Biomarker-Driven Computational PK/PD Modeling and Dosing Optimization of the Targeted Protein Degrader Fulvestrant in Estrogen Receptor–Positive Breast Cancer

Biomarker-Driven Computational PK/PD Modeling and Dosing Optimization of the Targeted Protein Degrader Fulvestrant in Estrogen Receptor–Positive Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biomarker-Driven Computational PK/PD Modeling and Dosing Optimization of the Targeted Protein Degrader Fulvestrant in Estrogen Receptor–Positive Breast Cancer Nikhil Srinivasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8227309/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Computational models that help establish dose, exposure and response relationship are essential for maximizing a drug’s clinical efficacy. This paper describes a novel biomarker-driven computational model that simulates the pharmacological effects of an important targeted protein degradation drug, fulvestrant (Faslodex), used to treat Estrogen Receptor (ER)-positive breast cancer. Fulvestrant promotes ER degradation in tumors, and this slows the disease progression. Using PK/PD modeling to optimize dosing to achieve maximal intratumoral ER degradation hence is key to improving fulvestrant efficacy. This PK/PD model development effort utilized fulvestrant’s pharmacokinetics (half-life: 14 h; clearance: 11 mL/kg/min), potency in tamoxifen-resistant cells (IC₅₀: 2.4 nM), tumor penetration (cell/plasma ratio: 0.7), plasma protein binding (~99%), and intracellular ER turnover rate (0.003 h⁻¹) data. Tumor growth inhibition in mice was simulated (doses: 25 and 200 mg/kg), and the model refined using in vivo xenograft tumor growth inhibition (TGI) data. This approach was translated to develop a human computational model and fine-tuned by aligning predicted ER degradation rates with reported patient biomarker data. Our model indicates that the current monthly 500 mg IM dose is unlikely to achieve sufficient ER degradation in patients, while a 250 mg weekly IM schedule could reduce ER levels by >90% in six months and >99% in ten months, potentially improving treatment outcomes. fulvestrant estrogen receptor positive breast cancer precision oncology selective estrogen receptor degrader biomarker pharmacokinetics pharmacodynamics tumor growth inhibition progression-free survival compartment model dose-response Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8227309","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558140739,"identity":"3d50e50f-8388-4c3f-a309-e46714ee4240","order_by":0,"name":"Nikhil 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Cancer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"fulvestrant, estrogen receptor positive breast cancer, precision oncology, selective estrogen receptor degrader, biomarker, pharmacokinetics, pharmacodynamics, tumor growth inhibition, progression-free survival, compartment model, dose-response","lastPublishedDoi":"10.21203/rs.3.rs-8227309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8227309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComputational models that help establish dose, exposure and response relationship are essential for maximizing a drug’s clinical efficacy. This paper describes a novel biomarker-driven computational model that simulates the pharmacological effects of an important targeted protein degradation drug, fulvestrant (Faslodex), used to treat Estrogen Receptor (ER)-positive breast cancer. Fulvestrant promotes ER degradation in tumors, and this slows the disease progression. Using PK/PD modeling to optimize dosing to achieve maximal intratumoral ER degradation hence is key to improving fulvestrant efficacy. This PK/PD model development effort utilized fulvestrant’s pharmacokinetics (half-life: 14 h; clearance: 11 mL/kg/min), potency in tamoxifen-resistant cells (IC₅₀: 2.4 nM), tumor penetration (cell/plasma ratio: 0.7), plasma protein binding (~99%), and intracellular ER turnover rate (0.003 h⁻¹) data. Tumor growth inhibition in mice was simulated (doses: 25 and 200 mg/kg), and the model refined using in vivo xenograft tumor growth inhibition (TGI) data. This approach was translated to develop a human computational model and fine-tuned by aligning predicted ER degradation rates with reported patient biomarker data. Our model indicates that the current monthly 500 mg IM dose is unlikely to achieve sufficient ER degradation in patients, while a 250 mg weekly IM schedule could reduce ER levels by \u0026gt;90% in six months and \u0026gt;99% in ten months, potentially improving treatment outcomes.\u003c/p\u003e","manuscriptTitle":"Biomarker-Driven Computational PK/PD Modeling and Dosing Optimization of the Targeted Protein Degrader Fulvestrant in Estrogen Receptor–Positive Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 17:43:17","doi":"10.21203/rs.3.rs-8227309/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9939b11a-28b3-4c05-b4d7-1f8e6ec798c0","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T03:39:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 17:43:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8227309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8227309","identity":"rs-8227309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}