# Medicines, Diseases, Indications, and Contraindications (MeDIC): a foundational resource to support drug repurposing

## TL;DR

MeDIC is a new open-source database that organizes drug, disease, and usage information to help find new drug uses.

## Contribution

MeDIC introduces a curated, ontology-mapped database of drugs and diseases from regulatory sources to aid drug repurposing.

## Key findings

- MeDIC includes curated data on drugs, diseases, indications, and contraindications from government sources.
- The database is ontology-mapped and available in multiple formats for easy use and integration.
- MeDIC is freely accessible and designed for adaptability and machine learning applications.

## Abstract

Drug databases typically aim to provide reference information on medications and their uses but often lack strict definitions of the terms drug (e.g. approved or a clinical candidate) or disease, and do not focus on any specific context of use. The recent emergence of biomedical knowledge graphs, which integrate diverse biomedical data into a contiguous, harmonized knowledge network, has enabled innovation in drug repurposing (identification of novel uses of existing drugs). This objective has created a new set of requirements and challenges for drug databases to be used for generating high-confidence, testable drug repurposing hypotheses. To address this challenge, we have developed MeDIC as an open, foundational database built from government regulatory sources only, which comprises highly curated lists of drugs (including combination therapies), diseases, indications (i.e. drug approvals to treat specific diseases), contraindications, and additional metadata. MeDIC allows for easy maintainability, open-source adaptability, and ongoing updates concordant with updates of primary sources. To facilitate downstream use, MeDIC is provided in a tabulated format, and each drug, disease, indication, or contraindication entry is mapped to multiple ontologies. We offer MeDIC as a web-based, freely accessible (https://medic.renci.org), downloadable (including lists and source code), searchable, and machine learning-friendly resource for patients, providers, and researchers.

Graphical Abstract