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---

name: bio-clinical-databases-pharmacogenomics description: Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics. tool_type: python primary_tool: requests measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

Pharmacogenomics

PharmGKB REST API

Query Drug-Gene Relationships

import requests

def get_pharmgkb_annotations(gene_symbol):
    '''Get PharmGKB clinical annotations for a gene'''
    url = f'https://api.pharmgkb.org/v1/data/clinicalAnnotation'
    params = {'view': 'base', 'location.genes.symbol': gene_symbol}
    response = requests.get(url, params=params)
    return response.json()['data']

annotations = get_pharmgkb_annotations('CYP2D6')
for ann in annotations[:5]:
    print(f"{ann['location']['genes'][0]['symbol']}: {ann['chemicals'][0]['name']}")

Query by Drug

def get_drug_annotations(drug_name):
    '''Get pharmacogenomic annotations for a drug'''
    url = 'https://api.pharmgkb.org/v1/data/clinicalAnnotation'
    params = {'view': 'base', 'chemicals.name': drug_name}
    response = requests.get(url, params=params)
    return response.json()['data']

warfarin_annotations = get_drug_annotations('warfarin')

Get Dosing Guidelines

def get_cpic_guidelines(gene_symbol):
    '''Get CPIC dosing guidelines for a gene'''
    url = 'https://api.pharmgkb.org/v1/data/guideline'
    params = {'view': 'base', 'relatedGenes.symbol': gene_symbol, 'source': 'CPIC'}
    response = requests.get(url, params=params)
    return response.json()['data']

guidelines = get_cpic_guidelines('CYP2C19')
for g in guidelines:
    print(f"{g['name']}: {g['chemicals'][0]['name']}")

Star Allele Interpretation

CYP2D6 Metabolizer Status

# CYP2D6 activity scores for common alleles
# Based on CPIC guidelines
CYP2D6_ACTIVITY = {
    '*1': 1.0,   # Normal function
    '*2': 1.0,   # Normal function
    '*3': 0.0,   # No function
    '*4': 0.0,   # No function
    '*5': 0.0,   # Gene deletion
    '*6': 0.0,   # No function
    '*9': 0.5,   # Decreased function
    '*10': 0.25, # Decreased function (common in East Asian)
    '*17': 0.5,  # Decreased function
    '*41': 0.5,  # Decreased function
}

def calculate_activity_score(allele1, allele2):
    '''Calculate CYP2D6 activity score from diplotype'''
    score1 = CYP2D6_ACTIVITY.get(allele1, 1.0)
    score2 = CYP2D6_ACTIVITY.get(allele2, 1.0)
    return score1 + score2

def get_metabolizer_status(activity_score):
    '''Convert activity score to metabolizer phenotype

    CPIC thresholds:
    - PM: 0
    - IM: 0 < score <= 1.25
    - NM: 1.25 < score <= 2.25
    - UM: > 2.25 (gene duplications)
    '''
    if activity_score == 0:
        return 'Poor Metabolizer (PM)'
    elif activity_score <= 1.25:
        return 'Intermediate Metabolizer (IM)'
    elif activity_score <= 2.25:
        return 'Normal Metabolizer (NM)'
    else:
        return 'Ultrarapid Metabolizer (UM)'

score = calculate_activity_score('*1', '*4')
status = get_metabolizer_status(score)
print(f'Activity score: {score}, Status: {status}')

CYP2C19 Interpretation

CYP2C19_ACTIVITY = {
    '*1': 1.0,   # Normal function
    '*2': 0.0,   # No function (most common loss-of-function)
    '*3': 0.0,   # No function
    '*17': 1.5,  # Increased function
}

def cyp2c19_phenotype(allele1, allele2):
    '''Determine CYP2C19 metabolizer status'''
    score = CYP2C19_ACTIVITY.get(allele1, 1.0) + CYP2C19_ACTIVITY.get(allele2, 1.0)
    if score == 0:
        return 'Poor Metabolizer'
    elif score < 1.5:
        return 'Intermediate Metabolizer'
    elif score <= 2.0:
        return 'Normal Metabolizer'
    elif score <= 2.5:
        return 'Rapid Metabolizer'
    else:
        return 'Ultrarapid Metabolizer'

Drug Interaction Lookup

def check_pgx_interaction(drug, gene, variant):
    '''Check for pharmacogenomic drug-gene-variant interaction'''
    url = 'https://api.pharmgkb.org/v1/data/variantAnnotation'
    params = {
        'chemicals.name': drug,
        'location.genes.symbol': gene
    }
    response = requests.get(url, params=params)
    annotations = response.json().get('data', [])

    for ann in annotations:
        if variant in str(ann.get('variant', {}).get('name', '')):
            return {
                'drug': drug,
                'gene': gene,
                'variant': variant,
                'phenotype': ann.get('phenotypes', []),
                'evidence': ann.get('evidenceLevel')
            }
    return None

Common PGx Gene-Drug Pairs

Gene	Drugs	Clinical Impact
CYP2D6	Codeine, tamoxifen, ondansetron	Efficacy, toxicity
CYP2C19	Clopidogrel, omeprazole, escitalopram	Efficacy, dosing
CYP2C9	Warfarin, phenytoin, NSAIDs	Bleeding risk, dosing
VKORC1	Warfarin	Dosing
TPMT	Azathioprine, mercaptopurine	Myelosuppression
DPYD	Fluorouracil, capecitabine	Severe toxicity
HLA-B*57:01	Abacavir	Hypersensitivity
HLA-B*15:02	Carbamazepine	SJS/TEN
SLCO1B1	Simvastatin	Myopathy risk
UGT1A1	Irinotecan	Neutropenia

Batch PGx Annotation

import pandas as pd

def annotate_pgx_variants(vcf_variants, pgx_genes):
    '''Annotate variants in pharmacogenes

    Args:
        vcf_variants: DataFrame with chrom, pos, ref, alt
        pgx_genes: List of pharmacogenes to check
    '''
    results = []
    for gene in pgx_genes:
        annotations = get_pharmgkb_annotations(gene)
        for ann in annotations:
            results.append({
                'gene': gene,
                'drug': ann['chemicals'][0]['name'] if ann.get('chemicals') else None,
                'phenotype': ann.get('phenotypes', []),
                'level': ann.get('levelOfEvidence')
            })
    return pd.DataFrame(results)

pgx_genes = ['CYP2D6', 'CYP2C19', 'CYP2C9', 'VKORC1', 'TPMT']
pgx_df = annotate_pgx_variants(vcf_df, pgx_genes)

Related Skills

• clinical-databases/clinvar-lookup - Pathogenicity classification
• variant-calling/clinical-interpretation - ACMG guidelines
• clinical-databases/variant-prioritization - Clinical filtering

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