Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis
Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis
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DOI：
10.1177/0961203316657437
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发表时间：
2016-09-01
期刊：
影响因子：
2.6
通讯作者：
Lipsky, P. E.
中科院分区：
文献类型：
--
作者：
Grammer, A. C.;Ryals, M. M.;Lipsky, P. E.
狼疮患者需要现代药物来有效和安全地治疗其疾病的特定表现。在过去的半个世纪中，只有一种新的治疗方法被美国食品和药物管理局（FDA）批准用于系统性红斑狼疮（SLE）。在2014-2015年，FDA批准了71种新药，其中只有一种针对风湿性疾病，没有一种被批准用于SLE。使用多种方法重新定位/重新利用批准用于其他疾病的药物是为狼疮患者寻找新治疗选择的一种可能手段。大数据分析从公正的角度解决了这一挑战，而文献挖掘和通过CoLT（联合狼疮治疗评分）系统评估的候选人的众包提供了一种基于假设的方法来对可能的临床应用的潜在治疗候选人进行排名。这两种方法都降低了风险，因为评估的候选药物在很大程度上已经在其他适应症的临床试验中进行了广泛的测试。通过对狼疮患者样本中差异表达基因的大数据分析，对基于假设的药物重新定位预测进行正交确认，突出了多管齐下的方法在狼疮药物重新定位中的有用性。目标是确定有可能具体影响疾病过程的新疗法。通过LRxL-STAT等众包网站，SLE患者和研究这种疾病的科学家参与思考可能对狼疮有效的新药（www.linkedin.com/in/lrxlstat）对于刺激在狼疮临床研究者网络LuCIN（www.linkedin.com/in/lucinstat）进行的小型概念验证试验中快速测试这些新型药物靶标在狼疮中的疗效所需的动力非常重要。
Lupus patients are in need of modern drugs to treat specific manifestations of their disease effectively and safely. In the past half century, only one new treatment has been approved by the US Food and Drug Administration (FDA) for systemic lupus erythematosus (SLE). In 2014-2015, the FDA approved 71 new drugs, only one of which targeted a rheumatic disease and none of which was approved for use in SLE. Repositioning/repurposing drugs approved for other diseases using multiple approaches is one possible means to find new treatment options for lupus patients. Big Data analysis approaches this challenge from an unbiased standpoint whereas literature mining and crowd sourcing for candidates assessed by the CoLTs (Combined Lupus Treatment Scoring) system provide a hypothesis-based approach to rank potential therapeutic candidates for possible clinical application. Both approaches mitigate risk since the candidates assessed have largely been extensively tested in clinical trials for other indications. The usefulness of a multi-pronged approach to drug repositioning in lupus is highlighted by orthogonal confirmation of hypothesis-based drug repositioning predictions by Big Data analysis of differentially expressed genes from lupus patient samples. The goal is to identify novel therapies that have the potential to affect disease processes specifically. Involvement of SLE patients and the scientists that study this disease in thinking about new drugs that may be effective in lupus though crowd-sourcing sites such as LRxL-STAT (www.linkedin.com/in/lrxlstat) is important in stimulating the momentum needed to test these novel drug targets for efficacy in lupus rapidly in small, proof-of-concept trials conducted by LuCIN, the Lupus Clinical Investigators Network (www.linkedin.com/in/lucinstat).