临床试验设计可行性评估工具：6维分析生成全面报告，预测入组与监管路径

tooluniverse-clinical-trial-design by mims-harvard/tooluniverse

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安装命令

npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-clinical-trial-design

医疗科技项目管理科研工具

🇨🇳中文介绍

临床试验设计可行性评估

通过分析6个研究维度，系统评估临床试验可行性。生成全面的可行性报告，包含定量入组预测、终点推荐和监管路径分析。

重要提示：在工具调用中始终使用英文术语（药物名称、疾病名称、生物标志物名称），即使用户使用其他语言书写。仅当英文术语无结果时，才尝试使用原始语言术语。使用用户的语言进行回复。

核心原则

1. 报告优先方法（强制要求）

切勿向用户展示工具输出。而是：

首先创建 [INDICATION]_trial_feasibility_report.md
用所有章节标题初始化
随着数据到达逐步更新
仅呈现最终报告

2. 证据分级系统

等级	符号	标准	示例
A	3星	监管接受，多个先例	相同适应症中FDA批准的终点
B	2星	临床验证，单一先例	相关适应症的3期试验

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3. 可行性评分 (0-100)

加权综合评分：

患者可及性 (30%)：人群规模 x 生物标志物流行率 x 地理分布
终点先例 (25%)：历史使用情况，监管接受度
监管清晰度 (20%)：路径已定义，存在先例
对照可行性 (15%)：标准治疗的可及性
安全性监测 (10%)：已知风险，监测方案已建立

解读：>=75 高（推进），50-74 中等（需要额外验证），<50 低（需要降低风险）

何时使用此技能

计划早期试验（侧重1/2期）
需要入组可行性评估
设计生物标志物筛选试验
评估终点策略
评估监管路径
比较试验设计方案
需要安全性监测计划

触发短语："clinical trial design", "trial feasibility", "enrollment projections", "endpoint selection", "trial planning", "Phase 1/2 design", "basket trial", "biomarker trial"

核心策略：6条研究路径

并行执行6个研究维度。每个路径的详细步骤请参见 STUDY_DESIGN_PROCEDURES.md。

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

报告结构 (14个章节)

创建包含全部14个章节的 [INDICATION]_trial_feasibility_report.md。完整模板及可填写字段请参见 REPORT_TEMPLATE.md。

执行摘要 - 可行性评分、关键发现、推进/停止建议
疾病背景 - 流行率、发病率、标准治疗、未满足需求
患者人群分析 - 基础人群、生物标志物筛选、资格漏斗、入组预测
生物标志物策略 - 主要生物标志物、替代方案、检测物流
终点选择与论证 - 主要/次要/探索性终点、统计学考量
对照分析 - 标准治疗、试验设计选项（单臂 vs 随机 vs 非劣效性）、药物来源
安全性终点与监测计划 - DLT定义、机制相关毒性、器官监测、SMC
研究设计建议 - 分期、设计类型、方案、资格标准、治疗计划、评估计划
入组与研究中心策略 - 研究中心选择、入组预测、招募策略
监管路径 - FDA路径、先例、pre-IND会议、IND时间线
预算与资源考量 - 成本驱动因素、时间线、FTE要求
风险评估 - 可行性风险、科学风险、缓解策略
成功标准与推进/停止决策 - 1/2期标准、中期分析、可行性评分卡
建议与后续步骤 - 最终建议、IND关键路径、替代设计

按研究路径的工具参考

路径 1：患者人群规模估算

OpenTargets_get_disease_id_description_by_name - 疾病查找
OpenTargets_get_diseases_phenotypes - 流行率数据
ClinVar_search_variants - 生物标志物突变频率
gnomAD_search_gene_variants - 人群等位基因频率
PubMed_search_articles - 流行病学文献
search_clinical_trials - 既往试验的入组可行性

路径 2：生物标志物流行率与检测

ClinVar_get_variant_details - 变异致病性
COSMIC_search_mutations - 癌症特异性突变频率
gnomAD_get_variant_details - 群体遗传学
PubMed_search_articles - CDx检测性能、指南

路径 3：对照选择

drugbank_get_drug_basic_info_by_drug_name_or_id - 药物信息
drugbank_get_indications_by_drug_name_or_drugbank_id - 批准适应症
drugbank_get_pharmacology_by_drug_name_or_drugbank_id - 机制
FDA_OrangeBook_search_drugs - 仿制药可及性
FDA_get_drug_approval_history - 批准详情
search_clinical_trials - 历史对照数据

路径 4：终点选择

search_clinical_trials - 先例试验、使用的终点
PubMed_search_articles - FDA接受历史、终点验证
FDA_get_drug_approval_history - 按适应症批准的终点

路径 5：安全性终点与监测

drugbank_get_pharmacology_by_drug_name_or_drugbank_id - 机制毒性
FDA_get_warnings_and_cautions_by_drug_name - FDA黑框警告
FAERS_search_reports_by_drug_and_reaction - 真实世界不良事件
FAERS_count_reactions_by_drug_event - AE频率
FAERS_count_death_related_by_drug - 严重结局
PubMed_search_articles - DLT定义、监测策略

路径 6：监管路径

FDA_get_drug_approval_history - 先例批准
PubMed_search_articles - 突破性疗法认定、FDA指南
search_clinical_trials - 监管先例（加速批准）

from tooluniverse import ToolUniverse

tu = ToolUniverse(use_cache=True)
tu.load_tools()

# 示例：EGFR+ NSCLC 试验可行性
# 步骤 1：疾病流行率
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']
)

# 步骤 2：生物标志物流行率
variants = tu.tools.ClinVar_search_variants(gene="EGFR", significance="pathogenic")

# 步骤 3：先例试验
trials = tu.tools.search_clinical_trials(
    condition="EGFR positive non-small cell lung cancer",
    status="completed", phase="2"
)

# 步骤 4：标准治疗对照
soc = tu.tools.FDA_OrangeBook_search_drugs(ingredient="osimertinib")

# 编译成可行性报告...

完整的6路径Python工作流和用例示例请参见 WORKFLOW_DETAILS.md。

与其他技能的集成

tooluniverse-drug-research : 研究机制、临床前数据
tooluniverse-disease-research : 深入探讨疾病生物学
tooluniverse-target-research : 验证药物靶点、必要性
tooluniverse-pharmacovigilance : 对照药物的上市后安全性
tooluniverse-precision-oncology : 生物标志物生物学、耐药机制

文件	内容
`REPORT_TEMPLATE.md`	包含可填写字段的完整14章节报告模板
`STUDY_DESIGN_PROCEDURES.md`	每条研究路径的详细步骤
`WORKFLOW_DETAILS.md`	完整的Python示例工作流和5个用例摘要
`BEST_PRACTICES.md`	最佳实践、常见陷阱、输出格式要求
`EXAMPLES.md`	额外示例
`QUICK_START.md`	快速入门指南

版本 : 1.0.0
最后更新 : 2026年2月
兼容于 : ToolUniverse 0.5+
重点 : 1/2期早期临床开发

🇺🇸English

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.

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 - Prevalence data
ClinVar_search_variants - Biomarker mutation frequency
gnomAD_search_gene_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_details - 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_drugs - Generic availability
FDA_get_drug_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
FDA_get_drug_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

FDA_get_drug_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_drugs(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

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`

Version Information

Version : 1.0.0
Last Updated : February 2026
Compatible with : ToolUniverse 0.5+
Focus : Phase 1/2 early clinical development

Weekly Installs

156

Repository

mims-harvard/to…universe

GitHub Stars

1.2K

First Seen

Feb 12, 2026

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