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

:

Report-first approach

- Create report file FIRST, then populate progressively

Patient-centric

- Every recommendation considers the individual patient's profile

Molecular-first matching

- Prioritize trials targeting patient's specific biomarkers

Evidence-graded

- Every recommendation has an evidence tier (T1-T4)

Quantitative scoring

- Trial Match Score (0-100) for every trial

Eligibility-aware

- Parse and evaluate inclusion/exclusion criteria

Actionable output

- Clear next steps, contact info, enrollment status

Source-referenced

- Every statement cites the tool/database source

Completeness checklist

- Mandatory section showing analysis coverage

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

Input Format

Parsed As

Example

Gene + amino acid change

Specific mutation

EGFR L858R

Gene + exon notation

Exon-level alteration

EGFR exon 19 deletion

Gene + fusion partner

Fusion

EML4-ALK fusion

Gene + amplification

Copy number gain

HER2 amplification

Gene + expression level

Expression biomarker

PD-L1 50%

Gene + status

Status biomarker

MSI-high, TMB-high

Gene + resistance

Resistance mutation

EGFR T790M

Gene Symbol Normalization

Common Alias

Official Symbol

Notes

HER2

ERBB2

Search both in trials

PD-L1

CD274

Often searched as "PD-L1" in trials

ALK

EML4-ALK is a fusion

VEGF

VEGFA

Often searched as "VEGF"

PD-1

PDCD1

Search as "PD-1" in trials

BRCA

BRCA1/BRCA2

Specify which BRCA gene

Phase 0: Tool Parameter Reference (CRITICAL)

BEFORE calling ANY tool

, verify its parameters from this reference table.

Clinical Trial Tools

Tool

Parameters

Notes

search_clinical_trials

query_term

(REQUIRED str),

condition

(str),

intervention

(str),

pageSize

(int, default 10),

pageToken

(str)

Main search. Returns

{studies: [{NCT ID, brief_title, brief_summary, overall_status, condition, phase}], nextPageToken, total_count}

clinical_trials_search

action

(REQUIRED, must be

"search_studies"

),

condition

(str),

intervention

(str),

limit

(int)

Alternative search. Returns

{total_count, studies: [{nctId, title, status, conditions}]}

clinical_trials_get_details

action

(REQUIRED, must be

"get_study_details"

),

nct_id

(REQUIRED str)

Full trial details. Returns

{nctId, title, summary, eligibility: {eligibilityCriteria}, ...}

get_clinical_trial_eligibility_criteria

nct_ids

(REQUIRED array),

eligibility_criteria

(REQUIRED str, use

"all"

)

Returns

[{NCT ID, eligibility_criteria}]

get_clinical_trial_locations

nct_ids

(REQUIRED array),

location

(REQUIRED str, use

"all"

)

Returns

[{NCT ID, locations: [{facility, city, state, country}]}]

get_clinical_trial_descriptions

nct_ids

(REQUIRED array),

description_type

(REQUIRED str:

"brief"

or

"full"

)

Returns

[{NCT ID, brief_title, official_title, brief_summary, detailed_description}]

get_clinical_trial_status_and_dates

nct_ids

(REQUIRED array),

status_and_date

(REQUIRED str, use

"all"

)

Returns

[{NCT ID, overall_status, start_date, primary_completion_date, completion_date}]

get_clinical_trial_conditions_and_interventions

nct_ids

(REQUIRED array),

condition_and_intervention

(REQUIRED str, use

"all"

)

Returns

[{NCT ID, condition, arm_groups, interventions}]

get_clinical_trial_outcome_measures

nct_ids

(REQUIRED array),

outcome_measures

(str:

"primary"

,

"secondary"

,

"all"

)

Returns

[{NCT ID, primary_outcomes, secondary_outcomes}]

extract_clinical_trial_outcomes

nct_ids

(REQUIRED array),

outcome_measure

(str)

Returns trial outcome results

extract_clinical_trial_adverse_events

nct_ids

(REQUIRED array),

adverse_event_type

(str)

Returns adverse event data

Molecular/Disease Tools

Tool

Parameters

Notes

MyGene_query_genes

query

(str),

species

(str)

Returns

{hits: [{symbol, entrezgene, ensembl: {gene}, name}]}

OpenTargets_get_target_id_description_by_name

targetName

(str)

Returns

{data: {search: {hits: [{id, name, description}]}}}

OpenTargets_get_disease_id_description_by_name

diseaseName

(str)

Returns

{data: {search: {hits: [{id, name, description}]}}}

OpenTargets_get_associated_drugs_by_target_ensemblID

ensemblId

(str),

size

(int)

Returns

{data: {target: {knownDrugs: {count, rows: [{drug: {id, name, isApproved}, phase, mechanismOfAction, disease: {id, name}}]}}}}

OpenTargets_get_associated_drugs_by_disease_efoId

efoId

(str),

size

(int)

Returns

{data: {disease: {knownDrugs: {count, rows: [...]}}}}

OpenTargets_get_drug_id_description_by_name

drugName

(str)

Returns

{data: {search: {hits: [{id, name, description}]}}}

OpenTargets_get_drug_mechanisms_of_action_by_chemblId

chemblId

(str)

Returns

{data: {drug: {mechanismsOfAction: {rows: [{mechanismOfAction, actionType, targetName, targets}]}}}}

OpenTargets_get_approved_indications_by_drug_chemblId

chemblId

(str)

Returns

{data: {drug: {approvedIndications: [efoIds]}}}

OpenTargets_target_disease_evidence

ensemblId

(str),

efoId

(str),

size

(int)

Returns target-disease evidence rows

CIViC Tools

Tool

Parameters

Notes

civic_search_variants

query

(str),

limit

(int)

Does NOT filter by query. Returns alphabetically sorted variants

civic_get_variants_by_gene

gene_id

(int, CIViC gene ID),

limit

(int)

Returns

{data: {gene: {variants: {nodes: [{id, name}]}}}}

. Max 100 per call

civic_search_evidence_items

query

(str),

limit

(int)

Does NOT filter by query. Returns evidence alphabetically

civic_get_variant

variant_id

(int)

Returns

{data: {variant: {id, name}}}

civic_search_therapies

query

(str),

limit

(int)

Search therapies

civic_search_diseases

query

(str),

limit

(int)

Search diseases

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
Tool
Parameters
Notes
drugbank_get_targets_by_drug_name_or_drugbank_id
query
,
case_sensitive
,
exact_match
,
limit
(ALL REQUIRED)
Returns
{results: [{drug_name, drugbank_id, targets: [{name, organism, actions}]}]}
drugbank_get_indications_by_drug_name_or_drugbank_id
query
,
case_sensitive
,
exact_match
,
limit
(ALL REQUIRED)
Returns drug indications
ChEMBL_search_drugs
query
(str),
limit
(int)
Returns
{status, data: {drugs: [...]}}
ChEMBL_get_drug_mechanisms
drug_chembl_id__exact
(str)
Returns drug mechanisms
fda_pharmacogenomic_biomarkers
drug_name
(opt str),
biomarker
(opt str),
limit
(opt int, default 10)
Returns
{count, shown, results: [{Drug, TherapeuticArea, Biomarker, LabelingSection}]}
. Use
limit=1000
to get all.
FDA_get_indications_by_drug_name
drug_name
(str),
limit
(int)
Returns FDA indications text
FDA_get_mechanism_of_action_by_drug_name
drug_name
(str),
limit
(int)
Returns FDA MoA text
FDA_get_clinical_studies_info_by_drug_name
drug_name
(str),
limit
(int)
Returns FDA clinical study info
FDA_get_adverse_reactions_by_drug_name
drug_name
(str),
limit
(int)
Returns adverse reactions
Disease Ontology Tools
Tool
Parameters
Notes
ols_search_efo_terms
query
(str),
limit
(int)
Returns
{data: {terms: [{iri, obo_id, short_form, label, description}]}}
ols_get_efo_term
term_id
(str)
Get specific EFO term details
ols_get_efo_term_children
term_id
(str)
Get child terms
Literature Tools
Tool
Parameters
Notes
PubMed_search_articles
query
(str),
max_results
(int)
Returns list of
{pmid, title, abstract, authors, journal, pub_date}
openalex_literature_search
query
(str),
limit
(int)
Returns literature results
PharmGKB Tools
Tool
Parameters
Notes
PharmGKB_search_genes
query
(str)
Returns gene pharmacogenomics data
PharmGKB_get_clinical_annotations
query
(str)
Returns clinical annotations
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+)-/\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
Tier
Symbol
Criteria
Score Impact
T1
[T1]
FDA-approved biomarker-drug, NCCN guideline
20 points
T2
[T2]
Phase III positive, clinical evidence
15 points
T3
[T3]
Phase I/II results, preclinical
10 points
T4
[T4]
Computational, mechanism inference
5 points
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

Criterion

Points

Trial sites in patient's state/city

5

Trial sites within 100 miles

3

Trial sites in same country

1

No location info or far away

0

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): Criterion Points Description Exact biomarker match 40 Trial requires patient's specific variant Gene-level match 30 Trial requires gene mutation, patient has specific variant Pathway match 20 Trial targets same pathway as patient's biomarker No molecular criteria 10 General disease trial Excluded biomarker 0 Patient's biomarker is in exclusion criteria Clinical Eligibility (0-25 points): Criterion Points Description All criteria met 25 Disease, stage, prior treatment all match Most criteria met 18 1-2 criteria unclear Some criteria met 10 Several criteria unclear Clearly ineligible 0 Fails major criterion Evidence Strength (0-20 points): Criterion Points Description FDA-approved combination 20 T1 evidence Phase III positive 15 T2 evidence Phase II promising 10 T3 evidence Phase I or no results 5 T4 evidence Trial Phase (0-10 points): Phase Points Phase III 10 Phase II 8 Phase I/II 6 Phase I 4 Geographic Feasibility (0-5 points): Criterion Points Patient's city/state 5 Same country 3 International only 1 Unknown 0 Recommendation Tiers Score Tier Label Action 80-100 Tier 1 Optimal Match Strongly recommend - contact site immediately 60-79 Tier 2 Good Match Recommend - discuss with care team 40-59 Tier 3 Possible Match Consider - needs further eligibility review 0-39 Tier 4 Exploratory Backup option - consider if Tier 1-3 unavailable 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) ...

|

|

Ranked Trial Matches

|

|

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

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]

|

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]

|

|

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 .

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:

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

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"

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 If no trials match the patient's biomarker: 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 For uncommon mutations (e.g., unusual EGFR variants): 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 For complex molecular profiles: 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 When patient meets some criteria but not others: 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 Known CIViC Gene IDs For direct CIViC lookups without search: Gene CIViC ID Gene CIViC ID ALK 1 MET 52 ABL1 4 PIK3CA 37 BRAF 5 ROS1 118 EGFR 19 RET 122 ERBB2 20 NTRK1 197 KRAS 30 NTRK2 560 TP53 45 NTRK3 561 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

安装

OpenTargets disease search

result

Fallback: OLS EFO search

ols_result

Normalize common aliases

alias_map

MyGene resolution

result

Check FDA pharmacogenomic biomarkers

fda_result

Check OpenTargets for drugs targeting this gene

(done in Phase 5)

Pattern: "GENE VARIANT" (e.g., "EGFR L858R")

mutation_match

Pattern: "GENE exon N deletion/insertion"

exon_match

Pattern: "GENE1-GENE2 fusion" or "GENE1/GENE2"

fusion_match

Pattern: "GENE amplification"

amp_match

Pattern: "PD-L1 XX%"

expression_match

Pattern: "MSI-high", "TMB-high"

status_match

Fallback: treat as gene name

Response: {studies: [{NCT ID, brief_title, brief_summary, overall_status, condition, phase}], nextPageToken, total_count}

No studies found

Search 1: Gene + alteration

biomarker_query

Process in batches of 10

all_criteria

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

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

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

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

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

Common biomarker patterns in eligibility text

Required biomarkers (in inclusion criteria)

inclusion_section

Look for gene mutation requirements

gene_patterns

In inclusion section

In exclusion section

Check for biomarker-agnostic / basket trial language

basket_terms

No molecular criteria - could be open to any

Check exclusions first

excluded_match

Check for exact gene match

matched_genes

Check for specific variant match

Look for specific mutation mentions in context

exact_variant_match

Check for pathway-level match (e.g., trial targets EGFR pathway, patient has EGFR mutation)

This requires domain knowledge mapping

Step 1: Resolve drug in OpenTargets

result

Step 2: Get mechanisms of action

moa_result

Step 3: Check approval

approval_result

Check if disease is mentioned in indications

First 10 for display

Start with simpler queries (more likely to return results)

query_terms

|

|

|

|

|

|

|

[Phase]

[Recruiting/Active/etc.]

[Sponsor]

[Date]

|

|

|