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name: 'pharmacogenomics-agent' description: 'AI-driven pharmacogenomic analysis for precision dosing and adverse event prediction using multi-omics data.' keywords:

• pharmacogenomics
• precision-dosing
• cpic-guidelines
• adverse-events
• multi-omics

measurable_outcome: 'Provides validated dosing recommendations for >50 drugs with 99% concordance to CPIC guidelines.' allowed-tools:

• read_file
• run_shell_command

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Pharmacogenomics Agent

The Pharmacogenomics Agent integrates AI and multi-omics data to predict individual drug responses, optimize medication dosing, and minimize adverse events. It implements CPIC guidelines while leveraging deep learning for complex polygenic drug response phenotypes.

When to Use This Skill

• When interpreting pharmacogenomic variants (CYP450, HLA, transporters) for drug selection.
• To predict drug response using transcriptomic and proteomic biomarkers.
• For calculating polygenic risk scores for drug efficacy/toxicity.
• When optimizing doses for narrow therapeutic index drugs.
• To identify drug-drug-gene interactions.

Core Capabilities

1. Variant Interpretation: Translates star allele genotypes (1/2) into metabolizer phenotypes and actionable CPIC recommendations.

1. Multi-Omics Response Prediction: Deep learning models (DeepDRA, MOViDA) integrate genomic, transcriptomic, and proteomic features for drug response prediction.

1. Polygenic Risk Scoring: Combines effects of thousands of variants to stratify patients beyond single-gene pharmacogenomics.

1. Adverse Event Prediction: Identifies genetic risk factors for serious adverse reactions (HLA associations, G6PD deficiency).

1. Dose Optimization: AI-guided dosing for warfarin, tacrolimus, fluoropyrimidines, thiopurines, and other PGx-guided drugs.

1. Drug-Drug-Gene Interactions: Detects complex interactions where genetic variants modify drug interaction severity.

CPIC-Guided Genes and Drugs

| Gene | Drugs | Clinical Impact | |------|-------|-----------------| | CYP2D6 | Codeine, tamoxifen, antidepressants | Metabolizer status affects efficacy/toxicity | | CYP2C19 | Clopidogrel, PPIs, antidepressants | Loss-of-function affects activation | | CYP2C9/VKORC1 | Warfarin | Dose requirements vary 10-fold | | TPMT/NUDT15 | Thiopurines | Myelosuppression risk | | DPYD | Fluoropyrimidines | Severe/fatal toxicity in deficient patients | | HLA-B57:01 | Abacavir | Hypersensitivity screening | | HLA-B15:02 | Carbamazepine | SJS/TEN risk in Asian populations |

Workflow

1. Input: Patient genotype data (VCF, genotyping array), medication list, clinical parameters.

1. Star Allele Calling: Translate variants to star alleles using Stargazer or PharmCAT.

1. Phenotype Assignment: Determine metabolizer status (PM, IM, NM, UM) for each gene.

1. Guideline Lookup: Retrieve CPIC/DPWG recommendations for patient's medications.

1. Multi-Omics Prediction: Apply deep learning for complex response phenotypes.

1. Output: Drug-specific recommendations, dose adjustments, alternative medications, interaction alerts.

Example Usage

User: "Interpret this patient's pharmacogenomic panel and provide recommendations for their current medications."

Agent Action:

python3 Skills/Precision_Medicine/Pharmacogenomics_Agent/pgx_analyzer.py \
    --genotype patient_pgx_panel.vcf \
    --medications current_meds.json \
    --guidelines cpic_dpwg \
    --risk_scores oncology_response \
    --output pgx_recommendations.json

AI Models for Drug Response

| Model | Architecture | Application | Performance | |-------|--------------|-------------|-------------| | DeepDRA | Autoencoders | Drug response from transcriptomics | AUC 0.99 | | MOViDA | Multi-omics VAE | Interpretable response prediction | State-of-art | | DrugCell | Graph neural network | Drug synergy prediction | Improved over baselines | | PaccMann | Multimodal attention | Cancer drug sensitivity | Clinical translation |

Polygenic Drug Response

Beyond single-gene PGx, polygenic scores capture:

• Efficacy polygenic scores: Statin LDL response, antidepressant remission
• Toxicity polygenic scores: Metformin GI intolerance, opioid dependence risk
• Combined scores: Integrating PRS with PGx for personalized prediction

Prerequisites

• Python 3.10+
• PharmCAT or Stargazer for star allele calling
• CPIC/DPWG guideline databases
• Deep learning frameworks (PyTorch)
• Optional: Expression data for multi-omics models

Related Skills

• Variant_Interpretation - For general variant classification
• Drug_Repurposing - For alternative drug identification
• Clinical_Trials - For PGx-guided trial matching

Implementation Notes

Clinical Integration:

• Returns structured FHIR-compatible recommendations
• Supports CDS Hooks for real-time EMR alerts
• Audit trail for clinical decision support

Quality Metrics:

• Validated against PharmGKB annotations
• Concordance with reference laboratory calls
• Regular updates with new CPIC guidelines

Author

AI Group - Biomedical AI Platform

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