- name
- digital-twin-patient-builder
- description
- Build digital twin patient models to test drug efficacy and toxicity
- version
- 1.0.0
- category
- AI/Tech
- tags
- []
- author
- AIPOCH
- license
- MIT
- status
- Draft
- risk_level
- High
- skill_type
- Hybrid (Tool/Script + Network/API)
- owner
- AIPOCH
- reviewer
- last_updated
- 2026-02-06
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
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
from scripts.main import DigitalTwinBuilder
builder = DigitalTwinBuilder()
twin = builder.build_twin(patient_data)
results = twin.simulate_drug_regimen(drug_profile, dose_range)
Technical Architecture
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)
{
"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)
{
"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
- Genotype Modeling: Parse drug metabolizing enzyme genotypes to predict metabolic phenotypes (ultrarapid/normal/poor metabolizer)
- Physiological Modeling: Calculate personalized pharmacokinetic parameters based on age, weight, and organ function
- Imaging Modeling: Extract tumor features to predict drug responsiveness
- Integrated Model: Multi-modal data fusion to build a comprehensive digital twin
- 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
# 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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
