633 lines
20 KiB
Markdown
633 lines
20 KiB
Markdown
---
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name: clinpgx-database
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description: "Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions."
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---
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# ClinPGx Database
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## Overview
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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.
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## When to Use This Skill
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This skill should be used when:
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- **Gene-drug interactions**: Querying how genetic variants affect drug metabolism, efficacy, or toxicity
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- **CPIC guidelines**: Accessing evidence-based clinical practice guidelines for pharmacogenetics
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- **Allele information**: Retrieving allele function, frequency, and phenotype data
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- **Drug labels**: Exploring FDA and other regulatory pharmacogenomic drug labeling
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- **Pharmacogenomic annotations**: Accessing curated literature on gene-drug-disease relationships
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- **Clinical decision support**: Using PharmDOG tool for phenoconversion and custom genotype interpretation
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- **Precision medicine**: Implementing pharmacogenomic testing in clinical practice
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- **Drug metabolism**: Understanding CYP450 and other pharmacogene functions
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- **Personalized dosing**: Finding genotype-guided dosing recommendations
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- **Adverse drug reactions**: Identifying genetic risk factors for drug toxicity
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## Installation and Setup
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### Python API Access
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The ClinPGx REST API provides programmatic access to all database resources. Basic setup:
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```bash
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uv pip install requests
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```
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### API Endpoint
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```python
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BASE_URL = "https://api.clinpgx.org/v1/"
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```
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**Rate Limits**:
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- 2 requests per second maximum
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- Excessive requests will result in HTTP 429 (Too Many Requests) response
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**Authentication**: Not required for basic access
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**Data License**: Creative Commons Attribution-ShareAlike 4.0 International License
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For substantial API use, notify the ClinPGx team at api@clinpgx.org
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## Core Capabilities
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### 1. Gene Queries
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**Retrieve gene information** including function, clinical annotations, and pharmacogenomic significance:
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```python
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import requests
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# Get gene details
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response = requests.get("https://api.clinpgx.org/v1/gene/CYP2D6")
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gene_data = response.json()
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# Search for genes by name
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response = requests.get("https://api.clinpgx.org/v1/gene",
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params={"q": "CYP"})
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genes = response.json()
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```
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**Key pharmacogenes**:
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- **CYP450 enzymes**: CYP2D6, CYP2C19, CYP2C9, CYP3A4, CYP3A5
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- **Transporters**: SLCO1B1, ABCB1, ABCG2
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- **Other metabolizers**: TPMT, DPYD, NUDT15, UGT1A1
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- **Receptors**: OPRM1, HTR2A, ADRB1
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- **HLA genes**: HLA-B, HLA-A
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### 2. Drug and Chemical Queries
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**Retrieve drug information** including pharmacogenomic annotations and mechanisms:
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```python
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# Get drug details
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response = requests.get("https://api.clinpgx.org/v1/chemical/PA448515") # Warfarin
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drug_data = response.json()
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# Search drugs by name
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response = requests.get("https://api.clinpgx.org/v1/chemical",
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params={"name": "warfarin"})
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drugs = response.json()
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```
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**Drug categories with pharmacogenomic significance**:
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- Anticoagulants (warfarin, clopidogrel)
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- Antidepressants (SSRIs, TCAs)
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- Immunosuppressants (tacrolimus, azathioprine)
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- Oncology drugs (5-fluorouracil, irinotecan, tamoxifen)
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- Cardiovascular drugs (statins, beta-blockers)
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- Pain medications (codeine, tramadol)
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- Antivirals (abacavir)
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### 3. Gene-Drug Pair Queries
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**Access curated gene-drug relationships** with clinical annotations:
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```python
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# Get gene-drug pair information
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"gene": "CYP2D6", "drug": "codeine"})
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pair_data = response.json()
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# Get all pairs for a gene
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"gene": "CYP2C19"})
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all_pairs = response.json()
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```
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**Clinical annotation sources**:
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- CPIC (Clinical Pharmacogenetics Implementation Consortium)
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- DPWG (Dutch Pharmacogenetics Working Group)
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- FDA (Food and Drug Administration) labels
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- Peer-reviewed literature summary annotations
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### 4. CPIC Guidelines
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**Access evidence-based clinical practice guidelines**:
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```python
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# Get CPIC guideline
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response = requests.get("https://api.clinpgx.org/v1/guideline/PA166104939")
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guideline = response.json()
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# List all CPIC guidelines
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response = requests.get("https://api.clinpgx.org/v1/guideline",
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params={"source": "CPIC"})
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guidelines = response.json()
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```
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**CPIC guideline components**:
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- Gene-drug pairs covered
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- Clinical recommendations by phenotype
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- Evidence levels and strength ratings
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- Supporting literature
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- Downloadable PDFs and supplementary materials
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- Implementation considerations
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**Example guidelines**:
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- CYP2D6-codeine (avoid in ultra-rapid metabolizers)
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- CYP2C19-clopidogrel (alternative therapy for poor metabolizers)
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- TPMT-azathioprine (dose reduction for intermediate/poor metabolizers)
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- DPYD-fluoropyrimidines (dose adjustment based on activity)
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- HLA-B*57:01-abacavir (avoid if positive)
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### 5. Allele and Variant Information
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**Query allele function and frequency data**:
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```python
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# Get allele information
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response = requests.get("https://api.clinpgx.org/v1/allele/CYP2D6*4")
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allele_data = response.json()
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# Get all alleles for a gene
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response = requests.get("https://api.clinpgx.org/v1/allele",
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params={"gene": "CYP2D6"})
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alleles = response.json()
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```
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**Allele information includes**:
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- Functional status (normal, decreased, no function, increased, uncertain)
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- Population frequencies across ethnic groups
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- Defining variants (SNPs, indels, CNVs)
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- Phenotype assignment
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- References to PharmVar and other nomenclature systems
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**Phenotype categories**:
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- **Ultra-rapid metabolizer** (UM): Increased enzyme activity
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- **Normal metabolizer** (NM): Normal enzyme activity
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- **Intermediate metabolizer** (IM): Reduced enzyme activity
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- **Poor metabolizer** (PM): Little to no enzyme activity
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### 6. Variant Annotations
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**Access clinical annotations for specific genetic variants**:
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```python
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# Get variant information
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response = requests.get("https://api.clinpgx.org/v1/variant/rs4244285")
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variant_data = response.json()
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# Search variants by position (if supported)
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response = requests.get("https://api.clinpgx.org/v1/variant",
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params={"chromosome": "10", "position": "94781859"})
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variants = response.json()
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```
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**Variant data includes**:
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- rsID and genomic coordinates
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- Gene and functional consequence
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- Allele associations
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- Clinical significance
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- Population frequencies
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- Literature references
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### 7. Clinical Annotations
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**Retrieve curated literature annotations** (formerly PharmGKB clinical annotations):
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```python
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# Get clinical annotations
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response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation",
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params={"gene": "CYP2D6"})
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annotations = response.json()
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# Filter by evidence level
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response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation",
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params={"evidenceLevel": "1A"})
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high_evidence = response.json()
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```
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**Evidence levels** (from highest to lowest):
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- **Level 1A**: High-quality evidence, CPIC/FDA/DPWG guidelines
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- **Level 1B**: High-quality evidence, not yet guideline
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- **Level 2A**: Moderate evidence from well-designed studies
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- **Level 2B**: Moderate evidence with some limitations
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- **Level 3**: Limited or conflicting evidence
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- **Level 4**: Case reports or weak evidence
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### 8. Drug Labels
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**Access pharmacogenomic information from drug labels**:
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```python
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# Get drug labels with PGx information
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response = requests.get("https://api.clinpgx.org/v1/drugLabel",
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params={"drug": "warfarin"})
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labels = response.json()
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# Filter by regulatory source
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response = requests.get("https://api.clinpgx.org/v1/drugLabel",
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params={"source": "FDA"})
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fda_labels = response.json()
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```
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**Label information includes**:
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- Testing recommendations
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- Dosing guidance by genotype
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- Warnings and precautions
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- Biomarker information
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- Regulatory source (FDA, EMA, PMDA, etc.)
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### 9. Pathways
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**Explore pharmacokinetic and pharmacodynamic pathways**:
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```python
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# Get pathway information
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response = requests.get("https://api.clinpgx.org/v1/pathway/PA146123006") # Warfarin pathway
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pathway_data = response.json()
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# Search pathways by drug
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response = requests.get("https://api.clinpgx.org/v1/pathway",
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params={"drug": "warfarin"})
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pathways = response.json()
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```
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**Pathway diagrams** show:
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- Drug metabolism steps
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- Enzymes and transporters involved
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- Gene variants affecting each step
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- Downstream effects on efficacy/toxicity
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- Interactions with other pathways
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## Query Workflow
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### Workflow 1: Clinical Decision Support for Drug Prescription
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1. **Identify patient genotype** for relevant pharmacogenes:
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```python
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# Example: Patient is CYP2C19 *1/*2 (intermediate metabolizer)
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response = requests.get("https://api.clinpgx.org/v1/allele/CYP2C19*2")
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allele_function = response.json()
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```
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2. **Query gene-drug pairs** for medication of interest:
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```python
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"gene": "CYP2C19", "drug": "clopidogrel"})
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pair_info = response.json()
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```
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3. **Retrieve CPIC guideline** for dosing recommendations:
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```python
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response = requests.get("https://api.clinpgx.org/v1/guideline",
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params={"gene": "CYP2C19", "drug": "clopidogrel"})
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guideline = response.json()
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# Recommendation: Alternative antiplatelet therapy for IM/PM
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```
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4. **Check drug label** for regulatory guidance:
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```python
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response = requests.get("https://api.clinpgx.org/v1/drugLabel",
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params={"drug": "clopidogrel"})
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label = response.json()
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```
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### Workflow 2: Gene Panel Analysis
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1. **Get list of pharmacogenes** in clinical panel:
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```python
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pgx_panel = ["CYP2C19", "CYP2D6", "CYP2C9", "TPMT", "DPYD", "SLCO1B1"]
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```
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2. **For each gene, retrieve all drug interactions**:
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```python
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all_interactions = {}
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for gene in pgx_panel:
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"gene": gene})
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all_interactions[gene] = response.json()
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```
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3. **Filter for CPIC guideline-level evidence**:
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```python
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for gene, pairs in all_interactions.items():
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for pair in pairs:
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if pair.get('cpicLevel'): # Has CPIC guideline
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print(f"{gene} - {pair['drug']}: {pair['cpicLevel']}")
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```
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4. **Generate patient report** with actionable pharmacogenomic findings.
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### Workflow 3: Drug Safety Assessment
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1. **Query drug for PGx associations**:
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```python
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response = requests.get("https://api.clinpgx.org/v1/chemical",
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params={"name": "abacavir"})
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drug_id = response.json()[0]['id']
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```
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2. **Get clinical annotations**:
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```python
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response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation",
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params={"drug": drug_id})
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annotations = response.json()
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```
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3. **Check for HLA associations** and toxicity risk:
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```python
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for annotation in annotations:
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if 'HLA' in annotation.get('genes', []):
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print(f"Toxicity risk: {annotation['phenotype']}")
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print(f"Evidence level: {annotation['evidenceLevel']}")
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```
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4. **Retrieve screening recommendations** from guidelines and labels.
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### Workflow 4: Research Analysis - Population Pharmacogenomics
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1. **Get allele frequencies** for population comparison:
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```python
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response = requests.get("https://api.clinpgx.org/v1/allele",
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params={"gene": "CYP2D6"})
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alleles = response.json()
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```
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2. **Extract population-specific frequencies**:
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```python
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populations = ['European', 'African', 'East Asian', 'Latino']
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frequency_data = {}
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for allele in alleles:
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allele_name = allele['name']
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frequency_data[allele_name] = {
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pop: allele.get(f'{pop}_frequency', 'N/A')
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for pop in populations
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}
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```
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3. **Calculate phenotype distributions** by population:
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```python
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# Combine allele frequencies with function to predict phenotypes
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phenotype_dist = calculate_phenotype_frequencies(frequency_data)
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```
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4. **Analyze implications** for drug dosing in diverse populations.
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### Workflow 5: Literature Evidence Review
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1. **Search for gene-drug pair**:
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```python
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"gene": "TPMT", "drug": "azathioprine"})
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pair = response.json()
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```
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2. **Retrieve all clinical annotations**:
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```python
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response = requests.get("https://api.clinpgx.org/v1/clinicalAnnotation",
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params={"gene": "TPMT", "drug": "azathioprine"})
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annotations = response.json()
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```
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3. **Filter by evidence level and publication date**:
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```python
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high_quality = [a for a in annotations
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if a['evidenceLevel'] in ['1A', '1B', '2A']]
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```
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4. **Extract PMIDs** and retrieve full references:
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```python
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pmids = [a['pmid'] for a in high_quality if 'pmid' in a]
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# Use PubMed skill to retrieve full citations
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```
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## Rate Limiting and Best Practices
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### Rate Limit Compliance
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```python
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import time
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def rate_limited_request(url, params=None, delay=0.5):
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"""Make API request with rate limiting (2 req/sec max)"""
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response = requests.get(url, params=params)
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time.sleep(delay) # Wait 0.5 seconds between requests
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return response
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# Use in loops
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genes = ["CYP2D6", "CYP2C19", "CYP2C9"]
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for gene in genes:
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response = rate_limited_request(
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"https://api.clinpgx.org/v1/gene/" + gene
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)
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data = response.json()
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```
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### Error Handling
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```python
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def safe_api_call(url, params=None, max_retries=3):
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"""API call with error handling and retries"""
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for attempt in range(max_retries):
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try:
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response = requests.get(url, params=params, timeout=10)
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if response.status_code == 200:
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return response.json()
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elif response.status_code == 429:
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# Rate limit exceeded
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wait_time = 2 ** attempt # Exponential backoff
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print(f"Rate limit hit. Waiting {wait_time}s...")
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time.sleep(wait_time)
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else:
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response.raise_for_status()
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except requests.exceptions.RequestException as e:
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print(f"Attempt {attempt + 1} failed: {e}")
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if attempt == max_retries - 1:
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raise
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time.sleep(1)
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```
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### Caching Results
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```python
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import json
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from pathlib import Path
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def cached_query(cache_file, api_func, *args, **kwargs):
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"""Cache API results to avoid repeated queries"""
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cache_path = Path(cache_file)
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if cache_path.exists():
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with open(cache_path) as f:
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return json.load(f)
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result = api_func(*args, **kwargs)
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with open(cache_path, 'w') as f:
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json.dump(result, f, indent=2)
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return result
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# Usage
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gene_data = cached_query(
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'cyp2d6_cache.json',
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rate_limited_request,
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"https://api.clinpgx.org/v1/gene/CYP2D6"
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)
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```
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## PharmDOG Tool
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PharmDOG (formerly DDRx) is ClinPGx's clinical decision support tool for interpreting pharmacogenomic test results:
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**Key features**:
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- **Phenoconversion calculator**: Adjusts phenotype predictions for drug-drug interactions affecting CYP2D6
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- **Custom genotypes**: Input patient genotypes to get phenotype predictions
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- **QR code sharing**: Generate shareable patient reports
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- **Flexible guidance sources**: Select which guidelines to apply (CPIC, DPWG, FDA)
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- **Multi-drug analysis**: Assess multiple medications simultaneously
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**Access**: Available at https://www.clinpgx.org/pharmacogenomic-decision-support
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**Use cases**:
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- Clinical interpretation of PGx panel results
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- Medication review for patients with known genotypes
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- Patient education materials
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- Point-of-care decision support
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## Resources
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### scripts/query_clinpgx.py
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Python script with ready-to-use functions for common ClinPGx queries:
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- `get_gene_info(gene_symbol)` - Retrieve gene details
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- `get_drug_info(drug_name)` - Get drug information
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- `get_gene_drug_pairs(gene, drug)` - Query gene-drug interactions
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- `get_cpic_guidelines(gene, drug)` - Retrieve CPIC guidelines
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- `get_alleles(gene)` - Get all alleles for a gene
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- `get_clinical_annotations(gene, drug, evidence_level)` - Query literature annotations
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- `get_drug_labels(drug)` - Retrieve pharmacogenomic drug labels
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- `search_variants(rsid)` - Search by variant rsID
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- `export_to_dataframe(data)` - Convert results to pandas DataFrame
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Consult this script for implementation examples with proper rate limiting and error handling.
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### references/api_reference.md
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Comprehensive API documentation including:
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- Complete endpoint listing with parameters
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- Request/response format specifications
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- Example queries for each endpoint
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- Filter operators and search patterns
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- Data schema definitions
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- Rate limiting details
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- Authentication requirements (if any)
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- Troubleshooting common errors
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Refer to this document when detailed API information is needed or when constructing complex queries.
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## Important Notes
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### Data Sources and Integration
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ClinPGx consolidates multiple authoritative sources:
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- **PharmGKB**: Curated pharmacogenomics knowledge base (now part of ClinPGx)
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- **CPIC**: Evidence-based clinical implementation guidelines
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- **PharmCAT**: Allele calling and phenotype interpretation tool
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- **DPWG**: Dutch pharmacogenetics guidelines
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- **FDA/EMA labels**: Regulatory pharmacogenomic information
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As of July 2025, all PharmGKB URLs redirect to corresponding ClinPGx pages.
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### Clinical Implementation Considerations
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- **Evidence levels**: Always check evidence strength before clinical application
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- **Population differences**: Allele frequencies vary significantly across populations
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- **Phenoconversion**: Consider drug-drug interactions that affect enzyme activity
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- **Multi-gene effects**: Some drugs affected by multiple pharmacogenes
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- **Non-genetic factors**: Age, organ function, drug interactions also affect response
|
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- **Testing limitations**: Not all clinically relevant alleles detected by all assays
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|
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### Data Updates
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|
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- ClinPGx continuously updates with new evidence and guidelines
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|
- Check publication dates for clinical annotations
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- Monitor ClinPGx Blog (https://blog.clinpgx.org/) for announcements
|
|
- CPIC guidelines updated as new evidence emerges
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- PharmVar provides nomenclature updates for allele definitions
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|
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### API Stability
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|
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- API endpoints are relatively stable but may change during development
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- Parameters and response formats subject to modification
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- Monitor API changelog and ClinPGx blog for updates
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- Consider version pinning for production applications
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- Test API changes in development before production deployment
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## Common Use Cases
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|
|
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### Pre-emptive Pharmacogenomic Testing
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Query all clinically actionable gene-drug pairs to guide panel selection:
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```python
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# Get all CPIC guideline pairs
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"cpicLevel": "A"}) # Level A recommendations
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actionable_pairs = response.json()
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```
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|
|
### Medication Therapy Management
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|
|
|
Review patient medications against known genotypes:
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|
|
```python
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patient_genes = {"CYP2C19": "*1/*2", "CYP2D6": "*1/*1", "SLCO1B1": "*1/*5"}
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medications = ["clopidogrel", "simvastatin", "escitalopram"]
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|
|
for med in medications:
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for gene in patient_genes:
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response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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params={"gene": gene, "drug": med})
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# Check for interactions and dosing guidance
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```
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|
|
### Clinical Trial Eligibility
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|
|
Screen for pharmacogenomic contraindications:
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|
|
```python
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# Check for HLA-B*57:01 before abacavir trial
|
|
response = requests.get("https://api.clinpgx.org/v1/geneDrugPair",
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|
params={"gene": "HLA-B", "drug": "abacavir"})
|
|
pair_info = response.json()
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|
# CPIC: Do not use if HLA-B*57:01 positive
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|
```
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|
|
## Additional Resources
|
|
|
|
- **ClinPGx website**: https://www.clinpgx.org/
|
|
- **ClinPGx Blog**: https://blog.clinpgx.org/
|
|
- **API documentation**: https://api.clinpgx.org/
|
|
- **CPIC website**: https://cpicpgx.org/
|
|
- **PharmCAT**: https://pharmcat.clinpgx.org/
|
|
- **ClinGen**: https://clinicalgenome.org/
|
|
- **Contact**: api@clinpgx.org (for substantial API use)
|