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.

There is increasing evidence that pregnancy alters the function of drug-metabolizing enzymes and drug transporters in a gestational-stage and tissue-specific manner. In vivo probe studies have shown that the activity of several hepatic cytochrome P450 enzymes, such as CYP2D6 and CYP3A4, is increased during pregnancy, whereas the activity of others, such as CYP1A2, is decreased. The activity of some renal transporters, including organic cation transporter and P-glycoprotein, also appears to be increased during pregnancy. Although much has been learned, significant gaps still exist in our understanding of the spectrum of drug metabolism and transport genes affected, gestational age–dependent changes in the activity of encoded drug metabolizing and transporting processes, and the mechanisms of pregnancy-induced alterations. In this issue of Drug Metabolism and Disposition, a series of articles is presented that address the predictability, mechanisms, and magnitude of changes in drug metabolism and transport processes during pregnancy. The articles highlight state-of-the-art approaches to studying mechanisms of changes in drug disposition during pregnancy, and illustrate the use and integration of data from in vitro models, animal studies, and human clinical studies. The findings presented show the complex inter-relationships between multiple regulators of drug metabolism and transport genes, such as estrogens, progesterone, and growth hormone, and their effects on enzyme and transporter expression in different tissues. The studies provide the impetus for a mechanism- and evidence-based approach to optimally managing drug therapies during pregnancy and improving treatment outcomes.

(2009). The role of CYP26 enzymes in retinoic acid clearance. Expert Opinion on Drug Metabolism & Toxicology: Vol. 5, No. 8, pp. 875-886.

The aim of this study was to evaluate the contribution of metabolites to drug-drug interactions (DDI) using the inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites as a model. Of the metabolites identified in vivo, 5-hydroxyomeprazole, 5′-O-desmethylomeprazole, omeprazole sulfone, and carboxyomeprazole had a metabolite to parent area under the plasma concentration–time curve (AUC_m/AUC_p) ratio ≥ 0.25 when either total or unbound concentrations were measured after a single 20-mg dose of omeprazole in a cocktail. All of the metabolites inhibited CYP2C19 and CYP3A4 reversibly. In addition omeprazole, omeprazole sulfone, and 5′-O-desmethylomeprazole were time dependent inhibitors (TDI) of CYP2C19, whereas omeprazole and 5′-O-desmethylomeprazole were found to be TDIs of CYP3A4. The in vitro inhibition constants and in vivo plasma concentrations were used to evaluate whether characterization of the metabolites affected DDI risk assessment. Identifying omeprazole as a TDI of both CYP2C19 and CYP3A4 was the most important factor in DDI risk assessment. Consideration of reversible inhibition by omeprazole and its metabolites would not identify DDI risk with CYP3A4, and with CYP2C19, reversible inhibition values would only identify DDI risk if the metabolites were included in the assessment. On the basis of inactivation data, CYP2C19 and CYP3A4 inhibition by omeprazole would be sufficient to identify risk, but metabolites were predicted to contribute 30–63% to the in vivo hepatic interactions. Therefore, consideration of metabolites may be important in quantitative predictions of in vivo DDIs. The results of this study show that, although metabolites contribute to in vivo DDIs, their relative abundance in circulation or logP values do not predict their contribution to in vivo DDI risk.