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. ADMETe Tools
・Drug Metabolism and pharmacokinetics Analysis Platform
  1. DruMAP
・Prediction models of pharmacokinetics parameters
  1. 1) fu,p (fraction unbound in plasma)
  2. 2) fu,brain (fraction unbound in brain homogenate)
Reference
  1. Esaki, T.; Watanabe, R.; Kawashima, H.; Ohashi, R.; Natsume-Kitatani, Y.; Nagao, C.; Mizuguchi, K., Data curation can improve the prediction accuracy of metabolic intrinsic clearance. Mol Inform. 2019 Jan;38(1-2):e1800086
  2. Watanabe R.; Esaki T.; Kawashima H.; Natsume-Kitatani Y.; Nagao C.; Ohashi R.; Mizuguchi K., Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges. Mol Pharm. 2018 Nov 5;15(11):5302-5311.
  3. Esaki, T.; Ohashi, R.; Watanabe, R.; Natsume-Kitatani, Y.; Kawashima, H.; Nagao, C.; Mizuguchi, K., Computational Model to Predict the Fraction of Unbound Drug in the Brain. J Chem Inf Model. 2019, in printing.