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整体定量阅读跨结构-活动关系算法用于预测皮肤细胞毒性.

Tarapong Srisongkram1

  • 1Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40000, Thailand.

Chemical research in toxicology
|December 4, 2023
PubMed
概括

一个新的算法,enQRASAR,使用化学结构准确预测皮肤刺激毒性. 这种方法减少了动物试验和化学品注册的成本.

科学领域:

  • 毒理学 毒理学 毒理学
  • 计算化学的计算化学
  • 化学信息学 化学信息学

背景情况:

  • 阅读 (RA) 和定量结构-活性关系 (QSAR) 对于填补化学安全评估中的数据缺口至关重要.
  • 这些方法利用化学结构和特性来预测未知的物质行为,最大限度地减少动物试验和成本.
  • 开发强大的预测模型对于有效的化学品注册和风险评估至关重要.

研究的目的:

  • 开发和验证一个堆叠组合定量阅读跨结构-活动关系 (enQRASAR) 算法.
  • 预测皮肤刺激毒性,特别是负日志细胞活力抑制度50% (pIC50) 对皮肤角质细胞.
  • 为评估化学细胞毒性提供可靠的计算工具.

主要方法:

  • 开发一个集成RA和QSAR原则的堆叠组合算法 (enQRASAR).
  • 使用pIC50作为终点预测皮肤刺激毒性.
  • 验证模型的适用性和可预测性,使用一次性交叉验证和外部测试数据集.

主要成果:

  • enQRASAR算法在统计学上证明了可靠的适合性,稳定性和可预测性.
  • 该模型实现了低预测误差,即使应用于FDA批准的药物.
  • 开发的算法有效地预测化学品的皮肤细胞毒性.

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结论:

  • enQRASAR算法是用于预测皮肤细胞毒性的经过验证和可靠的工具.
  • 这种计算方法有助于减少在化学安全评估中对动物进行测试的需求.
  • 公开可用的enQRASAR模型可以更容易地预测化学注册中未知的化合物的毒性.