A Combined Model for Predicting CYP3A4 Clinical Net Drug-Drug Interaction Based on CYP3A4 Inhibition, Inactivation, and Induction Determined in Vitro

ABSTRACT:Induction of the cytochrome P450 (P450) enzyme is a major concern in the drug discovery processes. To predict the clinical significance of enzyme induction, it is helpful to investigate pharmacokinetic alterations of a coadministered drug in a suitable animal model. In this study, we focus on the induction of CYP3A, which is involved in the metabolism of approximately 50% of marketed drugs and is inducible in both the liver and intestine. As a marker substrate for CYP3A activity, alprazolam (APZ) was selected and characterized using recombinant CYP3A enzymes expressed in Escherichia coli. Both human CYP3A4 and its cynomolgus P450 ortholog predominantly catalyzed APZ 4-hydroxylation with sigmoidal kinetics. When administered intravenously and orally to cynomolgus monkeys, APZ had moderate clearance; its first-pass extraction ratio after oral dosing was estimated to be 0.09 in the liver and 0.45 in the intestine. Pretreatment with multiple doses of rifampicin (20 mg/kg p.o. for 5 days), a known CYP3A inducer, significantly decreased plasma concentrations of APZ after intravenous and oral administrations (0.5 mg/kg), and first-pass extraction ratios were increased to 0.39 in the liver and 0.63 in the intestine. The results were comparable to those obtained in clinical drug-drug interaction (DDI) reports related to CYP3A induction, although the rate of recovery of CYP3A activity seemed to be slower than rates estimated in clinical studies. In conclusion, pharmacokinetic studies using APZ as a probe in monkeys may provide useful information regarding the prediction of clinical DDIs due to CYP3A induction.

Section: Discussionmentioning

confidence: 99%

Alprazolam as an In Vivo Probe for Studying Induction of CYP3A in Cynomolgus Monkeys

Ohtsuka¹,

Yoshikawa²,

Kozakai³

et al. 2010

“…12 and root mean squared error (rmse) (units of the parameter investigated) in eq. 13 (Sheiner and Beal, 1981;Fahmi et al, 2008). The gmfe does not allow over-and underpredictions to cancel each other out and indicates therefore an absolute deviation from the line of unity.…”

Section: Lc-ms/ms Analysis and List Of Chemicalsmentioning

confidence: 99%

Prediction of Human Intestinal First-Pass Metabolism of 25 CYP3A Substrates from In Vitro Clearance and Permeability Data

Gertz

Harrison²,

Houston³

et al. 2010

270

233

ABSTRACT:Intestinal first-pass metabolism may contribute to low oral drug bioavailability and drug-drug interactions, particularly for CYP3A substrates. The current analysis predicted intestinal availability (F G ) from in vitro metabolic clearance and permeability data of 25 drugs using the Q Gut model. The drug selection included a wide range of physicochemical properties and in vivo F G values (0.07-0.94). In vitro clearance data (CLu int ) were determined in human intestinal (HIM) and three liver (HLM) microsomal pools (n ‫؍‬ 105 donors) using the substrate depletion method. Apparent drug permeability (P app ) was determined in Caco-2 and Madin-Darby canine kidney cells transfected with human MDR1 gene (MDCK-MDR1 cells) under isotonic conditions (pH ‫؍‬ 7.4). In addition, effective permeability (P eff ) data, estimated from regression analyses to P app or physicochemical properties were used in the F G predictions. Determined CLu int values ranged from 0.022 to 76.7 l/min/pmol of CYP3A (zolpidem and nisoldipine, respectively). Differences in CLu int values obtained in HIM and HLM were not significant after normalization for tissue-specific CYP3A abundance, supporting their interchangeable usability. The F G predictions were most successful when P app data from Caco-2/MDCK-MDR1 cells were used directly; in contrast, the use of physicochemical parameters resulted in significant F G underpredictions. Good agreement between predicted and in vivo F G was noted for drugs with low to medium intestinal extraction (e.g., midazolam predicted F G value 0.54 and in vivo value 0.51). In contrast, low prediction accuracy was observed for drugs with in vivo F G <0.5, resulting in considerable underprediction in some instances, as for saquinavir (predicted F G is 6% of the observed value). Implications of the findings are discussed.

“…The extent of drug interactions was predicted using the eq. 1 that incorporates the effects of competitive inhibition and mechanismbased inhibition in both the intestine and liver (Fahmi et al, 2008): …”

mentioning

confidence: 99%

Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Wang¹

2010

ABSTRACT:Accurate prediction of the extent of mechanism-based CYP3A inhibition is critical in determining the timing of clinical drug interaction studies in drug development. To evaluate the prediction accuracy of the static and Simcyp time-based approaches, 54 clinical drug interactions involving mechanism-based CYP3A inhibitors were predicted using both methods. The Simcyp timebased approach generated better prediction when 0.03 h ؊1 was used as the hepatic CYP3A enzyme degradation rate constant (k deg ) value. Of the predictions 87 and 55% had an error less than 2 and 0. Drug-drug interactions remain an area of focus in drug discovery and development. Mechanism-based inhibition of CYP3A is one of the major causes of clinical drug-drug interactions and generally leads to greater concern, as highlighted by the list of moderate and strong CYP3A inhibitors in the Food and Drug Administration (2006) Drug Interaction Guidance. The inhibitory effects of a mechanism-based CYP3A inhibitor persist long after the compound is eliminated from the body because the recovery of CYP3A enzyme activity requires de novo protein synthesis or slow release of the enzyme from the enzyme-inhibitor complex. Because of the primary role of CYP3A in drug disposition, prediction of the clinical drug interaction potential of a mechanism-based CYP3A inhibitor would be helpful in guiding the timing and design of clinical studies.Several approaches have been developed to predict clinical outcomes of mechanism-based inhibition. The static mathematical model developed by Mayhew et al. (2000) is a commonly used approach to predict mechanism-based drug interactions from in vitro estimated inactivation parameters. Several modifications to the original model have been made to incorporate the effects of intestinal wall metabolism (Wang et al., 2004b), competitive inhibition, and induction (Fahmi et al., 2009). Nonetheless, these static models are only capable of predicting the average magnitude of drug interactions across a population, assuming that the steady state of enzyme inhibition has been reached. Temporal changes in inhibitor concentrations and CYP3A enzyme activities as well as interindividual variability in CYP3A enzyme levels and rate constants of enzyme degradation are not considered. In addition, it is difficult to assess the effects of dosing regimens (e.g., irregular dosing) on the extent of drug interactions using a static model.Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al., 2000;Zhang et al., 2009;Fenneteau et al., 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models. They can be used to simulate drug concentrationtime profiles and to explore the effects of various dosing regimens. Simcyp (Simcyp Limited, Sheffield, UK) is a commercially available absorption, distribution, metabolism, and...