在体内预测内分泌活动的预测
Daan A Jiskoot1, Jeroen L A Pennings2, Willie J G M Peijnenburg3
1Centre for Safety of Substances and Products, National Institute for Public Health and the Environment (RIVM), 3721MA, Bilthoven, The Netherlands; Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, 2300RA, Leiden, The Netherlands.
计算毒理学为识别内分泌破坏性化学物质 (EDCs) 提供了更快,更便宜和无动物的方法. 本综述强调了早期EDC检测和优先级的in silico方法的进展,重点关注关键的激素通路.
科学领域:
- 环境毒理学环境毒理学
- 计算化学是一种计算化学.
- 内分泌学 在内分泌学.
背景情况:
- 内分泌干扰化学物质 (EDC) 对健康构成重大风险.
- 传统的测试方法 (体外,体内) 是缓慢的,昂贵的,并且严重依赖动物模型.
- In silico方法为有效的EDC检测提供了一个有希望的替代方案.
研究的目的:
- 审查计算毒理学的进展,以便早期识别和优先考虑潜在的EDC.
- 专注于EDC对雌激素,雄激素,甲状腺和类固醇生成途径的影响.
- 讨论这些方法在监管环境中的应用.
主要方法:
- 基于连接体和基于结构的计算方法.
- 机器学习和深度学习算法.
- 分子对接,分子动力学和自由能量计算.
主要成果:
- 形方法在识别和优先考虑EDC方面取得了显著进展.
- 这些计算工具可以有效地分析关键的内分泌通路.
- 该审查综合了当前的应用和未来的潜力.
结论:
- 计算毒理学对于推动EDCs的早期检测至关重要.
- 需要进一步发展,才能充分发挥in silico方法在监管科学中的潜力.
- 增强的in silico策略承诺更高效和更道德的EDC评估.
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