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Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides.
Yang, Qi; Fan, Lili; Hao, Erwei; Hou, Xiaotao; Deng, Jiagang; Du, Zhengcai; Xia, Zhongshang.
Affiliation
  • Yang Q; School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China.
  • Fan L; Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China.
  • Hao E; School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China.
  • Hou X; Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China.
  • Deng J; Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China.
  • Du Z; Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China.
  • Xia Z; Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China.
Drug Chem Toxicol ; : 1-13, 2024 Jun 28.
Article in En | MEDLINE | ID: mdl-38938098
ABSTRACT
It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Drug Chem Toxicol Year: 2024 Document type: Article Affiliation country: China Publication country: EEUU / ESTADOS UNIDOS / ESTADOS UNIDOS DA AMERICA / EUA / UNITED STATES / UNITED STATES OF AMERICA / US / USA

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Drug Chem Toxicol Year: 2024 Document type: Article Affiliation country: China Publication country: EEUU / ESTADOS UNIDOS / ESTADOS UNIDOS DA AMERICA / EUA / UNITED STATES / UNITED STATES OF AMERICA / US / USA