{"title":"DRUGPATH: A New Database for Mapping Polypharmacology","authors":"Rajeev Jaundoo, T. Craddock","doi":"10.29173/aar92","DOIUrl":null,"url":null,"abstract":"While there are existing databases that curate only drug, target, or pathway data for instance, none of these alone are exhaustive. The Drug Gene Pathway (DRUGPATH) meta database was created as a response to the complex treatment required for various diseases including Gulf War Illness (GWI) and post-traumatic stress disorder (PTSD), where therapy involves using multiple drugs in combination. Here, drug-drug interactions can occur due to the promiscuous nature of pharmaceuticals, which can then lead to various side effects or can alternatively be utilized towards drug repurposing. The objective was to develop a database that maps the interactions between drugs, genes, pathways, and targets for use in the treatment of complex diseases, including the prediction of off-target interactions, otherwise known as side effects. Using MATLAB and Python scripts, interactions between known drugs, genes, targets, and pathways amalgamated from numerous expert-curated sources such as PharmGKB, DrugBank, DGIdb, ConsesusPathDB, Guide to PHARMACOLOGY, HUGO Gene Nomenclature Committee, Toxin and Toxin-Target Database, repoDB, the FDA’s National Drug Code database, etc. were mapped together. The raw data was first downloaded from its source and subsequently cleaned, where extraneous information such as data from non-humans, internal identifiers, timestamps, etc. were removed. The remaining information was then integrated into an SQLite database. DRUGPATH currently contains a total of 2,632,516 unique entries, and of these, there are 54,757 unique genes, 2,632,242 unique pathways, and 31,042 unique drugs. DRUGPATH allows researchers and clinicians to discern which pathways are affected by each drug, reducing the likelihood of an adverse drug reaction occurring. The incorporation of drug, gene, target, and pathway information makes DRUGPATH a powerful resource for predicting potential side effects when designing or refining a given drug combination therapy. Not only that, but we have additionally added the FDA status, half-life, and indication for each drug whenever possible for clinical applications of this database.","PeriodicalId":239812,"journal":{"name":"Alberta Academic Review","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alberta Academic Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29173/aar92","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
While there are existing databases that curate only drug, target, or pathway data for instance, none of these alone are exhaustive. The Drug Gene Pathway (DRUGPATH) meta database was created as a response to the complex treatment required for various diseases including Gulf War Illness (GWI) and post-traumatic stress disorder (PTSD), where therapy involves using multiple drugs in combination. Here, drug-drug interactions can occur due to the promiscuous nature of pharmaceuticals, which can then lead to various side effects or can alternatively be utilized towards drug repurposing. The objective was to develop a database that maps the interactions between drugs, genes, pathways, and targets for use in the treatment of complex diseases, including the prediction of off-target interactions, otherwise known as side effects. Using MATLAB and Python scripts, interactions between known drugs, genes, targets, and pathways amalgamated from numerous expert-curated sources such as PharmGKB, DrugBank, DGIdb, ConsesusPathDB, Guide to PHARMACOLOGY, HUGO Gene Nomenclature Committee, Toxin and Toxin-Target Database, repoDB, the FDA’s National Drug Code database, etc. were mapped together. The raw data was first downloaded from its source and subsequently cleaned, where extraneous information such as data from non-humans, internal identifiers, timestamps, etc. were removed. The remaining information was then integrated into an SQLite database. DRUGPATH currently contains a total of 2,632,516 unique entries, and of these, there are 54,757 unique genes, 2,632,242 unique pathways, and 31,042 unique drugs. DRUGPATH allows researchers and clinicians to discern which pathways are affected by each drug, reducing the likelihood of an adverse drug reaction occurring. The incorporation of drug, gene, target, and pathway information makes DRUGPATH a powerful resource for predicting potential side effects when designing or refining a given drug combination therapy. Not only that, but we have additionally added the FDA status, half-life, and indication for each drug whenever possible for clinical applications of this database.
虽然现有的数据库只管理药物、靶标或途径数据,但这些都不是详尽的。药物基因通路(DRUGPATH)元数据库的创建是为了响应各种疾病所需的复杂治疗,包括海湾战争病(GWI)和创伤后应激障碍(PTSD),其中治疗涉及多种药物联合使用。在这里,由于药物的混杂性质,药物-药物相互作用可能发生,这可能导致各种副作用,或者可以替代地用于药物再利用。目的是建立一个数据库,绘制药物、基因、途径和靶标之间的相互作用图,用于复杂疾病的治疗,包括预测脱靶相互作用,或称为副作用。使用MATLAB和Python脚本,从众多专家管理的来源(如PharmGKB、DrugBank、DGIdb、ConsesusPathDB、Guide to PHARMACOLOGY、HUGO基因命名委员会、Toxin and Toxin- target Database、repoDB、FDA的国家药物代码数据库等)中合并已知药物、基因、靶点和通路之间的相互作用被映射在一起。原始数据首先从其源下载,随后进行清理,其中删除了无关信息,例如来自非人类的数据、内部标识符、时间戳等。然后将剩余的信息集成到SQLite数据库中。DRUGPATH目前总共包含2,632,516个独特条目,其中有54,757个独特基因,2,632,242个独特通路和31,042个独特药物。DRUGPATH使研究人员和临床医生能够辨别每种药物影响哪些途径,从而减少发生药物不良反应的可能性。药物、基因、靶标和通路信息的结合使DRUGPATH成为设计或改进给定药物联合治疗时预测潜在副作用的强大资源。不仅如此,我们还额外添加了每种药物的FDA状态,半衰期和适应症,以便该数据库的临床应用。