一种对性敏感的方法来预测化学物质在人类胎盘屏障上的转移
Anish Gomatam1, Evans Coutinho2
1Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Mumbai, India.
Toxicology letters
|February 29, 2024
概括
这项研究引入了一种新的度敏感计算方法,以预测物质如何通过胎盘传输. 这种方法通过考虑分子结构来改善怀孕期间化学品和药物的安全性评估.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学计算化学
- 生殖生物学 生殖生物学
背景情况:
- 胎盘对于胎儿发育至关重要,但它对药物和化学物质的透性是复杂的.
- 鉴于子宫内化学物质暴露的增加,评估胎盘转移至关重要.
- 现有的模型往往无法解释立体化学,限制了预测人类胎盘转移的准确性.
研究的目的:
- 开发和验证对胎盘转移的定量结构-属性关系 (QSPR) 模型,这些模型对分子性敏感.
- 通过结合立体化学信息来解决当前QSPR模型中二维描述器的局限性.
- 提供可靠的预测工具来评估环境化学品和类似药物的胎盘安全性.
主要方法:
- 应用 EigenValue ANalySis (EVANS) 方法,一种对度敏感的 QSPR 方法.
- 使用机器学习算法开发环境化学品的回归模型 (PD I) 和类似药物化合物的分类模型 (PD II).
- 使用Shapley添加式解释 (SHAP) 解释模型发现,以确定关键的分子描述符.
主要成果:
- 基于EVANS的QSPR模型实现了最先进的性能.
- 对于PD I的支持向量机 (SVM) 回归产生了r2train=0.85和r2test=0.75.
- 对于PD II的逻辑回归实现了0.88准确度和0.93F1得分.
- 自相对应描述符被确定为模拟胎盘透性的关键.
结论:
- 对于精确建模胎盘化学转移而言,对度敏感的方法是必不可少的.
- 开发的基于 EVANS 的 QSPR 模型为胎盘转移提供了可靠的预测.
- 这些模型可以帮助在怀孕期间评估化学品和药物的安全性.
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