近年来,中药引起的肝损伤在全球范围内受到越来越多的关注。评估化合物在TCM中的肝毒性对于医生和监管机构都是必不可少和不可避免的。然而,到目前为止,尚无有效的方法来筛选TCM中的肝毒性成分。在本研究中,我们最初建立了药物诱导肝损伤(DILIs)的大规模数据集.然后,利用13种分子指纹/描述符和8种机器学习算法来开发DILI的单分类器,这导致了5416个单分类器。接下来,采用NaiveBayes算法集成每个机器学习算法的最佳单分类器,我们试图构建一个组合分类器。准确性,灵敏度,特异性,组合分类器的曲线下面积分别为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提供了更好的性能。在我们之前的研究中,我们基于发表在科学文献中的临床前证据,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,通过将其与在这项工作中开发的组合分类器相结合,我们提出了计算毒理学的第一个实例来筛选中药中的肝毒性成分。然后以何首乌(PmT)作为案例来研究所提出方法的可靠性。因此,PmT中共有25种成分被鉴定为肝毒性剂.结果与文献记录高度一致,表明我们的计算毒理学方法对于Pmt中肝毒性成分的筛选是可靠和有效的。在这项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。这项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,这将为进一步探索中药的肝毒性机制奠定基础。此外,这项工作中提出的方法可以应用于重点研究TCM/合成药物的其他不良反应。
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.