bio-clinical-databases-pharmacogenomics
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.
ソース情報
- リポジトリ
- zja2004/BGI-CLI
- ソースの最終更新活動
- 2026年3月19日 08:17
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- 英語
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- 2
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ソースの指示 · 読み取り専用プレビュー- 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
## 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
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