ClinPGx Database

Overview

ClinPGx (Clinical Pharmacogenomics Database) is a comprehensive resource for clinical pharmacogenomics information, successor to PharmGKB. It consolidates data from PharmGKB, CPIC, and PharmCAT, providing curated information on how genetic variation affects medication response. Access gene-drug pairs, clinical guidelines, allele functions, and drug labels for precision medicine applications.

When to Use This Skill

This skill should be used when:

• Gene-drug interactions: Querying how genetic variants affect drug metabolism, efficacy, or toxicity
• CPIC guidelines: Accessing evidence-based clinical practice guidelines for pharmacogenetics
• Allele information: Retrieving allele function, frequency, and phenotype data
• Drug labels: Exploring FDA and other regulatory pharmacogenomic drug labeling
• Pharmacogenomic annotations: Accessing curated literature on gene-drug-disease relationships
• Clinical decision support: Using PharmDOG tool for phenoconversion and custom genotype interpretation
• Precision medicine: Implementing pharmacogenomic testing in clinical practice
• Drug metabolism: Understanding CYP450 and other pharmacogene functions
• Personalized dosing: Finding genotype-guided dosing recommendations
• Adverse drug reactions: Identifying genetic risk factors for drug toxicity

Installation and Setup

Python API Access

The ClinPGx REST API provides programmatic access to all database resources. Basic setup:

uv pip install requests

API Endpoint

BASE_URL = "https://api.clinpgx.org/v1/"

Rate Limits:

• 2 requests per second maximum
• Excessive requests will result in HTTP 429 (Too Many Requests) response

Authentication: Not required for basic access

Data License: Creative Commons Attribution-ShareAlike 4.0 International License

For substantial API use, notify the ClinPGx team at [email protected]

Core Capabilities

1. Gene Queries

Retrieve gene information including function, clinical annotations, and pharmacogenomic significance:

import requests

# Get gene details
response = requests.get("https://api.clinpgx.org/v1/gene/CYP2D6")
gene_data = response.json()

# Search for genes by name
response = requests.get("https://api.clinpgx.org/v1/gene",
                       params={"q": "CYP"})
genes = response.json()

Key pharmacogenes:

• CYP450 enzymes: CYP2D6, CYP2C19, CYP2C9, CYP3A4, CYP3A5
• Transporters: SLCO1B1, ABCB1, ABCG2
• Other metabolizers: TPMT, DPYD, NUDT15, UGT1A1
• Receptors: OPRM1, HTR2A, ADRB1
• HLA genes: HLA-B, HLA-A

2. Drug and Chemical Queries

Retrieve drug information including pharmacogenomic annotations and mechanisms:

# Get drug details
response = requests.get("https://api.clinpgx.org/v1/chemical/PA448515")  # Warfarin
drug_data = response.json()

# Search drugs by name
response = requests.get("https://api.clinpgx.org/v1/chemical",
                       params={"name": "warfarin"})
drugs = response.json()

Drug categories with pharmacogenomic significance:

• Anticoagulants (warfarin, clopidogrel)
• Antidepressants (SSRIs, TCAs)
• Immunosuppressants (tacrolimus, azathioprine)
• Oncology drugs (5-fluorouracil, irinotecan, tamoxifen)
• Cardiovascular drugs (statins, beta-blockers)
• Pain medications (codeine, tramadol)
• Antivirals (abacavir)

3. Gene-Drug Pair Queries

Access curated gene-drug relationships with clinical annotations:

# Get gene-drug pair information
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
                       params={"gene": "CYP2D6", "drug": "codeine"})
pair_data = response.json()

# Get all pairs for a gene
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
                       params={"gene": "CYP2C19"})
all_pairs = response.json()

Clinical annotation sources:

• CPIC (Clinical Pharmacogenetics Implementation Consortium)
• DPWG (Dutch Pharmacogenetics Working Group)
• FDA (Food and Drug Administration) labels
• Peer-reviewed literature summary annotations

4. CPIC Guidelines

Access evidence-based clinical practice guidelines:

# Get CPIC guideline
response = requests.get("https://api.clinpgx.org/v1/guideline/PA166104939")
guideline = response.json()

# List all CPIC guidelines
response = requests.get("https://api.clinpgx.org/v1/guideline",
                       params={"source": "CPIC"})
guidelines = response.json()

CPIC guideline components:

• Gene-drug pairs covered
• Clinical recommendations by phenotype
• Evidence levels and strength ratings
• Supporting literature
• Downloadable PDFs and supplementary materials
• Implementation considerations

Example guidelines:

• CYP2D6-codeine (avoid in ultra-rapid metabolizers)
• CYP2C19-clopidogrel (alternative therapy for poor metabolizers)
• TPMT-azathioprine (dose reduction for intermediate/poor metabolizers)
• DPYD-fluoropyrimidines (dose adjustment based on activity)
• HLA-B*57:01-abacavir (avoid if positive)

5. Allele and Variant Information

Query allele function and frequency data:

# Get allele information
response = requests.get("https://api.clinpgx.org/v1/allele/CYP2D6*4")
allele_data = response.json()

# Get all alleles for a gene
response = requests.get("https://api.clinpgx.org/v1/allele",
                       params={"gene": "CYP2D6"})
alleles = response.json()

Allele information includes:

• Functional status (normal, decreased, no function, increased, uncertain)
• Population frequencies across ethnic groups
• Defining variants (SNPs, indels, CNVs)
• Phenotype assignment
• References to PharmVar and other nomenclature systems

Phenotype categories:

• Ultra-rapid metabolizer (UM): Increased enzyme activity
• Normal metabolizer (NM): Normal enzyme activity
• Intermediate metabolizer (IM): Reduced enzyme activity
• Poor metabolizer (PM): Little to no enzyme activity

6. Variant Annotations

Access clinical annotations for specific genetic variants:

# Get variant information
response = requests.get("https://api.clinpgx.org/v1/variant/rs4244285")
variant_data = response.json()

# Search variants by position (if supported)
response = requests.get("https://api.clinpgx.org/v1/variant",
                       params={"chromosome": "10", "position": "94781859"})
variants = response.json()

Variant data includes:

• rsID and genomic coordinates
• Gene and functional consequence
• Allele associations
• Clinical significance
• Population frequencies
• Literature references

7. Clinical Annotations

Retrieve curated literature annotations (formerly PharmGKB clinical annotations):

# Get clinical annotations
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation",
                       params={"gene": "CYP2D6"})
annotations = response.json()

# Filter by evidence level
response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation",
                       params={"evidenceLevel": "1A"})
high_evidence = response.json()

Evidence levels (from highest to lowest):

• Level 1A: High-quality evidence, CPIC/FDA/DPWG guidelines
• Level 1B: High-quality evidence, not yet guideline
• Level 2A: Moderate evidence from well-designed studies
• Level 2B: Moderate evidence with some limitations
• Level 3: Limited or conflicting evidence
• Level 4: Case reports or weak evidence

8. Drug Labels

Access pharmacogenomic information from drug labels:

# Get drug labels with PGx information
response = requests.get("https://api.clinpgx.org/v1/drugLabel",
                       params={"drug": "warfarin"})
labels = response.json()

# Filter by regulatory source
response = requests.get("https://api.clinpgx.org/v1/drugLabel",
                       params={"source": "FDA"})
fda_labels = response.json()

Label information includes:

• Testing recommendations
• Dosing guidance by genotype
• Warnings and precautions
• Biomarker information
• Regulatory source (FDA, EMA, PMDA, etc.)

9. Pathways

Explore pharmacokinetic and pharmacodynamic pathways:

# Get pathway information
response = requests.get("https://api.clinpgx.org/v1/pathway/PA146123006")  # Warfarin pathway
pathway_data = response.json()

# Search pathways by drug
response = requests.get("https://api.clinpgx.org/v1/pathway",
                       params={"drug": "warfarin"})
pathways = response.json()

Pathway diagrams show:

• Drug metabolism steps
• Enzymes and transporters involved
• Gene variants affecting each step