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Clinical Trial Design - Quick Start Guide

Status: ✅ WORKING - Pipeline fixed and tested Last Updated: 2026-02-09

Choose Your Implementation

Python SDK

Option 1: Use the Working Pipeline (RECOMMENDED)

# Import from either file (both work)
from python_implementation import TrialFeasibilityAnalyzer
# or: from trial_pipeline import TrialFeasibilityAnalyzer

# Initialize analyzer
analyzer = TrialFeasibilityAnalyzer()

# Analyze trial feasibility
report = analyzer.analyze(
    indication="EGFR-mutant non-small cell lung cancer",
    drug_name="osimertinib",
    phase="Phase 2"
)

# Report automatically saved to: Trial_Feasibility_osimertinib.md
print(f"Feasibility Score: {report['feasibility_score']}/100")

Option 2: Use Individual Tools

from tooluniverse import ToolUniverse

tu = ToolUniverse()
tu.load_tools()

# Disease information (Open Targets)
result = tu.tools.OpenTargets_get_disease_id_description_by_name(
    disease_name="non-small cell lung cancer"
)

# Drug profile (DrugBank)
result = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(
    query="osimertinib",  # ✅ Correct parameter
    case_sensitive=False,
    exact_match=False,
    limit=1
)

# Pharmacology (DrugBank)
result = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id(
    query="osimertinib",  # ✅ Correct parameter
    case_sensitive=False,
    exact_match=False,
    limit=1
)

# Safety data (DrugBank)
result = tu.tools.drugbank_get_safety_by_drug_name_or_drugbank_id(
    query="osimertinib",  # ✅ Correct parameter
    case_sensitive=False,
    exact_match=False,
    limit=1
)

# Precedent trials (ClinicalTrials.gov)
result = tu.tools.search_clinical_trials(
    condition="EGFR-mutant non-small cell lung cancer",
    intervention="osimertinib",
    max_results=10
)

# FDA warnings (FDA)
result = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(
    drug_name="osimertinib"
)

# Literature evidence (PubMed)
result = tu.tools.PubMed_search_articles(
    query='"EGFR-mutant NSCLC" AND "osimertinib"',
    max_results=20
)

# Prevalence data (PubMed)
result = tu.tools.PubMed_search_articles(
    query='"EGFR-mutant NSCLC" AND "prevalence"',
    max_results=5
)

MCP (Model Context Protocol)

Option 1: Conversational (Claude Desktop or Compatible Client)

Tell Claude:

"Analyze clinical trial feasibility for osimertinib in EGFR-mutant NSCLC using ToolUniverse"

Claude will follow the workflow from SKILL.md and use these tools:

  1. OpenTargets_get_disease_id_description_by_name - Disease identification
  2. drugbank_get_drug_basic_info_by_drug_name_or_id - Drug profile
  3. drugbank_get_pharmacology_by_drug_name_or_drugbank_id - Mechanism
  4. search_clinical_trials - Precedent trials
  5. drugbank_get_safety_by_drug_name_or_drugbank_id - Safety profile
  6. FDA_get_warnings_and_cautions_by_drug_name - FDA warnings
  7. PubMed_search_articles - Literature evidence

Option 2: Direct Tool Calls

Step 1: Disease Identification

Tool: OpenTargets_get_disease_id_description_by_name
Parameters:
{
  "disease_name": "EGFR-mutant non-small cell lung cancer"
}

Step 2: Drug Profile

Tool: drugbank_get_drug_basic_info_by_drug_name_or_id
Parameters:
{
  "query": "osimertinib",
  "case_sensitive": false,
  "exact_match": false,
  "limit": 1
}

Step 3: Pharmacology & Mechanism

Tool: drugbank_get_pharmacology_by_drug_name_or_drugbank_id
Parameters:
{
  "query": "osimertinib",
  "case_sensitive": false,
  "exact_match": false,
  "limit": 1
}

Step 4: Precedent Trials

Tool: search_clinical_trials
Parameters:
{
  "condition": "EGFR-mutant non-small cell lung cancer",
  "intervention": "osimertinib",
  "max_results": 10
}

Step 5: Safety Assessment (DrugBank)

Tool: drugbank_get_safety_by_drug_name_or_drugbank_id
Parameters:
{
  "query": "osimertinib",
  "case_sensitive": false,
  "exact_match": false,
  "limit": 1
}

Step 6: FDA Warnings

Tool: FDA_get_warnings_and_cautions_by_drug_name
Parameters:
{
  "drug_name": "osimertinib"
}

Step 7: Literature Evidence

Tool: PubMed_search_articles
Parameters:
{
  "query": "\"EGFR-mutant NSCLC\" AND \"osimertinib\"",
  "max_results": 20
}

Step 8: Prevalence Data

Tool: PubMed_search_articles
Parameters:
{
  "query": "\"EGFR-mutant NSCLC\" AND \"prevalence\"",
  "max_results": 5
}

Run Examples (Python SDK)

# Run the working pipeline
python trial_pipeline.py

# Generates report:
#   - Trial_Feasibility_osimertinib.md

What Works ✅

  • ✅ Disease identification (Open Targets)
  • ✅ Drug profiling (DrugBank - correct parameters)
  • ✅ Pharmacology data (DrugBank)
  • ✅ Safety assessment (DrugBank + FDA)
  • ✅ Precedent trial search (ClinicalTrials.gov)
  • ✅ Literature evidence (PubMed)
  • ✅ Prevalence estimation (PubMed proxy)
  • ✅ Feasibility scoring (0-100 scale)
  • ✅ Report generation (markdown)
  • ✅ Clinical interpretation

Pipeline Analysis Steps

The pipeline performs 6-step analysis:

  1. Patient Population Analysis

    • Disease identification (Open Targets)
    • Prevalence estimation (PubMed literature)

  2. Drug Profile Analysis

    • Drug identification (DrugBank)
    • Mechanism of action (DrugBank pharmacology)

  3. Precedent Trial Search

    • Similar trials (ClinicalTrials.gov)
    • Phase/status information

  4. Safety Assessment

    • Toxicity data (DrugBank)
    • FDA warnings (FDA labels)

  5. Literature Evidence

    • Published studies (PubMed)
    • Research support

  6. Feasibility Scoring

    • 0-100 score based on data availability
    • Clinical interpretation

Feasibility Score Interpretation

  • 75-100: HIGH FEASIBILITY - Strong precedent and data available
  • 50-74: MODERATE FEASIBILITY - Some gaps but viable
  • 25-49: LOW FEASIBILITY - Significant challenges
  • 0-24: VERY LOW FEASIBILITY - Major gaps in data/precedent

Known Limitations

⚠️ Data Availability: Some tools return empty results:

  • DrugBank may not include very new drugs (e.g., osimertinib)
  • ClinicalTrials.gov API may have limited search results
  • This is a data availability issue, not a code issue

⚠️ Novel Compounds: Experimental drugs may show low feasibility scores simply due to lack of historical data, not actual infeasibility

Tool Parameters (All Implementations)

These parameter names apply to both Python SDK and MCP:

Tool Parameter Correct Name Notes
drugbank_get_drug_basic_info Drug query query All DrugBank tools use this
drugbank_get_pharmacology Drug query query NOT drug_name_or_drugbank_id
drugbank_get_safety Drug query query NOT drug_name_or_drugbank_id
OpenTargets_get_disease_id Disease name disease_name Returns Ensembl disease IDs
search_clinical_trials Condition condition Separate from intervention
FDA_get_warnings_and_cautions Drug name drug_name Simple string parameter
PubMed_search_articles Search query query Supports PubMed query syntax

Note: Whether using Python SDK or MCP, the parameter names are the same

Files

  • trial_pipeline.py - Complete working pipeline ✅
  • python_implementation.py - Same pipeline (for consistency with other skills) ✅
  • SKILL.md - Skill documentation (framework)
  • EXAMPLES.md - Clinical scenarios (documentation)
  • README.md - Original readme
  • QUICK_START.md - This file

Fixed: 2026-02-09 - Pipeline now uses correct ToolUniverse tool parameters