Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
The paper introduces CTG-DB, an open-source pipeline that transforms the ClinicalTrials.gov XML archive into a relational database aligned with standardized adverse event (AE) terminology using MedDRA. This transformation addresses the limitations of ClinicalTrials.gov's registry-oriented architecture and heterogeneous AE terminology, which hinder systematic pharmacovigilance (PV) analytics. CTG-DB enables concept-level retrieval and cross-trial aggregation for scalable placebo-referenced safety analyses, facilitating the integration of clinical trial evidence into downstream PV signal detection.
Unlock cross-trial drug safety analysis with CTG-DB, an open-source tool that transforms ClinicalTrials.gov data into a standardized, relational database.
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.