García María C, Cuesta Sebastián A, Mora José R, Paz Jose L, Marrero-Ponce Yovani, Alexis Frank, Márquez Edgar A
Departamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de Quito, 170901, Quito, Ecuador.
Department of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
Sci Rep. 2025 Feb 3;15(1):4085. doi: 10.1038/s41598-025-86926-8.
Parkinson's disease (PD) is a complex neurodegenerative disorder that affects multiple neurotransmitters, and its exact cause is still unknown. Developing new drugs for PD is a lengthy and expensive process, making it difficult to find new treatments. This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. An ensemble modeling approach was employed to screen the DrugBank database, aiming to repurpose approved medications as potential treatments for Parkinson's disease (PD). The dataset was constructed using pIC50 values of various compounds targeting the inhibition of leucine-rich repeat kinase 2 (LRRK2). The best ensemble model showed exceptional predictive performance, with five-fold cross-validation and external validation metrics exceeding 0.8 (Qcv = 0.864 and Qext = 0.873). The DrugBank screening resulted in three promising drugs-triamterene, phenazopyridine, and CRA_1801-with predicted pIC50 values greater than 7, warranting further investigation as novel PD treatments. Molecular docking and molecular dynamics simulations were performed to provide a comprehensive understanding of the interactions between LRRK2 and the inhibitors in the data set and best molecules of the screening. Free energy of binding calculation along with hydrogen bond occupancy analysis and RMSD of the ligand in the pocket show CRA_1801 as the best candidate to be repurposed as LRRK2 inhibitor.
帕金森病（PD）是一种复杂的神经退行性疾病，会影响多种神经递质，其确切病因仍不清楚。开发治疗帕金森病的新药是一个漫长且昂贵的过程，因此很难找到新的治疗方法。本研究旨在创建一个详细的数据集，以便使用各种机器学习算法构建强大的预测模型。采用集成建模方法筛选药物银行数据库，旨在将已批准的药物重新用作帕金森病（PD）的潜在治疗方法。该数据集是使用针对富含亮氨酸重复激酶2（LRRK2）抑制作用的各种化合物的半数抑制浓度负对数（pIC50）值构建的。最佳集成模型显示出卓越的预测性能，五折交叉验证和外部验证指标超过0.8（交叉验证Q值=0.864，外部验证Q值=0.873）。药物银行筛选产生了三种有前景的药物——氨苯蝶啶、非那吡啶和CRA_1801——预测的pIC50值大于7，值得作为帕金森病的新型治疗方法进行进一步研究。进行了分子对接和分子动力学模拟，以全面了解数据集中LRRK2与抑制剂以及筛选出的最佳分子之间的相互作用。结合自由能计算以及口袋中配体的氢键占有率分析和均方根偏差（RMSD）表明，CRA_1801是最适合重新用作LRRK2抑制剂的候选药物。
