Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.
Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.
Biomolecules. 2019 Oct 7;9(10):577. doi: 10.3390/biom9100577.
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 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.
近年来，中药（TCM）引起的肝损伤已引起全球越来越多的关注。评估 TCM 中化合物的肝毒性对医生和监管机构都是必要和不可避免的。然而，到目前为止，还没有有效的方法来筛选 TCM 中的肝毒性成分。在本研究中，我们首先构建了一个大规模的药物性肝损伤（DILI）数据集。然后，我们利用 13 种分子指纹/描述符和 8 种机器学习算法为 DILI 开发了单分类器，得到了 5416 个单分类器。接下来，我们采用朴素贝叶斯算法整合每个机器学习算法的最佳单分类器，试图构建一个组合分类器。组合分类器的准确率、灵敏度、特异性和曲线下面积分别为 72.798、0.732、0.724 和 0.793。与之前的几项研究相比，组合分类器在交叉验证和外部验证中都表现出了更好的性能。在我们之前的研究中，我们根据科学文献中发表的临床前证据，开发了一个草药-肝毒性成分网络和一个草药诱导的肝损伤（HILI）数据集。在这里，我们将其与本工作中开发的组合分类器相结合，提出了一种计算毒理学方法，用于筛选 TCM 中的肝毒性成分。然后，以 Thunb（PmT）为例，考察了该方法的可靠性。结果共鉴定出 PmT 中的 25 种成分具有肝毒性。结果与文献记录高度一致，表明我们的计算毒理学方法用于筛选 PmT 中的肝毒性成分是可靠和有效的。本工作中开发的组合分类器可用于评估天然化合物和合成药物的肝毒性风险。本工作中提出的计算毒理学方法将有助于筛选 TCM 中的肝毒性成分，为进一步探索 TCM 的肝毒性机制奠定基础。此外，本工作中提出的方法可应用于研究 TCM/synthetic 药物其他不良反应的工作中。
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