Improving Bioavailability Through Predictive Modeling Of Solubility Enhancement Strategies

If you're working with a poorly soluble small-molecule compound, you already know the pain: screens that go on too long, material you can't afford to burn through, and timelines that slip before you've confirmed anything. A compound with aqueous solubility of just 0.015 mg/mL in FaSSIF media presented exactly that problem. Instead of defaulting to broad experimental screening, the team applied the Quadrant 2 predictive platform within the OSD Predict framework, combining quantum mechanics (QM) and molecular dynamics (MD) simulations with calculated molecular descriptors to model drug-excipient interactions computationally first.

Five candidate polymers emerged from in silico screening, including HPMCAS-M, PVAP, Soluplus, HPMCP-HP55, and PVPVA-64. Animal pharmacokinetic data then narrowed the field further, pointing to HPMCAS-M and PVAP as lead candidates. The HPMCAS-M amorphous solid dispersion, produced via spray drying and scaled to pilot scale using an MS-150 dryer, delivered roughly eight-fold improvement in Cmax and five-fold improvement in AUC relative to the crystalline form. That's the kind of exposure jump that moves a program into Phase I. Access the full case study to see how computational screening was structured and what it saved in experimental cycles.