{"id":141,"date":"2022-07-14T22:38:08","date_gmt":"2022-07-14T13:38:08","guid":{"rendered":"http:\/\/www-dsc.naist.jp\/ai-ships\/?page_id=141"},"modified":"2022-07-14T22:39:51","modified_gmt":"2022-07-14T13:39:51","slug":"result","status":"publish","type":"page","link":"http:\/\/www-dsc.naist.jp\/ai-ships\/en\/result\/","title":{"rendered":"Result"},"content":{"rendered":"\n
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<\/a><\/dt>

Uehara S, Yoneda N, Higuchi Y, Yamazaki H, Suemizu H<\/p>

Xenobiotica, DOI: 10.1080\/00498254.2021.1875515 <\/p><\/dd><\/td><\/tr>

Plasma, liver, and kidney exposures in rats after oral doses of industrial chemicals predicted using physiologically based pharmacokinetic models: A case study of per\ufb02uorooctane sulfonic acid<\/a><\/dt>

Kamiya Y, Yanagi M, Hina S, Shigeta K, Miura T, Yamazaki H<\/p>

J. Toxicol. Sci., 45(12), 763-767, 2020, DOI: 10.2131\/jts.45.763<\/p><\/dd><\/td><\/tr>

Predicted Contributions of Flavin-containing Monooxygenases to the N-Oxygenation of Drug Candidates Based on their Estimated Base Dissociation Constants<\/a><\/dt>

Taniguchi-Takizawa T, Kato H, Shimizu M, Yamazaki H<\/p>

Current Drug Metabolism, 22, 1-0, 2020, DOI: 10.2174\/1389200221666201207195758 <\/p><\/dd><\/td><\/tr>

Metabolic profiles of coumarin in human plasma extrapolated from a rat data set with a simplified physiologically based pharmacokinetic model<\/a><\/dt>

Miura T, Kamiya Y, Hina S, Kobayashi Y, Murayama N, Shimizu M, Yamazaki H<\/p>

J. Toxicol. Sci., 45(11), 695-700, 2020, DOI: 10.2131\/jts.45.695<\/p><\/dd><\/td><\/tr>

Prediction of the inhibitory activity of rat drug-metabolizing enzyme by in silico method<\/dt>

Nakamori M, Tohno R, Ambe K, Tohkin M, Sasaki T, Yoshinari K<\/p>

CBI Annual Meeting 2020, Online, Oct. 2020<\/p><\/dd><\/td><\/tr>

Physiologically Based Pharmacokinetic Models Predicting Renal and Hepatic Concentrations of Industrial Chemicals after Virtual Oral Doses in Rats<\/a><\/dt>

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

Chem. Res. Toxicol., 33(7), 1736\u20131751, 2020, DOI: 10.1021\/acs.chemrestox.0c00009<\/p><\/dd><\/td><\/tr>

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library <\/a><\/dt>

Matsuzaka Y, Uesawa Y<\/p>

Molecules, 25(12), 2764, 2020, DOI: 10.3390\/molecules25122764<\/p><\/dd><\/td><\/tr>

Transcriptomics-Driven Evaluation on Liver Toxicity using Adverse Outcome Pathways (AOP) <\/a><\/dt>

Akahori Y, Yamashita K, Ishida K, Saito F, Nakai M<\/p>

Yakugaku Zasshi, 140(4), 491-498, 2020, DOI: 10.1248\/yakushi.19-00190-3<\/p><\/dd><\/td><\/tr>

AI-based QSAR Modeling for Prediction of Active Compounds in MIE\/AOP<\/a><\/dt>

Uesawa Y<\/p>

Yakugaku Zasshi, 140(4), 499-505, 2020, DOI: 10.1248\/yakushi.19-00190-4<\/p><\/dd><\/td><\/tr>

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning<\/a><\/dt>

Matsuzaka Y, Hosaka T, Ogaito A, Yoshinari K, Uesawa Y<\/p>

Molecules, 25(6), 1317, 2020, DOI: 10.3390\/molecules25061317<\/p><\/dd><\/td><\/tr>

Application of cytochrome P450 reactivity on the characterization of chemical compounds and its association with repeated-dose toxicity<\/a><\/dt>

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<\/p>

Toxicol. Appl. Pharmacol., 388, 114854, 2020, DOI: 10.1016\/j.taap.2019.114854<\/p><\/dd><\/td><\/tr>

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance<\/a><\/dt>

Matsuzaka Y, Uesawa Y<\/p>

Front. Bioeng. Biotechnol., 7(485), 1-18, 2020 , DOI: 10.3389\/fbioe.2019.00485<\/p><\/dd><\/td><\/tr>

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<\/a><\/dt>

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

Toxicol. Rep., 7, 149-154, 2020, DOI: 10.1016\/j.toxrep.2020.01.004<\/p><\/dd><\/td><\/tr>

Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity<\/a><\/dt>

Yamazaki H, Kamiya Y<\/p>

Toxicol. Res., 35(4), 295-301,2019, DOI: 10.5487\/TR.2019.35.4.295<\/p><\/dd><\/td><\/tr>

Prediction Model with High-Performance Constitutive Androstane Receptor(CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K CompoundLibrary<\/a><\/dt>

Matsuzaka Y, Uesawa Y<\/p>

Int. J. Mol. Sci., 20(19), 4855, 2019, DOI: 10.3390\/ijms20194855<\/p><\/dd><\/td><\/tr>

Human plasma and liver concentrations of styrene estimated by combining a simple physiologically based pharmacokinetic model with rodent data<\/a><\/dt>

Miura T, Uehara S, Nakazato M, Kusama T, Toda A, Kamiya Y, Murayama N, Shimizu M, Suemizu H, Yamazaki H<\/p>

J. Toxicol. Sci., 44(8), 543-548, 2019, DOI: 10.2131\/jts.44.543<\/p><\/dd><\/td><\/tr>

Predictability of human pharmacokinetics of diisononyl phthalate (DINP) using chimeric mice with humanized liver.<\/a><\/dt>

Iwata H, Goto M, Sakai N, Suemizu H, Yamazaki H<\/p>

Xenobiotica, 49(11), 1311-1322, 2019, DOI: 10.1080\/00498254.2018.1564087<\/p><\/dd><\/td><\/tr>

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\u2013Activity Relationship (QSAR) Analysis<\/a><\/dt>

Matsuzaka Y, Uesawa Y<\/p>

Front. Bioeng. Biotechnol., 7(65), 1-15, 2019, DOI: 10.3389\/fbioe.2019.00065<\/p><\/dd><\/td><\/tr>

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<\/a><\/dt>

Miura T, Uehara S, Mizuno S, Yoshizawa M, Murayama N, Kamiya Y, Shimizu M, Suemizu H, Yamazaki H<\/p>

Chem. Res. Toxicol., 32(2), 333-340, 2019, DOI: 10.1021\/acs.chemrestox.8b00361<\/p><\/dd><\/td><\/tr>

Plasma and hepatic concentrations of chemicals after virtual oral administrations extrapolated using rat plasma data and simple physiologically based pharmacokinetic models<\/a><\/dt>

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

Chem. Res. Toxicol., 32(1), 211-218, 2019, DOI: 10.1021\/acs.chemrestox.8b00307<\/p><\/dd><\/td><\/tr>

Overview of AI-SHIPS Project<\/dt>

Funatsu K<\/p>

Tokyo AI-SHIPS International Symposium\u201cThe front line of development of in silico toxicity prediction system\u201d, Tokyo, Japan, Nov. 2018<\/p><\/dd><\/td><\/tr>

A physiologically based pharmacokinetic model to predict chemical concentrationsin livers after virtual oral doses<\/dt>

Yamazaki H<\/p>

Tokyo AI-SHIPS International Symposium\u201cThe front line of development of in silico toxicity prediction system\u201d, Tokyo, Japan, Nov. 2018<\/p><\/dd><\/td><\/tr>

Application of in vitro assays to development of mechanism-based in silico prediction system of hepatotoxicity<\/dt>

Yoshinari K<\/p>

Tokyo AI-SHIPS International Symposium\u201cThe front line of development of in silico toxicity prediction system\u201d, Tokyo, Japan, Nov. 2018<\/p><\/dd><\/td><\/tr>

Construction of a database contributing to development of mechanism-based insilico toxicity prediction system<\/dt>

Jun-ichi T<\/p>

Tokyo AI-SHIPS International Symposium\u201cThe front line of development of in silico toxicity prediction system\u201d, Tokyo, Japan, Nov. 2018<\/p><\/dd><\/td><\/tr>

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<\/a><\/dt>

Shimura K, Murayama N, Tanaka S, Onozeki S, Yamazaki H<\/p>

Xenobiotica, 49(5), 557-562, 2018, DOI: 10.1080\/00498254.2018.1482576<\/p><\/dd><\/td><\/tr>

Human plasma concentrations of trimethylamine N-oxide extrapolated using pharmacokinetic modeling based on metabolic profiles of deuterium-labeled trimethylamine in humanized-liver mice<\/a><\/dt>

Shimizu M, Suemizu H, Mizuno S, Kusama T, Miura T, Uehara S, Yamazaki H<\/p>

J. Toxicol. Sci., 43(6), 387-393, 2018, DOI: 10.2131\/jts.43.387<\/p><\/dd><\/td><\/tr>

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<\/a><\/dt>

Murayama N, Suemizu H, Uehara S, Kusama T, Mitsui M, Kamiya Y, Shimizu M, Guengerich FP, Yamazaki H<\/p>

J. Toxicol. Sci., 43(6), 369-375, 2018, DOI: 10.2131\/jts.43.369<\/p><\/dd><\/td><\/tr>

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

Miura T, Suemizu H, Goto M, Sakai N, Iwata H, Shimizu M, Yamazaki H<\/p>

Xenobiotica, 49(5), 513-520, 2019, DOI: 10.1080\/00498254.2018.1471753<\/p><\/dd><\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

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: […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_locale":"en_US","_original_post":"http:\/\/www-dsc.naist.jp\/ai-ships\/?page_id=80","footnotes":""},"class_list":["post-141","page","type-page","status-publish","has-post-thumbnail","hentry","en-US"],"acf":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"post-thumbnail":false},"uagb_author_info":{"display_name":"ad_t","author_link":"http:\/\/www-dsc.naist.jp\/ai-ships\/author\/ad_t\/"},"uagb_comment_info":0,"uagb_excerpt":"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:…","_links":{"self":[{"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/pages\/141","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/comments?post=141"}],"version-history":[{"count":1,"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/pages\/141\/revisions"}],"predecessor-version":[{"id":142,"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/pages\/141\/revisions\/142"}],"wp:attachment":[{"href":"http:\/\/www-dsc.naist.jp\/ai-ships\/wp-json\/wp\/v2\/media?parent=141"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}