An expert-driven literature review of “negative” chemicals for developmental neurotoxicity (DNT) in vitro assay evaluation

指南 计算机科学 工作流程 代理(哲学) 化学安全 危害 风险分析(工程) 毒理 生化工程 医学 工程类 化学 生物 病理 哲学 有机化学 认识论 数据库
作者
Melissa M. Martin,Nancy Baker,William K. Boyes,Kelly E. Carstens,Megan Culbreth,Mary E. Gilbert,Joshua Harrill,Johanna Nyffeler,Stephanie Padilla,Katie Paul Friedman,Timothy J. Shafer
出处
期刊:Neurotoxicology and Teratology [Elsevier BV]
卷期号:93: 107117-107117 被引量:9
标识
DOI:10.1016/j.ntt.2022.107117
摘要

To date, approximately 200 chemicals have been tested in US Environmental Protection Agency (EPA) or Organization for Economic Co-operation and Development (OECD) developmental neurotoxicity (DNT) guideline studies, leaving thousands of chemicals without traditional animal information on DNT hazard potential. To address this data gap, a battery of in vitro DNT new approach methodologies (NAMs) has been proposed. Evaluation of the performance of this battery will increase the confidence in its use to determine DNT chemical hazards. One approach to evaluate DNT NAM performance is to use a set of chemicals to evaluate sensitivity and specificity. Since a list of chemicals with potential evidence of in vivo DNT has been established, this study aims to develop a curated list of "negative" chemicals for inclusion in a "DNT NAM evaluation set". A workflow, including a literature search followed by an expert-driven literature review, was used to systematically screen 39 chemicals for lack of DNT effect. Expert panel members evaluated the scientific robustness of relevant studies to inform chemical categorizations. Following review, the panel discussed each chemical and made categorical determinations of "Favorable", "Not Favorable", or "Indeterminate" reflecting acceptance, lack of suitability, or uncertainty given specific limitations and considerations, respectively. The panel determined that 10, 22, and 7 chemicals met the criteria for "Favorable", "Not Favorable", and "Indeterminate", for use as negatives in a DNT NAM evaluation set. Ultimately, this approach not only supports DNT NAM performance evaluation but also highlights challenges in identifying large numbers of negative DNT chemicals.

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