Clinical Data Integration for Drug Safety

Clinical Data Integration for Drug Safety

End-to-end drug safety review pipeline that integrates FDA label information, FAERS spontaneous reports, disproportionality signal detection, pharmacogenomic biomarkers, clinical trial data, and published literature. Designed for regulatory assessments, pharmacovigilance, and clinical decision support.

Guiding principles:

1. Label is ground truth -- FDA-approved labeling is the authoritative starting point for known safety information
2. Signals need context -- a FAERS signal without label or literature corroboration is hypothesis-generating, not confirmatory
3. Disproportionality is not causation -- PRR/ROR measure reporting patterns, not causal relationships
4. Pharmacogenomics narrows risk -- PGx biomarkers can identify which patients face elevated risk
5. Progressive reporting -- create the report file early; update section by section
6. English-first queries -- use English drug names in all tool calls; respond in the user's language

Clinical data integration starts with data harmonization. Different hospitals code the same diagnosis differently (ICD-10 vs SNOMED). Before merging datasets, verify the coding system. Missing data is informative — a missing lab value may mean the test wasn't ordered (patient was stable) not that the result was normal.

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

Differentiation: This skill emphasizes regulatory-grade data integration across the full drug lifecycle. For focused FAERS signal detection with quantitative scoring, see tooluniverse-adverse-event-detection. For general pharmacovigilance workflows, see tooluniverse-pharmacovigilance.

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

When to Use

Typical triggers:

• "Give me a full safety review for [drug]"
• "What does the FDA label say about [drug] and [event]?"
• "Are there FAERS signals for [drug]?"
• "What pharmacogenomic biomarkers exist for [drug]?"
• "Find clinical trials studying [drug] safety"
• "Post-market surveillance summary for [drug]"
• "Compare safety profiles of [drug A] and [drug B]"

Core Data Sources

| Source | Type | Best For |