tooluniverse-clinical-trial-design

name tooluniverse-clinical-trial-design description Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math. 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 lookup OpenTargets_get_diseases_phenotypes_by_target_ensembl - Prevalence data ClinVar_search_variants - Biomarker mutation frequency gnomad_search_variants - Population allele frequencies PubMed_search_articles - Epidemiology literature search_clinical_trials - Enrollment feasibility from past trials PATH 2: Biomarker Prevalence & Testing ClinVar_get_variant_details - Variant pathogenicity COSMIC_search_mutations - Cancer-specific mutation frequencies gnomad_get_variant - Population genetics PubMed_search_articles - CDx test performance, guidelines PATH 3: Comparator Selection drugbank_get_drug_basic_info_by_drug_name_or_id - Drug info drugbank_get_indications_by_drug_name_or_drugbank_id - Approved indications drugbank_get_pharmacology_by_drug_name_or_drugbank_id - Mechanism FDA_OrangeBook_search_drug - Generic availability OpenFDA_get_approval_history - Approval details search_clinical_trials - Historical control data PATH 4: Endpoint Selection search_clinical_trials - Precedent trials, endpoints used PubMed_search_articles - FDA acceptance history, endpoint validation OpenFDA_get_approval_history - Approved endpoints by indication PATH 5: Safety Endpoints & Monitoring drugbank_get_pharmacology_by_drug_name_or_drugbank_id - Mechanism toxicity FDA_get_warnings_and_cautions_by_drug_name - FDA black box warnings FAERS_search_reports_by_drug_and_reaction - Real-world adverse events FAERS_count_reactions_by_drug_event - AE frequency FAERS_count_death_related_by_drug - Serious outcomes PubMed_search_articles - DLT definitions, monitoring strategies PATH 6: Regulatory Pathway OpenFDA_get_approval_history - Precedent approvals PubMed_search_articles - Breakthrough designations, FDA guidance search_clinical_trials - Regulatory precedents (accelerated approval) Quick Start Example 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. 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 # 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. Reference Files 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 Version Information Version : 1.0.0 Last Updated : February 2026 Compatible with : ToolUniverse 0.5+ Focus : Phase 1/2 early clinical development