Pharma & Life Sciences
Use Ontologies to Accelerate Discovery & Meet Regulatory Requirements
The Problem
Fragmented Data
Research, clinical, operational, claims, and sales data live in separate systems with no unified view — making it impossible to identify drug candidates, patient cohorts, or safety signals without lengthy integration projects.
Manual Workflows
Data preparation for regulatory submissions, clinical trial design, and pharmacovigilance relies heavily on manual processes — introducing risk, delay, and cost at every step of R&D.
Discovery Bottlenecks
R&D timelines stretch years and budgets spiral due to disconnected hypothesis generation, opaque AI reasoning, and regulatory transparency requirements that existing data infrastructure cannot meet.
The Perfect Ontology Solution
Why It Matters Now
- R&D timelines of 10–15 years and budgets of $2B+ per approved drug demand faster, cheaper discovery cycles
- Regulatory transparency (FDA, EMA) requires explainable, traceable AI-driven decisions
- Patient access to therapies depends on faster trial design and regulatory alignment
- AI provenance and explainability are now regulatory requirements, not just aspirations
- Competitive pressure from biotech and AI-native companies accelerates the need for connected data infrastructure
Solution & Features
- Unify research, clinical, operational, claims, and commercial sales data in one queryable ontology
- Map HCP prescribing patterns and influence networks across therapeutic areas
- Connect claims, EHR, and trial data for patient cohort identification
- Identify drug indications and repurposing opportunities through knowledge graph reasoning
- Value-level data lineage for regulatory submission and audit
- AI molecular insights with explainable, traceable reasoning chains
- Graph-level analytics without graph database infrastructure
Use Cases
- Drug repurposing — identify new indications from connected molecular and clinical data
- Patient cohort identification for clinical trial recruitment
- HCP influence mapping for targeted medical affairs
- Safety signal detection across pharmacovigilance databases
- Clinical trial optimisation and site selection
- Regulatory submission preparation with full data lineage
- AI-driven hypothesis generation with explainable reasoning
Benefits
- Shorter research cycles — go from hypothesis to validated candidate faster
- Lower clinical trial risk with better-matched patient cohorts
- Faster regulatory alignment with built-in lineage and explainability
- Rapid iteration on AI-driven discovery with traceable reasoning
- Graph-level insights across molecular, clinical, and commercial data without graph databases
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Resource Center
Perspectives on our customers, future of data, AI, and ontologies.
A whitepaper discussing the role of executable ontologies in enhancing enterprise security.
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