Digital Twin Patient Builder

Build digital twin patient models to test drug efficacy and toxicity in virtual environments

Digital Twin Patient Builder (ID: 208)

Function Overview

Build a "digital twin" model of a patient, integrating genotype, clinical history, and imaging data to test the efficacy and toxicity of different drug doses in a virtual environment.

Use Cases

• Personalized drug treatment plan design
• Drug dose optimization
• Adverse reaction risk assessment
• Clinical trial virtual simulation

Input

Data Type	Description	Format
genotype	Patient genotype data (SNPs, CNVs)	JSON
clinical_history	Clinical history and laboratory indicators	JSON
imaging_features	Imaging features (MRI, CT, etc.)	JSON

Output

Output Type	Description
efficacy_prediction	Efficacy prediction results
toxicity_prediction	Toxicity reaction prediction
optimal_dose	Optimal dose recommendation

Usage

Command Line Usage

bash

python scripts/main.py --patient patient_data.json --drug drug_profile.json --doses "[50, 100, 150]"

Parameters

Parameter	Type	Default	Required	Description
--patient	string	-	Yes	Path to patient data JSON file
--drug	string	-	Yes	Path to drug profile JSON file
--doses	string	-	Yes	Dose range to test (JSON array format)
--output, -o	string	-	No	Output file path for simulation results
--simulation-days	int	30	No	Number of days to simulate
--timestep	float	0.5	No	Simulation timestep in days

Python API

python

from scripts.main import DigitalTwinBuilder

builder = DigitalTwinBuilder()
twin = builder.build_twin(patient_data)
results = twin.simulate_drug_regimen(drug_profile, dose_range)

Technical Architecture

text

digital-twin-patient-builder/
├── SKILL.md              # This file
├── scripts/
│   └── main.py           # Core implementation
│
├── Core Components:
│   ├── PatientProfile    # Patient profile management
│   ├── GenotypeModel     # Genotype modeling
│   ├── ClinicalModel     # Clinical data modeling
│   ├── ImagingModel      # Imaging feature modeling
│   ├── DigitalTwin       # Digital twin main class
│   ├── PharmacokineticModel  # Pharmacokinetic model
│   └── DrugSimulator     # Drug simulator

Dependencies

• numpy >= 1.21.0
• scipy >= 1.7.0
• pandas >= 1.3.0

Example Data Format

Patient Data (patient_data.json)

json

{
  "patient_id": "P001",
  "genotype": {
    "CYP2D6": "*1/*4",
    "TPMT": "*1/*3C",
    "SNPs": {"rs12345": "AG", "rs67890": "CC"}
  },
  "clinical": {
    "age": 58,
    "weight": 70.5,
    "height": 170,
    "lab_values": {"creatinine": 1.2, "alt": 45, "ast": 38},
    "comorbidities": ["hypertension", "diabetes"]
  },
  "imaging": {
    "tumor_volume": 45.2,
    "perfusion_rate": 0.85,
    "texture_features": {"entropy": 5.2, "uniformity": 0.45}
  }
}

Drug Profile (drug_profile.json)

json

{
  "drug_name": "ExampleDrug",
  "drug_class": "chemotherapy",
  "metabolizing_enzymes": ["CYP2D6", "CYP3A4"],
  "target_genes": ["EGFR", "KRAS"],
  "pk_params": {
    "clearance": 15.5,
    "volume_distribution": 45.0,
    "half_life": 8.0
  },
  "efficacy_biomarkers": ["tumor_reduction", "survival_rate"],
  "toxicity_markers": ["neutropenia", "hepatotoxicity"]
}

Model Principles

1. Genotype Modeling: Parse drug metabolizing enzyme genotypes to predict metabolic phenotypes (ultrarapid/normal/poor metabolizer)
2. Physiological Modeling: Calculate personalized pharmacokinetic parameters based on age, weight, and organ function
3. Imaging Modeling: Extract tumor features to predict drug responsiveness
4. Integrated Model: Multi-modal data fusion to build a comprehensive digital twin
5. Drug Simulation: PBPK (physiologically-based pharmacokinetics) + PD (pharmacodynamics) model

References

• PBPK modeling guidelines (FDA, 2018)
• Pharmacogenomics in precision medicine (Nature Reviews, 2020)

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access	External API calls	High
File System Access	Read/write data	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Data handled securely	Medium

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

bash

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support