Classification of Hepatotoxicants Using HepG2 Cells: A Proof of Principle Study

With the number of new drug candidates increasing every year, there is a need for high-throughput human toxicity screenings. As the liver is the most important organ in drug metabolism and thus capable of generating relatively high levels of toxic metabolites, it is important to find a reliable strategy to screen for drug-induced hepatotoxicity. Microarray-based transcriptomics is a well-established technique in toxicogenomics research and is an ideal approach to screen for drug-induced injury at an early stage. The aim of this study was to prove the principle of classifying known hepatotoxicants and nonhepatotoxicants using their distinctive gene expression profiles in vitro in HepG2 cells. Furthermore, we undertook to subclassify the hepatotoxic compounds by investigating the subclass of cholestatic compounds. Prediction analysis for microarrays was used for classification of hepatotoxicants and nonhepatotoxicants, which resulted in an accuracy of 92% on the training set and 91% on the validation set, using 36 genes. A second model was set up with the goal of finding classifiers for cholestasis, resulting in 12 genes that appeared capable of correctly classifying 8 of the 9 cholestatic compounds, resulting in an accuracy of 93%. We were able to prove the principle that transcriptomic analyses of HepG2 cells can indeed be used to classify chemical entities for hepatotoxicity. Genes selected for classification of hepatotoxicity and cholestasis indicate that endoplasmic reticulum stress and the unfolded protein response may be important cellular effects of drug-induced liver injury. However, the number of compounds in both the training set and the validation set should be increased to improve the reliability of the prediction.

随着每年新增候选药物数量逐年攀升，高通量人类毒性筛选的需求愈发迫切。由于肝脏是药物代谢的核心器官，可生成相对高水平的毒性代谢产物，因此亟需开发可靠的策略以筛选药物诱导的肝毒性。基于微阵列的转录组学（Microarray-based transcriptomics）是毒理基因组学研究中应用成熟的技术，亦是早期筛查药物诱导组织损伤的理想手段。

本研究旨在验证通过体外培养HepG2细胞的独特基因表达谱，对已知肝毒性化合物与非肝毒性化合物进行分类的可行性。此外，本研究针对胆汁淤积性化合物子类展开分析，尝试对肝毒性化合物进行子类划分。

本研究采用微阵列预测分析（Prediction analysis for microarrays）实现肝毒性与非肝毒性化合物的分类，最终以36个基因在训练集上获得92%的分类准确率，在验证集上达到91%的准确率。随后构建的第二个分类模型旨在筛选胆汁淤积性化合物的分类标志物，最终得到12个有效基因，可准确区分9种胆汁淤积性化合物中的8种，分类准确率达93%。

本研究证实，通过HepG2细胞的转录组学分析，可有效对化学实体的肝毒性进行分类。用于肝毒性与胆汁淤积性分类筛选的基因提示，内质网应激与未折叠蛋白反应可能是药物诱导肝损伤的重要细胞效应。不过，当前训练集与验证集所包含的化合物数量均有待扩充，以进一步提升预测模型的可靠性。