Real-world application of physiologically based pharmacokinetic models in drug discovery

The utility of physiologically based pharmacokinetic (PBPK) models in support of drug development has been well documented. During the discovery stage, PBPK modeling has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection, and early formulation assessment. Previous review articles have proposed model-building and application strategies for PBPK-based first-inhuman predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees based on literature reviews to guide the application of PBPK models in the discovery setting. The goal of this minireview is to provide additional guidance on the real-world application of PBPK models in support of the discovery stage of drug development, to assist in decision making. We have illustrated our recommended approach through description of case examples where PBPK models have been successfully applied to aid in human pharmacokinetic projection, candidate selection, and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including preverification in preclinical species, the application of empirical scalars in the prediction of in vivo clearance from in vitro systems, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. Significance Statement: Through description of 3 case studies, this minireview highlights the fundamental principles of physiologically based pharmacokinetic application during drug discovery. These include preverification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. (c) 2024 American Society for Pharmacology and Experimental Therapeutics. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.