Knowledge Graphs for Pharmaceutical Research, Regulatory & Pharmacovigilance

A pharmaceutical company runs on documents that nobody outside the regulatory affairs team can read end-to-end: USPI labels, EMA SmPCs, IND/NDA modules, clinical study reports, FAERS narratives, and tens of thousands of MedDRA-coded adverse-event line listings. KnodeGraph reads all of it and assembles a graph of molecules, indications, mechanisms, comparators, populations, endpoints, AEs, and regulators. Medical affairs uses it to build evidence dossiers; pharmacovigilance uses it to spot signal clusters; regulatory teams use it to map a portfolio's labelling drift across jurisdictions.

Why Pharma teams choose KnodeGraph

  • ClinicalTrials.gov listed roughly 510,000 studies as of late 2024 (NLM annual report); a single oncology asset's competitive landscape routinely pulls from 80–200 of those protocols, each with structured eligibility, endpoint, and intervention metadata that maps cleanly to graph nodes.
  • MedDRA v27 (Q1 2024) defines 27,000+ Lowest Level Terms across 27 System Organ Classes — KnodeGraph stores both the verbatim AE text and the coded LLT/PT, so a reviewer sees the original narrative and the controlled-vocabulary node side by side.
  • FDA's Structured Product Labeling (SPL) standard publishes labels as XML with explicit sections for indications, contraindications, drug interactions, and warnings; ingestion turns each section into a typed subgraph rather than a flat block of prose.
  • EMA's PSUR/PBRER format and PMDA's J-PSUR have overlapping but non-identical section structures — a graph view reveals where a portfolio's narrative diverges between the two regulators (a common audit finding in 2023 PBRER inspections).
  • 21 CFR Part 11 and Annex 11 mandate audit trails and electronic-signature controls for GxP-regulated systems; KnodeGraph self-host preserves the original document, ties every extracted edge to a page-level provenance link, and never mutates source files — the architecture maps cleanly onto Part 11's integrity requirements.
  • ICD-10-CM (US) has ~73,000 codes; ICD-11 (WHO, 2022) introduces post-coordination — entity disambiguation against both code systems is exactly the kind of curation a pharmacist or coder can do faster in a graph staging UI than in a spreadsheet.
  • DrugBank's commercial release covers 17,000+ drugs and Open Targets links 60,000+ targets to disease evidence — KnodeGraph projects can reuse those public graphs as seed data and overlay the company's internal trial findings on top.

A typical engagement workflow

1.Pool the regulatory and scientific corpus

USPI labels, EMA SmPCs, EPARs, PMDA review reports, NICE technology appraisals, key NEJM/Lancet trial publications, your own CSRs, and the relevant ClinicalTrials.gov XMLs. KnodeGraph ingests SPL XML, PDF, DOCX, and CSV in the same project.

2.Pick a life-sciences template

Templates: 'Asset Dossier' (molecule, MoA, indication, comparators, endpoints), 'Pharmacovigilance Signal' (drug, AE, MedDRA PT, demographic, dechallenge), 'Regulatory Mapping' (label section, jurisdiction, change history, citation). Each shapes Claude's extraction toward the right entity types.

3.Reconcile codings

Free-text AE narratives often need MedDRA LLT/PT mapping; condition mentions need ICD-10-CM or SNOMED CT alignment. Claude proposes the codes; a medical reviewer confirms or corrects in the staging UI before they hit the graph.

4.Spot signals and gaps

Filter to 'reported_with' edges across drugs sharing a metabolic pathway — high-degree clusters of unexpected AE terms are early signal candidates. Filter to comparator nodes with no head-to-head trial edges to find the evidence gaps your medical affairs team should commission.

5.Hand off to regulatory or medical writing

Export label-section subgraphs as JSON for downstream eCTD module 2.5 drafting, or PNG/SVG for evidence-map figures in advisory board decks. The graph itself becomes a durable artefact across submission cycles.

Why KnodeGraph is the right fit

  • •Templates encode pharma-specific entity types (active moiety, route of administration, MedDRA PT, ICD-10-CM, ATC class) so extractions are typed and reviewable, not loose strings.
  • •Provenance back to the source page and section makes every edge audit-friendly — exactly what a regulatory inspector or pharmacovigilance auditor wants to see.
  • •Self-host plan deploys inside a GxP-validated environment with the company's own Anthropic API key under a BAA, supporting 21 CFR Part 11 / Annex 11 controls.
  • •100+ language support handles cross-regional regulatory work — Japanese PMDA review reports, Mandarin NMPA filings, and German BfArM correspondence land in the same graph as US/EU sources.
  • •Cheaper than a domain-specific BERT pipeline and faster than a Veeva Vault custom rollup — Pro at $14.99/mo lets a single medical affairs analyst pilot the workflow without procurement overhead.
  • •Cytoscape-based visualisation ships PNG/SVG ready for advisory-board decks, conference posters, and regulatory module figures.

Common roles that benefit

  • Medical affairs scientists building evidence dossiers across a therapeutic area
  • Pharmacovigilance analysts triaging FAERS / Eudravigilance signal patterns
  • Regulatory affairs writers maintaining label-change history across regions
  • Clinical operations teams mapping competitive trial landscapes for protocol design
  • HEOR researchers connecting trial endpoints to real-world outcome evidence
  • Medical information specialists answering HCP queries from a structured graph rather than a search bar

Regulatory and compliance context

  • 21 CFR Part 11 (FDA) and EU Annex 11 require audit trails, electronic signatures, and validated systems for GxP records — the self-hosted plan deploys inside a validated environment with controlled change management.
  • EMA's GVP modules (Good Pharmacovigilance Practices) require documented signal-detection methodology; a graph view of AE clusters with provenance to source narratives supports the GVP Module IX expectations.
  • FDA's 21 CFR 314.80 mandates expedited reporting of serious unexpected AEs — KnodeGraph does not replace the safety database but accelerates the literature-screening step that feeds it.
  • ICH E2D and E2E guidelines define the safety-data structures KnodeGraph entity templates align to (drug, indication, AE term, seriousness, causality).
  • GDPR Article 9 governs special-category health data; the self-hosted plan keeps any patient-level data inside the same compliance perimeter as the source CSRs.
  • EU AI Act (in force from 2024, full application 2026) classifies certain medical-AI systems as high-risk; KnodeGraph's human-in-the-loop staging workflow aligns with the Act's risk-management and human-oversight requirements.

KPIs this maps to

  • Hours per dossier on evidence synthesis (target: -40% with structured extraction across labels, EPARs, and key trials)
  • Signal-detection cycle time in pharmacovigilance (graph-based clustering shortens triage)
  • Coverage: % of in-scope ClinicalTrials.gov protocols and key publications represented in the asset graph
  • Label-discrepancy detection rate across USPI, SmPC, and J-PSUR for the same molecule
  • Cross-functional reuse: how often a medical affairs graph informs the next regulatory submission or HEOR analysis

Frequently Asked Questions

Is KnodeGraph validated for GxP / 21 CFR Part 11 environments?

The hosted SaaS is not a validated GxP system. For GxP-regulated workflows (CSR-level analysis, regulated label authoring, formal pharmacovigilance signal management), the self-hosted plan deploys inside a validated environment your quality team controls — your IQ/OQ/PQ scripts, your audit logs, your change control. The Anthropic API runs under your own BAA. Most teams use the hosted SaaS for medical affairs evidence work and the self-host for anything that lands in eCTD or a safety database.

Can it handle MedDRA, ICD-10-CM, ICD-11, SNOMED CT, and RxNorm coding?

Claude is well-trained on all of these and can propose codings inline during extraction. For production-grade coding (regulatory submission, safety database entry), export the graph as JSON and run it through a dedicated terminology service — UMLS Metathesaurus, MedDRA's official browser, or a vendor like Apelon — and feed the validated codes back. The graph staging UI is the right place to catch the obvious misalignments before that handoff.

How does this compare to Veeva Vault, Iqvia E360, or Embase?

Veeva Vault is a regulated content management system — KnodeGraph isn't trying to replace it. Iqvia E360 and Embase are vendor-curated literature platforms. KnodeGraph is the structured-extraction-and-mapping layer that sits over your own document set, whether that's checked out of Vault, downloaded from Embase, or pulled from ClinicalTrials.gov. Most pharma teams keep Vault as the system of record and use KnodeGraph for the synthesis step.

What about EMA / PMDA / NMPA documents in non-English languages?

Claude handles 100+ languages, including Japanese PMDA review reports, Mandarin NMPA filings, German BfArM correspondence, and French ANSM documents. The graph holds entities in their native script and language; relationships work the same way. For multinational asset graphs covering FDA + EMA + PMDA simultaneously, this is one of the strongest reasons teams pick KnodeGraph over English-only vendor tools.

Can I use it for FAERS / Eudravigilance signal detection?

It's a useful upstream tool but not a replacement for a safety database (Argus, ArisG, Veeva Safety). Drop in FAERS quarterly data extracts and a folder of relevant case-narrative PDFs; KnodeGraph builds the drug-AE-population graph and surfaces unexpected high-degree clusters. A pharmacovigilance scientist then triages those leads in the safety database. Treat KnodeGraph as the signal-screening layer, not the formal signal-management system.

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