Explainable Machine Learning Using Taguchi-QbD Data for Digital Design Space Mapping of Pregabalin Extended-Release Tablets

Explainable Machine Learning Using Taguchi-QbD Data for Digital Design Space Mapping of Pregabalin Extended-Release Tablets | 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 Explainable Machine Learning Using Taguchi-QbD Data for Digital Design Space Mapping of Pregabalin Extended-Release Tablets Deepender, Tarandeep Singh Walia, Sreenath Sriram, Dr. Nabeel Ahmad, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9683753/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This study applied explainable machine learning to published Taguchi-QbD data for pregabalin extended-release tablets to support formulation understanding and digital design space mapping. No new experimental batches were prepared; the analysis used formulation-response data from a previously developed pregabalin extended-release tablet system. A secondary data set containing 16 Taguchi formulations was prepared using six formulation and process variables: lipid material type, lipid quantity, hydrophilic matrix type, hydrophilic matrix quantity, filler type, and compression force. Hardness, friability, and dissolution at 1, 2, 4, and 8 h were selected as modeling responses. Ridge regression, random forest regression, extra trees regression, and Gaussian process regression were evaluated using leave-one-out cross-validation. Model-independent explainability was performed using permutation feature importance, partial dependence analysis, and surrogate decision-tree rules. A desirability-based virtual formulation screening approach was then used to identify high-desirability regions within the original experimental design limits. Dissolution at 2 h and 4 h showed the most useful cross-validated predictive performance, with mean absolute errors of 10.25% and 8.10%, respectively. Explainability analysis identified hydrophilic matrix quantity as the dominant variable controlling early and intermediate drug release, whereas lipid type and lipid quantity were more influential for tablet hardness and late-stage release behavior. Among the original Taguchi formulations, F15 showed the highest observed desirability, although it did not fully satisfy all dissolution criteria. Virtual screening identified high-desirability in silico candidates characterized mainly by high hydrophilic matrix loading and adequate lipid content. The published optimized formulation was positioned within a high-desirability AI-predicted region, supporting consistency between the explainable ML workflow and the original QbD optimization. These findings indicate that small-data explainable machine learning can complement QbD-based formulation analysis by extracting formulation rules and prioritizing design-space regions; however, AI-predicted candidates require experimental confirmation. Artificial Intelligence and Machine Learning Drug Discovery, Design, & Development Explainable machine learning Taguchi-QbD Pregabalin extended-release tablets Digital design space mapping Permutation feature importance Extended-release matrix tablets Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-9683753","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639558223,"identity":"bb05c462-a02a-432a-a3dc-9b361d9a9dec","order_by":0,"name":"Deepender","email":"","orcid":"https://orcid.org/0000-0002-0529-4007","institution":"School of Computer Applications, Lovely Professional University, Punjab, India","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Deepender","suffix":""},{"id":639558224,"identity":"771e060b-42a1-43ef-9cbf-89df7e9ba555","order_by":1,"name":"Tarandeep Singh 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