Adverse Drug Event Signal Detection & Analysis

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