Title of Research: 22-3-01

Prediction of internal concentrations of chemicals orally administered using
data-driven pharmacokinetic modeling
Principal Investigator: Prof. Hiroshi Yamazaki, PhD (Showa Pharmaceutical University,
Laboratory of Drug Metabolism and Pharmacokinetics), 3-3165, Higashi-tamagawa Gakuen,Machida,Tokyo
194-8543,
Japan.
(phone)+81-42-721-1406,(e-mail)hyamazak@ac.shoyaku.ac.jp.
Collaborator: Makiko Shimizu, ibid, (e-mail) shimizu@ac.shoyaku.ac.jp
Summary of Research: To evaluate internal exposures in humans without any reference toexperimental data, physiologically based pharmacokinetic (PBPK) modeling could be used.
The input parameters for PBPK models (i.e., fraction absorbed x intestinal availability,
absorption rate constants, volumes of the systemic circulation, and hepatic intrinsicclearances) were estimated for a panel of 355 chemicals using a light gradient boostingmachine learning algorithms (LightGBM) based on between 11 and 29 in silico-calculated
chemical descriptors. Parameters for human PBPK models for a diverse range of compounds
could be successfully estimated using chemical descriptors. This approach topharmacokinetic modeling has potential for application in computational toxicology and in theclinical setting for assessing the potential risk of general chemicals.
Timeline: From March 1, 2022 to February 28, 2023
Topics: The principal Investigator has been the recipient of Scientific Achievement Award from theInternational Society of Study of Xenobiotics (ISSX) for major contributions to the field ofxenobiotics in the Asia Pacific Region (Bangalore, India, 2023).
Publications:(1) Adachi, K., Shimizu, M., and Yamazaki, H. (2022) Updated in Silico Prediction Methods forFractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically
Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral
Doses in Humans. Biol Pharm Bull 45, 1812-1817.
(2) Kamiya, Y., Handa, K., Miura, T., Ohori, J., Kato, A., Shimizu, M., Kitajima, M., and Yamazaki, H.
(2022) Machine Learning Prediction of the Three Main Input Parameters of a Simplified
Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-DependentPlasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals. Biol Pharm
Bull 45, 124-128.
(3) Shimizu, M., Hayasaka, R., Kamiya, Y., and Yamazaki, H. (2022) Trivariate Linear Regression andMachine Learning Prediction of Possible Roles of Efflux Transporters in Estimated Intestinal
Permeability Values of 301 Disparate Chemicals. Biol Pharm Bull 45, 1142-1157.
(4) Kamiya, Y., Handa, K., Miura, T., Ohori, J., Shimizu, M., Kitajima, M., Shono, F., Funatsu, K., andYamazaki, H. (2022) Correction to "An Updated In Silico Prediction Method for Volumes ofSystemic Circulation of 323 Disparate Chemicals for Use in Physiologically Based
Pharmacokinetic Models to Estimate Plasma and Tissue Concentrations after Oral Doses in Rats".
Chem Res Toxicol 35, 1433.
(5) Adachi, K., Shimizu, M., and Yamazaki, H. (2022) Updated in silico prediction methods for
fractions absorbed and absorption rate constants of 372 disparate chemicals for use inphysiologically based pharmacokinetic models for estimating internal concentrations in rats. J
Toxicol Sci 47, 453-456.
(6) Miura, T., Uehara, S., Shimizu, M., Suemizu, H., and Yamazaki, H. (2022) Forward and reverse
dosimetry for aniline and 2,6-dimethylaniline in humans extrapolated from humanized-liver mouse
data using simplified physiologically based pharmacokinetic models. J Toxicol Sci 47, 531-538.