🧬 PharmaGuard — Pharmacogenomic Risk Prediction System

RIFT 2026 Hackathon | Pharmacogenomics / Explainable AI Track

Live Project demo https://coderbash-lucknow.onrender.com

CPIC Guidelines

🔗 Important Links

| Resource | URL | | Live Application | -:YOUR_DEPLOYED_URL_HERE | | Demo Video (LinkedIn)| -: YOUR_LINKEDIN_VIDEO_URL_HERE | | GitHub Repository** | -: https://github.com/goldigd05/CoderBash_Lucknow |

📌 Project Title

PharmaGuard — AI-powered Pharmacogenomic Risk Prediction System
RIFT 2026 Hackathon | Pharmacogenomics / Explainable AI Track

🎯 Problem Statement

Adverse drug reactions (ADRs) kill over 100,000 Americans annually. Many of these deaths are preventable through pharmacogenomic testing — analyzing how a patient's genetic variants affect how they metabolize drugs.

Current clinical workflows lack real-time, personalized, AI-powered decision support that can analyze a patient's VCF genetic data and instantly predict drug-specific risks with clinically actionable, CPIC-aligned recommendations.

PharmaGuard solves this by combining VCF parsing, pharmacogenomic knowledge, and Google Gemini LLM to deliver explainable, personalized drug risk predictions in seconds.

🏗️ Architecture Overview

┌──────────────────────────────────────────────┐ │ FRONTEND (React + Vite) │ │ VCF Upload → Drug Input → Results Dashboard │ └─────────────────────┬────────────────────────┘ │ POST /api/analyze ▼ ┌──────────────────────────────────────────────┐ │ BACKEND (FastAPI + Python) │ │ │ │ VCF Parser → PGx Analyzer → Risk Assessor │ │ ↓ │ │ Google Gemini LLM (XAI) │ │ ↓ │ │ JSON Output (RIFT Schema) │ └──────────────────────────────────────────────┘

🧪 Tech Stack

Layer	Technology
Backend Framework	FastAPI (Python 3.11+)
LLM Integration	Google Gemini 2.0 Flash (Free API)
VCF Parsing	Custom pure-Python VCF v4.2 parser
PGx Knowledge Base	CPIC-curated variant database
Frontend	React + Vite
Deployment	Render.com
API Docs	FastAPI Swagger UI (/docs)

🧬 Genes & Drugs Supported

Gene	Drug	Clinical Risk (PM)
CYP2D6	CODEINE	Ineffective — no morphine conversion
CYP2C19	CLOPIDOGREL	Ineffective — no antiplatelet activation
CYP2C9	WARFARIN	Toxic — severe bleeding risk
SLCO1B1	SIMVASTATIN	Toxic — myopathy/rhabdomyolysis
TPMT	AZATHIOPRINE	Toxic (Critical) — fatal myelosuppression
DPYD	FLUOROURACIL	Toxic (Critical) — fatal chemo toxicity

⚡ Risk Labels

Label	Color	Meaning
Safe	🟢 Green	Standard dosing appropriate
Adjust Dosage	🟡 Yellow	Dose modification required
Toxic	🔴 Red	High toxicity risk at standard dose
Ineffective	🟣 Purple	Drug will not work — use alternative
Unknown	⚪ Gray	Insufficient pharmacogenomic data

🚀 Installation & Setup

Prerequisites

• Python 3.11+
• Google Gemini API key (free at aistudio.google.com)

Backend Setup step by step

bash

1. Clone the repository

git clone https://github.com/goldigd05/CoderBash_Lucknow.git cd CoderBash_Lucknow/Backend

2. Create virtual environment

python -m venv venv

Windows

venv\Scripts\activate

Mac/Linux

source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment

cp .env.example .env

Add your GEMINI_API_KEY in .env file

5. Run the server

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

API live at: http://localhost:8000
Swagger docs at: http://localhost:8000/docs

📡 API Documentation

POST /api/analyze

Main analysis endpoint

Request — multipart/form-data:

Field	Type	Required	Description
vcf_file	File (.vcf)	✅	Patient VCF file, max 5MB
drugs	String	✅	Comma-separated drug names

Example: bash curl -X POST http://localhost:8000/api/analyze
-F "vcf_file=@sample_vcf/test_patient_001.vcf"
-F "drugs=CODEINE,WARFARIN"

Response — Exact JSON Schema: json { "patient_id": "PATIENT_XXXXXX", "drug": "CODEINE", "timestamp": "2026-02-20T10:30:00Z", "risk_assessment": { "risk_label": "Safe", "confidence_score": 0.88, "severity": "none" }, "pharmacogenomic_profile": { "primary_gene": "CYP2D6", "diplotype": "*1/*1", "phenotype": "NM", "detected_variants": [] }, "clinical_recommendation": { "action": "Standard dosing", "dosing_guidance": "...", "cpic_recommendation": "...", "urgency": "ROUTINE", "cpic_guideline_applicable": true }, "llm_generated_explanation": { "summary": "...", "mechanism": "...", "variant_impact": "...", "clinical_context": "...", "monitoring_parameters": "...", "model_used": "gemini-2.0-flash" }, "quality_metrics": { "vcf_parsing_success": true, "variants_analyzed_for_drug": 0, "confidence_level": "HIGH" } }

GET /api/health

Returns service status.

GET /api/drugs

Returns all supported drugs and their genes.

GET /api/genes

Returns all supported pharmacogenomic genes.

GET /api/schema

Returns the full output JSON schema.

🧪 Usage Examples

Test with Normal Patient (all Safe): bash

Upload sample_vcf/test_patient_002_normal.vcf

Drug: CODEINE,WARFARIN

Expected: Safe for all drugs

Test with Variant Patient: bash

Upload sample_vcf/test_patient_001.vcf

Drug: AZATHIOPRINE

Expected: Toxic (critical) — TPMT Poor Metabolizer

🤖 LLM Integration (Explainable AI)

PharmaGuard uses Google Gemini 2.0 Flash to generate explainable clinical explanations:

• Every explanation cites specific rsIDs from the patient's VCF
• 5 structured fields: summary, mechanism, variant impact, clinical context, monitoring
• Graceful fallback if API unavailable — structured template used
• Prompt engineered for clinician-grade pharmacogenomic language
• CPIC guideline citations included in every response

📋 CPIC Alignment

All risk assessments follow CPIC (Clinical Pharmacogenomics Implementation Consortium) guidelines:

• Strong/Moderate recommendation levels included
• Dosing guidance from validated CPIC tables
• Reference URLs to cpicpgx.org in every response

👥 Team Members

Name	Role
Dilip jagriti goldi
CoderBash tema name

📁 Project Structure

Backend/ ├── main.py ← FastAPI entry point ├── requirements.txt ├── .env.example ├── sample_vcf/ │ ├── test_patient_001.vcf ← Variant patient test file │ └── test_patient_002_normal.vcf ← Normal patient test file └── src/ ├── parsers/ │ └── vcf_parser.py ← VCF v4.2 parser ├── models/ │ └── pgx_knowledge.py ← CPIC knowledge base ├── analyzers/ │ ├── pgx_analyzer.py ← Diplotype/phenotype engine │ └── risk_assessor.py ← Risk lookup ├── services/ │ ├── llm_service.py ← Gemini API integration │ └── analysis_service.py ← Orchestrator └── routes/ ├── analysis.py ← API endpoints └── health.py ← Health check

🌐 Deployment

Deployed on Render.com

Deploy your own:

1. Push code to GitHub
2. render.com → New Web Service
3. Build Command: pip install -r requirements.txt
4. Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT
5. Environment Variable: GEMINI_API_KEY = your_key
6. Deploy → get live URL!

Built with ❤️ at RIFT 2026 Hackathon | May this algorithm save lives.