Introduction
Pharmaceutical data models are only as valuable as their semantic consistency. With increasing reliance on digital health records, regulatory data submissions, and automated pharmacovigilance systems, pharma companies must align their terminologies to ensure safe and compliant data exchange. Standards like MedDRA, SNOMED CT, and WHO Drug Dictionary (WHO-DD) are essential vocabularies in this landscape—but aligning them across systems, geographies, and departments is no small feat.
In this article, we explore how to model semantic alignment using Archi and Sparx EA, combining visual and metadata-driven approaches to create machine-readable, traceable, and standards-compliant pharmaceutical models.
1. The Need for Semantic Alignment in Pharma
Pharma organizations face semantic silos caused by:
- Different clinical trial platforms using SNOMED vs MedDRA
- Safety systems referencing WHO-DD but integrating with EMR systems using FHIR codes
- Inconsistent mapping between E2B(R3) regulatory submissions and internal dictionaries
Semantic inconsistencies lead to reporting delays, adverse event misclassification, and regulatory noncompliance.
2. Overview of Key Vocabularies
- MedDRA: Hierarchical terminology for adverse event classification, required in safety reporting and pharmacovigilance (ICH, EMA, FDA).
- SNOMED CT: Comprehensive clinical terminology used in electronic health records, diagnostics, and patient care.
- WHO-DD: Drug dictionary from the WHO used for product coding, clinical trial registration, and pharmacovigilance.
3. Modeling Terminology Structures in Sparx EA
Sparx EA allows you to define domain models for each terminology using UML class diagrams. You can:
- Create class hierarchies representing the MedDRA five-level structure (LLT → PT → HLT → HLGT → SOC)
- Model SNOMED CT as a graph of concepts and relationships (e.g., “is-a”, “has-finding-site”)
- Represent WHO-DD coding systems as UML stereotypes with tagged values (e.g., ATC, INN, Product Name)
Use reference data profiles to manage versions (e.g., MedDRA v25.0, SNOMED Jan 2024 release) across projects.
4. Visualizing Semantic Overlaps in Archi
Archi, with ArchiMate, enables visualization of semantic mappings between systems and domains:
- Map business processes (e.g., "Safety Reporting") to application components (e.g., "Oracle Argus") and their vocabularies
- Use Motivation layer to model the rationale for using a given standard (e.g., “Regulatory Mandate” → “Use MedDRA”)
- Visualize data object flows where translation or normalization occurs
Combine views to demonstrate where mappings exist and where gaps or risks occur.
5. Mapping Between Standards
Create canonical mapping diagrams using UML or ArchiMate:
- Map “Headache” in MedDRA to “Cephalalgia” in SNOMED
- Translate WHO-DD product codes to SNOMED CT substance IDs for clinical matching
- Document transformation rules in tagged values or documentation fields
These mappings can be exported as XMI or OWL to integrate with terminology servers or NLP engines.
6. Supporting Semantic Governance
EA tools like Sparx and Archi support governance through:
- Version control and model baselines for each terminology set
- Traceability between mappings, data lineage, and use cases (e.g., pharmacovigilance, labeling)
- Review workflows and model approvals using Prolaborate
7. Integration with External Ontologies
You can enhance your models by linking to external semantic resources:
- Link ArchiMate elements to SNOMED or MedDRA URIs
- Use RDF/OWL exports from Sparx to feed graph databases
- Integrate with terminology services like SNOMED Browser, NCBO BioPortal, or Ontoserver
8. Case Study: Semantic Modeling for Global PV System
A top-20 pharma company unified safety reporting across 110 countries. Using Sparx EA, they modeled adverse events using MedDRA, mapped to WHO-DD drug entities, and aligned data flows with SNOMED CT for integration into their EHR-linked RWE systems. The result:
- Reduced case reconciliation time by 60%
- Automated safety narrative translation in 8 languages
- Regulatory approval for digital reporting format across three agencies
Conclusion
Semantic interoperability is the future of pharmaceutical data governance. By using Sparx EA and Archi to create semantic models that align MedDRA, SNOMED, and WHO-DD, organizations can enhance regulatory compliance, improve patient safety, and enable automation at scale. Architecture is no longer just about systems—it’s about meaning.
Keywords
MedDRA, SNOMED CT, WHO-DD, Semantic Interoperability, Pharma Data Modeling, Sparx EA, Archi, ArchiMate, OWL, RDF, Terminology Mapping, Pharmacovigilance, E2B(R3), ICH Compliance, NLP in Safety, Ontology Modeling, Semantic Integration, Digital Health Standards, WHO Drug Dictionary, Clinical Terminology