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利用人工智能筛选文献和数据库探索鉴定未经验证的内分泌干扰化学特征化方法。

Identification of non-validated endocrine disrupting chemical characterization methods by screening of the literature using artificial intelligence and by database exploration.

机构信息

Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France.

Université Sorbonne Paris Nord, Bobigny, INSERM UMR-S 1124, Paris, France.

出版信息

Environ Int. 2021 Sep;154:106574. doi: 10.1016/j.envint.2021.106574. Epub 2021 Apr 23.

Abstract

BACKGROUND

Exposure to endocrine disrupting chemicals (EDCs) represents a critical public health threat. Several adverse health outcomes (e.g., cancers, metabolic and neurocognitive/neurodevelopmental disorders, infertility, immune diseases and allergies) are associated with exposure to EDCs. However, the regulatory tests that are currently employed in the EU to identify EDCs do not assess all of the endocrine pathways.

OBJECTIVE

Our objective was to explore the literature, guidelines and databases to identify relevant and reliable test methods which could be used for prioritization and regulatory pre-validation of EDCs in missing and urgent key areas.

METHODS

Abstracts of articles referenced in PubMed were automatically screened using an updated version of the AOP-helpFinder text mining approach. Other available sources were manually explored. Exclusion criteria (computational methods, specific tests for estrogen receptors, tests under validation or already validated, methods accepted by regulatory bodies) were applied according to the priorities of the French Public-privatE Platform for the Pre-validation of Endocrine disRuptors (PEPPER) characterisation methods.

RESULTS

226 unique non-validated methods were identified. These experimental methods (in vitro and in vivo) were developed for 30 species using diverse techniques (e.g., reporter gene assays and radioimmunoassays). We retrieved bioassays mainly for the reproductive system, growth/developmental systems, lipogenesis/adipogenicity, thyroid, steroidogenesis, liver metabolism-mediated toxicity, and more specifically for the androgen-, thyroid hormone-, glucocorticoid- and aryl hydrocarbon receptors.

CONCLUSION

We identified methods to characterize EDCs which could be relevant for regulatory pre-validation and, ultimately for the efficient prevention of EDC-related severe health outcomes. This integrative approach highlights a successful and complementary strategy which combines computational and manual curation approaches.

摘要

背景

接触内分泌干扰化学物质(EDCs)是一个严重的公共卫生威胁。许多不良健康后果(例如癌症、代谢和神经认知/神经发育障碍、不孕、自身免疫性疾病和过敏)都与 EDC 暴露有关。然而,目前欧盟用于识别 EDC 的监管测试并未评估所有内分泌途径。

目的

我们的目的是探索文献、指南和数据库,以确定相关和可靠的测试方法,可用于优先考虑和监管预验证缺失和紧急关键领域的 EDC。

方法

使用更新版本的 AOP-helpFinder 文本挖掘方法自动筛选 PubMed 中引用文章的摘要。手动探索其他可用来源。根据法国公私合作内分泌干扰物预验证平台(PEPPER)特征方法的优先级,应用排除标准(计算方法、特定的雌激素受体测试、正在验证或已经验证的测试、监管机构接受的方法)。

结果

确定了 226 种独特的未验证方法。这些实验方法(体外和体内)是为 30 个物种开发的,使用了多种技术(例如报告基因检测和放射免疫检测)。我们检索了主要用于生殖系统、生长/发育系统、脂肪生成/脂肪形成、甲状腺、类固醇生成、肝代谢介导的毒性的生物测定,更具体地用于雄激素、甲状腺激素、糖皮质激素和芳香烃受体。

结论

我们确定了可用于监管预验证的 EDC 特征方法,最终可有效预防 EDC 相关的严重健康后果。这种综合方法突出了一种成功且互补的策略,该策略结合了计算和手动策展方法。

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