Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
December 11th, 2016
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 overall goal of this software tool is to generate and prioritize drug repurposing hypothesis based on both genome-wide association study data and phenome-wide association study data. This method can help answer questions in the drug discovery field, including which available drugs can be repurposed when you use disease indications and which candidates have more support in the literature. The main advantage of this technique is that multiple entry points are available to explore the repurposing potential whether it be from the perspective of specific disease, gene, or drug.
Access the home page for RE:fine Drugs. Begin by entering a query term in the search bar. This term can be a generic drug name, a new disease indication or a gene.
Filter the search bar function to include only drug name, new disease indication, gene symbol, or all categories. The search bar includes an autofill function for query entries. Put in a keyword and click on the Search button.
The table of results can be stored by multiple options as listed in the top row. Navigate to the advanced search option to enable the drug information feature. Click on icon in the Info column for a particular drug.
Observe a page that list the P-Value for the association, disease name, drug name gene details, and drug details. Click on the Advanced button located on the right side of page in several options to further refine the results provided. The advanced search options include modifications to drug, association, disease, potential, gene, and action.
Export the results tables by clicking on the Export button on the right side of the page. Click on the Simple button to pull down the advanced search window. Under the advanced option drug tab, specify a particular drug indication or an additional drug name to filter the results.
Click on the association tab to filter the results by the significance level P-Value, adjusted P-Value with false discovery rate, effect size, and/or study type. Under the disease tab, specify a certain disease description for the predicted new use. Within the potential tab, filter the results according to wether the drug indication is contained in the DrugBank database, the number of medline abstracts with co-occurence of the drug and the disease, the number of ClinicalTrials.
gov database entries with this co-occurrence, as well as the repurposing potential. Under the gene tab, enter a single-nucleotide polymorphism identifier or gene symbol to filter the results by specific drug target genes. Click on the action tab and specify the drug action type against the drug target as either agonist, antagonist, other, unknown, or all.
In this example, the gene IL2RB was entered as a gene-based query and 12 drug repurposing hypotheses for the IL2RB gene are returned. The detailed information page for a particular drug repurposing hypothesis is provided from the info column. Shown here are only those drugs with a known indication for the transplant disease term.
The association tab allows the user to filter SNP phenotype relationships by statistical significance and by genetic effect size. Genetic effect size is defined as the odds of the presence of disease in individuals with the specific genotype over the odds in individuals without the SNP allele. The results under the potential tab can be filtered by a minimum number of five medline abstracts containing a co-occurrence of drug and disease terms.
In this example, all drug results under the gene tab are targeted for the IL2RB gene corresponding to the original query term. Finally, results filtered as agonists under the action tab return all drugs that act as agonists on the IL2RB gene. After watching this video, you should have a good understanding of how to use the RE:fine Drugs tool to generate and prioritize drug repurposing hypothesis.
Overview
This article describes the use of the web-based drug repurposing tool, RE:fine Drugs, which allows users to generate and prioritize drug repurposing hypotheses based on genomic and phenomic data. The protocol can be customized according to user preferences, including query types such as gene, drug, or disease.
Key Study Components
Area of Science
- Drug repurposing
- Genomic data analysis
- Phenomic data analysis
Background
- Drug repurposing can accelerate the drug discovery process.
- Genome-wide and phenome-wide association studies provide valuable data for hypothesis generation.
- RE:fine Drugs offers multiple entry points for exploring drug repurposing potential.
- The tool supports various query types to enhance user experience.
Purpose of Study
- To provide a flexible tool for generating drug repurposing hypotheses.
- To facilitate exploration of drug, gene, and disease relationships.
- To prioritize hypotheses based on existing literature and data.
Methods Used
- Users enter query terms related to drugs, diseases, or genes.
- The search function includes autofill and filtering options.
- Advanced search options allow for detailed filtering of results.
- Results can be exported for further analysis.
Main Results
- The tool returns multiple drug repurposing hypotheses based on user queries.
- Results can be filtered by significance levels and genetic effect sizes.
- Users can view detailed information about each hypothesis.
- Filtering options enhance the relevance of results based on user needs.
Conclusions
- RE:fine Drugs is a versatile tool for drug repurposing research.
- It enables researchers to leverage existing data for new therapeutic insights.
- The tool's flexibility supports various research inquiries in drug discovery.
Frequently Asked Questions
Executive Industry Relevance
Drug repurposing accelerates therapeutic discovery by leveraging existing safety profiles and clinical data, reducing time and cost in early-stage hypothesis generation. The RE:fine Drugs system integrates GWAS and PheWAS data to prioritize candidates with mechanistic and epidemiological support, enabling data-driven target validation. This approach enhances predictive confidence in selecting repurposing opportunities for portfolio triage and lead identification.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Interrogates therapeutic hypotheses by linking genetic variants to disease phenotypes through drug-gene-disease triads.
- Operational Value: Enables systematic exploration of repurposing candidates from disease, gene, or drug entry points.
- Predictive Value: Prioritizes candidates using literature support, association strength, and statistical significance to reduce mechanistic ambiguity.
Screening & Assay Development
- Scientific Value: Prepares biologically grounded hypotheses for downstream validation in phenotypic or target-based screens.
- Operational Value: Standardizes candidate selection through reproducible filtering by P-value, effect size, and literature co-occurrence.
- Scalability: Supports export of filtered results for integration into compound screening workflows and assay readiness assessments.
Translational & Preclinical Research
- Translational Value: Aligns genetically validated drug-disease associations with preclinical models for mechanistic de-risking.
- Predictive Confidence: Uses effect size and replication thresholds to support go/no-go decisions in target validation.
- Continuity: Bridges discovery-phase hypothesis generation with preclinical validation through exportable, annotation-rich datasets.
Pipeline & Workflow Integration
The RE:fine Drugs system supports early discovery by generating repurposing hypotheses that inform lead identification and preclinical validation stages through data-driven prioritization.
- Discovery Biology: Supports hypothesis testing by linking GWAS/PheWAS signals to drug targets and disease indications.
- Screening: Enhances assay readiness by delivering filtered, prioritized compound lists for validation in cellular or phenotypic models.
- Analytics: Delivers quantitative outputs including P-values, effect sizes, and literature metrics to enable cross-condition comparison.
- Translational Research: Connects genetic associations to disease mechanisms, supporting biomarker alignment and pathway validation.
- Enterprise Reuse: Functions as a reusable dashboard for iterative hypothesis generation across therapeutic areas and discovery campaigns.
Operational & Enterprise Impact
- Scientific Value: Increases target validation confidence through genetically anchored drug-disease associations.
- Operational Value: Ensures reproducibility via standardized filtering and exportable result tables.
- Strategic Value: Improves go/no-go decisions by reducing biological risk in early repurposing candidates.
- Portfolio Impact: Enables risk-adjusted prioritization of repurposing opportunities based on evidence strength and data quality.
Implementation Considerations
- Requires familiarity with GWAS/PheWAS interpretation and drug-target databases.
- Depends on access to genomic, phenotypic, and literature data sources for optimal performance.
- Necessitates cross-team standardization of filtering criteria for consistent hypothesis prioritization.
- Involves adaptation across disease areas and target classes through customizable query and filter settings.
- Limited by the completeness and accuracy of input GWAS/PheWAS datasets and drug annotation resources.
