University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Toxicol Sci. 2012 Apr;126(2):578-88. doi: 10.1093/toxsci/kfs023. Epub 2012 Jan 19.
A shift in toxicity testing from in vivo to in vitro may efficiently prioritize compounds, reveal new mechanisms, and enable predictive modeling. Quantitative high-throughput screening (qHTS) is a major source of data for computational toxicology, and our goal in this study was to aid in the development of predictive in vitro models of chemical-induced toxicity, anchored on interindividual genetic variability. Eighty-one human lymphoblast cell lines from 27 Centre d'Etude du Polymorphisme Humain trios were exposed to 240 chemical substances (12 concentrations, 0.26nM-46.0μM) and evaluated for cytotoxicity and apoptosis. qHTS screening in the genetically defined population produced robust and reproducible results, which allowed for cross-compound, cross-assay, and cross-individual comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited interindividual differences in cytotoxicity. Specifically, the qHTS in a population-based human in vitro model system has several unique aspects that are of utility for toxicity testing, chemical prioritization, and high-throughput risk assessment. First, standardized and high-quality concentration-response profiling, with reproducibility confirmed by comparison with previous experiments, enables prioritization of chemicals for variability in interindividual range in cytotoxicity. Second, genome-wide association analysis of cytotoxicity phenotypes allows exploration of the potential genetic determinants of interindividual variability in toxicity. Furthermore, highly significant associations identified through the analysis of population-level correlations between basal gene expression variability and chemical-induced toxicity suggest plausible mode of action hypotheses for follow-up analyses. We conclude that as the improved resolution of genetic profiling can now be matched with high-quality in vitro screening data, the evaluation of the toxicity pathways and the effects of genetic diversity are now feasible through the use of human lymphoblast cell lines.
从体内到体外的毒性测试转变可以有效地对化合物进行优先级排序,揭示新的机制,并实现预测建模。高通量筛选(qHTS)是计算毒理学的主要数据来源,我们在这项研究中的目标是帮助开发基于个体遗传变异性的化学诱导毒性的体外预测模型。从 27 个人类多态性研究中心的 81 个人类淋巴母细胞系中,暴露于 240 种化学物质(12 种浓度,0.26nM-46.0μM)并评估细胞毒性和细胞凋亡。在遗传定义的人群中进行 qHTS 筛选可产生稳健且可重复的结果,从而可以进行跨化合物、跨测定和跨个体的比较。一些化合物以相似的浓度对所有细胞类型均具有细胞毒性,而另一些化合物则具有细胞毒性的个体间差异。具体而言,基于人群的人类体外模型系统中的 qHTS 具有几个独特的方面,这些方面对毒性测试、化学物质优先级排序和高通量风险评估非常有用。首先,标准化和高质量的浓度反应谱,通过与以前的实验进行比较来确认重复性,可优先对具有细胞毒性个体间差异的化学物质进行排序。其次,对细胞毒性表型进行全基因组关联分析可以探索毒性个体间差异的潜在遗传决定因素。此外,通过分析人群水平的基础基因表达变异性与化学诱导毒性之间的相关性而得出的高度显著关联,提出了用于后续分析的合理作用机制假说。我们得出结论,由于遗传分析分辨率的提高现在可以与高质量的体外筛选数据相匹配,因此通过使用人类淋巴母细胞系,现在可以评估毒性途径和遗传多样性的影响。