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 improve prediction of food/drug interactions for high-risk oral drugs by using a lab model that mimics the gut to assess drug performance.
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.
NIH supports a consortium of centers refining human tissue models for infectious disease research and medical intervention testing to support regulatory acceptance; clinical trials are not allowed.
This grant funds innovative research to develop or improve broadly applicable models, materials, and methodologies for studying multiple body systems or diseases relevant to human health, excluding single disease or system studies.