Abstract
Backgrounds and AimsRecently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI.Materials and MethodsIn this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM.Results16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity.ConclusionOverall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.
Highlights
Liver injury induced by drug, novel foods or phytotherapy, known as hepatotoxicity, is still a major clinical and pharmaceutical concern (Amadi and Orisakwe, 2018; Hammann et al, 2018; Kyawzaw et al, 2018; Real et al, 2018)
Single Model Building and Evaluation In this study, 16 single models were developed by four different algorithms (ANN, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN)) using four common types of fingerprints (EState, MACCS, PubChem and Substructure fingerprint (SubFP))
In order to further validate the predictive capability of our models, they were applied to predict the test set consisting of 664 compounds
Summary
Liver injury induced by drug, novel foods or phytotherapy, known as hepatotoxicity, is still a major clinical and pharmaceutical concern (Amadi and Orisakwe, 2018; Hammann et al, 2018; Kyawzaw et al, 2018; Real et al, 2018). According to the data from United States National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), hepatotoxicity accounts for 50% of all liver failure cases in the United States (Tujios and Fontana, 2011). It is one of the leading causes of drug failure in trials and withdrawal from the market (Segall and Barber, 2014). Side effects (SE) aroused by TCM, especially herb induced liver injury (HILI), have been reported widely (Teschke et al, 2015; Amadi and Orisakwe, 2018; Jing and Teschke, 2018), which severely restricts the application of TCM (Lee et al, 2015; Kaplowitz, 2018).
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