AI-Driven Knowledge Management in PBPK Modeling

This document is a technical report that discusses the integration of AI-driven knowledge management in Physiologically Based Pharmacokinetic (PBPK) modeling, highlighting the challenges and opportunities presented by this approach. It outlines how high-quality data is essential for AI advancements and details the role of knowledge management in centralizing scientific knowledge, ensuring data quality, and supporting compliance with regulatory standards. The report identifies various challenges in AI adoption for PBPK modeling, including the diversity of data sources and the complexities of data curation. It also presents interoperable data models as a foundation for AI integration and discusses the automation of data extraction from unstructured sources. Furthermore, it addresses the creation of ontologies and knowledge graphs using AI, emphasizing the need for human validation to ensure accuracy. The report concludes with a case study on ocular PBPK extrapolation, illustrating the practical applications and outcomes of these methodologies.