drug-discovery-prompts/upstream/mims-harvard-ToolUniverse/skills/tooluniverse-clinical-trial-matching/TRIAL_SEARCH_PATTERNS.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Trial Search Patterns & Execution Strategy		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-clinical-trial-matching/TRIAL_SEARCH_PATTERNS.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Trial Search Patterns & Execution Strategy

Trial Search Functions

Disease-Based 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
    )

    if isinstance(result, str):
        return []
    return result.get('studies', [])

Biomarker-Specific Search

def search_trials_by_biomarker(tu, gene_symbol, alteration, disease_name=None, page_size=15):
    """Search trials mentioning specific biomarkers."""
    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', [])

Intervention-Based 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', [])

Alternative Search (ClinicalTrials_search_studies)

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.ClinicalTrials_search_studies(**params)
    return result.get('studies', [])

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

Batch Trial Detail Retrieval (Phase 3)

All batch functions process NCT IDs in groups of 10:

def get_trial_eligibility(tu, nct_ids):
    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

def get_trial_interventions(tu, nct_ids):
    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

def get_trial_locations(tu, nct_ids):
    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

def get_trial_status(tu, nct_ids):
    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

def get_trial_descriptions(tu, nct_ids):
    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

Geographic & Feasibility Analysis (Phase 7)

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]
    }

Alternative Options (Phase 8)

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):
    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])

Expanded Access Search

def search_expanded_access(tu, drug_name):
    result = tu.tools.search_clinical_trials(query_term=f'{drug_name} expanded access', pageSize=5)
    if isinstance(result, str):
        return []
    return result.get('studies', [])

Parallelization Opportunities

Parallel Group 1 (Phase 1 - all simultaneous):

• 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 - all simultaneous):

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

Parallel Group 3 (Phase 3 - all simultaneous):

• 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

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

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

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

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

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

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

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