Abdallah Rami M, Hasan Hisham E, Hammad Ahmad
Department of Pharmaceutical Sciences, Faculty of Pharmacy, Zarqa University, Zarqa, Jordan.
Department of Artificial Intelligence, Faculty of Information Technology, Middle East University, Amman, Jordan.
PLOS Digit Health. 2024 Apr 3;3(4):e0000483. doi: 10.1371/journal.pdig.0000483. eCollection 2024 Apr.
The transdermal route of drug administration has gained popularity for its convenience and bypassing the first-pass metabolism. Accurate skin permeability prediction is crucial for successful transdermal drug delivery (TDD). In this study, we address this critical need to enhance TDD. A dataset comprising 441 records for 140 molecules with diverse LogKp values was characterized. The descriptor calculation yielded 145 relevant descriptors. Machine learning models, including MLR, RF, XGBoost, CatBoost, LGBM, and ANN, were employed for regression analysis. Notably, LGBM, XGBoost, and gradient boosting models outperformed others, demonstrating superior predictive accuracy. Key descriptors influencing skin permeability, such as hydrophobicity, hydrogen bond donors, hydrogen bond acceptors, and topological polar surface area, were identified and visualized. Cluster analysis applied to the FDA-approved drug dataset (2326 compounds) revealed four distinct clusters with significant differences in molecular characteristics. Predicted LogKp values for these clusters offered insights into the permeability variations among FDA-approved drugs. Furthermore, an investigation into skin permeability patterns across 83 classes of FDA-approved drugs based on the ATC code showcased significant differences, providing valuable information for drug development strategies. The study underscores the importance of accurate skin permeability prediction for TDD, emphasizing the superior performance of nonlinear machine learning models. The identified key descriptors and clusters contribute to a nuanced understanding of permeability characteristics among FDA-approved drugs. These findings offer actionable insights for drug design, formulation, and prioritization of molecules with optimum properties, potentially reducing reliance on costly experimental testing. Future research directions include offering promising applications in pharmaceutical research and formulation within the burgeoning field of computer-aided drug design.
药物经皮给药途径因其便利性和避免首过代谢而受到欢迎。准确预测皮肤渗透性对于成功的经皮给药（TDD）至关重要。在本研究中，我们满足了这一增强TDD的关键需求。对一个包含140种具有不同LogKp值的分子的441条记录的数据集进行了表征。描述符计算产生了145个相关描述符。采用包括多元线性回归（MLR）、随机森林（RF）、极端梯度提升（XGBoost）、类别梯度提升（CatBoost）、轻量级梯度提升机（LGBM）和人工神经网络（ANN）在内的机器学习模型进行回归分析。值得注意的是，LGBM、XGBoost和梯度提升模型的表现优于其他模型，显示出卓越的预测准确性。确定并可视化了影响皮肤渗透性的关键描述符，如疏水性、氢键供体、氢键受体和拓扑极性表面积。对FDA批准的药物数据集（2326种化合物）进行的聚类分析揭示了四个不同的簇，其分子特征存在显著差异。这些簇的预测LogKp值为了解FDA批准药物之间的渗透性差异提供了见解。此外，基于解剖学治疗学及化学分类系统（ATC）代码对83类FDA批准药物的皮肤渗透性模式进行的调查显示出显著差异，为药物开发策略提供了有价值的信息。该研究强调了准确预测皮肤渗透性对TDD的重要性，强调了非线性机器学习模型的卓越性能。确定的关键描述符和簇有助于对FDA批准药物的渗透性特征有更细致入微的理解。这些发现为药物设计、配方以及具有最佳特性分子的优先级排序提供了可操作的见解，有可能减少对昂贵实验测试的依赖。未来的研究方向包括在计算机辅助药物设计的新兴领域中为药物研究和配方提供有前景的应用。
