A Minimal Model Framework for Robust CAR-T Cell and Oncolytic Virus Combination Therapy

A Minimal Model Framework for Robust CAR-T Cell and Oncolytic Virus Combination Therapy | 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 A Minimal Model Framework for Robust CAR-T Cell and Oncolytic Virus Combination Therapy Aisha Tursynkozha, Yang Kuang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8680401/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 Glioblastoma remains one of the most lethal brain cancers. Combination therapy using CAR-T cells and oncolytic viruses shows promise, yet the mechanisms underlying synergy remain poorly understood. We develop mathematical models to analyze IL-13R$\alpha$2-targeting CAR-T cells and the oncolytic virus C134 using patient-derived glioblastoma data. We present a minimal model framework for predicting combination immunotherapy outcomes. Applying timescale separation between rapid viral and slower cellular dynamics, we derive quasi-steady-state (QSS) approximations that reduce complexity while maintaining accuracy. The QSS model uses 9 parameters compared with 11 in the full model and achieves comparable fits. Model comparisons using the Akaike Information Criterion indicate that the QSS model is generally favored; it consistently yields lower AIC values for oncolytic virus monotherapy and produces lower AIC values in three of four combination therapy conditions. Models with and without CAR-T exhaustion produce identical fits, indicating that exhaustion dynamics do not improve predictions within the 72-hour observation window. Overall, our results demonstrate that simplified QSS formulations effectively capture viral dynamics and provide a practical framework for optimizing combination immunotherapies. Cancer Biology Mathematical and Theoretical Biology Computational Biology Glioblastoma CAR-T therapy Oncolytic virus Combination therapy Therapeutic synergy Mathematical modeling Full Text Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8680401","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579833155,"identity":"063605eb-7c66-491b-83bd-6d319b6c30ad","order_by":0,"name":"Aisha Tursynkozha","email":"","orcid":"","institution":"School of Artificial Intelligence and Data Science, Astana IT University, Astana, 010000, Kazakhstan","correspondingAuthor":false,"prefix":"","firstName":"Aisha","middleName":"","lastName":"Tursynkozha","suffix":""},{"id":579836824,"identity":"04608235-98ca-427b-8627-19c67045ef69","order_by":1,"name":"Yang Kuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACCTBZgcwhTssZkrUwtpGiRXJG8rOHX+fZ2RscYD54m4cYLdISaebGstuSEzccYEu2JkqLnESCmbTkNuYEgwM8ZtJEakn/Ji05px7oMP5vxGmRlsgxk/zYcJhxwwEeNuK0SPa8KZNmOHY8ceZhNmPLOcRokTievk3yR021Pd/x5oc33hCjBQSYwe5hJlY5CDD+IEX1KBgFo2AUjDwAAE7rK9x75IWmAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2774-4133","institution":"School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Kuang","suffix":""}],"badges":[],"createdAt":"2026-01-23 14:53:59","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8680401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8680401/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101206522,"identity":"0778acc1-e201-49df-a80a-b6399dee4337","added_by":"auto","created_at":"2026-01-27 09:56:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11275686,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8680401/v1_covered_4fe8b2dd-8f7e-42cf-93f6-803feb539fcc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Minimal Model Framework for Robust CAR-T Cell and Oncolytic Virus Combination Therapy\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Glioblastoma, CAR-T therapy, Oncolytic virus, Combination therapy, Therapeutic synergy, Mathematical modeling","lastPublishedDoi":"10.21203/rs.3.rs-8680401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8680401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlioblastoma remains one of the most lethal brain cancers. 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