Thienpont Anouck, Cho Eunnara, Williams Andrew, Meier Matthew J, Yauk Carole L, Beal Marc A, Van Goethem Freddy, Rogiers Vera, Vanhaecke Tamara, Mertens Birgit
Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel (VUB), Brussels 1090, Belgium; Department of Chemical and Physical Health Risks, Sciensano, Brussels 1050, Belgium.
Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada.
Toxicology. 2025 Aug;515:154165. doi: 10.1016/j.tox.2025.154165. Epub 2025 Apr 25.
In vitro transcriptomics holds promise for high-throughput, human-relevant data but is not yet integrated into regulatory decision-making due to the lack of standardized approaches. For genotoxicity assessment, transcriptomic biomarkers such as GENOMARK and TGx-DDI facilitate qualitative and quantitative analysis of complex in vitro transcriptomic datasets. However, advancing their use in quantitative testing requires standardized methods for deriving transcriptomic Points of Departure (tPoDs) and linking them to in vivo responses. Herein, we investigated different approaches to calculate tPoDs and applied in vitro to in vivo extrapolation to obtain administered equivalent doses (AEDs). Human HepaRG cells were exposed for 72 h to 10 known in vivo genotoxicants (glycidol, methyl methanesulfonate, nitrosodimethylamine, 4-nitroquinoline-N-oxide, aflatoxin B1, colchicine, cyclophosphamide, mitomycin C, ethyl methanesulfonate, and N-Nitroso-N-ethylurea) from the highest concentration that induces up to 50 % cytotoxicity through a range of lower concentrations. Gene expression data was generated using a customized version of the TempO-Seq® human S1500 + gene panel. The GENOMARK and TGx-DDI biomarkers produced genotoxic calls for all of these reference genotoxicants. Next, we performed benchmark concentration (BMC) modeling to generate both genotoxicity-specific biomarker (tPoD) and generic tPoDs (tPoD ). High-throughput toxicokinetic models estimated the human AEDs for these tPoDs, which were compared with (a) previously reported genotoxicity-specific AEDs from other New Approach Methodologies, and (b) in vivo PoDs from animal studies. We found that the generic AEDs were more conservative than genotoxicity-specific biomarker AEDs. For six of the nine genotoxicants, transcriptomic AEDs were lower than the in vivo PoDs; refined kinetic models may improve predictions. Overall, in vitro transcriptomic data in HepaRG cells provide protective estimates of in vivo genotoxic concentrations, consistent with other in vitro genotoxicity testing systems.
体外转录组学有望提供高通量、与人类相关的数据，但由于缺乏标准化方法，尚未纳入监管决策。对于遗传毒性评估，诸如GENOMARK和TGx-DDI等转录组学生物标志物有助于对复杂的体外转录组数据集进行定性和定量分析。然而，要推进它们在定量测试中的应用，需要标准化方法来推导转录组出发值（tPoD）并将其与体内反应联系起来。在此，我们研究了计算tPoD的不同方法，并应用体外到体内外推法来获得给药等效剂量（AED）。将人HepaRG细胞暴露于10种已知的体内遗传毒性剂（缩水甘油、甲磺酸甲酯、二甲基亚硝胺、4-硝基喹啉-N-氧化物、黄曲霉毒素B1、秋水仙碱、环磷酰胺、丝裂霉素C、甲磺酸乙酯和N-亚硝基-N-乙基脲）72小时，浓度范围从诱导高达50%细胞毒性的最高浓度到一系列较低浓度。使用定制版的TempO-Seq®人类S1500 +基因面板生成基因表达数据。GENOMARK和TGx-DDI生物标志物对所有这些参考遗传毒性剂都产生了遗传毒性判定。接下来，我们进行了基准浓度（BMC）建模，以生成遗传毒性特异性生物标志物（tPoD）和通用tPoD（tPoD ）。高通量毒物动力学模型估计了这些tPoD对应的人类AED，并将其与（a）先前报道的来自其他新方法学的遗传毒性特异性AED，以及（b）动物研究中的体内PoD进行比较。我们发现通用AED比遗传毒性特异性生物标志物AED更保守。对于9种遗传毒性剂中的6种，转录组AED低于体内PoD；改进的动力学模型可能会改善预测。总体而言，HepaRG细胞中的体外转录组数据提供了体内遗传毒性浓度的保护性估计，与其他体外遗传毒性测试系统一致。
