Jeffery L. Painter

Jeffery L. Painter, François Haguinet, Andrew Bate

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.

Maria Kefala, Jeffery L. Painter, Syed Tauhid Bukhari et al.

Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata. Methods: Current and historical Summaries of Product Characteristics (SmPCs) for all centrally authorized products (n=1,513) were retrieved from the EU Union Register of Medicinal Products (data lock: 15 December 2025). Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs. Regulatory metadata, including labelling changes, were programmatically extracted. Time indexing was based on the date of AE inclusion in the SmPC. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations. Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%). Safety updates peaked around 2012. Gastrointestinal, skin, and nervous system disorders were the most represented System Organ Classes. Drugs had a median of 48 AEs across 14 SOCs. Conclusions: The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.

Jeffery L. Painter, Venkateswara Rao Chalamalasetti, Raymond Kassekert et al.

Objective: To enhance the efficiency and accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural language queries (NLQs) into Structured Query Language (SQL) queries, leveraging a business context document. Materials and Methods: We utilized OpenAI's GPT-4 model within a retrieval-augmented generation (RAG) framework, enriched with a business context document, to transform NLQs into syntactically precise SQL queries. Each NLQ was presented to the LLM randomly and independently to prevent memorization. The study was conducted in three phases, varying query complexity, and assessing the LLM's performance both with and without the business context document. Results: Our approach significantly improved NLQ-to-SQL accuracy, increasing from 8.3\% with the database schema alone to 78.3\% with the business context document. This enhancement was consistent across low, medium, and high complexity queries, indicating the critical role of contextual knowledge in query generation. Discussion: The integration of a business context document markedly improved the LLM's ability to generate accurate and contextually relevant SQL queries. Performance achieved a maximum of 85\% when high complexity queries are excluded, suggesting promise for routine deployment. Conclusion: This study presents a novel approach to employing LLMs for safety data retrieval and analysis, demonstrating significant advancements in query generation accuracy. The methodology offers a framework applicable to various data-intensive domains, enhancing the accessibility and efficiency of information retrieval for non-technical users.