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Prediction of drug-induced liver injury by artificial intelligence based on adverse drug reaction reports

Research Project

Project/Area Number 18K14987
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 47060:Clinical pharmacy-related
Research InstitutionNagoya City University

Principal Investigator

Ambe Kaori  名古屋市立大学, 医薬学総合研究院(薬学), 助教 (70440625)

Project Period (FY) 2018-04-01 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Keywords薬物性肝障害 / JADER / インシリコ予測 / 機械学習 / 副作用 / ビッグデータ / 人工知能 / 機械学習法
Outline of Final Research Achievements

The purpose of this study was to utilize JADER as spontaneous adverse event reporting database and to predict drug-induced liver injury from chemical structure information of drugs using machine learning. The positive drugs and negative drugs for drug-induced liver injury were defined using the signal detection method and the number of reports. Using a random forest with molecular descriptors of drugs as a feature value, in silico prediction model that classify the posirive or negative of drug-induced liver injury was constructed, and the performance of sensitivity 0.90 and AUC 0.66 was obtained. Furthermore, combining JADER and literature information, AUC was improved to 0.84. It was suggested that drug-induced liver injury could be predicted by appropriately utilizing large-scale adverse event information and machine learning.

Academic Significance and Societal Importance of the Research Achievements

本研究の学術的意義として、開発した予測モデルは対象とする副作用に対して柔軟性があり、入手しやすい医薬品の化学構造情報を用いて適切に大規模な副作用情報を活用することで、様々な副作用の予測に応用することが可能である。薬物性肝障害の予測モデルを活用すればヒトでの臨床試験や市販後のリスク軽減が可能となり、本研究から得られた知見は新薬開発において候補化合物のスクリーニングを効率化することも期待されるなど社会的意義も大きい。

Report

(3 results)
  • 2019 Annual Research Report   Final Research Report ( PDF )
  • 2018 Research-status Report
  • Research Products

    (11 results)

All 2019 2018

All Journal Article (2 results) (of which Peer Reviewed: 2 results) Presentation (9 results) (of which Int'l Joint Research: 3 results,  Invited: 1 results)

  • [Journal Article] Comparison of the developmental/reproductive toxicity and hepatotoxicity of phthalate esters in rats using an open toxicity data source2019

    • Author(s)
      Ambe K, Sakakibara Y, Sakabe A, Makino H, Ochibe T, Tohkin M.
    • Journal Title

      The Journal of Toxicological Sciences

      Volume: 44 Issue: 4 Pages: 245-255

    • DOI

      10.2131/jts.44.245

    • NAID

      130007623321

    • ISSN
      0388-1350, 1880-3989
    • Related Report
      2018 Research-status Report
    • Peer Reviewed
  • [Journal Article] In silico prediction of chemical-induced hepatocellular hypertrophy using molecular descriptors.2018

    • Author(s)
      Ambe K, Ishihara K, Ochibe T, Ohya K, Tamura S, Inoue K, Yoshida M, Tohkin M.
    • Journal Title

      Toxicological Sciences

      Volume: 162 Issue: 2 Pages: 667-675

    • DOI

      10.1093/toxsci/kfx287

    • Related Report
      2018 Research-status Report
    • Peer Reviewed
  • [Presentation] JADERを用いた医薬品の重症皮膚副作用のin silico予測2019

    • Author(s)
      大矢和幸, 安部賀央里、頭金正博
    • Organizer
      第46回日本毒性学会学術年会
    • Related Report
      2019 Annual Research Report
  • [Presentation] In silico models for predicting hepatotoxicity and renal toxicity based on HESS database2019

    • Author(s)
      Tatsuya Ochibe, Kaori Ambe, Masahiro Tohkin
    • Organizer
      CBI 学会 2019 年大会
    • Related Report
      2019 Annual Research Report
  • [Presentation] In Silico Prediction of Severe Cutaneous Adverse Drug Reactions Using the Japanese Adverse Drug Event Report Database2019

    • Author(s)
      Kaori Ambe, Kazuyuki Ohya, Masahiro Tohkin
    • Organizer
      ICTXV2019 (15th International Congress of Toxicology)
    • Related Report
      2019 Annual Research Report
    • Int'l Joint Research
  • [Presentation] In Silico Models for the Predicting of the Repeated Dose Toxicity Based on HESS Database2019

    • Author(s)
      Tatsuya Ochibe, Kaori Ambe, Masahiro Tohkin
    • Organizer
      ICTXV2019 (15th International Congress of Toxicology)
    • Related Report
      2019 Annual Research Report
    • Int'l Joint Research
  • [Presentation] 毒性データベースを用いた in silico 安全性予測2019

    • Author(s)
      安部賀央里, 頭金正博
    • Organizer
      第1回医薬品毒性機序研究会
    • Related Report
      2018 Research-status Report
    • Invited
  • [Presentation] 機械学習法を利用した化学物質誘発性腎毒性の予測2019

    • Author(s)
      落部達也, 安部賀央里, 頭金正博
    • Organizer
      第1回医薬品毒性機序研究会
    • Related Report
      2018 Research-status Report
  • [Presentation] Development of in silico predictive classification models for chemical-induced hepatocellular hypertrophy based on molecular descriptors.2018

    • Author(s)
      Kaori Ambe, Tatsuya Ochibe, Kazuyuki Ohya, Masahiro Tohkin.
    • Organizer
      第18回国際薬理学・臨床薬理学会議 (WCP2018)
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] In silico Prediction of Severe Cutaneous Adverse Drug Reactions Based on the Adverse Event Reporting Database2018

    • Author(s)
      Kazuyuki Ohya, Kaori Ambe, Masahiro Tohkin
    • Organizer
      CBI学会2018年大会
    • Related Report
      2018 Research-status Report
  • [Presentation] JADERを用いたDeep Learningによる医薬品の重症皮膚副作用の予測2018

    • Author(s)
      大矢和幸, 安部賀央里, 頭金正博
    • Organizer
      第4回次世代を担う若手のためのレギュラトリーサイエンスフォーラム
    • Related Report
      2018 Research-status Report

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Published: 2018-04-23   Modified: 2021-02-19  

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