Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Mumbai, India.
Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Mumbai, India; St. John Institute of Pharmacy and Research, Palghar (E), Palghar, India.
Toxicol Lett. 2024 Apr;394:66-75. doi: 10.1016/j.toxlet.2024.02.012. Epub 2024 Feb 28.
The placenta is a membrane that separates the fetus from the maternal circulation, and in addition to protecting the fetus, plays a key role in fetal growth and development. With increasing drug use in pregnancy, it is imperative that reliable models of estimating placental permeability and safety be established. In vitro methods and animal models such as rodent placenta are limited in application since the species-specific nature of the placenta prevents meaningful extrapolations to humans. In this regard, in silico approaches such as quantitative structure-property relationships (QSPRs) are useful alternatives. However, despite evidence that drug transport across the placenta is stereoselective (i.e., governed by the spatial arrangement of the atoms in a molecule), many QSPR models for placental transfer have been built using 2D descriptors that do not account for chirality and stereochemistry. In this study, we apply a chirality-sensitive and proven QSPR methodology titled "EigenValue ANalySis" (EVANS) to build QSPR models for placental transfer. We deploy EVANS along with robust machine learning algorithms to build (i) regression models on a dataset of environmental chemicals (dataset PD I) followed by (ii) classification models on a set of drug-like compounds (dataset PD II). The best models were found to achieve state-of-the-art performance, with the support vector machine algorithm returning r=0.85,r=0.75 for PD I, and the logistic regression algorithm giving accuracy 0.88 and F1 score 0.93 for PD II. The best models were interpreted with the Shapley Additive Explanations paradigm, and it was found that autocorrelation descriptors are crucial for modelling placental permeability. In conclusion, we demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals, and present two predictive QSPR models that may reliably be used for prediction of placental transfer.
胎盘是一种将胎儿与母体循环分隔开的膜，除了保护胎儿外，还在胎儿生长和发育中起着关键作用。随着怀孕期间药物使用的增加，建立可靠的估计胎盘通透性和安全性的模型势在必行。由于胎盘的种属特异性限制了向人类的有意义外推，因此体外方法和啮齿动物胎盘等动物模型的应用受到限制。在这方面，定量构效关系（QSPR）等计算方法是有用的替代方法。然而，尽管有证据表明药物跨胎盘转运具有立体选择性（即受分子中原子的空间排列控制），但许多用于胎盘转运的 QSPR 模型都是使用不考虑手性和立体化学的 2D 描述符构建的。在这项研究中，我们应用了一种名为“特征值分析”（EVANS）的手性敏感且经过验证的 QSPR 方法来构建胎盘转运的 QSPR 模型。我们使用 EVANS 结合强大的机器学习算法，构建了（i）环境化学物质数据集（数据集 PD I）的回归模型，然后构建了（ii）一组类药化合物数据集（数据集 PD II）的分类模型。发现最佳模型的性能达到了最新水平，支持向量机算法对 PD I 的 r 值为 0.85，r 值为 0.75，逻辑回归算法对 PD II 的准确率为 0.88，F1 得分为 0.93。使用 Shapley 加法解释范式对最佳模型进行了解释，发现自相关描述符对于建模胎盘通透性至关重要。总之，我们证明了对建模化学物质胎盘转运需要采用手性敏感方法，并提出了两个可用于可靠预测胎盘转运的预测性 QSPR 模型。
