Quantitative Prediction of Interactions Mediated by Transporters and Cytochromes: Application to Organic Anion Transporting Polypeptides, Breast Cancer Resistance Protein and Cytochrome 2C8.

Abstract

The in vivo mechanistic static model (IMSM) is an effective method to predict the magnitude of drug-drug interactions (DDIs) mediated by cytochromes. The aim of this study was to extend the IMSM paradigm to DDIs mediated by organic anion transporting polypeptide (OATP)1Bs, breast cancer resistance protein (BCRP) and cytochrome 2C8. First, a generic model for this kind of interaction was established, and a literature search was then conducted to retrieve the area under the concentration-time curve (AUC) ratio of a large set of DDIs involving OATP1B1, OATP1B3, BCRP and cytochromes 2C8 or 3A4. The model was fitted to the data to estimate the characteristic parameters (contribution ratios [CRs] and inhibition or induction potencies [IXs]) by nonlinear regression, and the model was qualified by external validation on a different dataset. Lastly, the model was used to identify the risks of overexposure by DDIs of this type. A total of 27 substrates, 26 inhibitors, 3 inducers and 3 genetic variants were considered in the regression analysis. The number of observations (AUC ratios, denoted as Robs) was 101. Forty-six CRs and 47 IXs were estimated. The proportions of predictions within 0.67- to 1.5-fold and 0.5- to twofold Robs were 90% and 99%, respectively, for the internal validation, and 78% and 96%, respectively, for the external validation. The median fold-error was 1.03 (the ideal value is 1). The interquartile range of fold-error was 0.31, and the relative standard error of parameter estimates was, at most, 17%. The IMSM approach was successfully extended to DDIs mediated by OATP1Bs, BCRP and cytochromes 2C8 or 3A4. The method revealed good predictive performances by internal and external validation.