Integrating knowledge graphs and retrieval-augmented generation to identify clinical pharmacology gaps in infectious disease trial protocols
Year: 2026
Type: article
Abstract: Purpose: Clinical trial protocols contain critical clinical pharmacology information that is often scattered across different sections of these lengthy documents. The purpose was to evaluate whether a retrieval-augmented generation (RAG) protocol review integrated with knowledge graphs (KGs) could be used to obtain clinical pharmacology evidence and identify regulatory gaps. Methods: Clinical trial protocols were obtained from the Gates Foundation's publicly available corpus of clinical trials in low- and middle-income countries. The RAG protocol review was used to assess the protocols against Food & Drug Administration (FDA) guidance documents for hepatic impairment, exposure-response, population PK (PopPK), and physiologically based pharmacokinetic modeling (PBPK). Disease-specific KG analyses were conducted for tuberculosis (TB), human immunodeficiency virus (HIV), malaria, and neglected tropical diseases (NTD). KG models connecting studies to drugs, diseases, pharmacologic classes, organ-function exclusions, biomarkers, and pharmacokinetic (PK) parameter features were obtained. Results: Across 42 unique trials, the KG contained 223 nodes and 376 edges. TB protocols produced the largest disease-specific graph, whereas HIV had the highest frequency of PK studies. Gap analysis showed that hepatic impairment exclusions in TB were more frequent than PK studies. In the multi-guidance evaluation, faithfulness was high, answer relevancy was lower, and the clinical pharmacology metric showed guidance-dependent differences. Conclusion: The integrated KG-RAG approach can help highlight potential gaps in clinical pharmacology reporting in infectious disease clinical trial protocols.
Source: Frontiers in Pharmacology
Authors: Shreyas Waikar, Murali Ramanathan
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Awards: Research Foundation for the State University of New York - To offer a genAI-powered recommendation engine to improve the informativeness of pharmacokinetic and pharmacodynamic components of global health re..., Research Foundation for the State University of New York - To offer new evidence on best-practices for Foundation grantees to design pharmacometric portions of clinical research.
OpenAlex ID: W7221824095