Carstens Kelly E, Dönmez Arif, Hsieh Jui-Hua, Bartmann Kristina, Friedman Katie Paul, Koch Katharina, Scholze Martin, Fritsche Ellen
Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.
IUF - Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany.
Comput Toxicol. 2025 Jun 1;34. doi: 10.1016/j.comtox.2025.100360.
New approach methods (NAMs) have been prioritized to reduce the use of animals for chemical safety assessment while continuing to protect human health and the environment. A key challenge of generating toxicity data is the implementation of a standardized analysis approach for transparent and reproducible benchmark concentration (BMC) estimation and uncertainty quantification for assay developers, regulators, and other stakeholders. In this study, we compared the bioactivity results of 321 chemical samples from four established BMC analysis pipelines used for evaluation of developmental neurotoxicity (DNT) NAMs data: the ToxCast pipeline (tcpl), CRStats, DNT DIVER (Curvep and Hill pipelines). We found an overall activity hit call concordance of 77.2% and highly correlated BMC estimations (r = 0.92 ± 0.02 SD), demonstrating generally good agreement across pipelines. Discordance appeared to be explained predominantly by noise within the data and borderline activity (activity occuring near the benchmark response level). Evaluation of the BMC confidence intervals indicated that pipeline selection may impact the estimation of the BMC lower bound. Consideration of biphasic models appeared important for capturing biologically-relevant changes in activity in the DNT battery. Lastly, different approaches to compute 'selective' bioactivity (activity below the threshold of cytotoxicity) were compared, identifying the CRstats classification model as more stringent for classifying selective activity. Overall, these findings indicated greater confidence in NAMs bioactivity results and emphasize the importance of understanding strengths and uncertainties of concentration-response modeling pipelines for informing biological interpretation and application decision making.
新方法(NAMs)已被优先用于减少化学安全评估中动物的使用,同时继续保护人类健康和环境。生成毒性数据的一个关键挑战是为分析开发者、监管机构和其他利益相关者实施一种标准化分析方法,以进行透明且可重复的基准浓度(BMC)估计和不确定性量化。在本研究中,我们比较了来自用于评估发育神经毒性(DNT)NAMs数据的四个既定BMC分析流程的321个化学样品的生物活性结果:ToxCast流程(tcpl)、CRStats、DNT DIVER(Curvep和Hill流程)。我们发现总体活性命中调用一致性为77.2%,且BMC估计高度相关(r = 0.92 ± 0.02标准差),表明各流程之间总体一致性良好。不一致似乎主要由数据中的噪声和临界活性(在基准响应水平附近出现的活性)所解释。对BMC置信区间的评估表明,流程选择可能会影响BMC下限的估计。考虑双相模型对于捕捉DNT试验组中生物学相关的活性变化似乎很重要。最后,比较了计算“选择性”生物活性(低于细胞毒性阈值的活性)的不同方法,确定CRstats分类模型在分类选择性活性方面更为严格。总体而言,这些发现表明对NAMs生物活性结果更有信心,并强调了解浓度 - 反应建模流程的优势和不确定性对于指导生物学解释和应用决策的重要性。