Drug safety and adverse event analysis — FAERS spontaneous-report mining, FDA black-box warnings, signal detection (PRR, ROR, IC), risk factors by demographic/comorbidity, and label change tracking. Use for post-market safety surveillance, AE signal investigation, drug-AE association strength scorin

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. Pharmacovigilance Safety Analyzer Systematic drug safety analysis using FAERS adverse event data, FDA labeling, PharmGKB pharmacogenomics, and clinical trial safety signals. KEY PRINCIPLES : 1. Report first approach Create report file FIRST, update progressively 2. Signal quantification Use disproportionality measures (PRR, ROR) 3. Severity stratification Prioritize serious/fatal events 4. Multi source triangulation FAERS, labels, trials, literature 5. Pharmacogenomic context Include genetic risk factors 6. Actionable output Risk benefit summary with recommendations 7. English first queries Always use English drug names in tool calls When to Use Apply when user asks: "What are the safety concerns for [drug]?" "What adverse events are associated with [drug]?" "Is [drug] safe? What are the risks?" "Compare safety profiles of [drug A] vs [drug B]" "Pharmacovigilance analysis for [drug]" Clinical Reasoning Framework Reasoning Strategy 1: On Target vs Off Target Thinking Ask: is this adverse effect a predictable extension of the drug's mechanism (on target), or something the mechanism doesn't explain (off target)? On target effects are dose dependent and predictable. Off target effects are often idiosyncratic and harder to predict. How to apply this : 1. Look up the drug's primary mechanism of action (use ChEMBL or DailyMed label) 2. For each reported adverse event, ask: "Does this follow logically from what the drug does to its target?" If yes, it is on target toxicity — expect dose dependence and manage with dose reduction 3. If the adverse event cannot be explained by the primary mechanism, consider off target receptor binding or reactive metabolite formation. These require different management (drug discontinuation, not dose adjustment) 4. Use KEGG pathway data to identify metabolic routes that could produce toxic intermediates Reasoning Strategy 2: Timeline as Diagnostic Tool When did the adverse event start relative to drug initiation? The timeline alone narrows the mechanism: Hours = anaphylaxis, immediate hypersensitivity, or direct pharmacological overshoot Days = serum sickness, cytotoxic reactions, cumulative pharmacological effects 1 6 weeks = delayed hypersensitivity (SJS/TEN, DRESS), organ accumulation Months = chronic toxicity, cumulative organ damage Years = long term cumulative effects How to apply this : When reviewing FAERS case reports, always check the time to onset field. If the reported timeline is biologically implausible for the proposed mechanism, suspect confounding or misattribution. A reaction appearing years after drug start is unlikely to be immune mediated but could be chronic accumulation. Reasoning Strategy 3: Dose Dependent vs Idiosyncratic Classification This distinction determines monitoring strategy and management: Dose dependent (Type A) : Predictable from pharmacology. Dose response relationship exists. Can be managed by dose reduction. These are on target toxicities pushed too far. Idiosyncratic (Type B) : Not predictable from pharmacology alone. No clear dose response. Often immune mediated or due to metabolic idiosyncrasy (e.g., genetic variation in drug metabolism). Drug must be stopped — dose reduction will not help. Mixed : Some reactions are dose dependent in most patients but become idiosyncratic in genetically susceptible individuals. When you see a "Type A" reaction occurring at unexpectedly low doses, suspect a pharmacogenomic contributor. How to apply this : When evaluating a safety signal, classify it as Type A or B. This determines whether you recommend dose adjustment (Type A) or drug avoidance with potential pharmacogenomic screening (Type B). Reasoning Strategy 4: The Naranjo Algorithm for Causality Classification When investigating a suspected drug adverse event, the Naranjo algorithm asks: (1) Did the event appear after the drug was given? (2) Did it improve when the drug was stopped? (3) Did it reappear when restarted? (4) Could other causes explain it? Score each question to classify causality. Reasoning Strategy 5: The Rechallenge Question Did the event recur when the drug was restarted? Positive rechallenge is the strongest evidence for causation in an individual case. But rechallenge is often unethical for serious reactions, so absence of rechallenge data doesn't exonerate the drug. How to apply this : When reviewing case narratives or FAERS reports, check for dechallenge (did the event resolve when the drug was stopped?) and rechallenge (did it recur on re exposure?). A positive dechallenge + positive rechallenge is near definitive. Negative dechallenge weakens the causal link considerably. Reasoning Strategy 5: Disproportionality Reasoning A signal in FAERS means the drug event pair is REPORTED more than expected. It does not mean the drug CAUSES the event. Think about reporting biases: Serious events get reported more than mild ones New drugs get reported more than old ones (Weber effect) Drugs prescribed to sick populations get events attributed to them that may reflect the underlying disease Media attention or regulatory alerts create reporting spikes How to apply this : Always ask — what is the base rate of this event in the untreated population? A high PRR for "cardiac arrest" in a drug used by ICU patients may reflect the patient population, not the drug. Cross reference with clinical trial placebo arm rates when available. Reasoning Strategy 6: When to Use Tools vs Reason Use FAERS/OpenFDA tools to QUANTIFY a signal you have already hypothesized based on mechanism. Do not mine FAERS without a hypothesis — you will find spurious associations. The correct sequence : 1. Reason about mechanism first (what adverse events are plausible given this drug's pharmacology?) 2. Form specific hypotheses (e.g., "this drug may cause QT prolongation because it blocks hERG channels") 3. Query tools to test each hypothesis (FAERS for reporting frequency, DailyMed for label warnings, PharmGKB for genetic risk factors) 4. Interpret results in context (is the signal consistent with the mechanism? Is the timeline plausible? Are there confounders?) Reasoning Strategy 7: Pharmacogenomic Risk Assessment Rather than memorizing gene drug pairs, apply this reasoning framework: 1. Identify the drug's metabolic pathway (use KEGG or DailyMed label): Which CYP enzymes metabolize it? Is it a prodrug requiring activation? 2. Assess the consequence of altered metabolism : For active drugs, poor metabolizers accumulate the drug (toxicity risk). For prodrugs, poor metabolizers fail to activate (efficacy failure). Ultra rapid metabolizers show the opposite pattern. 3. Check for immune mediated risk : If the drug is associated with severe cutaneous reactions (SJS/TEN, DRESS) or hypersensitivity syndrome, query PharmGKB for HLA associations. These are population specific. 4. Use PharmGKB evidence levels to guide action : Level 1A/1B (guideline based) = actionable now. Level 2A/2B = may inform. Level 3 = not clinically actionable yet. Query PharmGKB search drugs(query=...) and CPIC list guidelines to get current pharmacogenomic annotations rather than relying on memorized associations, which may be outdated. Critical Workflow Requirements Report First Approach (MANDATORY) 1. Create [DRUG] safety report.md FIRST with all section headers and [Researching...] placeholders 2. Apply mechanistic reasoning first (on target toxicity, time to onset, dose vs. idiosyncratic, PGx) 3. Progressively update as data is gathered 4. Output separate data files: [DRUG] adverse events.csv and [DRUG] pharmacogenomics.csv Citation Requirements (MANDATORY) Every safety signal MUST include source tool, data period, PRR, case counts, and serious/fatal breakdown. Tool Parameter Reference (CRITICAL) Tool WRONG Parameter CORRECT Parameter FAERS count reactions by drug event drug drug name FAERS filter serious events American spelling (e.g., "Hemorrhage") MedDRA British spelling (e.g., "Haemorrhage") FAERS stratify by demographics Requiring adverse event adverse event is optional (omit for all event stratification) DailyMed search spls name drug name PharmGKB search drugs drug query OpenFDA search drug events drug name search Workflow Overview Phase 0: Mechanistic Reasoning (DO THIS BEFORE TOOLS) 1. Identify drug class and primary mechanism (use DailyMed label or ChEMBL) 2. Apply on target vs off target thinking (Strategy 1) to predict plausible adverse events 3. Estimate expected time to onset for each predicted event (Strategy 2) 4. Classify each as dose dependent vs idiosyncratic (Strategy 3) 5. Formulate specific, testable safety hypotheses to guide tool queries (Strategy 6) Phase 1: Drug Disambiguation 1. Search DailyMed via DailyMed search spls(drug name=...) for NDC, SPL setid, generic name 2. Search ChEMBL via ChEMBL search drugs(query=...) for molecule ID, max phase 3. Document: generic name, brand names, drug class, mechanism, approval date Phase 2: Adverse Event Profiling (FAERS) 1. Query FAERS count reactions by drug event(drug name=..., limit=50) for top events 2. For each event, get detailed breakdown (serious, fatal, hospitalization counts) 3. Calculate PRR: (A/B) / (C/D) where A=drug+event, B=drug+any, C=event+any other, D=total other 4. Apply signal thresholds: PRR 2.0 (signal), 3.0 (strong signal), case count = 3 Severity classification : Fatal (highest priority), Life threatening, Hospitalization, Disability, Other serious, Non serious FAERS filter serious events MedDRA Spelling (CRITICAL) FAERS filter serious events uses MedDRA preferred terms which follow British English spelling conventions. Common examples: Incorrect (American) Correct (MedDRA/British) HEMORRHAGE Haemorrhage ANEMIA Anaemia EDEMA Oedema DIARRHEA Diarrhoea LEUKOPENIA Leucopenia ESOPHAGITIS Oesophagitis The adverse event parameter should use the exact MedDRA preferred term spelling . When in doubt, first query FAERS count reactions by drug event to see the exact event names as they appear in the FAERS database, then use those exact strings. Additional FAERS notes: adverse event is now correctly appended to the OpenFDA query in filter serious events FAERS stratify by demographics : adverse event is optional — when omitted, stratification covers all events for the drug. Sex codes: 0=Unknown, 1=Male, 2=Female See [SIGNAL DETECTION.md](SIGNAL DETECTION.md) for detailed disproportionality formulas and example output tables. Phase 3: Label Warning Extraction 1. Get label via DailyMed get spl by setid(setid=...) 2. Extract: boxed warnings, contraindications, warnings/precautions, drug interactions 3. Categorize severity: Boxed Warning Contraindication Warning Precaution Phase 4: Pharmacogenomic Risk 1. Search PharmGKB search drugs(query=...) for clinical annotations 2. Document actionable variants with evidence levels (1A/1B/2A/2B/3) 3. Note CPIC/DPWG guideline status PGx Evidence Levels : Level Description Action 1A CPIC/DPWG guideline, implementable Follow guideline 1B CPIC/DPWG guideline, annotation Consider testing 2A VIP annotation, moderate evidence May inform 2B VIP annotation, weaker evidence Research 3 Low level annotation Not actionable Phase 5: Clinical Trial Safety 1. Search search

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