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 ```python 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 ```python 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 ```python 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 ```python # 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 ```python 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. ```python 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. ```python 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