You will be presented with the full text of a biomedical research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.

Paper text:
the costly and inefficient processes associated with traditional drug discovery approaches , including high - throughput drug and lead compound screening , are contributing to delays in translating research discoveries into therapies for patients .
dollars and 15 - 20 years is required to bring a new drug from the bench to the bedside .
further , 52% of drugs fail during development in phase 1 clinical trials , and only 25% of compounds that enter phase 2 proceed into full phase 3 clinical studies .
the goal of drug repurposing or drug repositioning is to renew failed drugs and/or find novel indications for approved drugs in order to deliver new therapies to patients faster and with a higher success rate .
drug repurposing may decrease the timeline to make drugs available for use in patients to 3 - 12 years .
important medical applications for drug repurposing include : diseases with poor prognosis and low survival rates , drug - resistant diseases , underfunded disease research areas and impoverished and underserved patient populations .
computational drug repurposing is defined as the process of designing and validating automated workflows that can generate hypotheses for new indications for a drug candidate .
existing computational drug repurposing methods have been categorized target - based , knowledge - based , signature - based , network - based , and targeted - mechanism - based , and can be oriented from gene , disease or drug perspectives .
furthermore , computational approaches may further accelerate proof - of - concept validation experiments and small - scale clinical studies for repurposed drug candidates .
we have previously reported on " re : fine drugs " , a freely available , interactive web - based tool for drug repurposing hypothesis generation based on the transitive theory of drug - gene - disease relationships .
the overall goal of this method is to systematically integrate diverse types of drug , genetic and clinical data to enable drug repurposing for users from diverse communities , including clinical , industry and regulatory communities .
the foundational methods for this system have been previously reported for the use of genome - wide association study ( gwas ) and phenome - wide association study ( phewas ) data in drug repurposing research .
the novel combination of these types of data distinguishes our webtool from other target - based methods .
the re : fine drugs system currently contains 60,911 drug repurposing hypotheses covering 916 drugs , 567 genes and 1,770 diseases . the webtool provides a user - friendly interface for researchers to interactively search drug repurposing hypotheses and prioritize them using diverse criteria .
for instance , users can filter drug repurposing hypotheses with support in the biomedical literature and clinical trials database , significant p - values , association odds ratios or by specific indications .
access the homepage for " re : fine drugs " at the following link : http://drug-repurposing.nationwidechildrens.org .
begin by entering a query term in the search bar from any of the following three categories : drug ( generic drug name ) , disease ( new disease indication ) or gene ( official hgnc gene symbol ) . filter the search bar function to include only " drug name " , " new disease indication " , " gene symbol " or " all " categories .
the search bar includes an auto - fill function for query entries . put in a keyword , and click on the " search " button .
sort the table of results by any of the following columns : " drug " , " registered indication " , " p - value " , " p - value adjusted " , " odds ratio " , " study " , " new indication " , " drug bank indication " , " # of medline abstracts " , " # of clinical trial registry " , " potential " , " snp " , " gene " or " action " .
observe a page that lists the all the corresponding information including p - value for the association , disease name , drug name , gene details ( ncbi gene link ) and drug details ( drugbank link ) .
click on the " advanced " button located on the right side of the page , and several options to further refine the results are provided .
the advanced search options include modifications to the following : drug , association , disease , potential , gene and action .
export results tables by clicking on the " export " button on the right side of the page .
click on the " simple " button in order to fold down the advanced search window . under the advanced option " drug " tab , specify a particular drug indication or an additional drug name to filter results . under the " association " tab , filter results by the significance level p - value , adjusted p - value with fdr , effect size ( odds ratio ) , and/or study type ( gwas , phewas or both ) . under the " disease " tab ,
specify a certain disease description for the predicted new use . under the " potential " tab , filter results according to any of the following criteria : ( i ) whether the drug indication is contained in the drugbank database , ( ii ) number of medline abstracts with co - occurrence of the drug and the disease , ( iii ) number of clinicaltrials.gov database entries with co - occurrence of the drug and the disease and ( iv ) repurposing potential .
note : the repurposing potential option describes the novelty of the discovery : ( i ) known : relationship already exists in the drugbank database , ( ii ) strongly supported : some support in both clinical trial registry and the medline abstracts , ( iii ) likely : some support in either clinical trial registry or the medline abstracts and ( iv ) novel : no evidence in clinical trial registry nor in medline abstracts . under the " gene " tab ,
enter a snp identifier or gene symbol to filter results by specific drug target genes . under the " action " tab ,
specify the drug action type against the drug target(s ) , including agonist , antagonist , other , unknown or all ( source : drugbank database ) .
access the homepage for " re : fine drugs " at the following link : http://drug-repurposing.nationwidechildrens.org .
begin by entering a query term in the search bar from any of the following three categories : drug ( generic drug name ) , disease ( new disease indication ) or gene ( official hgnc gene symbol ) . filter the search bar function to include only " drug name " , " new disease indication " , " gene symbol " or " all " categories .
the search bar includes an auto - fill function for query entries . put in a keyword , and click on the " search " button .
sort the table of results by any of the following columns : " drug " , " registered indication " , " p - value " , " p - value adjusted " , " odds ratio " , " study " , " new indication " , " drug bank indication " , " # of medline abstracts " , " # of clinical trial registry " , " potential " , " snp " , " gene " or " action " .
observe a page that lists the all the corresponding information including p - value for the association , disease name , drug name , gene details ( ncbi gene link ) and drug details ( drugbank link ) .
click on the " advanced " button located on the right side of the page , and several options to further refine the results are provided .
the advanced search options include modifications to the following : drug , association , disease , potential , gene and action .
export results tables by clicking on the " export " button on the right side of the page .
click on the " simple " button in order to fold down the advanced search window . under the advanced option " drug " tab , specify a particular drug indication or an additional drug name to filter results . under the " association " tab ,
filter results by the significance level p - value , adjusted p - value with fdr , effect size ( odds ratio ) , and/or study type ( gwas , phewas or both ) . under the " disease " tab , specify a certain disease description for the predicted new use . under the " potential " tab , filter results according to any of the following criteria : ( i ) whether the drug indication is contained in the drugbank database , ( ii ) number of medline abstracts with co - occurrence of the drug and the disease , ( iii ) number of clinicaltrials.gov database entries with co - occurrence of the drug and the disease and ( iv ) repurposing potential .
note : the repurposing potential option describes the novelty of the discovery : ( i ) known : relationship already exists in the drugbank database , ( ii ) strongly supported : some support in both clinical trial registry and the medline abstracts , ( iii ) likely : some support in either clinical trial registry or the medline abstracts and ( iv ) novel : no evidence in clinical trial registry nor in medline abstracts . under the " gene " tab ,
enter a snp identifier or gene symbol to filter results by specific drug target genes . under the " action " tab ,
specify the drug action type against the drug target(s ) , including agonist , antagonist , other , unknown or all ( source : drugbank database ) .
in this example , the gene " il2rb " was entered as a gene - based query , and was automatically recognized as such by the auto - fill function ( figure 1 ) .
the twelve drug repurposing hypotheses for the il2rb gene are returned , as shown in figure 2 .
detailed information page for a particular drug repurposing hypothesis , " daclizumab " in this case , is provided from the " info " column ( figure 3 ) .
the results were filtered on the drug tab was by all results corresponding to the " daclizumab " drug , as shown in figure 4 .
figure 5 shows only those drugs with a known indication for the " transplant " disease term ( source : drugbank database ) .
the association tab allows the user to filter snp - phenotype relationships by statistical significance ( p - value ) and genetic effect size ( odds ratio ) , defined as the ratio of the odds of presence of disease in individuals with a specific genotype ( snp allele ) over the odds of presence of disease in individuals without the snp allele .
figure 6 shows the results filtered under the association tab within the p - value range of 0.000001 to 0.05 .
figure 7 shows drug repurposing hypotheses specific for " asthma " based on the new indications we found in the study .
figure 8 shows results under the potential tab to filter by a minimum number of 5 medline abstracts containing a co - occurrence of drug and disease terms . in this example , all drugs results under the gene tab are targeted for the " il2rb " gene , corresponding to the original query term ( figure 9 ) .
finally , figure 10 shows results filtered under the " action " tab to return all drugs that act as agonists on the il2rb gene .
users may begin a query by entering a drug name , new disease indication or gene symbol .
links are also provided for the gwas and phewas reference papers describing methodologies for generating drug repurposing hypotheses .
figure 2:auto - fill function for query entries . as an example , the gene query term " il2rb " was automatically recognized as a gene term .
figure 3:drug repurposing results table produced from a gene - based query ( e.g. , il2rb ) .
clicking on the icon from the " info " column shows detailed information for the drug daclizumab .
figure 5:advanced search option under drug tab to filter by a specific drug . in this example , three results are shown for the drug daclizumab
figure 6:advanced search option under drug tab to filter by a specific disease indication from the drugbank database .
all drugs with a known indication for the disease term " transplant " are shown .
figure 7 : advanced search option under association tab to filter by significance level . in this case , eight results are provided whose association significance level falls within the p - value range of 0.000001 to 0.05 .
figure 8:advanced search option under disease tab to filter by a specific disease indication we extracted in this study . in this example , four results are shown for asthma as a new use disease indication.please click here to view a larger version of this figure . figure 9:advanced search option under potential tab to filter by a co - occurrence of drug and disease terms in medline abstracts .
in this example , four results are shown that are supported by a minimum number of 5 medline abstracts containing a co - occurrence of drug and disease terms .
figure 10:advanced search option under gene tab to filter by a specific gene symbol , where all results correspond to the il2rb gene used for the original query . in this example , all drugs results under the gene tab are targeted for the " il2rb " gene , corresponding to the original query term . advanced search option under action tab to filter by agonist drugs only . in this example , six results are returned for all drugs that act as agonists on the il2rb gene .
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 .  
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 .
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 .
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 .
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 .
finally , future studies will be needed to compare this system to other target - based drug repurposing methods .
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 .