Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration

Developing in silico models to predict the brain penetration of drugs remains a challenge owing to the intricate involvement of multiple transport systems in the blood brain barrier, and the necessity to consider a combination of multiple pharmacokinetic parameters. P-glycoprotein (P-gp) is one of the most important transporters affecting the brain penetration of drugs. Here, we developed an in silico prediction model for P-gp efflux potential in brain capillary endothelial cells (BCEC). Using the representative values of P-gp net efflux ratio in BCEC, we proposed a novel prediction system for brain-to-plasma concentration ratio (K-p,K-brain) and unbound brain-to-plasma concentration ratio (K-p,K- uu,K-brain) of P-gp substrates. We validated the proposed prediction system using newly acquired experimental brain penetration data of 28 P-gp substrates. Our system improved the predictive accuracy of brain penetration of drugs using only chemical structure information compared with that of previous studies.

Automated summary

A computer model was developed to predict the efflux potential of drugs through brain capillary endothelial cells' P-gp, proposing a novel system for evaluating brain-to-plasma concentration ratios of P-gp substrates. Validation with experimental data improved the accuracy of predicting drug penetration across the blood-brain barrier, solely based on chemical structure information, compared to previous studies.