A comparative study of biostatistical pipelines for benchmark concentration modeling of in vitro screening assays

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2025-06-01 DOI:10.1016/j.comtox.2025.100360

Kelly E. Carstens , Arif Dönmez , Jui-Hua Hsieh , Kristina Bartmann , Katie Paul Friedman , Katharina Koch , Martin Scholze , Ellen Fritsche

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引用次数: 0

Abstract

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.

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体外筛选试验基准浓度建模的生物统计管道的比较研究

新的方法（NAMs）已得到优先考虑，以减少使用动物进行化学品安全评估，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检测开发人员、监管机构和其他利益相关者实施透明和可重复的基准浓度（BMC）估计和不确定度量化的标准化分析方法。在这项研究中，我们比较了用于评估发育神经毒性（DNT） NAMs数据的四种已建立的BMC分析管道中的321种化学样品的生物活性结果：ToxCast管道（tcpl）， CRStats管道，DNT DIVER （curve和Hill管道）。我们发现，总体活动的呼叫一致性为77.2%，BMC估计高度相关（r = 0.92±0.02 SD），表明管道之间的一致性总体良好。不一致似乎主要由数据中的噪声和边界活动（在基准响应水平附近发生的活动）来解释。对BMC置信区间的评估表明，管道选择可能会影响BMC下界的估计。考虑双相模型对于捕获DNT电池活性的生物学相关变化似乎很重要。最后，对计算“选择性”生物活性（低于细胞毒性阈值的活性）的不同方法进行了比较，确定CRstats分类模型对于分类选择性活性更为严格。总的来说，这些发现表明了对NAMs生物活性结果的更大信心，并强调了理解浓度-反应建模管道的优势和不确定性对于为生物学解释和应用决策提供信息的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Toxicology Computer Science-Computer Science Applications

CiteScore

5.50

自引率

0.00%

发文量

审稿时长

56 days

期刊介绍： Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs