A simplified approach to predict CYP3A-mediated drug–drug interactions at early drug discovery: validation with clinical data

Abstract

1. The present study evaluates which factors should be incorporated into a simplified approach to reasonably predict CYP3A-mediated drug–drug interaction (DDI) at an early drug discovery stage.

2. CYP3A IC₅₀ values were obtained using human liver microsomes (HLM) and hepatocytes. Plasma and microsomal protein binding and in vitro hepatocyte partition coefficient (K_p) were also determined for 10 drugs. Therapeutic human maximum plasma concentrations (C_max) were retrieved from the literature. DDI predictions were performed using an equation incorporating the fraction of the substrate metabolized by CYP3A with the total or free plasma C_max, with or without correction for hepatocyte K_p.

3. Based on the K_i data from HLM, the use of total C_max provided a prediction of DDI within 2-fold of the observed clinical values for 9 out of 10 drugs.

4. In comparison, free drug corrections for both C_max and K_i values from HLM led to an underprediction of DDI (>3-fold error for five drugs).

5. Data from hepatocytes showed, in general, lower prediction accuracy than data from HLM.

6. CYP3A-mediated DDIs can be predicted with a high level of accuracy based on K_i estimates from HLM data and the total therapeutic plasma C_max of the inhibitors. This approach should be widely applicable to the assessment of clinically significant DDIs risk in early drug discovery programs

• CYP3A inhibition
• drug–drug interaction
• human