Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of <i>in vitro</i> data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.

监管机构负责评估大量化学品的内分泌干扰潜力，而针对此类化学品往往缺乏或完全缺乏支撑决策的相关数据。优先筛选出最受关注的化学品，对其开展进一步筛查以评估对人类与野生生物的潜在危害，是该工作流程的初始步骤。
本文收集了经优化的、用于检测低亲和力雌激素受体（ER）结合型化学品的体外（in vitro）实验数据，并基于吉尔曼·维特（Gilman Veith）及其团队开创的定量结构-活性关系（QSAR，Quantitative Structure-Activity Relationship）方法与原则，依托该数据集构建基于效应的化学品分类体系，以应对环境监管难题。
本次构建的基于效应的化学品分类体系遵循上述QSAR原则，聚焦于目标监管领域内的化学品类型——即非甾体类工业化学品，并基于一项作用机制假说：这类结构与17β-雌二醇（17ß-estradiol，简称E2）看似迥异的非甾体类化学品，可通过两种不同的结合模式与ER发生相互作用。
研究团队同时对所有受试化学品开展了溶解度测试，以此最大限度减少假阴性结果，并为化学品无活性的判定结果提供可靠性保障。
本研究通过该方式收集的高质量数据集，被用于构建用于化学品优先级排序的ER专家系统，相关内容将在本期刊的姊妹论文中详细阐述。