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  • Review Article
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Predicting in vivo drug interactions from in vitro drug discovery data

Key Points

  • Safety concerns related to drug–drug interactions caused by the inhibition of cytochrome P450 (CYP) enzymes by co-administered drugs has led to the removal of several drugs from the market. The FDA has since issued guidelines for the introduction of in vitro metabolic studies early in the drug discovery process to determine the inhibitory properties of new chemical entities (NCEs), but these tests are subject to uncertainties and sources of error which confound the ability to extrapolate in vitro data to the in vivo condition.

  • Most metabolic inhibition screens involve incubating the test drug with one or more CYP enzymes and a substrate probe compound and measuring the amount of product using fluorescence assays or liquid chromatography–mass spectrometry (LC–MS). This is used to calculate various parameters such as the inhibition constant (Ki) which can be extrapolated to a model that better reflects the in vivo condition, typically the hepatic clearance model.

  • The hepatic clearance model relies on several assumptions regarding the distribution of enzyme, substrate and inhibitor. Evidence suggests that predicting CYP-based drug interactions in qualitative terms is feasible using in vitro data, but that accurate quantitative predictions of a drug's inhibitory potential can be confounded by various biochemical and biophysical factors that exist in vivo.

  • This review provides an overview of how various factors can affect the accuracy of in vitro–in vivo extrapolation. These include nonspecific protein binding of a drug, buffer and solvent effects on enzyme kinetics, irreversible inhibition (in which an inhibitor binds to and destroys the CYP enzyme) and the issue of atypical kinetics (such as positive cooperativity or partial inhibition caused by effector molecules binding ortho- or allo-sterically to the enzyme).

  • The authors conclude that in vitro information should ultimately be combined with other information about inhibitor plasma concentration or target–drug interaction as part of optimally designed DDI studies.

Abstract

In vitro screening for drugs that inhibit cytochrome P450 enzymes is well established as a means for predicting potential metabolism-mediated drug interactions in vivo. Given that these predictions are based on enzyme kinetic parameters observed from in vitro experiments, the miscalculation of the inhibitory potency of a compound can lead to an inaccurate prediction of an in vivo drug interaction, potentially precluding a safe drug from advancing in development or allowing a potent inhibitor to 'slip' into the patient population. Here, we describe the principles underlying the generation of in vitro drug metabolism data and highlight commonly encountered uncertainties and sources of bias and error that can affect extrapolation of drug–drug interaction information to the clinical setting.

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Figure 1: Routes of elimination of the top 200 most prescribed drugs in 2002.
Figure 2: Assessing drug–drug interaction potential for new chemical entities at stages in drug development.
Figure 3: Possible mechanisms of inhibition for the cytochrome P450s.
Figure 4: Potential outcomes for a test compound interacting with a substrate-dependent drug metabolic pathway.

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Correspondence to Larry C. Wienkers.

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DATABASES

Entrez Gene

CYP1A2

CYP2C19

CYP2C9

CYP2D6

CYP3A4

Glossary

AREA UNDER THE CURVE

(AUC). The area under the plasma–concentration curve of a substance expressed as a function of time that represents the exposure or measurement of the quantity of drug in the body.

ADVERSE DRUG REACTION

Any response to a drug that is noxious, unintended and occurs at doses normally used in humans for the prophylaxis, diagnosis or therapy of disease.

FIRST-ORDER REACTION

First-order kinetics reflects an enzymatic or chemical reaction in which the rate is directly proportional to the concentration of reactant, or any reaction changing at a constant fractional rate.

PARTITION RATIO

The ratio of released product to enzyme inactivation, which therefore reflects the efficiency of inactivation of the system. For example, metabolically activated inactivator with a large partition ratio (favours dissociation) leading to formation of released metabolites.

CYP METABOLIC-INTERMEDIATE (MI) COMPLEX

This occurs when intermediates generated during the oxidative metabolism of certain compounds bind to the haem of the cyp enzyme and form a stable (quasi-irreversible) ferrous haem complex.

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Wienkers, L., Heath, T. Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 4, 825–833 (2005). https://doi.org/10.1038/nrd1851

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