Technical Whitepapers and Methodology Guides
In-depth publications on benefit-risk methodology, AI in pharmacovigilance, and regulatory science.
The ArcaScience Methodology: AI-Driven Benefit-Risk Analysis
Comprehensive overview of the platform's scientific foundation. Covers the AS Profiling Base data architecture, 24 AI model taxonomy, BRAT framework implementation, and regulatory alignment with FDA and EMA guidance.
BRAT Framework Implementation: From Theory to Automated Practice
How ArcaScience operationalizes the Benefit-Risk Action Team (BRAT) framework. Step-by-step guide to automated value tree construction, effects table generation, and scenario modeling for regulatory submissions.
Domain-Specific AI vs General-Purpose Models for Pharmacovigilance
Comparative analysis of approach effectiveness. Benchmark study demonstrating why purpose-trained models outperform general LLMs for adverse event extraction, MedDRA coding, and causality assessment.
Automating PSUR/PBRER: A Technical Guide
ICH E2C(R2) alignment and automation methodology. Detailed walkthrough of data integration, signal detection, literature synthesis, and submission-ready document generation with full regulatory traceability.
Signal Detection at Scale: Methods and Validation
Disproportionality analysis and AI-augmented detection. Technical deep-dive on PRR, ROR, MGPS, BCPNN methods enhanced with deep learning for 3x faster signal identification and reduced false positives.
Benefit-Risk Evidence for HTA Bodies: Extending Regulatory Analysis
From regulatory BRA to payer value story. How to repurpose regulatory benefit-risk analysis for NICE, G-BA, and other health technology assessment submissions with comparative effectiveness evidence.