CTG-DB: An Ontology-Based Transformation of ClinicalTrials.gov to Enable Cross-Trial Drug Safety Analyses

TL;DR

This paper introduces CTG-DB, an open-source database transforming ClinicalTrials.gov data into a standardized, ontology-based format to facilitate scalable, cross-trial drug safety analyses and pharmacovigilance.

Contribution

It presents a novel pipeline that converts CT.gov data into a MedDRA-aligned relational database, enabling systematic safety data integration and analysis.

Findings

Supports arm-level denominators and comparator arms

Enables cross-trial safety concept retrieval

Facilitates scalable pharmacovigilance analyses

Abstract

ClinicalTrials .gov (CT .gov) is the largest publicly accessible registry of clinical studies, yet its registry-oriented architecture and heterogeneous adverse event (AE) terminology limit systematic pharmacovigilance (PV) analytics. AEs are typically recorded as investigator-reported text rather than standardized identifiers, requiring manual reconciliation to identify coherent safety concepts. We present the ClinicalTrials .gov Transformation Database (CTG-DB), an open-source pipeline that ingests the complete CT .gov XML archive and produces a relational database aligned to standardized AE terminology using the Medical Dictionary for Regulatory Activities (MedDRA). CTG-DB preserves arm-level denominators, represents placebo and comparator arms, and normalizes AE terminology using deterministic exact and fuzzy matching to ensure transparent and reproducible mappings. This framework enables concept-level retrieval and cross-trial aggregation for scalable placebo-referenced safety analyses and integration of clinical trial evidence into downstream PV signal detection.