Prediction of in vivo drug-drug interactions from in vitro data: impact of incorporating parallel pathways of drug elimination and inhibitor absorption rate constant

Abstract

Aims: Success of the quantitative prediction of drug-drug interactions via inhibition of CYP-mediated metabolism from the inhibitor concentration at the enzyme active site ([I]) and the in vitro inhibition constant (K(i)) is variable. The aim of this study was to examine the impact of the fraction of victim drug metabolized by a particular CYP (f(mCYP)) and the inhibitor absorption rate constant (k(a)) on prediction accuracy.

Methods: Drug-drug interaction studies involving inhibition of CYP2C9, CYP2D6 and CYP3A4 (n = 115) were investigated. Data on f(mCYP) for the probe substrates of each enzyme and k(a) values for the inhibitors were incorporated into in vivo predictions, alone or in combination, using either the maximum hepatic input or the average systemic plasma concentration as a surrogate for [I]. The success of prediction (AUC ratio predicted within twofold of in vivo value) was compared using nominal values of f(mCYP) = 1 and k(a) = 0.1 min(-1).

Results: The incorporation of f(mCYP) values into in vivo predictions using the hepatic input plasma concentration resulted in 84% of studies within twofold of in vivo value. The effect of k(a) values alone significantly reduced the number of over-predictions for CYP2D6 and CYP3A4; however, less precision was observed compared with the f(mCYP). The incorporation of both f(mCYP) and k(a) values resulted in 81% of studies within twofold of in vivo value.

Conclusions: The incorporation of substrate and inhibitor-related information, namely f(mCYP) and k(a), markedly improved prediction of 115 interaction studies with CYP2C9, CYP2D6 and CYP3A4 in comparison with [I]/K(i) ratio alone.

Figure 1

Relationship between the observed AUC ratio and the [I]_in/K_i ratio for 146 drug–drug interactions involving CYP2C9 (○), CYP2D6 (▵) and CYP3A4 (•). The line shown is the theoretical relationship based on equation 1. The shaded areas represent the regions corresponding to negative and positive drug–drug interactions as defined by the borderlines of an AUC ratio of 2 and an [I]/K_i of 1 [2]

Figure 2

Determination of f_mCYP for midazolam. Relationship between the AUC ratio observed in vivo and [I]_in/K_i ratio for 10 drug–drug interactions involving midazolam as the victim drug

Figure 3

Relationship between the AUC ratio observed in vivo and the AUC ratio predicted for 115 drug–drug interaction studies involving CYP2C9 (○), CYP2D6 (▵) and CYP3A4 (•). The plots represent predictions using the maximum hepatic input concentration—equation 1 (A), incorporating both the f_mCYP, equation 2 (B), refined k_a value (C) and both f_mCYP and k_a(D). Solid line represents line of unity, whereas dashed lines represent the twofold limit in prediction accuracy. The shaded areas represent the regions corresponding to negative and positive drug–drug interactions as defined by the borderlines of an AUC ratio of 2 and an [I]/K_i of 1 [2]

Figure 4

Relationship between the observed AUC ratio in vivo and AUC ratio predicted for 115 drug–drug interaction studies involving CYP2C9 (○), CYP2D6 (▵) and CYP3A4 (•). The plots represent predictions using the average systemic total drug plasma concentration ([I]_av) (A), and incorporating f_mCYP data (B). The solid line represents line of unity, whereas dashed lines represent the twofold limit in prediction accuracy

Figure 5

Relationship between [I]_in and [I]_av for 115 drug–drug interaction studies involving CYP2C9 (○), CYP2D6 (▪) and CYP3A4 (▴)