Integrated ML-Based Strategy Identifies Drug Repurposing for Idiopathic Pulmonary Fibrosis

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.

据估算，全球约有300万人群受特发性肺纤维化（Idiopathic Pulmonary Fibrosis, IPF）影响。IPF是一种病因不明的疾病，以肺部瘢痕组织形成为特征，可引发进行性呼吸系统疾病。目前，美国食品药品监督管理局（FDA）仅批准了两款针对IPF的特异性小分子药物，这使得IPF治疗药物的快速研发成为迫切需求。此外，全新药物研发（de novo drug development）耗时耗力，成功率不足10%。对于这种罕见散发性疾病，药物重定位目前是实现药物快速上市的最可行方案。传统药物重定位流程通常先借助计算工具筛选FDA已获批药物，但该方式的筛选命中率较低。本研究开发了一种集成机器学习的药物重定位策略，通过引入基于机器学习的预对接（predock）预测，辅以文献验证、基因集富集分析（Gene Set Enrichment Analysis, GSEA）验证及药物通路预测，以显著降低假阳性结果。本研究将该策略应用于1480款FDA已获批药物及当前处于IPF临床试验阶段的药物，针对"TGFB1"、"TGFB2"、"PDGFR-α"、"SMAD-2/3"、"FGF-2"等多种蛋白进行筛选，共得到247款候选药物，其中27款具备潜在重定位价值。文献及GSEA验证结果显示，247款候选药物中有72款（29.14%）已被用于IPF治疗研究，13款（5.2%）已应用于肺纤维化治疗，另有20款（8%）已在囊性纤维化、肾纤维化等其他纤维化疾病中开展测试。对剩余142款药物进行通路预测，共得到118条不同的通路。进一步分析发现，118条通路中有29条直接或间接参与IPF发病过程，其中11条为直接参与。此外，本研究还提出了15种潜在药物组合，其在IPF治疗中可展现出较强的协同效应。本文所报道的药物重定位策略，将有助于快速开发IPF及其他相关疾病的治疗药物。