Drug Interaction Checker

Check for interactions between multiple medications, including severity classification and mechanism explanations.

Features

• Multi-drug analysis: Check interactions between 2+ medications simultaneously
• Severity classification: Critical / Major / Moderate / Minor / Unknown
• Mechanism explanation: Pharmacological basis for each interaction
• Clinical guidance: Recommendations for management

Severity Levels

Usage

Python Script

python scripts/main.py --drugs "Warfarin" "Aspirin" "Ibuprofen"

As a Module

from scripts.main import check_interactions

result = check_interactions(["Metformin", "Simvastatin", "Amlodipine"])

Parameters

Output Format

{
  "drugs_checked": ["Drug A", "Drug B"],
  "interactions": [
    {
      "drug_pair": ["Drug A", "Drug B"],
      "severity": "Major",
      "mechanism": "Pharmacodynamic synergism...",
      "effect": "Increased bleeding risk",
      "recommendation": "Avoid combination or monitor INR closely"
    }
  ],
  "summary": {
    "critical": 0,
    "major": 1,
    "moderate": 0,
    "minor": 0
  }
}

Data Sources

This skill uses a curated drug interaction database stored in references/interactions_db.json. The database includes:

• FDA-approved drug interaction data
• Known metabolic pathways (CYP450 enzymes)
• Pharmacodynamic interactions
• Common supplement interactions

Limitations

• Database may not include all possible drug combinations
• Always consult healthcare professionals for medical decisions
• Does not account for patient-specific factors (age, renal function, etc.)
• Not a substitute for professional medical advice

Technical Difficulty

High - Requires extensive pharmacological knowledge database, accurate severity classification, and clear mechanism explanations.

References

See references/ directory for:

• interactions_db.json - Drug interaction database
• severity_criteria.md - Classification criteria
• cyp450_substrates.json - Metabolic pathway data

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

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

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