The pharmaceutical industry operates under some of the most stringent regulatory requirements globally. From clinical trials to post-market surveillance, compliance reporting demands precision, transparency, and exhaustive documentation—often spanning 10+ years of data. Yet, for many organizations, this process remains manual, error-prone, and siloed.

In a recent livestream hosted by Semantic Arts, experts Doug Beeson, Irina Filitovich, and Eliud Polanco explored how knowledge graphs and semantic technologies can revolutionize regulatory compliance within pharmaceutical organizations. 

This blog recaps the key themes, lessons learned, and actionable strategies to automate compliance reporting while building reusable data assets.

The Compliance Challenge: Why Manual Processes Fall Short

The 10-Year Data Integration Problem

Developing a drug involves decades of data generated across fragmented teams. Pre-clinical research encompasses target identification, in-vitro assays, and animal studies. This is followed by clinical trials that include phases I-III and safety/efficacy monitoring. Manufacturing processes generate batch records and quality control data, while post-market surveillance involves tracking adverse events.

Each stage relies on specialized systems (LIMS, ERPs, CRMs) with siloed data models. By the time companies compile a Common Technical Document (CTD)—a 1,000+ page submission to regulators like the FDA or EMA—teams spend months manually reconciling spreadsheets, PDFs, and legacy databases. As Eliud Polanco notes, “A typical marketing authorization application can take 40 people 24 months to compile. This isn’t sustainable.”

The Cost of Fragmented Terminology

Inconsistent terminology further amplifies these inefficiencies. A “test result” might be labeled differently in a Pre-Clinical Research system versus a Clinical Trial system. Legacy systems often lack machine-readable metadata, forcing manual mapping. Additionally, global submissions require multilingual labeling, which complicates harmonization efforts.

Without standardized vocabularies, organizations risk delays, misinterpretations, and non-compliance. These challenges highlight the need for a more integrated approach to managing regulatory data.

The Solution: Knowledge Graphs as a Unified Data Layer

Shift from Data Integration to Knowledge Management

Traditional ETL pipelines solve immediate problems but create technical debt. Knowledge graphs, by contrast, prioritize reusable, interoperable data assets. A semantic layer acts as a central hub, harmonizing data from disparate systems (ERP, LIMS, CRMs) using RDF triples and SPARQL queries. Federated queries extract insights across multiple databases without physical consolidation, while ontologies define relationships between entities (e.g., Drug → Batch → Manufacturing Site) to contextualize data.

As Doug Beeson emphasizes, “Regulatory compliance isn’t a one-time project. It’s about building a living knowledge asset.” This perspective transforms compliance from a burden into a strategic opportunity.

Ontologies Bridge the Terminology Gap

Ontologies like gistPharma (Semantic Arts’ domain-specific framework) provide standardized taxonomies with pre-defined terms for assays, drug formulations, and clinical outcomes. They enable interoperability by aligning internal data with public biomedical ontologies (e.g., SNOMED, MeSH) for regulator-friendly reporting. These frameworks also offer flexibility to extend models to capture proprietary processes while maintaining compliance.

With data semantically linked, organizations can generate eCTD sections dynamically via SPARQL, validate compliance in real-time by flagging missing toxicity reports, and update submissions globally when manufacturing processes change. This connectivity creates efficiencies previously impossible with traditional data management approaches.

Lessons Learned from the Frontlines

Start Small, Scale with Governance

Implementation success depends on starting with high-impact use cases such as adverse event reporting. Involving domain experts like scientists and QA teams ensures the semantic layer addresses real-world needs. Organizations should adopt iterative governance to refine taxonomies as standards evolve, creating a feedback loop that continually improves the knowledge framework.

Embrace Open Standards

Rather than reinventing the wheel, companies should leverage public ontologies like Mondo and MedDRA. Initiatives such as Pistoia Alliance’s Pharma Ontology Project curate industry-wide best practices that can accelerate implementation. Building on these established foundations saves time while ensuring compatibility with broader industry standards.

Think Beyond Compliance

A semantic layer unlocks secondary benefits beyond regulatory reporting. It can accelerate drug repurposing by revealing connections between compounds and indications. The structured data creates AI/ML-ready datasets for predictive analytics. Perhaps most importantly, it enables cross-functional collaboration between R&D and manufacturing teams, breaking down traditional organizational silos.

From Reactive to Proactive Compliance

Regulators increasingly demand machine-readable submissions, as evidenced by the FDA’s FHIR initiative. Knowledge graphs future-proof organizations by enabling real-time API-based reporting, reducing validation cycles via pre-harmonized data, and supporting AI-driven audits and anomaly detection. These capabilities transform compliance from a reactive burden into a proactive advantage.

Conclusion

Automating regulatory compliance isn’t about replacing humans with algorithms. It’s about empowering teams with contextualized, reusable knowledge that transcends silos. By adopting semantic technologies, pharma companies can transform a decade-long burden into a strategic asset—one that accelerates innovation while ensuring patient safety.

Next Steps:

Attend the Knowledge Graph Conference (May 6–9, NYC) for our live workshop on this topic.

Connect with Fluree or Semantic Arts to kickstart a 90-day scoped trial.