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Pharmacogenomics

AIVA's pharmacogenomics features help you identify variants in drug-metabolizing genes, assess their impact on drug response, and review prescribing guidelines. These tools connect genomic findings to actionable medication management decisions.

Overview

Pharmacogenomics (PGx) examines how genetic variants influence drug metabolism, efficacy, and adverse reactions. AIVA supports PGx analysis through:

  • PGx Analysis Card: A dedicated analysis card in the Analysis Hub showing drug-variant interactions and metabolizer phenotypes.
  • Knowledge Graph queries: Explore drug-gene-protein relationships through the Knowledge Graph.
  • AIVA Chat: Ask the AI assistant about specific drug-gene interactions, guidelines, and evidence.

Key Pharmacogenes

AIVA's PGx analysis covers established pharmacogenes, including:

Gene Drug Examples Clinical Impact
CYP2D6 Codeine, tramadol, tamoxifen, fluoxetine Metabolizer status affects drug activation and clearance
CYP2C19 Clopidogrel, omeprazole, voriconazole Poor metabolizers may have reduced drug efficacy or increased toxicity
CYP2C9 Warfarin, phenytoin, celecoxib Dose adjustment may be required based on metabolizer status
CYP3A⅘ Tacrolimus, cyclosporine, statins Affects first-pass metabolism and bioavailability
DPYD Fluorouracil, capecitabine Deficiency increases risk of severe/fatal toxicity
TPMT Azathioprine, mercaptopurine Deficiency requires significant dose reduction
UGT1A1 Irinotecan, atazanavir Reduced glucuronidation affects drug clearance
VKORC1 Warfarin Affects warfarin sensitivity and dosing
SLCO1B1 Simvastatin, rosuvastatin Increased risk of myopathy
HLA-B Abacavir, carbamazepine, allopurinol Hypersensitivity reactions

Using the PGx Card

The PGx card in the Analysis Hub provides a structured view of pharmacogenomic findings:

Step 1: Open the PGx Card

  1. Navigate to the Analysis Hub for your sample.
  2. Select the Pharmacogenomics category (or any category containing the PGx card).
  3. The PGx card loads automatically.

Step 2: Review Drug-Variant Interactions

The card displays:

  • Variants found: Pharmacogene variants detected in your sample.
  • Metabolizer phenotype: Predicted metabolizer status based on the variant(s) (e.g., Poor Metabolizer, Normal Metabolizer).
  • Affected medications: Drugs whose metabolism or efficacy is affected by the detected variants.
  • Clinical action: Recommended prescribing modifications (dose adjustment, alternative drug, contraindication).

Step 3: Review Prescribing Guidelines

For each drug-gene interaction, the card provides links to:

  • CPIC guidelines: Clinical Pharmacogenetics Implementation Consortium recommendations.
  • PharmGKB annotations: Detailed evidence summaries and clinical annotations.
  • FDA label information: Pharmacogenomic biomarker information from FDA-approved drug labels.

Metabolizer Phenotypes

AIVA predicts metabolizer phenotypes based on detected variants:

Phenotype Description Clinical Implication
Ultra-Rapid Metabolizer Increased enzyme activity May require higher doses; prodrug activation may be excessive
Rapid Metabolizer Above-normal enzyme activity Similar considerations as ultra-rapid, often less pronounced
Normal Metabolizer Typical enzyme activity Standard dosing expected to be effective
Intermediate Metabolizer Reduced enzyme activity May need dose reduction for some drugs
Poor Metabolizer Minimal or absent enzyme activity Significant dose reduction or alternative drug often required

Clinical context required

Metabolizer phenotype predictions are based on known variant-phenotype associations. Additional factors (drug interactions, organ function, comorbidities) influence actual drug response and should be considered in prescribing decisions.

PGx in AIVA Chat

You can also perform pharmacogenomic analysis through AIVA Chat:

  • "List all variants in pharmacogenes from my sample."
  • "What is the CYP2D6 metabolizer status based on the variants in my data?"
  • "What drugs should be prescribed with caution given the DPYD variants in this sample?"
  • "Search for CPIC guidelines for CYP2C19 and clopidogrel."

AIVA uses the Genomic Data Query, Knowledge Graph, and Web Search tools to compile pharmacogenomic information.

Evidence Levels

PharmGKB classifies drug-gene associations by evidence level:

Level Description
1A Annotation in a CPIC or other clinical guideline
1B Annotation supported by strong clinical evidence
2A Known pharmacogene with moderate clinical evidence
2B Moderate clinical evidence for the association
3 Low-level evidence or in vitro data
4 Case reports or preliminary evidence

AIVA displays the evidence level alongside each drug-gene interaction so you can assess the strength of the recommendation.

Integrating PGx into Reports

Pharmacogenomic findings can be included in clinical reports:

  1. Flag relevant PGx variants using the flagging system.
  2. Add prescribing notes as comments.
  3. Use the Pharmacogenomic Report template for structured PGx reporting.
  4. Link flagged variants to the report.
  5. Use AI Auto-Fill to generate prescribing recommendation summaries.