Background: Physiologically based pharmacokinetic (PBPK) models utilize computer-based simulations to predict the pharmacokinetics of drugs. By using mathematical modeling techniques consisting of differential equations to simulate blood flow, tissue compositions, and organ properties, the pharmacokinetic properties of drugs can be better understood. Specifically, PBPK models can provide predictive information about drug absorption, distribution, metabolism, and excretion (ADME). The information gained from PBPK models can be useful in both drug discovery, development, and regulatory science. PBPK models can help to address some of the ethical dilemmas that arise during the drug development process, particularly when examining patient populations where testing a new drug may have significant ethical concerns. Patient populations where significant physiological change (i.e., pregnancy, pediatrics, geriatrics, organ impairment populations, etc.) and pathophysiological influences resulting in PK changes can also benefit from PBPK modeling. Additionally, PBPK models can be utilized to predict variations in drug metabolism resulting from genetic polymorphisms, age, and disease states. Methods: In this mini-review, we examine the various applications of PBPK models in drug metabolism. Current research articles related to drug metabolism in genetics, life-stages, and disease states were reviewed. Results: Several key factors in genetics, life-stage, and disease states that affect metabolism in PBPK models are identified. In genetics, the role of CYP enzymes, genetic polymorphisms, and ethnicity may influence metabolism. Metabolism generally changes over time from neonate, pediatric, adult, geriatric, and perinatal populations. Disease states such as renal and hepatic impairment, weight and other acute and chronic diseases also can also alter metabolism. Several examples of PBPK models applying these physiological changes have been published. Conclusions: The utilization and recognition of these specific areas in PBPK modeling can aid in personalized dosing strategy, clinical trial optimization, and regulatory submission.
Gonnabathula et al. (Tue,) studied this question.