Skill: aipoch-ai/digital-twin-patient-builder (version 0.1.0)

--- name: digital-twin-patient-builder description: Build digital twin patient models to test drug efficacy and toxicity in virtual environments 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 ```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 ``` 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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)