Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health

Linking chemical data from the Comparative... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/14-1266" }, "headline": "Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the...", "datePublished": "2025-11-17T15:42:04", "dateModified": "2026-04-27T09:07:26", "author": [ { "@type": "Person", "name": "Allan Peter Davis" }, { "@type": "Person", "name": "Thomas C. Wiegers" }, { "@type": "Person", "name": "Daniela Sciaky" }, { "@type": "Person", "name": "Fern Barkalow" }, { "@type": "Person", "name": "Brent Wyatt" }, { "@type": "Person", "name": "Jolene Wiegers" }, { "@type": "Person", "name": "Roy McMorran" }, { "@type": "Person", "name": "Sakib Abrar" }, { "@type": "Person", "name": "Carolyn J. Mattingly" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Chemicals can perturb gene functions to affect chronic human diseases, and a significant amount of biological knowledge involved in environmental health is available in public databases. Combining information across resources can assist in the discovery of novel testable hypotheses related to how chemical exposures influence human diseases, such as autism. Methods The Comparative Toxicogenomics Database (CTD) is a public resource that provides curated content for chemicals, genes, phenotypes, diseases, and exposures. The AOP-Wiki is a repository of adverse outcome pathways (AOPs) that provide defined biological frameworks describing disease processes. Here, we intersect CTD toxicogenomic content with the AOP-Wiki to identify environmental chemicals that could potentially modulate key steps in autism. Results We identify numerous chemical stressors that intersect with the individual events of the autism AOP, including bisphenol compounds, per/polyfluoroalkyl substances, pesticides, metals, and air pollutants, suggesting a wide range of environmental factors that could synergize to potentially affect autism. By integrating additional CTD curated content for three autism-associated chemicals (bisphenol A, particulate matter, and valproic acid), we discover other mechanisms, including specific genes (e.g., SLC1A1, GSTP1, CNTNAP2) and phenotypes (e.g., lipid metabolism, inflammatory response, social behavior) that can be used to help refine or expand this AOP or create an entirely new pathway for autism. Furthermore, related diseases are identified to build interconnected networks, mechanistically linking autism to fatty liver disease, intellectual disability, and cancer. Conclusions We demonstrate the value of integrating content from different resources to address environmental health questions related to autism etiology and co-morbidities. Importantly, our methodology is easily adapted for any AOP in the AOP-Wiki to identify potential environmental influences on the disease process and help support or refine AOPs. This analysis underscores the importance of standardizing public databases to make them efficiently interoperable for enhanced shared utility across the numerous bioknowledge digital landscapes. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1266/v1", "name": "Linking chemical data from the Comparative Toxicogenomics Database..." } } ] } Home Browse Linking chemical data from the Comparative Toxicogenomics Database... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Davis AP, Wiegers TC, Sciaky D et al. Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.12688/f1000research.172567.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] Allan Peter Davis https://orcid.org/0000-0002-5741-7128 1 , Thomas C. Wiegers 1 , Daniela Sciaky https://orcid.org/0009-0000-3182-9493 1 , [...] Fern Barkalow https://orcid.org/0009-0000-5308-1535 1 , Brent Wyatt 1 , Jolene Wiegers 1 , Roy McMorran 1 , Sakib Abrar 1 , Carolyn J. Mattingly 1,2 Allan Peter Davis https://orcid.org/0000-0002-5741-7128 1 , Thomas C. Wiegers 1 , [...] Daniela Sciaky https://orcid.org/0009-0000-3182-9493 1 , Fern Barkalow https://orcid.org/0009-0000-5308-1535 1 , Brent Wyatt 1 , Jolene Wiegers 1 , Roy McMorran 1 , Sakib Abrar 1 , Carolyn J. Mattingly 1,2 PUBLISHED 17 Nov 2025 Author details Author details 1 Department of Biological Sciences, North Carolina Sate University, Raleigh, North Carolina, 27695, USA 2 Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina, 27695, USA Allan Peter Davis Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation Thomas C. Wiegers Roles: Software, Writing – Review & Editing Daniela Sciaky Roles: Data Curation, Writing – Review & Editing Fern Barkalow Roles: Data Curation, Writing – Review & Editing Brent Wyatt Roles: Data Curation, Writing – Review & Editing Jolene Wiegers Roles: Software, Writing – Review & Editing Roy McMorran Roles: Software Sakib Abrar Roles: Software Carolyn J. Mattingly Roles: Funding Acquisition, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Cheminformatics gateway. This article is included in the AOP Mechanistic Data-Oriented Coordination collection. Abstract Background Chemicals can perturb gene functions to affect chronic human diseases, and a significant amount of biological knowledge involved in environmental health is available in public databases. Combining information across resources can assist in the discovery of novel testable hypotheses related to how chemical exposures influence human diseases, such as autism. Methods The Comparative Toxicogenomics Database (CTD) is a public resource that provides curated content for chemicals, genes, phenotypes, diseases, and exposures. The AOP-Wiki is a repository of adverse outcome pathways (AOPs) that provide defined biological frameworks describing disease processes. Here, we intersect CTD toxicogenomic content with the AOP-Wiki to identify environmental chemicals that could potentially modulate key steps in autism. Results We identify numerous chemical stressors that intersect with the individual events of the autism AOP, including bisphenol compounds, per/polyfluoroalkyl substances, pesticides, metals, and air pollutants, suggesting a wide range of environmental factors that could synergize to potentially affect autism. By integrating additional CTD curated content for three autism-associated chemicals (bisphenol A, particulate matter, and valproic acid), we discover other mechanisms, including specific genes (e.g., SLC1A1, GSTP1, CNTNAP2) and phenotypes (e.g., lipid metabolism, inflammatory response, social behavior) that can be used to help refine or expand this AOP or create an entirely new pathway for autism. Furthermore, related diseases are identified to build interconnected networks, mechanistically linking autism to fatty liver disease, intellectual disability, and cancer. Conclusions We demonstrate the value of integrating content from different resources to address environmental health questions related to autism etiology and co-morbidities. Importantly, our methodology is easily adapted for any AOP in the AOP-Wiki to identify potential environmental influences on the disease process and help support or refine AOPs. This analysis underscores the importance of standardizing public databases to make them efficiently interoperable for enhanced shared utility across the numerous bioknowledge digital landscapes. READ ALL READ LESS Keywords environmental chemicals, adverse outcome pathways, autism, molecular mechanisms, database, disease networks, interoperability Corresponding Author(s) Allan Peter Davis ( [email protected] ) Close Corresponding author: Allan Peter Davis Competing interests: No competing interests were disclosed. Grant information: This work was supported by the National Institute of Environmental Health Sciences [U24 ES033155]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Davis AP et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Davis AP, Wiegers TC, Sciaky D et al. Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.12688/f1000research.172567.1 ) First published: 17 Nov 2025, 14 :1266 ( https://doi.org/10.12688/f1000research.172567.1 ) Latest published: 27 Apr 2026, 14 :1266 ( https://doi.org/10.12688/f1000research.172567.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction Numerous public databases exist that provide extensive content related to chemicals, genes, phenotypes, biological networks, and diseases. 1 The shared use of standardized vocabularies facilitates data interoperability, integration, and exchange between these resources to help understand human health. 2 Synergy arising from combining information from different sources can lead to novel discoveries and testable hypotheses. Most chronic human diseases are the result of a complex, multifactorial interplay between environmental agents and genetics, 3 , 4 and chemical exposure is an important element of the environment. Here, we integrate the content of two distinct publicly available databases to illuminate how environmental chemicals could affect autism, a human chronic disease influenced by numerous genes as well as environmental conditions. 5 For over two decades, the Comparative Toxicogenomics Database (CTD: https://ctdbase.org ) has manually curated the scientific literature to annotate toxicogenomic information in a structured format using FAIR controlled vocabularies ( https://ctdbase.org/about/ctdDataFairness.jsp ), providing detailed, contextualized interactions between chemicals, genes, phenotypes, anatomy terms, diseases, and exposures. 6 Currently, CTD includes over 4.1 million manually curated interactions describing relationships between 19,400 chemicals, 57,000 cross-species gene products, 6,900 phenotypes, 1,000 anatomical terms, and 7,200 diseases, curated from over 148,000 research papers ( https://ctdbase.org/about/dataStatus.go ). In turn, CTD integrates these curated interactions to generate “Inference Networks”, predictive associations based upon shared intermediates 7 as well as “CGPD-tetramers”, modular four-unit blocks of information that prospectively relate an initiating chemical with an interacting gene and an intermediate phenotype to a disease outcome in a step-wise direction. 8 Importantly, CTD includes the original source articles as evidence for every curated statement used to generate all inferences and tetramers, providing transparency and traceability. CTD inferences and tetramers can be used to computationally fill the molecular knowledge gaps connecting chemical exposure to a variety of disease outcomes 8 – 10 as well as group chemicals by mechanistic-induced adverse endpoints. 11 The AOP-Wiki ( https://aopwiki.org ) is the official repository for a community-driven international development of adverse outcome pathways (AOPs), coordinated by the global policy forum Organization for Economic Co-operation and Development (OECD). 12 The AOP is a modular knowledge framework that relates intermediate key events (KE) by key event edge relationships (KER) at different levels of biological knowledge, connecting a molecular initiating event (MIE) to an adverse outcome (AO) to describe the critical check-points for a disease pathway. 13 AOPs can be used to organize and model biological knowledge as well as provide the necessary mechanistic information for new approach methodologies (NAMs) and integrated approaches to testing and assessment (IATA) for chemical risk analysis. 13 – 17 Currently, the AOP-Wiki contains 532 AOPs in various stages of endorsement, the majority of which (74%) are flagged as “empty” with still no official endorsement, indicating a critical need to increase AOP testing, refinement, verification, and approval. Importantly, AOPs are defined as stressor-independent and chemical-agnostic, although they are typically reported with a list of assigned “prototypical stressors” that have been used to experimentally document key steps in the pathway and provide empirical support for the AOP, especially the KERs. 18 Independently, chemical stressors have been layered onto established AOPs to intertwine “stressor-AOP networks” that help inform chemical influence (both single and mixed chemical exposures) on interconnected disease pathways 19 and identify health outcomes associated with exposure to, for example, plastic additives 20 and inorganic cadmium. 21 Integrating toxicogenomic information from CTD with AOPs supplies the molecular mechanisms associated with chemical exposure to modulate biological pathways. Here, as a use case demonstration, we expand upon this concept of intersecting chemical data from CTD to the AOP-Wiki to identify and prioritize potential environmental influences for autism and discover additional mechanistic data that can be used to help support, inform, refine, and expand AOP development and interconnectivity. Importantly, these methods can be adapted for any AOP in the AOP-Wiki. This study underscores the importance of standardizing public databases to make them interoperable and increase their utility, and we discuss ways to further enhance data type connections between CTD and AOP-Wiki going forward. Methods CTD data version and analysis Analysis was performed using CTD data available in September 2025 (revision 17923). CTD is updated with new content on a monthly basis ( https://ctdbase.org/about/dataStatus.go ); consequently, query results described in this text may vary over time. For analysis, CTD data pages and query results were downloaded (available formats: CSV, Excel, XML, or TSV) into spreadsheets, and the sorting, advanced filtering, and subtotaling functions provided in Excel were used to survey and count the unique data types. The online tools Venny 2.1 ( https://bioinfogp.cnb.csic.es/tools/venny/index.html ), and InteractiVenn 22 were used to compare and find shared data types. CTD data for autism and phenotypes CTD uses the MEDIC disease hierarchy as a FAIR controlled vocabulary for annotating disease outcomes. 23 In MEDIC, “Autism Spectrum Disorder” (ASD; MESH:D000067877) is part of the mental disorder branch and is a parent to several descendant diseases, including “Autistic Disorder” (MESH:D001321), “Asperger Syndrome” (MESH:D020817), and “Adenylosuccinate lyase deficiency” (MESH:C538235); collectively, we refer to this set of diseases simply as “autism”. Since MEDIC is a hierarchy, all associated data (e.g., chemicals, genes, and phenotypes) annotated to descendant terms are subsumed and displayed to the parent term and can be retrieved by simply using ASD as the query term. All query results were combined and filtered to remove any duplicates arising from descendant terms. CTD operationally distinguishes “phenotypes” from “diseases”, wherein a phenotype is defined by a molecular, cellular, or physiological process term that does not exist in the MEDIC vocabulary (e.g., cell migration, retinoic acid receptor signaling pathway, apoptosis, regulation of blood pressure, etc.). To annotate chemical-induced phenotype data, CTD uses the Gene Ontology (GO) as a controlled FAIR vocabulary for phenotypes 24 ; thus, all CTD phenotypes are defined by GO terms and their GO accession identifiers (GO:ID). CTD term mapping to autism AOP events to derive intersecting chemicals We searched the public AOP-Wiki database (version 2.7, 25 April 2025) with the term “autism” to retrieve AOP:522 ( https://aopwiki.org/aops/522 ) entitled “estrogen antagonism leading to increased risk of autism-like behavior” ( Figure 1 ). This AOP was last updated 25 January 2024 and is composed of six linked events: MIE:112 (“antagonism, estrogen receptor”), KE:2207 (“inhibition, ERK1/2 signaling pathway”), KE:195 (“inhibition, NMDARs” or “deceased, NMDAR expression”), KE:2208 (“aberrant, synaptic formation and plasticity”), KE:386 (“decrease of neuronal network function”), and AO:2209 (“autism-like behavior”). The edges connecting KE nodes to each other are referred to as key event relationships (KER). As currently structured in the AOP-Wiki, AOP:522 is bifurcated, with KE:195 as an offshoot event that joins the AOP at the KE:2208 junction ( Figure 1 ). We reviewed the individual AOP event terms to manually identify corresponding terms in CTD; sometimes the best match was to one or more genes and/or phenotypes ( Table 1 ). We used expansive queries when appropriate to fully cover the concepts best reflected in each KE; for example, we queried for chemicals that could affect any aspect of the estrogen receptor genes (gene expression, protein expression, protein activity, protein translocation, etc.). Since the GO is structured as a hierarchy, CTD queries with phenotype terms return data associated with the direct query term itself as well as associated data for all descendants of the phenotype term, providing comprehensive data coverage. Chemicals interacting with genes and phenotypes were retrieved using either CTD’s Batch Query tool ( https://ctdbase.org/tools/batchQuery.go ), Chemical-Gene Interaction Query page ( https://ctdbase.org/query.go?type=ixn ), or Chemical-Phenotype Interaction Query page ( https://ctdbase.org/query.go?type=phenotype ). For each AOP event, CTD chemicals with directly curated interactions associated with the corresponding CTD genes/phenotypes were downloaded; duplicate listings derived from multiple queries were removed. For example, for MIE:112 (“antagonism, estrogen receptor”), we performed four independent CTD queries: first, we used the CTD Chemical-Gene Interaction Query tool to independently retrieve 1,088 and 603 chemicals that interacted with the genes for estrogen receptors 1 and 2 (ESR1, GENE:2099; ESR2, GENE:2100), respectively; next, we used the CTD Chemical-Phenotype Interaction Query tool to retrieve 180 chemicals that modulate the phenotype “intracellular estrogen receptor signaling pathway” (GO:0030520) and eight chemicals that modulate the phenotype “nuclear estrogen receptor activity” (GO:0030284); after compiling these four independent results and filtering out duplicates, we identified a set of 1,189 unique chemicals that modulate any one of the CTD mechanistic terms corresponding to AOP event MIE:112. This iterative process was performed for all six AOP events, using the corresponding CTD terms for queries ( Table 1 ) to derive 3,648 unique chemicals distributed over the six events comprising AOP:522 ( Table S1 in Extended data 25 ). Chemicals were given a score of 1-6 based upon the number of AOP:522 events with which the chemical interacted, allowing the set to be ranked and prioritized for chemicals with the greatest intersection to AOP:522. Prioritized chemicals were group into categories by searching the CTD Chemical vocabulary ( https://ctdbase.org/voc.go?type=chem ) for shared term parentage and/or using web-based searches for chemical definitions. Figure 1. Mapping AOP:522 events to CTD mechanisms to find intersecting environmental chemicals from CTD. AOP:522 (“estrogen antagonism leading to increased risk of autism-like behavior”) is currently the only autism related pathway in the AOP-Wiki and is composed of one MIE with four KEs ending in the AO of autism-like behavior. KERs connecting KEs are represented as black lines. As represented in the AOP-Wiki, this AOP is bifurcated at the KE:195 event (“inhibition, NMDARs”, which at other times in the AOP-Wiki is entitled “decreased, NMDAR expression”). To enable interoperability, each AOP event is mapped to a corresponding CTD mechanistic data type, including a mix of CTD genes (GENE:IDs), phenotypes (GO:IDs), and the disease “Autism Spectrum Disorder” (MESH:ID). The CTD terms are described in the text ( Table 1 ) and are used to query and retrieve sets of CTD chemical stressors that can intersect each AOP event. In total, 3,648 unique chemicals are distributed across the six events of AOP:522, with the most numerous modulating the ERK1/ERK2 signaling pathway (KE:2207), followed by neuronal network function (KE:386) and estrogen receptor activity (MIE:112). Table 1. CTD terms mapped to the six events of AOP:522 and the number of CTD chemicals annotated to each term. AOP:ID AOP term CTD:ID CTD term No. CTD chemicals MIE:112 Antagonism, estrogen receptor GENE:2099 ESR1 1,088 GENE:2100 ESR2 603 GO:0030520 Intracellular estrogen receptor signaling pathway 180 GO:0030284 Nuclear estrogen receptor activity 8 KE:2207 Inhibition, ERK1/2 signaling pathway GENE:5594 MAPK1 2,045 GENE:5595 MAPK3 2,018 GO:0070371 ERK1 and ERK2 cascade 269 GO:0004707 MAP kinase activity 34 KE:195 Inhibition, NMDARs or deceased, NMDAR expression n/a GRIN_wildcard 549 GO:0004972 NMDA glutamate receptor activity 5 GO:0017146 NMDA selective glutamate receptor complex 0 GO:0098989 NMDA selective glutamate receptor signaling pathway 9 GO:2000310 Regulation of NMDA receptor activity 13 KE:2208 Aberrant, synaptic formation and plasticity GO:0045202 Synapse 25 GO:0007416 Synapse assembly 25 GO:0050808 Synapse organization 69 GO:0099536 Synaptic signaling 450 KE:386 Decreased neuronal network function GO:0007399 Nervous system development 620 GO:0050877 Nervous system process 880 AO:2209 Autism-like behavior MESH:D000067877 Autism Spectrum Disorder 109 Constructing a new AOP series for autism using CTD content The CTD Tetramers tool ( https://ctdbase.org/query.go?type=tetramer ) was used to retrieve computational tetramers via three independent queries with the chemical field set to bisphenol A (MESH:C006780), particulate matter (MESH:D052638), or valproic acid (MESH:D014635) and the disease field set to Autism Spectrum Disorder (MESH:D000067877), which also retrieves data for descendant disease terms. The resulting three datasets were manually combined in a spreadsheet and analyzed to identify the gene-phenotype pairs common to all three chemical-disease outputs, and this identified tetramer subset was uploaded to CTD’s Chord Diagram Generator tool ( https://ctdbase.org/tools/chord.go?window=upload ) to visualize the information as a chord diagram and identify frequently used gene and phenotype components. 26 The phenotypes “response to oxidative stress” (GO:0006979), “glutathione metabolic process” (GO:0006749), “lipid metabolic process” (GO:0006629), “inflammatory response” (GO:0006954), “social behavior” (GO:0035176), and “locomotory behavior” (GO:0007626) were revealed as some of the most frequently used terms in the subset and were selected to manually construct a new AOP series. Since this dataset is derived from CTD tetramers, by operational definition, all three chemicals (bisphenol A, particulate matter, and valproic acid) have a direct interaction with all retrieved genes, phenotypes, and autism in CTD; similarly, all retrieved genes are directly annotated to every phenotype and directly curated to autism in CTD. The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes. Finding autism-related diseases from shared CTD mechanisms The CTD Tetramers tool was used to retrieve tetramers for independent queries for the 19 identified CTD gene or phenotype equivalent mechanistic terms that correspond to the first five events of AOP:522 ( Figure 1 ; Table 1 ): MIE:122 (GENE:2099, GENE:2100, GO:0030520, GO:0030284), KE:2207 (GENE:5594, GENE:5595, GO:0070371, GO:0004707), KE:195 (GENE:GRIN-wildcard, GO:0004972, GO:0017146, GO:00988989, GO:2000310), KE:2208 (GO:0045202, GO:0007416, GO:0050808, GO:0099536), and KE:386 (GO:0007399, GO:0050577). The tetramer results for each independent query were downloaded, compiled to identify a unique set of diseases for each of the five AOP events, and compared by Venn analysis. Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated. Results and discussion Discovering environmental chemicals that can intersect with an autism AOP As a use case, we queried the AOP-Wiki with the term “autism” to retrieve AOP:522 (“estrogen antagonism leading to increased risk of autism-like behavior”, https://aopwiki.org/aops/522 ). Currently, this AOP has the status of “empty” and is listed as “open for adoption”. The model was published in 2024 using public database content from numerous independent resources, including CTD, by looking for autism-related genes that also interact with two endocrine-disrupting chemicals, diethylhexyl phthalate (DEHP) and bisphenol A 27 as prototypical stressors. The model is composed of six events: one MIE, four KEs, and one AO ( Figure 1 ). We sought to leverage these six AOP events in order to expand upon the list of potentially associated chemicals beyond the original two endocrine disruptors. To identify chemicals in CTD that can intersect with these six AOP events, the AOP-Wiki terms were mapped to corresponding terms in CTD, and the selected CTD terms were used to retrieve sets of interacting chemicals from CTD ( Table 1 ), as such: MIE:112 (“antagonism, estrogen receptor ”). This MIE maps to four CTD data types, including two estrogen receptor genes ESR1 (GENE:2099) and ESR2 (GENE:2100) and two phenotypes “intracellular estrogen receptor signaling pathway” (GO:0030520) and “nuclear estrogen receptor activity” (GO:0030284). From this, we compiled a set of 1,189 unique CTD chemicals that have curated interactions with at least one of the four data types and can therefore affect MIE:112. KE:2207 (“inhibition, ERK1/2 signaling pathway” ). This KE maps to four CTD data types, composed of two mitogen-activated protein kinase genes MAPK1 (GENE:5594) and MAPK3 (GENE:5595) and two phenotypes “ERK1 and ERK2 cascade” (GO:0070371) and “MAP kinase activity” (GO:0004707). There are 2,157 unique chemicals curated in CTD that can affect KE:2207. KE:195 (“inhibition, NMDARs” or “deceased, NMDAR expression ”). This KE is described using two different names in the AOP-Wiki, referring to either the “inhibition” or “decreased expression” of N-methyl-D-aspartate receptors. Official nomenclature refers to these genes as “glutamate receptors” which use the gene symbol prefix of “GRIN” (e.g., GRIN1, GRIN2A, GRIN2B, etc.). To map this data type in CTD, we first queried for all chemical-gene interactions wherein the gene field was wildcarded using an asterisk (“GRIN*”) to maximize gene coverage. The downloaded interactions were then manually vetted to include only “GRIN”-based genes of which 23 were found, and the chemicals that interact with those 23 genes were collected. Additionally, this KE broadly maps to four CTD phenotypes, including: “NMDA glutamate receptor activity” (GO:0004972), “NMDA selective glutamate receptor complex” (GO:0017146), “NDMA selective glutamate receptor signaling pathway” (GO:0098989), and “regulation of NMDA receptor activity” (GO:2000310). In total, for all gene and phenotype queries, there are 557 unique CTD chemicals that can affect KE:195. KE:2208 (“aberrant, synaptic formation and plasticity ”). This KE maps to the four CTD phenotypes: “synapse” (GO:0045202), “synapse assembly” (GO:0007416), “synapse organization” (GO:0050808), and “synaptic signaling” (GO:0099536). There are 502 unique CTD chemicals that can affect KE:2208. KE:386 (“decreased neuronal network function ”). This KE broadly maps to the two CTD phenotypes “nervous system development” (GO:0007399) and “nervous system process” (GO:0050877) to include both neuronal development and function. There are 1,230 unique CTD chemicals that can affect KE:386. AO:2209 (“autism-like behavior ”). This AO maps to the CTD disease “Autism Spectrum Disorder” (ASD; MESH:D000067877), which is a parent term in the disease hierarchy and includes data for “Autistic Disorder” (MESH:D001321), “Asperger Syndrome” (MESH:D020817), and “Adenylosuccinate lyase deficiency” (MESH:C538235), a metabolic disease that presents with some symptoms of autism. We collectively refer to this set of disease terms as “autism”. To explore environmental influences on the etiology of autism, we used CTD chemicals annotated as “marker/mechanism” direct evidence to ASD (or one its descendants). There are 109 unique chemicals curated in CTD that can affect AO:2209. In total, 3,648 unique chemicals are distributed across the six events of AOP:522 ( Figure 1 , and Table S1 in Extended data 25 ). To help identify and prioritize the top environmental chemicals, we ranked the list by the number of individual AOP events with which each chemical interacts. Of the 3,648 chemicals, 76 modulate five or more of the AOP events ( Figure 2 ), and this set includes both bisphenol A and DEHP which are the original prototypical stressors used to construct AOP:522 in the AOP-Wiki ( https://aopwiki.org/aops/522#prototypical-stressors ). There are 12 chemicals that intersect with all six events of AOP:522, including bisphenol A, valproic acid, perfluorooctane sulfonic acid (PFOS), chlorpyrifos, decamethrin, glyphosate, particulate matter, aluminum, cadmium, manganese, 2,2’,4,4’-tetrabromodiphenyl ether (PBDE-47), and testosterone. Many of the 76 ranked chemicals that modulate five or more of the AOP events can be grouped as medications/preventatives (e.g., acetaminophen, valproic acid, folic acid, sodium fluoride), air pollutants, pesticides (e.g., paraquat, diazinon, imidacloprid), several per/polyfluoroalkyl substances (PFAS), metals (e.g., lead, copper, zinc), phthalates, and environmental pollutants ( Figure 2 ). These chemicals suggest that a wide range of environmental factors (independently or together) may modulate many of the key events currently included in AOP:522 to influence autism pathways. 28 Figure 2. Prioritizing CTD chemical stressors for AOP:522. The 76 chemicals that interact with five or more of the six events in AOP:522 are depicted by a filled-in box showing the intersection between the chemical and the AOP events (listed at top). Twelve chemicals (red font) interact with all six AOP events. Many of these prioritized chemicals can be grouped into categories, including bisphenols, medications, PFAS, pesticides, air pollutants, metals, phthalates, etc. Bisphenol A and diethylhexyl phthalate (blue stars) are the prototypical stressors originally used to build AOP:522. Importantly, this approach of linking toxicogenomic content for environmental chemicals from CTD with AOPs provides molecular mechanisms for experiments and targeted assays to test modulation of the key elements of the AOP:522 pathway by any of the top-ranked environmental stressors to more definitively explore and confirm the possible influence of environmental factors on autism. This analysis is especially pertinent for studying the effects of co-exposed chemical mixtures, as environmental influences can be multifactorial. For example, low fluoride exposure induces autism-related neurotoxicity but only in the presence of aluminum cations, 29 and while prenatal exposure to either copper or butyl phthalates can be associated with depressive symptoms, only co-exposure to both has a significant association with autism. 30 Both air pollution and vehicle emissions are highly complex mixtures and have been linked to autism, 31 , 32 but it becomes important to identify the specific compounds of those mixtures and evaluate them experimentally. Using environmental chemicals from CTD to discover additional events for autism AOP Next, CTD environmental chemicals were leveraged to discover potential additional mechanisms that could expand AOP:522 (and fill in gaps) or generate completely new AOPs for autism. As a spectrum disease, autism exhibits a diverse and wide-ranging set of complex symptoms with different levels of severity affecting social interaction, communication, and behavior, suggesting the plausibility for a variety of biological pathways leading to an outcome. 33 We selected bisphenol A, the original prototypical stressor used to construct AOP:522 27 ; valproic acid, an anti-convulsant drug whose use during pregnancy has been associated with autism in children 34 and is used experimentally to induce animal models of the disease 35 ; and particulate matter, a common element of air pollution, that has some evidence of association with autism from prenatal or early-life exposure. 36 All three of these chemicals additionally have curated exposure data in CTD correlating their exposure to autism in humans at the population level ( https://ctdbase.org/detail.go?type=disease&acc=MESH%3AD000067877&view=expStudies ). We looked for additional molecular/cellular events that might help inform or refine AOP:522 by identifying mechanisms (i.e., genes and phenotypes) used by bisphenol A, particulate matter, and valproic acid in relationship to autism in CTD in an effort to find shared processes. We used the CTD Tetramers tool, 37 a user-friendly online tool that quickly generates computational solutions called CGPD-tetramers that are derived by integrating five curated supporting lines of literature-based evidence from CTD to construct a modular four-unit block linking an initiating chemical, an interactive gene, an intermediate phenotype event, and a disease outcome ( Figure 3A ). Three independent queries were made using bisphenol A, particulate matter, and valproic acid as the chemical inputs and ASD as the disease output to retrieve 2,021, 2,161, and 1,373 computational tetramers, respectively ( Figure 3B-C , and Tables S2-S4 in Extended data 25 ). The three sets of tetramers share 291 gene-phenotype paired intermediates (GP-dimers), composed of 136 genes and 53 phenotypes, that relate the three chemicals to autism, providing new potential key mechanisms to be included in a disease pathway. Some of the top shared genes underlying connections between all three chemicals and autism ( Figure 3D ) include those that play a role as environmental sensors/detoxifiers (e.g., ABCG2, GSTP1, ALDH1A1) or function in neural health (e.g., SLC1A1, ALOX12, CNTNAP2, COMT, NOTCH1), helping to link environmental responses and neuronal phenotypes to autism. The most frequent phenotype shared by all three chemicals related to autism is lipid metabolism ( Figure 3D ). Additional highlighted phenotypes include regulation of cell population proliferation and/or apoptosis, inflammatory response, oxidative stress, social and locomotory behavior, glutathione metabolism, cholesterol metabolism, heart development, heart rate, blood pressure, and cytosolic calcium ion concentration ( Figure 3D ). Figure 3. Leveraging CTD tetramers to find additional potential mechanisms for autism. (A) CTD tetramers are modular four-unit blocks of computed information linking an initiating chemical (C), an interacting gene (G), an intermediate phenotype (P), and a disease (D) outcome. To generate a tetramer, five lines of supporting evidence must already exist in CTD. Importantly, if a tetramer is generated, then, by operational definition, the chemical must have a directly curated relationship to the gene, phenotype, and disease in CTD, and every gene must be annotated to the phenotype and disease in CTD (dotted box, with five small arrows connecting all four blocks of the tetramer). (B) The online tool CTD Tetramers allows users to easily and quickly generate tetramers for any chemical, gene, phenotype, and/or disease of interest, by filling in any one or more of the four appropriate fields on the query page. Here, three independent queries were made for three chemicals as the input (bisphenol A, particulate matter, and valproic acid) with ASD as the disease output (which retrieves data for both ASD and AD) for each query. (C) Results include 2,021 tetramers for bisphenol A (using 306 genes and 477 phenotypes), 2,161 tetramers for particulate matter (using 340 genes and 316 phenotypes), and 1,373 tetramers for valproic acid (using 387 genes and 144 phenotypes). From these three tetramer sets, 291 shared GP-dimers are found for all three chemicals linked to autism, and involve 136 genes and 53 phenotypes. (D) The subset of tetramers representing only the shared 291 GP-dimers are visualized as a chord diagram, linking the three initiating chemicals (blue nodes), 136 genes (green nodes), and 53 phenotypes (purple nodes) to autism (red node, wherein data for ASD and AD are depicted as a single node), to illuminate potentially key mechanisms for consideration to refine or expand autism disease pathways. We selected six of these prominent tetramer-identified key phenotypes and their associated 93 genes to manually construct a prospective pathway of linked events relating the three chemical stressors to autism ( Figure 4 ). In this strategy, CTD tetramers provide the literature-based content to build a highly interconnected mechanistic map that includes chemicals and genes directly curated to autism, genes and phenotypes directly curated to the three chemical stressors, and overlapping genes shared between the phenotypes as mechanistic links connecting the key events. Here, an oxidative stress response is linked to cellular metabolism (affecting the levels of glutathione and lipids, such as cholesterol), inflammatory responses, and both social and locomotor behavior. The edge relationships between each phenotype are further supported by shared genes ( Figure 4 ), which interact with each of the three chemicals and independently have curated relationships to autism. This novel AOP series has multiple levels of evidentiary support from CTD for the relationship edges connecting the phenotype events, and is supported in the extrinsic literature, wherein the role of oxidative stress, glutathione metabolism, impaired lipid metabolism, inflammatory processes, and both social and locomotory behaviors have all been found to promote the pathogenesis of or be a characteristic of autism 38 – 43 ; as well, non-autistic-related associations connect oxidative stress, dietary lipids/cholesterol, and inflammation with changes in social behavior. 44 – 46 Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 ( Figure 4 ), as well as provide new mechanisms to develop additional targeted assays. 47 Figure 4. Constructing a new AOP series identified by CTD environmental chemicals associated with autism. We used 93 genes and six phenotypes identified as part of the shared GP-dimers from tetramers linking bisphenol A, particulate matter, and valproic acid to autism in CTD to manually build an intricately connected, novel AOP series for autism, spanning molecular, cellular, system, and behavioral phenotypes (purple boxes with GO:IDs). Since data are derived from CTD tetramers (see Figure 3A ), by definition, all three chemical stressors (blue box) have directly curated relationships to every gene, phenotype, and autism in CTD (not shown), and every gene also has a directly curated relationship to autism (not shown) and is annotated to their associated phenotypes (gene number in small circle for each phenotype). Genes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events. This novel, manually generated AOP can serve as a framework to construct an entirely new AOP for autism or be used to refine or expand (gray downward arrows) AOP:522 from the AOP-Wiki (bottom). Using CTD to discover related diseases to build interconnected networks for autism One of the hallmarks of AOPs is that the individual events are modular and can be re-used in other AOPs, 48 similar to the way CTD tetramers are modular and the underlying chemicals, genes, phenotypes, and diseases can be used in other tetramers. 8 This modular nature makes it possible to interrelate other AOPs (and CTD tetramers) that use the same data types. Thus, extrinsic diseases linked to the same corresponding CTD terms for the AOP:522 events can be connected to autism via their shared mechanisms. Here, we use the same CTD gene and phenotype terms that were previously mapped to the first five AOP events of MIE:112, KE:2207, KE:195, KE:2208, and KE:386 ( Figure 1 , Table 1 ), and we utilize the CTD Tetramers tool to retrieve and compile 149, 619, 55, 471, and 842 tetramer-derived diseases, respectively, for each of the five events for AOP:522 ( Figure 5 ). When the five datasets are analyzed, 17 shared diseases are identified that can be simultaneously associated with all five AOP events. Both ASD and AD are in this subset and also included are a variety of cancer-related outcomes, hyperalgesia, and intellectual disability, which have been reported with autism. 49 – 51 If KE:195 (“inhibition, NMDARs”), which yielded the fewest number of tetramer-derived diseases and is represented as the bifurcated step in the original AOP:522, is excluded from the analysis, an additional 108 shared diseases are identified that co-use just the four remaining events MIE:112, KE:2207, KE:2208, and KE:386. These additional diseases include cardiac arrhythmias, congenital heart defects, non-alcoholic fatty liver disease (NAFLD), colitis, and status epilepticus, all of which have been associated with autism. 52 – 56 Figure 5. Discovering CTD diseases related to autism via shared intermediate mechanistic events. The first five AOP:522 events map to 19 corresponding CTD gene and phenotype terms (gray boxes with GENE:IDs and GO:IDs; also see Table 1 ). These CTD terms are independently used as query inputs in the CTD Tetramers tool to retrieve the tetramer-associated diseases compiled for each AOP mechanistic step. Sets of 149, 619, 55, 471, and 842 tetramer-derived CTD diseases are linked to MIE:112, KE:2207, KE:195, KE:2208, and KE:386, respectively (for simplicity, the AOP is drawn linear, and not bifurcated). A Venn analysis identifies 17 shared diseases simultaneously associated with all five AOP events. If KE:195 (the KE with the lowest number of diseases) is dropped from the analysis, an additional 108 shared diseases are identified (partial list shown) for the remaining four AOP events together (MIE:112, KE:2207, KE:2208, and KE:386). Disease pathways that include the same modular events can be interconnected ( Figure 6 ), which may inform possible comorbidities for autism. To date, there are no AOPs in the AOP-Wiki that use all five of the same events used in the AOP:522 autism pathway, but individual events are included in a few limited AOPs. The initiating event MIE:112 (“antagonism, estrogen receptor”) is also used in AOP:443 leading to “metastatic breast cancer” (AO:1982) and independently in AOP:595 resulting in “decreased sperm quantity” (AO:520). As well, both KE:2208 (“aberrant, synaptic formation and plasticity”) and KE:386 (“decrease of neuronal network function”) are also part of two independent AOPs ending in “impairment, learning or memory” (AO:341), and KE:386 is additionally used in other AOPs resulting in the similar outcomes of “cognitive function, decreased” (AO:402) and “memory loss” (AO:1941). Figure 6. Building interconnected disease networks via shared CTD mechanisms. Five linked events composing AOP:522 result in autism-like behavior (AO:2209). Some of these individual modular events, however, are also used in other AOPs (but not all in conjunction with each other) resulting in other outcomes, such as MIE:112 ending in “metastatic breast cancer” (AO:1982) in AOP:443 or in “decreased sperm quantity” (AO:520) for the unrelated AOP:595. Both KE:2208 and KE:386 are involved in “impairment, learning or memory” (AO:341), but individually via numerous different AOPs, and KE:386 also leads to a related outcome of “memory loss” (AO:1941), yet by another AOP:429. Five of the AOP events, however, when mapped into their corresponding CTD mechanistic terms, and in simultaneous conjunction with each other (set 1), can be leveraged to discover 17 shared diseases that interconnect autism with numerous other health outcomes, such as intellectual disability and hyperalgesia. If KE:195 (the bifurcated event) is removed from analysis (set 2), an additional 108 shared diseases are identified, including congenital heart defects, non-alcoholic fatty liver disease (NAFLD), and status epilepticus. Many of these computed outcomes currently exist in the AOP-Wiki as AO or KE terms (dashed arrows). CTD, on the other hand, discovers many more potential connected disease pathways that, importantly, include all five AOP events concurrently: MIE:112, KE:2207, KE:195, KE:2208, and KE:386 ( Figure 5 ), allowing additional mechanistic and interrelated disease networks to be constructed. Many of these computed outcomes have corresponding KE and AO terms in the AOP-Wiki to help construct AOP networks ( Figure 6 ). Limitations and strengths A limitation to this study is that the AOP-Wiki does not impose controlled vocabularies or standardized formatting when new MIEs, KEs, or AOs are created and submitted by researchers. 17 Thus, to intersect CTD chemical content with AOPs, KE terms from the AOP-Wiki first must be manually mapped to corresponding terms in CTD, which is rarely a straightforward process, as some KE terms can be mapped to multiple phenotypes as well as genes, such as KE:2207 (“inhibition of ERK1/2 signaling pathway”) mapped here to both a CTD biological phenotype (“ERK1 and ERK2 cascade”, GO:0070371) as well as specific CTD target genes MAPK1 (GENE:5594) and MAPK3 (GENE:5595) whose activity can be modulated by chemicals. Additionally, many KE terms are incompletely described in the AOP-Wiki, such as KE:195, which sometimes is referred to as “decreased NMDAR expression” and other times as “inhibition, NMDARs”. To accommodate for this subtlety, we mapped KE:195 to CTD mechanistic terms reflecting both NMDAR gene expression as well as NMDAR activity. Bias can be introduced in the manual mapping of AOP-Wiki terms to matched mechanisms in CTD; for example, what exactly do the AOP authors mean by the KE term “decreased neuronal network function” (KE:386)? Here, the KE term is not defined in the AOP-Wiki, so it is left to each user to interpret this event, as we did here to refer to any impaired development (GO:0007399) or processes (GO:0050877) of the nervous system. To help initiate interoperability, the AOP-Wiki does provide a rudimentary download file ( https://aopwiki.org/downloads/aop_ke_ec.tsv ) that maps many (but not all) AOP event terms to some GO terms (as well as other vocabularies), but the source of this mapping is unclear, as well as how the mapping was performed; nonetheless, it can serve as an important initial guide to start linking AOP event terms with CTD terms via shared GO terms. Additionally, some biomedical data translators 57 may be able to assist in rendering mappings between KE and GO terms impartially. In 2022, CTD manually processed and mapped the then-current AOs from the AOP-Wiki to equivalent CTD disease or phenotype terms 37 ; however, over time, AO terms have been edited and new ones have been added to the AOP-Wiki, making the CTD mapping outdated. Other methods have been developed to increase the usability of AOP information, such as converting the AOP-Wiki into semantic web formats 58 or curating relevant KEs to gene sets associated with pathways, phenotypes, and GO terms 59 ; but these deliverables are catered mostly toward users proficient in programming. Finally, the AOP-Wiki does provide a list of more than 840 prototypical stressors currently used in their AOPs ( https://aopwiki.org/stressors ), and these stressors are defined using common and stable chemical identifiers, including Chemical Abstract Service (CAS) numbers, Distributed Structure-Searchable Toxicity Identifiers (DTXSIDs), and International Chemical Identifier (InChI) keys, all of which are also used to define chemicals at CTD, providing an accessible integration point between these resources with respect to chemoinformatics. 60 Enhancing the AOP-Wiki with a dedicated team of professional biocurators could help to ensure data standardization and harmonization. Furthermore, requiring AOP contributors to include suggested FAIR controlled terms with their initial submissions, 17 especially defining KEs with suitable GO terms (a commonly used ontology in bioinformatics), 61 would be a significant step in opening up AOP data to more biological databases and researchers. Lastly, we note a caveat about relying solely on CTD tetramers as a source of mechanistic data for building new AOPs and discovering interconnected disease networks. CTD tetramers require five lines of literature-based evidence for the constituent, curated interactions between a chemical, gene, phenotype, and disease ( Figure 3A ). If any one of the five curated statements does not exist in CTD, the tetramer will not be generated. Thus, tetramers represent a more restrictive subset of computational solutions. As a counterpoint, the strength to tetramers is that they provide a more detailed and comprehensive view of outcome pathways than inferred relationships, which do not include all four data types. Importantly, CTD is not a static resource, as new literature is curated and added on a monthly basis, and the number of available tetramers increases over time. Furthermore, tetramers now can be prioritized and ranked by their calculated weighted “evidence strength score” to enable users to sort tetramers by the number of underlying articles from which mechanistic events were originally curated. 6 To complement tetramers, the less-restrictive predictions generated from CTD “Inference Networks” can also be used in modeling. Future directions CTD is exploring new avenues to better enable the intersection of both CTD content and CTD tools with AOP datasets, such as an automated process to electronically transform CTD tetramer query results into mechanistic-linked maps connected by shared chemical and gene edges (see Figure 4 ); this is especially important in that the KERs connecting individual KEs in an AOP are often described as the critical core unit in supporting and advancing the success of an AOP. 62 As well, if AOP terms become controlled and stabilized, direct hyperlinks and accession interoperability between the shared data types of these two resources will advance accessibility and reusability, 17 similar to how CTD currently provides links and interoperable searches for chemicals to both PubChem 63 and CompTox, 64 genes to NCBI-Gene, 65 phenotypes to AmiGO, 66 anatomy terms to both Uberon 67 and Cell Ontology, 68 and diseases to Disease Ontology. 69 Conclusion We describe a method to explore environmental health issues by intersecting toxicogenomic chemical data from CTD with AOPs from the AOP-Wiki to show how public resources can be leveraged to discover new information about disease pathways. Here, we present autism as a use case in our analysis, but the same methodology can be adapted for any AOP in the AOP-Wiki. More than 3,600 chemical stressors are identified that could potentially influence AOP:522 for autism; of these, 76 chemicals can be prioritized (because they intersect with a preponderance of the AOP events), including medications/preventatives, air pollutants, pesticides, PFAS compounds, metals, phthalates, and environmental pollutants, suggesting a wide-range of environmental factors with the potential to influence autism etiologies and outcomes. These identified chemicals further discover additional environmental sensor and neural health genes as well as oxidative stress and metabolic, inflammatory, and behavioral mechanisms for consideration to expand or refine AOP:522 or to generate a new disease pathway for autism. Finally, additional diseases that use the same intermediate mechanisms discerned by CTD can be interconnected to build extensive comorbidity networks for autism. Importantly, CTD provides the supporting literature used to generate these testable mechanistic pathways. Leveraging this information to improve and refine AOP construction and validation may facilitate the process of official endorsement and status advancement for AOPs. This work underscores the importance of harmonizing public databases to increase their interoperability and utility across the bioknowledge landscape. Data availability Underlying data All CTD content and analysis tools are freely available for non-commercial users at https://ctdbase.org . Extended data Figshare: Environmental chemicals from the Comparative Toxicogenomics Database linked to autism disease pathways. https://doi.org/10.6084/m9.figshare.30384805.v1 25 The project contains the following extended datasets: 1. Table S1. xlsx (CTD environmental chemicals distributed across six events of AOP:522 for autism). 2. Table S2. xlsx (CTD tetramers for bisphenol A and Autism Spectrum Disorder). 3. Table S3. xlsx (CTD tetramers for particulate matter and Autism Spectrum Disorder). 4. Table S4. xlsx (CTD tetramers for valproic acid and Autism Spectrum Disorder). Extended data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References 1. Mattingly CJ: Chemical databases for environmental health and clinical research. Toxicol. Lett. 2009; 186 (1): 62–65. PubMed Abstract | Publisher Full Text | Free Full Text 2. Davis AP, Wiegers J, Wiegers TC, et al. : Public data sources to support systems toxicology applications. Curr Opin Toxicol. 2019; 16 : 17–24. PubMed Abstract | Publisher Full Text | Free Full Text 3. Boyce WT, Sokolwski MB, Robinson GE: Genes and environments, development and time. Proc. Natl. Acad. Sci. USA. 2020; 117 (38): 23235–23241. PubMed Abstract | Publisher Full Text | Free Full Text 4. Motsinger-Reif AA, Reif DM, Akhtari SF, et al. : Gene-environment interactions within a precision environmental health framework. Cell Genom. 2024; 4 (7): 100591. PubMed Abstract | Publisher Full Text | Free Full Text 5. Cheroni C, Caporale N, Testa G: Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology. Mol. Autism. 2020; 11 (1): 69. PubMed Abstract | Publisher Full Text | Free Full Text 6. Davis AP, Wiegers TC, Sciaky D, et al. : Comparative Toxicogenomics Database’s 20th anniversary: update 2025. Nucleic Acids Res. 2025; 53 (D1): D1328–D1334. PubMed Abstract | Publisher Full Text | Free Full Text 7. Davis AP, Murphy CG, Rosenstein MC, et al. : The Comparative Toxicogenomics Database facilitates identification and understanding of chemical-gene-disease associations: arsenic as a case study. BMC Med. Genet. 2008; 1 : 48. PubMed Abstract | Publisher Full Text | Free Full Text 8. Davis AP, Wiegers TC, Grondin CJ, et al. : Leveraging the Comparative Toxicogenomics Database to Fill in Knowledge Gaps for Environmental Health: A Test Case for Air Pollution-induced Cardiovascular Disease. Toxicol. Sci. 2020; 177 (2): 392–404. PubMed Abstract | Publisher Full Text | Free Full Text 9. Grondin CJ, Davis AP, Wiegers JA, et al. : Predicting molecular mechanisms, pathways, and health outcomes induced by Juul e-cigarette aerosol chemicals using the Comparative Toxicogenomics Database. Curr Res Toxicol. 2021; 2 : 272–281. PubMed Abstract | Publisher Full Text | Free Full Text 10. Chai Z, Zhao C, Jin Y, et al. : Generating adverse outcome pathway (AOP) of inorganic arsenic-induced adult male reproductive impairment via integration of phenotypic analysis in comparative toxicogenomics database (CTD) and AOP wiki. Toxicol. Appl. Pharmacol. 2021; 411 : 115370. PubMed Abstract | Publisher Full Text 11. Canzler S, Lehmann J, Schor J, et al. : From toxicogenomics data to cumulative assessment groups: a framework for chemical grouping. Arch. Toxicol. 2025; PubMed Abstract | Publisher Full Text 12. Jaylet T, Coustillet T, Smith NM, et al. : Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps. Front Toxicol. 2024; 6 : 1285768. PubMed Abstract | Publisher Full Text | Free Full Text 13. Ankley GT, Bennett RS, Erickson RJ, et al. : Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 2010; 29 (3): 730–741. PubMed Abstract | Publisher Full Text 14. Ankley GT, Edwards SW: The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology. Curr Opin Toxicol. 2018; 9 : 1–7. PubMed Abstract | Publisher Full Text | Free Full Text 15. Perkins EJ, Woolard EA, Garcia-Reyero N: Integration of Adverse Outcome Pathways, Causal Networks and ’Omics to Support Chemical Hazard Assessment. Front Toxicol. 2022; 4 : 786057. PubMed Abstract | Publisher Full Text | Free Full Text 16. Tollefsen KE, Scholz S, Cronin MT, et al. : Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA). Regul. Toxicol. Pharmacol. 2014; 70 (3): 629–640. PubMed Abstract | Publisher Full Text 17. Mortensen HM, Gromelski M, Hench G, et al. : The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways. Comput Toxicol. 2025; 35 : 100368. PubMed Abstract | Publisher Full Text | Free Full Text 18. Organization for Economic Co-operation and Development: AOP Developers’ Handbook, v 2.7. (released May 30, 2024). Reference Source 19. Aguayo-Orozco A, Audouze K, Siggaard T, et al. : sAOP: linking chemical stressors to adverse outcomes pathway networks. Bioinformatics. 2019; 35 (24): 5391–5392. PubMed Abstract | Publisher Full Text | Free Full Text 20. Sahoo AK, Chivukula N, Madgaonkar SR, et al. : Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives. Arch. Toxicol. 2024; 98 (10): 3299–3321. PubMed Abstract | Publisher Full Text | Free Full Text 21. Sahoo AK, Chivukula N, Ramesh K, et al. : An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity. Sci. Total Environ. 2024; 920 : 170968. PubMed Abstract | Publisher Full Text 22. Heberle H, Meirelles GV, da Silva FR , et al. : InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics. 2015; 16 (1): 169. PubMed Abstract | Publisher Full Text | Free Full Text 23. Davis AP, Wiegers TC, Rosenstein MC, et al. : MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database. Database (Oxford). 2012; 2012 : bar065. PubMed Abstract | Publisher Full Text | Free Full Text 24. Davis AP, Wiegers TC, Wiegers J, et al. : Chemical-Induced Phenotypes at CTD Help Inform the Predisease State and Construct Adverse Outcome Pathways. Toxicol. Sci. 2018; 165 (1): 145–156. PubMed Abstract | Publisher Full Text | Free Full Text 25. Davis AP: Environmental chemicals from the Comparative Toxicogenomics Database linked to autism disease pathways. Dataset. figshare. 2025. Publisher Full Text 26. Wyatt B, Davis AP, Wiegers TC, et al. : Transforming environmental health datasets from the comparative toxicogenomics database into chord diagrams to visualize molecular mechanisms. Front Toxicol. 2024; 6 : 1437884. PubMed Abstract | Publisher Full Text | Free Full Text 27. Cui K, Li L, Li K, et al. : AOP-based framework for predicting the joint action mode of di-(2-ethylhexyl) phthalate and bisphenol A co-exposure on autism spectrum disorder. Neurotoxicology. 2024; 104 : 75–84. PubMed Abstract | Publisher Full Text 28. Carter CJ, Blizard RA: Autism genes are selectively targeted by environmental pollutants including pesticides, heavy metals, bisphenol A, phthalates and many others in food, cosmetics or household products. Neurochem. Int. 2016; 101 : 83–109. Publisher Full Text 29. Strunecka A, Strunecky O: Chronic Fluoride Exposure and the Risk of Autism Spectrum Disorder. Int. J. Environ. Res. Public Health. 2019; 16 (18): 3431. PubMed Abstract | Publisher Full Text | Free Full Text 30. Tasi T-L, Hsieh C-J, Wu M-T, et al. : Co-exposure to toxic metals and phthalates in pregnant women and their children’s mental health problems aged four years - Taiwan Maternal and Infant Cohort Study (TMICS). Environ. Int. 2023; 173 : 107804. PubMed Abstract | Publisher Full Text 31. Ghassabian A, Dickerson AS, Wang Y, et al. : Prenatal Air Pollution Exposure and Autism Spectrum Disorder in the ECHO Consortium. Environ. Health Perspect. 2025. PubMed Abstract | Publisher Full Text 32. O’Sharkey K, Mitra S, Chow T, et al. : Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990-2018). Environ. Int. 2025; 201 : 109562. PubMed Abstract | Publisher Full Text 33. Leisman G, Melillo R: Autism Spectrum Disorder: What Do We Know and Where Do We Go? Brain Sci. 2025; 15 (9): 1010. PubMed Abstract | Publisher Full Text | Free Full Text 34. Christensen J, Gronborg TK, Sorensen MJ, et al. : Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism. JAMA. 2013; 309 (1): 1696–1703. PubMed Abstract | Publisher Full Text | Free Full Text 35. Nicolini C, Fahnestock M: The valproic acid-induced rodent model of autism. Exp. Neurol. 2019; 299 (Pt A): 217–227. Publisher Full Text 36. Bae HJ, Mony TJ, Park SJ: Association between Prenatal Particulate Matter Exposure and Neuropsychiatric Disorders Development. Biomol. Ther (Seoul). 2025; 33 (4): 557–571. PubMed Abstract | Publisher Full Text | Free Full Text 37. Davis AP, Wiegers TC, Wiegers J, et al. : CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health. Toxicol. Sci. 2023; 195 (2): 155–168. PubMed Abstract | Publisher Full Text | Free Full Text 38. Kuzniar-Palka A: The Role of Oxidative Stress in Autism Spectrum Disorder Pathophysiology, Diagnosis and Treatment. Biomedicine. 2025; 13 (2): 388. PubMed Abstract | Publisher Full Text | Free Full Text 39. Bjorklund G, Dosa MD, Maes M, et al. : The impact of glutathione metabolism in autism spectrum disorder. Pharmacol. Res. 2021; 166 : 105437. PubMed Abstract | Publisher Full Text 40. El-Ansary A, Chirumbolo S, Bhat RS, et al. : The Role of Lipidomics in Autism Spectrum Disorder. Mol. Diagn. Ther. 2020; 24 (1): 31–48. PubMed Abstract | Publisher Full Text 41. Turbe H, Waeckel L, Dechelotte B: Overview of prospects for inflammation pathways in autism spectrum disorders. Encéphale. 2020; 46 (5): 404–407. PubMed Abstract | Publisher Full Text 42. Barak B, Feng G: Neurobiology of social behavior abnormalities in autism and Williams syndrome. Nat. Neurosci. 2016; 19 (6): 647–655. PubMed Abstract | Publisher Full Text | Free Full Text 43. Sandhu A, Rawat K, Gautam V, et al. : Ameliorating effect of pioglitazone on prenatal valproic acid-induced behavioral and neurobiological abnormalities in autism spectrum disorder in rats. Pharmacol. Biochem. Behav. 2024; 237 : 173721. PubMed Abstract | Publisher Full Text 44. Muller TE, Nunes SZ, Silviera A, et al. : Repeated ethanol exposure alters social behavior and oxidative stress parameters of zebrafish. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2017; 79 (Pt B): 105–111. PubMed Abstract | Publisher Full Text 45. Kaplan JR, Fontenot MB, Manuck SB, et al. : Influence of dietary lipids on agonistic and affiliative behavior in Macaca fascicularis. Am. J. Primatol. 1996; 38 (4): 333–347. PubMed Abstract | <a target="xrefwindow" id="d10303e2626" href="https://doi.org/10.1002/(SICI)1098-2345(1996)38:4 Publisher Full Text 46. Eisenberger NI, Moieni M, Inagaki TK, et al. : In Sickness and in Health: The Co-Regulation of Inflammation and Social Behavior. Neuropsychopharmacology. 2017; 42 (1): 242–253. PubMed Abstract | Publisher Full Text | Free Full Text 47. Saarimaki LA, Morikka J, Pavel A, et al. : Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach. Adv. Sci. (Weinh). 2023; 10 (2): e2203984. PubMed Abstract | Publisher Full Text | Free Full Text 48. Villeneuve DL, Crump D, Garcia-Reyero N, et al. : Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol. Sci. 2014; 142 (2): 312–320. PubMed Abstract | Publisher Full Text | Free Full Text 49. Kao H-T, Buka SL, Kelsey KT, et al. : The correlation between rates of cancer and autism: an exploratory ecological investigation. PLoS One. 2010; 5 (2): e9372. PubMed Abstract | Publisher Full Text | Free Full Text 50. Garriga-Cazoria H, Roman-Juan J, Marti L, et al. : Chronic Pain in Autism: A Systematic Review. Clin. J. Pain. 2025; 41 (8): e1296. PubMed Abstract | Publisher Full Text 51. Chiurazzi P, Kiani AK, Miertus J, et al. : Genetic analysis of intellectual disability and autism. Acta Biomed. 2020; 91 (13-S): e2020003. Publisher Full Text 52. Hutt C, Forrest SJ, Richer J: Cardiac arrhythmia and behaviour in autistic children. Acta Psychiatr. Scand. 1975; 51 (5): 361–372. PubMed Abstract | Publisher Full Text 53. Nayar K, Katz L, Heinrich K, et al. : Autism spectrum disorder and congenital heart disease: a narrative review of the literature. Cardiol. Young. 2023; 33 (6): 843–853. PubMed Abstract | Publisher Full Text 54. Sweeny KF, Lee CK, Mitchell PD, et al. : A Cross-Sectional Retrospective Review of Neuropsychiatric Comorbidities in Children With Non-Alcoholic Fatty Liver Disease. Clin. Pediatr. (Phila). 2025; 64 (9): 1274–1280. PubMed Abstract | Publisher Full Text 55. Li W, He X, Tan C, et al. : Causal Relationship Between Autism Spectrum Disorder and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization Study. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2025; 198 (2): e33012. PubMed Abstract | Publisher Full Text 56. Besag FMC: Epilepsy in patients with autism: links, risks and treatment challenges. Neuropsychiatr. Dis. Treat. 2017; 14 : 1–10. PubMed Abstract | Publisher Full Text | Free Full Text 57. Biomedical Data Translator Consortium: Toward A Universal Biomedical Data Translator. Clin. Transl. Sci. 2019; 12 (2): 86–90. PubMed Abstract | Publisher Full Text | Free Full Text 58. Martens M, Evelo CT, Willighagen EL: Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content. Appl in vitro Toxicol. 2022; 8 (1): 2–13. PubMed Abstract | Publisher Full Text | Free Full Text 59. Saarimaki LA, Fratello M, Pavel A, et al. : A curated gene and biological system annotation of adverse outcome pathways related to human health. Sci. Data. 2023; 10 (1): 409. PubMed Abstract | Publisher Full Text | Free Full Text 60. Willians AJ, Richard AM: Three pillars for ensuring public access and integrity of chemical databases powering cheminformatics. Aust. J. Chem. 2025; 17 (1): 40. PubMed Abstract | Publisher Full Text | Free Full Text 61. Ashburner M, Ball CA, Blake JA, et al. : Gene ontology: tool for the unification of biology. Nat. Genet. 2000; 25 (1): 25–29. PubMed Abstract | Publisher Full Text | Free Full Text 62. Svingen T, Villeneuve DL, Knapen D, et al. : A Pragmatic Approach to Adverse Outcome Pathway Development and Evaluation. Toxicol. Sci. 2021; 184 (2): 183–190. PubMed Abstract | Publisher Full Text | Free Full Text 63. Kim S, Chen J, Cheng T, et al. : PubChem 2025 update. Nucleic Acids Res. 2025; 53 (D1): D1516–D1525. PubMed Abstract | Publisher Full Text | Free Full Text 64. Williams AJ, Grulke CM, Edwards J, et al. : The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. Aust. J. Chem. 2017; 9 (1): 61. PubMed Abstract | Publisher Full Text | Free Full Text 65. Sayers EW, Beck J, Bolton EE, et al. : Database resources of the National Center for Biotechnology Information in 2025. Nucleic Acids Res. 2025; 53 (D1): D20–D29. PubMed Abstract | Publisher Full Text | Free Full Text 66. Carbon S, Ireland A, Mungall CJ, et al. : AmiGO: online access to ontology and annotation data. Bioinformatics. 2009; 25 (2): 288–289. PubMed Abstract | Publisher Full Text | Free Full Text 67. Mungall CJ, Torniai C, Gkoutos GV, et al. : Uberon, an integrative multi-species anatomy ontology. Genome Biol. 2012; 13 (1): R5. PubMed Abstract | Publisher Full Text | Free Full Text 68. Diehl AD, Meehan TF, Bradford YM, et al. : The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J. Biomed. Semantics. 2016; 7 (1): 44. PubMed Abstract | Publisher Full Text | Free Full Text 69. Schriml LM, Munro JB, Schor M, et al. : The Human Disease Ontology 2022 update. Nucleic Acids Res. 2022; 50 (D1): D1255–D1261. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 17 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Biological Sciences, North Carolina Sate University, Raleigh, North Carolina, 27695, USA 2 Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina, 27695, USA Allan Peter Davis Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation Thomas C. Wiegers Roles: Software, Writing – Review & Editing Daniela Sciaky Roles: Data Curation, Writing – Review & Editing Fern Barkalow Roles: Data Curation, Writing – Review & Editing Brent Wyatt Roles: Data Curation, Writing – Review & Editing Jolene Wiegers Roles: Software, Writing – Review & Editing Roy McMorran Roles: Software Sakib Abrar Roles: Software Carolyn J. Mattingly Roles: Funding Acquisition, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was supported by the National Institute of Environmental Health Sciences [U24 ES033155]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 27 Apr 2026, 14:1266 https://doi.org/10.12688/f1000research.172567.2 version 1 Published: 17 Nov 2025, 14:1266 https://doi.org/10.12688/f1000research.172567.1 Copyright © 2025 Davis AP et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Davis AP, Wiegers TC, Sciaky D et al. Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.12688/f1000research.172567.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 17 Nov 2025 Views 0 Cite How to cite this report: Chorley BN. Reviewer Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r467900 ) The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-467900 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Apr 2026 Brian N Chorley , US Environmental Protection Agency, Research Triangle Park, North Carolina, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190304.r467900 This manuscript by Davis et al. provides a case study to leverage the Comparative Toxicogenomics Database (CTD) to provide chemical context and/or supplemental content for Adverse Outcome Pathways (AOPs) in the AOP-Wiki. The use case here in an AOP associated ... Continue reading READ ALL This manuscript by Davis et al. provides a case study to leverage the Comparative Toxicogenomics Database (CTD) to provide chemical context and/or supplemental content for Adverse Outcome Pathways (AOPs) in the AOP-Wiki. The use case here in an AOP associated with autism, specifically AOP:522 "Estrogen antagonism leading to an increased risk of autism-like behavior". Two approaches leveraging the CTD were outlines: First, 6 KEs of AOP:522 were associated with GO and MESH terms, linking to chemicals using CTD search tools. Second, "CTD Tetramers" generated chemical-gene-phenotype-disease blocks associated with autism linked exposures (BPA, valproic acid, and PM) to derive literature-driven AOPs to both enhance existing AOPs (such as AOP:522) or create new ones for hypothesis generation, testing, or tool building. The manuscript is well-written, logically arranged, easy to follow and enhanced with helpful figures. The outlined procedures provide a theoretical blueprint to enhance existing AOPs, or generate new AOPS, using the continuously updated content of the CTD. Therefore, this manuscript suggests a path to fill in numerous gaps for existing “empty” AOPs, as well creating new content for AOs of interest. The steps provided, however, entail many manual steps that may be somewhat subjective and result in a daunting number of results. I have some suggestions, primarily minor, that should enhance the content of this manuscript. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals : In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. “Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: molecular biology, biomarkers, AOPs I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Chorley BN. Reviewer Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r467900 ) The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-467900 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 27 Apr 2026 Allan Davis , Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 27 Apr 2026 Author Response 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or ... Continue reading 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. AUTHORS’ RESPONSE : There are several different ways to retrieve CTD chemicals that intersect with the mapped AOP steps, including the Chemical-Gene Interaction Query (for chemicals interacting with mapped gene terms), the Chemical-Phenotype Interaction Query (for chemicals interacting with mapped phenotype terms), or the CTD Batch Query (for chemicals interacting with either mapped genes or mapped phenotypes). As well, users can simply go to the respective CTD gene page or CTD phenotype page and download the chemical set from the “Chemical” data-tab from each page. We think modifying Table 1 with a new column that simply repeats “Chemical-Gene Interaction Query” for each gene term and “Chemical-Phenotype Interaction Query” for each phenotype term would be distracting. Instead, in Supplementary Figure S2 we now demonstrate how to retrieve these chemical sets with comprehensive step-by-step instructions using the CTD Batch Query and include a detailed diagram as well. We also have made this distinction (between gene and phenotype queries) clearer in the “Methods” section with added text. 2. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. AUTHORS’ RESPONSE : We have now added a detailed Supplementary Figure S4 (figshare) that describes step-by-step instructions on how to construct this new AOP series, including identifying the tetramer genes that are shared between different tetramer phenotypes, and how these shared gene edges can be used to connect adjacent phenotypes and help design testable hypotheses to find supporting KER evidence. 3. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? AUTHORS’ RESPONSE : A complete list of the AOPs and AOs associated with the five MIE/KEs of AOP:522 is now provided as Supplementary Figure S6 (figshare). 4. From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks). We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process (figshare). 5. “ Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. AUTHORS’ RESPONSE : Our intention by that statement was to suggest that users could “refine and/or expand” AOP:522 by inclusion of some of the additional mechanistic key events derived from CTD tetramers. This is also suggested in Figure 4, which states in the legend: “This novel, manually generated AOP can serve as a framework to construct an entirely new AOP for autism or be used to refine or expand (gray downward arrows) AOP:522 from the AOP-Wiki.” 6. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. AUTHORS’ RESPONSE : We admit we have never heard of (nor seen) an UpSet plot before, and we feel that a casual reader also will not be familiar with how to read/interpret one. Hence, we prefer to keep our original Venn diagram in the full text, but we have since learned how to create an UpSet plot and now include it as an alternative visualization in new Supplementary Figure S5 (figshare), and point users to that alternative plot in both the text and Figure 5 legend. 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. AUTHORS’ RESPONSE : There are several different ways to retrieve CTD chemicals that intersect with the mapped AOP steps, including the Chemical-Gene Interaction Query (for chemicals interacting with mapped gene terms), the Chemical-Phenotype Interaction Query (for chemicals interacting with mapped phenotype terms), or the CTD Batch Query (for chemicals interacting with either mapped genes or mapped phenotypes). As well, users can simply go to the respective CTD gene page or CTD phenotype page and download the chemical set from the “Chemical” data-tab from each page. We think modifying Table 1 with a new column that simply repeats “Chemical-Gene Interaction Query” for each gene term and “Chemical-Phenotype Interaction Query” for each phenotype term would be distracting. Instead, in Supplementary Figure S2 we now demonstrate how to retrieve these chemical sets with comprehensive step-by-step instructions using the CTD Batch Query and include a detailed diagram as well. We also have made this distinction (between gene and phenotype queries) clearer in the “Methods” section with added text. 2. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. AUTHORS’ RESPONSE : We have now added a detailed Supplementary Figure S4 (figshare) that describes step-by-step instructions on how to construct this new AOP series, including identifying the tetramer genes that are shared between different tetramer phenotypes, and how these shared gene edges can be used to connect adjacent phenotypes and help design testable hypotheses to find supporting KER evidence. 3. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? AUTHORS’ RESPONSE : A complete list of the AOPs and AOs associated with the five MIE/KEs of AOP:522 is now provided as Supplementary Figure S6 (figshare). 4. From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks). We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process (figshare). 5. “ Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. AUTHORS’ RESPONSE : Our intention by that statement was to suggest that users could “refine and/or expand” AOP:522 by inclusion of some of the additional mechanistic key events derived from CTD tetramers. This is also suggested in Figure 4, which states in the legend: “This novel, manually generated AOP can serve as a framework to construct an entirely new AOP for autism or be used to refine or expand (gray downward arrows) AOP:522 from the AOP-Wiki.” 6. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. AUTHORS’ RESPONSE : We admit we have never heard of (nor seen) an UpSet plot before, and we feel that a casual reader also will not be familiar with how to read/interpret one. Hence, we prefer to keep our original Venn diagram in the full text, but we have since learned how to create an UpSet plot and now include it as an alternative visualization in new Supplementary Figure S5 (figshare), and point users to that alternative plot in both the text and Figure 5 legend. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 27 Apr 2026 Allan Davis , Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 27 Apr 2026 Author Response 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or ... Continue reading 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. AUTHORS’ RESPONSE : There are several different ways to retrieve CTD chemicals that intersect with the mapped AOP steps, including the Chemical-Gene Interaction Query (for chemicals interacting with mapped gene terms), the Chemical-Phenotype Interaction Query (for chemicals interacting with mapped phenotype terms), or the CTD Batch Query (for chemicals interacting with either mapped genes or mapped phenotypes). As well, users can simply go to the respective CTD gene page or CTD phenotype page and download the chemical set from the “Chemical” data-tab from each page. We think modifying Table 1 with a new column that simply repeats “Chemical-Gene Interaction Query” for each gene term and “Chemical-Phenotype Interaction Query” for each phenotype term would be distracting. Instead, in Supplementary Figure S2 we now demonstrate how to retrieve these chemical sets with comprehensive step-by-step instructions using the CTD Batch Query and include a detailed diagram as well. We also have made this distinction (between gene and phenotype queries) clearer in the “Methods” section with added text. 2. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. AUTHORS’ RESPONSE : We have now added a detailed Supplementary Figure S4 (figshare) that describes step-by-step instructions on how to construct this new AOP series, including identifying the tetramer genes that are shared between different tetramer phenotypes, and how these shared gene edges can be used to connect adjacent phenotypes and help design testable hypotheses to find supporting KER evidence. 3. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? AUTHORS’ RESPONSE : A complete list of the AOPs and AOs associated with the five MIE/KEs of AOP:522 is now provided as Supplementary Figure S6 (figshare). 4. From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks). We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process (figshare). 5. “ Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. AUTHORS’ RESPONSE : Our intention by that statement was to suggest that users could “refine and/or expand” AOP:522 by inclusion of some of the additional mechanistic key events derived from CTD tetramers. This is also suggested in Figure 4, which states in the legend: “This novel, manually generated AOP can serve as a framework to construct an entirely new AOP for autism or be used to refine or expand (gray downward arrows) AOP:522 from the AOP-Wiki.” 6. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. AUTHORS’ RESPONSE : We admit we have never heard of (nor seen) an UpSet plot before, and we feel that a casual reader also will not be familiar with how to read/interpret one. Hence, we prefer to keep our original Venn diagram in the full text, but we have since learned how to create an UpSet plot and now include it as an alternative visualization in new Supplementary Figure S5 (figshare), and point users to that alternative plot in both the text and Figure 5 legend. 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. AUTHORS’ RESPONSE : There are several different ways to retrieve CTD chemicals that intersect with the mapped AOP steps, including the Chemical-Gene Interaction Query (for chemicals interacting with mapped gene terms), the Chemical-Phenotype Interaction Query (for chemicals interacting with mapped phenotype terms), or the CTD Batch Query (for chemicals interacting with either mapped genes or mapped phenotypes). As well, users can simply go to the respective CTD gene page or CTD phenotype page and download the chemical set from the “Chemical” data-tab from each page. We think modifying Table 1 with a new column that simply repeats “Chemical-Gene Interaction Query” for each gene term and “Chemical-Phenotype Interaction Query” for each phenotype term would be distracting. Instead, in Supplementary Figure S2 we now demonstrate how to retrieve these chemical sets with comprehensive step-by-step instructions using the CTD Batch Query and include a detailed diagram as well. We also have made this distinction (between gene and phenotype queries) clearer in the “Methods” section with added text. 2. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. AUTHORS’ RESPONSE : We have now added a detailed Supplementary Figure S4 (figshare) that describes step-by-step instructions on how to construct this new AOP series, including identifying the tetramer genes that are shared between different tetramer phenotypes, and how these shared gene edges can be used to connect adjacent phenotypes and help design testable hypotheses to find supporting KER evidence. 3. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? AUTHORS’ RESPONSE : A complete list of the AOPs and AOs associated with the five MIE/KEs of AOP:522 is now provided as Supplementary Figure S6 (figshare). 4. From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks). We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process (figshare). 5. “ Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. AUTHORS’ RESPONSE : Our intention by that statement was to suggest that users could “refine and/or expand” AOP:522 by inclusion of some of the additional mechanistic key events derived from CTD tetramers. This is also suggested in Figure 4, which states in the legend: “This novel, manually generated AOP can serve as a framework to construct an entirely new AOP for autism or be used to refine or expand (gray downward arrows) AOP:522 from the AOP-Wiki.” 6. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. AUTHORS’ RESPONSE : We admit we have never heard of (nor seen) an UpSet plot before, and we feel that a casual reader also will not be familiar with how to read/interpret one. Hence, we prefer to keep our original Venn diagram in the full text, but we have since learned how to create an UpSet plot and now include it as an alternative visualization in new Supplementary Figure S5 (figshare), and point users to that alternative plot in both the text and Figure 5 legend. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Obrien J. Reviewer Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r462004 ) The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-462004 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Mar 2026 Jason Obrien , Environment and Climate Change Canada, Ottawa, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190304.r462004 Study Summary Data from the Comparative Toxicogenomic Database were linked to an Adverse Outcome Pathway from the AOPwiki to identify potentially useful chemical, gene, phenotype, disease and AOP relationships. The resulting relationships are potentially useful for ... Continue reading READ ALL Study Summary Data from the Comparative Toxicogenomic Database were linked to an Adverse Outcome Pathway from the AOPwiki to identify potentially useful chemical, gene, phenotype, disease and AOP relationships. The resulting relationships are potentially useful for identifying exposures that may influence the apical adverse outcome, or provide mechanistic support for the AOP. The authors selected AOP 522 (“Estrogen antagonism leading to increased risk of autism-like behavior”) as a pilot case study, but the approach could in theory be applied to any AOP. The authors identified >3000 chemicals in the CTD database that have some relationship to one or more of the Key Events (KEs) in AOP 522, 76 of which had relationships with five or more of the AOPs six KEs. Of these 76 chemicals, the authors selected three (Bisphenol A, valproic acid and particulate matter) to identify 136 genes and 53 phenotypes related to these chemicals and autism. The authors propose that the identified genes and phenotypes can form the basis of additional KEs related to the AOP, or be used to provide mechanistic support existing KEs. To demonstrate this, the authors selected 6 of the resulting phenotypes to construct a hypothetical AOP linking oxidative stress to autism. Finally, the authors used the CTD database to identify several other diseases that may have mechanistic overlap with AOP 522. Evaluation Summary Overall, this manuscript describes a well-conceived and executed study. The introduction presents clear and concise background information, with clear and justified objectives. The methods are clearly described and reproducible (I was even successful at replicating some of the CTD search results). The results are clearly conveyed and adequately discussed. I have only a few minor comments below for the author’s consideration. Specific Comments Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. Comment regarding selected AOP: The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) Comment regarding KE:386 mapping: The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. Comment 1 regarding support for AOP: The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. Comment 2 regarding support for AOP: The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. Comment regarding hypothetical AOP: How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? Minor Typo : Page 7, paragraph 1: Prioritized chemicals were group ED into categories Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Obrien J. Reviewer Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r462004 ) The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-462004 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 27 Apr 2026 Allan Davis , Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 27 Apr 2026 Author Response 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, ... Continue reading 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. AUTHORS’ RESPONSE : We have edited the “Methods” section to make it more generic and instead moved the specific “results” to the more appropriate “Results” section of the manuscript. 2. Comment regarding selected AOP : The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) AUTHORS’ RESPONSE : In response to similar comments from Reviewer 1, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 3. Comment regarding KE:386 mapping : The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. AUTHORS’ RESPONSE : As the reviewer notes, we believe a limitation in this study is the mapping of KE terms (often weakly defined or susceptible to interpretation) from the AOP-Wiki to their best corresponding matches in CTD. We discuss this in the “Results” section for KE:195 (which is sometimes described as “NMDAR inhibition” and at other times as “decreased NMDAR expression” in the AOP-Wiki). For AOP:522, KE:2208 (“aberrant, synaptic formation and plasticity”) is immediately upstream to KE:386 (“decrease of neuronal network function”). However, KE:2208 is poorly defined in the AOP-Wiki (with only a key event title and no other context) while the downstream KE:386 is almost overly defined with extensive descriptions in different sections of the webpage (ranging from the measurement of synaptic activity to neuron and brain development). This makes it challenging to interpret the best mapping for these two supposedly distinct KEs. To further highlight this issue, we have added two new passages to the “Results”: “Mapping terms across databases, however, is not always straightforward and can be subject to interpretation, and individual users can exercise flexibility to arrive at different translations, especially with respect to the level of granularity. In the AOP-Wiki, KE terms are defined in multiple sections, including the “event title” (a mandatory descriptive title for the KE), the “event component” (an optional ontology term, if known or applicable), and an “event description” (an optional descriptive passage about the biological state being measured and its role in biology). We attempted to balance these descriptions for each unique KE to make the best mappings to CTD.” and then later: “While KE:386 uses a broad event title (“decreased neuronal network function”), its event component is more nuanced (“decreased synaptic signaling”), which conceptually overlaps with the upstream key event KE:2208 (see above). However, the event description for KE:386 details the neuronal network processes in the developing and mature brain. To limit duplicative mapping used for KE:2208 (“synaptic signaling” GO:0099536), and, more importantly, to cover the essential features of neuron and brain development, we decided to map this KE more broadly to two CTD phenotypes: “nervous system development” (GO:0007399) and “nervous system process” (GO:0050877) which includes “neurogenesis” (GO:0022008), “brain development” (GO:0007420), and “transmission of nerve impulse” (GO:0019226), among many other neuronal functions and processes”. 4. Comment 1 regarding support for AOP : The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. AUTHORS’ RESPONSE : We have now added text to elaborate this point in the “Results”: “Linking literature-based chemical-gene-phenotype events (curated from mechanistic studies) into biologically plausible pathways can support KERs as well as provide testable hypotheses for additional experiments to quantify empirical evidence connecting these intermediate events in response to the same chemical stressor and via shared mechanistic genes (25).” 5. Comment 2 regarding support for AOP : The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. AUTHORS’ RESPONSE : Using CTD tetramers to discover new potential intermediate mechanistic phenotypes/KEs and construct new AOP series creates a highly interconnected map of linked phenotypes/KEs that have numerous edges via shared chemical-gene, chemical-phenotype, and gene-phenotype relationships (see Figure 4); this offers curated literature-based support for KERs as well as new avenues for developing additional experiments to further validate the KERs. Also, results from CTD Tetramers Query are ranked by an “Evidence Strength Score”, calculated as the product score of the number of references used in each of the four lines of supporting evidence needed to construct a CGPD-tetramer. Thus, computational tetramers with a higher number of source articles across the five supporting lines of evidence categories will have a higher calculated “Evidence Strength Score” and be ranked at the top of the output results, providing users with an option to filter tetramers with a higher amount of underlying literature support; this is also diagrammed as step 4 in the newly added Supplementary Figure S3. We have added to the description of our new model AOP constructed from shared tetramer data, describing how these identified tetramers provide “overlapping genes shared between the phenotypes as mechanistic links connecting the key events and provide support for KER evidence”. This is also currently described in the Figure 4 legend: “Genes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events”. 6. Comment regarding hypothetical AOP : How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks, per Reviewer 3) and manually ordered them at four levels of biological organization. We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process. 7. Minor Typo : Page 7, paragraph 1: Prioritized chemicals were groupED into categories AUTHORS’ RESPONSE : We have fixed this typo. 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. AUTHORS’ RESPONSE : We have edited the “Methods” section to make it more generic and instead moved the specific “results” to the more appropriate “Results” section of the manuscript. 2. Comment regarding selected AOP : The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) AUTHORS’ RESPONSE : In response to similar comments from Reviewer 1, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 3. Comment regarding KE:386 mapping : The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. AUTHORS’ RESPONSE : As the reviewer notes, we believe a limitation in this study is the mapping of KE terms (often weakly defined or susceptible to interpretation) from the AOP-Wiki to their best corresponding matches in CTD. We discuss this in the “Results” section for KE:195 (which is sometimes described as “NMDAR inhibition” and at other times as “decreased NMDAR expression” in the AOP-Wiki). For AOP:522, KE:2208 (“aberrant, synaptic formation and plasticity”) is immediately upstream to KE:386 (“decrease of neuronal network function”). However, KE:2208 is poorly defined in the AOP-Wiki (with only a key event title and no other context) while the downstream KE:386 is almost overly defined with extensive descriptions in different sections of the webpage (ranging from the measurement of synaptic activity to neuron and brain development). This makes it challenging to interpret the best mapping for these two supposedly distinct KEs. To further highlight this issue, we have added two new passages to the “Results”: “Mapping terms across databases, however, is not always straightforward and can be subject to interpretation, and individual users can exercise flexibility to arrive at different translations, especially with respect to the level of granularity. In the AOP-Wiki, KE terms are defined in multiple sections, including the “event title” (a mandatory descriptive title for the KE), the “event component” (an optional ontology term, if known or applicable), and an “event description” (an optional descriptive passage about the biological state being measured and its role in biology). We attempted to balance these descriptions for each unique KE to make the best mappings to CTD.” and then later: “While KE:386 uses a broad event title (“decreased neuronal network function”), its event component is more nuanced (“decreased synaptic signaling”), which conceptually overlaps with the upstream key event KE:2208 (see above). However, the event description for KE:386 details the neuronal network processes in the developing and mature brain. To limit duplicative mapping used for KE:2208 (“synaptic signaling” GO:0099536), and, more importantly, to cover the essential features of neuron and brain development, we decided to map this KE more broadly to two CTD phenotypes: “nervous system development” (GO:0007399) and “nervous system process” (GO:0050877) which includes “neurogenesis” (GO:0022008), “brain development” (GO:0007420), and “transmission of nerve impulse” (GO:0019226), among many other neuronal functions and processes”. 4. Comment 1 regarding support for AOP : The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. AUTHORS’ RESPONSE : We have now added text to elaborate this point in the “Results”: “Linking literature-based chemical-gene-phenotype events (curated from mechanistic studies) into biologically plausible pathways can support KERs as well as provide testable hypotheses for additional experiments to quantify empirical evidence connecting these intermediate events in response to the same chemical stressor and via shared mechanistic genes (25).” 5. Comment 2 regarding support for AOP : The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. AUTHORS’ RESPONSE : Using CTD tetramers to discover new potential intermediate mechanistic phenotypes/KEs and construct new AOP series creates a highly interconnected map of linked phenotypes/KEs that have numerous edges via shared chemical-gene, chemical-phenotype, and gene-phenotype relationships (see Figure 4); this offers curated literature-based support for KERs as well as new avenues for developing additional experiments to further validate the KERs. Also, results from CTD Tetramers Query are ranked by an “Evidence Strength Score”, calculated as the product score of the number of references used in each of the four lines of supporting evidence needed to construct a CGPD-tetramer. Thus, computational tetramers with a higher number of source articles across the five supporting lines of evidence categories will have a higher calculated “Evidence Strength Score” and be ranked at the top of the output results, providing users with an option to filter tetramers with a higher amount of underlying literature support; this is also diagrammed as step 4 in the newly added Supplementary Figure S3. We have added to the description of our new model AOP constructed from shared tetramer data, describing how these identified tetramers provide “overlapping genes shared between the phenotypes as mechanistic links connecting the key events and provide support for KER evidence”. This is also currently described in the Figure 4 legend: “Genes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events”. 6. Comment regarding hypothetical AOP : How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks, per Reviewer 3) and manually ordered them at four levels of biological organization. We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process. 7. Minor Typo : Page 7, paragraph 1: Prioritized chemicals were groupED into categories AUTHORS’ RESPONSE : We have fixed this typo. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 27 Apr 2026 Allan Davis , Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 27 Apr 2026 Author Response 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, ... Continue reading 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. AUTHORS’ RESPONSE : We have edited the “Methods” section to make it more generic and instead moved the specific “results” to the more appropriate “Results” section of the manuscript. 2. Comment regarding selected AOP : The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) AUTHORS’ RESPONSE : In response to similar comments from Reviewer 1, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 3. Comment regarding KE:386 mapping : The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. AUTHORS’ RESPONSE : As the reviewer notes, we believe a limitation in this study is the mapping of KE terms (often weakly defined or susceptible to interpretation) from the AOP-Wiki to their best corresponding matches in CTD. We discuss this in the “Results” section for KE:195 (which is sometimes described as “NMDAR inhibition” and at other times as “decreased NMDAR expression” in the AOP-Wiki). For AOP:522, KE:2208 (“aberrant, synaptic formation and plasticity”) is immediately upstream to KE:386 (“decrease of neuronal network function”). However, KE:2208 is poorly defined in the AOP-Wiki (with only a key event title and no other context) while the downstream KE:386 is almost overly defined with extensive descriptions in different sections of the webpage (ranging from the measurement of synaptic activity to neuron and brain development). This makes it challenging to interpret the best mapping for these two supposedly distinct KEs. To further highlight this issue, we have added two new passages to the “Results”: “Mapping terms across databases, however, is not always straightforward and can be subject to interpretation, and individual users can exercise flexibility to arrive at different translations, especially with respect to the level of granularity. In the AOP-Wiki, KE terms are defined in multiple sections, including the “event title” (a mandatory descriptive title for the KE), the “event component” (an optional ontology term, if known or applicable), and an “event description” (an optional descriptive passage about the biological state being measured and its role in biology). We attempted to balance these descriptions for each unique KE to make the best mappings to CTD.” and then later: “While KE:386 uses a broad event title (“decreased neuronal network function”), its event component is more nuanced (“decreased synaptic signaling”), which conceptually overlaps with the upstream key event KE:2208 (see above). However, the event description for KE:386 details the neuronal network processes in the developing and mature brain. To limit duplicative mapping used for KE:2208 (“synaptic signaling” GO:0099536), and, more importantly, to cover the essential features of neuron and brain development, we decided to map this KE more broadly to two CTD phenotypes: “nervous system development” (GO:0007399) and “nervous system process” (GO:0050877) which includes “neurogenesis” (GO:0022008), “brain development” (GO:0007420), and “transmission of nerve impulse” (GO:0019226), among many other neuronal functions and processes”. 4. Comment 1 regarding support for AOP : The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. AUTHORS’ RESPONSE : We have now added text to elaborate this point in the “Results”: “Linking literature-based chemical-gene-phenotype events (curated from mechanistic studies) into biologically plausible pathways can support KERs as well as provide testable hypotheses for additional experiments to quantify empirical evidence connecting these intermediate events in response to the same chemical stressor and via shared mechanistic genes (25).” 5. Comment 2 regarding support for AOP : The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. AUTHORS’ RESPONSE : Using CTD tetramers to discover new potential intermediate mechanistic phenotypes/KEs and construct new AOP series creates a highly interconnected map of linked phenotypes/KEs that have numerous edges via shared chemical-gene, chemical-phenotype, and gene-phenotype relationships (see Figure 4); this offers curated literature-based support for KERs as well as new avenues for developing additional experiments to further validate the KERs. Also, results from CTD Tetramers Query are ranked by an “Evidence Strength Score”, calculated as the product score of the number of references used in each of the four lines of supporting evidence needed to construct a CGPD-tetramer. Thus, computational tetramers with a higher number of source articles across the five supporting lines of evidence categories will have a higher calculated “Evidence Strength Score” and be ranked at the top of the output results, providing users with an option to filter tetramers with a higher amount of underlying literature support; this is also diagrammed as step 4 in the newly added Supplementary Figure S3. We have added to the description of our new model AOP constructed from shared tetramer data, describing how these identified tetramers provide “overlapping genes shared between the phenotypes as mechanistic links connecting the key events and provide support for KER evidence”. This is also currently described in the Figure 4 legend: “Genes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events”. 6. Comment regarding hypothetical AOP : How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks, per Reviewer 3) and manually ordered them at four levels of biological organization. We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process. 7. Minor Typo : Page 7, paragraph 1: Prioritized chemicals were groupED into categories AUTHORS’ RESPONSE : We have fixed this typo. 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. AUTHORS’ RESPONSE : We have edited the “Methods” section to make it more generic and instead moved the specific “results” to the more appropriate “Results” section of the manuscript. 2. Comment regarding selected AOP : The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) AUTHORS’ RESPONSE : In response to similar comments from Reviewer 1, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 3. Comment regarding KE:386 mapping : The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. AUTHORS’ RESPONSE : As the reviewer notes, we believe a limitation in this study is the mapping of KE terms (often weakly defined or susceptible to interpretation) from the AOP-Wiki to their best corresponding matches in CTD. We discuss this in the “Results” section for KE:195 (which is sometimes described as “NMDAR inhibition” and at other times as “decreased NMDAR expression” in the AOP-Wiki). For AOP:522, KE:2208 (“aberrant, synaptic formation and plasticity”) is immediately upstream to KE:386 (“decrease of neuronal network function”). However, KE:2208 is poorly defined in the AOP-Wiki (with only a key event title and no other context) while the downstream KE:386 is almost overly defined with extensive descriptions in different sections of the webpage (ranging from the measurement of synaptic activity to neuron and brain development). This makes it challenging to interpret the best mapping for these two supposedly distinct KEs. To further highlight this issue, we have added two new passages to the “Results”: “Mapping terms across databases, however, is not always straightforward and can be subject to interpretation, and individual users can exercise flexibility to arrive at different translations, especially with respect to the level of granularity. In the AOP-Wiki, KE terms are defined in multiple sections, including the “event title” (a mandatory descriptive title for the KE), the “event component” (an optional ontology term, if known or applicable), and an “event description” (an optional descriptive passage about the biological state being measured and its role in biology). We attempted to balance these descriptions for each unique KE to make the best mappings to CTD.” and then later: “While KE:386 uses a broad event title (“decreased neuronal network function”), its event component is more nuanced (“decreased synaptic signaling”), which conceptually overlaps with the upstream key event KE:2208 (see above). However, the event description for KE:386 details the neuronal network processes in the developing and mature brain. To limit duplicative mapping used for KE:2208 (“synaptic signaling” GO:0099536), and, more importantly, to cover the essential features of neuron and brain development, we decided to map this KE more broadly to two CTD phenotypes: “nervous system development” (GO:0007399) and “nervous system process” (GO:0050877) which includes “neurogenesis” (GO:0022008), “brain development” (GO:0007420), and “transmission of nerve impulse” (GO:0019226), among many other neuronal functions and processes”. 4. Comment 1 regarding support for AOP : The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. AUTHORS’ RESPONSE : We have now added text to elaborate this point in the “Results”: “Linking literature-based chemical-gene-phenotype events (curated from mechanistic studies) into biologically plausible pathways can support KERs as well as provide testable hypotheses for additional experiments to quantify empirical evidence connecting these intermediate events in response to the same chemical stressor and via shared mechanistic genes (25).” 5. Comment 2 regarding support for AOP : The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. AUTHORS’ RESPONSE : Using CTD tetramers to discover new potential intermediate mechanistic phenotypes/KEs and construct new AOP series creates a highly interconnected map of linked phenotypes/KEs that have numerous edges via shared chemical-gene, chemical-phenotype, and gene-phenotype relationships (see Figure 4); this offers curated literature-based support for KERs as well as new avenues for developing additional experiments to further validate the KERs. Also, results from CTD Tetramers Query are ranked by an “Evidence Strength Score”, calculated as the product score of the number of references used in each of the four lines of supporting evidence needed to construct a CGPD-tetramer. Thus, computational tetramers with a higher number of source articles across the five supporting lines of evidence categories will have a higher calculated “Evidence Strength Score” and be ranked at the top of the output results, providing users with an option to filter tetramers with a higher amount of underlying literature support; this is also diagrammed as step 4 in the newly added Supplementary Figure S3. We have added to the description of our new model AOP constructed from shared tetramer data, describing how these identified tetramers provide “overlapping genes shared between the phenotypes as mechanistic links connecting the key events and provide support for KER evidence”. This is also currently described in the Figure 4 legend: “Genes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events”. 6. Comment regarding hypothetical AOP : How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks, per Reviewer 3) and manually ordered them at four levels of biological organization. We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process. 7. Minor Typo : Page 7, paragraph 1: Prioritized chemicals were groupED into categories AUTHORS’ RESPONSE : We have fixed this typo. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Mortensen HM. Reviewer Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r456179 ) The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-456179 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Mar 2026 Holly M Mortensen , US Environmental Protection Agency, Research Triangle Park, North Carolina, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190304.r456179 Issue with selected AOP -Status “open for adoption” AOP 522 is classified as "open for adoption" and is poorly populated in the AOP Wiki Cui et al: Previous study has shown that prenatal ... Continue reading READ ALL Issue with selected AOP -Status “open for adoption” AOP 522 is classified as "open for adoption" and is poorly populated in the AOP Wiki Cui et al: Previous study has shown that prenatal or postnatal exposure to EEDs could increase the risk of ASD in children (Mandy and Lai, 2016). Mandy and Lai, 2016 clearly state that "Although the studies generally showed a positive association between EDCs and ASD, after considering the strengths and limitations, we concluded that the overall strength of evidence supporting an association between prenatal exposure to EDCs and later ASD in humans remains "limited" and inconclusive. Further well-conducted prospective studies are warranted to clarify the role of EDCs on ASD development." Manuscript would benefit from an analysis workflow. The authors present a complicated analysis routine that is difficult to follow. Individual steps are described in sufficient detail, but the reader is left to follow. Suggest a general diagram, eg. Flow chart, decision tree to describe steps taken in the present analyses. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility. Subjective or inconsistent use of terms, for example, would invalidate findings. Suggest performing on >1 AOP to test level of reproducibility. This reviewer is concerned with the validity and consistence of gene-chemical vs. chemical phenotype groupings with various levels of confidence. Authors do not specify confidence level or thresholding for association groups. Same comment for “shared term percentage”-not reproducible with the current workflow explanations. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100. “Estrogen receptors regulate a multitude of biological and physiological processes” (Fuentes, 2019, doi: 10.1016/bs.apcsb.2019.01.001) Estrogenic effects occur via multiple mechanisms, pathways and receptors, some of which are not fully understood at present (Marino , 2006 doi: 10.2174/138920206779315737) Supplemental training materials are suggested to facilitate reproducibility of the methods presented Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). The standardization of mapping molecular identifiers to AOPs is an area of need in the AOP field. However, the Authors use term mapping from AOP-Wiki key events to map to CTD phenotype terms. This manual process could be improved for reproducibility and accuracy. Suggest implementing ontology-based gene mapping to CTD chemical-gene pairs and possibly using a publicly available AOP tool that provides this mapping, like the AOP-DB (Mortensen, Senn, 2021; Pittman, 2018); AOP-DB RDF (Mortensen, Martens, 2022) or AOP-WIKI EXPLORER (Saurav, 2024) , followed by mapping to ctd chem-gene or manually to phenotype. The AOP-DB ( Mortensen, Senn , 2021) actually maps AOP molecular KE (entrez genes ) directly to CTD-Gene tables, as well as DTXIDs, which could minimize the manual curation (and chance for error). Please reference the literature and relevant contributions in this area In Limitations and Strengths section Suggest referencing the citations (Ives, et al 2017; Pittman, 2018; Mortensen, Senn 2021). “some biomedical data translators” are reference to assist in mapping KE to gene ontology terms”. Suggest referencing the extensive work in this area that preceded/contributed to the cited contributions (Pittman, 2018; Mortensen, Senn 2021; Mortensen, Martens, 2022) as well as other semantic mapping approaches (Saurav, 2024). “Other methods have been developed to increase the usability of AOP information, such as converting the AOP-Wiki into semantic web formats 58 or curating relevant KEs to gene sets associated with pathways, phenotypes, and GO terms 59 ”. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mortensen HM. Reviewer Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r456179 ) The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-456179 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 27 Apr 2026 Allan Davis , Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 27 Apr 2026 Author Response 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to ... Continue reading 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to help inform, refine, expand, and hopefully advance the status of underdeveloped AOPs. When we initially submitted our manuscript (17 November 2025), the AOP-Wiki contained 532 AOPs, of which only 7% were designated as endorsed by WPHA/WNT, with the remaining 93% of the AOP-Wiki content described as empty (74%), under development (16%), under review (3%), and none with ESCA approval (0%). In our minds, these statistics are indicative of a critical need for new methodologies to advance the status of these languishing AOPs as a way to improve the overall utility and resourcefulness of the AOP-Wiki. The AOP used in our demonstration (i.e., AOP:522) is a good representative candidate, because, as this reviewer noted, it is “open for adoption”, currently poorly populated in the AOP-Wiki itself, and has equivocal limiting supporting evidence from the literature (although enough to warrant the construction of and deposition in the AOP-Wiki). As Mari-Bauset et al. (2018) [rather than the mistakenly cited Mandy and Lai, 2016] conclude in their article about EDC and ASD: “incomplete understanding of biological mechanisms precludes the establishment of a causal relationship”, supporting the primary need to better understand the biological mechanistic steps that can link these disrupting chemicals to ASD, such as the development of a more robust AOP. In our manuscript, we demonstrate how researchers can (1) leverage CTD to identify environmental chemicals that could modulate autism etiology via this AOP and (2) then use those identified chemicals to point researchers in mechanistically-supported directions to further develop and refine AOP:522 to a better position for testability and advancement. To help clarify our intention, we have improved the text. In response to similar comments from Reviewer 2, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 2. Manuscript would benefit from an analysis workflow AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 3. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility AUTHORS’ RESPONSE : The crux of this work is to demonstrate how users can leverage CTD chemical content and readily integrate it with AOPs to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with AOP:522 and (2) then use these chemicals to discover new, potential mechanistic steps to further develop and refine AOP:522 to a better position for testability and advancement. A step-by-step instruction guide is now provided (see new Supplemental Figures at figshare) for all the manual queries and analyses, enabling both validation and reproducibility. However, it is important to understand that CTD is updated each month with new curated content from the recently published literature; this is one of the strengths of CTD. Thus, query results can change on a monthly basis (as currently explained in our “Methods” section). Importantly, results from newer queries do not invalidate findings, but rather provide additional data from the more recent literature. In Figure 2, we grouped the 76 prioritized chemicals simply as a display convenience (e.g., clustering together similar chemicals, such as metals, phthalates, pesticides, etc.). The clustering itself does not impart any additional knowledge to the figure (compared to not clustering the chemicals). We performed that task, as described in the manuscript, using web searches or shared term parentage in the CTD Chemical vocabulary hierarchy. For example, “dibutyl phthalate”, “diethylhexyl phthalate”, and “monobutyl phthalate”, all share “Phthalic Acids” as a common parent in the CTD chemical hierarchy, and thus can be grouped as such. Figure 2 is just as valid and reproducible if the chemicals were not clustered, and instead listed alphabetically. 4. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100 AUTHORS’ RESPONSE : In our manuscript, we did not base estrogenic effects only on the two estrogen genes (ESR1 and ESR2). Rather, we mapped this MIE:112 to four CTD terms (Table 1): the two estrogen receptor genes ESR1 and ESR2 plus, importantly, two additional estrogen-signaling related phenotypes (which include all of their GO-phenotype descendants as well) to capture chemicals that still affect the estrogen receptor signaling pathway but without necessarily affecting the specific estrogen receptors. It is important to understand that at CTD a chemical-phenotype interaction does not (necessarily) have to involve a gene, but is simply a curated annotation describing how a chemical causes a biological outcome reported in the literature; e.g., “chemical X results in decreased intracellular estrogen receptor signaling pathway in MCF-7 cells” is a chemical-phenotype interaction reported by an author without any discussion or knowledge of the genes involved. This specifically enables CTD to capture literature-based chemical-induced outcomes without having to know a priori what genes are involved. We would direct the reviewer to our detailed description of this CTD curation module to better understand what we mean as chemical-phenotype interactions (Davis et al., 2018). This CTD curation paradigm allows CTD to capture important data for chemical-induced events directly, without having to involve any genes (either known or unknown by the author). Thus, chemicals that affect estrogen receptor signaling by “mechanisms which are not understood” (Marino, 2006) can still be captured in CTD because we curate the direct chemical-phenotype relationship itself, obviating the need to invoke any unknown gene/mechanism. If a user chooses to broaden their interpretation of “estrogen receptor antagonism”, they can select a more expansive phenotype in the ontology by using a parent term, such as “steroid hormone mediated pathway” (GO:0043401), which now expands the query range and at this writing returns 231 unique chemicals instead of the original 180 chemicals shown in Table 1 for the more specific phenotype. Curating phenotype data as an ontology (instead of as presumptive “gene set) is an advantage, and enables users to broaden or narrow their searches by navigating the vocabulary to the level they prefer. If deemed necessary, users can always map this particular AOP step to additional genes beyond ESR1 and ESR2 if they prefer: this will only bring back additional chemical data for analysis. Finally, a user could try to leverage Natural Language Processing (NLP)-based mapping tools that attempt to link KEs first to “gene lists” and then find CTD chemicals that interact with those gene sets. NLP methods, however, are notoriously complex and difficult to follow and validate for the non-technical user, and, unfortunately, the results may go out of date quickly unless the NLP tool is updated on a consistent schedule; in fact, spot checks by CTD found some strange and equivocal mappings (discussed below) performed by such NLP methods. The main point here, however, is that individual users have the ability to leverage CTD to their level of specificity, comfort, and skill level to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with this AOP and (2) then use these chemicals to discover new, potential mechanistic steps to further develop, expand, and improve the AOP to a better position for testability and advancement. Different users might decide to use different terms to retrieve data, and that’s perfectly fine. The user ultimately decides how large of a data-net he/she wants to cast and then can decide which molecular mechanisms retrieved from those results make the most sense to include in a testable updated/new AOP. CTD provides the chemical data-set and evidence to fill in the mechanistic knowledge gaps connecting exposure to an adverse outcome. To better reflect this, we edited all occurrences in the “Results and Discussion” of “This KE mapped to…” to the better phrase of “We mapped this KE to…” to emphasize that users are in control of the process here. 5. Supplemental training materials are suggested to facilitate reproducibility of the methods presented. AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 6. Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). AUTHORS’ RESPONSE : We wholeheartedly agree and promote the idea of the AOP research community coming together to diligently standardize their mappings, identification, and definitions. We attempted to use many of the third party tools currently promoted by the AOP-Wiki in our original mapping and analysis, but unfortunately, we began to realize that many of these resources are unreliable, as they were either outdated, non-intuitive as to how to use, no longer available as a web application, or simply returned generic links back to the AOP-Wiki without providing any new context: AOP-DB (as defined by Mortensen, Senn, 2021; Pittman, 2018) is listed as a third-party tool on the AOP-Wiki, but is no longer accessible from its stated URL, and the EPA site appears to have been last updated May 2021 and also is inaccessible from its stated URL. Wiki Kaptis has a copyright stamp of 2022, and the information menu states that it only contains data extracted from the AOP-Wiki up to 2023. Our searches using official AOP-Wiki terms such as: AOP:522, autism (AO:2209), estrogen receptor antagonism (MIE:112), ERK1/2 inhibition (KE:2207), and aberrant synaptic formation and plasticity (KE:2208) yielded no results. The AOP-KB is a tool provided by the OECD, but again, appears to be limited in functionality. A user can enter any term (e.g., “estrogen receptor”) and retrieve term matches to AOPs and KEs such as “Androgen receptor activation leading to prostate cancer”, but this result simply links back to the AOP-Wiki, with no explanation as to why this AOP would be returned as a result for “estrogen receptor”. It is not obvious how the AOP-KB determined that “estrogen receptor” is involved in this AOP. Similarly, a search with “autism” returns no results, suggesting the AOP-KB is also not current and out-of-date with the AOP-Wiki. AOP-WIKI EXPLORER (as defined by Kumar et al. 2024) is no longer accessible from its stated URL. We next attempted to use a tool that employs NLP to map curated genes to KEs (Saarimaki et al. (2023), but the resource also seems to be out of date, and spot-checks performed by us gave questionable results: e.g., MIE:112 (“estrogen receptor antagonism”) was mapped to 59 genes, but surprisingly did not include ESR2, the second critical estrogen receptor in humans; similarly, KE:195 (“NMDARs inhibition”) contained only seven GRIN genes, missing 16 other GRIN genes that we included in our CTD analysis. The other KEs we checked (KE:2207, KE:2208, KE:2209) do not even exist in the file, presumably because of its outdatedness. Based upon these unsatisfying and inconsistent approaches, in this manuscript, we decided to map the six AOP terms to CTD genes and phenotypes manually ourselves, as a typical CTD user would find it necessary to do. Going forward, it would be more advantageous if the AOP-Wiki recommended tools that are more easily and readily accessible, stable, intuitive to use, and designed for data currency, otherwise they risk becoming out of date with diminishing value as time goes on. 7. Please reference the literature and relevant contributions in this area. AUTHORS’ RESPONSE : Thank you. To the “Limitations and Strengths” section, we have now added citations for: Ives et al. (2017): Creating a Structured AOP Knowledgebase via Ontology-Based Annotations; Pittman et al. (2018): AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks; Mortensen et al. (2021): The 2021 update of the EPA’s adverse outcome pathway database; and Kumar et al. (2024): AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models. 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to help inform, refine, expand, and hopefully advance the status of underdeveloped AOPs. When we initially submitted our manuscript (17 November 2025), the AOP-Wiki contained 532 AOPs, of which only 7% were designated as endorsed by WPHA/WNT, with the remaining 93% of the AOP-Wiki content described as empty (74%), under development (16%), under review (3%), and none with ESCA approval (0%). In our minds, these statistics are indicative of a critical need for new methodologies to advance the status of these languishing AOPs as a way to improve the overall utility and resourcefulness of the AOP-Wiki. The AOP used in our demonstration (i.e., AOP:522) is a good representative candidate, because, as this reviewer noted, it is “open for adoption”, currently poorly populated in the AOP-Wiki itself, and has equivocal limiting supporting evidence from the literature (although enough to warrant the construction of and deposition in the AOP-Wiki). As Mari-Bauset et al. (2018) [rather than the mistakenly cited Mandy and Lai, 2016] conclude in their article about EDC and ASD: “incomplete understanding of biological mechanisms precludes the establishment of a causal relationship”, supporting the primary need to better understand the biological mechanistic steps that can link these disrupting chemicals to ASD, such as the development of a more robust AOP. In our manuscript, we demonstrate how researchers can (1) leverage CTD to identify environmental chemicals that could modulate autism etiology via this AOP and (2) then use those identified chemicals to point researchers in mechanistically-supported directions to further develop and refine AOP:522 to a better position for testability and advancement. To help clarify our intention, we have improved the text. In response to similar comments from Reviewer 2, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 2. Manuscript would benefit from an analysis workflow AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 3. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility AUTHORS’ RESPONSE : The crux of this work is to demonstrate how users can leverage CTD chemical content and readily integrate it with AOPs to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with AOP:522 and (2) then use these chemicals to discover new, potential mechanistic steps to further develop and refine AOP:522 to a better position for testability and advancement. A step-by-step instruction guide is now provided (see new Supplemental Figures at figshare) for all the manual queries and analyses, enabling both validation and reproducibility. However, it is important to understand that CTD is updated each month with new curated content from the recently published literature; this is one of the strengths of CTD. Thus, query results can change on a monthly basis (as currently explained in our “Methods” section). Importantly, results from newer queries do not invalidate findings, but rather provide additional data from the more recent literature. In Figure 2, we grouped the 76 prioritized chemicals simply as a display convenience (e.g., clustering together similar chemicals, such as metals, phthalates, pesticides, etc.). The clustering itself does not impart any additional knowledge to the figure (compared to not clustering the chemicals). We performed that task, as described in the manuscript, using web searches or shared term parentage in the CTD Chemical vocabulary hierarchy. For example, “dibutyl phthalate”, “diethylhexyl phthalate”, and “monobutyl phthalate”, all share “Phthalic Acids” as a common parent in the CTD chemical hierarchy, and thus can be grouped as such. Figure 2 is just as valid and reproducible if the chemicals were not clustered, and instead listed alphabetically. 4. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100 AUTHORS’ RESPONSE : In our manuscript, we did not base estrogenic effects only on the two estrogen genes (ESR1 and ESR2). Rather, we mapped this MIE:112 to four CTD terms (Table 1): the two estrogen receptor genes ESR1 and ESR2 plus, importantly, two additional estrogen-signaling related phenotypes (which include all of their GO-phenotype descendants as well) to capture chemicals that still affect the estrogen receptor signaling pathway but without necessarily affecting the specific estrogen receptors. It is important to understand that at CTD a chemical-phenotype interaction does not (necessarily) have to involve a gene, but is simply a curated annotation describing how a chemical causes a biological outcome reported in the literature; e.g., “chemical X results in decreased intracellular estrogen receptor signaling pathway in MCF-7 cells” is a chemical-phenotype interaction reported by an author without any discussion or knowledge of the genes involved. This specifically enables CTD to capture literature-based chemical-induced outcomes without having to know a priori what genes are involved. We would direct the reviewer to our detailed description of this CTD curation module to better understand what we mean as chemical-phenotype interactions (Davis et al., 2018). This CTD curation paradigm allows CTD to capture important data for chemical-induced events directly, without having to involve any genes (either known or unknown by the author). Thus, chemicals that affect estrogen receptor signaling by “mechanisms which are not understood” (Marino, 2006) can still be captured in CTD because we curate the direct chemical-phenotype relationship itself, obviating the need to invoke any unknown gene/mechanism. If a user chooses to broaden their interpretation of “estrogen receptor antagonism”, they can select a more expansive phenotype in the ontology by using a parent term, such as “steroid hormone mediated pathway” (GO:0043401), which now expands the query range and at this writing returns 231 unique chemicals instead of the original 180 chemicals shown in Table 1 for the more specific phenotype. Curating phenotype data as an ontology (instead of as presumptive “gene set) is an advantage, and enables users to broaden or narrow their searches by navigating the vocabulary to the level they prefer. If deemed necessary, users can always map this particular AOP step to additional genes beyond ESR1 and ESR2 if they prefer: this will only bring back additional chemical data for analysis. Finally, a user could try to leverage Natural Language Processing (NLP)-based mapping tools that attempt to link KEs first to “gene lists” and then find CTD chemicals that interact with those gene sets. NLP methods, however, are notoriously complex and difficult to follow and validate for the non-technical user, and, unfortunately, the results may go out of date quickly unless the NLP tool is updated on a consistent schedule; in fact, spot checks by CTD found some strange and equivocal mappings (discussed below) performed by such NLP methods. The main point here, however, is that individual users have the ability to leverage CTD to their level of specificity, comfort, and skill level to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with this AOP and (2) then use these chemicals to discover new, potential mechanistic steps to further develop, expand, and improve the AOP to a better position for testability and advancement. Different users might decide to use different terms to retrieve data, and that’s perfectly fine. The user ultimately decides how large of a data-net he/she wants to cast and then can decide which molecular mechanisms retrieved from those results make the most sense to include in a testable updated/new AOP. CTD provides the chemical data-set and evidence to fill in the mechanistic knowledge gaps connecting exposure to an adverse outcome. To better reflect this, we edited all occurrences in the “Results and Discussion” of “This KE mapped to…” to the better phrase of “We mapped this KE to…” to emphasize that users are in control of the process here. 5. Supplemental training materials are suggested to facilitate reproducibility of the methods presented. AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 6. Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). AUTHORS’ RESPONSE : We wholeheartedly agree and promote the idea of the AOP research community coming together to diligently standardize their mappings, identification, and definitions. We attempted to use many of the third party tools currently promoted by the AOP-Wiki in our original mapping and analysis, but unfortunately, we began to realize that many of these resources are unreliable, as they were either outdated, non-intuitive as to how to use, no longer available as a web application, or simply returned generic links back to the AOP-Wiki without providing any new context: AOP-DB (as defined by Mortensen, Senn, 2021; Pittman, 2018) is listed as a third-party tool on the AOP-Wiki, but is no longer accessible from its stated URL, and the EPA site appears to have been last updated May 2021 and also is inaccessible from its stated URL. Wiki Kaptis has a copyright stamp of 2022, and the information menu states that it only contains data extracted from the AOP-Wiki up to 2023. Our searches using official AOP-Wiki terms such as: AOP:522, autism (AO:2209), estrogen receptor antagonism (MIE:112), ERK1/2 inhibition (KE:2207), and aberrant synaptic formation and plasticity (KE:2208) yielded no results. The AOP-KB is a tool provided by the OECD, but again, appears to be limited in functionality. A user can enter any term (e.g., “estrogen receptor”) and retrieve term matches to AOPs and KEs such as “Androgen receptor activation leading to prostate cancer”, but this result simply links back to the AOP-Wiki, with no explanation as to why this AOP would be returned as a result for “estrogen receptor”. It is not obvious how the AOP-KB determined that “estrogen receptor” is involved in this AOP. Similarly, a search with “autism” returns no results, suggesting the AOP-KB is also not current and out-of-date with the AOP-Wiki. AOP-WIKI EXPLORER (as defined by Kumar et al. 2024) is no longer accessible from its stated URL. We next attempted to use a tool that employs NLP to map curated genes to KEs (Saarimaki et al. (2023), but the resource also seems to be out of date, and spot-checks performed by us gave questionable results: e.g., MIE:112 (“estrogen receptor antagonism”) was mapped to 59 genes, but surprisingly did not include ESR2, the second critical estrogen receptor in humans; similarly, KE:195 (“NMDARs inhibition”) contained only seven GRIN genes, missing 16 other GRIN genes that we included in our CTD analysis. The other KEs we checked (KE:2207, KE:2208, KE:2209) do not even exist in the file, presumably because of its outdatedness. Based upon these unsatisfying and inconsistent approaches, in this manuscript, we decided to map the six AOP terms to CTD genes and phenotypes manually ourselves, as a typical CTD user would find it necessary to do. Going forward, it would be more advantageous if the AOP-Wiki recommended tools that are more easily and readily accessible, stable, intuitive to use, and designed for data currency, otherwise they risk becoming out of date with diminishing value as time goes on. 7. Please reference the literature and relevant contributions in this area. AUTHORS’ RESPONSE : Thank you. To the “Limitations and Strengths” section, we have now added citations for: Ives et al. (2017): Creating a Structured AOP Knowledgebase via Ontology-Based Annotations; Pittman et al. (2018): AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks; Mortensen et al. (2021): The 2021 update of the EPA’s adverse outcome pathway database; and Kumar et al. (2024): AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 27 Apr 2026 Allan Davis , Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 27 Apr 2026 Author Response 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to ... Continue reading 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to help inform, refine, expand, and hopefully advance the status of underdeveloped AOPs. When we initially submitted our manuscript (17 November 2025), the AOP-Wiki contained 532 AOPs, of which only 7% were designated as endorsed by WPHA/WNT, with the remaining 93% of the AOP-Wiki content described as empty (74%), under development (16%), under review (3%), and none with ESCA approval (0%). In our minds, these statistics are indicative of a critical need for new methodologies to advance the status of these languishing AOPs as a way to improve the overall utility and resourcefulness of the AOP-Wiki. The AOP used in our demonstration (i.e., AOP:522) is a good representative candidate, because, as this reviewer noted, it is “open for adoption”, currently poorly populated in the AOP-Wiki itself, and has equivocal limiting supporting evidence from the literature (although enough to warrant the construction of and deposition in the AOP-Wiki). As Mari-Bauset et al. (2018) [rather than the mistakenly cited Mandy and Lai, 2016] conclude in their article about EDC and ASD: “incomplete understanding of biological mechanisms precludes the establishment of a causal relationship”, supporting the primary need to better understand the biological mechanistic steps that can link these disrupting chemicals to ASD, such as the development of a more robust AOP. In our manuscript, we demonstrate how researchers can (1) leverage CTD to identify environmental chemicals that could modulate autism etiology via this AOP and (2) then use those identified chemicals to point researchers in mechanistically-supported directions to further develop and refine AOP:522 to a better position for testability and advancement. To help clarify our intention, we have improved the text. In response to similar comments from Reviewer 2, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 2. Manuscript would benefit from an analysis workflow AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 3. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility AUTHORS’ RESPONSE : The crux of this work is to demonstrate how users can leverage CTD chemical content and readily integrate it with AOPs to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with AOP:522 and (2) then use these chemicals to discover new, potential mechanistic steps to further develop and refine AOP:522 to a better position for testability and advancement. A step-by-step instruction guide is now provided (see new Supplemental Figures at figshare) for all the manual queries and analyses, enabling both validation and reproducibility. However, it is important to understand that CTD is updated each month with new curated content from the recently published literature; this is one of the strengths of CTD. Thus, query results can change on a monthly basis (as currently explained in our “Methods” section). Importantly, results from newer queries do not invalidate findings, but rather provide additional data from the more recent literature. In Figure 2, we grouped the 76 prioritized chemicals simply as a display convenience (e.g., clustering together similar chemicals, such as metals, phthalates, pesticides, etc.). The clustering itself does not impart any additional knowledge to the figure (compared to not clustering the chemicals). We performed that task, as described in the manuscript, using web searches or shared term parentage in the CTD Chemical vocabulary hierarchy. For example, “dibutyl phthalate”, “diethylhexyl phthalate”, and “monobutyl phthalate”, all share “Phthalic Acids” as a common parent in the CTD chemical hierarchy, and thus can be grouped as such. Figure 2 is just as valid and reproducible if the chemicals were not clustered, and instead listed alphabetically. 4. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100 AUTHORS’ RESPONSE : In our manuscript, we did not base estrogenic effects only on the two estrogen genes (ESR1 and ESR2). Rather, we mapped this MIE:112 to four CTD terms (Table 1): the two estrogen receptor genes ESR1 and ESR2 plus, importantly, two additional estrogen-signaling related phenotypes (which include all of their GO-phenotype descendants as well) to capture chemicals that still affect the estrogen receptor signaling pathway but without necessarily affecting the specific estrogen receptors. It is important to understand that at CTD a chemical-phenotype interaction does not (necessarily) have to involve a gene, but is simply a curated annotation describing how a chemical causes a biological outcome reported in the literature; e.g., “chemical X results in decreased intracellular estrogen receptor signaling pathway in MCF-7 cells” is a chemical-phenotype interaction reported by an author without any discussion or knowledge of the genes involved. This specifically enables CTD to capture literature-based chemical-induced outcomes without having to know a priori what genes are involved. We would direct the reviewer to our detailed description of this CTD curation module to better understand what we mean as chemical-phenotype interactions (Davis et al., 2018). This CTD curation paradigm allows CTD to capture important data for chemical-induced events directly, without having to involve any genes (either known or unknown by the author). Thus, chemicals that affect estrogen receptor signaling by “mechanisms which are not understood” (Marino, 2006) can still be captured in CTD because we curate the direct chemical-phenotype relationship itself, obviating the need to invoke any unknown gene/mechanism. If a user chooses to broaden their interpretation of “estrogen receptor antagonism”, they can select a more expansive phenotype in the ontology by using a parent term, such as “steroid hormone mediated pathway” (GO:0043401), which now expands the query range and at this writing returns 231 unique chemicals instead of the original 180 chemicals shown in Table 1 for the more specific phenotype. Curating phenotype data as an ontology (instead of as presumptive “gene set) is an advantage, and enables users to broaden or narrow their searches by navigating the vocabulary to the level they prefer. If deemed necessary, users can always map this particular AOP step to additional genes beyond ESR1 and ESR2 if they prefer: this will only bring back additional chemical data for analysis. Finally, a user could try to leverage Natural Language Processing (NLP)-based mapping tools that attempt to link KEs first to “gene lists” and then find CTD chemicals that interact with those gene sets. NLP methods, however, are notoriously complex and difficult to follow and validate for the non-technical user, and, unfortunately, the results may go out of date quickly unless the NLP tool is updated on a consistent schedule; in fact, spot checks by CTD found some strange and equivocal mappings (discussed below) performed by such NLP methods. The main point here, however, is that individual users have the ability to leverage CTD to their level of specificity, comfort, and skill level to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with this AOP and (2) then use these chemicals to discover new, potential mechanistic steps to further develop, expand, and improve the AOP to a better position for testability and advancement. Different users might decide to use different terms to retrieve data, and that’s perfectly fine. The user ultimately decides how large of a data-net he/she wants to cast and then can decide which molecular mechanisms retrieved from those results make the most sense to include in a testable updated/new AOP. CTD provides the chemical data-set and evidence to fill in the mechanistic knowledge gaps connecting exposure to an adverse outcome. To better reflect this, we edited all occurrences in the “Results and Discussion” of “This KE mapped to…” to the better phrase of “We mapped this KE to…” to emphasize that users are in control of the process here. 5. Supplemental training materials are suggested to facilitate reproducibility of the methods presented. AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 6. Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). AUTHORS’ RESPONSE : We wholeheartedly agree and promote the idea of the AOP research community coming together to diligently standardize their mappings, identification, and definitions. We attempted to use many of the third party tools currently promoted by the AOP-Wiki in our original mapping and analysis, but unfortunately, we began to realize that many of these resources are unreliable, as they were either outdated, non-intuitive as to how to use, no longer available as a web application, or simply returned generic links back to the AOP-Wiki without providing any new context: AOP-DB (as defined by Mortensen, Senn, 2021; Pittman, 2018) is listed as a third-party tool on the AOP-Wiki, but is no longer accessible from its stated URL, and the EPA site appears to have been last updated May 2021 and also is inaccessible from its stated URL. Wiki Kaptis has a copyright stamp of 2022, and the information menu states that it only contains data extracted from the AOP-Wiki up to 2023. Our searches using official AOP-Wiki terms such as: AOP:522, autism (AO:2209), estrogen receptor antagonism (MIE:112), ERK1/2 inhibition (KE:2207), and aberrant synaptic formation and plasticity (KE:2208) yielded no results. The AOP-KB is a tool provided by the OECD, but again, appears to be limited in functionality. A user can enter any term (e.g., “estrogen receptor”) and retrieve term matches to AOPs and KEs such as “Androgen receptor activation leading to prostate cancer”, but this result simply links back to the AOP-Wiki, with no explanation as to why this AOP would be returned as a result for “estrogen receptor”. It is not obvious how the AOP-KB determined that “estrogen receptor” is involved in this AOP. Similarly, a search with “autism” returns no results, suggesting the AOP-KB is also not current and out-of-date with the AOP-Wiki. AOP-WIKI EXPLORER (as defined by Kumar et al. 2024) is no longer accessible from its stated URL. We next attempted to use a tool that employs NLP to map curated genes to KEs (Saarimaki et al. (2023), but the resource also seems to be out of date, and spot-checks performed by us gave questionable results: e.g., MIE:112 (“estrogen receptor antagonism”) was mapped to 59 genes, but surprisingly did not include ESR2, the second critical estrogen receptor in humans; similarly, KE:195 (“NMDARs inhibition”) contained only seven GRIN genes, missing 16 other GRIN genes that we included in our CTD analysis. The other KEs we checked (KE:2207, KE:2208, KE:2209) do not even exist in the file, presumably because of its outdatedness. Based upon these unsatisfying and inconsistent approaches, in this manuscript, we decided to map the six AOP terms to CTD genes and phenotypes manually ourselves, as a typical CTD user would find it necessary to do. Going forward, it would be more advantageous if the AOP-Wiki recommended tools that are more easily and readily accessible, stable, intuitive to use, and designed for data currency, otherwise they risk becoming out of date with diminishing value as time goes on. 7. Please reference the literature and relevant contributions in this area. AUTHORS’ RESPONSE : Thank you. To the “Limitations and Strengths” section, we have now added citations for: Ives et al. (2017): Creating a Structured AOP Knowledgebase via Ontology-Based Annotations; Pittman et al. (2018): AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks; Mortensen et al. (2021): The 2021 update of the EPA’s adverse outcome pathway database; and Kumar et al. (2024): AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models. 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to help inform, refine, expand, and hopefully advance the status of underdeveloped AOPs. When we initially submitted our manuscript (17 November 2025), the AOP-Wiki contained 532 AOPs, of which only 7% were designated as endorsed by WPHA/WNT, with the remaining 93% of the AOP-Wiki content described as empty (74%), under development (16%), under review (3%), and none with ESCA approval (0%). In our minds, these statistics are indicative of a critical need for new methodologies to advance the status of these languishing AOPs as a way to improve the overall utility and resourcefulness of the AOP-Wiki. The AOP used in our demonstration (i.e., AOP:522) is a good representative candidate, because, as this reviewer noted, it is “open for adoption”, currently poorly populated in the AOP-Wiki itself, and has equivocal limiting supporting evidence from the literature (although enough to warrant the construction of and deposition in the AOP-Wiki). As Mari-Bauset et al. (2018) [rather than the mistakenly cited Mandy and Lai, 2016] conclude in their article about EDC and ASD: “incomplete understanding of biological mechanisms precludes the establishment of a causal relationship”, supporting the primary need to better understand the biological mechanistic steps that can link these disrupting chemicals to ASD, such as the development of a more robust AOP. In our manuscript, we demonstrate how researchers can (1) leverage CTD to identify environmental chemicals that could modulate autism etiology via this AOP and (2) then use those identified chemicals to point researchers in mechanistically-supported directions to further develop and refine AOP:522 to a better position for testability and advancement. To help clarify our intention, we have improved the text. In response to similar comments from Reviewer 2, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 2. Manuscript would benefit from an analysis workflow AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 3. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility AUTHORS’ RESPONSE : The crux of this work is to demonstrate how users can leverage CTD chemical content and readily integrate it with AOPs to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with AOP:522 and (2) then use these chemicals to discover new, potential mechanistic steps to further develop and refine AOP:522 to a better position for testability and advancement. A step-by-step instruction guide is now provided (see new Supplemental Figures at figshare) for all the manual queries and analyses, enabling both validation and reproducibility. However, it is important to understand that CTD is updated each month with new curated content from the recently published literature; this is one of the strengths of CTD. Thus, query results can change on a monthly basis (as currently explained in our “Methods” section). Importantly, results from newer queries do not invalidate findings, but rather provide additional data from the more recent literature. In Figure 2, we grouped the 76 prioritized chemicals simply as a display convenience (e.g., clustering together similar chemicals, such as metals, phthalates, pesticides, etc.). The clustering itself does not impart any additional knowledge to the figure (compared to not clustering the chemicals). We performed that task, as described in the manuscript, using web searches or shared term parentage in the CTD Chemical vocabulary hierarchy. For example, “dibutyl phthalate”, “diethylhexyl phthalate”, and “monobutyl phthalate”, all share “Phthalic Acids” as a common parent in the CTD chemical hierarchy, and thus can be grouped as such. Figure 2 is just as valid and reproducible if the chemicals were not clustered, and instead listed alphabetically. 4. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100 AUTHORS’ RESPONSE : In our manuscript, we did not base estrogenic effects only on the two estrogen genes (ESR1 and ESR2). Rather, we mapped this MIE:112 to four CTD terms (Table 1): the two estrogen receptor genes ESR1 and ESR2 plus, importantly, two additional estrogen-signaling related phenotypes (which include all of their GO-phenotype descendants as well) to capture chemicals that still affect the estrogen receptor signaling pathway but without necessarily affecting the specific estrogen receptors. It is important to understand that at CTD a chemical-phenotype interaction does not (necessarily) have to involve a gene, but is simply a curated annotation describing how a chemical causes a biological outcome reported in the literature; e.g., “chemical X results in decreased intracellular estrogen receptor signaling pathway in MCF-7 cells” is a chemical-phenotype interaction reported by an author without any discussion or knowledge of the genes involved. This specifically enables CTD to capture literature-based chemical-induced outcomes without having to know a priori what genes are involved. We would direct the reviewer to our detailed description of this CTD curation module to better understand what we mean as chemical-phenotype interactions (Davis et al., 2018). This CTD curation paradigm allows CTD to capture important data for chemical-induced events directly, without having to involve any genes (either known or unknown by the author). Thus, chemicals that affect estrogen receptor signaling by “mechanisms which are not understood” (Marino, 2006) can still be captured in CTD because we curate the direct chemical-phenotype relationship itself, obviating the need to invoke any unknown gene/mechanism. If a user chooses to broaden their interpretation of “estrogen receptor antagonism”, they can select a more expansive phenotype in the ontology by using a parent term, such as “steroid hormone mediated pathway” (GO:0043401), which now expands the query range and at this writing returns 231 unique chemicals instead of the original 180 chemicals shown in Table 1 for the more specific phenotype. Curating phenotype data as an ontology (instead of as presumptive “gene set) is an advantage, and enables users to broaden or narrow their searches by navigating the vocabulary to the level they prefer. If deemed necessary, users can always map this particular AOP step to additional genes beyond ESR1 and ESR2 if they prefer: this will only bring back additional chemical data for analysis. Finally, a user could try to leverage Natural Language Processing (NLP)-based mapping tools that attempt to link KEs first to “gene lists” and then find CTD chemicals that interact with those gene sets. NLP methods, however, are notoriously complex and difficult to follow and validate for the non-technical user, and, unfortunately, the results may go out of date quickly unless the NLP tool is updated on a consistent schedule; in fact, spot checks by CTD found some strange and equivocal mappings (discussed below) performed by such NLP methods. The main point here, however, is that individual users have the ability to leverage CTD to their level of specificity, comfort, and skill level to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with this AOP and (2) then use these chemicals to discover new, potential mechanistic steps to further develop, expand, and improve the AOP to a better position for testability and advancement. Different users might decide to use different terms to retrieve data, and that’s perfectly fine. The user ultimately decides how large of a data-net he/she wants to cast and then can decide which molecular mechanisms retrieved from those results make the most sense to include in a testable updated/new AOP. CTD provides the chemical data-set and evidence to fill in the mechanistic knowledge gaps connecting exposure to an adverse outcome. To better reflect this, we edited all occurrences in the “Results and Discussion” of “This KE mapped to…” to the better phrase of “We mapped this KE to…” to emphasize that users are in control of the process here. 5. Supplemental training materials are suggested to facilitate reproducibility of the methods presented. AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 6. Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). AUTHORS’ RESPONSE : We wholeheartedly agree and promote the idea of the AOP research community coming together to diligently standardize their mappings, identification, and definitions. We attempted to use many of the third party tools currently promoted by the AOP-Wiki in our original mapping and analysis, but unfortunately, we began to realize that many of these resources are unreliable, as they were either outdated, non-intuitive as to how to use, no longer available as a web application, or simply returned generic links back to the AOP-Wiki without providing any new context: AOP-DB (as defined by Mortensen, Senn, 2021; Pittman, 2018) is listed as a third-party tool on the AOP-Wiki, but is no longer accessible from its stated URL, and the EPA site appears to have been last updated May 2021 and also is inaccessible from its stated URL. Wiki Kaptis has a copyright stamp of 2022, and the information menu states that it only contains data extracted from the AOP-Wiki up to 2023. Our searches using official AOP-Wiki terms such as: AOP:522, autism (AO:2209), estrogen receptor antagonism (MIE:112), ERK1/2 inhibition (KE:2207), and aberrant synaptic formation and plasticity (KE:2208) yielded no results. The AOP-KB is a tool provided by the OECD, but again, appears to be limited in functionality. A user can enter any term (e.g., “estrogen receptor”) and retrieve term matches to AOPs and KEs such as “Androgen receptor activation leading to prostate cancer”, but this result simply links back to the AOP-Wiki, with no explanation as to why this AOP would be returned as a result for “estrogen receptor”. It is not obvious how the AOP-KB determined that “estrogen receptor” is involved in this AOP. Similarly, a search with “autism” returns no results, suggesting the AOP-KB is also not current and out-of-date with the AOP-Wiki. AOP-WIKI EXPLORER (as defined by Kumar et al. 2024) is no longer accessible from its stated URL. We next attempted to use a tool that employs NLP to map curated genes to KEs (Saarimaki et al. (2023), but the resource also seems to be out of date, and spot-checks performed by us gave questionable results: e.g., MIE:112 (“estrogen receptor antagonism”) was mapped to 59 genes, but surprisingly did not include ESR2, the second critical estrogen receptor in humans; similarly, KE:195 (“NMDARs inhibition”) contained only seven GRIN genes, missing 16 other GRIN genes that we included in our CTD analysis. The other KEs we checked (KE:2207, KE:2208, KE:2209) do not even exist in the file, presumably because of its outdatedness. Based upon these unsatisfying and inconsistent approaches, in this manuscript, we decided to map the six AOP terms to CTD genes and phenotypes manually ourselves, as a typical CTD user would find it necessary to do. Going forward, it would be more advantageous if the AOP-Wiki recommended tools that are more easily and readily accessible, stable, intuitive to use, and designed for data currency, otherwise they risk becoming out of date with diminishing value as time goes on. 7. Please reference the literature and relevant contributions in this area. AUTHORS’ RESPONSE : Thank you. To the “Limitations and Strengths” section, we have now added citations for: Ives et al. (2017): Creating a Structured AOP Knowledgebase via Ontology-Based Annotations; Pittman et al. (2018): AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks; Mortensen et al. (2021): The 2021 update of the EPA’s adverse outcome pathway database; and Kumar et al. (2024): AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 17 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 27 Apr 26 Version 1 17 Nov 25 read read read Holly M Mortensen , US Environmental Protection Agency, Research Triangle Park, USA Jason Obrien , Environment and Climate Change Canada, Ottawa, Canada Brian N Chorley , US Environmental Protection Agency, Research Triangle Park, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Chorley B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Apr 2026 | for Version 1 Brian N Chorley , US Environmental Protection Agency, Research Triangle Park, North Carolina, USA 0 Views copyright © 2026 Chorley B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript by Davis et al. provides a case study to leverage the Comparative Toxicogenomics Database (CTD) to provide chemical context and/or supplemental content for Adverse Outcome Pathways (AOPs) in the AOP-Wiki. The use case here in an AOP associated with autism, specifically AOP:522 "Estrogen antagonism leading to an increased risk of autism-like behavior". Two approaches leveraging the CTD were outlines: First, 6 KEs of AOP:522 were associated with GO and MESH terms, linking to chemicals using CTD search tools. Second, "CTD Tetramers" generated chemical-gene-phenotype-disease blocks associated with autism linked exposures (BPA, valproic acid, and PM) to derive literature-driven AOPs to both enhance existing AOPs (such as AOP:522) or create new ones for hypothesis generation, testing, or tool building. The manuscript is well-written, logically arranged, easy to follow and enhanced with helpful figures. The outlined procedures provide a theoretical blueprint to enhance existing AOPs, or generate new AOPS, using the continuously updated content of the CTD. Therefore, this manuscript suggests a path to fill in numerous gaps for existing “empty” AOPs, as well creating new content for AOs of interest. The steps provided, however, entail many manual steps that may be somewhat subjective and result in a daunting number of results. I have some suggestions, primarily minor, that should enhance the content of this manuscript. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals : In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. “Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise molecular biology, biomarkers, AOPs I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 27 Apr 2026 Allan Davis, Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 1. From Methods/CTD term mapping to autism AOP events to derive intersecting chemicals: In Table 1, add a column that explicitly states which CTD search tool (Chemical-Gene Interaction Query or Chemical-Phenotype Interaction Query) was used to map to CTD chemicals. Although it may be obvious, this clearly links the process to the results listed. AUTHORS’ RESPONSE : There are several different ways to retrieve CTD chemicals that intersect with the mapped AOP steps, including the Chemical-Gene Interaction Query (for chemicals interacting with mapped gene terms), the Chemical-Phenotype Interaction Query (for chemicals interacting with mapped phenotype terms), or the CTD Batch Query (for chemicals interacting with either mapped genes or mapped phenotypes). As well, users can simply go to the respective CTD gene page or CTD phenotype page and download the chemical set from the “Chemical” data-tab from each page. We think modifying Table 1 with a new column that simply repeats “Chemical-Gene Interaction Query” for each gene term and “Chemical-Phenotype Interaction Query” for each phenotype term would be distracting. Instead, in Supplementary Figure S2 we now demonstrate how to retrieve these chemical sets with comprehensive step-by-step instructions using the CTD Batch Query and include a detailed diagram as well. We also have made this distinction (between gene and phenotype queries) clearer in the “Methods” section with added text. 2. From Methods/Constructing a new AOP series for autism using CTD content : “The gene sets for each selected phenotype were manually collected and compared against each other to find subsets of shared genes that could be used to manually link the phenotypes.” It is not clear what was done here. Were the greater number of shared genes selected to link to phenotypes or were frequent subsets of genes selected? Please explain in more detail. AUTHORS’ RESPONSE : We have now added a detailed Supplementary Figure S4 (figshare) that describes step-by-step instructions on how to construct this new AOP series, including identifying the tetramer genes that are shared between different tetramer phenotypes, and how these shared gene edges can be used to connect adjacent phenotypes and help design testable hypotheses to find supporting KER evidence. 3. From Methods/Finding autism-related diseases from shared CTD mechanisms : “Each AOP event for AOP:522 was also queried in the AOP-Wiki to find other AOPs and AOs with which they were associated.” I would like to see a complete list of AOs associated with these events other than those selected in Fig 6. Can this be made as a supplement? AUTHORS’ RESPONSE : A complete list of the AOPs and AOs associated with the five MIE/KEs of AOP:522 is now provided as Supplementary Figure S6 (figshare). 4. From Results/Using environmental chemicals from CTD to discover additional events for autism AOP : “We selected six of these prominent tetramer-identified key phenotypes…” Mark these somehow in Fig 3D for easy reference. Also, it isn’t clear why these six specifically were selected. Please better describe the selection process used. AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks). We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process (figshare). 5. “ Importantly, this new proposed AOP series computed from CTD tetramers can be used to create an entirely new pathway for autism or help refine and/or expand the current AOP:522 (Figure 4), as well as provide new mechanisms to develop additional targeted assays.” It is not clear how this new AOP helps define AOP:522. Suggest examples here or simply only state this is a new AOP. AUTHORS’ RESPONSE : Our intention by that statement was to suggest that users could “refine and/or expand” AOP:522 by inclusion of some of the additional mechanistic key events derived from CTD tetramers. This is also suggested in Figure 4, which states in the legend: “This novel, manually generated AOP can serve as a framework to construct an entirely new AOP for autism or be used to refine or expand (gray downward arrows) AOP:522 from the AOP-Wiki.” 6. Figure 5 : Suggest using an UpSet plot rather than a complex Venn. This provides an easier-to-follow comparison. AUTHORS’ RESPONSE : We admit we have never heard of (nor seen) an UpSet plot before, and we feel that a casual reader also will not be familiar with how to read/interpret one. Hence, we prefer to keep our original Venn diagram in the full text, but we have since learned how to create an UpSet plot and now include it as an alternative visualization in new Supplementary Figure S5 (figshare), and point users to that alternative plot in both the text and Figure 5 legend. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Chorley BN. Peer Review Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r467900) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-467900 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Obrien J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Mar 2026 | for Version 1 Jason Obrien , Environment and Climate Change Canada, Ottawa, Canada 0 Views copyright © 2026 Obrien J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Study Summary Data from the Comparative Toxicogenomic Database were linked to an Adverse Outcome Pathway from the AOPwiki to identify potentially useful chemical, gene, phenotype, disease and AOP relationships. The resulting relationships are potentially useful for identifying exposures that may influence the apical adverse outcome, or provide mechanistic support for the AOP. The authors selected AOP 522 (“Estrogen antagonism leading to increased risk of autism-like behavior”) as a pilot case study, but the approach could in theory be applied to any AOP. The authors identified >3000 chemicals in the CTD database that have some relationship to one or more of the Key Events (KEs) in AOP 522, 76 of which had relationships with five or more of the AOPs six KEs. Of these 76 chemicals, the authors selected three (Bisphenol A, valproic acid and particulate matter) to identify 136 genes and 53 phenotypes related to these chemicals and autism. The authors propose that the identified genes and phenotypes can form the basis of additional KEs related to the AOP, or be used to provide mechanistic support existing KEs. To demonstrate this, the authors selected 6 of the resulting phenotypes to construct a hypothetical AOP linking oxidative stress to autism. Finally, the authors used the CTD database to identify several other diseases that may have mechanistic overlap with AOP 522. Evaluation Summary Overall, this manuscript describes a well-conceived and executed study. The introduction presents clear and concise background information, with clear and justified objectives. The methods are clearly described and reproducible (I was even successful at replicating some of the CTD search results). The results are clearly conveyed and adequately discussed. I have only a few minor comments below for the author’s consideration. Specific Comments Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. Comment regarding selected AOP: The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) Comment regarding KE:386 mapping: The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. Comment 1 regarding support for AOP: The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. Comment 2 regarding support for AOP: The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. Comment regarding hypothetical AOP: How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? Minor Typo : Page 7, paragraph 1: Prioritized chemicals were group ED into categories Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 27 Apr 2026 Allan Davis, Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 1. Comment regarding Methods section: There are a lot of “results” presented in the methods section. For example, the number of chemicals returned from queries is reported on page 6, paragraph 1; the most frequent phenotypes were reported on page 7, paragraph 2. This is not a major problem, per se, but it is a bit unconventional. AUTHORS’ RESPONSE : We have edited the “Methods” section to make it more generic and instead moved the specific “results” to the more appropriate “Results” section of the manuscript. 2. Comment regarding selected AOP : The AOP selected, AOP:522, is a very underdeveloped AOP. Although the authors do allude to this in their discussion (the AOP is “empty” and “open for adoption”) I suggest that this point can be made more clear and maybe earlier in the paper (for example in the intro). For example, this could be emphasized more in the last paragraph of the introduction (e.g. We identified intersecting data between CTD and AOPwiki to reveal chemical, gene, phenotype relationships that could potentially be used as supporting evidence to strengthen an autism-related AOP that is in the early stages of development.) AUTHORS’ RESPONSE : In response to similar comments from Reviewer 1, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 3. Comment regarding KE:386 mapping : The CTD terms selected for this KE do not seem very well matched. The KE is called “decreased neuronal network function”, and based on the description in AOPwiki, the KE is largely related to measured synaptic activity. The CTD terms are “nervous system development” and “nervous system process”, which seem more general than what the KE describes. The authors do address this later in the limitations section, but might be better to mention in the mapping section. AUTHORS’ RESPONSE : As the reviewer notes, we believe a limitation in this study is the mapping of KE terms (often weakly defined or susceptible to interpretation) from the AOP-Wiki to their best corresponding matches in CTD. We discuss this in the “Results” section for KE:195 (which is sometimes described as “NMDAR inhibition” and at other times as “decreased NMDAR expression” in the AOP-Wiki). For AOP:522, KE:2208 (“aberrant, synaptic formation and plasticity”) is immediately upstream to KE:386 (“decrease of neuronal network function”). However, KE:2208 is poorly defined in the AOP-Wiki (with only a key event title and no other context) while the downstream KE:386 is almost overly defined with extensive descriptions in different sections of the webpage (ranging from the measurement of synaptic activity to neuron and brain development). This makes it challenging to interpret the best mapping for these two supposedly distinct KEs. To further highlight this issue, we have added two new passages to the “Results”: “Mapping terms across databases, however, is not always straightforward and can be subject to interpretation, and individual users can exercise flexibility to arrive at different translations, especially with respect to the level of granularity. In the AOP-Wiki, KE terms are defined in multiple sections, including the “event title” (a mandatory descriptive title for the KE), the “event component” (an optional ontology term, if known or applicable), and an “event description” (an optional descriptive passage about the biological state being measured and its role in biology). We attempted to balance these descriptions for each unique KE to make the best mappings to CTD.” and then later: “While KE:386 uses a broad event title (“decreased neuronal network function”), its event component is more nuanced (“decreased synaptic signaling”), which conceptually overlaps with the upstream key event KE:2208 (see above). However, the event description for KE:386 details the neuronal network processes in the developing and mature brain. To limit duplicative mapping used for KE:2208 (“synaptic signaling” GO:0099536), and, more importantly, to cover the essential features of neuron and brain development, we decided to map this KE more broadly to two CTD phenotypes: “nervous system development” (GO:0007399) and “nervous system process” (GO:0050877) which includes “neurogenesis” (GO:0022008), “brain development” (GO:0007420), and “transmission of nerve impulse” (GO:0019226), among many other neuronal functions and processes”. 4. Comment 1 regarding support for AOP : The authors state that their results “provide molecular mechanisms for experiments and targeted assays to test modulation of the key elements of AOP:522”. I completely agree with this, but feel that this point can be elaborated on a bit. Specifically, these results can be used to identity experiments in the literature or design new experiments that can be used as supporting evidence (for example KER evidence) to further develop and strengthen this AOP. AUTHORS’ RESPONSE : We have now added text to elaborate this point in the “Results”: “Linking literature-based chemical-gene-phenotype events (curated from mechanistic studies) into biologically plausible pathways can support KERs as well as provide testable hypotheses for additional experiments to quantify empirical evidence connecting these intermediate events in response to the same chemical stressor and via shared mechanistic genes (25).” 5. Comment 2 regarding support for AOP : The authors propose that the identified relationships can be used to “expand AOP development and interconnectivity”. The authors nicely show how these results can be used to identify potentially new KEs or create AOP networks. However, one of the greatest challenges in AOP development is providing support for the Key Event Relationships (KERs). While the authors do mention this briefly in the future directions section, it would have been nice to see an example of how the CTD results can be integrated using AOP theory to provide empirical support for the KERs. AUTHORS’ RESPONSE : Using CTD tetramers to discover new potential intermediate mechanistic phenotypes/KEs and construct new AOP series creates a highly interconnected map of linked phenotypes/KEs that have numerous edges via shared chemical-gene, chemical-phenotype, and gene-phenotype relationships (see Figure 4); this offers curated literature-based support for KERs as well as new avenues for developing additional experiments to further validate the KERs. Also, results from CTD Tetramers Query are ranked by an “Evidence Strength Score”, calculated as the product score of the number of references used in each of the four lines of supporting evidence needed to construct a CGPD-tetramer. Thus, computational tetramers with a higher number of source articles across the five supporting lines of evidence categories will have a higher calculated “Evidence Strength Score” and be ranked at the top of the output results, providing users with an option to filter tetramers with a higher amount of underlying literature support; this is also diagrammed as step 4 in the newly added Supplementary Figure S3. We have added to the description of our new model AOP constructed from shared tetramer data, describing how these identified tetramers provide “overlapping genes shared between the phenotypes as mechanistic links connecting the key events and provide support for KER evidence”. This is also currently described in the Figure 4 legend: “Genes shared between any two phenotypes (boxes with listed gene symbols connected by dotted arcs) provide additional mechanistic links further supporting the numerous KERs between the events”. 6. Comment regarding hypothetical AOP : How did the authors select the six phenotypes from the 53 common phenotypes to include in their prospective AOP? Similarly, how did the authors select the order of the phenotypes in the prospective AOP? Were these decisions data driven, or based on expert knowledge? AUTHORS’ RESPONSE : We selected six of the most prominent phenotypes illuminated in the chord diagram of Figure 3D (now identified in that figure with asterisks, per Reviewer 3) and manually ordered them at four levels of biological organization. We have added expanded text for clarification in the “Results”. As well, we have added a new Supplementary Figure S4 to show the step-by-step instructions for this process. 7. Minor Typo : Page 7, paragraph 1: Prioritized chemicals were groupED into categories AUTHORS’ RESPONSE : We have fixed this typo. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Obrien J. Peer Review Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r462004) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-462004 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Mortensen H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Mar 2026 | for Version 1 Holly M Mortensen , US Environmental Protection Agency, Research Triangle Park, North Carolina, USA 0 Views copyright © 2026 Mortensen H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Issue with selected AOP -Status “open for adoption” AOP 522 is classified as "open for adoption" and is poorly populated in the AOP Wiki Cui et al: Previous study has shown that prenatal or postnatal exposure to EEDs could increase the risk of ASD in children (Mandy and Lai, 2016). Mandy and Lai, 2016 clearly state that "Although the studies generally showed a positive association between EDCs and ASD, after considering the strengths and limitations, we concluded that the overall strength of evidence supporting an association between prenatal exposure to EDCs and later ASD in humans remains "limited" and inconclusive. Further well-conducted prospective studies are warranted to clarify the role of EDCs on ASD development." Manuscript would benefit from an analysis workflow. The authors present a complicated analysis routine that is difficult to follow. Individual steps are described in sufficient detail, but the reader is left to follow. Suggest a general diagram, eg. Flow chart, decision tree to describe steps taken in the present analyses. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility. Subjective or inconsistent use of terms, for example, would invalidate findings. Suggest performing on >1 AOP to test level of reproducibility. This reviewer is concerned with the validity and consistence of gene-chemical vs. chemical phenotype groupings with various levels of confidence. Authors do not specify confidence level or thresholding for association groups. Same comment for “shared term percentage”-not reproducible with the current workflow explanations. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100. “Estrogen receptors regulate a multitude of biological and physiological processes” (Fuentes, 2019, doi: 10.1016/bs.apcsb.2019.01.001) Estrogenic effects occur via multiple mechanisms, pathways and receptors, some of which are not fully understood at present (Marino , 2006 doi: 10.2174/138920206779315737) Supplemental training materials are suggested to facilitate reproducibility of the methods presented Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). The standardization of mapping molecular identifiers to AOPs is an area of need in the AOP field. However, the Authors use term mapping from AOP-Wiki key events to map to CTD phenotype terms. This manual process could be improved for reproducibility and accuracy. Suggest implementing ontology-based gene mapping to CTD chemical-gene pairs and possibly using a publicly available AOP tool that provides this mapping, like the AOP-DB (Mortensen, Senn, 2021; Pittman, 2018); AOP-DB RDF (Mortensen, Martens, 2022) or AOP-WIKI EXPLORER (Saurav, 2024) , followed by mapping to ctd chem-gene or manually to phenotype. The AOP-DB ( Mortensen, Senn , 2021) actually maps AOP molecular KE (entrez genes ) directly to CTD-Gene tables, as well as DTXIDs, which could minimize the manual curation (and chance for error). Please reference the literature and relevant contributions in this area In Limitations and Strengths section Suggest referencing the citations (Ives, et al 2017; Pittman, 2018; Mortensen, Senn 2021). “some biomedical data translators” are reference to assist in mapping KE to gene ontology terms”. Suggest referencing the extensive work in this area that preceded/contributed to the cited contributions (Pittman, 2018; Mortensen, Senn 2021; Mortensen, Martens, 2022) as well as other semantic mapping approaches (Saurav, 2024). “Other methods have been developed to increase the usability of AOP information, such as converting the AOP-Wiki into semantic web formats 58 or curating relevant KEs to gene sets associated with pathways, phenotypes, and GO terms 59 ”. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 27 Apr 2026 Allan Davis, Department of Biological Sciences, North Carolina Sate University, Raleigh, 27695, USA 1. Issue with selected AOP-Status “open for adoption”. AUTHORS’ RESPONSE : The intention of our manuscript was to show how researchers can leverage CTD in a variety of ways to help inform, refine, expand, and hopefully advance the status of underdeveloped AOPs. When we initially submitted our manuscript (17 November 2025), the AOP-Wiki contained 532 AOPs, of which only 7% were designated as endorsed by WPHA/WNT, with the remaining 93% of the AOP-Wiki content described as empty (74%), under development (16%), under review (3%), and none with ESCA approval (0%). In our minds, these statistics are indicative of a critical need for new methodologies to advance the status of these languishing AOPs as a way to improve the overall utility and resourcefulness of the AOP-Wiki. The AOP used in our demonstration (i.e., AOP:522) is a good representative candidate, because, as this reviewer noted, it is “open for adoption”, currently poorly populated in the AOP-Wiki itself, and has equivocal limiting supporting evidence from the literature (although enough to warrant the construction of and deposition in the AOP-Wiki). As Mari-Bauset et al. (2018) [rather than the mistakenly cited Mandy and Lai, 2016] conclude in their article about EDC and ASD: “incomplete understanding of biological mechanisms precludes the establishment of a causal relationship”, supporting the primary need to better understand the biological mechanistic steps that can link these disrupting chemicals to ASD, such as the development of a more robust AOP. In our manuscript, we demonstrate how researchers can (1) leverage CTD to identify environmental chemicals that could modulate autism etiology via this AOP and (2) then use those identified chemicals to point researchers in mechanistically-supported directions to further develop and refine AOP:522 to a better position for testability and advancement. To help clarify our intention, we have improved the text. In response to similar comments from Reviewer 2, we have edited the last paragraph of the “Introduction” to now more clearly explain that our test AOP is underdeveloped, open for adoption, and currently contains limited data, and that by intersecting CTD content with this AOP, users can discover chemicals, genes, and phenotypes that can help develop and strengthen this AOP. 2. Manuscript would benefit from an analysis workflow AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 3. Manual queries and analyses are performed at several points in the workflow, which raise the question of validation and reproducibility AUTHORS’ RESPONSE : The crux of this work is to demonstrate how users can leverage CTD chemical content and readily integrate it with AOPs to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with AOP:522 and (2) then use these chemicals to discover new, potential mechanistic steps to further develop and refine AOP:522 to a better position for testability and advancement. A step-by-step instruction guide is now provided (see new Supplemental Figures at figshare) for all the manual queries and analyses, enabling both validation and reproducibility. However, it is important to understand that CTD is updated each month with new curated content from the recently published literature; this is one of the strengths of CTD. Thus, query results can change on a monthly basis (as currently explained in our “Methods” section). Importantly, results from newer queries do not invalidate findings, but rather provide additional data from the more recent literature. In Figure 2, we grouped the 76 prioritized chemicals simply as a display convenience (e.g., clustering together similar chemicals, such as metals, phthalates, pesticides, etc.). The clustering itself does not impart any additional knowledge to the figure (compared to not clustering the chemicals). We performed that task, as described in the manuscript, using web searches or shared term parentage in the CTD Chemical vocabulary hierarchy. For example, “dibutyl phthalate”, “diethylhexyl phthalate”, and “monobutyl phthalate”, all share “Phthalic Acids” as a common parent in the CTD chemical hierarchy, and thus can be grouped as such. Figure 2 is just as valid and reproducible if the chemicals were not clustered, and instead listed alphabetically. 4. Concern with estrogenic effects being based only on ESR1, GENE:2099; ESR2, GENE:2100 AUTHORS’ RESPONSE : In our manuscript, we did not base estrogenic effects only on the two estrogen genes (ESR1 and ESR2). Rather, we mapped this MIE:112 to four CTD terms (Table 1): the two estrogen receptor genes ESR1 and ESR2 plus, importantly, two additional estrogen-signaling related phenotypes (which include all of their GO-phenotype descendants as well) to capture chemicals that still affect the estrogen receptor signaling pathway but without necessarily affecting the specific estrogen receptors. It is important to understand that at CTD a chemical-phenotype interaction does not (necessarily) have to involve a gene, but is simply a curated annotation describing how a chemical causes a biological outcome reported in the literature; e.g., “chemical X results in decreased intracellular estrogen receptor signaling pathway in MCF-7 cells” is a chemical-phenotype interaction reported by an author without any discussion or knowledge of the genes involved. This specifically enables CTD to capture literature-based chemical-induced outcomes without having to know a priori what genes are involved. We would direct the reviewer to our detailed description of this CTD curation module to better understand what we mean as chemical-phenotype interactions (Davis et al., 2018). This CTD curation paradigm allows CTD to capture important data for chemical-induced events directly, without having to involve any genes (either known or unknown by the author). Thus, chemicals that affect estrogen receptor signaling by “mechanisms which are not understood” (Marino, 2006) can still be captured in CTD because we curate the direct chemical-phenotype relationship itself, obviating the need to invoke any unknown gene/mechanism. If a user chooses to broaden their interpretation of “estrogen receptor antagonism”, they can select a more expansive phenotype in the ontology by using a parent term, such as “steroid hormone mediated pathway” (GO:0043401), which now expands the query range and at this writing returns 231 unique chemicals instead of the original 180 chemicals shown in Table 1 for the more specific phenotype. Curating phenotype data as an ontology (instead of as presumptive “gene set) is an advantage, and enables users to broaden or narrow their searches by navigating the vocabulary to the level they prefer. If deemed necessary, users can always map this particular AOP step to additional genes beyond ESR1 and ESR2 if they prefer: this will only bring back additional chemical data for analysis. Finally, a user could try to leverage Natural Language Processing (NLP)-based mapping tools that attempt to link KEs first to “gene lists” and then find CTD chemicals that interact with those gene sets. NLP methods, however, are notoriously complex and difficult to follow and validate for the non-technical user, and, unfortunately, the results may go out of date quickly unless the NLP tool is updated on a consistent schedule; in fact, spot checks by CTD found some strange and equivocal mappings (discussed below) performed by such NLP methods. The main point here, however, is that individual users have the ability to leverage CTD to their level of specificity, comfort, and skill level to (1) identify environmental chemicals that could potentially modulate autism etiology via interaction with this AOP and (2) then use these chemicals to discover new, potential mechanistic steps to further develop, expand, and improve the AOP to a better position for testability and advancement. Different users might decide to use different terms to retrieve data, and that’s perfectly fine. The user ultimately decides how large of a data-net he/she wants to cast and then can decide which molecular mechanisms retrieved from those results make the most sense to include in a testable updated/new AOP. CTD provides the chemical data-set and evidence to fill in the mechanistic knowledge gaps connecting exposure to an adverse outcome. To better reflect this, we edited all occurrences in the “Results and Discussion” of “This KE mapped to…” to the better phrase of “We mapped this KE to…” to emphasize that users are in control of the process here. 5. Supplemental training materials are suggested to facilitate reproducibility of the methods presented. AUTHORS’ RESPONSE : We have added a new comprehensive file of Supplemental Figures that includes a diagram of the overall approach and provide the step instructions and workflow for data acquisition and analyses, allowing for reproducibility of the methods presented. This file is now available as an additional extended data set via the updated figshare link. 6. Process of manual mapping as presented could be improved (AOP Wiki KE terms to CTD phenotype). AUTHORS’ RESPONSE : We wholeheartedly agree and promote the idea of the AOP research community coming together to diligently standardize their mappings, identification, and definitions. We attempted to use many of the third party tools currently promoted by the AOP-Wiki in our original mapping and analysis, but unfortunately, we began to realize that many of these resources are unreliable, as they were either outdated, non-intuitive as to how to use, no longer available as a web application, or simply returned generic links back to the AOP-Wiki without providing any new context: AOP-DB (as defined by Mortensen, Senn, 2021; Pittman, 2018) is listed as a third-party tool on the AOP-Wiki, but is no longer accessible from its stated URL, and the EPA site appears to have been last updated May 2021 and also is inaccessible from its stated URL. Wiki Kaptis has a copyright stamp of 2022, and the information menu states that it only contains data extracted from the AOP-Wiki up to 2023. Our searches using official AOP-Wiki terms such as: AOP:522, autism (AO:2209), estrogen receptor antagonism (MIE:112), ERK1/2 inhibition (KE:2207), and aberrant synaptic formation and plasticity (KE:2208) yielded no results. The AOP-KB is a tool provided by the OECD, but again, appears to be limited in functionality. A user can enter any term (e.g., “estrogen receptor”) and retrieve term matches to AOPs and KEs such as “Androgen receptor activation leading to prostate cancer”, but this result simply links back to the AOP-Wiki, with no explanation as to why this AOP would be returned as a result for “estrogen receptor”. It is not obvious how the AOP-KB determined that “estrogen receptor” is involved in this AOP. Similarly, a search with “autism” returns no results, suggesting the AOP-KB is also not current and out-of-date with the AOP-Wiki. AOP-WIKI EXPLORER (as defined by Kumar et al. 2024) is no longer accessible from its stated URL. We next attempted to use a tool that employs NLP to map curated genes to KEs (Saarimaki et al. (2023), but the resource also seems to be out of date, and spot-checks performed by us gave questionable results: e.g., MIE:112 (“estrogen receptor antagonism”) was mapped to 59 genes, but surprisingly did not include ESR2, the second critical estrogen receptor in humans; similarly, KE:195 (“NMDARs inhibition”) contained only seven GRIN genes, missing 16 other GRIN genes that we included in our CTD analysis. The other KEs we checked (KE:2207, KE:2208, KE:2209) do not even exist in the file, presumably because of its outdatedness. Based upon these unsatisfying and inconsistent approaches, in this manuscript, we decided to map the six AOP terms to CTD genes and phenotypes manually ourselves, as a typical CTD user would find it necessary to do. Going forward, it would be more advantageous if the AOP-Wiki recommended tools that are more easily and readily accessible, stable, intuitive to use, and designed for data currency, otherwise they risk becoming out of date with diminishing value as time goes on. 7. Please reference the literature and relevant contributions in this area. AUTHORS’ RESPONSE : Thank you. To the “Limitations and Strengths” section, we have now added citations for: Ives et al. (2017): Creating a Structured AOP Knowledgebase via Ontology-Based Annotations; Pittman et al. (2018): AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks; Mortensen et al. (2021): The 2021 update of the EPA’s adverse outcome pathway database; and Kumar et al. (2024): AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Mortensen HM. Peer Review Report For: Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental health [version 1; peer review: 3 approved with reservations] . F1000Research 2025, 14 :1266 ( https://doi.org/10.5256/f1000research.190304.r456179) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1266/v1#referee-response-456179 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Linking chemical data from the Comparative...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-1266/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-1266/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-1266/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Davis AP et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/14-1266/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/14-1266", templates : { twitter : "Linking chemical data from the Comparative Toxicogenomics Database.... Davis AP et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-1266/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/172567/190304") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "190304"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "451328": 0, "479503": 0, "479505": 0, "479504": 0, "451366": 0, "451367": 0, "451364": 0, "451365": 0, "451362": 0, "451363": 0, "451360": 0, "451361": 0, "449367": 0, "449374": 0, "449375": 0, "449372": 0, "449373": 0, "449370": 0, "449371": 0, "449368": 0, "449369": 0, "449376": 0, "434030": 0, "434031": 0, "434028": 0, "434029": 0, "434026": 0, "434027": 0, "434034": 0, "434035": 0, "434032": 0, "434033": 0, "439182": 0, "439183": 0, "439180": 0, "439181": 0, "439178": 0, "439179": 0, "480663": 0, "480662": 0, "480661": 0, "480660": 0, "461203": 0, "439186": 0, "480659": 0, "439187": 0, "480658": 0, "439184": 0, "480657": 0, "439185": 0, "480656": 0, "480665": 0, "480664": 0, "461999": 0, "461998": 0, "462007": 0, "462006": 0, "462005": 0, "462004": 24, "462003": 0, "462002": 0, "462001": 0, "462000": 0, "444350": 0, "467903": 0, "444351": 0, "442047": 0, "467902": 0, "444348": 0, "467901": 0, "444349": 0, "467900": 25, "467899": 0, "444346": 0, "444347": 0, "467898": 0, "442054": 0, "442055": 0, "442052": 0, "442053": 0, "444354": 0, "442050": 0, "467907": 0, "442051": 0, "467906": 0, "444352": 0, "442048": 0, "467905": 0, "444353": 0, "442049": 0, "467904": 0, "442056": 0, "444645": 0, "456182": 0, "456183": 0, "456180": 0, "456181": 0, "456178": 0, "456179": 44, "456176": 0, "456177": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "e4732701-a42d-488a-a605-d0fc09438c38"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });