Computational Model To Predict the Fraction of Unbound Drug in the Brain-Reference-Cited by-同舟云学术

Computational Model To Predict the Fraction of Unbound Drug in the Brain

Published:2019-07-01 Issue:7 Volume:59 Page:3251-3261
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Esaki Tsuyoshi¹^ORCID,Ohashi Rikiya¹²^ORCID,Watanabe Reiko¹^ORCID,Natsume-Kitatani Yayoi¹³^ORCID,Kawashima Hitoshi¹^ORCID,Nagao Chioko¹³^ORCID,Mizuguchi Kenji¹³^ORCID

Affiliation:

1. Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Osaka, Ibaraki 567-0085, Japan

2. Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda, Saitama 335-8505, Japan

3. Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Osaka, Ibaraki 567-0085, Japan

Funder

Japan Agency for Medical Research and Development

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00180

Reference54 articles.

1. Physiologically Based Modeling of 1,2,4-Trimethylbenzene Inhalation Toxicokinetics

2. PKQuest: a general physiologically based pharmacokinetic model. Introduction and application to propranolol

3. Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection