临床试验检索与资格预筛｜个人思维图谱小站

正文

TRIAL_SEARCH_PATTERNS.md

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:

Wrap in try/except
Check for empty results
Use fallback tools when primary fails
Document what failed in completeness checklist
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"

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

Pattern 2: Immunotherapy Selection

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

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

Pattern 3: Basket Trial Identification

Input: "Any solid tumor with NTRK fusion"

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

Pattern 4: Post-Progression Options

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

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

Pattern 5: Geographic Search

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

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

Edge Case Handling

No Matching Trials Found

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

Rare Biomarkers

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

Multiple Biomarkers

Search for each biomarker independently
Search for combination biomarker trials
Identify trials that require multiple biomarkers
Score based on most actionable biomarker
Flag potential synergistic drug targets

Conflicting Eligibility

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

临床试验检索与资格预筛

正文