A computationally informed framework for lipid-based formulation developability: Integrating predictive models with...

Area of Science:

• Pharmaceutical Sciences
• Drug Delivery
• Computational Chemistry

Background:

• Poorly water-soluble drugs (PWSDs) present significant formulation challenges.
• Traditional methods for selecting lipid-based formulations (LBFs) are empirical and time-consuming.
• Efficient bio-enabling strategies are crucial for advancing PWSD development.

Purpose of the Study:

• To introduce a computationally informed framework for streamlining LBF development.
• To integrate predictive in silico tools for assessing LBF suitability.
• To guide formulation decisions for PWSDs using a data-driven approach.

Main Methods:

• Developed a framework combining in silico tools (food effect, lipid solubility, biopharmaceutical dose number predictions).
• Screened a database of >200 FDA-approved oral drugs (2010-2023) using the computational framework.
• Applied tailored strategies like lipophilic salt synthesis for challenging molecules.

Main Results:

• The framework successfully classified drug candidates based on formulation risk.
• Identified suitable LBF types (Type-I, Type-IIIa) for low- and medium-risk molecules.
• Demonstrated the effectiveness of lipophilic salts for high-risk drugs with dose-loading and dose number limitations.

Conclusions:

• The computational framework enables rapid, data-driven formulation decisions for PWSDs.
• Reduces reliance on empirical methods, improving R&D efficiency.
• Offers a practical roadmap for accelerating LBF adoption in early-stage drug development.