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 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)

1. Create the report file FIRST:

   • File name: [DRUG]_safety_report.md
   • Initialize with all section headers
   • Add placeholder text: [Researching...]

2. Progressively update as you gather data

3. 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_event	drug	drug_name
DailyMed_search_spls	name	drug_name
PharmGKB_search_drug	drug	query
OpenFDA_get_drug_events	drug_name	search

---

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)

Signal Thresholds:

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

• Generic name resolved
• Brand names listed
• Drug class identified
• ChEMBL/DrugBank ID obtained
• Mechanism of action stated

Phase 2: FAERS Analysis

• ≥20 adverse events queried
• PRR calculated for top events
• Serious/fatal counts included
• Signal thresholds applied
• Time period stated

Phase 3: Label Warnings

• Boxed warnings extracted (or "None")
• Contraindications listed
• Key warnings summarized
• Drug interactions noted

Phase 4: Pharmacogenomics

• PharmGKB queried
• Actionable variants listed (or "None")
• Evidence levels provided
• Testing recommendations stated

Phase 5: Clinical Trials

• Phase 3/4 trials searched
• Serious AE rates compared
• Discontinuation rates noted

Phase 6: Signal Prioritization

• Signals ranked by score
• Critical signals flagged
• Actions recommended

Phase 7-8: Synthesis

• Risk-benefit assessment provided
• Monitoring recommendations listed
• Patient counseling points included

---

Fallback Chains

Primary Tool	Fallback 1	Fallback 2
FAERS_count_reactions_by_drug_event	OpenFDA_get_drug_events	Literature search
DailyMed_get_spl_by_set_id	FDA_drug_label_search	DailyMed website
PharmGKB_search_drug	CPIC_get_guidelines	Literature search
search_clinical_trials	ClinicalTrials.gov API	PubMed for trial results

---

Tool Reference

See TOOLS_REFERENCE.md for complete tool documentation.