End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support for drug safety.
End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support 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:
Label is ground truth -- FDA-approved labeling is the authoritative starting point for known safety information
Signals need context -- a FAERS signal without label or literature corroboration is hypothesis-generating, not confirmatory
Disproportionality is not causation -- PRR/ROR measure reporting patterns, not causal relationships
Pharmacogenomics narrows risk -- PGx biomarkers can identify which patients face elevated risk
Progressive reporting -- create the report file early; update section by section
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
FDA Labels (DailyMed)
Regulatory
Approved safety information, boxed warnings, drug interactions
Output: {studies, nextPageToken, total_count} or string if no results
Workflow:
Search for safety-focused trials: "[drug] safety" or "[drug] adverse events"
Search for Risk Evaluation and Mitigation Strategies (REMS) trials
Look for post-marketing requirement (PMR) studies
Note trial status (recruiting, completed, terminated) and primary endpoints
Query tip: Simple queries work best. Complex multi-word queries often return no results. Search "[drug name]" first, then filter by safety-related keywords in the results.
Phase 5: Literature Evidence
Objective: Find published safety studies, case reports, and meta-analyses.
Tools:
PubMed_search_articles -- search biomedical literature
Input: query (search term), optional limit
Output: list of articles (plain list of dicts, NOT {articles: [...]})
Workflow:
Search: "[drug] adverse events" or "[drug] safety"
Search: "[drug] [specific adverse event]" for signals found in Phase 2
Look for systematic reviews and meta-analyses
Prioritize: meta-analyses > RCTs > cohort studies > case reports
Phase 6: Integrated Safety Report
Synthesize all phases into a cohesive report:
Drug Overview -- identity, class, mechanism, approval date, indications
Labeled Safety Information -- boxed warnings, key contraindications, known adverse reactions
Post-Market Signals -- FAERS signals with disproportionality metrics, compared to label
Distinguish: known and labeled vs known but under-labeled vs potential new signal
