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 scoring, and pharmacovigilance reports.
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 scoring, and pharmacovigilance reports.
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.
Multi-source triangulation - FAERS, labels, trials, literature
Pharmacogenomic context - Include genetic risk factors
Actionable output - Risk-benefit summary with recommendations
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:
Look up the drug's primary mechanism of action (use ChEMBL or DailyMed label)
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
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)
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.
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:
Reason about mechanism first (what adverse events are plausible given this drug's pharmacology?)
Form specific hypotheses (e.g., "this drug may cause QT prolongation because it blocks hERG channels")
Query tools to test each hypothesis (FAERS for reporting frequency, DailyMed for label warnings, PharmGKB for genetic risk factors)
Interpret results in context (is the signal consistent with the mechanism? Is the timeline plausible? Are there confounders?)
Rather than memorizing gene-drug pairs, apply this reasoning framework:
Identify the drug's metabolic pathway (use KEGG or DailyMed label): Which CYP enzymes metabolize it? Is it a prodrug requiring activation?
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.
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.
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)
Create [DRUG]_safety_report.md FIRST with all section headers and [Researching...] placeholders
Apply mechanistic reasoning first (on-target toxicity, time-to-onset, dose vs. idiosyncratic, PGx)
Progressively update as data is gathered
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 (BEFORE tools)
On-target toxicity, time-to-onset, dose vs idiosyncratic, PGx risk
Phase 1: Drug Disambiguation
-> Resolve drug name, get identifiers (ChEMBL, DrugBank)
Phase 2: Adverse Event Profiling (FAERS)
-> Query FAERS, calculate PRR, stratify by seriousness
Phase 3: Label Warning Extraction
-> DailyMed boxed warnings, contraindications, precautions
Phase 4: Pharmacogenomic Risk
-> PharmGKB clinical annotations, high-risk genotypes
Phase 5: Clinical Trial Safety
-> ClinicalTrials.gov Phase 3/4 safety data
Phase 5.5: Pathway & Mechanism Context
-> KEGG drug metabolism, target pathway analysis
Phase 5.6: Literature Intelligence
-> PubMed, BioRxiv/MedRxiv, OpenAlex citation analysis
Phase 6: Signal Prioritization
-> Rank by PRR x severity x frequency
Phase 7: Report Synthesis
Phase 0: Mechanistic Reasoning (DO THIS BEFORE TOOLS)
Identify drug class and primary mechanism (use DailyMed label or ChEMBL)
Apply on-target vs off-target thinking (Strategy 1) to predict plausible adverse events
Estimate expected time-to-onset for each predicted event (Strategy 2)
Classify each as dose-dependent vs idiosyncratic (Strategy 3)
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 for detailed disproportionality formulas and example output tables.
Phase 3: Label Warning Extraction
Get label via DailyMed_get_spl_by_setid(setid=...)
Extract: boxed warnings, contraindications, warnings/precautions, drug interactions
Known/Expected (manage clinically): Low PRR, in label
Cross-check against mechanistic prediction: A signal not predicted mechanistically warrants additional scrutiny (possible confounding, reporting bias, or genuinely novel finding).
Output Report
Save as [DRUG]_safety_report.md. See REPORT_TEMPLATES.md for the full report structure and example outputs.
Evidence Grading
Tier
Criteria
Example
T1
PRR >10, fatal outcomes, boxed warning
Lactic acidosis
T2
PRR 3-10, serious outcomes
Hepatotoxicity
T3
PRR 2-3, moderate concern
Hypoglycemia
T4
PRR <2, known/expected
GI side effects
Fallback Chains
Primary Tool
Fallback 1
Fallback 2
FAERS_count_reactions_by_drug_event
OpenFDA_search_drug_events
Literature search
DailyMed_search_spls
OpenFDA_search_drug_labels
DailyMed website
PharmGKB_search_drugs
CPIC_list_guidelines
Literature search
search_clinical_trials
ClinicalTrials.gov API
PubMed for trial results
Completeness Checklist
See CHECKLIST.md for the full phase-by-phase verification checklist.
