The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey.
Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey.
Toxicol Sci. 2020 Apr 1;174(2):178-188. doi: 10.1093/toxsci/kfaa005.
Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.
肝毒性是药物开发过程中淘汰的主要原因。传统的临床前和临床研究评估肝毒性责任是昂贵和耗时的。随着高通量筛选的出现，有大量的体外毒性数据可用于为新的药物和化学物质的风险评估提供信息。为此，我们整理并合并了从文献和公共资源中获得的所有可用的体内肝毒性数据，这些数据产生了一个包含肝毒性分类的 4089 种化合物的综合数据库。在将原始化学物质数据库分为建模和测试集之后，使用内部数据挖掘工具自动提取 PubChem 测定数据，并根据体外测定中结构片段和细胞反应之间的关系对其进行聚类。对所得的 PubChem 测定聚类进行了进一步研究。在交叉验证过程中，从几个测定聚类中获得的生物数据显示出对肝毒性的高预测性，并且选择了这些测定来评估测试集化合物。读值结果表明，如果新化合物含有特定的鉴定化学片段（即分子起始事件）并且在相关的选定 PubChem 测定中显示出活性反应，则该化合物在体内可能具有肝毒性。此外，从建模结果中得出了几种可能导致毒性的机制，包括核受体信号的改变和 DNA 修复的抑制。这种建模策略可以进一步应用于其他复杂的化学毒性现象（例如，发育和生殖毒性）以及药物疗效的研究。
