Huang Shan-Han, Tung Chun-Wei, Fülöp Ferenc, Li Jih-Heng
Ph.D. Program in Toxicology and School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan.
Ph.D. Program in Toxicology and School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan; National Environmental Health Research Center, National Health Research Institutes, Taiwan.
Food Chem Toxicol. 2015 Apr;78:71-7. doi: 10.1016/j.fct.2015.01.020. Epub 2015 Feb 4.
The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM.
天然物质被认为是安全的这种观念使得中药在全球疾病的治疗和预防中广受欢迎。然而,由于缺乏科学验证,这样的假设往往具有误导性。为了评估中药的安全性,计算机模拟筛选在节省资源和时间以及完全可重复性方面比传统实验室方法具有主要优势。为了筛选中药活性成分的肝毒性,首先利用来自肝毒性知识库的药物建立了定量构效关系(QSAR)模型。这些药物用从临床试验和上市后监测中获得的药物性肝损伤信息进行注释。经过嵌套10倍交叉验证后,该模型的准确率、灵敏度和特异性分别为79.1%、91.2%、53.8%。对91种常见草药的知名成分进行外部验证,得到了较高的准确率(87%)。在筛选世界上最大的中药数据库——台湾中药数据库后,总共6853种(74.8%)成分被预测具有肝毒性潜力。我们的模型预测具有最高肝毒性潜力的100种化学成分通过已发表的文献进一步得到验证。我们的研究表明,该模型可以作为评估中药安全性的补充工具。