Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study

preprint OA: closed
Full text JSON View at publisher

Abstract

Background Melasma is a common dyschromia, mainly found in women with darker skin types. Although asymptomatic, melasma significantly impacts patients’ quality of life. Due to this complex pathogenesis, melasma is difficult to treat. Plant and plant-derived products have been explored as alternatives for the treatment of melasma. Methods This study utilized network pharmacology coupled with molecular docking and molecular dynamics simulations to investigate the molecular mechanisms of three selected Cassipourea metabolites in the treatment of melasma. Results Of the 202 genes obtained from the 14 profiled metabolites, only PTGS2, TYR, ESR2, and ESR1 were common among metabolites and targets implicated in melasma. From this, The gene ontology highlighted the intracellular steroid hormone receptor, signalling pathway, macromolecular complex, and estrogen receptor activity as the top enriched functional annotations, while the KEGG pathway analysis identified five signalling pathways, from which the prolactin signalling pathway, endocrine resistance, and estrogen signalling pathway were implicated in the pathogenesis of melasma. These pathways were further connected by their linkage to ESR2 and ESR1., Of all Cassipourea metabolites and standards, with afzelechin having the highest docking score for both gens. Further binding interaction analysis showed that ESR2-bound tamoxifen had the highest binding free energy of -47.68 kcal/mol, however, among the interacting Cassipourea metabolites, sitosterol-glycoside exhibited the highest negative binding affinity for both ESR2 (-40.50 kcal/mol) and ESR1 (-78.97 kcal/mol) over 150 ns simulation, suggesting its potential as a dual modulator. Altogether, the metabolites presented remarkable binding stability and thermodynamic compactness with the apo-genes. Conclusion The finding that the selected Cassipourea metabolites are associated with the genes and enzymes implicated in melasma pathogenesis, together with their significant binding effects on the enriched genes, suggests their regulatory potential on the profiled targets and, consequently, in the treatment of melasma.
Full text 167,912 characters · extracted from preprint-html · click to expand
Mechanisms of Selected Cassipourea Metabolites... | 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/13-952" }, "headline": "Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular...", "datePublished": "2024-08-22T11:13:22", "dateModified": "2024-08-22T11:13:22", "author": [ { "@type": "Person", "name": "Nomakhosi Mpofana" }, { "@type": "Person", "name": "Christina Peter" }, { "@type": "Person", "name": "Halimat Yusuf Lukman" }, { "@type": "Person", "name": "Mokgadi Ursula Makgobole" }, { "@type": "Person", "name": "Ncoza Cordelia Dlova" }, { "@type": "Person", "name": "Nceba Gqaleni" }, { "@type": "Person", "name": "Ahmed Hussein" }, { "@type": "Person", "name": "Saheed Sabiu" } ], "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 Melasma is a common dyschromia, mainly found in women with darker skin types. Although asymptomatic, melasma significantly impacts patients’ quality of life. Due to this complex pathogenesis, melasma is difficult to treat. Plant and plant-derived products have been explored as alternatives for the treatment of melasma. Methods This study utilized network pharmacology coupled with molecular docking and molecular dynamics simulations to investigate the molecular mechanisms of three selected Cassipourea metabolites in the treatment of melasma. Results Of the 202 genes obtained from the 14 profiled metabolites, only PTGS2, TYR, ESR2, and ESR1 were common among metabolites and targets implicated in melasma. From this, The gene ontology highlighted the intracellular steroid hormone receptor, signalling pathway, macromolecular complex, and estrogen receptor activity as the top enriched functional annotations, while the KEGG pathway analysis identified five signalling pathways, from which the prolactin signalling pathway, endocrine resistance, and estrogen signalling pathway were implicated in the pathogenesis of melasma. These pathways were further connected by their linkage to ESR2 and ESR1., Of all Cassipourea metabolites and standards, with afzelechin having the highest docking score for both gens. Further binding interaction analysis showed that ESR2-bound tamoxifen had the highest binding free energy of -47.68 kcal/mol, however, among the interacting Cassipourea metabolites, sitosterol-glycoside exhibited the highest negative binding affinity for both ESR2 (-40.50 kcal/mol) and ESR1 (-78.97 kcal/mol) over 150 ns simulation, suggesting its potential as a dual modulator. Altogether, the metabolites presented remarkable binding stability and thermodynamic compactness with the apo-genes. Conclusion The finding that the selected Cassipourea metabolites are associated with the genes and enzymes implicated in melasma pathogenesis, together with their significant binding effects on the enriched genes, suggests their regulatory potential on the profiled targets and, consequently, in the treatment of melasma. " } { "@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/13-952/v1", "name": "Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment:..." } } ] } Home Browse Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Mpofana N, Peter C, Lukman HY et al. Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :952 ( https://doi.org/10.12688/f1000research.153996.1 ) 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 Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] Nomakhosi Mpofana https://orcid.org/0000-0002-3007-1147 1,2 , Christina Peter https://orcid.org/0000-0001-7239-9493 3 , Halimat Yusuf Lukman 3 , [...] Mokgadi Ursula Makgobole https://orcid.org/0000-0001-6530-7079 2 , Ncoza Cordelia Dlova 1 , Nceba Gqaleni 4 , Ahmed Hussein 5 , Saheed Sabiu 3 Nomakhosi Mpofana https://orcid.org/0000-0002-3007-1147 1,2 , Christina Peter https://orcid.org/0000-0001-7239-9493 3 , [...] Halimat Yusuf Lukman 3 , Mokgadi Ursula Makgobole https://orcid.org/0000-0001-6530-7079 2 , Ncoza Cordelia Dlova 1 , Nceba Gqaleni 4 , Ahmed Hussein 5 , Saheed Sabiu 3 PUBLISHED 22 Aug 2024 Author details Author details 1 Dermatology, University of KwaZulu-Natal School of Clinical Medicine, Durban, KwaZulu-Natal, South Africa 2 Department of Somatology, Durban University of Technology - Ritson Campus, Durban, KwaZulu-Natal, South Africa 3 Biotechnology, Durban University of Technology - Steve Biko Campus, Durban, KwaZulu-Natal, South Africa 4 Discipline of Traditional Medicine, University of KwaZulu-Natal - Howard College Campus, Durban, KwaZulu-Natal, South Africa 5 Department of Chemistry, Cape Peninsula University of Technology - Bellville Campus, Bellville, Western Cape, South Africa Nomakhosi Mpofana Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Christina Peter Roles: Formal Analysis, Investigation, Writing – Review & Editing Halimat Yusuf Lukman Roles: Formal Analysis, Investigation, Visualization, Writing – Review & Editing Mokgadi Ursula Makgobole Roles: Investigation, Methodology, Writing – Review & Editing Ncoza Cordelia Dlova Roles: Supervision, Writing – Review & Editing Nceba Gqaleni Roles: Funding Acquisition, Supervision, Writing – Review & Editing Ahmed Hussein Roles: Supervision, Writing – Review & Editing Saheed Sabiu Roles: Formal Analysis, Software, Validation, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Melasma is a common dyschromia, mainly found in women with darker skin types. Although asymptomatic, melasma significantly impacts patients’ quality of life. Due to this complex pathogenesis, melasma is difficult to treat. Plant and plant-derived products have been explored as alternatives for the treatment of melasma. Methods This study utilized network pharmacology coupled with molecular docking and molecular dynamics simulations to investigate the molecular mechanisms of three selected Cassipourea metabolites in the treatment of melasma. Results Of the 202 genes obtained from the 14 profiled metabolites, only PTGS2, TYR, ESR2, and ESR1 were common among metabolites and targets implicated in melasma. From this, The gene ontology highlighted the intracellular steroid hormone receptor, signalling pathway, macromolecular complex, and estrogen receptor activity as the top enriched functional annotations, while the KEGG pathway analysis identified five signalling pathways, from which the prolactin signalling pathway, endocrine resistance, and estrogen signalling pathway were implicated in the pathogenesis of melasma. These pathways were further connected by their linkage to ESR2 and ESR1., Of all Cassipourea metabolites and standards, with afzelechin having the highest docking score for both gens. Further binding interaction analysis showed that ESR2-bound tamoxifen had the highest binding free energy of -47.68 kcal/mol, however, among the interacting Cassipourea metabolites, sitosterol-glycoside exhibited the highest negative binding affinity for both ESR2 (-40.50 kcal/mol) and ESR1 (-78.97 kcal/mol) over 150 ns simulation, suggesting its potential as a dual modulator. Altogether, the metabolites presented remarkable binding stability and thermodynamic compactness with the apo-genes. Conclusion The finding that the selected Cassipourea metabolites are associated with the genes and enzymes implicated in melasma pathogenesis, together with their significant binding effects on the enriched genes, suggests their regulatory potential on the profiled targets and, consequently, in the treatment of melasma. READ ALL READ LESS Keywords melasma, Cassipourea species, Network pharmacology, Molecular dynamics simulation, Signalling pathways Corresponding Author(s) Nomakhosi Mpofana ( [email protected] ) Close Corresponding author: Nomakhosi Mpofana Competing interests: No competing interests were disclosed. Grant information: This work was supported in part by the National Research Foundation of South Africa (Grant Number: 138179), the Department of Science and Innovation (DSI) “Cosmeceutical Concepts and Product Development” project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 Mpofana N 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: Mpofana N, Peter C, Lukman HY et al. Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :952 ( https://doi.org/10.12688/f1000research.153996.1 ) First published: 22 Aug 2024, 13 :952 ( https://doi.org/10.12688/f1000research.153996.1 ) Latest published: 22 Aug 2024, 13 :952 ( https://doi.org/10.12688/f1000research.153996.1 ) 1. Introduction The skin is the largest organ in the human body. It forms a significant anatomical barrier between internal and external environments. 1 The body is continuously exposed to various physical and chemical exogenous polluting substances. 2 Ultraviolet radiation (UVR) from excessive sun exposure is the primary exogenous factor that harms the skin. This process has various harmful effects on the skin as it alters the composition of the skin, causing elastic fiber accumulation, collagen reduction, and degeneration leading to wrinkles, sagging, and glycosaminoglycan deposition, resulting in premature aging, known as photoaging. 3 Moreover, overexposure to ultraviolet (UV) rays stimulates melanin synthesis owing to the rapid proliferation of melanocytes. Furthermore; to stimulated melanin synthesis, excessive exposure to sunlight, especially UVA and UVB, causes overexpression of reactive oxygen species (ROS) that harm lipids, proteins, and deoxyribonucleic acids. 4 Melanin is produced in the epidermis of the skin via a pathway known as melanogenesis, with tyrosinase playing a key role as the rate-limiting enzyme. 5 The enzyme catalyzes three steps in melanin biosynthesis: hydroxylation of tyrosine to 3,4-dihydroxyphenylalanine (DOPA), oxidation of DOPA to DOPA quinone, and conversion of 5,6-dihydroxyindole to indolequinone. 3 , 6 Thus, tyrosinase is the primary target for determining skin-lightening agents for cosmetic applications or skin lightening. 5 While melanin shields the skin from UV radiation, excessive production can result in dermatological hyperpigmentation of the skin in clinical conditions such as dark spots, freckles, melasma, solar lentigo, linea nigra, and post-inflammatory hyperpigmentation (PIH), which can affect the appearance of the skin. 7 Melasma is a common dermatological condition characterized by hyperpigmentation (light brown or dark brown), flaky or reticular patches, and macules that appear on the facial skin, and much less often on the neck and forearms. This condition primarily affects adult females, especially those with darker skin phototypes (Fitzpatrick skin phototypes III-VI). 8 , 9 Although common to females, men are also affected; the incidence of melasma is estimated at 1% worldwide; however, it varies between 8.8-50% of at-risk populations. 10 , 11 The pathogenesis of melasma is complex, as it is linked to melanocytes, keratin-forming cells, endothelial cells, fibroblasts, and alterations in the basement membrane. 12 , 13 Visible light and UV exposure, hormonal changes, and genetic predisposition have all been linked to the onset of melasma. 12 – 14 Melasma can also be caused by various factors such as the use of photosensitizing medication, thyroid diseases, ovarian tumors, hepatopathies, parasitic infestation, certain foods, and stress. 9 Thyroid dysfunction, menstrual cycle irregularities, and insulin resistance which may also be caused by hormonal imbalance, have been linked to the diagnosis of melasma. 9 , 14 Although not life-threatening, being a facial disorder, melasma is disfiguring, and as such, impacts patients’ lives and psychological well-being, potentially leading to anxiety, depression, and other disorders. 12 – 15 Owing to this complex pathogenesis, melasma is difficult to treat. Currently, there is no cure or standardized treatment for melasma. Hydroquinone is a popular skin-whitening agent that inhibits tyrosinase. However, it is not suitable for all skin types, and prolonged and unsupervised use can cause undesirable side effects such as dermatitis, edema, allergic reactions, and ochronosis. 15 , 16 Common treatments include sun protection, topical creams, such as niacinamide, vitamins A and C, oral medications, chemical peels, laser and light therapy, tranexamic acid, and microneedling. However, these treatments are prone to chemical irritation, inflammatory reactions, and hyperpigmentation. Other disadvantages include the possibility of recurrence and unpredictable efficacy. 12 – 15 Patients often prefer the use of complementary and alternative medicines to supplement or replace traditional treatments, licorice extract, arbutin, and kojic acid are among the many tyrosinase inhibitors used in skin-lightening treatments. 6 , 15 Additionally, tyrosinase inhibitors from phyto-molecules are equally valuable for scavenging ROS, which cause skin damage from excessive sun exposure. 5 Since ancient times, medicinal plants have been used as therapeutic agents, dating back to 4000-5000 BC. 17 The biological activities of plants are unique to specific species or groups, which supports the idea that a plant’s secondary metabolites are taxonomically distinct. 18 Screening of such secondary metabolites as active compounds from plants has led to the invention of a drug discovery process in pharmaceutical science for isolating new natural drugs with efficient protection and treatment roles against various diseases. 18 Studies have explored natural products that inhibit UV-induced ROS, suppress enzymes, and reduce melanin formation as potential alternatives to current treatments have been conducted. This strategic shift is due to the adverse effects of synthetic agents. 19 Phytocompounds such as aloesin, arbutin, licorice, hesperidin, gentisic acid, flavonoids, niacinamide, polyphenols, and yeast derivatives have demonstrated great potential to inhibit melanogenesis without harming melanocytes. 4 , 18 Topical botanicals are becoming increasingly popular for skin care because of their perceived safety, low side effects, formulation stability, efficacy, cost-effectiveness, and quick metabolism when applied to the skin compared with conventional treatments. 12 , 17 , 20 According to ethnobotanical literature, topical application is the most commonly used mode of application because it guarantees direct and immediate interaction of specific botanical compounds with the site of action. 21 – 23 Recently, three Cassipourea species ( Cassipourea flanaganii, Cassipourea malosana, and Cassipourea gummiflua Tul. Verticillata ) have been reported to be used for skin lightening by women in rural areas in the Eastern Cape and Kwa Zulu-Natal provinces of South Africa. 20 Although common names of the species are often used interchangeably by the rural community, the phytochemical comparison showed that each species is distinct, but they share skin-lightening characteristics. All three plants have been demonstrated to be effective and safe for use as topical skin lighteners, with no side effects. 17 , 22 – 24 In this context, the biological activities of the three Cassipourea species were systematically analyzed for their potential molecular mechanisms in the treatment of melasma using network pharmacology. Network pharmacology is an emerging science that examines the “compound-protein/gene-disease” system, thus, it is an effective measure for describing the intricacy of biological systems, drugs, and diseases from a network-based context. 19 , 25 However, to gain further insight into the binding interaction and stability of the metabolites and hub targets, molecular docking and dynamics simulations were conducted. 2. Methods 2.1 Acquisition of Cassipourea metabolites Active compounds from the three Cassipourea species screened through Chromatography-Mass Spectrometry (LC-MS/MS) analysis in the negative mode were used to generate a library of compounds for Cassipourea. The LCMS/MS analysis detected twenty-four compounds from various chemical classes, including fatty acids, steroids, di- and triterpenoids, flavonoids, and phenolic acids ( https://doi.org/10.6084/m9.figshare.26418361.v1 ) (accessed on 01 August 2024). Eighteen compounds were tentatively identified. 20 The compounds were identified based on their structure and molecular mass, which shared similarities with known substances ( https://doi.org/10.6084/m9.figshare.26418361.v1 ) (accessed on 01 August 2024). This was validated further with previous reports, characteristic fragmentation patterns, and data from a large bank and the SciFinder database. 2.2. Drug-likeness and pharmacokinetic screening of Cassipourea metabolites The metabolites of the Cassipourea species were evaluated using Lipinski’s rule of five (Ro5) for drug-likeness properties of the metabolites. 26 The SwissADME server ( http://www.swissadme.ch/ ; accessed April 01, 2024) was used to predict the absorption, distribution, metabolism, and excretion properties of metabolites. 27 2.3 Acquisition of target genes related to Cassipourea metabolites and melasma The metabolite target genes were mined from two independent databases. Genes related to Cassipourea metabolites, whose SMILES were available on PubChem (Afzelechin, azelaic acid, cassipourol, chlorogenic acid, chrysin 8-C glucoside, decahydroretinol, emodin 6,8 dimethyl ether, hexose, isorhamnetin-3-O-rhamnoside, lupeol, lyoniside, methyl linoleate, sitosterol-glycoside and tricin) were identified from the Swiss Target Prediction (STP) database ( http://www.swisstargetprediction.ch/ ) (accessed April 01, 2024) using the Simplified Molecular Input Line Entry System (SMILES) retrieved from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) (accessed April 01, 2024). Genes related to melasma were acquired from the Online Mendelian Inheritance in Man (OMIM) database ( https://www.omim.org/ ) (accessed on April 01, 2024) and the GeneCards database ( https://www.genecards.org/ ) (accessed April 01, 2024). The Venny 2.1.0 ( https://bioinfogp.cnb.csic.es/tools/venny /) tool was used to identify and characterize intersecting target genes 28 between Cassipourea metabolites and melasma. 29 2.4 Generation of protein-protein interaction (PPI) network The Search Tool for the Retrieval of Interacting Genes (STRING) database ( https://string-db.org/ ) (accessed April 01, 2024) was used to construct a PPI network. 30 The parameters for the analysis were set to Homo sapiens with a confidence level of < 0.4, followed by the input of common target genes between metabolites and melasma. The PPI network was then classified using Cytoscape v3.8.2. 28 A degree algorithm was used to identify key genes in the network ( Equation 1 ). 31 (1) Deg ( v ) = | N ( v ) | where N(v) = a node neighbor, and v = each node’s neighbors. 2.5 Analysis of the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment parameters To illustrate the roles of the identified common targets in biological processes (BP), cellular components (CC), and molecular function (MF), gene ontology was conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool ( https://david.ncifcrf.gov/tools.jsp ), 32 with search parameters fixed to Homo sapiens. The GO and KEGG enrichment pathway analysis plots were generated using SRPlot ( http://www.bioinformatics.com.cn/en ), and from the KEGG pathway enrichment, the most significant pathway was selected based on the lowest false discovery rate (FDR). 33 , 34 2.6 Construction of the pathway compound target network (PCT) The PCT comprising melasma-related signaling pathways, their interacting genes, and metabolites were constructed using the Cytomerger plugin in Cytoscape software v3.9.1. 35 Thereafter, network topology analysis was conducted with edges depicting node interactions and their degrees of significance. 35 2.7 Molecular docking Crystal X-ray structures of the target genes ESR2 [PDB: 2GIU] and ESR1 [PDB: 1QKU] were downloaded from the RCSB Protein Data Bank (PDB) ( https://www.rcsb.org/ ) (accessed on April 01, 2024) and optimized by the elimination of all non-standard residues, co-crystallized ligands, and water molecules. Thereafter, ESR2 and ESR1 interacting metabolites and reference standards for melasma and the gene agonists tranexamic acid and tamoxifen, respectively, were developed. 3D conformers (ligands) were obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) (accessed April 01, 2024). Ligands were optimized using the Open Babel program plug-in on Python Prescription v 0.9.5 (PyRx) 36 through the addition of Gasteiger charges. Binding at the gene active site was confirmed by the identification and selection of ESR2 and ESR1 active site amino acid residues. Docking studies were conducted on PyRx, from which the grid box coordinates, in correlation with the x-y-z coordinates, were ascertained using the BIOVIA Discovery Studio v21.1.0. 37 Following molecular docking, the top five ligands with the highest negative docking score relative to their reference standards were visualized using Discovery Studio for their docking interactions before the simulation process. To avoid pseudo-positive binding conformations, the docking protocol was validated by measuring the root mean square deviation (RMSD) of docked ligands from the reference pocket containing native ligands (estradiol) in the experimental co-crystal Where an RMSD of 0.5 Å was obtained for both docking validations ( Figure 1 ). 38 Figure 1. (a) Superimposition of ESR1 native ligand estradiol (green), gene modulator: tamoxifen (blue), reference standard: tranexamic acid (purple) and top performing plant metabolite afzelechin (red), with an RMSD of 0.5 Å. (b) Superimposition of ESR2 native ligand estradiol (green), gene modulator: tamoxifen (blue), reference standard: tranexamic acid (purple) and top performing plant metabolite afzelechin (red), with an RMSD of 0.5 Å. 2.8 Molecular Dynamic (MD) simulations MD simulations were conducted at 150 ns using the graphical processing unit (GPU) version of the AMBER 18 package (force field with FF18SB variant) of the Center for High Performance Computing (CHPC), Cape Town, South Africa. 39 Ligand atomic partial charges were harnessed through ANTECHAMBER using a general amber force field (GAFF) and restrained electrostatic potential (RESP). The systems were neutralized using hydrogen atoms and Na + and Cl − counter ions from the Leap module. The amino acid residues of ESR2 and ESR1 were subsequently numbered and encased in an orthorhombic box of TIP3P water molecules, such that all atoms were at most 8 Å away from any box edge. The SHAKE algorithm was used to constrain hydrogen atom bonds within simulated systems, with each simulation comprising a 2 fs step-size, concurrent with the isobaric-isothermal ensemble (NPT) randomized seeding, a Langevin thermostat with a collision frequency of 1.0 ps, 1 bar, 2 ps pressure-coupling constant, and 300 K temperature. Thereafter, along with the binding free energy, the post-dynamic parameters, namely the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and solvent-accessible surface area of each protein-ligand system were assessed. 40 3. Results 3.1 Cassipourea metabolites library A total number of 18 common metabolites were identified from the previously characterized Cassipourea species. They are comprised of different chemical classes, including fatty acids, steroids, di- and tri-terpenoids, flavonoids, and phenolic acids ( Table 1 ). Table 1. Common metabolites identified from the three investigated Cassipourea species. Metabolites Molecular formula Metabolite class 2α,3α-Epoxyflavan-5,7,4′-triol-(4β → 8)-afzelechin C 30 H 24 O 10 Flavonoids Afzelechin C 15 H 14 O 5 Flavonoids Azelaic acid C 9 H 16 O 4 Fatty acids Cassipourol C 20 H 38 O Triterpenoids Chlorogenic acid C 16 H 18 O 9 Phenolics Chrysin 8-C-glucoside C 27 H 44 O 3 Flavonoids Decahydroretinol C 20 H 40 O Tocopherol Ellisinin A C 18 H 18 O 2 Sesquiterpenes Emodin 6,8-dimethyl ether C 17 H 14 O 5 Anthraquinone derivative Ent-atis-16-en-19-oic acid C 30 H 24 O 10 Di-terpenoids Hexose (glucose) C 6 H 12 O Monosaccharide Isorhamnetin-3-O-rhamnoside C 22 H 22 O 11 Flavonoids Lupeol C 30 H 50 O Triterpenoids Lupeol stearate C 48 H 84 O 2 Triterpene ester Lyoniside C 27 H 36 O 12 Iridoid glycoside Methyl linoleate C 19 H 34 O 2 Fatty acid methyl ester Sitosterol glycoside C 35 H 60 O 6 Sterol glycoside derivate Tricin C 17 H 14 O 7 Methylated flavonoids 3.2 Drug-likeness, Pharmacokinetic screening of Cassipourea metabolites Of the 14 common metabolites of Cassipourea species whose SMILES were available in PubChem, only lyoniside had three violations of Lipinski’s Ro5 (molecular weight ≤ 500 g/mol, hydrogen bond donor and acceptor ≤ 5 and 10, respectively, bioavailability score ≤ 0.55, and lipophilicity (MLOGP) ≤ 4.5), as it had a molecular weight of 552.57 g/mol, hydrogen bond acceptor and donor of 12 and 6, respectively; other metabolites had ≤ 2 violations ( Table 2 ). Pharmacokinetic analysis revealed that, except for sitosterol-glycosides, the metabolites were soluble in water to varying degrees. The metabolites also possess relatively high gastrointestinal absorption, are mostly non-substrate for glycoproteins, and are impermeable to the blood-brain barrier (BBB). However, azelaic acid, decahydroretinol, and emodin 6,8 dimethyl showed BBB permeability ( Table 2 ). The metabolites demonstrated significant non-inhibition of the cytochrome P (cytochrome P450) isoenzymes ( Table 2 ). Table 2. Pharmacokinetic and Lipinski’s Ro5 screening of Cassipourea metabolites. Melasma metabolites MW (g/mol) HBA HBD LOGP BS LV Sol. GIA BBB-P Pgp-S CYP1A2 CYP2C19 CYP2C9 CYP2D6 CYP3A4 M1: Afzelechin 274.27 5 4 1.38 0.55 0 Soluble High No Yes No No No No No M2: Azelaic acid 188.22 4 2 1.44 0.85 0 Very soluble High Yes No No No No No No M3: Cassipourol 294.52 1 1 3.90 0.55 1 Mod. Soluble High No No No No Yes Yes No M4: Chlorogenic acid 354.31 9 6 0.96 0.11 1 Very soluble Low No No No No No No No M5: Chrysin 8-C-glucoside 416.38 9 6 2.18 0.55 1 Soluble Low No No No No No No No M6: Decahydroretinol 286.45 1 1 4.05 0.55 1 Mod. soluble High Yes No Yes No Yes No No M7: Emodin 6,8 dimethyl ether 298.29 5 1 2.66 0.55 0 Mod. soluble High Yes No Yes Yes Yes No Yes M8: Hexose (glucose) 180.16 6 5 0.24 0.55 0 Highly soluble Low No Yes No No No No No M9: Isorhamnetin-3-O-rhamnoside 462.40 11 6 2.26 0.17 2 Soluble Low No Yes No No No No Yes M10: Lupeol 426.72 1 1 4.68 0.55 1 Poorly soluble Low No No No No No No No M11: Lyoniside 552.57 12 6 4.07 0.17 3 Soluble Low No Yes No No No No No M12: Methyl linoleate 294.47 2 0 4.61 0.55 1 Mod. soluble High No No Yes No Yes No No M13: Sitosterol-glycoside 576.85 6 4 4.98 0.55 1 Poorly soluble Low No No No No No No No M14: Tricin 330.29 7 3 2.58 0.55 0 Mod. soluble High No No Yes No Yes Yes Yes 3.3 Acquisition of target genes related to Cassipourea metabolites and melasma In total, 644 and 545 Cassipourea metabolite genes were collected from the STP and SEA databases, respectively. A total of 202 genes were common in both databases. Of the 34 melasma genes acquired from the GeneCards database and 24282 genes acquired from OMIM, only 31 genes were common to both. A further probe of the gene interactions identified four genes directly linked to C assipourea and melasma ( Figure 2 ). Figure 2. Common genes between cassipourea metabolites and melasma, extrapolated from the relevant databases. 3.4 Generation of protein-protein interaction (PPI) network The PPI network constructed from common genes from melasma and C assipourea metabolites comprised 4 nodes and 12 edges, an average node degree of 2, and an enrichment p -value of 0.0107 ( Figure 3 ). The identified estrogen receptors 1 and 2 (ESR1 and 2), prostaglandin-endoperoxide synthase 2 (PTGS2), and tyrosinase (TYR) genes interact with each other. Figure 3. PPI network analysis of common genes between C assipourea metabolites and melasma. 3.5 Analysis of the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genome (KEGG) enrichment parameters Gene ontology analysis indicated the potential effect of Cassipourea metabolites on BP, CC, and MF relative to melasma. Amongst the BP, the intracellular steroid hormone receptor signalling pathway (2.0 × 10 −3 ) was identified as the most enriched, followed by response to vitamin D (2.9 × 10 −3 ) and cellular response to estrogen stimulus (3.4 × 10 −3 ). Of the CCs, only the macromolecular complex (9.8 × 10 −2 ) was marginally enriched. Estrogen receptor activity (6.3 × 10 −4 ) was both the most enriched MF within the GO, followed closely by enzyme binding (1.2 × 10 −3 ) and estrogen response element binding (1.7 × 10 −3 ) ( Figure 4 ). Figure 4. GO enrichment analysis of interacting C assipourea metabolites and melasma genes. KEGG pathway enrichment analysis generated five pathways, three of which were related to melasma ( Figure 5 ). All three pathways, namely the prolactin signaling pathway (0.0238), estrogen signaling pathway (0.0238), and endocrine resistance (0.0238), shared the same FDR. To account for this, pathway strength was used to screen for the most enriched pathways. The prolactin signaling pathway was subsequently identified as the most prominent pathway ( Table 2 ). Figure 5. Enriched signalling pathways from the KEGG analysis of the common C assipourea metabolites and melasma genes. 3.6 Construction of the pathway compound target network The resultant PCT comprised the prolactin signaling pathway, estrogen signaling pathway, and endocrine resistance ( Figure 7 ) encompassing 13 nodes, 20 edges, and an average node degree of 3.07. From the top three pathways identified, the prolactin signalling pathway was highlighted as the most significant for its greater pathway strength ( Figure 6 ). Network topology analysis identified seven metabolites that interact with the significant genes ESR1 and ESR2 ( Figures 8 and 9 ). Figure 6. PCT network depicting the interactions of C assipourea metabolites with the most enriched pathway. Figure 7. PCT network depicting the interactions of C assipourea metabolites with the three most enriched signalling pathways. Figure 8. Interaction of ESR1 gene with 7 Cassipourea metabolites (yellow) and the most significant pathways (green) common to melasma and Cassipourea metabolites. Figure 9. Interaction of ESR2 gene with 7 Cassipourea metabolites (yellow) and the most significant pathway (green) common to melasma and Cassipourea metabolites. 3.7 Molecular docking From the ESR2 bound C assipourea metabolites, hexose (-5.4 kcal/mol), lupeol (-5.4 kcal/mol) and sitosterol-glycoside (-4.9 kcal/mol) had lower negative docking scores than tamoxifen (-6.2 kcal/mol) and tranexamic acid (-5.5 kcal/mol) ( Table 3 ). Except for decahydroretinol (-6.0 kcal/mol), all ESR1-bound metabolites exhibited higher negative docking scores than both tamoxifen (-6.1 kcal/mol) and tranexamic acid (-5.9 kcal/mol) ( Table 4 ). Observably, when bound to both ESR2 and ESR1, afzelechin exhibited the highest negative docking scores of -7.3 kcal/mol and -8.8 kcal/mol, respectively. Regarding ESR2, afzelechin formed 27 intermolecular interactions comprising two hydrogen bonds (Ile373, Arg346), 12 van der Waals interactions (Met340, Ile376, Leu362, Met294, Phe377, Leu380, Leu354, Val487, Thr299, Trp335, Leu301, Arg346), and nine other important interactions, viz. hydrophobic interactions (Glu305, Leu343, Phe356, Ala302, Leu339, Leu476, Met336, Met295, Leu298). Twenty intermolecular interactions and bonds were observed in ESR1 bound afzelechin namely, two hydrogen bonds (Phe404, Met517), 13 Van der Waals interactions (Arg394, Glu353, Thr347, Leu346, Met343, His524, Leu525, Met421, Gly521, Ser518, Ile424, Leu428, Leu387), and five other important interactions (Met388, Leu384, Leu391, Ala350, Leu349) ( Tables 4 and 5 ). Table 3. Profiles of the 5 signalling pathways enriched in KEGG analysis of the common C assipourea metabolites and melasma genes. #term ID Pathways Strength FDR Target genes hsa01522 Endocrine resistance 2.02 0.0238 ESR2, ESR1 hsa04915 Estrogen signalling pathway 1.87 0.0238 ESR2, ESR1 hsa04917 Prolactin signaling pathway 2.16 0.0238 ESR2, ESR1 hsa05200 Pathways in cancer 1.46 0.0238 ESR2, PTGS2, ESR1 hsa05224 Breast cancer 1.83 0.0238 ESR2, ESR1 Table 4. Docking scores and structural interactions of the plant metabolites and their reference standards bound to ESR2. Metabolite name Docking scores (kcal/mol) No. of interactions No. of hydrogen bonds and interacting residues No. of Van der Waal forces and interacting residues Other important interactions and residues Tranexamic acid -5.5 10 2 (Arg346, Leu339) 5 (Met340, Glu305, Leu301, Met336, Met295) 3 (Phe356, Leu343, Leu298) Tamoxifen -6.2 21 1 (Ile373) 12 (Met340, Ile376, Leu362, Met294, Phe377, Leu380, Leu354, Val487, Thr299, Trp335, Leu301, Arg346) 9 (Glu305, Leu343, Phe356, Ala302, Leu339, Leu476, Met336, Met295, Leu298) Afzelechin -7.3 17 2 (Ile373, Arg346) 11 (Met295, Gly472, Met336, Ala302, Leu339, Leu301, Glu305, Met340, Leu298, Leu380, Phe377) 4 (His475, Ile376, Phe356, Leu343) Tricin -6.6 13 4 (Pro277, Glu305, Val338, Lys401) 6 (Pro278, Val280, Leu339, Met341, Gly342, Tyr397) 3 (His279, Arg346, Trp345) Decahydroretinol -6.4 15 - 11 (Trp345, Gly342, Lys401, Trp312, Met309, Leu339, Pro277, His308, Glu305, Pro278, Val280) 4 (Ala357, Pro358, His279, Arg346) Emodin 6,8 dimethyl ether -6.4 19 1 (Glu305) 9 (Arg346, Leu380, Met340, Gly472, Thr299, Val487, Ala302, Leu301, Leu339) 9 (Leu343, Phe356, Met336, His475, Ile376, Ile373, Leu476, Met295, Leu298) Hexose (glucose) -5.4 13 5 (Glu305, Arg346, Gly342, Lys402, Val338) 8 (Pro278, Val280, His308, Pro277, Met309, Trp312, Leu339, Tyr397) - Lupeol -5.4 8 1 (Pro277) 7 (Glu276, Pro278, Arg346, His279, Pro358, Ile355, His350) Sitosterol-glycoside -4.9 14 3 (Asn470, Ser333, Glu332) 10 (Arg466, Glu337, Cys334, Ser469, Met336, Trp335, Met473, Tyr488, Glu474, Asn478) 1 (Leu477) Table 5. Docking scores and structural interactions of the plant metabolites and their reference standards bound to ESR1. Metabolite name Docking scores (kcal/mol) No. of interactions No. of hydrogen bonds and interaction residues No. of Van der Waal forces and interactions residues Other important interactions and residues Tranexamic acid -5.9 12 2 (Arg394, Glu353) 7 (Leu384, Leu391, Leu349, Leu346, Leu525, Leu540, Thr347) 3 (Phe404, Leu387, Ala350) Tamoxifen -6.1 18 - 8 (Gly521, Met421, Leu525, Met343, Thr347, Phe404, Glu353, Arg394) 10 (Ile424, Leu384, Met388, Leu391, Leu428, Leu349, Ala350, Leu387, Leu346, His524) Afzelechin -8.8 20 2 (Phe404, Met517) 13 (Arg394, Glu353, Thr347, Leu346, Met343, His524, Leu525, Met421, Gly521, Ser518, Ile424, Leu428, Leu387) 5 (Met388, Leu384, Leu391, Ala350, Leu349) Cassipourol -8.1 20 - 13 (Arg394, Glu353, Leu349, Thr347, Leu540, Leu346, Leu540, Gly521, Ile424, Leu428, Phe425, Met421, Met522, Met388) 7 (Leu387, Phe404, Ala350, Leu391, Leu384, Leu525, Trp383) Sitosterol-glycoside -7.7 28 - 21 (Pro324, Lys449, Glu323, Ile326, Gly390, Met388, Leu346, Gly521, Ile424, Leu384, Met522, Trp383, His524, Leu540, Met421, Phe425, Met343, Thr347, Leu349, Met357, Ile386) 7 (Leu525, Ala350, Arg394, Glu353, Leu387, Phe404, Leu391) Emodin 6,8 dimethyl ether -7.7 19 1 (Leu387) 5 (Met517, Gly521, Leu428, Met421, Gly353, Thr347) 12 (Met388, Ile424, Leu384, Met528, Met343, His524, Leu525, Leu346, Ala350, Phe404, Leu349, Leu391) Lupeol -6.9 10 1 (Glu397) 8 (Met396, Gly442, Leu320, Glu323, Ile326, Pro325, Pro324, Arg394) 1 (Trp393) Tricin -6.5 19 2 (Met517, Met388) 8 (Leu428, Leu384, Gly521, Met421, His524, Thr347, Glu353, Arg394) 9 (Phe404, Ala350, Leu391, Met343, Leu346, Leu525, Ile424, Leu387, Leu349) Decahydroretinol -6.0 11 1 (Met438) 8 (Asn439, Arg503, Leu495, Met490, Glu444, Gln441, Glu443, Lys492) 2 (Leu489, Ala493) 3.8 Molecular dynamic simulations of C assipourea metabolites with the enriched genes Regarding ESR2 bound systems, all C assipourea metabolites displayed lower negative binding affinities (ranging from -18.71 kcal/mol to -40.50 kcal/mol) than tamoxifen (-47.68 kcal/mol) and higher negative binding affinities than tranexamic acid (17, 60 kcal/mol). From the top performing C assipourea metabolites bound to ESR1, sitosterol-glycoside (-78.97 kcal/mol), decahydroretinol (-63,34 kcal/mol), lupeol (-61.23 kcal/mol), emodin 6,8 dimethyl ether (-56.45 kcal/mol), and tricin (-48.22 kcal/mol), had higher negative binding affinities than tamoxifen (-47.61 kcal/mol) and tranexamic acid (-16.68 kcal/mol), with emodin 6,8 dimethyl ether (-36.26 kcal/mol) exhibiting the lowest negative binding affinity ( Table 6 ). Table 6. Thermodynamic analysis of the Cassipourea metabolites bound to ESR2 and ESR1 systems over 150 ns. Energy framework (kcal/mol) Complex ΔE vdW ΔE elec ΔG gas ΔG solv ΔG bind ESR2 Ref. STD: Tranexamic acid -20.31±2.46 -146.65±9.22 -166.95±8.96 149.35±8.50 -17.60±2.58 GM: Tamoxifen -50.70±3.05 -139.49±17.17 -190.19±16.79 142.51±15.29 -47.68±3.63 M1: Afzelechin -41.15±2.65 -16.99±6.36 -58.15±5.81 21.58±3.29 -36.57±3.40 M6: Decahydroretinol -35.04±3.36 -16.80±4.44 -56.40±4.99 21.20±2.96 -35.21±4.09 M7: Emodin 6,8 dimethyl ether -46.45±2.38 -9.66±2.00 -56.12±3.04 16.23±1.62 -39.88±2.67 M8: Hexose -17.64±4.87 -61.88±11.61 -79.52±8.85 50.82±7.96 -28.70±4.19 M10: Lupeol -23.83±5.42 -4.27±6.14 -28.10±9.91 9.39±4.38 -18.71±6.53 M13: Sitosterol-glycoside -34.53±4.07 -40.70±11.07 -75.62±10.67 35.12±7.55 -40.50±4.96 M14: Tricin -36.85±4.41 -50.67±6.99 -87.52±8.29 50.57±5.23 -36.95±5.49 ESR1 Ref. STD: Tranexamic acid -22.01± 2.32 -148.81± 15.75 -170.82±15.63 154.14±13.56 -16.68± 3.77 GM: Tamoxifen -50.46 ± 2.78 -152.53 ± 13.11 -202.99 ± 13.36 155.37 ± 12.28 -47.61 ± 3.11 M1: Afzelechin -41.19 ± 2.96 -33.01 ± 4.32 -74.20 ± 4.05 28.67 ± 2.09 -45.52 ± 2.99 M3: Cassipourol -49.70 ± 2.57 -6.75 ± 2.52 -67.54 ± 4.05 4.20 ± 1.74 -63.34 ± 3.89 M6: Decahydroretinol -48.43 ± 2.59 -13.27 ± 4.41 -66.42 ± 4.99 9.97 ± 2.06 -56.45 ± 4.42 M7: Emodin 6,8 dimethyl ether -43.32 ± 2.43 -5.96 ± 2.88 -49.28 ± 4.16 13.02 ± 3.13 -36.26 ± 2.43 M10: Lupeol -58.12 ± 2.66 -1.76 ± 2.87 -59.88 ± 3.43 -1.34 ± 2.35 -61.23 ± 3.02 M13: Sitosterol-glycoside -75.58 ± 4.11 -38.12 ± 9.66 -114.16 ± 8.96 35.19 ± 9.12 -78.97 ± 5.52 M14: Tricin -47.40 ± 3.38 -20.37 ± 8.58 -67.77 ± 6.97 19.55 ± 5.40 -48.22 ± 3.09 With the exception of ESR2-hexose and ESR1-tricin bound complexes at 90 ns and 120 ns, respectively, all trajectories of ESR2 and ESR1 bound and unbound systems were relatively stable and experienced minimal fluctuations ( Figure 10a and 10b ). Among ESR2-bound metabolites, tranexamic acid (1.44 Å) had the lowest RMSD value. Along with tranexamic acid, afzelechin (1.60 Å), emodin 6,8 dimethyl ether (1.58 Å) and sitosterol glycoside (1.59 Å) exhibited RMSD values comparable to or lower than apo-ESR2 (1.60 Å). Excluding hexose (1.98 Å), all ESR2-bound metabolites had lower RMSD values than tamoxifen (1.98 Å) ( Table 7 ). In ESR1 bound systems, tamoxifen (1.51 Å) had the lowest RMSD value. Conversely, tranexamic acid (1.91 Å) and sitosterol-glycoside (1.91 Å) had the highest RMSD values among the ESR1-bound metabolites. Whereas tamoxifen (1.51 Å), afzelechin (1.61 Å), cassipourol (1.70 Å), decahydroretinol (1.55 Å), emodin 6,8 dimethyl ether (1.55 Å) and lupeol (1.78 Å) had similar or lower RMSD values than apo-ESR1 (1.78) ( Table 7 ). Figure 10. Comparative RMSD of a) ESR2 and b) ESR1 bound systems with Cassipourea metabolites over 150 ns. Table 7. Post-dynamic analysis of the Cassipourea metabolites bound to ESR2 and ESR1 systems over 150 ns. Dynamics Complex RMSD (Å) ROG (Å) RMSF (Å) SASA (Å) ESR2 Apo-ESR2 1.60±0.17 1.20±0.60 17.70±0.09 12411.51±304.13 Ref STD: Tranexamic acid 1.44±0.17 1.15±0.51 17.76±0.06 12291.71±258.62 GM: Tamoxifen 1.83±0.18 1.21±0.59 17.64±0.06 12227.35±290.42 M1: Afzelechin 1.60±0.17 1.20±0.59 17.70±0.07 12103.98±265.74 M6: Decahydroretinol 1.80±0.25 1.33±0.65 17.59±0.08 12216.00±291.91 M7: Emodin 6,8 dimethyl ether 1.58±0.19 1.17±0.59 17.62±0.07 121116.71±256.02 M8: Hexose 1.98±0.31 1.50±0.72 17.60±0.11 12123.38±321.02 M10: Lupeol 1.72±0.17 1.34±0.68 17.71±0.08 12475.06±292.96 M13: Sitosterol-glycoside 1.59±0.21 1.27±0.60 17.74±0.07 12397.75±347.87 M14: Tricin 1.75±0.27 1.34±0.66 17.63±0.07 11892.74±337.47 ESR1 Apo-ESR1 1.78± 0.2 18.67 ±0.81 1.175± 0.55 13584.25 ± 234 Ref. STD: Tranexamic acid 1.91±0.32 18.81 ±0.62 1.19± 0.80 13706.67 ± 276 GM: Tamoxifen 1.51±0.23 18.71 ±0.53 1.07± 0.57 13588.03 ± 321 M1: Afzelechin 1.61± 0.16 18.54 ±0.75 1.05± 0.45 12995.41 ± 248 M3: Cassipourol 1.70±0.21 18.66 ±0.63 1.28± 0.53 13672.21 ± 312 M6: Decahydroretinol 1.55±0.19 18.62 ±0.42 1.11± 0.63 13132.37 ± 278 M7: Emodin 6,8 dimethyl ether 1.55±0.32 18.69 ±0.86 1.21± 0.46 13615.75 ± 265 M10: Lupeol 1.78±0.23 18.64 ±0.75 1.17± 0.74 13602.97 ± 213 M13: Sitosterol-glycoside 1.91±0.24 18.62 ±0.44 1.30± 0.66 13451.55 ± 314 M14: Tricin 1.81±0.21 18.52 ±0.60 1.18± 0.30 13287.54 ± 254 Trajectories of ESR2 bound systems experienced a multitude of high-rising fluctuations on amino acid residues 290, 215-218, and 402-412 and again from residues to 469-482 ( Figure 11a ). All ESR2 bound and unbound systems had comparable RMSF values, with hexose (1.50 Å) and tranexamic acid (1.15 Å) exhibiting the highest and lowest RMSF values, respectively. Against apo-ESR2 (1.20 Å), tranexamic acid, afzelechin (1.20 Å), and emodin 6,8 dimethyl ether (1.17 Å) had lower RMSF values ( Table 7 ). In the ESR1 bound and unbound systems, fluctuations in residues 30, 70, 230, and 247, and from residues 155-165 were prominent ( Figure 11b ). Besides sitosterol-glycoside (1.30 Å), cassipourol (1.28 Å), and emodin 6,8 dimethyl ether (1.21 Å), all other ESR1 bound metabolites had comparable or lower RMSF values than apo-ESR1 (1.18 Å) and tranexamic acid (1.19 Å). From the ESR1-bound metabolites, only afzelechin (1.05 Å) had a lower RMSF value than apo-ESR1, tranexamic acid, and tamoxifen (1.07 Å) ( Table 7 ). Figure 11. Comparative RMSF of a) ESR2 and b) ESR1 bound systems with Cassipourea metabolites over 150 ns. In the ESR2 and ESR1 bound and unbound systems, no complex fluctuations were observed ( Figure 12a and 12b ). The ESR2 bound systems exhibited non-significant differences in their RoG values, with decahydroretinol (17.59 Å), emodin 6,8 dimethyl ether (17.62 Å) and hexose (17.60 Å) displaying lower RoG values than tranexamic acid (17.76 Å), apo-ESR2 (17.70) and tamoxifen (17.64 Å) ( Table 7 ). There was a marginal variance in ESR1 bound the RoG values. Excluding emodin 6,8 dimethyl ether (18.69 Å), all ESR1 bound metabolites complexes had lower RoG values than apo-ESR1 (18.67 Å), tranexamic acid (18.81 Å) and tamoxifen (18.71 Å), with tricin (18.52 Å) displaying the lowest RoG value ( Table 7 ). Figure 12. Comparative RoG of a) ESR2 and b) ESR1 bound systems with Cassipourea metabolites over 150 ns. Excluding ESR2 bound lupeol and ESR1 bound tricin, the trajectories of ESR2 and ESR1 bound and unbound systems experienced minor disruptions ( Figure 13a and 13b ). Except for sitosterol-glycoside (12397.75 Å) and lupeol (12475.06 Å), ESR2-bound metabolites exhibit lower mean SASA values than tranexamic acid (12291.71 Å), tamoxifen (12227.35 Å) and apo-ESR2 (12411.51 Å), with tricin (11892.74 Å) exerting the most impact on the SASA ( Table 7 ). In ESR1 bound and unbound systems, tranexamic acid (13706.67 Å) had the highest SASA value. ESR1 bound cassipourol (13672.21 Å), Emodin 6,8 dimethyl ether (13615.75 Å) and lupeol (13602.97 Å) exhibited higher SASA values than tamoxifen (13558.03 Å) and apo-ESR1 (13584.25 Å), with afzelechin (12995.41 Å) exhibiting the lowest SASA value ( Table 7 ). Figure 13. Comparative SASA plots of a) ESR2 and b) ESR1 bound systems with Cassipourea metabolites over 150 ns. The 2D interaction plots of ESR2 bound systems displayed the formation of hydrogen bonds (carbon-hydrogen, pi-hydrogen donor, and conventional), hydrophobic interactions (pi-pi-T-shaped, alkyl, pi-alkyl, pi-sulfur, pi-cation, amide-pi-stacked, attractive charge, salt bridge, and unfavorable donor-donor bonds), and van der Waals interactions ( Figure 14 ). From 0 ns to 150 ns, afzelechin is unable to maintain hydrogen bond formation (4 → 1) and forgoes its unfavorable donor-donor bond to form a π-sulfur bond, thus maintaining six important interactions and bonds ( Figure 14a ). Decahydroretinol retains six hydrophobic interactions at 0 ns and 150 ns but is unable to maintain its hydrogen bonds (2 → 1) I ( Figure 14b ). Emodin 6,8 dimethyl ether formed nine hydrophobic interactions and three hydrogen bonds at 0 ns, which were reduced to seven hydrophobic interactions and two hydrogen bonds at 150 ns ( Figure 14c . However, at 0 and 150 ns, hexose formed four and seven hydrogen bonds, respectively, and experienced a decrease in the total intramolecular interactions from 15 to 13 ( Figure 14d ). At 150 ns, lupeol was unable to maintain hydrogen bond formation on Arg81 and retained only four hydrophobic interactions ( Figure 14e ). Sitosterol-glycoside retained four hydrogen bonds at 0 ns and 150 ns and formed a hydrophobic alkyl interaction on Met292 at 150 ns ( Figure 14f ). Regarding tricin, at 0 ns and 150, the number of hydrogen bonds decreased from five to four, and the number of hydrophobic interactions increased from three to four ( Figure 14g ). Based on its interactions at 0 ns, tranexamic acid was unable to retain its hydrogen bond (one), hydrophobic interactions, and intramolecular interactions (12) at 150 ns ( Figure 14h ). Although tamoxifen was able to maintain its hydrophobic interactions (seven) at 0 and 150 ns, it did not retain its hydrogen bond formation (one) or intramolecular interactions (22) ( Figure 14i ). Figure 14. a: 2D interaction plot of ESR2 and afzelechin over 150 ns simulation. b: 2D interaction plot of ESR2 and decahydroretinol over 150 ns simulation. c: 2D interaction plot of ESR2 and emodin 6,8 dimethyl ether over 150 ns simulation. d: 2D interaction plot of ESR2 and hexose over 150 ns simulation. e: 2D interaction plot of ESR2 and lupeol over 150 ns simulation. f: 2D interaction plot of ESR2 and sitosterol glycoside over 150 ns simulation. g: 2D interaction plot of ESR2 and tricin over 150 ns simulation. h: 2D interaction plot of ESR2 and tranexamic acid over 150 ns simulation. i: 2D interaction plot of ESR2 and tamoxifen over 150 ns simulation. A probe into the binding interactions of ESR1 and its metabolites revealed the presence of hydrogen bonds, van der Waals interactions, and hydrophobic interactions such as pi-sulfur, pi-alkyl, alkyl, pi-pi-T-shaped, amide-pi-stacked, pi-sigma, and unfavorable donor-donor interactions ( Figure 15 ). At 0 and 150 ns, afzelechin retained its five hydrophobic interactions and formed four hydrogen bonds at 150 ns ( Figure 15a ). From 0 to 150 ns, cassipourol forms nine hydrophobic interactions and two hydrogen bonds ( Figure 15b ). Decahydroretinol maintains one hydrogen bond and, in total, forms 11 hydrophobic interactions from 0 to 150 ns ( Figure 15c ). Against emodin 6,8 dimethyl ether’s intramolecular interactions at 0 ns, hydrogen bond formation increased to two, and hydrophobic and total intramolecular interactions decreased to nine and 18, respectively (Figue 15d). Lupeol exhibited hydrogen bond formation on Phe101 and an increase in hydrophobic (10) and intramolecular interactions (23) ( Figure 15e ). Sitosterol-glycoside forms three hydrogen bonds and replaces the three unfavorable bumps with eight hydrophobic alkyl interactions (Figure 16f). At 0ns and 150 ns, tricin forms two and five hydrogen bonds, respectively, and forgoes the unfavorable acceptor bond at 0ns for eight hydrophobic interactions ( Figure 15g ). In tranexamic acid, from 0 ns to 150 ns, hydrogen bond formation increased to three, intramolecular interactions were altered to 18, and hydrophobic interactions decreased to two ( Figure 15h ). Similarly, in tamoxifen, hydrogen bonds and intramolecular interactions are increased to two and 22, respectively, and hydrophobic interactions are reduced to six ( Figure 15i ). Figure 15. a: 2D interaction plot of ESR1 and afzelechin over 150 ns simulation. b: 2D interaction plot of ESR1 and cassipourol over 150 ns simulation. c: 2D interaction plot of ESR1 and decahydroretinol over 150 ns simulation. d: 2D interaction plot of ESR1 and emodin 6,8 dimethyl ether over 150 ns simulation. e: 2D interaction plot of ESR1 and lupeol over 150 ns simulation. f: 2D interaction plot of ESR1 and sitosterol glycoside over 150 ns simulation. g: 2D interaction plot of ESR1 and tricin over 150 ns simulation. h: 2D interaction plot of ESR1 and tranexamic acid over 150 ns simulation. i: 2D interaction plot of ESR1 and tamoxifen over 150 ns simulation. 4. Discussion Several studies have established the benefits of natural products, including plants, for various biological purposes. 41 Metabolites identified in the Cassipourea species are reportedly implicated in skin pigmentation, treatment of skin diseases, and inhibition of tyrosinase activity. 20 , 24 Specifically, azelaic acid, a dicarboxylic acid found in many grains, has been reported to possess anti-tyrosinase and anti-inflammatory effects, 42 whereas lupeol and its derivatives are implicated in wound healing. 43 These studies corroborate the therapeutic potential and identify putative leads of Cassipourea metabolites in melasma. Interestingly, Cassipourea metabolites demonstrated remarkable drug-likeness and pharmacokinetic properties, indicating their use not only as potential topical agents but also as oral drug candidates for melasma treatment. This is evident by the finding that most metabolites are in conformity with Lipinski’s Ro5 for oral drug candidates and show appreciable bioavailability for therapeutic applications. In addition, the metabolites were relatively non-inhibitors of CYP450 enzymes; hence, they can be easily biotransformed into more polar and excretable by-products in biological systems. The GIA and Pgp-S profiles also potentiated Cassipourea for both applications. For example, sitosterol-glycoside and lupeol are hydrophobic compounds, and their low GIA and poor solubilities are suggestive of their biological effect being more applicable topically with potentially longer-lasting and better effects on the skin due to their hydrophobicity. To gain further insight into the mechanism of action of Cassipourea metabolites in melasma treatment, NP and MD simulations were conducted. Although only four genes were common between Cassipourea metabolites and melasma, they have been reported to exert significant effects on skin pigmentation and melasma. Factors that reportedly affect the pathogenesis of melasma include genetics and hormonal activity. 44 Estrogen is a female sex hormone that influences skin pigmentation by stimulating melanin synthesis in melanocytes, improving skin tonicity, thickness, healing, elasticity, and preventing age-related diseases when produced in adequate amounts. 45 Estrogen levels differ with stages in women; for example, estrogen levels gradually rise from the pre-menopausal to childbearing stages, followed by a decline during post-menopause. This could be attributed to the reduction in melanin synthesis with increasing age. 45 Estrogen exerts its effects through signalling pathways and genes that are important in skin physiology and pathology. Dysregulation of estrogen gene activity in the skin might result in abnormal skin pigmentation, including melasma. Furthermore, overexpression of ESR2 has been implicated in melasma. 46 Melasma is more prominent during pregnancy, hormone replacement therapy, and oral contraceptive use. 47 The observation that estrogen genes, receptors, and signalling pathways were some of the most enriched genes in the PPI, the most significant molecular functions in the GO analysis, and the second most significant signalling pathway KEGG, respectively, in this study lend credence to the vital role of Cassipourea metabolites in melasma treatment. The PTGS2 gene (cyclooxygenase 2) stimulates the synthesis of prostanoids, such as prostaglandins, thromboxanes, and protacyclins, involved in inflammation caused by the 20-C atom arachidonic acid. 48 Therefore, through the modulation of PTGS2, Cassipourea metabolites may regulate melasma-induced inflammatory responses. Furthermore, sitosterol has been reported to be an anti-inflammatory agent. 49 The involvement of tyrosinase (TYR) in the PPI network corroborates the findings of Mpofana et al. 2023, 20 who stated that Cassipourea metabolites possess an anti-tyrosinase effect. TYR regulates melanin synthesis; hence, its inhibition aids in replenishing normal skin pigmentation and decreasing the risk of melasma. The finding that the intracellular steroid hormone receptor signalling pathway was the most enriched biological process in the GO analysis denotes the potential of Cassipourea metabolites in the treatment of melasma. Other biological processes, such as cellular response to estrogen and estradiol stimulus, estrogen receptor signalling pathway, and regulation of inflammatory response, have also been implicated in skin pigmentation. However, steroid hormones, such as estrogen, estradiol, and progesterone, have been implicated in the control of skin pigmentation via certain receptors. 50 These hormones, as well as the overexpression of tyrosinase and melanocyte-stimulating hormone, are attributed to the excessive pigmentation observed during pregnancy. 51 The interaction of these steroid hormones and signalling pathways is suggestive of the possible role of Cassipourea in preventing pregnancy-induced melasma. Interestingly, the signalling pathways and genes identified in this study are in line with the study by Yin et al., 25 who studied the mechanism of action of Croci stigma on melasma . The hyperstimulation of melanocytes by UV irradiation also induces high melanin secretion and ROS generation, resulting in cellular damage. 44 Vitamin D plays an important role in skin functions, including skin homeostasis and anti-inflammatory, immune, and cell proliferation responses that are essential for wound healing and restoration of skin pigmentation anomalies. 52 The observation that response to vitamin D was one of the most enriched biological processes denotes the ability of Cassipourea metabolites to stimulate vitamin D production, which is necessary to prevent UV-induced melasma. However, the macromolecular complex was the only enriched cellular component. This complex is vital to cellular processes. It is composed of assemblies of proteins and nucleic acids involved in transcriptional and translational processes that convert DNA to RNA and protein, respectively. In signaling pathways, these assemblies are transient, 53 which might imply that melasma temporarily affects signaling pathways and that skin anomalies could be restored to normalcy upon appropriate treatment. Prolactin, a hormone secreted during lactation and reproduction, is involved in metabolism, immunoregulation, protection, growth, and development. The binding of prolactin to the prolactin receptor initiates the prolactin signaling pathway. 54 The observation that the prolactin signalling pathway was the most enriched in the KEGG analysis signifies that the functions of prolactin, its predominance in females who are largely affected by melasma, and the activation of its signalling pathway during the reproductive process, the regulation of this pathway could also serve as a target for the action of Cassipourea metabolites. Furthermore, the most significant genes (ESR2 and ESR1) in this pathway were analyzed for their binding affinity, stability, and interaction with top-ranked metabolites. Molecular docking identifies the top therapeutic metabolites by assessing their binding affinity for the active site of selected receptors to further augment the understanding of their bioactive mechanisms of action. 55 Higher negative docking scores are associated with better docking poses and fitness. 56 Except for ESR2-bound hexose, lupeol, sitosterol-glycoside, and ESR1-bound decahydroretinol, the higher negative docking scores of receptor-bound Cassipourea metabolites, relative to tamoxifen and tranexamic acid, are suggestive of their better biological activity. 57 Afzelechin acts as a dual modulator of both ESR2 and ESR1, with the highest negative docking scores within each receptor-bound system, further highlighting its potential therapeutic application. However, molecular docking merely assesses the crystallographic binding orientation of ligands, while molecular dynamic (MD) simulations help to elucidate binding stability. 58 To better understand the biomolecular affinity and efficacy of Cassipourea metabolites as therapeutic candidates, their thermodynamic binding free energies were analyzed over 150 ns, 59 with higher negative values indicative of greater binding affinity. The higher negative binding free energy of tamoxifen than that of ESR2-bound metabolites suggests that tamoxifen offers marginal superiority as an ESR2 modulator. The lower negative binding free energy of tranexamic acid relative to that of ESR2-bound metabolites indicates that Cassipourea metabolites are more thermodynamically stable. Comparatively, the higher negative binding free energies of ESR1 bound cassipourol, decahydroretinol, lupeol, tricin, and sitosterol-glycoside complexes with tamoxifen and tranexamic acid were presumed to be stable and had better modulatory effects on ESR1. Moreover, MD simulations identified sitosterol-glycoside as a dual modulator of ESR2 and ESR1 because of its high negative binding free energy and superior thermodynamic stability among all other Cassipourea metabolites, further augmenting its biological activity in melasma-based therapeutics. Thereafter, the post-dynamic parameters RMSD, RMSF, RoG, and SASA of the ESR2 and ESR1 bound systems were assessed at 150 ns for better insight into their thermodynamic stability. 60 RMSD measures the stability of protein-ligand systems by quantifying the change between their initial and final structural conformation, with lower deviations (values) denoting greater stability. 61 According to Umar et al., 62 an RMSD value of ≤ 3 Å is an acceptable measurement of protein-ligand stability, validating the stability of ESR2 and ESR1 bound and unbound systems. The lower RMSD of the ESR2-bound tranexamic acid and ESR1-bound tamoxifen complexes is presumed to be due to their better stability as modulators of their respective genes. Conversely, the higher RMSD of ESR2-bound tamoxifen and ESR1-bound tranexamic acid systems compared to Cassipourea metabolites suggests their potential stability with the ESR2 and ESR1 genes. Comparatively, the lower RMSD of ESR2-bound emodin ethyl ether and sitosterol-glycoside and ESR1-bound afzelechin, cassipourol, decahydroretinol, emodin 6,8 dimethyl ether, and lupeol against their apo-genes indicated that these metabolites formed stable complexes with their relative genes. RMSF analysis of ESR2 and ESR1 bound and unbound systems measures the flexibility of protein-ligand interactions. Higher RMSF values and fluctuations denote greater flexibility but less stability, whereas lower RMSF values and fluctuations indicate greater stability. 63 Generally, an RMSF value <2 Å is an optimal measure of ligand flexibility. 64 Thus, all metabolites bound to ESR2 and ESR1 exhibited suitable flexibility, with ESR2-bound emodin 6,8 dimethyl ether and ESR1-bound afzelechin, decahydroretinol, and lupeol exerting stabilizing effects on their relative apo-genes. However, among Cassipourea metabolites, tranexamic acid may potentiate greater stability as an ESR2 modulator, and tamoxifen may exhibit comparable flexibility as an alternate ESR1 modulator. The RoG measures the time-dependent compactness of protein-ligand systems, with lower values indicating greater overall compactness and stability. 65 The similar RoG values of the ESR2 and ESR1 bound systems relative to their apo-genes are suggestive of the marginal impact exerted by Cassipourea metabolites on ESR2 and ESR1 protein folding. The lower RoG values of ESR2-bound decahydroretinol, emodin 6.8 dimethyl ether, and hexose complexes and ESR1-bound afzelechin, cassipourol, decahydroretinol, sitosterol-glycoside, lupeol, and tricin complexes relative to tranexamic acid, tamoxifen, and their apo-genes are indicative of their greater compactness, stability, and suitability as ESR2 and ESR1 modulators. The SASA plot was used to measure the rate of interaction between complexes and their surrounding hydrophobic environment, with higher values denoting greater environmental exposure and lower values depicting more stable systems. 66 The lower SASA values of ESR2-bound metabolites (excluding lupeol) and ESR1-bound metabolites (excluding emodin 6,8 dimethyl ether, cassipourol, and lupeol) relative to their apo-genes suggest Cassipourea metabolites induce protein-ligand stability and compactness through protein folding. The minor fluctuations experienced by ESR2-bound lupeol at 125 ns and ESR1-bound tricin and cassipourol at 85 ns and 105 ns, respectively, suggest less interaction during that period. The lower SASA values of ESR2 bound afzelechin, hexose, emodin 6,8 dimethyl ether, decahydroretinol, and tricin complexes and ESR1 bound afzelechin, decahydroretinol, sitosterol-glycoside, and tricin complexes relative to tamoxifen and tranexamic acid are presumptive of their weaker interactions with the surrounding hydrophobic environment, resulting in better stability and modulation of ESR2 and ESR1. The presence of hydrogen bonds significantly induces protein-ligand stability, binding specificity, and several pharmacokinetic properties such as metabolism and adsorption. 67 The 2D interaction plots of ESR2 and ESR1 bound systems also formed van der Waals and hydrophobic interactions, both of which augment their stability and affinity. In both ESR2 and ESR1 systems, sitosterol-glycoside could retain and steadily increase its hydrogen bond formation, corroborating its highest negative binding free energy among Cassipourea metabolites in both systems. The higher negative binding free energy of ESR2-bound tamoxifen can be attributed to the formation of multiple hydrophobic interactions throughout the 150 ns simulation. 5. Conclusions The results of this study provide pharmacological support for the development of an alternative treatment for the management of hypermelanosis disorders such as melasma. The constituents of Cassipourea have potential commercial value. The finding that several sex hormones implicated in melasma, transcription processes, and vitamin D are highly involved in this study is a strong indication of the multi-target pathways by which Cassipourea metabolites potentially exert effects on melasma. The potential regulation of estrogen synthesis by these metabolites, depicted by their binding interactions, stability, and compactness, is suggestive of melanogenesis control for normal skin pigmentation and the possible restoration of melasma skin. Further studies are recommended to validate and assess the safety of these metabolites. Ethical statement The study was conducted following the approval of the University of KwaZulu-Natal Biomedical Research Ethics Committee (UKZN BREC) (protocol reference number: BREC/00002721/2021). Statement of contribution Conceptualization: NM; methodology: NM; CP and SS; software: SS.; formal analysis: SS, CP, and HYL; investigation: NM, CP, and SS.; resources: SS; writing: original draft preparation: NM, CP, and HYL; writing: review and editing, NM, CP, HYL, SS, and MUM.; visualization, SS; supervision: NCD, NG, and AH; funding acquisition: NM and NG. All authors have read, agreed to, and approved the final version of the manuscript. Data availability statement Underlying data FIGSHARE: Compounds elucidated from the three selected Cassipourea species as well as phytochemical comparison, https://doi.org/10.6084/m9.figshare.26418361.v1 . 68 The project contains the following underlying data: • Compounds elucidated from the three selected Cassipourea species • Phytochemical comparison of the compounds Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Reporting guidelines All the methods used in the study were adequately detailed in the “Methodology Section” of the manuscript and the followed the standard guidelines on reporting computational biology studies. References 1. Gilaberte Y, et al. : Anatomy and Function of the Skin. Nanoscience in dermatology. Elsevier; 2016; pp. 1–14. Publisher Full Text 2. Singh M, Sharma V: Spectrophotometric determination of Sun Protection Factor and antioxidant potential of an herbal mixture. Br. Biotechnol. J. 2016; 10 : 1–8. Publisher Full Text 3. Narayanaswamy N, Duraisamy A, Balakrishnan K: Screening of some medicinal plants for their antityrosinase and antioxidant activities. Int. J. PharmTech Res. 2011; 3 : 1107–1112. 4. Chatatikun M, Chiabchalard A: Thai plants with high antioxidant levels, free radical scavenging activity, anti-tyrosinase and anti-collagenase activity. BMC Complement. Altern. Med. 2017; 17 : 1–9. 5. Mukherjee PK, et al. : Validation of medicinal herbs for anti-tyrosinase potential. J. Herb. Med. 2018; 14 : 1–16. Publisher Full Text 6. Hsieh P-W, et al. : Hydroquinone-salicylic acid conjugates as novel anti-melasma actives show superior skin targeting compared to the parent drugs. J. Dermatol. Sci. 2014; 76 (2): 120–131. PubMed Abstract | Publisher Full Text 7. Yasui H, Sakurai H: Age-dependent generation of reactive oxygen species in the skin of live hairless rats exposed to UVA light. Exp. Dermatol. 2003; 12 (5): 655–661. PubMed Abstract | Publisher Full Text 8. Mpofana N, Abrahamse H: The management of melasma on skin types V and VI using light emitting diode treatment. Photomed. Laser Surg. 2018; 36 (10): 522–529. PubMed Abstract | Publisher Full Text 9. Piętowska Z, Nowicka D, Szepietowski JC: Understanding melasma-how can pharmacology and cosmetology procedures and prevention help to achieve optimal treatment results? A narrative review. Int. J. Environ. Res. Public Health. 2022; 19 (19): 12084. PubMed Abstract | Publisher Full Text | Free Full Text 10. Neagu N, et al. : Melasma treatment: a systematic review. J. Dermatol. Treat. 2022; 33 (4): 1816–1837. Publisher Full Text 11. Ogbechie-Godec OA, Elbuluk N: Melasma: an up-to-date comprehensive review. Dermatol. Ther. 2017; 7 : 305–318. PubMed Abstract | Publisher Full Text | Free Full Text 12. Guo EL, et al. : Combination Treatment Approach to Melasma. Adv. Cosmet. Surg. 2021; 4 (1): 97–107. Publisher Full Text 13. Rajanala S, Maymone MB, Vashi NA: Melasma pathogenesis: a review of the latest research, pathological findings, and investigational therapies. Dermatol. Online J. 2019; 25 (10). Publisher Full Text 14. Mpofana N, et al. : The Effect of melasma on the quality of life in people with darker skin types living in Durban, South Africa. Int. J. Environ. Res. Public Health. 2023; 20 (22): 7068. PubMed Abstract | Publisher Full Text | Free Full Text 15. Nomakhosi M, Heidi A: Natural options for management of melasma, a review. J. Cosmet. Laser Ther. 2018; 20 (7-8): 470–481. PubMed Abstract | Publisher Full Text 16. Morgado-Carrasco D, et al. : Melasma: The need for tailored photoprotection to improve clinical outcomes. Photodermatol. Photoimmunol. Photomed. 2022; 38 (6): 515–521. PubMed Abstract | Publisher Full Text | Free Full Text 17. Mpofana N, et al. : An Investigation into the Acute and Subacute Toxicity of Extracts of Cassipourea flanaganii Stem Bark In Vivo. Plants. 2023; 12 (12): 2281. PubMed Abstract | Publisher Full Text | Free Full Text 18. Zhu W, Gao J: The use of botanical extracts as topical skin-lightening agents for the improvement of skin pigmentation disorders. J. Investig. Dermatol. Symp. Proc. 2008. Elsevier. 19. Fahmy NM, et al. : Chemical exploration of different extracts from Phytolacca americana leaves and their potential utilization for global health problems: ın silico and network pharmacology validation. J. Biomol. Struct. Dyn. 2024; 1–21. PubMed Abstract | Publisher Full Text 20. Mpofana N, et al. : Analysis of Three Species of Cassipourea Traditionally Used for Hypermelanosis in Selected Provinces in South Africa. Int. J. Mol. Sci. 2023; 25 (1): 237. PubMed Abstract | Publisher Full Text | Free Full Text 21. Mwinga J, et al. : Botanicals used for cosmetic purposes by Xhosa women in the Eastern Cape, South Africa. S. Afr. J. Bot. 2019; 126 : 4–10. Publisher Full Text 22. Thibane V, et al. : The cosmetic potential of plants from the Eastern Cape Province traditionally used for skincare and beauty. S. Afr. J. Bot. 2019; 122 : 475–483. Publisher Full Text 23. Thibane V, et al. : Modulation of the enzyme activity of secretory phospholipase A2, lipoxygenase and cyclooxygenase involved in inflammation and disease by extracts from some medicinal plants used for skincare and beauty. S. Afr. J. Bot. 2019; 120 : 198–203. Publisher Full Text 24. Langat MK, et al. : The effect of isolates from Cassipourea flanaganii (Schinz) alston, a plant used as a skin lightning agent, on melanin production and tyrosinase inhibition. J. Ethnopharmacol. 2021; 264 : 113272. PubMed Abstract | Publisher Full Text 25. Yin W, et al. : The mechanism of Croci stigma in the treatment of melasma based on network pharmacology and molecular docking. J. Cosmet. Dermatol. 2023; 22 (7): 2105–2114. PubMed Abstract | Publisher Full Text 26. Lipinski CA, et al. : Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997; 23 (1-3): 3–25. Publisher Full Text 27. Daina A, Michielin O, Zoete V: SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017; 7 (1): 42717. PubMed Abstract | Publisher Full Text | Free Full Text 28. Shannon P, et al. : A software environment for integrated models of biomolecular interaction networks.2003; 13 : 2498–2504. PubMed Abstract | Publisher Full Text | Free Full Text 29. Fan Z, et al. : Network Pharmacology and experimental validation to reveal the pharmacological mechanisms of chongcaoyishen decoction against chronic kidney disease. Front. Mol. Biosci. 2022; 9 : 847812. PubMed Abstract | Publisher Full Text | Free Full Text 30. Szklarczyk D, et al. : The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021; 49 (D1): D605–D612. PubMed Abstract | Publisher Full Text | Free Full Text 31. Zeng Z, et al. : Network pharmacology and molecular docking-based prediction of the mechanism of Qianghuo Shengshi decoction against rheumatoid arthritis. Biomed. Res. Int. 2021; 2021 : 1–12. PubMed Abstract | Publisher Full Text | Free Full Text 32. Huang DW, et al. : The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007; 8 : R183–R116. Publisher Full Text 33. Wu T, et al. : clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The innovation. 2021; 2 (3): 100141. PubMed Abstract | Publisher Full Text | Free Full Text 34. Yu G, et al. : DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 2014; 31 (4): 608–609. PubMed Abstract | Publisher Full Text 35. Sakle NS, More SA, Mokale SN: A network pharmacology-based approach to explore potential targets of Caesalpinia pulcherima: An updated prototype in drug discovery. Sci. Rep. 2020; 10 (1): 17217. PubMed Abstract | Publisher Full Text | Free Full Text 36. Dallakyan S, Olson AJ: Small-molecule library screening by docking with PyRx. Methods Mol. Biol. 2015; 1263 : 243–250. PubMed Abstract | Publisher Full Text 37. Biovia DS: Discovery Studio, version 21.1. 0. San Diego: Dassault Systèmes; 2021; 627. 38. Lukman H, et al. : Modulation of dipeptidyl peptidase by Rooibos tea metabolites towards type 2 diabetes care: Evidence from molecular dynamics simulation and density functional theory. Scientific African. 2024; 24 : e02173. Publisher Full Text 39. Nair PC, Miners JO: Molecular dynamics simulations: from structure function relationships to drug discovery. In Silico Pharmacol. 2014; 2 : 1–4. Publisher Full Text 40. Aribisala JO, S’thebe NW, Sabiu S: In silico exploration of phenolics as modulators of penicillin binding protein (PBP) 2× of Streptococcus pneumoniae. Sci. Rep. 2024; 14 (1): 8788. PubMed Abstract | Publisher Full Text | Free Full Text 41. Ibrahim SO, et al. : An Insight into the Physicochemical, Drug-likeness, Pharmacokinetics and Toxicity Profile of Kigelia africana (Lam) Bioactive Compounds. Al-Bahir Journal for Engineering and Pure Sciences. 2024; 4 (1): 4. Publisher Full Text 42. Albzea W, et al. : Azelaic Acid Versus Hydroquinone for Managing Patients With Melasma: Systematic Review and Meta-Analysis of Randomized Controlled Trials. Cureus. 2023; 15 (7). Publisher Full Text 43. Malinowska MA, et al. : The effect of the new lupeol derivatives on human skin cells as potential agents in the treatment of wound healing. Biomolecules. 2021; 11 (6): 774. PubMed Abstract | Publisher Full Text | Free Full Text 44. Espósito ACC, et al. : Update on Melasma—Part I: Pathogenesis. Dermatol. Ther. 2022; 12 (9): 1967–1988. PubMed Abstract | Publisher Full Text | Free Full Text 45. Caruntu C, et al. : The role of estrogens and estrogen receptors in melanoma development and progression. Acta Endocrinologica (Bucharest). 2016; 12 (2): 234–241. PubMed Abstract | Publisher Full Text | Free Full Text 46. Liu W, Chen Q, Xia Y: New mechanistic insights of melasma. Clin. Cosmet. Investig. Dermatol. 2023; 16 : 429–442. PubMed Abstract | Publisher Full Text | Free Full Text 47. Handel A, et al. : Risk factors for facial melasma in women: a case–control study. Br. J. Dermatol. 2014; 171 (3): 588–594. PubMed Abstract | Publisher Full Text 48. Smith WL, Garavito RM, DeWitt DL: Prostaglandin endoperoxide H synthases (cyclooxygenases)-1 and− 2. J. Biol. Chem. 1996; 271 (52): 33157–33160. Publisher Full Text 49. Paniagua-Pérez R, et al. : Evaluation of the anti-inflammatory capacity of beta-sitosterol in rodent assays. Afr. J. Tradit. Complement. Altern. Med. 2017; 14 (1): 123–130. 50. Natale CA, et al. : Sex steroids regulate skin pigmentation through nonclassical membrane-bound receptors. elife. 2016; 5 : e15104. PubMed Abstract | Publisher Full Text | Free Full Text 51. Goandal NF, Rungby J, Karmisholt KE: The role of sex hormones in the pathogenesis of melasma. Ugeskr. Laeger. 2022; 184 (6): V10210769–V10210769. 52. Chen Q, Liu L, Zhang Y: Vitamin D and wound healing: Assessing skin barrier function and implications for chloasma treatment. Int. Wound J. 2024; 21 (1): e14541. PubMed Abstract | Publisher Full Text | Free Full Text 53. Appasamy SD, et al. : Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data. Sci Data. 2023; 10 (1): 853. PubMed Abstract | Publisher Full Text | Free Full Text 54. Radhakrishnan A, et al. : A pathway map of prolactin signaling. Springer; 2012. 55. Agu P, et al. : Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep. 2023; 13 (1): 13398. PubMed Abstract | Publisher Full Text | Free Full Text 56. Akoonjee A, et al. : Waste to Medicine: Evidence from Computational Studies on the Modulatory Role of Corn Silk on the Therapeutic Targets Implicated in Type 2 Diabetes Mellitus. Biology. 2023; 12 (12): 1509. PubMed Abstract | Publisher Full Text | Free Full Text 57. Macip G, et al. : Haste makes waste: a critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition. Med. Res. Rev. 2022; 42 (2): 744–769. PubMed Abstract | Publisher Full Text | Free Full Text 58. Ramírez D, Caballero J: Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules. 2018; 23 (5): 1038. PubMed Abstract | Publisher Full Text | Free Full Text 59. Hata H, et al. : Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model. Biophys. Physicobiol. 2021; 18 : 305–316. PubMed Abstract | Publisher Full Text | Free Full Text 60. Rampadarath A, et al. : Molecular bioprospection of Helianthus annuus L.(sunflower) cypsela for antidiabetic therapeutics through network pharmacology, density functional theory and molecular dynamics simulation. S. Afr. J. Bot. 2023; 162 : 72–95. Publisher Full Text 61. Aier I, Varadwaj PK, Raj U: Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci. Rep. 2016; 6 (1): 34984. PubMed Abstract | Publisher Full Text | Free Full Text 62. Ramírez D, Caballero J: Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target? Int. J. Mol. Sci. 2016; 17 (4): 525. PubMed Abstract | Publisher Full Text | Free Full Text 63. Singh K, et al. : Computational insights and in vitro validation of antibacterial potential of shikimate pathway-derived phenolic acids as NorA efflux pump inhibitors. Molecules. 2022; 27 (8): 2601. PubMed Abstract | Publisher Full Text | Free Full Text 64. Umar AK, et al. : Structure-based computational screening of 470 natural quercetin derivatives for identification of SARS-CoV-2 Mpro inhibitor. PeerJ. 2023; 11 : e14915. Publisher Full Text 65. Aribisala JO, Sabiu S: Cheminformatics identification of phenolics as modulators of penicillin-binding protein 2a of Staphylococcus aureus: A structure–activity-relationship-based study. Pharmaceutics. 2022; 14 (9): 1818. 66. Sulyman AO, et al. : Bioprospection of Selected Plant Secondary Metabolites as Modulators of the Proteolytic Activity of Plasmodium falciparum Plasmepsin V. Biomed. Res. Int. 2023; 2023 . PubMed Abstract | Publisher Full Text | Free Full Text 67. Williams M, Ladbury J: Hydrogen bonds in protein-ligand complexes. Protein-ligand interactions: from molecular recognition to drug design.2003; 137–161. 68. Mpofana N: Compounds elucidated from the three selected Cassipourea species as well as phytochemical comparison. figshare. Figure. 2024. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 22 Aug 2024 ADD YOUR COMMENT Comment Author details Author details 1 Dermatology, University of KwaZulu-Natal School of Clinical Medicine, Durban, KwaZulu-Natal, South Africa 2 Department of Somatology, Durban University of Technology - Ritson Campus, Durban, KwaZulu-Natal, South Africa 3 Biotechnology, Durban University of Technology - Steve Biko Campus, Durban, KwaZulu-Natal, South Africa 4 Discipline of Traditional Medicine, University of KwaZulu-Natal - Howard College Campus, Durban, KwaZulu-Natal, South Africa 5 Department of Chemistry, Cape Peninsula University of Technology - Bellville Campus, Bellville, Western Cape, South Africa Nomakhosi Mpofana Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Christina Peter Roles: Formal Analysis, Investigation, Writing – Review & Editing Halimat Yusuf Lukman Roles: Formal Analysis, Investigation, Visualization, Writing – Review & Editing Mokgadi Ursula Makgobole Roles: Investigation, Methodology, Writing – Review & Editing Ncoza Cordelia Dlova Roles: Supervision, Writing – Review & Editing Nceba Gqaleni Roles: Funding Acquisition, Supervision, Writing – Review & Editing Ahmed Hussein Roles: Supervision, Writing – Review & Editing Saheed Sabiu Roles: Formal Analysis, Software, Validation, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was supported in part by the National Research Foundation of South Africa (Grant Number: 138179), the Department of Science and Innovation (DSI) “Cosmeceutical Concepts and Product Development” project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 22 Aug 2024, 13:952 https://doi.org/10.12688/f1000research.153996.1 Copyright © 2024 Mpofana N 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 Mpofana N, Peter C, Lukman HY et al. Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :952 ( https://doi.org/10.12688/f1000research.153996.1 ) 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 22 Aug 2024 Views 0 Cite How to cite this report: Jagdeo J. Reviewer Report For: Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :952 ( https://doi.org/10.5256/f1000research.168967.r316598 ) The direct URL for this report is: https://f1000research.com/articles/13-952/v1#referee-response-316598 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 10 Sep 2024 Jared Jagdeo , Veterans Affairs New York Harbor Healthcare System - Brooklyn Campus, State University of New York, Downstate Health Sciences University, Brooklyn, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.168967.r316598 Abstract : The background, methods, results, and conclusion are briefly described. The authors clearly state the study objectives and a concise rationale for the study. The experimental results and conclusions are well represented, giving readers a good sense of the research ... Continue reading READ ALL Abstract : The background, methods, results, and conclusion are briefly described. The authors clearly state the study objectives and a concise rationale for the study. The experimental results and conclusions are well represented, giving readers a good sense of the research and the key takeaways. In the methods section of the abstract, the information provided is general and does not provide sufficient detail to readers. Additional information regarding the three specific metabolites, how the molecular docking and simulation were performed, what softwares were utilized and structures used, etc. would help provide substance and clarity. Introduction: The authors present a thorough background on the known pathogenesis of melasma, the role of melanin, and existing treatment options. An epidemiological background is also provided but could benefit from including the disproportionate impact and disease burden on populations with skin of color, as well as their more limited treatment options. Additionally, since melasma is known as the 'mask of pregnancy,' the association with pregnancy should be included. This would offer a more robust introduction and more effectively communicate the impact of this research. The sentences addressing the value and history of secondary metabolites could be strengthened by providing concrete example(s) of previously discovered secondary metabolites that have played 'protection and treatment roles against various diseases.' We also recommend the authors provide more comprehensive details on the current evidence for the three Cassipourea species. This would help strengthen the justification for their analyses. Lastly, the authors should grammatically revise the first sentence of the sixth paragraph: 'Studies have explored natural products that inhibit UV-induced ROS, suppress enzymes, and reduce melanin formation as potential alternatives to current treatments have been conducted.' Methods: The methods effectively support the goals of the experiment. The organization and division into subsections make the protocol easy to follow. The purpose of each analysis is clear and well justified. However, additional context would strengthen the robustness of the methodology. Specifically, in section 2.1, we recommend that the authors list the three Cassipourea species. In section 2.2, we suggest briefly describing Lipinski’s Rule of Five (as referenced later in Section 3.2) and its purpose within the context of this study. Results: The results are presented clearly with supporting tables and figures. The figures are well displayed and effectively convey the data in a concise and logical manner. The information supports the goals of the experiment and the conclusions drawn. However, in all figure legends, the authors should not only describe the methods used but also briefly summarize the main findings from the figures and their corresponding experiments. Additionally, the authors should redefine all abbreviations used within the figures for clarity. Lastly, in Figure 2, the GeneCards/OMIM Venn diagram requires clarification and correction, as the number of genes identified as common to both GeneCards and OMIM exceeds the number of metabolite genes identified by GeneCards alone. Discussion: The discussion highlights the novelty and value of this research, efficiently yet comprehensively discussing each of the study's results. The authors effectively convey how each analysis and result builds upon the previous one, providing sufficient contextual information for readers to fully appreciate the implications of the findings. While Figures 14 and 15 are helpful visual representations, we recommend adjusting the sizing and formatting of the keys for better readability. Additionally, we suggest examining any limitations of the study, including those related to the technologies the authors relied on to perform the analyses. Conclusion: The conclusion briefly and effectively summarizes the study's purpose and the key takeaways from the discussion. We recommend expanding upon potential future directions, including future research and clinical applications. General comments: The paper is well-written overall. The information is presented clearly and completely. The writing style is scientific and professional. The goals of the study, the methods, and results/conclusions are clear, justified, and strong. Our specific comments are as stated above. Decision: Minor revisions 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? Partly 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? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: melasma, phototherapy, keloids, basic science research, AN, cosmetics, skin of color 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 Jagdeo J. Reviewer Report For: Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :952 ( https://doi.org/10.5256/f1000research.168967.r316598 ) The direct URL for this report is: https://f1000research.com/articles/13-952/v1#referee-response-316598 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 25 Sep 2024 Nomakhosi Mpofana , Dermatology, University of KwaZulu-Natal School of Clinical Medicine, Durban, South Africa 25 Sep 2024 Author Response Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern ... Continue reading Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern about the sufficiency of details provided for replication, we would like to ask for your help in identifying specific methods or aspects of our analysis that you found unclear, in order for us to amend the sections as required. Are all the source data underlying the results available to ensure full reproducibility?: Partly Response: Thank you for your comment. To better address this comment, please advise which specific aspects of the results you found unclear or inadequately supported by the source data. Identifying these areas will allow us to provide the necessary information and increase the transparency of our results. Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern about the sufficiency of details provided for replication, we would like to ask for your help in identifying specific methods or aspects of our analysis that you found unclear, in order for us to amend the sections as required. Are all the source data underlying the results available to ensure full reproducibility?: Partly Response: Thank you for your comment. To better address this comment, please advise which specific aspects of the results you found unclear or inadequately supported by the source data. Identifying these areas will allow us to provide the necessary information and increase the transparency of our results. Competing Interests: Authors declare no competing interests . Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 25 Sep 2024 Nomakhosi Mpofana , Dermatology, University of KwaZulu-Natal School of Clinical Medicine, Durban, South Africa 25 Sep 2024 Author Response Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern ... Continue reading Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern about the sufficiency of details provided for replication, we would like to ask for your help in identifying specific methods or aspects of our analysis that you found unclear, in order for us to amend the sections as required. Are all the source data underlying the results available to ensure full reproducibility?: Partly Response: Thank you for your comment. To better address this comment, please advise which specific aspects of the results you found unclear or inadequately supported by the source data. Identifying these areas will allow us to provide the necessary information and increase the transparency of our results. Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern about the sufficiency of details provided for replication, we would like to ask for your help in identifying specific methods or aspects of our analysis that you found unclear, in order for us to amend the sections as required. Are all the source data underlying the results available to ensure full reproducibility?: Partly Response: Thank you for your comment. To better address this comment, please advise which specific aspects of the results you found unclear or inadequately supported by the source data. Identifying these areas will allow us to provide the necessary information and increase the transparency of our results. Competing Interests: Authors declare no competing interests . Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 22 Aug 2024 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 Version 1 22 Aug 24 read Jared Jagdeo , State University of New York, Downstate Health Sciences University, Brooklyn, 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 © 2024 Jagdeo 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. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. 10 Sep 2024 | for Version 1 Jared Jagdeo , Veterans Affairs New York Harbor Healthcare System - Brooklyn Campus, State University of New York, Downstate Health Sciences University, Brooklyn, USA 0 Views copyright © 2024 Jagdeo 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. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. 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 Abstract : The background, methods, results, and conclusion are briefly described. The authors clearly state the study objectives and a concise rationale for the study. The experimental results and conclusions are well represented, giving readers a good sense of the research and the key takeaways. In the methods section of the abstract, the information provided is general and does not provide sufficient detail to readers. Additional information regarding the three specific metabolites, how the molecular docking and simulation were performed, what softwares were utilized and structures used, etc. would help provide substance and clarity. Introduction: The authors present a thorough background on the known pathogenesis of melasma, the role of melanin, and existing treatment options. An epidemiological background is also provided but could benefit from including the disproportionate impact and disease burden on populations with skin of color, as well as their more limited treatment options. Additionally, since melasma is known as the 'mask of pregnancy,' the association with pregnancy should be included. This would offer a more robust introduction and more effectively communicate the impact of this research. The sentences addressing the value and history of secondary metabolites could be strengthened by providing concrete example(s) of previously discovered secondary metabolites that have played 'protection and treatment roles against various diseases.' We also recommend the authors provide more comprehensive details on the current evidence for the three Cassipourea species. This would help strengthen the justification for their analyses. Lastly, the authors should grammatically revise the first sentence of the sixth paragraph: 'Studies have explored natural products that inhibit UV-induced ROS, suppress enzymes, and reduce melanin formation as potential alternatives to current treatments have been conducted.' Methods: The methods effectively support the goals of the experiment. The organization and division into subsections make the protocol easy to follow. The purpose of each analysis is clear and well justified. However, additional context would strengthen the robustness of the methodology. Specifically, in section 2.1, we recommend that the authors list the three Cassipourea species. In section 2.2, we suggest briefly describing Lipinski’s Rule of Five (as referenced later in Section 3.2) and its purpose within the context of this study. Results: The results are presented clearly with supporting tables and figures. The figures are well displayed and effectively convey the data in a concise and logical manner. The information supports the goals of the experiment and the conclusions drawn. However, in all figure legends, the authors should not only describe the methods used but also briefly summarize the main findings from the figures and their corresponding experiments. Additionally, the authors should redefine all abbreviations used within the figures for clarity. Lastly, in Figure 2, the GeneCards/OMIM Venn diagram requires clarification and correction, as the number of genes identified as common to both GeneCards and OMIM exceeds the number of metabolite genes identified by GeneCards alone. Discussion: The discussion highlights the novelty and value of this research, efficiently yet comprehensively discussing each of the study's results. The authors effectively convey how each analysis and result builds upon the previous one, providing sufficient contextual information for readers to fully appreciate the implications of the findings. While Figures 14 and 15 are helpful visual representations, we recommend adjusting the sizing and formatting of the keys for better readability. Additionally, we suggest examining any limitations of the study, including those related to the technologies the authors relied on to perform the analyses. Conclusion: The conclusion briefly and effectively summarizes the study's purpose and the key takeaways from the discussion. We recommend expanding upon potential future directions, including future research and clinical applications. General comments: The paper is well-written overall. The information is presented clearly and completely. The writing style is scientific and professional. The goals of the study, the methods, and results/conclusions are clear, justified, and strong. Our specific comments are as stated above. Decision: Minor revisions 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? Partly 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? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise melasma, phototherapy, keloids, basic science research, AN, cosmetics, skin of color 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 25 Sep 2024 Nomakhosi Mpofana, Dermatology, University of KwaZulu-Natal School of Clinical Medicine, Durban, South Africa Reviewer comment: Are sufficient details of methods and analysis provided to allow replication by others?: Partly Response: Thank you for your feedback. To address your concern about the sufficiency of details provided for replication, we would like to ask for your help in identifying specific methods or aspects of our analysis that you found unclear, in order for us to amend the sections as required. Are all the source data underlying the results available to ensure full reproducibility?: Partly Response: Thank you for your comment. To better address this comment, please advise which specific aspects of the results you found unclear or inadequately supported by the source data. Identifying these areas will allow us to provide the necessary information and increase the transparency of our results. View more View less Competing Interests Authors declare no competing interests . reply Respond Report a concern Jagdeo J. Peer Review Report For: Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :952 ( https://doi.org/10.5256/f1000research.168967.r316598) 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/13-952/v1#referee-response-316598 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 = "Mechanisms of Selected Cassipourea Metabolites...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-952/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-952/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-952/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Mpofana N 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/13-952/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/13-952", templates : { twitter : "Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment:.... Mpofana N et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/13-952/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/153996/168967") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "168967"); $(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 = { "332164": 0, "332161": 0, "332160": 0, "332163": 0, "332162": 0, "320927": 0, "320932": 0, "320933": 0, "320934": 0, "320935": 0, "320928": 0, "320929": 0, "320930": 0, "320931": 0, "382124": 0, "320936": 0, "316597": 0, "338871": 0, "316598": 14, "316599": 0, "338870": 0, "382128": 0, "316604": 0, "324412": 0, "316605": 0, "324413": 0, "316606": 0, "324414": 0, "324415": 0, "316600": 0, "316601": 0, "338872": 0, "316602": 0, "324410": 0, "316603": 0, "324411": 0, "324416": 0, "324417": 0, "324418": 0, "324419": 0, "349023": 0, "374245": 0, "374244": 0, "374247": 0, "374246": 0, "374241": 0, "374243": 0, "374242": 0, "402287": 0, "374249": 0, "374248": 0, "402294": 0, "402295": 0, "402292": 0, "402293": 0, "402290": 0, "402291": 0, "402288": 0, "402289": 0, "332157": 0, "332156": 0, "332159": 0, "332158": 0, "332155": 0, "402296": 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 = "5801e5b3-4264-4712-92a6-aab87fb66851"; 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(",","/")); } }); });

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00