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

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.

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

“…Small molecules were predicted against rheumatoid arthritis using an integrated approach of ML and DL [ 147 ]. Another study performed using an AI-based method identified the hepatotoxic ingredient from Chinese traditional medicine [ 148 ]. Predictive models have been developed for screening liver toxicity induced due to drugs using ML algorithms [ 110 ].…”

A review on machine learning approaches and trends in drug discovery

“…MACCS (Molecular ACCess System) Keys are another of the most used structural keys [36] , [37] , [39] , [38] . Sometimes they are known as MDL keys, which bear the name of the company that developed them.…”

“…They are fingerprints based on topological routes, which represent all possible connectivity routes defined by a specific fingerprint through an input compound [36] , [39] , [37] , [41] . They mainly focus on chemical connectivity information of synthetic compounds.…”

Integrated Strategy of Network Pharmacological Prediction and Experimental Validation Elucidate Possible Mechanism of Bu-Yang Herbs in Treating Postmenopausal Osteoporosis via ESR1

“…Hopkins proposed the concept of “network pharmacology” and it’s becoming increasingly popular among researchers in recent years ( Tang et al, 2020 ; Xu J. et al, 2019b ). The characteristic of network pharmacology is following the holistic theory of TCM(S. He et al, 2019b ). The basic opinion of network pharmacology is well suited for exploring the mechanisms of multi-components and multi-targets drugs, so it is an ideal approach in investigating and identifying the mechanism of the TCM formula ( Guo et al, 2019 ; Huang et al, 2020 ; Liu et al, 2020 ; Zhang et al, 2019 ).…”