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

  • J Med Chem. 2021 Mar 11;64(5):2725-2738. doi: 10.1021/acs.jmedchem.0c02011.
Reiko Watanabe  1 ,  Tsuyoshi Esaki  2 ,  Rikiya Ohashi  1  3 ,  Masataka Kuroda  1  3 ,  Hitoshi Kawashima  1 ,  Hiroshi Komura  4 ,  Yayoi Natsume-Kitatani  1 ,  Kenji Mizuguchi  1  5
Affiliations
  • 1. Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
  • 2. The Center for Data Science Education and Research, Shiga University, Hikone, Shiga 522-8522, Japan.
  • 3. Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, Muraoka-Higashi, Fujisawa, Kanagawa 251-8555, Japan.
  • 4. URA Center, Osaka City University, Osaka 545-0051, Japan.
  • 5. Laboratory of In-Silico Drug Design, Center of Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
PMID: 33619967 DOI: 10.1021/acs.jmedchem.0c02011
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.

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