薬剤誘発性肝障害の予測評価における化学構造的特徴の解析とATC分類の利用について

清水 佑記; 米川 恵理; 山崎 弘量; 佐々木 崇光; 渡邉 美智子; 志津 怜太; 保坂 卓臣; 竹下 潤一; 吉成 浩一

doi:10.14869/toxpt.47.1.0_P-39S

The 47th Annual Meeting of the Japanese Society of Toxicology

Session ID : P-39S

DOI https://doi.org/10.14869/toxpt.47.1.0_P-39S

Conference information

Host: The Japanese Society of Toxicology

Name : The 47th Annual Meeting of the Japanese Society of Toxicology

Date : 2020 -

Poster

Analysis of chemical and structural features and use of ATC classification in predictive evaluation of drug-induced liver injury

*Yuki SHIMIZU, Eri YONEKAWA, Hirokazu YAMAZAKI, Takamitsu SASAKI, Michiko WATANABE, Ryota SHIZU, Takuomi HOSAKA, Jun-ichi TAKESHITA, Kouichi YOSHINARI

Author information

*Yuki SHIMIZU
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Eri YONEKAWA
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Hirokazu YAMAZAKI
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Takamitsu SASAKI
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Michiko WATANABE
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Ryota SHIZU
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Takuomi HOSAKA
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka
Jun-ichi TAKESHITA
Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology
Kouichi YOSHINARI
Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka

CONFERENCE PROCEEDINGS FREE ACCESS

Details

Abstract

The purpose of this study was to identify chemical and structural features associated with drug-induced liver injury (DILI) using in silico methods. The model drugs’ data were obtained from the US FDA’s Liver Toxicity Knowledge Base and literature. The 2D chemical descriptors were calculated with Dragon 7 software. In the decision tree analysis of 432 DILI and 220 no-DILI drugs, 64% and 71% of DILI and no-DILI drugs, respectively, were correctly classified by AMW. By individual analyses of each ATC class 1 group, highly accurate discriminant models were obtained for classes A, C, M and S.

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