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.
4. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
5. **Quantitative scoring** - Trial Match Score (0-100) for every trial
6. **Eligibility-aware** - Parse and evaluate inclusion/exclusion criteria
7. **Actionable output** - Clear next steps, contact info, enrollment status
8. **Source-referenced** - Every statement cites the tool/database source
9. **Completeness checklist** - Mandatory section showing analysis coverage
10. **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.

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## 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`

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## 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](./MATCHING_ALGORITHMS.md).

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## 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
```

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## Critical Tool Parameters

### Clinical Trial Search Tools

| Tool | Key Parameters | Notes |
|------|---------------|-------|
| `search_clinical_trials` | `query_term` (REQ), `condition`, `intervention`, `pageSize` | Main search (ClinicalTrials.gov, U.S./global) |
| `search_clinical_trials` | `action="search_studies"` (REQ), `condition`, `intervention`, `limit` | Alternative search |
| `get_clinical_trial_descriptions` | `action="get_study_details"` (REQ), `nct_id