A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study.

Shuaibing He,Xiaobo Sun,Ting Zhu,Xuelian Zhang,Guibo Sun,Shan Lu

doi:10.3390/biom9100577

Abstract

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.

Highlights

IntroductionTraditional Chinese Medicines (TCMs) have been widely consumed in manyAsian countries
For thousands of years, Traditional Chinese Medicines (TCMs) have been widely consumed in manyAsian countries
As a case, we proposed a computational toxicology approach to screen the hepatotoxic ingredients in TCMs by combining the combined classifier constructed in this work and the herb-hepatotoxic ingredient network and herb-induced liver injury (HILI) dataset published in our prior studies [47,48]

Summary

Introduction

Traditional Chinese Medicines (TCMs) have been widely consumed in manyAsian countries. As all drugs do, TCMs possess a series of side effects. As one of the major concerns of TCMs, hepatotoxicity has gained more and more attention recently. Hundreds of TCMs or their extracts have been reported to possess potential hepatotoxicity [7,8,9]. Several special databases focused on TCM-induced liver injuries have been developed, such as Hepatox (http://www.hepatox.org) and HDS hepatotoxicity databases [10]. In recent studies focused on the etiology of drug-induced liver injury (DILI) in several Asian countries, TCMs were found to be the leading induction factors of DILI [11,12]. Studies focused on the hepatotoxicity of TCMs are urgent and imperative

Methods

Results

Discussion

Conclusion