ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States; National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, United States.
ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States; ORISE Postdoctoral Research Participant, United States.
Reprod Toxicol. 2019 Oct;89:145-158. doi: 10.1016/j.reprotox.2019.07.012. Epub 2019 Jul 21.
The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
毒性参考数据库（ToxRefDB）将 5000 多项体内毒性研究的信息进行了结构化处理，这些研究主要是按照美国环境保护署和国家毒理学计划的指导方针或规范进行的，现已将其构建为一个公共资源，用于培训和验证预测模型。本文介绍了 ToxRefDB 版本 2.0（ToxRefDBv2）的开发情况。根据亚急性、亚慢性、慢性、发育和多代生殖设计的指导方针，对终点进行了注释（例如必需、非必需），将阴性反应与未测试的反应区分开来。使用基准剂量（BMD）建模软件提取了近 28000 个数据集的定量数据，并对近 400 个终点的近 28000 个数据集进行了剂量反应建模，并进行了存储。受控词汇的实现提高了数据质量；根据指导方针的要求进行标准化，并与统一医学语言系统（UMLS）交叉引用，将 ToxRefDBv2 的观察结果与与 UMLS 相关联的词汇联系起来，包括 PubMed 医学主题词。ToxRefDBv2 允许与其他资源进行更多的连接，并极大地提高了预测毒理学的定量和定性效用。
