FDA Guideline Search

Quickly search and retrieve FDA industry guidelines by therapeutic area.

Features

• Search FDA guidelines by therapeutic area (oncology, cardiology, neurology, etc.)
• Filter by document type (draft, final, ICH guidelines)
• Download and cache guideline documents
• Search within document content

Usage

Python Script

python scripts/main.py --area <therapeutic_area> [options]

Parameters

Examples

# Search oncology guidelines
python scripts/main.py --area oncology

# Search for rare disease draft guidelines
python scripts/main.py --area "rare disease" --type draft

# Search with download
python scripts/main.py --area cardiology --download --limit 10

Technical Details

• Source: FDA CDER/CBER Guidance Documents Database
• API: FDA Open Data / Web scraping with rate limiting
• Cache: Local PDF storage in references/cache/
• Difficulty: Medium

Output Format

Results are returned as structured JSON:

{
  "query": {
    "area": "oncology",
    "type": "all",
    "limit": 20
  },
  "total_found": 45,
  "guidelines": [
    {
      "title": "Clinical Trial Endpoints for the Approval of Cancer Drugs...",
      "document_number": "FDA-2020-D-0623",
      "issue_date": "2023-03-15",
      "type": "Final",
      "therapeutic_area": "Oncology",
      "pdf_url": "https://www.fda.gov/.../guidance.pdf",
      "local_path": "references/cache/..."
    }
  ]
}

References

• FDA Search Strategy
• Therapeutic Area Mappings
• FDA API Documentation

Limitations

• Rate limited to 10 requests/minute to respect FDA servers
• Some historical documents may not have digital PDFs
• ICH guidelines require separate search scope

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
• 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

No additional Python packages required.

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