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

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

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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