tooluniverse-pharmacovigilance

What this skill does

## 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

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## 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]"

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## 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

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### 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.

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### 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).

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### 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.

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### 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.

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### 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?)

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### 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.

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## 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