Ulm J Wes, Barthélémy Florian, Nelson Stanley F
Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Center for Duchenne Muscular Dystrophy at UCLA, Los Angeles, CA, United States.
Front Cell Dev Biol. 2023 Aug 17;11:1226707. doi: 10.3389/fcell.2023.1226707. eCollection 2023.
Duchenne Muscular Dystrophy (DMD)'s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations.
杜氏肌营养不良症（DMD）复杂的多系统病理生理学，再加上多年药物筛选和随访的高昂成本，阻碍了新治疗方法的探索。在此，我们进行了一项基于系统历史和文本挖掘的试点可行性研究，以探索已确立或先前测试过的药物作为潜在DMD治疗药物的潜力。我们的方法采用了一种受斯旺森关联启发的方法，以揭示具有药理学意义的DMD靶点与针对不相关疾病开发的药物之间有意义但在很大程度上隐藏的深层语义联系。具体而言，我们将基于分子靶点的医学主题词（MeSH）术语和类别作为高产出的生物信息学代理，用分类元数据有效地标记相关文献。为了识别有前景的线索，我们全面收集了2011年及后续年份的已发表报告。然后，我们确定了相关细胞靶点的不同MeSH术语或类别标签与药物一起被引用的最早年份，以及该相关靶点本身首次被最终确定对DMD具有治疗价值的年份。通过比较药物被确定为DMD治疗候选药物的最早年份与首次实际报告证实这一点的年份，我们计算了一个延迟发现指数（IDD），它作为斯旺森关联潜在知识的一个指标。利用这些发现，我们从通过基于MeSH的斯旺森关联子集化的先前未关联文章或药物银行存储库中的目标类别中识别数据。这使我们能够识别出对DMD重新利用特别感兴趣且未经测试的新的高潜力小分子候选药物，值得进一步研究。
