Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods.
Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods.
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DOI：
10.1038/srep12337
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发表时间：
2015-07-27
影响因子：
4.6
通讯作者：
Zink D
中科院分区：
文献类型：
--
作者：
Kandasamy K;Chuah JK;Su R;Huang P;Eng KG;Xiong S;Li Y;Chia CS;Loo LH;Zink D
肾近端小管是药物毒性的主要靶点。在药物开发过程中预测近端肾小管毒性仍然很困难。迄今为止，尚未开发出任何基于诱导多能干细胞衍生的肾细胞的体外方法。在这里，我们开发了一种快速的一步方案，用于将人诱导多能干细胞（hiPSC）分化为近端小管样细胞。这些近端小管样细胞在分化8天后具有>90%的纯度，并且可以直接用于化合物筛选。通过评价细胞对30种化合物的反应来确定细胞的肾毒性预测性能。结果是使用一种名为随机森林的机器学习算法自动确定的。通过这种方式，可以预测人体近端肾小管毒性，训练准确率为99.8%，测试准确率为87.0%。此外，我们研究了这些hiPSC衍生的肾细胞中损伤和药物诱导的细胞通路的潜在机制，结果与人类和动物数据一致。我们的方法将能够开发个性化或疾病特异性的基于hiPSC的肾脏体外模型，用于化合物筛选和肾毒性预测。
The renal proximal tubule is a main target for drug-induced toxicity. The prediction of proximal tubular toxicity during drug development remains difficult. Any in vitro methods based on induced pluripotent stem cell-derived renal cells had not been developed, so far. Here, we developed a rapid 1-step protocol for the differentiation of human induced pluripotent stem cells (hiPSC) into proximal tubular-like cells. These proximal tubular-like cells had a purity of >90% after 8 days of differentiation and could be directly applied for compound screening. The nephrotoxicity prediction performance of the cells was determined by evaluating their responses to 30 compounds. The results were automatically determined using a machine learning algorithm called random forest. In this way, proximal tubular toxicity in humans could be predicted with 99.8% training accuracy and 87.0% test accuracy. Further, we studied the underlying mechanisms of injury and drug-induced cellular pathways in these hiPSC-derived renal cells, and the results were in agreement with human and animal data. Our methods will enable the development of personalized or disease-specific hiPSC-based renal in vitro models for compound screening and nephrotoxicity prediction.