Agent skill

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.

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-clinical-databases-pharmacogenomics

SKILL.md

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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results