SIDEKICK: A Semantically Integrated Resource for Drug Effects, Indications, and Contraindications

TL;DR

SIDEKICK is a comprehensive knowledge graph integrating drug effects, indications, and contraindications from FDA labels, enhancing semantic reasoning and interoperability for pharmacovigilance and drug repurposing.

Contribution

We developed SIDEKICK, a novel semantic resource using LLMs and ontology mapping to standardize drug safety data from FDA labels, improving upon existing datasets.

Findings

Outperforms SIDER and ONSIDES in drug repurposing tasks.

Processed over 50,000 drug labels with ontology mapping.

Enables automated safety surveillance and phenotype-based analysis.

Abstract

Pharmacovigilance and clinical decision support systems utilize structured drug safety data to guide medical practice. However, existing datasets frequently depend on terminologies such as MedDRA, which limits their semantic reasoning capabilities and their interoperability with Semantic Web ontologies and knowledge graphs. To address this gap, we developed SIDEKICK, a knowledge graph that standardizes drug indications, contraindications, and adverse reactions from FDA Structured Product Labels. We developed and used a workflow based on Large Language Model (LLM) extraction and Graph-Retrieval Augmented Generation (Graph RAG) for ontology mapping. We processed over 50,000 drug labels and mapped terms to the Human Phenotype Ontology (HPO), the MONDO Disease Ontology, and RxNorm. Our semantically integrated resource outperforms the SIDER and ONSIDES databases when applied to the task of drug repurposing by side effect similarity. We serialized the dataset as a Resource Description Framework (RDF) graph and employed the Semanticscience Integrated Ontology (SIO) as upper level ontology to further improve interoperability. Consequently, SIDEKICK enables automated safety surveillance and phenotype-based similarity analysis for drug repurposing.