Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides

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.

Keywords:

• Gardenia
• hepatotoxicity
• machine learning
• PCA
• QSAR

Author contributions

Qi Yang proposed innovative ideas and wrote the first draft and code of this article. Lili Fan was responsible for review and revision, Erwei Hao, Xiaotao Hou, Zhengcai Du, and Zhongshang Xia were responsible for collecting data and providing resources, and Jiagang Deng was responsible for providing project support. All authors finally reviewed this article and agreed to submit it.

Disclosure statement

The authors declare that they have no conflict of interest.

Data availability statement

All data and materials are available upon request.

Additional information

Funding

This work was supposed by Operation Subsidy Project of Guangxi Key Laboratory of TCM Efficacy Research in 2020, Guangxi, China (Grant No: 20–065-38).