{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusion: The integrated KG-RAG approach can help highlight potential gaps in clinical pharmacology reporting in infectious disease clinical trial protocols.",
  "abstract_shown": true,
  "affiliations": [
    "Artificial Intelligence and Clinical Pharmacology Laboratory, Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shreyas Waikar"
    },
    {
      "affs": [
        0
      ],
      "name": "Murali Ramanathan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6fqj7mz87frf8.bib",
  "cited_by": 0,
  "doi": "10.3389/fphar.2026.1955011",
  "fulltext_url": "https://europepmc.org/articles/PMC13713297",
  "id": "6fqj7mz87frf8",
  "issns": [
    "1663-9812"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1955011/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
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  "openalex": "W7221824095",
  "pdf_url": "https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2026.1955011/pdf",
  "pmcid": "PMC13713297",
  "pmid": "42952574",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/6fqj7mz87frf8.ris",
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    {
      "first_seen": "2026-10-30T12:20:06Z",
      "native_id": "10.3389/fphar.2026.1955011",
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      "url": "https://www.frontiersin.org/articles/10.3389/fphar.2026.1955011/full"
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    {
      "first_seen": "2026-10-31T12:23:52Z",
      "native_id": "PMC13713297",
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      "url": "https://doi.org/10.3389/fphar.2026.1955011"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Integrating knowledge graphs and retrieval-augmented generation to identify clinical pharmacology gaps in infectious disease trial protocols",
  "title_text": "Integrating knowledge graphs and retrieval-augmented generation to identify clinical pharmacology gaps in infectious disease trial protocols",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:20:59Z",
  "url": "https://research.draconitic.com/paper/6fqj7mz87frf8",
  "venue": "Frontiers in Pharmacology",
  "versions": []
}