Reliability and Extension of Quantitative Prediction of CYP3A4-Mediated Drug Interactions Based on Clinical Data

Abstract

Abstract. An approach was proposed in 2007 for quantitative predictions of cytochrome P450 (CYP)3A4-mediated drug-drug interactions. It is based on two characteristic parameters: the contribution ratio (CR; i.e., the fraction of victim drug clearance by CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when given with the inhibitor and the AUC of the victim drug when it is given alone. So far, these parameters were established for 21 substrates and 17 inhibitors. The goals of our study were to test the assumption of substrate independence of the potency of inhibitors in vivo and to estimate the CR and IR for an extended list of substrates and inhibitors of CYP3A4. The assumption of independence of IRs from the substrate was evaluated on a set of eight victim drugs and eight inhibitors. Forty-four AUC ratios were available. This assumption was rejected in four cases, but it did not result in more than a twofold error in AUC ratio predictions. The extended list of substrates and inhibitors was defined by a thorough literature search. Fifty-nine AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 37 substrates and 25 inhibitors, respectively. The mean prediction error of the ratios was 0.02, while the mean absolute prediction error was 0.58. Predictive distributions for 917 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far.

Comparison of the Static In Vivo Approach to a Physiologically Based Pharmacokinetic Approach for Metabolic Drug–Drug Interactions Prediction

“…Even after accounting for the differences in ketoconazole dose (200 or 400 mg) or interval between midazolam and ketoconazole intake, the variability of the AUC ratio between studies remains large. The typical standard deviation of an AUC ratio of 1, 10 or 20 was estimated to 1, 3.16 or 4.5, respectively, in our former study [19].…”

“…The second assumption may be an issue for CYP3A4 only, because the other CYPs have negligible activity in the gut, so F g and F g * are close to one. With CYP3A4, as shown earlier by simulation [20] and by studying DDIs with the rather selective inhibitor grapefruit juice [19], the lack of consideration of the variation of F g has been shown to have little impact on the predictive performances.…”

“…The website returns predicted AUC ratios for DDIs (171 substrates, 94 inhibitors and 21 inducers) mediated by CYPs 1A2, 2D6, 2C9, 2C19 and 3A4 [14][15][16][17][18][19], allowing 171 × (94 + 21) = 19,665 DDIs to be predicted. Multiple interactions between two drugs, involving several CYPs or simultaneous inhibition and induction, are also accommodated.…”

“…The last approach rely on in vivo data only, coupled with a mechanistic static interaction model, and will be denoted in vivo mechanistic static model (IMSM). It was introduced by Ohno [12,13] and extended in several directions by our group [14][15][16][17][18][19]. It demonstrated good predictive performances on moderate size studies (n = 50-100) devoted to DDIs mediated by each of the main CYPs.…”

A Prediction Model of Drug Exposure in Cirrhotic Patients According to Child–Pugh Classification

“…To further illustrate this possibility, the AUC ratios for the interaction between fluconazole and midazolam were predicted. Midazolam was chosen because the CYP3A4 CR is high for this drug (CR 3A4 = 0.91) and fluconazole is a potent inhibitor of this enzyme (IR 3A4 = 0.85 for 400 mg/day) [24]. The magnitude of the interaction was quantified by an AUC ratio, as shown in the following equation, where int represents the case of coadministration of the two drugs and the asterisk designates a cirrhotic patient:…”