Physiologically based pharmacokinetic (PBPK) modeling and simulation is a tool that can help predict the pharmacokinetics of drugs in humans and evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug exposure. The use of this tool is increasing at all stages of the drug development process. This report reviews recent instances of the use of PBPK in decision-making during regulatory review. The examples are based on Center for Drug Evaluation and Research reviews of several submissions for investigational new drugs (INDs) and new drug applications (NDAs) received between July 2008 and June 2010. The use of PBPK modeling and simulation facilitated the following types of decisions: the need to conduct specific clinical pharmacology studies, specific study designs, and appropriate labeling language. The report also discusses the challenges encountered when PBPK modeling and simulation were used in these cases and recommends approaches to facilitating full utilization of this tool.

Predicting clinically significant drug interactions during drug development is a challenge for the pharmaceutical industry and regulatory agencies. Since the publication of the US Food and Drug Administration's (FDA's) first in vitro and in vivo drug interaction guidance documents in 1997 and 1999, researchers and clinicians have gained a better understanding of drug interactions. This knowledge has enabled the FDA and the industry to progress and begin to overcome these challenges. The FDA has continued its efforts to evaluate methodologies to study drug interactions and communicate recommendations regarding the conduct of drug interaction studies, particularly for CYP-based and transporter-based drug interactions, to the pharmaceutical industry. A drug interaction Web site was established to document the FDA's current understanding of drug interactions (http://www.fda.gov/cder/drug/drugInteractions/default.htm). This report provides an overview of the evolution of the drug interaction guidances, includes a synopsis of the steps taken by the FDA to revise the original drug interaction guidance documents, and summarizes and highlights updated sections in the current guidance document, Drug Interaction Studies-Study Design, Data Analysis, and Implications for Dosing and Labeling.

ABSTRACT:Pharmaceutical industry investigators routinely evaluate the potential for a new drug to modify cytochrome P450 (P450) activities by determining the effect of the drug on in vitro probe reactions that represent activity of specific P450 enzymes. The in vitro findings obtained with one probe substrate are usually extrapolated to the compound's potential to affect all substrates of the same enzyme. Due to this practice, it is important to use the right probe substrate and to conduct the experiment under optimal conditions. Surveys conducted by reviewers in CDER indicated that the most common in vitro probe reactions used by industry investigators include the following: phenacetin O-deethylation for CYP1A2, coumarin 7-hydroxylation for CYP2A6, 7-ethoxy-4-trifluoromethyl coumarin O-dealkylation for CYP2B6, tolbutamide 4-hydroxylation for CYP2C9, S-mephenytoin 4-hydroxylation for CYP2C19, bufuralol 1-hydroxylation for CYP2D6, chlorzoxazone 6-hydroxylation for CYP2E1, and testosterone 6␤-hydroxylation for CYP3A4. We reviewed the validation information in the literature on these reactions and other frequently used reactions, including caffeine N3-demethylation for CYP1A2, S-mephenytoin N-demethylation for CYP2B6, S-warfarin 7-hydroxylation for CYP2C9, dextromethorphan O-demethylation for CYP2D6, and midazolam 1-hydroxylation for CYP3A4. The available information indicates that we need to continue the search for better probe substrates for some enzymes. For CYP3A4-based drug interactions it may be necessary to evaluate two or more probe substrates. In many cases, the probe reaction represents a particular enzyme activity only under specific experimental conditions. Investigators must consider appropriateness of probe substrates and experimental conditions when conducting in vitro drug interaction studies and when extrapolating the results to in vivo situations.During the drug-candidate screening and development process, investigators often conduct two types of in vitro drug metabolism studies to assess the potential for P450 1 -based drug interactions. One type of study characterizes the metabolic pathway of the new drug and the potential for other drugs to modify the metabolism of the new drug. The other type of study evaluates the potential for the new drug to alter the metabolism of other drugs. Due to the availability of antibodies against specific P450 enzymes, cDNA-expressed enzymes, purified enzymes, and selective chemical inhibitors, the unequivocal identification of the major P450 isoform responsible for the metabolism of a new drug can be easily established. However, predicting the potential for the new drug to alter the metabolism of other drugs usually relies on the evaluation of the effect of the new drug on the rate of a probe reaction that represents a specific P450 enzyme activity. The second type of evaluation is more challenging and is the focus of this review.

These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

Translation of in vitro antiviral activity to the in vivo setting is crucial to identify potentially effective dosing regimens of hydroxychloroquine. In vitro EC50/EC90 values for hydroxychloroquine should be compared to the in vivo free extracellular tissue concentration, which is similar to the free plasma hydroxychloroquine concentration.

Many intrinsic and extrinsic factors can affect an individual patient's drug exposure and response. The US Food and Drug Administration (FDA) has published a number of guidances that recommend how and when to evaluate these factors during drug development. The most recent FDA draft guidance on drug interactions provides advice for in vitro and in vivo drug interaction studies, including suggestions for study design, dosing strategies and analysis, and interpretation of data for medical product labels. The draft guidance updated the FDA's recommendations on the evaluation of important cytochrome P450 (CYP) enzyme- and transporter-based drug interactions during drug development.

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.

Chronic kidney disease, or renal impairment (RI) can increase plasma levels for drugs that are primarily renally cleared and for some drugs whose renal elimination is not a major pathway. We constructed physiologically based pharmacokinetic (PBPK) models for 3 nonrenally eliminated drugs (sildenafil, repaglinide, and telithromycin). These models integrate drug-dependent parameters derived from in vitro, in silico, and in vivo data, and system-dependent parameters that are independent of the test drugs. Plasma pharmacokinetic profiles of test drugs were simulated in subjects with severe RI and normal renal function, respectively. The simulated versus observed areas under the concentration versus time curve changes (AUCR, severe RI/normal) were comparable for sildenafil (2.2 vs 2.0) and telithromycin (1.6 vs 1.9). For repaglinide, the initial, simulated AUCR was lower than that observed (1.2 vs 3.0). The underestimation was corrected once the estimated changes in transporter activity were incorporated into the model. The simulated AUCR values were confirmed using a static, clearance concept model. The PBPK models were further used to evaluate the changes in pharmacokinetic profiles of sildenafil metabolite by RI and of telithromycin by RI and co-administration with ketoconazole. The simulations demonstrate the utility and challenges of the PBPK approach in evaluating the pharmacokinetics of nonrenally cleared drugs in subjects with RI.