PharmaGuard: AI-Powered Pharmacogenomic Risk Assessment

Live Demo: [https://pharma-guard-xi.vercel.app/)
LinkedIn Demo Video: [https://www.linkedin.com/posts/kshitij-kj-jain-422025342_rift2026-pharmaguard-pharmacogenomics-ugcPost-7430411725461696513-1OtM?utm_source=share&utm_medium=member_desktop&rcm=ACoAAFXJ5hoBUz7esGLvvjsw-U0_Mhuo9yf44Oo)
GitHub Repository: [https://github.com/Harsh99830/PharmGuard)

Problem Overview

Adverse drug reactions cause over 100,000 deaths annually in the United States. Many of these deaths are preventable through pharmacogenomic testing — analyzing how genetic variants affect drug metabolism.

PharmaGuard is an AI-powered web application that analyzes patient genetic data (VCF files) and drug names to predict personalized pharmacogenomic risks and provide clinically actionable recommendations with LLM-generated explanations.

Architecture Overview

The system consists of a React frontend communicating with a FastAPI backend, which processes VCF files and integrates with OpenAI for clinical explanations. The application follows CPIC guidelines for pharmacogenomic recommendations.

Technology Stack

Frontend

React 19.2.0 - Modern React framework with hooks
Vite 7.3.1 - Fast build tool and development server
TailwindCSS 4.2.0 - Utility-first CSS framework
React Router DOM 7.13.0 - Client-side routing
Framer Motion 12.34.2 - Animation library
React Icons 5.5.0 - Icon library

Backend

FastAPI - Modern web framework for building APIs
Uvicorn - ASGI server for FastAPI
cyvcf2 - Efficient VCF file parsing
OpenAI - LLM integration for clinical explanations
Python-dotenv - Environment variable management

Core Features

VCF File Processing

Supported Format: Variant Call Format v4.2
File Size Limit: Up to 5 MB
Genes Analyzed: CYP2D6, CYP2C19, CYP2C9, SLCO1B1, TPMT, DPYD
Validation: Comprehensive VCF structure validation

Drug Risk Assessment

Supported Drugs: CODEINE, WARFARIN, CLOPIDOGREL, SIMVASTATIN, AZATHIOPRINE, FLUOROURACIL
Risk Categories: Safe, Adjust Dosage, Toxic, Ineffective, Unknown
Confidence Scoring: 0.0-1.0 scale with severity levels

Clinical Intelligence

CPIC Guidelines Integration: Evidence-based dosing recommendations
LLM-Generated Explanations: Clinical insights with variant citations
Phenotype Prediction: PM, IM, NM, RM, URM classification
Diplotype Analysis: Star allele identification

Output Schema

The application generates structured JSON output matching the exact required schema:

{
  "patient_id": "PATIENT_XXX",
  "drug": "DRUG_NAME",
  "timestamp": "ISO8601_timestamp",
  "risk_assessment": {
    "risk_label": "Safe|Adjust Dosage|Toxic|Ineffective|Unknown",
    "confidence_score": 0.0,
    "severity": "none|low|moderate|high|critical"
  },
  "pharmacogenomic_profile": {
    "primary_gene": "GENE_SYMBOL",
    "diplotype": "*X/*Y",
    "phenotype": "PM|IM|NM|RM|URM|Unknown",
    "detected_variants": [
      {
        "rsid": "rsXXXX",
        "gene": "GENE_SYMBOL",
        "star_allele": "*X",
        "clinical_significance": "Pathogenic|Likely pathogenic|Benign",
        "frequency": "0.XX"
      }
    ]
  },
  "clinical_recommendation": {
    "action": "Continue|Adjust dose|Avoid|Consider alternative",
    "dosage_adjustment": "Specific instructions",
    "monitoring_requirements": "Required monitoring",
    "alternative_drugs": ["Alternative options"]
  },
  "llm_generated_explanation": {
    "summary": "Clinical summary",
    "mechanism": "Biological mechanism explanation",
    "evidence_level": "Level of evidence",
    "citations": ["Relevant citations"]
  },
  "quality_metrics": {
    "vcf_parsing_success": true,
    "variant_coverage": 0.0,
    "annotation_completeness": 0.0
  }
}

Installation and Setup

Prerequisites

Python 3.8+
Node.js 18+
npm or yarn

Backend Setup

Navigate to backend directory:
```
cd backend
```

Create virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:
```
pip install -r requirements.txt
```

Set up environment variables:

cp .env.example .env
# Edit .env with your OpenAI API key

Start the backend server:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

Navigate to frontend directory:
```
cd frontend
```
Install dependencies:
```
npm install
```

Set up environment variables:

cp .env.example .env
# Edit .env with your backend URL

Start the development server:
```
npm run dev
```

Docker Deployment (Optional)

# Build and run with Docker Compose
docker-compose up --build

Deployment

Vercel Deployment (Frontend)

Install Vercel CLI:
```
npm i -g vercel
```
Deploy from frontend directory:
```
cd frontend
vercel --prod
```

Backend Deployment Options

Render: Connect GitHub repository for automatic deployment
Heroku: Use Procfile and requirements.txt
AWS/GCP/Azure: Container-based deployment

API Documentation

Endpoints

POST `/api/analyze`

Analyzes VCF file and drug input for pharmacogenomic risk assessment.

Request:

Content-Type: multipart/form-data
Body:
- file: VCF file (max 5MB)
- drugs: Comma-separated drug names

Response: JSON object matching the schema above

GET `/api/health`

Health check endpoint.

Response:

{
  "status": "healthy",
  "timestamp": "ISO8601_timestamp"
}

GET `/api/supported-drugs`

Returns list of supported drugs.

Response:

{
  "drugs": ["CODEINE", "WARFARIN", "CLOPIDOGREL", "SIMVASTATIN", "AZATHIOPRINE", "FLUOROURACIL"]
}

Usage Examples

Example 1: Single Drug Analysis

curl -X POST "http://localhost:8000/api/analyze" \
  -F "file=@sample.vcf" \
  -F "drugs=CODEINE"

Example 2: Multiple Drug Analysis

curl -X POST "http://localhost:8000/api/analyze" \
  -F "file=@sample.vcf" \
  -F "drugs=CODEINE,WARFARIN,CLOPIDOGREL"

Sample VCF Files

Sample VCF files are provided in the /test-data directory:

sample_patient_1.vcf - Normal metabolizer profile
sample_patient_2.vcf - Poor metabolizer profile
sample_patient_3.vcf - Ultra-rapid metabolizer profile

Web Interface Features

1. File Upload Interface

Drag-and-drop or file picker functionality
Real-time VCF file validation
File size indicator and progress tracking
Error handling for invalid formats

2. Drug Input

Multi-select dropdown with search functionality
Comma-separated text input support
Drug information tooltips
Input validation and suggestions

3. Results Display

Color-coded risk assessment (Green/Yellow/Red)
Expandable sections for detailed analysis
Interactive variant visualization
Downloadable JSON reports
Copy-to-clipboard functionality

4. Error Handling

User-friendly error messages
Graceful degradation for missing data
Input validation feedback
Retry mechanisms for failed uploads

Clinical Guidelines Integration

The system integrates with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines:

Gene-Drug Pairs: Evidence-based recommendations
Dosing Tables: Specific dosage adjustments
Phenotype Translation: Star allele to phenotype mapping
Alternative Therapies: Recommended alternatives when needed

Testing

Running Tests

# Backend tests
cd backend
python -m pytest tests/

# Frontend tests
cd frontend
npm test

Test Coverage

VCF parsing validation
Risk assessment accuracy
JSON schema compliance
UI component testing
Integration testing

Security and Privacy

HIPAA Compliance: No PHI storage in logs
Data Encryption: HTTPS/TLS encryption
File Validation: Malicious file detection
API Rate Limiting: Prevent abuse
Input Sanitization: XSS protection

Performance Metrics

VCF Processing: < 2 seconds for 5MB files
API Response Time: < 500ms average
UI Load Time: < 3 seconds
Memory Usage: < 512MB per request

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: support@pharmaguard.app

License

This project is licensed under the MIT License - see the LICENSE file for details.

Team Members

Lead Developer: Your Name
Backend Engineer: Team Member
Frontend Engineer: Team Member
Clinical Consultant: Team Member

Acknowledgments

CPIC Consortium for pharmacogenomic guidelines
PharmGKB for drug-gene interaction data
OpenAI for LLM capabilities
RIFT 2026 for the opportunity to build this solution

Disclaimer: This tool is for educational and research purposes only. Clinical decisions should not be based solely on this application. Always consult with qualified healthcare professionals and refer to official CPIC guidelines for clinical use.

#RIFT2026 #PharmaGuard #Pharmacogenomics #AIinHealthcare

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