Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

• Published in: Drug metabolism and disposition Vol. 43; no. 11; pp. 1823 - 1837
• Main Authors: Sager, Jennifer E., Yu, Jingjing, Ragueneau-Majlessi, Isabelle, Isoherranen, Nina
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.11.2015; The American Society for Pharmacology and Experimental Therapeutics
• Subjects: Animals; Computer Simulation; Drug Interactions - physiology; Humans; Minireview; Models, Biological; Pharmaceutical Preparations - metabolism; Pharmacokinetics; CDER; FDA; PD; CHMP; IVIVE; DDI; EMA; IQ; PBPK; PK; AUC
• ISSN: 0090-9556; 1521-009X; 1521-009X
• DOI: 10.1124/dmd.115.065920

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms “PBPK” and “physiologically based pharmacokinetic model” to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.