Articles and use cases on pharmaceutical and medical knowledge management: ontologies, semantic search, AI-ready data, and regulatory intelligence.
Regulatory submissions must demonstrate that every claim traces back to verified data. In most organisations those links are maintained as narrative text across dozens of documents. A knowledge graph-based traceability layer makes the evidence chain machine-readable, queryable, and automatically verifiable — reducing preparation time and improving reviewer confidence.
An ontology is only as valuable as the governance processes that keep it accurate, current, and trusted. Data governance for ontology-managed knowledge assets requires specific organisational structures, change control processes, and quality metrics that differ from conventional data governance frameworks.
Prior regulatory approvals — public assessment reports, review memoranda, approval letters — contain a vast and largely untapped knowledge base about what evidence regulators consider sufficient for specific approval decisions. Structured mining of this precedent knowledge transforms regulatory strategy from experience-dependent art to evidence-informed science.
Most pharmaceutical organisations have accumulated internal clinical terminologies — project-specific coding systems, legacy database value sets, local disease classifications — that must be mapped to MedDRA or SNOMED CT for regulatory reporting and cross-system interoperability. Building defensible, maintainable mappings requires a systematic methodology.
ICH M11 defines a harmonised structure for clinical study protocols and introduces the concept of a digital protocol that can be machine-processed by regulatory agencies. Implementing M11 with a semantic data model transforms protocol authoring from a document process into a knowledge management process.
IDMP — the ISO standard for Identification of Medicinal Products — requires pharmaceutical data to be expressed using standardised reference data in precisely defined data structures. Organisations that have invested in ontology-driven data governance find IDMP compliance far more achievable than those that have not.