"RE: İnce ilaçlar kullanılması" İlaç repurposing Hipotez Üretim Sistemi
Summary
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
Yüksek verimli ilaç ve kurşun bileşik taraması da dahil olmak üzere geleneksel ilaç keşfi yaklaşımları ile ilişkili masraflı ve verimsiz süreçler, hastalar 1,2 tedavilerin araştırmalar keşifler çeviri gecikmeler katkıda bulunmaktadır. 1 milyar dolar ve 15-20 yıllık bir ortalama başucunda 3 tezgah yeni bir ilaç getirmek için gereklidir. Dahası, ilaçların% 52 faz 1 klinik çalışmalarda gelişimi sırasında başarısız ve faz 2 girin bileşiklerin sadece% 25'i tam faz 3 klinik çalışmalarda 4 içine geçin. ilaç repurposing veya ilaç yeniden konumlandırma hedefi başarısız ilaçları yenilemek ve / veya daha hızlı hastalara yeni tedaviler sunmak ve daha yüksek bir başarı oranı ile amacıyla onaylanan ilaçlar için yeni endikasyonlar bulmaktır. İlaç yeniden amaç 3-12 yıl 5 hastalarda kullanılmak üzere ilaçların kullanılabilir hale getirmek için zaman çizelgesini azaltabilir. İlaç repurposing için önemli tıbbi uygulamalar şunlardır: fakir Prognos ile hastalıklarıve düşük sağkalım oranları, ilaca dirençli hastalıklar, yeterince hastalık araştırma alanları ve yoksul ve yeterli hizmet alamayan hasta popülasyonları.
Bilgisayar destekli ilaç yeniden amaç tasarımı ve bir ilaç adayı 6 için yeni endikasyonlar için hipotezler üretebilir otomatik iş akışları doğrulama süreci olarak tanımlanır. Mevcut hesaplama ilaç repurposing yöntemleri, hedef bazlı, bilgiye dayalı, imza tabanlı, ağ tabanlı kategorize ve hedefli bir mekanizma bazlı ve gen, hastalık veya ilaç perspektiflerden yönlendirilebilir edilmiştir. Ayrıca, hesaplamalı yaklaşımlar daha repurposed ilaç adayları 7 proof-of-concept doğrulama deneyleri ve küçük ölçekli klinik çalışmalar hızlandırabilir. Biz daha önce "RE: İnce İlaçlar" bildirdin, İlaç-Gen-Hastalık ilişkileri 8 geçişli teorisine dayalı ilaç repurposing hipotez üretimi için bir serbestçe kullanılabilir, etkileşimli web tabanlı bir araç. genel gBu yöntemin oal sistematik klinik, sanayi ve düzenleyici topluluklar da dahil olmak üzere çeşitli topluluklar, kullanıcılar için repurposing ilaç sağlamak için ilaç, genetik ve klinik verilerin farklı türlerini entegre etmektir. Bu sistem için temel yöntemler, daha önce genom boyu bağlantı çalışması (GWAS) ve araştırmayı 9,10 repurposing ilaç phenome çapında dernek çalışması (PheWAS) veri kullanımı için rapor edilmiştir. Veri bu tür yeni bir kombinasyonu diğer hedef bazlı yöntemler 6,11 bizim webtool ayırır.
RE: İnce İlaçlar sistemi şu anda 916 ilaç, 567 gen ve 1770 hastalıkları kapsayan 60.911 ilaç repurposing hipotez içeriyor. webtool interaktif ilaç yeniden amaç hipotezler arama ve çeşitli kriterleri kullanarak bunları öncelik araştırmacılar için kullanıcı dostu bir arayüz sağlar. Örneğin, kullanıcılar biyomedikal literatürde destek ve klinik çalışmalarda databa ilaç yeniden amaç hipotez süzebilirsinizse, önemli p-değerleri, dernek odds oranları veya belirli endikasyonlar tarafından. Bu sistem için tek şart Internet erişimi.
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.
Divulgations
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 |
References
- Borisy, A. A., et al. Systematic discovery of multicomponent therapeutics. Proc Natl Acad Sci U S A. 100 (13), 7977-7982 (2003).
- Zhang, L., et al. High-throughput synergy screening identifies microbial metabolites as combination agents for the treatment of fungal infections. Proc Natl Acad Sci U S A. 104 (11), 4606-4611 (2007).
- Adams, C. P., Brantner, V. V. Estimating the cost of new drug development: is it really 802 million dollars?. Health Aff (Millwood). 25 (2), 420-428 (2006).
- Bunnage, M. E. Getting pharmaceutical R&D back on target. Nat Chem Biol. 7 (6), 335-339 (2011).
- Ashburn, T. T., Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 3 (8), 673-683 (2004).
- Hurle, M. R., et al. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 93 (4), 335-341 (2013).
- Jin, G., Wong, S. T. Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today. 19 (5), 637-644 (2014).
- Moosavinasab, S., et al. 34;RE:fine Drugs": An Interactive Dashboard to Access Drug Repurposing Opportunities. Database (Oxford). , (2016).
- Zhang, J., et al. Use of genome-wide association studies for cancer research and drug repositioning. PLoS One. 10 (3), e0116477 (2015).
- Rastegar-Mojarad, M., Ye, Z., Kolesar, J. M., Hebbring, S. J., Lin, S. M. Opportunities for drug repositioning from phenome-wide association studies. Nat Biotechnol. 33 (4), 342-345 (2015).
- Li, J., et al. A survey of current trends in computational drug repositioning. Brief Bioinform. 17 (1), 2-12 (2016).
- Denny, J. C., et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 31 (12), 1102-1110 (2013).
- Wang, H., et al. Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets genome-wide association studies. Clin Pharmacol Ther. 97 (5), 451-454 (2015).
- Sanseau, P., et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 30 (4), 317-320 (2012).
- Xu, H., et al. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc. 22 (1), 179-191 (2015).
