Drug herbestemming Hypothese Generation Met behulp van de "RE: fine Drugs" System
概要
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.
Abstract
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.
Introduction
De dure en inefficiënte processen die verband houden met de traditionele drug discovery aanpak, met inbegrip van high-throughput drug en lead compound screening, dragen bij tot vertragingen bij het vertalen van onderzoek ontdekkingen in therapieën voor patiënten 1,2. Een gemiddelde van $ 1000000000 en 15-20 jaar is nodig om een nieuw geneesmiddel van de bank aan het bed 3 te brengen. Voorts 52% van geneesmiddelen falen tijdens de ontwikkeling in fase 1 klinische onderzoeken, en slechts 25% verbindingen die fase 2 voert verder volledig in fase 3 klinische studies 4. Het doel van het geneesmiddel herbestemming of drugs herpositionering is om mislukte drugs vernieuwen en / of zoek nieuwe indicaties voor goedgekeurde geneesmiddelen om nieuwe therapieën te leveren aan patiënten sneller en met een hoger slagingspercentage. Drug herbestemming kan de tijdlijn om drugs beschikbaar voor gebruik bij patiënten tot 3-12 jaar 5 te verminderen. Belangrijke medische toepassingen voor drug herbestemming zijn: ziekten met een slechte Prognosis en lage overlevingskansen, resistente ziekten, ondergefinancierd ziekte onderzoeksgebieden en arme en achtergestelde patiëntenpopulaties.
Computational drug herbestemming wordt gedefinieerd als het proces van het ontwerpen en valideren van geautomatiseerde workflows die hypotheses voor nieuwe indicaties voor een kandidaat-geneesmiddel 6 kan genereren. Bestaande computational drug herbestemming methoden zijn ingedeeld target-based, op kennis gebaseerde, op handtekeningen gebaseerde, netwerk-gebaseerde, en gerichte-mechanisme-gebaseerde, en kan worden georiënteerd van gen, ziekte of drug perspectieven. Bovendien kan computationele benaderingen verder te versnellen proof-of-concept validatie experimenten en kleinschalige klinische studies voor hergebruikt kandidaat-geneesmiddelen 7. We hebben eerder gemeld op "RE: boete Drugs", een vrij verkrijgbaar, interactieve web-based tool voor drug herbestemming hypothese te genereren op basis van de transitieve theorie van de Drug-Gene-Disease relaties 8. De algehele gOAL van deze methode is om systematisch integreren verschillende soorten drugs, genetische en klinische gegevens drug inschakelen herbestemming voor gebruikers van verschillende gemeenschappen, met inbegrip van klinische, ondernemingen en regelgevende gemeenschappen. De fundamentele methoden voor dit systeem zijn eerder gemeld voor het gebruik van genoom-brede associatie studie (GWAS) en phenome-brede associatie studie (PheWAS) gegevens in drug herbestemming onderzoek 9,10. De nieuwe combinatie van deze typen data onderscheidt onze webtool uit andere target-based methoden 6,11.
De RE: fijne Drugs systeem bevat op dit moment 60.911 drug herbestemming hypotheses die 916 drugs, 567 genen en 1770 ziekten. De webtool biedt een gebruiksvriendelijke interface voor onderzoekers om interactief te zoeken drug herbestemming hypotheses en prioriteren ze met behulp van diverse criteria. Zo kunnen gebruikers filteren drug herbestemming hypotheses met ondersteuning in de biomedische literatuur en klinische proeven databaSE, significante p-waarden, vereniging odds ratio of specifieke indicaties. De enige vereiste voor dit systeem is toegang tot internet.
Protocol
Representative Results
Discussion
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.
開示
The authors have nothing to disclose.
Acknowledgements
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.
Materials
| 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 |
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