Decision-Centric Explainable AI for QbD Optimization of Ultrasound-Triggered Drug- Loaded Microbubbles and Control Strategy Development

Decision-Centric Explainable AI for QbD Optimization of Ultrasound-Triggered Drug- Loaded Microbubbles and Control Strategy Development | 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 Decision-Centric Explainable AI for QbD Optimization of Ultrasound-Triggered Drug- Loaded Microbubbles and Control Strategy Development Anup Lal Yadav, Sonal Solanki, Nikunj Solanki, Vishin Patil, Sreenath Sriram, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8947026/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 A decision-centric workflow was developed to link a QbD-defined design space with interpretable surrogate modeling for rapid selection of ultrasound-triggered doxorubicin-loaded chitosan microbubbles. A 15-run Box–Behnken design spanning chitosan, palmitic acid, and Pluronic F68 was used to quantify three critical quality attributes (CQAs)—mean size, encapsulation efficiency (EE), and burst release at 40 s (Burst40)—and to train response-specific surrogates evaluated by leave-one-out cross-validation (LOOCV). The DoE produced a broad performance envelope (size 2.35–4.85 µm; EE 60–82%; Burst40 55.6–95%) with clear composition-driven trade-offs, notably between EE and acoustic responsiveness. A regularized polynomial surrogate (PolyRidge) provided strong LOOCV performance for size (R²=0.885; RMSE = 0.264 µm) and EE (R²=0.871; RMSE = 2.18%), whereas Burst40 was only moderately predictable (R²=0.501; RMSE = 8.61%), consistent with threshold-like cavitation and microstructure sensitivity. Digital screening with multi-objective desirability and explainability (SHAP, permutation importance, partial dependence, LIME) shortlisted manufacturable candidates; experimental validation showed QbD/RSM was better calibrated for absolute size and EE, while both modeling approaches remained similarly limited for Burst40. Importantly, shortlisted formulations preserved suppressed baseline release and pronounced ultrasound-enhanced release, yielding trigger-dependent cytotoxicity and increased apoptotic commitment in MCF-7 cells. Quality by Design Chitosan microbubbles Surrogate modeling PolyRidge Explainable AI Burst release. Full Text Additional Declarations No competing interests reported. Supplementary Files supplementrypaper.docx 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8947026","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596295591,"identity":"2d6e01c0-fcef-40a7-85fc-4895828c3d8d","order_by":0,"name":"Anup Lal Yadav","email":"","orcid":"","institution":"School of Engineering and Technology, CGC University","correspondingAuthor":false,"prefix":"","firstName":"Anup","middleName":"Lal","lastName":"Yadav","suffix":""},{"id":596295593,"identity":"a19f13e5-9245-4caa-97b5-c4517500c408","order_by":1,"name":"Sonal Solanki","email":"","orcid":"","institution":"Hon. 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A 15-run Box\u0026ndash;Behnken design spanning chitosan, palmitic acid, and Pluronic F68 was used to quantify three critical quality attributes (CQAs)\u0026mdash;mean size, encapsulation efficiency (EE), and burst release at 40 s (Burst40)\u0026mdash;and to train response-specific surrogates evaluated by leave-one-out cross-validation (LOOCV). The DoE produced a broad performance envelope (size 2.35\u0026ndash;4.85 \u0026micro;m; EE 60\u0026ndash;82%; Burst40 55.6\u0026ndash;95%) with clear composition-driven trade-offs, notably between EE and acoustic responsiveness. A regularized polynomial surrogate (PolyRidge) provided strong LOOCV performance for size (R\u0026sup2;=0.885; RMSE\u0026thinsp;=\u0026thinsp;0.264 \u0026micro;m) and EE (R\u0026sup2;=0.871; RMSE\u0026thinsp;=\u0026thinsp;2.18%), whereas Burst40 was only moderately predictable (R\u0026sup2;=0.501; RMSE\u0026thinsp;=\u0026thinsp;8.61%), consistent with threshold-like cavitation and microstructure sensitivity. Digital screening with multi-objective desirability and explainability (SHAP, permutation importance, partial dependence, LIME) shortlisted manufacturable candidates; experimental validation showed QbD/RSM was better calibrated for absolute size and EE, while both modeling approaches remained similarly limited for Burst40. Importantly, shortlisted formulations preserved suppressed baseline release and pronounced ultrasound-enhanced release, yielding trigger-dependent cytotoxicity and increased apoptotic commitment in MCF-7 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Decision-Centric Explainable AI for QbD Optimization of Ultrasound-Triggered Drug- Loaded Microbubbles and Control Strategy Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 03:36:26","doi":"10.21203/rs.3.rs-8947026/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":"c768d4a6-2486-41f4-adfa-925f0e6e9aa2","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T17:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 03:36:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8947026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8947026","identity":"rs-8947026","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}