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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

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

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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

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)

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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

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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

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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

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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
    }

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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
    }

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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

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