National Institutes of Biomedical Innovation, Health and Nutrition
Institute for Protein Research

Development of a pharmacokinetics database and prediction models

  • 1. Development of an integrated database Information on thousands of drug compounds from public databases were extracted and all the relevant pharmacokinetic data on these compounds were collected. We also acquired both in vitro and in vivo experimental data. The database schema for integrating the public and in-house data were created. The database is going to opened to the public.
  • 2. Construction of an in silico model for predicting pharmacokinetic parameters We aimed to construct a structure-activity relationship model for predicting physicochemical properties of compounds from their chemical structures.
  • 3. We have established a framework for public-private partnership to share proprietary data and prediction models with seven domestic pharmaceutical companies, and have built a database of high-quality pharmacokinetic/toxicity screening data and prediction models based on this database. The prediction models built through the public-private partnership and a part of the prediction models embedded in DruMAP have been incorporated into Fujitsu Digital Laboratory Platform "SCIQUICK".



  1.  Esaki, T., Watanabe, R., Kawashima, H., Ohashi, R., Natsume‐Kitatani, Y., Nagao, C., & Mizuguchi, K. (2018). Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance. Molecular Informatics, 38(1–2), e1800086. doi
  2.  Watanabe, R., Esaki, T., Kawashima, H., Natsume-Kitatani, Y., Nagao, C., Ohashi, R., & Mizuguchi, K. (2018). Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges. Molecular Pharmaceutics, 15(11), 5302–5311. doi
  3. Esaki, T., Ohashi, R., Watanabe, R., Natsume-Kitatani, Y., Kawashima, H., Nagao, C., & Mizuguchi, K. (2019). Computational Model To Predict the Fraction of Unbound Drug in the Brain. Journal of Chemical Information and Modeling, 59(7), 3251–3261. doi
  4.  Esaki, T., Ohashi, R., Watanabe, R., Natsume-Kitatani, Y., Kawashima, H., Nagao, C., Komura, H., & Mizuguchi, K. (2019). Constructing an in silico three-class predictor of human intestinal absorption with Caco-2 permeability and dried-DMSO solubility. Journal of Pharmaceutical Sciences. doi
  5.  Watanabe, R., Ohashi, R., Esaki, T., Kawashima, H., Natsume-Kitatani, Y., Nagao, C. & Mizuguchi, K. (2019). Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor. Scientific Reports, 9, Article number: 18782doi
  6.  Watanabe, R., Esaki,T., Ohashi, R., Kuroda, M., Kawashima, H., Komura, H., Natsume-Kitatani, Y., & Mizuguchi, K. (2021). Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration.  Journal of Medicinal Chemistry, 2021,64,5,2725-2738. doi
  7.  Komura,H., Watanabe, R., Kawashima, H., Ohashi, R., Kuroda, M., Sato, T., Honma, T., & Mizuguchi, K.  (2021). A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties. Drug Discovery Today In Pressdoi