Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility reports with evidence grading, enrollment projections, and trial design recommendations. Use when planning Phase 1/2 trials, assessing trial feasibility, or designing biomarker-driven studies.
Systematically assess clinical trial feasibility by analyzing 6 research dimensions. Produces comprehensive feasibility reports with quantitative enrollment projections, endpoint recommendations, and regulatory pathway analysis.
IMPORTANT: Always use English terms in tool calls (drug names, disease names, biomarker names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
Trial design starts with the question, not the methods. Answer these four questions before running any tools — they determine everything else:
These four answers determine sample size, duration, and trial design. Look them up from precedent trials and FDA guidance — do not derive them from first principles.
LOOK UP DON'T GUESS: Never assume what the standard of care is for an indication — look it up with DrugBank and FDA tools. Never assume an endpoint is FDA-accepted — verify with search_clinical_trials precedents and OpenFDA_get_approval_history. Never estimate prevalence from memory — use OpenTargets, gnomAD, or COSMIC.
DO NOT show tool outputs to user. Instead:
[INDICATION]_trial_feasibility_report.md FIRST| Grade | Symbol | Criteria | Examples |
|---|---|---|---|
| A | 3-star | Regulatory acceptance, multiple precedents | FDA-approved endpoint in same indication |
| B | 2-star | Clinical validation, single precedent | Phase 3 trial in related indication |
| C | 1-star | Preclinical or exploratory | Phase 1 use, biomarker validation ongoing |
| D | 0-star | Proposed, no validation | Novel endpoint, no precedent |
Weighted composite score:
Interpretation: >=75 HIGH (proceed), 50-74 MODERATE (additional validation), <50 LOW (de-risking required)
Apply when users:
Trigger phrases: "clinical trial design", "trial feasibility", "enrollment projections", "endpoint selection", "trial planning", "Phase 1/2 design", "basket trial", "biomarker trial"
Execute 6 parallel research dimensions. See STUDY_DESIGN_PROCEDURES.md for detailed steps per path.
Trial Design Query
|
+-- PATH 1: Patient Population Sizing
| Disease prevalence, biomarker prevalence, geographic distribution,
| eligibility criteria impact, enrollment projections
|
+-- PATH 2: Biomarker Prevalence & Testing
| Mutation frequency, testing availability, turnaround time,
| cost/reimbursement, alternative biomarkers
|
+-- PATH 3: Comparator Selection
| Standard of care, approved comparators, historical controls,
| placebo appropriateness, combination therapy
|
+-- PATH 4: Endpoint Selection
| Primary endpoint precedents, FDA acceptance history,
| measurement feasibility, surrogate vs clinical endpoints
|
+-- PATH 5: Safety Endpoints & Monitoring
| Mechanism-based toxicity, class effects, organ-specific monitoring,
| DLT history, safety monitoring plan
|
+-- PATH 6: Regulatory Pathway
Regulatory precedents (505(b)(1), 505(b)(2)), breakthrough therapy,
orphan drug, fast track, FDA guidance
Create [INDICATION]_trial_feasibility_report.md with all 14 sections. See REPORT_TEMPLATE.md for full templates with fillable fields.
OpenTargets_get_disease_id_description_by_name - Disease lookupOpenTargets_get_diseases_phenotypes_by_target_ensembl - Prevalence dataClinVar_search_variants - Biomarker mutation frequencygnomad_search_variants - Population allele frequenciesPubMed_search_articles - Epidemiology literaturesearch_clinical_trials - Enrollment feasibility from past trialsClinVar_get_variant_details - Variant pathogenicityCOSMIC_search_mutations - Cancer-specific mutation frequenciesgnomad_get_variant - Population geneticsPubMed_search_articles - CDx test performance, guidelinesdrugbank_get_drug_basic_info_by_drug_name_or_id - Drug infodrugbank_get_indications_by_drug_name_or_drugbank_id - Approved indicationsdrugbank_get_pharmacology_by_drug_name_or_drugbank_id - MechanismFDA_OrangeBook_search_drug - Generic availabilityOpenFDA_get_approval_history - Approval detailssearch_clinical_trials - Historical control datasearch_clinical_trials - Precedent trials, endpoints usedPubMed_search_articles - FDA acceptance history, endpoint validationOpenFDA_get_approval_history - Approved endpoints by indicationdrugbank_get_pharmacology_by_drug_name_or_drugbank_id - Mechanism toxicityFDA_get_warnings_and_cautions_by_drug_name - FDA black box warningsFAERS_search_reports_by_drug_and_reaction - Real-world adverse eventsFAERS_count_reactions_by_drug_event - AE frequencyFAERS_count_death_related_by_drug - Serious outcomesPubMed_search_articles - DLT definitions, monitoring strategiesOpenFDA_get_approval_history - Precedent approvalsPubMed_search_articles - Breakthrough designations, FDA guidancesearch_clinical_trials - Regulatory precedents (accelerated approval)from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True)
tu.load_tools()
# Example: EGFR+ NSCLC trial feasibility
# Step 1: Disease prevalence
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(
diseaseName="non-small cell lung cancer"
)
prevalence = tu.tools.OpenTargets_get_diseases_phenotypes(
efoId=disease_info['data']['id']
)
# Step 2: Biomarker prevalence
variants = tu.tools.ClinVar_search_variants(gene="EGFR", significance="pathogenic")
# Step 3: Precedent trials
trials = tu.tools.search_clinical_trials(
condition="EGFR positive non-small cell lung cancer",
status="completed", phase="2"
)
# Step 4: Standard of care comparator
soc = tu.tools.FDA_OrangeBook_search_drug(ingredient="osimertinib")
# Compile into feasibility report...
See WORKFLOW_DETAILS.md for the complete 6-path Python workflow and use case examples.
When ToolUniverse tools return limited trial metadata, use the ClinicalTrials.gov v2 API directly:
import requests, pandas as pd
# Search with pagination (all lung cancer immunotherapy trials with results)
all_studies = []
token = None
while True:
params = {"query.cond": "lung cancer", "query.intr": "immunotherapy",
"filter.overallStatus": "COMPLETED", "filter.results": "WITH_RESULTS", "pageSize": 100}
if token: params["pageToken"] = token
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json()
all_studies.extend(resp.get("studies", []))
token = resp.get("nextPageToken")
if not token: break
# Extract structured data
rows = []
for s in all_studies:
proto = s.get("protocolSection", {})
rows.append({
"nctId": proto.get("identificationModule", {}).get("nctId"),
"title": proto.get("identificationModule", {}).get("briefTitle"),
"enrollment": proto.get("designModule", {}).get("enrollmentInfo", {}).get("count"),
"phase": proto.get("designModule", {}).get("phases", [None])[0] if proto.get("designModule", {}).get("phases") else None,
})
df = pd.DataFrame(rows)
# FDA drug approval history
drug = "pembrolizumab"
fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json()
See tooluniverse-data-wrangling skill for pagination, error handling, and bulk download patterns.
| File | Content |
|---|---|
REPORT_TEMPLATE.md | Full 14-section report template with fillable fields |
STUDY_DESIGN_PROCEDURES.md | Detailed steps for each of the 6 research paths |
WORKFLOW_DETAILS.md | Complete Python example workflow and 5 use case summaries |
BEST_PRACTICES.md | Best practices, common pitfalls, output format requirements |
EXAMPLES.md | Additional examples |
QUICK_START.md | Quick start guide |