mims-harvard/tooluniverse-adverse-event-detection

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.

Adverse Drug Event Signal Detection & Analysis

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:

1. Signal quantification first - Every adverse event must have PRR/ROR/IC with confidence intervals
2. Serious events priority - Deaths, hospitalizations, life-threatening events always analyzed first
3. Multi-source triangulation - FAERS + FDA labels + OpenTargets + DrugBank + literature
4. Context-aware assessment - Distinguish drug-specific vs class-wide vs confounding signals
5. Report-first approach - Create report file FIRST, update progressively
6. Evidence grading mandatory - T1 (regulatory/boxed warning) through T4 (computational)
7. English-first queries - Always use English drug names in tool calls, respond in user's language

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:

1. Biologically plausible? Given the drug's mechanism of action and targets, does this adverse event make sense? An off-target kinase inhibitor causing cardiac events is plausible; a topical agent causing systemic toxicity needs more scrutiny. LOOK UP DON'T GUESS — use OpenTargets_get_drug_mechanisms_of_action_by_chemblId and drugbank_get_targets_by_drug_name_or_drugbank_id to check targets before asserting plausibility.
2. Timing consistent? Acute reactions (within hours/days) suggest immune or direct pharmacologic mechanism. Delayed reactions (weeks/months) suggest cumulative toxicity or idiosyncratic response. Check FAERS time-to-onset distribution.
3. Could confounders explain it? Patients taking this drug likely have the underlying disease — compare against background rate in that population, not the general population. Class-wide signals (appearing for all drugs in the class) suggest mechanism-based rather than molecule-specific toxicity.

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:

1. Prior reports? Are there previous conclusive reports of this reaction? Check FDA label (FDA_get_adverse_reactions_by_drug_name) and literature (PubMed_search_articles). Yes = +1.
2. Temporal relationship? Did the AE appear after drug administration? Onset within expected pharmacokinetic window (1-5 half-lives) = +2. Use FAERS_stratify_by_demographics for time-to-onset data.
3. Dechallenge? Did the AE improve when the drug was stopped? Positive dechallenge = +1. Look for rechallenge/dechallenge case reports in literature.
4. Rechallenge? Did the AE reappear when the drug was restarted? Positive rechallenge = +2 (strongest single piece of evidence for causality).
5. Alternative causes? Could the underlying disease, concomitant drugs, or other factors explain the AE? Check drugbank_get_drug_interactions_by_drug_name_or_id for interacting drugs.
6. Dose-response? Did the reaction worsen with higher doses or improve with lower doses? Dose-dependent AEs suggest on-target toxicity.
7. Drug level confirmation? Was the drug detected in body fluids at toxic concentrations?

• Score: Definite (>=9), Probable (5-8), Possible (1-4), Doubtful (<=0).
• Even without individual patient data, you can estimate causality from aggregate FAERS signals + label evidence + mechanistic plausibility.

Reference files (in this directory):

• PHASE_DETAILS.md - Detailed tool calls, code examples, and output templates per phase
• REPORT_TEMPLATE.md - Full report template and completeness checklist
• TOOL_REFERENCE.md - Tool parameter reference and fallback chains
• QUICK_START.md - Quick examples and common drug names

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When to Use

Apply when user asks:

• "What are the safety signals for [drug]?"
• "Detect adverse events for [drug]"
• "Is [drug] associated with [adverse event]?"
• "What are the FAERS signals for [drug]?"
• "Compare safety of [drug A] vs [drug B] for [adverse event]"
• "What are the serious adverse events for [drug]?"
• "Are there emerging safety signals for [drug]?"
• "Post-market surveillance report for [drug]"
• "Pharmacovigilance signal detection for [drug]"

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.

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Workflow Overview

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)
    |
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)
    |
Phase 3: FDA Label Safety Information
  Boxed warnings, contraindications
  Warnings and precautions, adverse reactions
  Drug interactions, special populations
    |
Phase 4: Mechanism-Based Adverse Event Context
  Target-based AE prediction (OpenTargets safety)
  Off-target effects, ADMET predictions
  Drug class effects comparison
    |
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

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Phase Summaries

Phase 0: Input Parsing & Drug Disambiguation

Resolve drug name to ChEMBL ID, DrugBank ID. Get mechanism of action, blackbox warning status, targets, and approved indications.

• Tools: 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_chemblId

Phase 1: FAERS Adverse Event Profiling

Query 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.

• Tools: 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_hierarchy

Phase 2: Disproportionality Analysis (Signal Detection)

CRITICAL 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.

• Tools: FAERS_calculate_disproportionality, FAERS_stratify_by_demographics
• MedDRA term level note: FAERS_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.
• Signal criteria: PRR >= 2.0 AND lower CI > 1.0 AND N >= 3
• Strength: Strong (PRR >= 5), Moderate (PRR 3-5), Weak (PRR 2-3)
• See PHASE_DETAILS.md for full signal classification table

Phase 3: FDA Label Safety Information

Extract 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.

• Tools: 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_name

Phase 4: Mechanism-Based Adverse Event Context

Get target safety profile, OpenTargets adverse events, ADMET toxicity predictions (if SMILES available), and drug warnings.

• Tools: 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_chemblId

Phase 5: Comparative Safety Analysis

Head-to-head comparison with class members using FAERS_compare_drugs. Aggregate class AEs. Identify class-wide vs drug-specific signals.

• Tools: FAERS_compare_drugs, FAERS_count_additive_adverse_reactions, FAERS_count_additive_seriousness_classification

Phase 6: Drug-Drug Interactions & Risk Factors

Extract DDIs from FDA label, DrugBank, and DailyMed. Query PharmGKB for pharmacogenomic risk factors and dosing guidelines. Check FDA PGx biomarkers.

• Tools: 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_biomarkers

Phase 7: Literature Evidence

Search PubMed, OpenAlex, and EuropePMC for safety studies, case reports, and preprints.

• Tools: PubMed_search_articles, openalex_search_works, EuropePMC_search_articles

Phase 8: Risk Assessment & Safety Signal Score

Calculate 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.

Phase 9: Report Synthesis

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.

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Edge Cases

• No FAERS reports: Skip Phases 1-2; rely on FDA label, mechanism predictions, literature
• Generic vs Brand name: Try both in FAERS; use OpenTargets_get_drug_chembId_by_generic_name to resolve
• Drug combinations: Use FAERS_count_additive_adverse_reactions for aggregate class analysis
• Confounding by indication: Compare AE profile to the disease being treated; note limitation in report
• Drugs with boxed warnings: Score component automatically 25/25 for label warnings; prioritize boxed warning events