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

The 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.