Ahmed Faheem, Samantasinghar Anupama, Bae Myung Ae, Choi Kyung Hyun
Department of Mechatronics Engineering, Jeju National University, Jeju 63243, Republic of Korea.
Therapeutics and Biotechnology Division, Korea Research Institute of Chemical Technology, Daejeon 34114, Korea.
ACS Omega. 2024 Jun 27;9(27):29870-29883. doi: 10.1021/acsomega.4c03796. eCollection 2024 Jul 9.
Idiopathic pulmonary fibrosis (IPF) affects an estimated global population of around 3 million individuals. IPF is a medical condition with an unknown cause characterized by the formation of scar tissue in the lungs, leading to progressive respiratory disease. Currently, there are only two FDA-approved small molecule drugs specifically for the treatment of IPF and this has created a demand for the rapid development of drugs for IPF treatment. Moreover, denovo drug development is time and cost-intensive with less than a 10% success rate. Drug repurposing currently is the most feasible option for rapidly making the drugs to market for a rare and sporadic disease. Normally, the repurposing of drugs begins with a screening of FDA-approved drugs using computational tools, which results in a low hit rate. Here, an integrated machine learning-based drug repurposing strategy is developed to significantly reduce the false positive outcomes by introducing the predock machine-learning-based predictions followed by literature and GSEA-assisted validation and drug pathway prediction. The developed strategy is deployed to 1480 FDA-approved drugs and to drugs currently in a clinical trial for IPF to screen them against "TGFB1", "TGFB2", "PDGFR-a", "SMAD-2/3", "FGF-2", and more proteins resulting in 247 total and 27 potentially repurposable drugs. The literature and GSEA validation suggested that 72 of 247 (29.14%) drugs have been tried for IPF, 13 of 247 (5.2%) drugs have already been used for lung fibrosis, and 20 of 247 (8%) drugs have been tested for other fibrotic conditions such as cystic fibrosis and renal fibrosis. Pathway prediction of the remaining 142 drugs was carried out resulting in 118 distinct pathways. Furthermore, the analysis revealed that 29 of 118 pathways were directly or indirectly involved in IPF and 11 of 29 pathways were directly involved. Moreover, 15 potential drug combinations are suggested for showing a strong synergistic effect in IPF. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating IPF and other related conditions.
特发性肺纤维化（IPF）估计影响全球约300万人。IPF是一种病因不明的病症，其特征是肺部形成瘢痕组织，导致进行性呼吸系统疾病。目前，美国食品药品监督管理局（FDA）仅批准了两种专门用于治疗IPF的小分子药物，这引发了对快速开发IPF治疗药物的需求。此外，从头开始研发药物既耗时又成本高昂，成功率不到10%。对于一种罕见且散发的疾病，药物重新利用目前是使药物快速上市的最可行选择。通常，药物重新利用始于使用计算工具对FDA批准的药物进行筛选，但其命中率较低。在此，开发了一种基于机器学习的综合药物重新利用策略，通过引入基于对接前机器学习的预测，随后进行文献和基因集富集分析（GSEA）辅助验证以及药物途径预测，显著减少假阳性结果。所开发的策略应用于1480种FDA批准的药物以及目前正在进行IPF临床试验的药物，针对“转化生长因子β1（TGFB1）”、“转化生长因子β2（TGFB2）”、“血小板衍生生长因子受体α（PDGFR-a）”、“SMAD-2/3”、“成纤维细胞生长因子2（FGF-2）”等多种蛋白质进行筛选，共得到247种药物，其中27种具有潜在重新利用价值。文献和GSEA验证表明，247种药物中有72种（29.14%）已用于IPF试验，247种中有13种（5.2%）已用于肺纤维化治疗，247种中有20种（8%）已针对其他纤维化病症如囊性纤维化和肾纤维化进行了测试。对其余142种药物进行了途径预测，得到118条不同的途径。此外，分析显示118条途径中有29条直接或间接参与IPF，其中29条中有11条直接参与。此外，还提出了15种潜在的药物组合，它们在IPF中显示出强烈的协同作用。本文报道的药物重新利用策略将有助于快速开发治疗IPF和其他相关病症的药物。
