Prediction of Drug-Drug Interactions for Highly Plasma Protein Bound Compounds.

Abstract

Accurate prediction of drug-drug interactions (DDI) from in vitro data is important, as it provides insights on clinical DDI risk and study design. Historically, the lower limit of plasma fraction unbound (f_u,p) is set at 1% for DDI prediction of highly bound compounds by the regulatory agencies due to the uncertainty of the f_u,p measurements. This leads to high false positive DDI predictions for highly bound compounds. The recently published ICH M12 DDI guideline allows the use of experimental f_u,p for DDI prediction of highly bound compounds. To further build confidence in DDI prediction of highly bound compounds using experimental f_u,p values, we evaluated a set of drugs with f_u,p < 1% and clinical DDI > 20% using both basic and mechanistic static models. All the compounds evaluated were flagged for DDI risk with the mechanistic model using experimental f_u,p values. There was no false negative DDI prediction. Similarly, using the basic model, the DDI risk of all the compounds was identified except for CYP2D6 inhibition of almorexant. The totality of the data demonstrates that the DDI potential of highly bound compounds can be predicted accurately when actual protein binding numbers are measured.