Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
Pharmacovigilance Safety Analyzer
Systematic drug safety analysis using FAERS adverse event data, FDA labeling, PharmGKB pharmacogenomics, and clinical trial safety signals.
KEY PRINCIPLES :
Report-first approach - Create report file FIRST, update progressivelySignal quantification - Use disproportionality measures (PRR, ROR)Severity stratification - Prioritize serious/fatal eventsMulti-source triangulation - FAERS, labels, trials, literaturePharmacogenomic context - Include genetic risk factorsActionable output - Risk-benefit summary with recommendationsEnglish-first queries - Always use English drug names and search terms in tool calls, even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language
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?"
"Should I be concerned about [specific adverse event] with [drug]?"
"Compare safety profiles of [drug A] vs [drug B]"
"Pharmacovigilance analysis for [drug]"
Critical Workflow Requirements
1. Report-First Approach (MANDATORY)
Create the report file FIRST :
File name: [DRUG]_safety_report.md
Initialize with all section headers
Add placeholder text: [Researching...]
Progressively update as you gather data
Output separate data files :
[DRUG]_adverse_events.csv - Ranked AEs with counts/signals
[DRUG]_pharmacogenomics.csv - PGx variants and recommendations
2. Citation Requirements (MANDATORY) Every safety signal MUST include source:
### Signal: Hepatotoxicity
- **PRR**: 3.2 (95% CI: 2.8-3.7)
- **Cases**: 1,247 reports
- **Serious**: 892 (71.5%)
- **Fatal**: 23
*Source: FAERS via `FAERS_count_reactions_by_drug_event` (Q1 2020 - Q4 2025)*
Phase 0: Tool Verification CRITICAL : Verify tool parameters before calling.
Known Parameter Corrections Tool WRONG Parameter CORRECT Parameter FAERS_count_reactions_by_drug_eventdrugdrug_nameDailyMed_search_splsnamedrug_namePharmGKB_search_drugdrugqueryOpenFDA_get_drug_eventsdrug_namesearch
Workflow Overview Phase 1: Drug Disambiguation
├── Resolve drug name (brand → generic)
├── Get identifiers (RxCUI, ChEMBL, DrugBank)
└── Identify drug class and mechanism
↓
Phase 2: Adverse Event Profiling (FAERS)
├── Query FAERS for drug-event pairs
├── Calculate disproportionality (PRR, ROR)
├── Stratify by seriousness
└── OUTPUT: Ranked AE table
↓
Phase 3: Label Warning Extraction
├── DailyMed boxed warnings
├── Contraindications
├── Warnings and precautions
└── OUTPUT: Label safety summary
↓
Phase 4: Pharmacogenomic Risk
├── PharmGKB clinical annotations
├── High-risk genotypes
├── Dosing recommendations
└── OUTPUT: PGx risk table
↓
Phase 5: Clinical Trial Safety
├── ClinicalTrials.gov safety data
├── Phase 3/4 discontinuation rates
├── Serious AEs in trials
└── OUTPUT: Trial safety summary
↓
Phase 5.5: Pathway & Mechanism Context (NEW)
├── KEGG: Drug metabolism pathways
├── Reactome: Mechanism-linked pathways
├── Target pathway analysis
└── OUTPUT: Mechanistic safety context
↓
Phase 5.6: Literature Intelligence (ENHANCED)
├── PubMed: Published safety studies
├── BioRxiv/MedRxiv: Recent preprints
├── OpenAlex: Citation analysis
└── OUTPUT: Literature evidence
↓
Phase 6: Signal Prioritization
├── Rank by PRR × severity × frequency
├── Identify actionable signals
├── Risk-benefit assessment
└── OUTPUT: Prioritized signal list
↓
Phase 7: Report Synthesis
Phase 1: Drug Disambiguation
1.1 Resolve Drug Identity def resolve_drug(tu, drug_query):
"""Resolve drug name to standardized identifiers."""
identifiers = {}
# DailyMed for NDC and SPL
dailymed = tu.tools.DailyMed_search_spls(drug_name=drug_query)
if dailymed:
identifiers['ndc'] = dailymed[0].get('ndc')
identifiers['setid'] = dailymed[0].get('setid')
identifiers['generic_name'] = dailymed[0].get('generic_name')
# ChEMBL for molecule data
chembl = tu.tools.ChEMBL_search_drugs(query=drug_query)
if chembl:
identifiers['chembl_id'] = chembl[0].get('molecule_chembl_id')
identifiers['max_phase'] = chembl[0].get('max_phase')
return identifiers
1.2 Output for Report ## 1. Drug Identification
| Property | Value |
|----------|-------|
| **Generic Name** | Metformin |
| **Brand Names** | Glucophage, Fortamet, Glumetza |
| **Drug Class** | Biguanide antidiabetic |
| **ChEMBL ID** | CHEMBL1431 |
| **Mechanism** | AMPK activator, hepatic gluconeogenesis inhibitor |
| **First Approved** | 1994 (US) |
*Source: DailyMed via `DailyMed_search_spls`, ChEMBL*
Phase 2: Adverse Event Profiling
2.1 FAERS Query Strategy def get_faers_events(tu, drug_name, top_n=50):
"""Query FAERS for adverse events."""
# Get event counts
events = tu.tools.FAERS_count_reactions_by_drug_event(
drug_name=drug_name,
limit=top_n
)
# For each event, get detailed breakdown
detailed_events = []
for event in events:
detail = tu.tools.FAERS_get_event_details(
drug_name=drug_name,
reaction=event['reaction']
)
detailed_events.append({
'reaction': event['reaction'],
'count': event['count'],
'serious': detail.get('serious_count', 0),
'fatal': detail.get('death_count', 0),
'hospitalization': detail.get('hospitalization_count', 0)
})
return detailed_events
2.2 Disproportionality Analysis Proportional Reporting Ratio (PRR) :
PRR = (A/B) / (C/D)
Where:
A = Reports of drug X with event Y
B = Reports of drug X with any event
C = Reports of event Y with any drug (excluding X)
D = Total reports (excluding drug X)
Measure Signal Threshold Strong Signal PRR >2.0 >3.0 Chi-squared >4.0 >10.0 N (case count) ≥3 ≥10
2.3 Severity Classification Category Definition Priority Fatal Death outcome Highest Life-threatening Immediate death risk Very High Hospitalization Required/prolonged hospitalization High Disability Persistent impairment High Congenital anomaly Birth defect High Other serious Medical intervention required Medium Non-serious No serious criteria Low
2.4 Output for Report ## 2. Adverse Event Profile (FAERS)
**Data Period**: Q1 2020 - Q4 2025
**Total Reports for Drug**: 45,234
### 2.1 Top Adverse Events by Frequency
| Rank | Adverse Event | Reports | PRR | 95% CI | Serious (%) | Fatal |
|------|---------------|---------|-----|--------|-------------|-------|
| 1 | Diarrhea | 8,234 | 2.3 | 2.1-2.5 | 12% | 3 |
| 2 | Nausea | 6,892 | 1.8 | 1.6-2.0 | 8% | 0 |
| 3 | Lactic acidosis | 1,247 | 15.2 | 12.8-17.9 | 89% ⚠️ | 156 ⚠️ |
| 4 | Hypoglycemia | 2,341 | 2.1 | 1.9-2.4 | 34% | 8 |
| 5 | Vitamin B12 deficiency | 892 | 8.4 | 7.2-9.8 | 23% | 0 |
### 2.2 Serious Adverse Events Only
| Adverse Event | Serious Reports | Fatal | PRR | Signal |
|---------------|-----------------|-------|-----|--------|
| Lactic acidosis | 1,110 | 156 | 15.2 | **STRONG** ⚠️ |
| Acute kidney injury | 678 | 34 | 4.2 | Moderate |
| Hepatotoxicity | 234 | 12 | 3.1 | Moderate |
### 2.3 Signal Interpretation
**Strong Signal: Lactic Acidosis** ⚠️
- PRR of 15.2 indicates 15x higher reporting rate than expected
- 89% classified as serious
- 156 fatalities (12.5% case fatality)
- **Known class effect of biguanides**
- Risk factors: renal impairment, hypoxia, contrast agents
*Source: FAERS via `FAERS_count_reactions_by_drug_event`*
Phase 3: Label Warning Extraction
3.1 DailyMed Query def extract_label_warnings(tu, setid):
"""Extract safety sections from FDA label."""
label = tu.tools.DailyMed_get_spl_by_set_id(setid=setid)
warnings = {
'boxed_warning': label.get('boxed_warning'),
'contraindications': label.get('contraindications'),
'warnings_precautions': label.get('warnings_and_precautions'),
'adverse_reactions': label.get('adverse_reactions'),
'drug_interactions': label.get('drug_interactions')
}
return warnings
3.2 Warning Severity Categories Category Symbol Description Boxed Warning ⬛ Most serious, life-threatening Contraindication 🔴 Must not use Warning 🟠 Significant risk Precaution 🟡 Use caution
3.3 Output for Report ## 3. FDA Label Safety Information
### 3.1 Boxed Warning ⬛
**LACTIC ACIDOSIS**
> Metformin can cause lactic acidosis, a rare but serious complication.
> Risk increases with renal impairment, sepsis, dehydration, excessive
> alcohol intake, hepatic impairment, and acute heart failure.
>
> **Contraindicated in patients with eGFR <30 mL/min/1.73m²**
### 3.2 Contraindications 🔴
| Contraindication | Rationale |
|------------------|-----------|
| eGFR <30 mL/min/1.73m² | Lactic acidosis risk |
| Acute/chronic metabolic acidosis | May worsen acidosis |
| Hypersensitivity to metformin | Allergic reaction |
### 3.3 Warnings and Precautions 🟠
| Warning | Clinical Action |
|---------|-----------------|
| Vitamin B12 deficiency | Monitor B12 levels annually |
| Hypoglycemia with insulin | Reduce insulin dose |
| Radiologic contrast | Hold 48h around procedure |
| Surgical procedures | Hold day of surgery |
*Source: DailyMed via `DailyMed_get_spl_by_set_id`*
Phase 4: Pharmacogenomic Risk
4.1 PharmGKB Query def get_pharmacogenomics(tu, drug_name):
"""Get pharmacogenomic annotations."""
# Search PharmGKB
pgx = tu.tools.PharmGKB_search_drug(query=drug_name)
annotations = []
for result in pgx:
if result.get('clinical_annotation'):
annotations.append({
'gene': result['gene'],
'variant': result['variant'],
'phenotype': result['phenotype'],
'recommendation': result['recommendation'],
'level': result['level_of_evidence']
})
return annotations
4.2 PGx Evidence Levels Level Description Clinical 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
4.3 Output for Report ## 4. Pharmacogenomic Risk Factors
### 4.1 Clinically Actionable Variants
| Gene | Variant | Phenotype | Recommendation | Level |
|------|---------|-----------|----------------|-------|
| SLC22A1 | rs628031 | Reduced OCT1 | Reduced metformin response | 2A |
| SLC22A1 | rs36056065 | Loss of function | Consider alternative | 2A |
| ATM | rs11212617 | Increased response | Standard dosing | 3 |
### 4.2 Clinical Implications
**OCT1 (SLC22A1) Poor Metabolizers**:
- ~9% of Caucasians carry two loss-of-function alleles
- Reduced hepatic uptake of metformin
- May have decreased efficacy
- Consider higher doses or alternative agent
**No CPIC/DPWG guidelines currently exist for metformin**
*Source: PharmGKB via `PharmGKB_search_drug`*
Phase 5: Clinical Trial Safety
5.1 ClinicalTrials.gov Query def get_trial_safety(tu, drug_name):
"""Get safety data from clinical trials."""
# Search completed phase 3/4 trials
trials = tu.tools.search_clinical_trials(
intervention=drug_name,
phase="Phase 3",
status="Completed",
pageSize=20
)
safety_data = []
for trial in trials:
if trial.get('results_posted'):
results = tu.tools.get_clinical_trial_results(
nct_id=trial['nct_id']
)
safety_data.append(results.get('adverse_events'))
return safety_data
5.2 Output for Report ## 5. Clinical Trial Safety Data
### 5.1 Phase 3 Trial Summary
| Trial | N | Duration | Serious AEs (Drug) | Serious AEs (Placebo) | Deaths |
|-------|---|----------|-------------------|----------------------|--------|
| UKPDS | 1,704 | 10 yr | 12.3% | 14.1% | 8.2% vs 9.1% |
| DPP | 1,073 | 3 yr | 4.2% | 3.8% | 0.1% |
| SPREAD | 884 | 2 yr | 5.1% | 4.9% | 0.2% |
### 5.2 Common Adverse Events in Trials
| Adverse Event | Drug (%) | Placebo (%) | Difference |
|---------------|----------|-------------|------------|
| Diarrhea | 53% | 12% | +41% ⚠️ |
| Nausea | 26% | 8% | +18% |
| Flatulence | 12% | 6% | +6% |
| Asthenia | 9% | 6% | +3% |
*Source: ClinicalTrials.gov via `search_clinical_trials`*
Phase 5.5: Pathway & Mechanism Context (NEW)
5.5.1 Drug Metabolism Pathways (KEGG) def get_drug_pathway_context(tu, drug_name, drug_targets):
"""Get pathway context for mechanistic safety understanding."""
# KEGG drug metabolism
metabolism = tu.tools.kegg_search_pathway(
query=f"{drug_name} metabolism"
)
# Target pathways
target_pathways = {}
for target in drug_targets:
pathways = tu.tools.kegg_get_gene_info(gene_id=f"hsa:{target}")
target_pathways[target] = pathways.get('pathways', [])
return {
'metabolism_pathways': metabolism,
'target_pathways': target_pathways
}
5.5.2 Output for Report ## 5.5 Pathway & Mechanism Context
### Drug Metabolism Pathways (KEGG)
| Pathway | Relevance | Safety Implication |
|---------|-----------|-------------------|
| Drug metabolism - cytochrome P450 | Primary metabolism | CYP2C9 interactions |
| Gluconeogenesis inhibition | MOA | Lactic acidosis mechanism |
| Mitochondrial complex I | Off-target | Lactic acid accumulation |
### Target Pathway Analysis
**Primary Target: AMPK**
- Pathway: AMPK signaling (hsa04152)
- Downstream: mTOR inhibition, autophagy
- Safety relevance: Explains metabolic effects
**Mechanistic Basis for Key AEs**:
| Adverse Event | Pathway Mechanism |
|---------------|-------------------|
| Lactic acidosis | Mitochondrial complex I inhibition |
| GI intolerance | Serotonin release in gut |
| B12 deficiency | Intrinsic factor interference |
*Source: KEGG, Reactome*
Phase 5.6: Literature Intelligence (ENHANCED)
5.6.1 Published Safety Studies def comprehensive_safety_literature(tu, drug_name, key_aes):
"""Search all literature sources for safety evidence."""
# PubMed: Peer-reviewed
pubmed = tu.tools.PubMed_search_articles(
query=f'"{drug_name}" AND (safety OR adverse OR toxicity)',
limit=30
)
# BioRxiv: Preprints
biorxiv = tu.tools.BioRxiv_search_preprints(
query=f"{drug_name} mechanism toxicity",
limit=10
)
# MedRxiv: Clinical preprints
medrxiv = tu.tools.MedRxiv_search_preprints(
query=f"{drug_name} safety",
limit=10
)
# Citation analysis for key papers
key_papers = pubmed[:10]
for paper in key_papers:
citation = tu.tools.openalex_search_works(
query=paper['title'],
limit=1
)
paper['citations'] = citation[0].get('cited_by_count', 0) if citation else 0
return {
'pubmed': pubmed,
'preprints': biorxiv + medrxiv,
'key_papers': key_papers
}
5.6.2 Output for Report ## 5.6 Literature Evidence
### Key Safety Studies
| PMID | Title | Year | Citations | Finding |
|------|-------|------|-----------|---------|
| 29234567 | Metformin and lactic acidosis: meta-analysis | 2020 | 245 | Risk 4.3/100,000 |
| 28765432 | Long-term cardiovascular outcomes... | 2019 | 567 | CV benefit confirmed |
| 30123456 | B12 deficiency prevalence study | 2021 | 123 | 30% after 4 years |
### Recent Preprints (Not Peer-Reviewed)
| Source | Title | Posted | Relevance |
|--------|-------|--------|-----------|
| MedRxiv | Novel metformin safety signal in elderly | 2024-01 | Age-related risk |
| BioRxiv | Gut microbiome and metformin GI effects | 2024-02 | Mechanistic |
**⚠️ Note**: Preprints have NOT undergone peer review.
### Evidence Summary
| Evidence Type | Count | High-Impact |
|---------------|-------|-------------|
| Systematic reviews | 12 | 5 |
| RCTs with safety data | 28 | 8 |
| Mechanistic studies | 15 | 3 |
| Case reports | 45 | - |
*Source: PubMed, BioRxiv, MedRxiv, OpenAlex*
Phase 6: Signal Prioritization
6.1 Signal Scoring Formula Signal Score = PRR × Severity_Weight × log10(Case_Count + 1)
Severity Weights:
- Fatal: 10
- Life-threatening: 8
- Hospitalization: 5
- Disability: 5
- Other serious: 3
- Non-serious: 1
6.2 Output for Report ## 6. Prioritized Safety Signals
### 6.1 Critical Signals (Immediate Attention)
| Signal | PRR | Fatal | Score | Action |
|--------|-----|-------|-------|--------|
| Lactic acidosis | 15.2 | 156 | 482 | Boxed warning exists |
| Acute kidney injury | 4.2 | 34 | 89 | Monitor renal function |
### 6.2 Moderate Signals (Monitor)
| Signal | PRR | Serious | Score | Action |
|--------|-----|---------|-------|--------|
| Hepatotoxicity | 3.1 | 234 | 52 | Check LFTs if symptoms |
| Pancreatitis | 2.8 | 178 | 41 | Monitor lipase |
### 6.3 Known/Expected (Manage Clinically)
| Signal | PRR | Frequency | Management |
|--------|-----|-----------|------------|
| Diarrhea | 2.3 | 18% | Start low, titrate slow |
| Nausea | 1.8 | 12% | Take with food |
| B12 deficiency | 8.4 | 2% | Annual monitoring |
Report Template File : [DRUG]_safety_report.md
# Pharmacovigilance Safety Report: [DRUG]
**Generated**: [Date] | **Query**: [Original query] | **Status**: In Progress
---
## Executive Summary
[Researching...]
---
## 1. Drug Identification
### 1.1 Drug Information
[Researching...]
---
## 2. Adverse Event Profile (FAERS)
### 2.1 Top Adverse Events
[Researching...]
### 2.2 Serious Adverse Events
[Researching...]
### 2.3 Signal Analysis
[Researching...]
---
## 3. FDA Label Safety Information
### 3.1 Boxed Warnings
[Researching...]
### 3.2 Contraindications
[Researching...]
### 3.3 Warnings and Precautions
[Researching...]
---
## 4. Pharmacogenomic Risk Factors
### 4.1 Actionable Variants
[Researching...]
### 4.2 Testing Recommendations
[Researching...]
---
## 5. Clinical Trial Safety
### 5.1 Trial Summary
[Researching...]
### 5.2 Adverse Events in Trials
[Researching...]
---
## 6. Prioritized Safety Signals
### 6.1 Critical Signals
[Researching...]
### 6.2 Moderate Signals
[Researching...]
---
## 7. Risk-Benefit Assessment
[Researching...]
---
## 8. Clinical Recommendations
### 8.1 Monitoring Recommendations
[Researching...]
### 8.2 Patient Counseling Points
[Researching...]
### 8.3 Contraindication Checklist
[Researching...]
---
## 9. Data Gaps & Limitations
[Researching...]
---
## 10. Data Sources
[Will be populated as research progresses...]
Evidence Grading Tier Symbol 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 ℹ️ PRR <2, known/expected GI side effects
Completeness Checklist
Phase 1: Drug Identification
Phase 2: FAERS Analysis
Phase 3: Label Warnings
Phase 4: Pharmacogenomics
Phase 5: Clinical Trials
Phase 6: Signal Prioritization
Phase 7-8: Synthesis
Fallback Chains Primary Tool Fallback 1 Fallback 2 FAERS_count_reactions_by_drug_eventOpenFDA_get_drug_eventsLiterature search DailyMed_get_spl_by_set_idFDA_drug_label_searchDailyMed website PharmGKB_search_drugCPIC_get_guidelinesLiterature search search_clinical_trialsClinicalTrials.gov APIPubMed for trial results
Tool Reference 