Successful Prediction of Human Steady-State Unbound Brain-to-Plasma Concentration Ratio of P-gp Substrates Using the Proteomics-Informed Relative Expression Factor Approach.

In order to optimize central nervous system (CNS) drug development, accurate prediction of the drug's human steady-state unbound brain interstitial fluid-to-plasma concentration ratio (K ) is critical, especially for drugs that are effluxed by the multiple drug resistance transporters (e.g., P-glycoprotein, P-gp). Due to lack of good in vitro human blood-brain barrier models, we and others have advocated the use of a proteomics-informed relative expressive factor (REF) approach to predict K . Therefore, we tested the success of this approach in humans, with a focus on P-gp substrates, using brain positron emission tomography imaging data for verification. To do so, the efflux ratio (ER) of verapamil, N-desmethyl loperamide, and metoclopramide was determined in human P-gp-transfected MDCKII cells using the Transwell assay. Then, using the ER estimate, K of the drug was predicted using REF (ER approach). Alternatively, in vitro passive and P-gp-mediated intrinsic clearances (CLs) of these drugs, estimated using a five-compartmental model, were extrapolated to in vivo using REF (active CL) and brain microvascular endothelial cells protein content (passive CL). The ER approach successfully predicted K of all three drugs within twofold of observed data and within 95% confidence interval of the observed data for verapamil and N-desmethyl loperamide. Using the in vitro-to-in vivo extrapolated clearance approach, K was reasonably well predicted but not the brain unbound interstitial fluid drug concentration-time profile. Therefore, we propose that the ER approach be used to predict K of CNS candidate drugs to enhance their success in development.