bio-clinical-databases-pharmacogenomics

Version Compatibility

Reference examples tested with: pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed

package and adapt the example to match the actual API rather than retrying.

Pharmacogenomics

PharmGKB REST API

Goal: Retrieve drug-gene clinical annotations and dosing guidelines from PharmGKB.

Approach: Query PharmGKB REST endpoints by gene symbol or drug name and parse JSON annotation records.

"Find pharmacogenomic annotations for this gene" → Query PharmGKB for clinical annotations linking genes to drug response.

• Python: requests.get() against PharmGKB API (requests)

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

Goal: Determine metabolizer phenotype from CYP star allele diplotypes using CPIC activity scores.

Approach: Sum per-allele activity scores and classify into PM/IM/NM/UM categories based on CPIC thresholds.

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

Goal: Check whether a specific drug-gene-variant combination has a known pharmacogenomic interaction.

Approach: Query PharmGKB variant annotation endpoint filtered by drug and gene, then match to the target variant.

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

Goal: Annotate a cohort of variants across multiple pharmacogenes with drug interaction data.

Approach: Iterate over a list of pharmacogenes, fetch PharmGKB annotations for each, and collect results into a DataFrame.

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