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• W3132353234 endingPage "2738" @default.
• W3132353234 startingPage "2725" @default.
• W3132353234 abstract "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 (Kp,brain) and unbound brain-to-plasma concentration ratio (Kp,uu,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." @default.
• W3132353234 created "2021-03-01" @default.
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• W3132353234 date "2021-02-23" @default.
• W3132353234 modified "2023-10-16" @default.
• W3132353234 title "Development of an <i>In Silico</i> Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration" @default.
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• W3132353234 doi "https://doi.org/10.1021/acs.jmedchem.0c02011" @default.
• W3132353234 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33619967" @default.
• W3132353234 hasPublicationYear "2021" @default.
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