Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Sciences, Harvard Medical SchoolBoston, MA.
Renal Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA.
Toxicol Sci. 2019 May 1;169(1):54-69. doi: 10.1093/toxsci/kfz021.
The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify biomarkers that not only predict nephrotoxic compounds but also provide hints toward their mechanism of toxicity. Gene expression and high-content imaging-derived phenotypical data from 46 diverse kidney toxicants were analyzed using Random Forest machine learning. Imaging features capturing changes in cell morphology and nucleus texture along with mRNA levels of HMOX1 and SQSTM1 were identified as the most powerful predictors of toxicity. These biomarkers were validated by their ability to accurately predict kidney toxicity of four out of six candidate therapeutics that exhibited toxicity only in late stage preclinical/clinical studies. Network analysis of similarities in toxic phenotypes was performed based on live-cell high-content image analysis at seven time points. Using compounds with known mechanism as reference, we could infer potential mechanisms of toxicity of candidate therapeutics. In summary, we report an approach to generate a multidimensional biomarker panel for mechanistic de-risking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities.
在新的化学实体进入人体之前的开发过程中早期未能预测其肾脏毒性仍然是一个关键问题。在这里，我们使用原代人肾细胞，并应用了一种系统生物学方法，该方法结合了多维数据集和机器学习，以识别不仅可以预测肾毒性化合物而且还可以提供其毒性机制线索的生物标志物。使用随机森林机器学习分析了来自 46 种不同肾毒性化合物的基因表达和高内涵成像衍生的表型数据。被鉴定为最有力的毒性预测因子的是捕获细胞形态和核纹理变化以及 HMOX1 和 SQSTM1 的 mRNA 水平的成像特征。这些生物标志物通过其能够准确预测仅在后期临床前/临床研究中显示毒性的六种候选治疗药物中的四种的肾脏毒性的能力得到了验证。基于活细胞高内涵图像分析在七个时间点进行了毒性表型相似性的网络分析。使用具有已知机制的化合物作为参考，我们可以推断候选治疗药物的潜在毒性机制。总之，我们报告了一种方法，用于为新的治疗候选物和化学实体在体外进行机制风险降低和肾脏毒性预测生成多维生物标志物面板。
