Human Genetics and Pharmacogenomics.
Safety Assessment and Laboratory Animal Resources, Merck & Co., Inc., West Point, Pennsylvania 19486-0004.
Toxicol Sci. 2020 May 1;175(1):98-112. doi: 10.1093/toxsci/kfaa026.
The robust transcriptional plasticity of liver mediated through xenobiotic receptors underlies its ability to respond rapidly and effectively to diverse chemical stressors. Thus, drug-induced gene expression changes in liver serve not only as biomarkers of liver injury, but also as mechanistic sentinels of adaptation in metabolism, detoxification, and tissue protection from chemicals. Modern RNA sequencing methods offer an unmatched opportunity to quantitatively monitor these processes in parallel and to contextualize the spectrum of dose-dependent stress, adaptation, protection, and injury responses induced in liver by drug treatments. Using this approach, we profiled the transcriptional changes in rat liver following daily oral administration of 120 different compounds, many of which are known to be associated with clinical risk for drug-induced liver injury by diverse mechanisms. Clustering, correlation, and linear modeling analyses were used to identify and optimize coexpressed gene signatures modulated by drug treatment. Here, we specifically focused on prioritizing 9 key signatures for their pragmatic utility for routine monitoring in initial rat tolerability studies just prior to entering drug development. These signatures are associated with 5 canonical xenobiotic nuclear receptors (AHR, CAR, PXR, PPARα, ER), 3 mediators of reactive metabolite-mediated stress responses (NRF2, NRF1, P53), and 1 liver response following activation of the innate immune response. Comparing paradigm chemical inducers of each receptor to the other compounds surveyed enabled us to identify sets of optimized gene expression panels and associated scoring algorithms proposed as quantitative mechanistic biomarkers with high sensitivity, specificity, and quantitative accuracy. These findings were further qualified using public datasets, Open TG-GATEs and DrugMatrix, and internal development compounds. With broader collaboration and additional qualification, the quantitative toxicogenomic framework described here could inform candidate selection prior to committing to drug development, as well as complement and provide a deeper understanding of the conventional toxicology study endpoints used later in drug development.
肝脏通过外源物质受体表现出强大的转录灵活性，使其能够快速有效地应对各种化学应激源。因此，药物引起的肝脏基因表达变化不仅作为肝损伤的生物标志物，而且作为代谢、解毒和化学物质引起的组织保护适应的机制哨兵。现代 RNA 测序方法提供了一个无与伦比的机会，可以并行定量监测这些过程，并将药物治疗引起的肝脏中依赖剂量的应激、适应、保护和损伤反应的范围置于上下文中。使用这种方法，我们对大鼠肝脏在每天口服给予 120 种不同化合物后的转录变化进行了分析，其中许多化合物已知通过不同机制与药物引起的肝损伤的临床风险相关。聚类、相关和线性建模分析用于识别和优化受药物处理调节的共表达基因特征。在这里，我们特别关注根据其在进入药物开发之前进行的大鼠耐受性研究中的常规监测的实用效用来对 9 个关键特征进行优先级排序。这些特征与 5 个经典外源物质核受体（AHR、CAR、PXR、PPARα、ER）、3 个活性代谢物介导的应激反应调节剂（NRF2、NRF1、P53）和 1 个固有免疫反应激活后的肝脏反应有关。将每个受体的范式化学诱导剂与调查的其他化合物进行比较，使我们能够识别出优化的基因表达面板集，并提出作为具有高灵敏度、特异性和定量准确性的定量机制生物标志物的相关评分算法。使用公共数据集 Open TG-GATEs 和 DrugMatrix 以及内部开发的化合物进一步对这些发现进行了验证。通过更广泛的合作和进一步的资格认证，这里描述的定量毒理学框架可以在承诺进行药物开发之前为候选药物的选择提供信息，并且可以补充和更深入地了解药物开发后期使用的常规毒理学研究终点。
