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. Tools

1. DruMAP

1. 1) fu,p (fraction unbound in plasma)
2. 2) fu,brain (fraction unbound in brain homogenate)
3. 3) F_a (absorbance), P_app (Caco-2 permeability), D-sol (solubility)

Reference

1. 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
2.  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
3.  Esaki, T., Watanabe, R., Kawashima, H., Ohashi, R., Natsume‐Kitatani, Y., Nagao, C., & Mizuguchi, K. (2019). Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance. Molecular Informatics, 38(1–2), e1800086. doi
4.  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