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

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.

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Publication Details

Journal
Nucleic Acids Research
Published
2025-12-12
DOI
https://doi.org/10.1093/nar/gkaf1312
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00

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article

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

О Н Ли, May Lim, David C. Fajgenbaum, Kathleen Carter et al.
Nucleic Acids Research
Biomedical Text Mining and Ontologies
article

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

О Н Ли, May Lim, David C. Fajgenbaum, Kathleen Carter, Sabrina Toro, C. Bizon, Elizabeth M. Sharp, Nicolas Matentzoglu, Kushal Koirala, Marcello DeLuca, Tudor Ionel Oprea, Kevin Schaper, Melissa Haendel, Jianchun Li, Alexander Tropsha, E.L. Ding, Laurens Vijnck, Pascal Brokmeier, Jacques Vergine, Charlie Hempstead, Piotr Kaniewski
article en

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.

Nucleic Acids ResearchVol. 54(D1)
University of North Carolina at Chapel Hill (US), Senologic Hellenic Society (GR), Semantic Designs (United States) (US), Expert System (Italy) (IT), ILC Dover (United States) (US), Renaissance Computing Institute (US), University of Pennsylvania (US), Philadelphia University (US)
Advanced Research Projects Agency
Openalex Percentile: Top 64%
Biomedical Text Mining and Ontologies
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