Tooluniverse Clinical Trial Matching

Clinical Trial Matching for Precision Medicine

Transform patient molecular profiles and clinical characteristics into prioritized clinical trial recommendations. Searches ClinicalTrials.gov and cross-references with molecular databases (CIViC, OpenTargets, ChEMBL, FDA) to produce evidence-graded, scored trial matches.

KEY PRINCIPLES:

1. Report-first approach - Create report file FIRST, then populate progressively
2. Patient-centric - Every recommendation considers the individual patient's profile
3. Molecular-first matching - Prioritize trials targeting patient's specific biomarkers

Molecular Matching Priority

Match patients to trials by molecular profile FIRST (specific mutations), then by disease stage, then by prior treatments. A patient with EGFR L858R should match to EGFR-targeted trials regardless of other factors.

1. Evidence-graded - Every recommendation has an evidence tier (T1-T4)
2. Quantitative scoring - Trial Match Score (0-100) for every trial
3. Eligibility-aware - Parse and evaluate inclusion/exclusion criteria
4. Actionable output - Clear next steps, contact info, enrollment status
5. Source-referenced - Every statement cites the tool/database source
6. Completeness checklist - Mandatory section showing analysis coverage
7. English-first queries - Always use English terms in tool calls. Respond in user's language

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

When to Use

Apply when user asks:

• "What clinical trials are available for my NSCLC with EGFR L858R?"
• "Patient has BRAF V600E melanoma, failed ipilimumab - what trials?"
• "Find basket trials for NTRK fusion"
• "Breast cancer with HER2 amplification, post-CDK4/6 inhibitor trials"
• "KRAS G12C colorectal cancer clinical trials"
• "Immunotherapy trials for TMB-high solid tumors"
• "Clinical trials near Boston for lung cancer"
• "What are my options after failing osimertinib for EGFR+ NSCLC?"

NOT for (use other skills instead):

• Single variant interpretation without trial focus -> Use tooluniverse-cancer-variant-interpretation
• Drug safety profiling -> Use tooluniverse-adverse-event-detection
• Target validation -> Use tooluniverse-drug-target-validation
• General disease research -> Use tooluniverse-disease-research

Input Parsing

Required Input

• Disease/cancer type: Free-text disease name (e.g., "non-small cell lung cancer", "melanoma")

Strongly Recommended

• Molecular alterations: One or more biomarkers (e.g., "EGFR L858R", "KRAS G12C", "PD-L1 50%", "TMB-high")
• Stage/grade: Disease stage (e.g., "Stage IV", "metastatic", "locally advanced")
• Prior treatments: Previous therapies and outcomes (e.g., "failed platinum chemotherapy", "progressed on osimertinib")

Optional

• Performance status: ECOG or Karnofsky score
• Geographic location: City/state for proximity filtering
• Trial phase preference: I, II, III, IV, or "any"
• Intervention type: drug, biological, device, etc.
• Recruiting status preference: recruiting, not yet recruiting, active

For biomarker parsing rules and gene symbol normalization, see MATCHING_ALGORITHMS.md.

Workflow Overview

Input: Patient profile (disease + biomarkers + stage + prior treatments)

Phase 1: Patient Profile Standardization
  - Resolve disease to EFO/ontology IDs (OpenTargets, OLS)
  - Parse molecular alterations to gene + variant
  - Resolve gene symbols to Ensembl/Entrez IDs (MyGene)
  - Classify biomarker actionability (FDA-approved vs investigational)

Phase 2: Broad Trial Discovery
  - Disease-based trial search (ClinicalTrials.gov)
  - Biomarker-specific trial search
  - Intervention-based search (for known drugs targeting patient's biomarkers)
  - Deduplicate and collect NCT IDs

Phase 3: Trial Characterization (batch, groups of 10)
  - Eligibility criteria, conditions/interventions, locations, status, descriptions

Phase 4: Molecular Eligibility Matching
  - Parse eligibility text for biomarker requirements
  - Match patient's molecular profile to trial requirements
  - Score molecular eligibility (0-40 points)

Phase 5: Drug-Biomarker Alignment
  - Identify trial intervention drugs and mechanisms (OpenTargets, ChEMBL)
  - FDA approval status for biomarker-drug combinations
  - Classify drugs (targeted therapy, immunotherapy, chemotherapy)

Phase 6: Evidence Assessment
  - FDA-approved biomarker-drug combinations
  - Clinical trial results (PubMed), CIViC evidence, PharmGKB
  - Evidence tier classification (T1-T4)

Phase 7: Geographic & Feasibility Analysis
  - Trial site locations, enrollment status, proximity scoring

Phase 8: Alternative Options
  - Basket trials, expanded access, related studies

Phase 9: Scoring & Ranking (0-100 composite score)
  - Tier classification: Optimal (80-100) / Good (60-79) / Possible (40-59) / Exploratory (0-39)

Phase 10: Report Synthesis
  - Executive summary, ranked trial list, evidence grading, completeness checklist

Critical Tool Parameters

Clinical Trial Search Tools

Geographic coverage: ClinicalTrials.gov is U.S.-centric but global; many EU/EEA-only trials appear only in the EU CTIS register, and UK/international trials in ISRCTN. For a comprehensive search — or any patient who could enroll outside the U.S. — query search_clinical_trials, CTIS_search_trials, and ISRCTN_search_trials, then merge (the three registers list largely disjoint trials; ISRCTN records carry DOI/EudraCT/NCT cross-refs you can use to dedupe against the others). Each register has its own id namespace and detail tool: NCT→get_clinical_trial_*, CT number→CTIS_get_trial, ISRCTN id→ISRCTN_get_trial.

Batch Trial Detail Tools (all take nct_ids array)

Gene/Disease Resolution