RepoDB: A Standard Database for Drug Repositioning
RepoDB is a comprehensive database designed to support drug repositioning research by providing a standard set of drug repositioning successes and failures. This resource enables fair and reproducible benchmarking of computational repositioning methods by offering curated data on approved drugs, their therapeutic indications, and clinical trial outcomes.
Overview
Drug repositioning (also known as drug repurposing) involves finding new therapeutic uses for existing approved drugs. RepoDB addresses the critical need for standardized datasets in this field by providing:
- Repositioning Successes: Drugs that have been successfully repositioned for new indications
- Repositioning Failures: Clinical trials that did not result in successful repositioning
- Approved Drug-Indication Pairs: Current therapeutic uses of approved drugs
- Clinical Trial Data: Outcomes from ClinicalTrials.gov
Data Sources and Methodology
RepoDB integrates data from two primary sources:
DrugCentral
- Comprehensive database of approved drugs and their indications
- Provides information on drug properties, targets, and therapeutic uses
- Ensures data quality and standardization
ClinicalTrials.gov
- Clinical trial registry maintained by the NIH
- Source of clinical trial outcomes and repositioning attempts
- Provides data on both successful and failed repositioning efforts
Key Features
Web Interface Functionality
- Drug-centric searching: Explore repositioning data by specific drugs
- Disease-centric searching: Find repositioning opportunities by therapeutic area
- Interactive visualizations: Explore data characteristics and relationships
- Full dataset download: Access complete RepoDB data for computational analysis
Data Characteristics
- Curated drug repositioning successes and failures
- Standardized drug and disease identifiers
- Clinical trial phase information
- Temporal data on drug approvals and repositioning events
Applications
Research Applications
- Computational Method Benchmarking: Standard dataset for evaluating repositioning algorithms
- Drug Discovery: Identify potential repositioning opportunities
- Clinical Research: Understand patterns in successful and failed repositioning
- Pharmacovigilance: Study drug safety across different indications
Educational Use
- Teaching drug repositioning concepts
- Demonstrating computational drug discovery methods
- Case studies in pharmaceutical research
Data Updates
RepoDB is regularly updated to maintain current and comprehensive coverage:
- Original Version (2017): Developed by Harvard School of Medicine Patel Group
- 2020 Update: Enhanced by University of New Mexico with updated sources
- 2023 Update: Latest version with current DrugCentral, AACT, and UMLS data
Citation Guidelines
When using RepoDB, please cite:
- Brown AS, Patel CJ. A standard database for drug repositioning. Sci Data. 2017;4:170029.
Access and Usage
Data Download
- Full dataset available through the web interface
- CSV format for computational analysis
- Regular updates ensure data currency
Web Interface
- Interactive exploration at: https://unmtid-shinyapps.net/shiny/repodb/
- No registration required for basic access
- Educational and research use encouraged
License and Terms
RepoDB is released under the Creative Commons Attribution 4.0 International License, making it freely available for:
- Educational purposes
- Scientific research
- Commercial applications (with attribution)
Development Team
Current Maintainers
- University of New Mexico: Translational Informatics Division
- Data Science Team: Jeremy Yang and collaborators
Original Developers
- Harvard School of Medicine: Patel Group
- Principal Investigators: AS Brown and CJ Patel
Technical Specifications
- Data Format: CSV, web-accessible
- Update Frequency: Periodic updates with new source data
- Access Method: Web interface and direct download
- API: Web-based search and filtering capabilities