使用HuskinDB的碎片贡献模型来预测皮肤透性
Laura J Waters1, David J Cooke2, Xin Ling Quah2
1School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK. l.waters@hud.ac.uk.
Scientific data
|November 23, 2023
概括
这项研究引入了一种新的非动物模型,用于仅使用化学功能组数据来预测人类皮肤的透性. 这简化了预测复合物透到皮肤的过程,而不需要复杂的物理化学特性.
科学领域:
- 药理动力学 药理动力学
- 计算化学计算化学
- 皮肤病学 皮肤病学
背景情况:
- 传统的皮肤透模型依赖于动物数据和物理化学性质.
- 预测人体皮肤的透性对于药物开发和化学安全评估至关重要.
- 现有的方法可能很复杂,可能不能完全代表人类生理学.
研究的目的:
- 开发一种新的,非动物模型来预测人类皮肤的透性.
- 使用一个免费访问的人类皮肤透数据库 (HuskinDB).
- 建立基于化学碎片数据的简化预测方法.
主要方法:
- 开发基于碎片的模型,使用来自HuskinDB数据库的功能组数据.
- 分析了常见的功能组及其对皮肤透性的可计算影响.
- 使用已知人类皮肤透值的化合物的验证模型.
主要成果:
- 创建基于功能组的类型和数量的预测模型.
- 证明这些模型的预测不需要物理化学性质.
- 对各种化合物的人体皮肤透率 (logKp) 进行了准确的预测.
结论:
- 基于碎片的模型提供了一种简化的非动物方法来预测人类皮肤的透性.
- 这种方法减少了对动物试验和复杂的物理化学性质计算的依赖.
- 开发的模型适用于含有分析功能组的化合物,简化了透性评估.
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