A computationally informed framework for lipid-based formulation developability: Integrating predictive models with case studies: Journal of Pharmaceutical Sciences

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dc.contributor.authorRyan, Callum D.
dc.contributor.authorO'Shea, Joseph
dc.contributor.authorGriffin, Brendan
dc.contributor.funderScience Foundation Ireland(SFI)
dc.date.accessioned2026-05-11T09:50:13Z
dc.date.available2026-05-11T09:50:13Z
dc.date.issued2026-03-24
dc.description.abstractThe increasing prevalence of poorly water-soluble drugs (PWSDs) in development pipelines has intensified the need for bio-enabling strategies such as lipid-based formulations (LBFs). However, formulation selection for optimal bioavailability often relies on empirical, resource-intensive approaches. This study introduces a computationally informed developability framework that integrates predictive models to streamline LBF development. The framework combines in silico tools for assessing key determinants of LBF suitability—including predictions of food effect, lipid solubility, and biopharmaceutical dose number (Do)—with tailored strategies such as lipophilic salt synthesis for challenging molecules. A database of >200 FDA-approved oral drugs (2010–2023) was screened through the computational framework, and four case studies (dapagliflozin, mavacamten, ospemifene, flibanserin) illustrate the framework’s ability to classify risk and guide formulation decisions. Predictions identified low-risk candidates for or type-I LBFs, highlighted medium-risk molecules requiring type-IIIa formulations, and demonstrated the utility of lipophilic salts for drugs which are high-risk with respect to dose-loading and dose number limitations. This integrated approach enables rapid, data-driven formulation decisions, reducing reliance on trial-and-error methods and supporting early-stage development of PWSDs. By coupling computational predictions with targeted experimental validation, the framework offers a practical roadmap for accelerating LBF adoption and improving R&D efficiency. © 2026 The Authors.en
dc.description.versionPublished Version
dc.format.extent12
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid104262
dc.identifier.authororcidRyan, Callum D.
dc.identifier.authororcidO'Shea, Joseph§0000-0001-9461-8730
dc.identifier.authororcidGriffin, Brendan§0000-0001-5433-8398
dc.identifier.citationRyan, C D, O'Shea, J & Griffin, B 2026, 'A computationally informed framework for lipid-based formulation developability: Integrating predictive models with case studies : Journal of Pharmaceutical Sciences', J. Pharm. Sci., vol. 115, no. 6, 104262. https://doi.org/10.1016/j.xphs.2026.104262
dc.identifier.doi10.1016/j.xphs.2026.104262
dc.identifier.issn0022-3549
dc.identifier.issued6
dc.identifier.journaltitleJ. Pharm. Sci.
dc.identifier.otherRIS: urn:1BD4006FB1C3A8EC6B7FAAC2540EE2AD
dc.identifier.otherORCID: /0000-0001-5433-8398/work/214352576
dc.identifier.otherORCID: /0000-0001-9461-8730/work/214353746
dc.identifier.urihttps://hdl.handle.net/10468/18737
dc.identifier.volume115
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.urihttps://www.scopus.com/pages/publications/105036428318?origin=resultslist
dc.rights©2026, the Authors. Published by Elsevier Inc. on behalf of American Pharmacists Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectComputational predictions
dc.subjectDecision trees
dc.subjectLipid-based formulations
dc.subjectPoorly soluble drugs
dc.subject[Pharmacy]
dc.titleA computationally informed framework for lipid-based formulation developability: Integrating predictive models with case studies: Journal of Pharmaceutical Sciencesen
dc.typeArticle (peer-reviewed)
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