In vivo animal models for investigating potential CYP3A- and Pgp-mediated drug-drug interactions
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Updated: Jul 19, 2026

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures
Published on: March 28, 2017
Punit H Marathe1, A David Rodrigues
1Metabolism and Pharmacokinetics, Bristol-Myers Squibb, Princeton, NJ 08543, USA. punit.marathe@bms.com
Abstract:
With the advent of polytherapy it has become prudent to minimize, as much as possible, the potential for drug-drug interactions. Towards this end, the metabolic and transporter pathways involved in the disposition of a drug candidate (phenotyping) are evaluated in vitro employing available human tissue and specific reagents. Likewise, in vitro screening for inhibition and induction of drug-metabolizing enzymes and transporters is conducted also. Such in vitro human data can be made available prior to human dosing and enable in vitro to in vivo-based predictions of clinical outcomes. Despite some success, however, in vitro systems are not dynamic and sometimes fail to predict drug-drug interactions for a variety of reasons. In comparison, relatively less effort has been made to evaluate predictions based on data derived from in vivo animal models. This review will attempt to summarize different examples from the literature where animal models have been used to predict cytochrome P450 3A (CYP3A)- and P-glycoprotein (Pgp)-based drug-drug interactions. When employing data from animal models one needs to be aware of species differences in pharmacokinetics, clearance pathways and selectivity and affinity of probe substrates and inhibitors. Because of these differences, in vivo animal studies alone, cannot be predictive of human drug-drug interactions. Despite these caveats, the information obtained from validated in vivo animal models may prove useful when used in conjunction with in vitro-in vivo extrapolation methods. Such an integrated data set can be used to select drug candidates with a reduced drug interaction potential.
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