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

Jeffery L. Painter, François Haguinet, Andrew Bate

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.

CTG-DB: An Ontology-Based Transformation of ClinicalTrials.gov to Enable Cross-Trial Drug Safety 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.
Paper Structure (20 sections, 1 equation, 2 figures, 1 table)

This paper contains 20 sections, 1 equation, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Data source and acquisition
  4. XML parsing and intermediate representations
  5. Demographic extraction and harmonization
  6. Adverse event terminology normalization to MedDRA
  7. Relational schema design and implementation
  8. Database creation and ingestion workflow
  9. Case study: placebo-referenced cross-trial screening
  10. Case study implementation
  11. Event definition and MedDRA grouping
  12. Arm-level event proportion
  13. Placebo aggregation and reference thresholds
  14. Comparative metrics
  15. Visualization of arm-level screening results
  16. ...and 5 more sections

Figures (2)

  • Figure 1: Conceptual overview of the CTG-DB relational schema showing the primary tables and relationships linking trial metadata, arm-level structures, and adverse event normalization to MedDRA. Arm-level denominators and comparator structure are preserved to support placebo-referenced cross-trial aggregation. Only principal tables and relationships are shown.
  • Figure 2: Arm-level adverse event proportions across anonymized investigational products (A–C) and placebo. Each bar represents the proportion of participants experiencing the MedDRA-normalized event within a single clinical trial arm. The red dashed line indicates the 75th percentile of aggregated placebo proportions and the blue dashed line indicates the maximum observed placebo proportion. Trial phase is color-coded.