Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.
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.
Automated pipeline for detecting, quantifying, and contextualizing adverse drug event signals using FAERS disproportionality analysis, FDA label mining, mechanism-based prediction, and literature evidence. Produces a quantitative Safety Signal Score (0-100) for regulatory and clinical decision-making.
KEY PRINCIPLES:
REASONING STRATEGY — Start Here: Start with the signal: What adverse event was reported more than expected? (PRR >= 2.0, N >= 3, lower CI > 1.0 is the threshold). Then ask three questions in order:
OpenTargets_get_drug_mechanisms_of_action_by_chemblId and drugbank_get_targets_by_drug_name_or_drugbank_id to check targets before asserting plausibility.Causality Assessment — Naranjo Algorithm Reasoning: When determining whether an adverse event is drug-caused (not just associated), apply these steps systematically. LOOK UP DON'T GUESS — search FAERS and FDA labels for each criterion:
FDA_get_adverse_reactions_by_drug_name) and literature (PubMed_search_articles). Yes = +1.FAERS_stratify_by_demographics for time-to-onset data.drugbank_get_drug_interactions_by_drug_name_or_id for interacting drugs.Reference files (in this directory):
PHASE_DETAILS.md - Detailed tool calls, code examples, and output templates per phaseREPORT_TEMPLATE.md - Full report template and completeness checklistTOOL_REFERENCE.md - Tool parameter reference and fallback chainsQUICK_START.md - Quick examples and common drug namesApply when user asks:
Differentiation from tooluniverse-pharmacovigilance: This skill focuses specifically on signal detection and quantification using disproportionality analysis (PRR, ROR, IC) with statistical rigor, produces a quantitative Safety Signal Score (0-100), and performs comparative safety analysis across drug classes.
Phase 0: Input Parsing & Drug Disambiguation
Parse drug name, resolve to ChEMBL ID, DrugBank ID
Identify drug class, mechanism, and approved indications
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Phase 1: FAERS Adverse Event Profiling
Top adverse events by frequency
Seriousness and outcome distributions
Demographics (age, sex, country)
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Phase 2: Disproportionality Analysis (Signal Detection)
Calculate PRR, ROR, IC with 95% CI for each AE
Apply signal detection criteria
Classify signal strength (Strong/Moderate/Weak/None)
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Phase 3: FDA Label Safety Information
Boxed warnings, contraindications
Warnings and precautions, adverse reactions
Drug interactions, special populations
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Phase 4: Mechanism-Based Adverse Event Context
Target-based AE prediction (OpenTargets safety)
Off-target effects, ADMET predictions
Drug class effects comparison
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Phase 5: Comparative Safety Analysis
Compare to drugs in same class
Identify unique vs class-wide signals
Head-to-head disproportionality comparison
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Phase 6: Drug-Drug Interactions & Risk Factors
Known DDIs causing AEs
Pharmacogenomic risk factors (PharmGKB)
FDA PGx biomarkers
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Phase 7: Literature Evidence
PubMed safety studies, case reports
OpenAlex citation analysis
Preprint emerging signals (EuropePMC)
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Phase 8: Risk Assessment & Safety Signal Score
Calculate Safety Signal Score (0-100)
Evidence grading (T1-T4) for each signal
Clinical significance assessment
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Phase 9: Report Synthesis & Recommendations
Monitoring recommendations
Risk mitigation strategies
Completeness checklist
Resolve drug name to ChEMBL ID, DrugBank ID. Get mechanism of action, blackbox warning status, targets, and approved indications.
OpenTargets_get_drug_chembId_by_generic_name, OpenTargets_get_drug_mechanisms_of_action_by_chemblId, OpenTargets_get_drug_blackbox_status_by_chembl_ID, drugbank_get_safety_by_drug_name_or_drugbank_id, drugbank_get_targets_by_drug_name_or_drugbank_id, OpenTargets_get_drug_indications_by_chemblIdQuery FAERS for top adverse events, seriousness distribution, outcomes, demographics, and death-related events. Filter serious events by type (death, hospitalization, life-threatening). Get MedDRA hierarchy rollup.
FAERS_count_reactions_by_drug_event, FAERS_count_seriousness_by_drug_event, FAERS_count_outcomes_by_drug_event, FAERS_count_patient_age_distribution, FAERS_count_death_related_by_drug, FAERS_count_reportercountry_by_drug_event, FAERS_filter_serious_events, FAERS_rollup_meddra_hierarchyCRITICAL PHASE. For each top adverse event (at least 15-20), calculate PRR, ROR, IC with 95% CI. Classify signal strength. Stratify strong signals by demographics.
FAERS_calculate_disproportionality, FAERS_stratify_by_demographicsFAERS_count_reactions_by_drug_event filters by MedDRA Lowest Level Term (reactionmeddraverse) while FAERS_calculate_disproportionality uses Preferred Terms. Case counts can differ dramatically — always use disproportionality analysis as the primary signal metric, not raw counts.PHASE_DETAILS.md for full signal classification tableExtract boxed warnings, contraindications, warnings/precautions, adverse reactions, drug interactions, and special population info. Note: {error: {code: "NOT_FOUND"}} is normal when a section does not exist.
FDA_get_boxed_warning_info_by_drug_name, FDA_get_contraindications_by_drug_name, FDA_get_warnings_by_drug_name, FDA_get_adverse_reactions_by_drug_name, FDA_get_drug_interactions_by_drug_name, FDA_get_pregnancy_or_breastfeeding_info_by_drug_name, FDA_get_geriatric_use_info_by_drug_name, FDA_get_pediatric_use_info_by_drug_name, FDA_get_pharmacogenomics_info_by_drug_nameGet target safety profile, OpenTargets adverse events, ADMET toxicity predictions (if SMILES available), and drug warnings.
OpenTargets_get_target_safety_profile_by_ensemblID, OpenTargets_get_drug_adverse_events_by_chemblId, ADMETAI_predict_toxicity, ADMETAI_predict_CYP_interactions, OpenTargets_get_drug_warnings_by_chemblIdHead-to-head comparison with class members using FAERS_compare_drugs. Aggregate class AEs. Identify class-wide vs drug-specific signals.
FAERS_compare_drugs, FAERS_count_additive_adverse_reactions, FAERS_count_additive_seriousness_classificationExtract DDIs from FDA label, DrugBank, and DailyMed. Query PharmGKB for pharmacogenomic risk factors and dosing guidelines. Check FDA PGx biomarkers.
FDA_get_drug_interactions_by_drug_name, drugbank_get_drug_interactions_by_drug_name_or_id, DailyMed_parse_drug_interactions, PharmGKB_search_drugs, PharmGKB_get_drug_details, PharmGKB_get_dosing_guidelines, fda_pharmacogenomic_biomarkersSearch PubMed, OpenAlex, and EuropePMC for safety studies, case reports, and preprints.
PubMed_search_articles, openalex_search_works, EuropePMC_search_articlesCalculate Safety Signal Score (0-100) from four components: FAERS signal strength (0-35), serious AEs (0-30), FDA label warnings (0-25), literature evidence (0-10). Grade each signal T1-T4. See PHASE_DETAILS.md for scoring rubric.
Generate comprehensive markdown report with executive summary, all phase outputs, monitoring recommendations, risk mitigation strategies, patient counseling points, and completeness checklist. See REPORT_TEMPLATE.md for full template.
OpenTargets_get_drug_chembId_by_generic_name to resolveFAERS_count_additive_adverse_reactions for aggregate class analysis