| 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 |
