Clinical Trial Design Feasibility Assessment
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.
Reasoning Before Searching
Trial design starts with the question, not the methods. Answer these four questions before running any tools — they determine everything else:
- What is the primary endpoint? Is it overall survival (gold standard but slow), PFS (faster but surrogate), ORR (single-arm friendly but not always accepted), or a biomarker (needs validation as surrogate first)? The endpoint determines FDA pathway, statistical design, and duration.
- Who is the population? Broad unselected vs. biomarker-enriched. Enriched populations have higher response rates, allowing smaller trials — but require a validated companion diagnostic and reduce the eligible patient pool.
- What is the comparator? Placebo (only if no standard of care exists), active control (requires non-inferiority or superiority framing), or single-arm with historical control (acceptable for rare diseases or breakthrough designations, but FDA scrutiny is high).
- Is the effect size realistic given the mechanism? A 20% improvement in ORR over SOC requires ~100 patients per arm. A 50% improvement requires ~30. If the mechanism only justifies a 10% improvement, the trial may be underpowered regardless of design. Check precedent effect sizes in similar trials before committing to an endpoint.
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.
Core Principles
1. Report-First Approach (MANDATORY)
DO NOT show tool outputs to user. Instead:
- Create
[INDICATION]_trial_feasibility_report.md FIRST
- Initialize with all section headers
- Progressively update as data arrives
- Present only the final report
2. Evidence Grading System
| 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 |
3. Feasibility Score (0-100)
Weighted composite score:
- Patient Availability (30%): Population size x biomarker prevalence x geography
- Endpoint Precedent (25%): Historical use, regulatory acceptance
- Regulatory Clarity (20%): Pathway defined, precedents exist
- Comparator Feasibility (15%): Standard of care availability
- Safety Monitoring (10%): Known risks, monitoring established
Interpretation: >=75 HIGH (proceed), 50-74 MODERATE (additional validation), <50 LOW (de-risking required)
When to Use This Skill
Apply when users:
- Plan early-phase trials (Phase 1/2 emphasis)
- Need enrollment feasibility assessment
- Design biomarker-selected trials
- Evaluate endpoint strategies
- Assess regulatory pathways
- Compare trial design options
- Need safety monitoring plans
Trigger phrases: "clinical trial design", "trial feasibility", "enrollment projections", "endpoint selection", "trial planning", "Phase 1/2 design", "basket trial", "biomarker trial"
Core Strategy: 6 Research Paths
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
Report Structure (14 Sections)
Create [INDICATION]_trial_feasibility_report.md with all 14 sections. See REPORT_TEMPLATE.md for full templates with fillable fields.
- Executive Summary - Feasibility score, key findings, go/no-go recommendation
- Disease Background - Prevalence, incidence, SOC, unmet need
- Patient Population Analysis - Base population, biomarker selection, eligibility funnel, enrollment projections
- Biomarker Strategy - Primary biomarker, alternatives, testing logistics
- Endpoint Selection & Justification - Primary/secondary/exploratory endpoints, statistical considerations
- Comparator Analysis - SOC, trial design options (single-arm vs randomized vs non-inferiority), drug sourcing
- Safety Endpoints & Monitoring Plan - DLT definition, mechanism-based toxicities, organ monitoring, SMC
- Study Design Recommendations - Phase, design type, schema, eligibility, treatment plan, assessment schedule
- Enrollment & Site Strategy - Site selection, enrollment projections, recruitment strategies
- Regulatory Pathway - FDA pathway, precedents, pre-IND meeting, IND timeline
- Budget & Resource Considerations - Cost drivers, timeline, FTE requirements
- Risk Assessment - Feasibility risks, scientific risks, mitigation strategies
- Success Criteria & Go/No-Go Decision - Phase 1/2 criteria, interim analysis, feasibility scorecard
- Recommendations & Next Steps - Final recommendation, critical path to IND, alternative designs
Tool Reference by Research Path
PATH 1: Patient Population Sizing
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 trials
PATH 2: Biomarker Prevalence & Testing
ClinVar_get_variant_details - Variant pathogenicityCOSMIC_search_mutations - Cancer-specific mutation frequenciesgnomad_get_variant - Population geneticsPubMed_search_articles - CDx test performance, guidelines
PATH 3: Comparator Selection
drugbank_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 data
PATH 4: Endpoint Selection
search_clinical_trials - Precedent trials, endpoints usedPubMed_search_articles - FDA acceptance history, endpoint validationOpenFDA_get_approval_history - Approved endpoints by indication
PATH 5: Safety Endpoints & Monitoring
drugbank_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 strategies
PATH 6: Regulatory Pathway
OpenFDA_get_approval_history - Precedent approvalsPubMed_search_articles - Breakthrough designations, FDA guidancesearch_clinical_trials - Regulatory precedents (accelerated approval)
Quick Start Example
from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True)
tu.load_tools()
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']
)
variants = tu.tools.ClinVar_search_variants(gene="EGFR", significance="pathogenic")
trials = tu.tools.search_clinical_trials(
condition="EGFR positive non-small cell lung cancer",
status="completed", phase="2"
)
soc = tu.tools.FDA_OrangeBook_search_drug(ingredient="osimertinib")
See WORKFLOW_DETAILS.md for the complete 6-path Python workflow and use case examples.
Integration with Other Skills
- tooluniverse-drug-research: Investigate mechanism, preclinical data
- tooluniverse-disease-research: Deep dive on disease biology
- tooluniverse-target-research: Validate drug target, essentiality
- tooluniverse-pharmacovigilance: Post-market safety for comparator drugs
- tooluniverse-precision-oncology: Biomarker biology, resistance mechanisms
Programmatic Access (Beyond Tools)
When ToolUniverse tools return limited trial metadata, use the ClinicalTrials.gov v2 API directly:
import requests, pandas as pd
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
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<
