Quantitative Prediction of Drug–Drug Interactions Involving Inhibitory Metabolites in Drug Development: How Can Physiologically Based Pharmacokinetic Modeling Help?

Abstract

This subteam under the Drug Metabolism Leadership Group (Innovation and Quality Consortium) investigated the quantitative role of circulating inhibitory metabolites in drug–drug interactions using physiologically based pharmacokinetic (PBPK) modeling. Three drugs with major circulating inhibitory metabolites (amiodarone, gemfibrozil, and sertraline) were systematically evaluated in addition to the literature review of recent examples. The application of PBPK modeling in drug interactions by inhibitory parent–metabolite pairs is described and guidance on strategic application is provided.

Quantitative Prediction of Drug–Drug Interactions Involving Inhibitory Metabolites by Physiologically Based Pharmacokinetic Models: Is it Worth It?

“…We read with interest the article of Templeton et al 1. on the quantitative prediction of drug‐drug interactions involving metabolites.…”

“…Most often, the goal is simply to predict the increase in the victim drug area under the curve (AUC) at steady state caused by the exposure to the inhibitory entity (the drug parent and its metabolites), because this is sufficient to estimate the risks associated with drug‐drug interactions, guide clinical decisions, and establish prescribing information. Due to its complexity, as acknowledged by Templeton et al .,1 the physiologically based pharmacokinetic procedure requires extensive validation and is sensitive to many modeling and experimental assumptions. As a result, it is time‐consuming and costly.…”

“…Due to its principle, IMSM can readily accommodate drug‐drug interactions involving multiple species (enantiomers, metabolites of the inhibitor), because the inhibition potency reflects the action of all molecular species at the site of interaction. A few examples are shown in Table 1 1, 3, 4. Of note, the interactions with bupropion were published in 2016, but were predicted using the parameters published by our group in 2011 5…”

Response to “Quantitative Prediction of Drug‐Drug Interactions Involving Inhibitory Metabolites by Physiologically Based Pharmacokinetic Models: Is It Worth?”

“…In our recently published article,1 we demonstrated that physiologically based pharmacokinetic (PBPK) modeling provides mechanistic understanding for the observed clinical drug–drug interactions (DDIs) caused by inhibitory metabolite(s). However, we recommended a step‐wise approach (i.e., flow chart) to predict DDIs involving inhibitory metabolites based on the development stage and available data.…”

“…As outlined in our position paper,1 static models are useful in predicting DDIs when data are limited or early DDI risk assessment is needed in compound progression. However, mechanistic PBPK models will be more informative for quantitative assessment and simulation of complex DDIs involving inhibitory metabolites.…”