Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Abstract

Background and Objective Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives. Methods True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction ( F 2), basic static model ( R 3) and relative induction score (RIS), was used to characterize clinical induction risk. Results The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state ( C max,ss,u ) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively. Conclusions These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13318-022-00763-y.

Dicloxacillin‐warfarin drug–drug interaction—A register‐based study and in vitro investigations in 3D spheroid primary human hepatocytes

“…IVIVE of DDIs caused by induction is typically adjusted with correction factors or by correlation methods, especially for CYP3A4 33,34,42 . Such methods are needed because of a pronounced variability in the in vitro estimation of CYP3A4 induction.…”

“…14 IVIVE of DDIs caused by induction is typically adjusted with correction factors or by correlation methods, especially for CYP3A4. 33,34,42 Such methods are needed because of a pronounced variability in the in vitro estimation of CYP3A4 induction. This is likely not related to interindividual variability but rather to the weak performance of 2D culture format of PHHs.…”

“…To predict the magnitude of clinical DDI from in vitro parameters, an estimation of maximum hepatic inlet concentration (I h ) of dicloxacillin (Equation 2) and combining I h with the vitro parameters (Equation 3) are needed. 33,34 The mechanistic static model (Equation 3) and estimation of I h (Equation 2) are recommended by the US Food and Drug Administration for the estimation of clinical DDI based on the in vitro DDI parameters and the plasma concentration of perpetrator drug. 35…”

Beyond the Michaelis–Menten: Accurate Prediction of Drug Interactions Through Cytochrome P450 3A4 Induction

“…Finally, one possible reason for the inaccurate predictions of the new equation is inaccuracy in the estimated inducer drug concentration in the enterocytes. Recently, a study demonstrated that using unbound average hepatic inlet concentration as the input for gut concentration leads to better predictions of DDIs 44 …”

“…Recently, a study demonstrated that using unbound average hepatic inlet concentration as the input for gut concentration leads to better predictions of DDIs. 44 Further work is needed to extend the applicability of our approach. First, we have only collected CYP3A4-related interaction pairs of inducers and substrates to evaluate the prediction accuracy.…”