Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128, Mainz, Germany.
Division of Cancer Genome Research, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), National Center for Tumor Diseases (NCT), Heidelberg, Germany.
Arch Toxicol. 2021 Jul;95(7):2485-2495. doi: 10.1007/s00204-021-03058-4. Epub 2021 May 22.
The majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.
由于不可接受的毒性和副作用，大多数候选药物在批准阶段失败。建立一个有效的毒性预测平台至关重要，可以提高药物发现过程的效率。为此，我们开发了一个基于机器学习策略的毒性预测平台。通过建立一个包含五个参数（心律失常、心力衰竭、心脏阻滞、高血压、心肌梗死）的模型来进行心脏毒性预测，并用 DataWarrior 和 Pro-Tox-II 软件进行额外的毒性预测，如肝毒性、生殖毒性、致突变性和致癌性。作为一个案例研究，我们选择了青蒿素衍生物来评估该平台，并提供了一系列安全的青蒿素衍生物。青蒿素最早是从青蒿中发现的，被描述为一种抗疟化合物，后来发现了它的抗癌特性。在这里，随机森林特征选择算法被用于建立心脏毒性模型。所有五种心脏毒性指征的 AUC 评分均高于 0.830。使用 374 种青蒿素衍生物的化学文库作为案例研究，有 7 种化合物（去氧青蒿素、3-羟基去氧青蒿素、3-去氧青蒿素、青蒿素呋喃乙酸酯-d3、去氧青蒿素、青蒿素 G、青蒿素 B）通过了毒性筛选过程，除了心脏毒性外，还具有肝毒性、致突变性、致癌性和生殖毒性。用心肌细胞系 AC16 进行的实验验证支持了心脏毒性计算模型的预测结果。用青蒿素 B 处理 AC16 细胞的转录组学分析显示，其基因表达谱与对照化合物地塞米松相似。用斑马鱼模型进行的体内实验进一步证实了计算和体外数据，因为只观察到微摩尔范围内的轻微心脏毒性。总之，我们的机器学习方法结合了体外和体内实验，代表了一种预测候选药物心脏毒性的合适方法。
