Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: “RE:fine Drugs.” This protocol can be modified to a user’s preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options.
The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created “RE:fine Drugs,” a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the “RE:fine Drugs” system and explores the different advanced options through a case study.
与传统药物发现方法,包括高通量药物和先导化合物筛选相关的昂贵和低效的过程,是造成延误翻译研究发现转化为治疗病人1,2。 1十亿美元和15 – 20年的平均时需携带从板凳上一个新的药物床边3。此外,药品52%的第一阶段临床试验在开发过程中失败了,只有25%即进入第二阶段的化合物进行充分三期临床研究4。再利用药物或药物重新定位的目标是续约失败的药物和/或找到批准的药品适应症新颖,以提供新的治疗方法给患者更快的速度和更高的成功率。药品再利用可能会降低时间表,使现有的药物在患者中使用,以3-12岁5。药品再利用的重要医疗应用包括:与差PROGNOS疾病是低存活率,抗药性疾病,资金不足的疾病的研究领域和贫困和缺医少药的病人群体。
计算药物再利用被定义为设计和验证自动化的工作流程,可以生成用于为候选药物6新适应症假设的过程。现有的计算药物再利用的方法已被归类目标为基础,以知识为基础的,基于签名的,基于网络和基于目标的机制,并可以从基因,疾病或药物的角度来导向。此外,计算方法可能会进一步加速证明了概念验证性实验和小规模临床研究为改变用途的候选药物7。我们曾报道的“RE:精药品”,一个免费的,互动式的网上药品再利用产生假设基于药物基因与疾病的关系8的传递理论工具。整体摹这种方法的OAL是系统集成不同类型的药物,遗传和临床资料,以使药物再利用从不同的社区,包括临床,工业和监管社区用户。该系统的基本方法先前已经报道了利用全基因组关联分析(GWAS),并在药物phenome范围内的关联研究(PheWAS)数据再利用的研究9,10。这些类型的数据的新组合,我们区别于其他基于目标的方法6,11 webtool。
该RE:精药物制度目前包含60911药物再利用假设覆盖916药品,567个基因和1,770疾病。该webtool为研究人员提供交互式搜索药物重复利用的假设和使用不同的标准优先考虑他们一个友好的用户界面。例如,用户可以过滤药物再利用假设用在生物医学文献支持和临床试验databaSE,显著p值,关联比值比或具体指示。此系统的唯一要求是上网。
The protocol described here for the RE:fine Drugs interactive dashboard can be modified in different ways according to the user’s preferences. This method uniquely integrates GWAS and PheWAS data as a novel paradigm underlying drug repurposing hypothesis generation. Specifically, this system provides access to both 52,966 PheWAS associations and 7,945 GWAS associations with advanced options to filter the results by the study type, effect size and/or significance level. Another advantage of this method over existing computational drug repurposing tools is that queries may be made from drug, gene or disease perspectives.
There are several limitations to this method. Currently, the PheWAS data is limited to primarily adult patient population from five institutions contained in the Electronic Medical Records and Genomics (eMERGE) network with a mean age of 69.5 years 12. Additionally, the “repurposing potential” feature uses co-occurrence of search terms in Medline abstracts as one of its criteria. It is well known that text mining methods using co-occurrence have limitations with respect to syntactical structure and literature bias. Thus, we recommend this feature be used as a starting point to explore the potential novelty and/or evidence supporting specific drug repurposing hypotheses and recommend additional investigation into the biomedical literature and clinical trial databases.
Future directions for this work not described here would be to extend this database to additional sources of GWAS and PheWAS data as they become available. Similar efforts to systematically translate results from large-scale GWAS studies into drug repurposing hypotheses have been previously published 9,13-14. It may be useful to compare these different workflows to predict drug candidates from GWAS data in future studies. Additionally, several other methods exist to computationally generate drug repurposing hypotheses from different data sources, including: genomics, transcriptomics, chemical structures, drug side effect profiles, as previously summarized 6,11. Future methodological advancements could also include automating drug combination predictions and providing information on drug toxicity to guide follow up studies for drug candidates.
Furthermore, the hypotheses generated from RE:fine Drugs may be further validated using electronic health records, before initiating clinical trials 15. Finally, future studies will be needed to compare this system to other target-based drug repurposing methods.
The authors have nothing to disclose.
This work was partially supported by the National Institutes of Health (NIH) Clinical and Translational Science Awards (CTSA) Grant (UL1TR001070) to the Ohio State University’s Center for Clinical and Translational Science (CCTS) and the National Library Of Medicine under Award Number T15LM011270.
Access the homepage for “RE:fine Drugs” at the following link: http://drug-repurposing.nationwidechildrens.org. | n/a | n/a | The only requirement for this system is Internet access |