Agent skills
tooluniverse-pharmacovigilance

Agent skill

tooluniverse-pharmacovigilance

Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data. Calculates disproportionality measures (PRR, ROR), identifies serious adverse events, assesses pharmacogenomic risk variants. Use when asked about drug safety, adverse events, post-market surveillance, or risk-benefit assessment.

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SKILL.md

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 progressively
Signal quantification - Use disproportionality measures (PRR, ROR)
Severity stratification - Prioritize serious/fatal events
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 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:

markdown

### 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

python

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

markdown

## 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

python

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

markdown

## 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

python

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

markdown

## 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

python

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

markdown

## 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

python

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

markdown

## 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)

python

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

markdown

## 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

python

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

markdown

## 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

markdown

## 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

markdown

# 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.

Maintainer

FreedomIntelligence Core maintainer

Source details

Full Name: FreedomIntelligence/OpenClaw-Medical-Skills
Branch: main
Path in repo: skills/tooluniverse-pharmacovigilance
Topics: claude-code skills openclaw awesome clawhub openclaw-skills medical nanoclaw

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SKILL.md

Pharmacovigilance Safety Analyzer

When to Use

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

2. Citation Requirements (MANDATORY)

Phase 0: Tool Verification

Known Parameter Corrections

Workflow Overview

Phase 1: Drug Disambiguation

1.1 Resolve Drug Identity

1.2 Output for Report

Phase 2: Adverse Event Profiling

2.1 FAERS Query Strategy

2.2 Disproportionality Analysis

2.3 Severity Classification

2.4 Output for Report

Phase 3: Label Warning Extraction

3.1 DailyMed Query

3.2 Warning Severity Categories

3.3 Output for Report

Phase 4: Pharmacogenomic Risk

4.1 PharmGKB Query

4.2 PGx Evidence Levels

4.3 Output for Report

Phase 5: Clinical Trial Safety

5.1 ClinicalTrials.gov Query

5.2 Output for Report

Phase 5.5: Pathway & Mechanism Context (NEW)

5.5.1 Drug Metabolism Pathways (KEGG)

5.5.2 Output for Report

Phase 5.6: Literature Intelligence (ENHANCED)

5.6.1 Published Safety Studies

5.6.2 Output for Report

Phase 6: Signal Prioritization

6.1 Signal Scoring Formula

6.2 Output for Report

Report Template

Evidence Grading

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

Tool Reference

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