This grant aims to create PBPK models for long-acting injectable suspensions and implants, predicting drug release by linking formulation attributes to in-vivo performance.
This grant aims to combine machine learning and computational fluid dynamics to improve generic inhaled drug development and approval through alternative bioequivalence studies.
This grant seeks to develop AI/ML models using real-world data to monitor clinical outcomes when patients switch between complex generics and brand-name drugs.
This grant aims to identify drug and formulation factors causing alcohol-induced rapid release from modified-release drugs, improving testing and development for safer generics.
Accelerate precision probiotic therapies by identifying person-specific features affecting probiotic responses to enhance clinical outcomes.