tooluniverse-clinical-trial-matching（tooluniverse 临床试验匹配）

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
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

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 (e.g., "ECOG 0-1")
• Geographic location: City/state for proximity filtering (e.g., "Boston, MA")
• Trial phase preference: I, II, III, IV, or "any"
• Intervention type: drug, biological, device, etc.
• Recruiting status preference: recruiting, not yet recruiting, active

Biomarker Parsing Rules

Gene Symbol Normalization

Phase 0: Tool Parameter Reference (CRITICAL)

BEFORE calling ANY tool, verify its parameters from this reference table.

Clinical Trial Tools

Molecular/Disease Tools

CIViC Tools

Known CIViC Gene IDs: EGFR=19, BRAF=5, ALK=1, ABL1=4, KRAS=30, TP53=45, ERBB2=20, NTRK1=197, NTRK2=560, NTRK3=561, PIK3CA=37, MET=52, ROS1=118, RET=122, BRCA1=2370, BRCA2=2371

Drug Information Tools

Disease Ontology Tools

Literature Tools

PharmGKB Tools

Workflow Overview

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

Phase 1: Patient Profile Standardization
  - Resolve disease to EFO/ontology IDs
  - Parse molecular alterations to gene + variant
  - Resolve gene symbols to Ensembl/Entrez IDs
  - 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)
  - Collect NCT IDs for detailed analysis

Phase 3: Trial Characterization
  - Get eligibility criteria for top candidate trials
  - Get conditions and interventions
  - Get locations and status
  - Get trial descriptions and phase information

Phase 4: Molecular Eligibility Matching
  - Parse eligibility criteria text for biomarker requirements
  - Match patient's molecular profile to trial requirements
  - Score molecular eligibility

Phase 5: Drug-Biomarker Alignment
  - Identify trial intervention drugs
  - Check drug mechanisms against patient biomarkers (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 for similar patients (PubMed)
  - CIViC clinical evidence
  - PharmGKB pharmacogenomics
  - Drug safety profiles

Phase 7: Geographic & Feasibility Analysis
  - Trial site locations
  - Enrollment status and dates
  - Distance from patient location (if provided)

Phase 8: Alternative Options
  - Basket trials (biomarker-driven, tumor-agnostic)
  - Expanded access and compassionate use
  - Related trials with different study designs

Phase 9: Scoring & Ranking
  - Calculate Trial Match Score (0-100) for each trial
  - Tier classification (Optimal/Good/Possible/Exploratory)
  - Rank by composite score
  - Generate recommendations

Phase 10: Report Synthesis
  - Executive summary (top 3 trials)
  - Patient profile summary
  - Ranked trial list with detailed analysis
  - Alternative options
  - Evidence grading
  - Completeness checklist

Phase 1: Patient Profile Standardization

Goal: Resolve all patient inputs to standardized identifiers for cross-database queries.

1.1 Disease Resolution

def resolve_disease(tu, disease_name):
    """Resolve disease name to EFO ID and standard terminology."""
    # OpenTargets disease search
    result = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName=disease_name)
    hits = result.get('data', {}).get('search', {}).get('hits', [])

    if hits:
        disease_info = hits[0]
        return {
            'efo_id': disease_info.get('id'),
            'name': disease_info.get('name'),
            'description': disease_info.get('description'),
            'original_input': disease_name
        }

    # Fallback: OLS EFO search
    ols_result = tu.tools.ols_search_efo_terms(query=disease_name, limit=5)
    ols_terms = ols_result.get('data', {}).get('terms', [])
    if ols_terms:
        term = ols_terms[0]
        return {
            'efo_id': term.get('short_form'),
            'name': term.get('label'),
            'description': term.get('description', [''])[0] if term.get('description') else '',
            'original_input': disease_name
        }

    return {'efo_id': None, 'name': disease_name, 'description': '', 'original_input': disease_name}

Response: {efo_id: "EFO_0003060", name: "non-small cell lung carcinoma", description: "...", original_input: "..."}

1.2 Gene/Biomarker Resolution

def resolve_gene(tu, gene_symbol):
    """Resolve gene symbol to cross-database IDs."""
    # Normalize common aliases
    alias_map = {
        'HER2': 'ERBB2', 'HER-2': 'ERBB2',
        'PD-L1': 'CD274', 'PDL1': 'CD274',
        'PD-1': 'PDCD1', 'PD1': 'PDCD1',
        'VEGF': 'VEGFA',
    }
    normalized = alias_map.get(gene_symbol.upper(), gene_symbol)

    # MyGene resolution
    result = tu.tools.MyGene_query_genes(query=normalized, species='human')
    hits = result.get('hits', [])

    gene_hit = None
    for hit in hits:
        if hit.get('symbol', '').upper() == normalized.upper():
            gene_hit = hit
            break
    if not gene_hit and hits:
        gene_hit = hits[0]

    if gene_hit:
        ensembl = gene_hit.get('ensembl', {})
        ensembl_id = ensembl.get('gene') if isinstance(ensembl, dict) else (ensembl[0].get('gene') if isinstance(ensembl, list) and ensembl else None)
        return {
            'symbol': gene_hit.get('symbol'),
            'entrez_id': gene_hit.get('entrezgene'),
            'ensembl_id': ensembl_id,
            'name': gene_hit.get('name'),
            'original_input': gene_symbol
        }

    return {'symbol': gene_symbol, 'entrez_id': None, 'ensembl_id': None, 'name': None, 'original_input': gene_symbol}

1.3 Biomarker Actionability Classification

Classify each biomarker using FDA pharmacogenomic biomarkers list:

def classify_biomarker_actionability(tu, gene_symbol, alteration):
    """Classify biomarker as FDA-approved, guideline, or investigational."""
    # Check FDA pharmacogenomic biomarkers
    fda_result = tu.tools.fda_pharmacogenomic_biomarkers()
    fda_biomarkers = fda_result.get('results', [])

    fda_match = [b for b in fda_biomarkers if gene_symbol.upper() in str(b.get('Biomarker', '')).upper()]

    if fda_match:
        return {
            'level': 'FDA-approved',
            'drugs': [b.get('Drug') for b in fda_match],
            'labeling_sections': [b.get('LabelingSection') for b in fda_match]
        }

    # Check OpenTargets for drugs targeting this gene
    # (done in Phase 5)

    return {'level': 'investigational', 'drugs': [], 'labeling_sections': []}

1.4 Parse Molecular Alterations

def parse_biomarker(biomarker_text):
    """Parse free-text biomarker into structured components."""
    import re

    # Pattern: "GENE VARIANT" (e.g., "EGFR L858R")
    mutation_match = re.match(r'(\w+)\s+([A-Z]\d+[A-Z])', biomarker_text, re.IGNORECASE)
    if mutation_match:
        return {'gene': mutation_match.group(1), 'alteration': mutation_match.group(2), 'type': 'mutation'}

    # Pattern: "GENE exon N deletion/insertion"
    exon_match = re.match(r'(\w+)\s+exon\s+(\d+)\s+(\w+)', biomarker_text, re.IGNORECASE)
    if exon_match:
        return {'gene': exon_match.group(1), 'alteration': f'exon {exon_match.group(2)} {exon_match.group(3)}', 'type': 'exon_alteration'}

    # Pattern: "GENE1-GENE2 fusion" or "GENE1/GENE2"
    fusion_match = re.match(r'(\w+)[-/](\w+)\s*(fusion)?', biomarker_text, re.IGNORECASE)
    if fusion_match:
        return {'gene': fusion_match.group(2), 'alteration': f'{fusion_match.group(1)}-{fusion_match.group(2)}', 'type': 'fusion', 'partner': fusion_match.group(1)}

    # Pattern: "GENE amplification"
    amp_match = re.match(r'(\w+)\s+amplification', biomarker_text, re.IGNORECASE)
    if amp_match:
        return {'gene': amp_match.group(1), 'alteration': 'amplification', 'type': 'amplification'}

    # Pattern: "PD-L1 XX%"
    expression_match = re.match(r'([\w-]+)\s+(\d+%|high|low|positive|negative)', biomarker_text, re.IGNORECASE)
    if expression_match:
        return {'gene': expression_match.group(1), 'alteration': expression_match.group(2), 'type': 'expression'}

    # Pattern: "MSI-high", "TMB-high"
    status_match = re.match(r'(MSI|TMB|dMMR|MMR)[-\s]*(high|low|stable|deficient|proficient)', biomarker_text, re.IGNORECASE)
    if status_match:
        return {'gene': status_match.group(1), 'alteration': status_match.group(2), 'type': 'status'}

    # Fallback: treat as gene name
    return {'gene': biomarker_text.split()[0], 'alteration': ' '.join(biomarker_text.split()[1:]), 'type': 'unknown'}

Phase 2: Broad Trial Discovery

Goal: Cast a wide net to find all potentially relevant clinical trials.

2.1 Disease-Based Trial Search

def search_trials_by_disease(tu, disease_name, status_filter=None, phase_filter=None, page_size=20):
    """Search ClinicalTrials.gov by disease/condition."""
    query_parts = []
    if status_filter:
        query_parts.append(f'AREA[OverallStatus]{status_filter}')
    if phase_filter:
        query_parts.append(phase_filter)

    query_term = ' AND '.join(query_parts) if query_parts else disease_name

    result = tu.tools.search_clinical_trials(
        condition=disease_name,
        query_term=query_term if query_parts else disease_name,
        pageSize=page_size
    )

    # Response: {studies: [{NCT ID, brief_title, brief_summary, overall_status, condition, phase}], nextPageToken, total_count}
    if isinstance(result, str):
        return []  # No studies found

    return result.get('studies', [])

2.2 Biomarker-Specific Trial Search

def search_trials_by_biomarker(tu, gene_symbol, alteration, disease_name=None, page_size=15):
    """Search trials mentioning specific biomarkers."""
    # Search 1: Gene + alteration
    biomarker_query = f'{gene_symbol} {alteration}' if alteration else gene_symbol

    result = tu.tools.search_clinical_trials(
        condition=disease_name if disease_name else '',
        query_term=biomarker_query,
        pageSize=page_size
    )

    if isinstance(result, str):
        return []

    return result.get('studies', [])

2.3 Intervention-Based Trial Search

def search_trials_by_intervention(tu, drug_name, disease_name=None, page_size=10):
    """Search trials by intervention/drug name."""
    result = tu.tools.search_clinical_trials(
        condition=disease_name if disease_name else '',
        intervention=drug_name,
        query_term=drug_name,
        pageSize=page_size
    )

    if isinstance(result, str):
        return []

    return result.get('studies', [])

2.4 Alternative Search (clinical_trials_search)

Use as a complement to the main search:

def search_trials_alternative(tu, condition, intervention=None, limit=10):
    """Alternative trial search with different API endpoint."""
    params = {
        'action': 'search_studies',
        'condition': condition,
        'limit': limit
    }
    if intervention:
        params['intervention'] = intervention

    result = tu.tools.clinical_trials_search(**params)

    return result.get('studies', [])

2.5 Deduplication

def deduplicate_trials(trial_lists):
    """Merge and deduplicate trials from multiple searches."""
    seen_ncts = set()
    unique_trials = []

    for trials in trial_lists:
        for trial in trials:
            nct = trial.get('NCT ID') or trial.get('nctId', '')
            if nct and nct not in seen_ncts:
                seen_ncts.add(nct)
                unique_trials.append(trial)

    return unique_trials

Phase 3: Trial Characterization

Goal: Get detailed information for the top candidate trials.

3.1 Get Eligibility Criteria (Batch)

def get_trial_eligibility(tu, nct_ids):
    """Get eligibility criteria for multiple trials."""
    # Process in batches of 10
    all_criteria = []
    for i in range(0, len(nct_ids), 10):
        batch = nct_ids[i:i+10]
        result = tu.tools.get_clinical_trial_eligibility_criteria(
            nct_ids=batch,
            eligibility_criteria='all'
        )
        if isinstance(result, list):
            all_criteria.extend(result)

    return all_criteria
    # Returns: [{NCT ID, eligibility_criteria: "Inclusion Criteria:\n...\nExclusion Criteria:\n..."}]

3.2 Get Conditions and Interventions (Batch)

def get_trial_interventions(tu, nct_ids):
    """Get conditions, arm groups, and interventions for multiple trials."""
    all_interventions = []
    for i in range(0, len(nct_ids), 10):
        batch = nct_ids[i:i+10]
        result = tu.tools.get_clinical_trial_conditions_and_interventions(
            nct_ids=batch,
            condition_and_intervention='all'
        )
        if isinstance(result, list):
            all_interventions.extend(result)

    return all_interventions
    # Returns: [{NCT ID, condition, arm_groups: [{label, type, description, interventionNames}], interventions: [{type, name, description}]}]

3.3 Get Locations (Batch)

def get_trial_locations(tu, nct_ids):
    """Get trial site locations."""
    all_locations = []
    for i in range(0, len(nct_ids), 10):
        batch = nct_ids[i:i+10]
        result = tu.tools.get_clinical_trial_locations(
            nct_ids=batch,
            location='all'
        )
        if isinstance(result, list):
            all_locations.extend(result)

    return all_locations
    # Returns: [{NCT ID, locations: [{facility, city, state, country}]}]

3.4 Get Status and Dates (Batch)

def get_trial_status(tu, nct_ids):
    """Get enrollment status and key dates."""
    all_status = []
    for i in range(0, len(nct_ids), 10):
        batch = nct_ids[i:i+10]
        result = tu.tools.get_clinical_trial_status_and_dates(
            nct_ids=batch,
            status_and_date='all'
        )
        if isinstance(result, list):
            all_status.extend(result)

    return all_status
    # Returns: [{NCT ID, overall_status, start_date, primary_completion_date, completion_date}]

3.5 Get Full Descriptions (Batch)

def get_trial_descriptions(tu, nct_ids):
    """Get detailed trial descriptions."""
    all_descriptions = []
    for i in range(0, len(nct_ids), 10):
        batch = nct_ids[i:i+10]
        result = tu.tools.get_clinical_trial_descriptions(
            nct_ids=batch,
            description_type='full'
        )
        if isinstance(result, list):
            all_descriptions.extend(result)

    return all_descriptions
    # Returns: [{NCT ID, brief_title, official_title, brief_summary, detailed_description}]

Phase 4: Molecular Eligibility Matching

Goal: Determine how well the patient's molecular profile matches each trial's requirements.

4.1 Parse Eligibility Text for Biomarker Requirements

def extract_biomarker_requirements(eligibility_text):
    """Extract biomarker requirements from eligibility criteria text."""
    import re

    requirements = {
        'required_biomarkers': [],
        'excluded_biomarkers': [],
        'biomarker_agnostic': False
    }

    if not eligibility_text:
        return requirements

    text_upper = eligibility_text.upper()

    # Common biomarker patterns in eligibility text
    # Required biomarkers (in inclusion criteria)
    inclusion_section = eligibility_text.split('Exclusion Criteria')[0] if 'Exclusion Criteria' in eligibility_text else eligibility_text
    exclusion_section = eligibility_text.split('Exclusion Criteria')[1] if 'Exclusion Criteria' in eligibility_text else ''

    # Look for gene mutation requirements
    gene_patterns = [
        r'(?:EGFR|KRAS|BRAF|ALK|ROS1|RET|MET|NTRK|HER2|ERBB2|PIK3CA|BRCA|PD-?L1|MSI|TMB|dMMR)',
    ]

    for pattern in gene_patterns:
        # In inclusion section
        for match in re.finditer(pattern, inclusion_section, re.IGNORECASE):
            gene = match.group(0).upper()
            context = inclusion_section[max(0, match.start()-100):match.end()+100]
            requirements['required_biomarkers'].append({
                'gene': gene,
                'context': context.strip()
            })

        # In exclusion section
        for match in re.finditer(pattern, exclusion_section, re.IGNORECASE):
            gene = match.group(0).upper()
            context = exclusion_section[max(0, match.start()-100):match.end()+100]
            requirements['excluded_biomarkers'].append({
                'gene': gene,
                'context': context.strip()
            })

    # Check for biomarker-agnostic / basket trial language
    basket_terms = ['tumor-agnostic', 'histology-independent', 'basket', 'any solid tumor', 'all comers', 'biomarker-selected']
    if any(term in text_upper.lower() for term in basket_terms):
        requirements['biomarker_agnostic'] = True

    return requirements

4.2 Score Molecular Match

def score_molecular_match(patient_biomarkers, trial_requirements):
    """Score molecular match between patient and trial (0-40 points)."""
    if not trial_requirements['required_biomarkers'] and not trial_requirements['excluded_biomarkers']:
        # No molecular criteria - could be open to any
        return 10, 'No specific molecular criteria (general trial)'

    patient_genes = {b['gene'].upper() for b in patient_biomarkers}
    required_genes = {b['gene'].upper() for b in trial_requirements['required_biomarkers']}
    excluded_genes = {b['gene'].upper() for b in trial_requirements['excluded_biomarkers']}

    # Check exclusions first
    excluded_match = patient_genes & excluded_genes
    if excluded_match:
        return 0, f'Patient biomarker(s) {excluded_match} are in exclusion criteria'

    if not required_genes:
        return 10, 'No specific biomarker requirements found'

    # Check for exact gene match
    matched_genes = patient_genes & required_genes
    if matched_genes:
        # Check for specific variant match
        # Look for specific mutation mentions in context
        exact_variant_match = False
        for req in trial_requirements['required_biomarkers']:
            for pb in patient_biomarkers:
                if pb['gene'].upper() == req['gene'].upper():
                    alt = pb.get('alteration', '').upper()
                    if alt and alt in req.get('context', '').upper():
                        exact_variant_match = True
                        break

        if exact_variant_match:
            return 40, f'Exact biomarker match: {matched_genes} with specific variant'
        else:
            return 30, f'Gene-level match: {matched_genes} (specific variant match unclear)'

    # Check for pathway-level match (e.g., trial targets EGFR pathway, patient has EGFR mutation)
    # This requires domain knowledge mapping
    return 5, 'No direct biomarker match found'

Phase 5: Drug-Biomarker Alignment

Goal: Verify that trial drugs actually target the patient's biomarkers.

5.1 Identify Trial Drugs and Mechanisms

def get_drug_mechanism_info(tu, drug_name):
    """Get drug mechanism, targets, and approval status."""
    # Step 1: Resolve drug in OpenTargets
    result = tu.tools.OpenTargets_get_drug_id_description_by_name(drugName=drug_name)
    hits = result.get('data', {}).get('search', {}).get('hits', [])

    if not hits:
        return {'drug_name': drug_name, 'chembl_id': None, 'mechanisms': [], 'is_approved': False}

    drug_info = hits[0]
    chembl_id = drug_info.get('id')

    # Step 2: Get mechanisms of action
    moa_result = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId=chembl_id)
    moa_rows = moa_result.get('data', {}).get('drug', {}).get('mechanismsOfAction', {}).get('rows', [])

    mechanisms = []
    for row in moa_rows:
        targets = row.get('targets', [])
        mechanisms.append({
            'mechanism': row.get('mechanismOfAction'),
            'action_type': row.get('actionType'),
            'target_name': row.get('targetName'),
            'target_genes': [t.get('approvedSymbol') for t in targets]
        })

    # Step 3: Check approval
    approval_result = tu.tools.OpenTargets_get_drug_approval_status_by_chemblId(chemblId=chembl_id)

    return {
        'drug_name': drug_name,
        'chembl_id': chembl_id,
        'description': drug_info.get('description'),
        'mechanisms': mechanisms,
        'is_approved': 'approved' in drug_info.get('description', '').lower()
    }

5.2 Score Drug-Biomarker Alignment

def score_drug_biomarker_alignment(patient_gene_symbols, drug_mechanisms):
    """Check if trial drug targets patient's biomarkers."""
    patient_genes_upper = {g.upper() for g in patient_gene_symbols}

    for mech in drug_mechanisms:
        target_genes = {g.upper() for g in mech.get('target_genes', [])}
        if patient_genes_upper & target_genes:
            return True, f"Drug targets {patient_genes_upper & target_genes} via {mech.get('mechanism')}"

    return False, "No direct target overlap with patient biomarkers"

Phase 6: Evidence Assessment

Goal: Assess evidence strength for drug efficacy in similar patient populations.

6.1 FDA Approval Evidence

def check_fda_approval(tu, drug_name, disease_name):
    """Check FDA approval status and labeled indications."""
    result = tu.tools.FDA_get_indications_by_drug_name(drug_name=drug_name, limit=3)

    indications = result.get('results', [])
    for ind in indications:
        ind_text = str(ind.get('indications_and_usage', ''))
        # Check if disease is mentioned in indications
        if any(term.lower() in ind_text.lower() for term in disease_name.split()):
            return {
                'approved': True,
                'indication_text': ind_text[:500],
                'brand_name': ind.get('openfda.brand_name', []),
                'evidence_tier': 'T1'
            }

    return {'approved': False, 'indication_text': '', 'brand_name': [], 'evidence_tier': 'T3'}

6.2 Literature Evidence

def get_literature_evidence(tu, gene, alteration, drug_name, disease_name):
    """Search PubMed for evidence of drug efficacy for this biomarker."""
    query = f'{gene} {alteration} {drug_name} {disease_name} clinical trial'
    result = tu.tools.PubMed_search_articles(query=query, max_results=5)

    articles = result if isinstance(result, list) else result.get('articles', [])
    return articles

6.3 CIViC Evidence (if available)

def get_civic_evidence(tu, gene_symbol, civic_gene_id):
    """Get CIViC clinical evidence for gene variants."""
    if not civic_gene_id:
        return []

    result = tu.tools.civic_get_variants_by_gene(gene_id=civic_gene_id, limit=100)
    variants = result.get('data', {}).get('gene', {}).get('variants', {}).get('nodes', [])
    return variants

6.4 Evidence Tier Classification

Phase 7: Geographic & Feasibility Analysis

Goal: Assess practical feasibility of trial enrollment.

7.1 Location Analysis

def analyze_trial_locations(locations_data, patient_location=None):
    """Analyze trial site locations and proximity."""
    if not locations_data:
        return {'total_sites': 0, 'countries': [], 'us_states': [], 'nearest': None}

    locations = locations_data.get('locations', [])
    countries = list(set(loc.get('country', '') for loc in locations if loc.get('country')))
    us_states = list(set(loc.get('state', '') for loc in locations if loc.get('country') == 'United States' and loc.get('state')))

    return {
        'total_sites': len(locations),
        'countries': countries,
        'us_states': us_states,
        'has_us_sites': 'United States' in countries,
        'locations': locations[:10]  # First 10 for display
    }

7.2 Geographic Scoring

Phase 8: Alternative Options

Goal: Identify basket trials, expanded access, and related studies.

8.1 Basket Trial Search

IMPORTANT: ClinicalTrials.gov search is sensitive to query complexity. Overly specific queries like "NTRK fusion tumor agnostic" may return zero results. Use simpler queries and combine results.

def search_basket_trials(tu, biomarker, page_size=10):
    """Search for basket/biomarker-driven trials.

    NOTE: Use simpler queries first (e.g., 'NTRK solid tumor'),
    then more specific ones. Complex multi-word queries often fail.
    """
    # Start with simpler queries (more likely to return results)
    query_terms = [
        f'{biomarker} solid tumor',
        f'{biomarker}',
        f'{biomarker} basket',
    ]

    all_trials = []
    for query in query_terms:
        result = tu.tools.search_clinical_trials(
            query_term=query,
            pageSize=page_size
        )
        if not isinstance(result, str):
            all_trials.extend(result.get('studies', []))

    return deduplicate_trials([all_trials])

8.2 Expanded Access Search

def search_expanded_access(tu, drug_name):
    """Search for expanded access / compassionate use programs."""
    result = tu.tools.search_clinical_trials(
        query_term=f'{drug_name} expanded access',
        pageSize=5
    )

    if isinstance(result, str):
        return []

    return result.get('studies', [])

Phase 9: Trial Match Scoring System

Score Components (Total: 0-100)

Molecular Match (0-40 points):

Clinical Eligibility (0-25 points):

Evidence Strength (0-20 points):

Trial Phase (0-10 points):

Geographic Feasibility (0-5 points):

Recommendation Tiers

Phase 10: Report Synthesis

Report Template

The final report should follow this structure:

# Clinical Trial Matching Report

**Patient**: [Disease type] with [biomarker(s)]
**Date**: [Current date]
**Trials Analyzed**: [N total] | **Top Matches**: [N with score >= 60]

---

## Executive Summary

**Top 3 Trial Recommendations**:

1. **[NCT ID]** - [Brief title] (Score: XX/100, Tier N)
   - Phase: [Phase], Status: [Status]
   - Why: [Key reason for match]

2. **[NCT ID]** - [Brief title] (Score: XX/100, Tier N)
   ...

3. **[NCT ID]** - [Brief title] (Score: XX/100, Tier N)
   ...

---

## Patient Profile Summary

| Parameter | Value | Standardized |
|-----------|-------|-------------|
| Disease | [input] | [EFO name] (EFO_XXXX) |
| Biomarker(s) | [input] | [gene: variant, type] |
| Stage | [input] | [standardized] |
| Prior Treatment | [input] | [standardized] |
| Performance Status | [input] | [ECOG score] |
| Location | [input] | [city, state] |

### Biomarker Actionability
| Biomarker | Actionability Level | FDA-Approved Drugs | Evidence |
|-----------|--------------------|--------------------|----------|
| [gene variant] | [FDA-approved/investigational] | [drugs] | [T1/T2/T3/T4] |

---

## Ranked Trial Matches

### Trial 1: [NCT ID] - [Title]

**Trial Match Score: XX/100** (Tier N: [Label])

| Component | Score | Details |
|-----------|-------|---------|
| Molecular Match | XX/40 | [explanation] |
| Clinical Eligibility | XX/25 | [explanation] |
| Evidence Strength | XX/20 | [explanation] |
| Trial Phase | XX/10 | [phase] |
| Geographic | XX/5 | [location info] |

**Trial Details**:
- **Phase**: [Phase]
- **Status**: [Recruiting/Active/etc.]
- **Sponsor**: [Sponsor]
- **Start Date**: [Date]
- **Estimated Completion**: [Date]

**Interventions**:
- [Drug name]: [Mechanism] | [Dosing info if available]
- [Comparator]: [Description]

**Molecular Eligibility Match**:
- Required biomarkers: [list]
- Patient match: [Exact/Gene-level/Pathway/None]
- Notes: [details]

**Clinical Eligibility Assessment**:
- Disease type: [Match/Mismatch]
- Stage: [Match/Mismatch/Unclear]
- Prior treatment: [Match/Mismatch/Unclear]
- Performance status: [Match/Mismatch/Unclear]

**Evidence for Efficacy**:
- FDA approval: [Yes/No for this indication]
- Clinical results: [Phase III/II/I data if available]
- Mechanism alignment: [Drug targets patient's biomarker: Yes/No]
- Literature: [Key references]

**Trial Sites** (first 5):
- [City, State, Country]
- ...

**Next Steps**: [Contact info, enrollment instructions]

[Repeat for each matched trial]

---

## Trials by Category

### Targeted Therapy Trials
[List trials with targeted agents matching patient's biomarkers]

### Immunotherapy Trials
[List immunotherapy trials, noting PD-L1/TMB/MSI requirements]

### Combination Therapy Trials
[List trials with drug combinations]

### Basket/Platform Trials
[List biomarker-agnostic or multi-arm trials]

---

## Additional Testing Recommendations

If the patient has not been tested for certain biomarkers, these trials would become relevant:

| Biomarker | Test Needed | Trials Unlocked | Priority |
|-----------|-------------|----------------|----------|
| [e.g., TMB] | [NGS panel] | [NCT IDs] | [High/Medium/Low] |

---

## Alternative Options

### Expanded Access Programs
[List any expanded access or compassionate use programs]

### Off-Label Options
[FDA-approved drugs for other indications with same biomarker]

---

## Evidence Grading Summary

| Evidence Tier | Count | Description |
|--------------|-------|-------------|
| T1 (FDA/Guideline) | N | FDA-approved biomarker-drug, clinical guideline |
| T2 (Clinical) | N | Phase III data, robust clinical evidence |
| T3 (Emerging) | N | Phase I/II, preclinical evidence |
| T4 (Exploratory) | N | Computational, mechanism inference |

---

## Completeness Checklist

| Analysis Step | Status | Source |
|--------------|--------|--------|
| Disease standardization | [Done/Partial/Failed] | [OpenTargets/OLS] |
| Gene resolution | [Done/Partial/Failed] | [MyGene] |
| Biomarker actionability | [Done/Partial/Failed] | [FDA biomarkers] |
| Disease trial search | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Biomarker trial search | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Intervention trial search | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Eligibility parsing | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Drug mechanism analysis | [Done/Partial/Failed] | [OpenTargets/ChEMBL] |
| Evidence assessment | [Done/Partial/Failed] | [FDA/PubMed/CIViC] |
| Location analysis | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Basket trial search | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Expanded access search | [Done/Partial/Failed] | [ClinicalTrials.gov] |
| Scoring & ranking | [Done/Partial/Failed] | [Composite] |

---

## Disclaimer

This report is for informational and research purposes only. Clinical trial eligibility is ultimately determined by the trial investigators based on complete medical records. Patients should discuss all options with their healthcare team. Trial availability and status may change; verify current status at [ClinicalTrials.gov](https://clinicaltrials.gov).

## Sources

All data sourced from:
- ClinicalTrials.gov (trial search, eligibility, locations, status)
- OpenTargets Platform (drug-target associations, disease ontology)
- CIViC (clinical variant interpretations)
- ChEMBL (drug mechanisms, targets)
- FDA (approved indications, pharmacogenomic biomarkers, drug labels)
- DrugBank (drug targets, indications)
- PharmGKB (pharmacogenomics)
- PubMed/NCBI (literature evidence)
- OLS/EFO (disease ontology)
- MyGene (gene identifier resolution)

Execution Strategy

Parallelization Opportunities

Many tool calls can be executed in parallel to speed up the workflow:

Parallel Group 1 (Phase 1 - can all run simultaneously):

• MyGene_query_genes for each gene
• OpenTargets_get_disease_id_description_by_name for disease
• ols_search_efo_terms for disease
• fda_pharmacogenomic_biomarkers (no params)

Parallel Group 2 (Phase 2 - can all run simultaneously):

• search_clinical_trials with disease condition
• search_clinical_trials with biomarker query
• search_clinical_trials with intervention query
• clinical_trials_search as alternative

Parallel Group 3 (Phase 3 - can all run simultaneously):

• get_clinical_trial_eligibility_criteria for all NCT IDs
• get_clinical_trial_conditions_and_interventions for all NCT IDs
• get_clinical_trial_locations for all NCT IDs
• get_clinical_trial_status_and_dates for all NCT IDs
• get_clinical_trial_descriptions for all NCT IDs

Parallel Group 4 (Phases 5-6 - for each drug):

• OpenTargets_get_drug_id_description_by_name for drug
• OpenTargets_get_drug_mechanisms_of_action_by_chemblId for drug
• FDA_get_indications_by_drug_name for drug
• PubMed_search_articles for evidence

Error Handling

For each tool call:

1. Wrap in try/except
2. Check for empty results
3. Use fallback tools when primary fails
4. Document what failed in completeness checklist
5. Never let one failure block the entire analysis

Performance Optimization

• Batch NCT IDs in groups of 10 for detail tools
• Limit initial search to 20-30 trials per search strategy
• Focus detailed analysis on top 15-20 candidates after initial filtering
• Cache gene/disease resolution results for reuse across phases

Common Use Patterns

Pattern 1: Targeted Therapy Matching (Most Common)

Input: "NSCLC patient with EGFR L858R, failed platinum chemotherapy"

1. Resolve: NSCLC -> EFO_0003060, EGFR -> ENSG00000146648
2. Search: "non-small cell lung cancer" + "EGFR mutation" + "EGFR L858R"
3. Filter: Recruiting trials with EGFR molecular requirements
4. Match: Score trials by EGFR L858R specificity
5. Drugs: Identify TKIs (osimertinib, erlotinib, etc.) in trial arms
6. Evidence: Check FDA approval of EGFR TKIs for NSCLC
7. Report: Prioritize targeted therapy trials, include immunotherapy options

Pattern 2: Immunotherapy Selection

Input: "Melanoma, TMB-high, PD-L1 positive, failed ipilimumab"

1. Resolve: Melanoma -> EFO_0000756
2. Search: "melanoma" + "TMB" + "PD-L1" + "immunotherapy"
3. Filter: Trials requiring PD-L1 or TMB testing
4. Match: Score by TMB/PD-L1 requirements
5. Drugs: Identify checkpoint inhibitors (pembrolizumab, nivolumab)
6. Evidence: Check FDA approval for TMB-high indications
7. Report: Focus on anti-PD-1/PD-L1 trials, combination immunotherapy

Pattern 3: Basket Trial Identification

Input: "Any solid tumor with NTRK fusion"

1. Resolve: NTRK genes (NTRK1, NTRK2, NTRK3)
2. Search: "NTRK fusion" + "tumor agnostic" + "basket"
3. Filter: Biomarker-agnostic trials
4. Match: Score by NTRK-specific inclusion criteria
5. Drugs: Identify larotrectinib, entrectinib
6. Evidence: FDA tissue-agnostic approval for larotrectinib
7. Report: Highlight tumor-agnostic approval, broad eligibility

Pattern 4: Post-Progression Options

Input: "Breast cancer, failed CDK4/6 inhibitors, ESR1 mutation"

1. Resolve: Breast cancer -> EFO_0000305, ESR1 -> ENSG00000091831
2. Search: "breast cancer" + "ESR1" + "CDK4/6 resistance"
3. Filter: Trials for post-CDK4/6 setting
4. Match: Score by ESR1 mutation and prior treatment requirements
5. Drugs: Identify novel endocrine agents, SERDs, ESR1-targeting drugs
6. Evidence: Check clinical data for post-CDK4/6 options
7. Report: Focus on resistance-overcoming strategies

Pattern 5: Geographic Search

Input: "Lung cancer trials within 100 miles of Boston"

1. Search: "lung cancer" (broad)
2. Get locations for all candidate trials
3. Filter: Sites in Massachusetts and nearby states
4. Score: High geographic feasibility for Boston-area sites
5. Report: Prioritize by proximity, include contact info

Edge Case Handling

No Matching Trials Found

If no trials match the patient's biomarker:

1. Broaden search to gene-level (remove specific variant)
2. Search for pathway-level trials
3. Search basket trials
4. Suggest additional biomarker testing
5. Report alternative options (off-label, compassionate use)

Rare Biomarkers

For uncommon mutations (e.g., unusual EGFR variants):

1. Search gene-level trials (any EGFR mutation)
2. Search mechanism-level trials (TKI trials)
3. Check CIViC for any evidence on this specific variant
4. Note variant rarity in report
5. Suggest discussion with molecular tumor board

Multiple Biomarkers

For complex molecular profiles:

1. Search for each biomarker independently
2. Search for combination biomarker trials
3. Identify trials that require multiple biomarkers
4. Score based on most actionable biomarker
5. Flag potential synergistic drug targets

Conflicting Eligibility

When patient meets some criteria but not others:

1. Score partial match transparently
2. Highlight which criteria are met/unmet
3. Note if unmet criteria are waivable
4. Suggest contacting PI for edge cases
5. Provide alternative trials without conflicting criteria

Known CIViC Gene IDs

For direct CIViC lookups without search:

Report File Naming Convention

Save reports as:

clinical_trial_matching_[DISEASE]_[BIOMARKER]_[DATE].md

Example: clinical_trial_matching_NSCLC_EGFR_L858R_2026-02-15.md