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Integrated in Vitro Analysis for the in Vivo Prediction of Cytochrome P450-Mediated Drug-Drug Interactions

Discovery Drug Metabolism and Pharmacokinetics, AstraZeneca R&D Charnwood, Loughborough, Leicestershire, United Kingdom

Unbound IC₅₀ (IC_50,u) values of 15 drugs were determined in eight recombinantly expressed human cytochromes P450 (P450s) and human hepatocytes, and the data were used to simulate clinical area under the plasma concentration-time curve changes (AUC) on coadministration with prototypic CYP2D6 substrates. Significant differences in IC_50,u values between enzyme sources were observed for quinidine (0.02 µM in recombinant CYP2D6 versus 0.5 µM in hepatocytes) and propafenone (0.02 versus 4.1 µM). The relative contribution of individual P450s toward the oxidative metabolism of clinical probes desipramine, imipramine, tolterodine, propranolol, and metoprolol was estimated via determinations of intrinsic clearance using recombinant P450s (rP450s). Simulated AUC were compared with those observed in vivo via the ratios of unbound inhibitor concentration at the entrance to the liver to inhibition constants determined against rP450s ([I]_in,u/K_i) and incorporating parallel substrate elimination pathways. For this dataset, there were 20% false negatives (observed AUC 2, predicted AUC < 2), 77% correct predictions, and 3% false positives. Thus, the [I]_in,u/K_i approach appears relatively successful at estimating the degree of clinical interactions and can be incorporated into drug discovery strategies. Using a Simcyp ADME (absorption, metabolism, distribution, elimination) simulator (Simcyp Ltd., Sheffield, UK), there were 3% false negatives, 94% correct simulations, and 3% false positives. False-negative predictions were rationalized as a result of mechanism-based inhibition, production of inhibitory metabolites, and/or hepatic uptake. Integrating inhibition and reaction phenotyping data from automated rP450 screens have shown applicability to predict the occurrence and degree of in vivo drug-drug interactions, and such data may identify the clinical consequences for candidate drugs as both "perpetrators" and "victims" of P450-mediated interactions.

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