ClinicalTrials.gov Parser

Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov.

Use Cases

• Trial Monitoring: Track status changes of specific clinical trials
• Competitive Intelligence: Monitor competitor trial activities and milestones
• Recruitment Tracking: Get updates on enrollment status
• Completion Alerts: Monitor trial completion and results posting

Parameters

Parameter	Type	Default	Required	Description
--sponsor	string	-	No	Trial sponsor name
--condition	string	-	No	Medical condition/disease
--status	string	-	No	Trial status (Recruiting, Completed, etc.)
--trials	string	-	No	Comma-separated trial IDs (NCT numbers)
--output	string	json	No	Output format (json, csv)
--days	int	30	No	Number of days for monitoring

Usage

from scripts.main import ClinicalTrialsMonitor

# Initialize monitor
monitor = ClinicalTrialsMonitor()

# Search for trials
trials = monitor.search_trials(
    sponsor="Pfizer",
    condition="Diabetes",
    status="Recruiting"
)

# Get trial details
trial = monitor.get_trial("NCT05108922")

# Check for status changes
changes = monitor.check_status_changes(trial_ids=["NCT05108922"])

CLI Usage

# Search trials
python scripts/main.py search --sponsor "Pfizer" --condition "Diabetes"

# Get trial details
python scripts/main.py get NCT05108922

# Monitor status changes
python scripts/main.py monitor --trials NCT05108922,NCT05108923 --output json

# Generate summary report
python scripts/main.py report --sponsor "Pfizer" --days 30

API Methods

Method	Description
search_trials()	Search trials with filters
get_trial(nct_id)	Get detailed trial information
check_status_changes()	Check for status updates
get_recruitment_status()	Get enrollment updates
generate_summary()	Generate competitor summary

Technical Details

• API: ClinicalTrials.gov API v2
• Rate Limit: 10 requests/second
• Data Format: JSON
• Difficulty: Medium

References

• See references/api-docs.md for API documentation
• See references/status-codes.md for trial status definitions
• See references/examples.md for usage examples

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

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