Result

Methyl-hydroxylation and subsequent oxidation to produce carboxylic acid is the major metabolic pathway of tolbutamide in chimeric TK-NOG mice transplanted with human hepatocytes

Uehara S, Yoneda N, Higuchi Y, Yamazaki H, Suemizu H

Xenobiotica, DOI: 10.1080/00498254.2021.1875515

Plasma, liver, and kidney exposures in rats after oral doses of industrial chemicals predicted using physiologically based pharmacokinetic models: A case study of perfluorooctane sulfonic acid

Kamiya Y, Yanagi M, Hina S, Shigeta K, Miura T, Yamazaki H

J. Toxicol. Sci., 45(12), 763-767, 2020, DOI: 10.2131/jts.45.763

Predicted Contributions of Flavin-containing Monooxygenases to the N-Oxygenation of Drug Candidates Based on their Estimated Base Dissociation Constants

Taniguchi-Takizawa T, Kato H, Shimizu M, Yamazaki H

Current Drug Metabolism, 22, 1-0, 2020, DOI: 10.2174/1389200221666201207195758

Metabolic profiles of coumarin in human plasma extrapolated from a rat data set with a simplified physiologically based pharmacokinetic model

Miura T, Kamiya Y, Hina S, Kobayashi Y, Murayama N, Shimizu M, Yamazaki H

J. Toxicol. Sci., 45(11), 695-700, 2020, DOI: 10.2131/jts.45.695

Prediction of the inhibitory activity of rat drug-metabolizing enzyme by in silico method

Nakamori M, Tohno R, Ambe K, Tohkin M, Sasaki T, Yoshinari K

CBI Annual Meeting 2020, Online, Oct. 2020

Physiologically Based Pharmacokinetic Models Predicting Renal and Hepatic Concentrations of Industrial Chemicals after Virtual Oral Doses in Rats

Kamiya Y, Otsuka S, Miura T, Yoshizawa M, Nakano A, Iwasaki M, Kobayashi Y, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H

Chem. Res. Toxicol., 33(7), 1736–1751, 2020, DOI: 10.1021/acs.chemrestox.0c00009

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

Matsuzaka Y, Uesawa Y

Molecules, 25(12), 2764, 2020, DOI: 10.3390/molecules25122764

Transcriptomics-Driven Evaluation on Liver Toxicity using Adverse Outcome Pathways (AOP)

Akahori Y, Yamashita K, Ishida K, Saito F, Nakai M

Yakugaku Zasshi, 140(4), 491-498, 2020, DOI: 10.1248/yakushi.19-00190-3

AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP

Uesawa Y

Yakugaku Zasshi, 140(4), 499-505, 2020, DOI: 10.1248/yakushi.19-00190-4

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning

Matsuzaka Y, Hosaka T, Ogaito A, Yoshinari K, Uesawa Y

Molecules, 25(6), 1317, 2020, DOI: 10.3390/molecules25061317

Application of cytochrome P450 reactivity on the characterization of chemical compounds and its association with repeated-dose toxicity

Watanabe M, Sasaki T, Takeshita J, Kushida M, Shimizu Y, Oki H, Kitsunai Y, Nakayama H, Saruhashi H, Ogura R, Shizu R, Hosaka T, Yoshinari K

Toxicol. Appl. Pharmacol., 388, 114854, 2020, DOI: 10.1016/j.taap.2019.114854

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

Matsuzaka Y, Uesawa Y

Front. Bioeng. Biotechnol., 7(485), 1-18, 2020 , DOI: 10.3389/fbioe.2019.00485

Determination and prediction of permeability across intestinal epithelial cell monolayer of a diverse range of industrial chemicals/drugs for estimation of oral absorption as a putative marker of hepatotoxicity

Kamiya Y, Takaku H, Yamada R, Akase C, Abe Y, Sekiguchi Y, Murayama N, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H

Toxicol. Rep., 7, 149-154, 2020, DOI: 10.1016/j.toxrep.2020.01.004

Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity

Yamazaki H, Kamiya Y

Toxicol. Res., 35(4), 295-301,2019, DOI: 10.5487/TR.2019.35.4.295

Prediction Model with High-Performance Constitutive Androstane Receptor(CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K CompoundLibrary

Matsuzaka Y, Uesawa Y

Int. J. Mol. Sci., 20(19), 4855, 2019, DOI: 10.3390/ijms20194855

Human plasma and liver concentrations of styrene estimated by combining a simple physiologically based pharmacokinetic model with rodent data

Miura T, Uehara S, Nakazato M, Kusama T, Toda A, Kamiya Y, Murayama N, Shimizu M, Suemizu H, Yamazaki H

J. Toxicol. Sci., 44(8), 543-548, 2019, DOI: 10.2131/jts.44.543

Predictability of human pharmacokinetics of diisononyl phthalate (DINP) using chimeric mice with humanized liver.

Iwata H, Goto M, Sakai N, Suemizu H, Yamazaki H

Xenobiotica, 49(11), 1311-1322, 2019, DOI: 10.1080/00498254.2018.1564087

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap, a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

Matsuzaka Y, Uesawa Y

Front. Bioeng. Biotechnol., 7(65), 1-15, 2019, DOI: 10.3389/fbioe.2019.00065

Steady-state human pharmacokinetics of monobutyl phthalate predicted by physiologically based pharmacokinetic modeling using single-dose data from humanized-liver mice orally administered with dibutyl phthalate

Miura T, Uehara S, Mizuno S, Yoshizawa M, Murayama N, Kamiya Y, Shimizu M, Suemizu H, Yamazaki H

Chem. Res. Toxicol., 32(2), 333-340, 2019, DOI: 10.1021/acs.chemrestox.8b00361

Plasma and hepatic concentrations of chemicals after virtual oral administrations extrapolated using rat plasma data and simple physiologically based pharmacokinetic models

Kamiya Y, Otsuka S, Miura T, Takaku H, Yamada R, Nakazato M, Nakamura H, Mizuno S, Shono F, Funatsu K, Yamazaki H

Chem. Res. Toxicol., 32(1), 211-218, 2019, DOI: 10.1021/acs.chemrestox.8b00307

Overview of AI-SHIPS Project

Funatsu K

Tokyo AI-SHIPS International Symposium“The front line of development of in silico toxicity prediction system”, Tokyo, Japan, Nov. 2018

A physiologically based pharmacokinetic model to predict chemical concentrationsin livers after virtual oral doses

Yamazaki H

Tokyo AI-SHIPS International Symposium“The front line of development of in silico toxicity prediction system”, Tokyo, Japan, Nov. 2018

Application of in vitro assays to development of mechanism-based in silico prediction system of hepatotoxicity

Yoshinari K

Tokyo AI-SHIPS International Symposium“The front line of development of in silico toxicity prediction system”, Tokyo, Japan, Nov. 2018

Construction of a database contributing to development of mechanism-based insilico toxicity prediction system

Jun-ichi T

Tokyo AI-SHIPS International Symposium“The front line of development of in silico toxicity prediction system”, Tokyo, Japan, Nov. 2018

Suitable albumin concentrations for enhanced drug oxidation activities mediated by human liver microsomal cytochrome P450 2C9 and other forms predicted with unbound fractions and partition/distribution coefficients of model substrates

Shimura K, Murayama N, Tanaka S, Onozeki S, Yamazaki H

Xenobiotica, 49(5), 557-562, 2018, DOI: 10.1080/00498254.2018.1482576

Human plasma concentrations of trimethylamine N-oxide extrapolated using pharmacokinetic modeling based on metabolic profiles of deuterium-labeled trimethylamine in humanized-liver mice

Shimizu M, Suemizu H, Mizuno S, Kusama T, Miura T, Uehara S, Yamazaki H

J. Toxicol. Sci., 43(6), 387-393, 2018, DOI: 10.2131/jts.43.387

Association of pharmacokinetic profiles of lenalidomide in human plasma simulated using pharmacokinetic data in humanized-liver mice with liver toxicity detected by human serum albumin RNA

Murayama N, Suemizu H, Uehara S, Kusama T, Mitsui M, Kamiya Y, Shimizu M, Guengerich FP, Yamazaki H

J. Toxicol. Sci., 43(6), 369-375, 2018, DOI: 10.2131/jts.43.369

Human urinary concentrations of monoisononyl phthalate estimated using physiologically based pharmacokinetic modeling and experimental pharmacokinetics in humanized-liver mice orally administered with diisononyl phthalate.

Miura T, Suemizu H, Goto M, Sakai N, Iwata H, Shimizu M, Yamazaki H

Xenobiotica, 49(5), 513-520, 2019, DOI: 10.1080/00498254.2018.1471753

Event Schedule

A symposium will be held in 2021. The details will be announced as soon as they are determined.