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Clinical Trial Design Feasibility Assessment

临床试验设计可行性评估

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.
通过分析6个研究维度，系统性评估临床试验可行性。生成包含定量入组预测、终点指标建议和监管路径分析的综合性可行性报告。
重要提示：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提问。仅当英文查询无结果时，才尝试使用原语言术语作为备选。请用用户使用的语言回复。

Core Principles

核心原则

1. Report-First Approach (MANDATORY)

1. 报告优先方法（强制要求）

DO NOT show tool outputs to user. Instead:
  1. Create 
    [INDICATION]_trial_feasibility_report.md
    FIRST
  2. Initialize with all section headers
  3. Progressively update as data arrives
  4. Present only the final report
请勿向用户展示工具输出结果，而是：
  1. 先创建
    [INDICATION]_trial_feasibility_report.md
  2. 初始化所有章节标题
  3. 随着数据获取逐步更新内容
  4. 仅展示最终报告

2. Evidence Grading System

2. 证据分级体系

GradeSymbolCriteriaExamples
A★★★Regulatory acceptance, multiple precedentsFDA-approved endpoint in same indication
B★★☆Clinical validation, single precedentPhase 3 trial in related indication
C★☆☆Preclinical or exploratoryPhase 1 use, biomarker validation ongoing
D☆☆☆Proposed, no validationNovel endpoint, no precedent
等级符号判定标准示例
A★★★监管机构认可，多先例支持相同适应症下FDA获批的终点指标
B★★☆临床验证，单一先例支持相关适应症的3期试验
C★☆☆临床前或探索性研究1期试验使用，生物标志物验证进行中
D☆☆☆仅为提议，无验证依据新型终点指标，无先例参考

3. Feasibility Score (0-100)

3. 可行性评分（0-100分）

Weighted composite score:
  • Patient Availability (30%): Population size × biomarker prevalence × 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
加权综合评分：
  • 患者可及性（30%）：群体规模 × 生物标志物患病率 × 地域分布
  • 终点指标先例（25%）：历史使用情况、监管机构认可度
  • 监管清晰度（20%）：路径明确，有先例参考
  • 对照药可行性（15%）：标准治疗方案的可及性
  • 安全性监测（10%）：已知风险、监测体系完善程度

When to Use This Skill

适用场景

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"
当用户有以下需求时适用：
  • 规划早期阶段试验（重点为1/2期）
  • 需要入组可行性评估
  • 设计生物标志物筛选型试验
  • 评估终点指标策略
  • 评估监管路径
  • 对比试验设计方案
  • 需要安全性监测计划
触发短语："临床试验设计"、"试验可行性"、"入组预测"、"终点选择"、"试验规划"、"1/2期设计"、"篮式试验"、"生物标志物试验"

Quick Start

快速入门

python
from tooluniverse import ToolUniverse

tu = ToolUniverse(use_cache=True)
tu.load_tools()
python
from tooluniverse import ToolUniverse

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

Example: EGFR+ NSCLC trial feasibility

示例：EGFR+ NSCLC试验可行性评估

indication = "EGFR-mutant non-small cell lung cancer" biomarker = "EGFR L858R"
indication = "EGFR-mutant non-small cell lung cancer" biomarker = "EGFR L858R"

Step 1: Get disease prevalence

步骤1：获取疾病患病率

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'] )
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: Estimate biomarker prevalence

步骤2：估算生物标志物患病率

EGFR mutations: ~15% of NSCLC in US, ~50% in Asia

EGFR突变：在美国NSCLC患者中占比约15%，在亚洲患者中占比约50%

variants = tu.tools.ClinVar_search_variants( gene="EGFR", significance="pathogenic" )
variants = tu.tools.ClinVar_search_variants( gene="EGFR", significance="pathogenic" )

Step 3: Find precedent trials

步骤3：查找先例试验

trials = tu.tools.search_clinical_trials( condition="EGFR positive non-small cell lung cancer", status="completed", phase="2" )
trials = tu.tools.search_clinical_trials( condition="EGFR positive non-small cell lung cancer", status="completed", phase="2" )

Step 4: Identify standard of care comparator

步骤4：确定标准治疗对照药

soc_drugs = tu.tools.FDA_OrangeBook_search_drugs( ingredient="osimertinib" # Current SOC for EGFR+ NSCLC )
soc_drugs = tu.tools.FDA_OrangeBook_search_drugs( ingredient="osimertinib" # EGFR+ NSCLC当前的标准治疗药物 )

Compile into feasibility report...

整理为可行性报告...


---

---

Core Strategy: 6 Research Paths

核心策略：6大研究路径

Execute 6 parallel research dimensions:
Trial Design Query (e.g., "EGFR+ NSCLC trial, Phase 2, ORR endpoint")
│
├─ PATH 1: Patient Population Sizing
│   ├─ Disease prevalence (OpenTargets_get_diseases_phenotypes)
│   ├─ Biomarker prevalence (ClinVar, gnomAD, literature)
│   ├─ Geographic distribution (clinical trials, epidemiology)
│   ├─ Eligibility criteria impact (age, comorbidities)
│   └─ Patient availability calculator
│
├─ PATH 2: Biomarker Prevalence & Testing
│   ├─ Mutation frequency (ClinVar, COSMIC, gnomAD)
│   ├─ Testing availability (CLIA labs, FDA-approved tests)
│   ├─ Test turnaround time
│   ├─ Cost and reimbursement
│   └─ Alternative biomarkers (correlates, surrogates)
│
├─ PATH 3: Comparator Selection
│   ├─ Standard of care (FDA_OrangeBook, guidelines)
│   ├─ Approved comparators (DrugBank, FDA labels)
│   ├─ Historical controls feasibility
│   ├─ Placebo appropriateness
│   └─ Combination therapy considerations
│
├─ PATH 4: Endpoint Selection
│   ├─ Primary endpoint precedents (search_clinical_trials)
│   ├─ FDA acceptance history (FDA_get_approval_history)
│   ├─ Measurement feasibility (imaging, biomarkers)
│   ├─ Time to event considerations
│   └─ Surrogate vs clinical endpoints
│
├─ PATH 5: Safety Endpoints & Monitoring
│   ├─ Mechanism-based toxicity (drugbank_get_pharmacology)
│   ├─ Class effect toxicities (FAERS_search_reports)
│   ├─ Organ-specific monitoring (liver, cardiac, etc.)
│   ├─ Dose-limiting toxicity history
│   └─ Safety monitoring plan
│
└─ PATH 6: Regulatory Pathway
    ├─ Regulatory precedents (505(b)(1), 505(b)(2))
    ├─ Breakthrough therapy potential
    ├─ Orphan drug designation (if rare)
    ├─ Fast track eligibility
    └─ FDA guidance documents
并行执行6个研究维度：
试验设计查询（例如："EGFR+ NSCLC试验，2期，ORR终点"）
│
├─ 路径1：患者群体规模测算
│   ├─ 疾病患病率（OpenTargets_get_diseases_phenotypes）
│   ├─ 生物标志物患病率（ClinVar, gnomAD, 文献）
│   ├─ 地域分布（临床试验、流行病学）
│   ├─ 入选标准影响（年龄、合并症）
│   └─ 患者可及性计算器
│
├─ 路径2：生物标志物患病率与检测
│   ├─ 突变频率（ClinVar, COSMIC, gnomAD）
│   ├─ 检测可及性（CLIA实验室、FDA获批检测方法）
│   ├─ 检测周转时间
│   ├─ 成本与报销情况
│   └─ 替代生物标志物（关联指标、替代终点）
│
├─ 路径3：对照药选择
│   ├─ 标准治疗方案（FDA_OrangeBook、指南）
│   ├─ 获批对照药（DrugBank、FDA标签）
│   ├─ 历史对照可行性
│   ├─ 安慰剂适用性
│   └─ 联合治疗考量
│
├─ 路径4：终点指标选择
│   ├─ 主要终点先例（search_clinical_trials）
│   ├─ FDA认可历史（FDA_get_approval_history）
│   ├─ 测量可行性（影像学、生物标志物）
│   ├─ 事件时间考量
│   └─ 替代终点 vs 临床终点
│
├─ 路径5：安全性终点与监测
│   ├─ 机制相关毒性（drugbank_get_pharmacology）
│   ├─ 类效应毒性（FAERS_search_reports）
│   ├─ 器官特异性监测（肝脏、心脏等）
│   ├─ 剂量限制性毒性历史
│   └─ 安全性监测计划
│
└─ 路径6：监管路径
    ├─ 监管先例（505(b)(1), 505(b)(2)）
    ├─ 突破性疗法潜力
    ├─ 孤儿药资格认定（如为罕见病）
    ├─ 快速通道资格
    └─ FDA指南文件

Report Structure (14 Sections)

报告结构（14个章节）

Create 
[INDICATION]_trial_feasibility_report.md
with:
创建
[INDICATION]_trial_feasibility_report.md
，包含以下内容：

1. Executive Summary

1. 执行摘要

markdown
undefined
markdown
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Clinical Trial Feasibility Report: [INDICATION]

临床试验可行性报告：[适应症]

Date: [YYYY-MM-DD] Trial Type: [Phase 1/2, biomarker-selected, basket, etc.] Primary Endpoint: [ORR, PFS, DLT, etc.] Feasibility Score: [0-100] - [LOW/MODERATE/HIGH]
日期：[YYYY-MM-DD] 试验类型：[1/2期、生物标志物筛选型、篮式试验等] 主要终点：[ORR、PFS、DLT等] 可行性评分：[0-100] - [低/中/高]

Key Findings

关键发现

  • Patient Availability: [Est. enrollable patients/year in US]
  • Enrollment Timeline: [Months to target N]
  • Endpoint Precedent: [Grade A/B/C/D] - [Description]
  • Regulatory Pathway: [505(b)(1), breakthrough, orphan, etc.]
  • Critical Risks: [Top 3 feasibility risks]
  • 患者可及性：[美国每年可入组患者数]
  • 入组 timeline：[达到目标样本量所需月数]
  • 终点指标先例：[A/B/C/D级] - [描述]
  • 监管路径：[505(b)(1)、突破性疗法、孤儿药等]
  • 关键风险：[可行性前3大风险]

Go/No-Go Recommendation

开展/不开展建议

[RECOMMEND PROCEED / RECOMMEND ADDITIONAL VALIDATION / DO NOT RECOMMEND]
Rationale: [2-3 sentence summary]
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[建议开展 / 建议补充验证 / 不建议开展]
理由：[2-3句话总结]
undefined

2. Disease Background

2. 疾病背景

  • Indication definition
  • Prevalence and incidence (with sources)
  • Current standard of care
  • Unmet medical need
  • Disease biology relevant to trial design
  • 适应症定义
  • 患病率与发病率（附来源）
  • 当前标准治疗方案
  • 未满足的医疗需求
  • 与试验设计相关的疾病生物学特性

3. Patient Population Analysis

3. 患者群体分析

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markdown
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3.1 Base Population Size

3.1 基础群体规模

  • US Incidence: [X per 100,000] [★★☆: Source]
  • Prevalence: [Y total patients in US] [★★★: CDC/NCI data]
  • Annual new cases: [Z patients/year]
  • 美国发病率：[每10万人中X例] [★★☆：来源]
  • 患病率：[美国总患者数Y] [★★★：CDC/NCI数据]
  • 年新增病例数：[Z例/年]

3.2 Biomarker Selection Impact

3.2 生物标志物选择的影响

  • Biomarker: [e.g., EGFR L858R mutation]
  • Prevalence in disease: [%] [★★★: ClinVar/COSMIC]
  • Geographic variation: [Asian vs. Caucasian, etc.]
  • Testing availability: [FDA-approved tests, CLIA labs]
  • 生物标志物：[例如：EGFR L858R突变]
  • 在疾病中的患病率：[%] [★★★：ClinVar/COSMIC]
  • 地域差异：[亚裔 vs 高加索裔等]
  • 检测可及性：[FDA获批检测方法、CLIA实验室]

3.3 Eligibility Criteria Funnel

3.3 入选标准漏斗分析

CriterionRemaining Patients% Retained
Base disease population[N]100%
Biomarker positive[N × biomarker %][%]
Age 18-75[N × age factor][%]
No prior therapy[N × treatment-naive %][%]
ECOG 0-1[N × performance factor][%]
Adequate organ function[N × eligibility factor][%]
FINAL ELIGIBLE POOL[N][%]
标准剩余患者数留存率
基础疾病群体[N]100%
生物标志物阳性[N × 生物标志物占比][%]
年龄18-75岁[N × 年龄因素][%]
未接受过治疗[N × 初治患者占比][%]
ECOG评分0-1[N × 体能状态因素][%]
器官功能充足[N × 入选资格因素][%]
最终符合条件的患者池[N][%]

3.4 Geographic Distribution

3.4 地域分布

  • High-incidence regions: [e.g., Asia 50%, US 15% for EGFR+]
  • Trial site implications
  • Recruitment strategy recommendations
  • 高发病区域：[例如：EGFR+患者中亚洲占50%，美国占15%]
  • 试验中心选址影响
  • 招募策略建议

3.5 Enrollment Projections

3.5 入组预测

Assumptions:
  • Eligible pool: [N patients/year in US]
  • Site activation: [M sites]
  • Screening success rate: [%]
  • Patients per site per month: [X]
Target Enrollment: [Total N] Projected Timeline: [Months] Sites Required: [Minimum M sites]
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假设条件：
  • 符合条件的患者池：[美国每年N例患者]
  • 激活中心数：[M个]
  • 筛选成功率：[%]
  • 每个中心每月入组患者数：[X]
目标入组量：[总N例] 预计 timeline：[月数] 所需中心数：[最少M个]
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4. Biomarker Strategy

4. 生物标志物策略

markdown
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4.1 Primary Biomarker

4.1 主要生物标志物

  • Biomarker: [Gene mutation, protein expression, etc.]
  • Prevalence: [%] [★★★: ClinVar data]
  • Assay Type: [NGS, IHC, PCR, etc.]
  • FDA-Approved Tests: [List CDx tests]
  • Turnaround Time: [Days]
  • Cost: [$X per test]
  • 生物标志物：[基因突变、蛋白表达等]
  • 患病率：[%] [★★★：ClinVar数据]
  • 检测方法：[NGS、IHC、PCR等]
  • FDA获批检测方法：[伴随诊断检测列表]
  • 周转时间：[天数]
  • 成本：[每例X美元]

4.2 Alternative/Complementary Biomarkers

4.2 替代/补充生物标志物

BiomarkerPrevalenceCorrelationTesting
[Alt 1][%][R²][Method]
[Alt 2][%][R²][Method]
生物标志物患病率相关性检测方法
[替代标志物1][%][R²][方法]
[替代标志物2][%][R²][方法]

4.3 Biomarker Testing Logistics

4.3 生物标志物检测 logistics

  • Pre-screening vs. screening approach
  • Central lab vs. local testing
  • Tissue vs. liquid biopsy (ctDNA)
  • Quality control requirements
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  • 预筛选 vs 筛选方案
  • 中心实验室 vs 本地检测
  • 组织活检 vs 液体活检（ctDNA）
  • 质量控制要求
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5. Endpoint Selection & Justification

5. 终点指标选择与论证

markdown
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5.1 Primary Endpoint

5.1 主要终点

Proposed: [e.g., Objective Response Rate (ORR)]
Regulatory Precedent [★★★]:
  • [N] FDA approvals in [indication] using ORR (2015-2024)
  • Recent example: [Drug] approved [Year] (ORR XX%, n=YY)
  • Source: search_clinical_trials, FDA_get_approval_history
Measurement Feasibility:
  • Assessment method: [RECIST 1.1, irRECIST, etc.]
  • Imaging modality: [CT, MRI, PET]
  • Assessment frequency: [Every X weeks]
  • Independent review: [Yes/No, cost]
Statistical Considerations:
  • Expected ORR: [%] (based on [source])
  • Null hypothesis: [%]
  • Sample size: [N] (α=0.05, β=0.20, two-sided)
  • Response duration: [Median months]
提议：[例如：客观缓解率（ORR）]
监管先例 [★★★]：
  • [N]项FDA在[适应症]中使用ORR的获批案例（2015-2024年）
  • 近期案例：[药物]于[年份]获批（ORR XX%，n=YY）
  • 来源：search_clinical_trials、FDA_get_approval_history
测量可行性：
  • 评估方法：[RECIST 1.1、irRECIST等]
  • 影像学手段：[CT、MRI、PET]
  • 评估频率：[每X周]
  • 独立评审：[是/否，成本]
统计学考量：
  • 预期ORR：[%]（基于[来源]）
  • 原假设：[%]
  • 样本量：[N]（α=0.05，β=0.20，双侧检验）
  • 缓解持续时间：[中位月数]

5.2 Secondary Endpoints

5.2 次要终点

EndpointEvidence GradeFeasibilityRationale
Progression-Free Survival (PFS)★★★HighFDA-accepted, precedent in [trials]
Duration of Response (DoR)★★☆HighStandard in oncology
Overall Survival (OS)★★★Low (early phase)Follow-up for long-term
[Biomarker response]★☆☆MediumExploratory, mechanistic
终点证据等级可行性理由
无进展生存期（PFS）★★★高FDA认可，[试验]中有先例
缓解持续时间（DoR）★★☆高肿瘤学领域标准指标
总生存期（OS）★★★低（早期阶段）需长期随访
[生物标志物响应]★☆☆中探索性、机制性研究

5.3 Exploratory Endpoints

5.3 探索性终点

  • Pharmacodynamic biomarkers (proof-of-mechanism)
  • ctDNA clearance (liquid biopsy)
  • Quality of life (PRO-CTCAE)
  • Correlative science (tumor profiling)
  • 药效学生物标志物（机制验证）
  • ctDNA清除（液体活检）
  • 生活质量（PRO-CTCAE）
  • 相关性研究（肿瘤谱分析）

5.4 Endpoint Risks & Mitigation

5.4 终点指标风险与缓解措施

  • Risk: [Low response rate → sample size inflation]
  • Mitigation: [Adaptive design, interim analysis]
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  • 风险：[缓解率低 → 样本量膨胀]
  • 缓解措施：[适应性设计、中期分析]
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6. Comparator Analysis

6. 对照药分析

markdown
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6.1 Standard of Care

6.1 标准治疗方案

Current SOC: [Drug name(s)]
  • FDA approval: [Year] [★★★: FDA_OrangeBook]
  • Efficacy: [ORR/PFS from pivotal trial]
  • Limitations: [Resistance, toxicity, access]
SOC Comparator Feasibility: [HIGH/MEDIUM/LOW]
当前标准治疗：[药物名称]
  • FDA获批时间：[年份] [★★★：FDA_OrangeBook]
  • 疗效：[关键试验中的ORR/PFS]
  • 局限性：[耐药性、毒性、可及性]
标准治疗对照药可行性：[高/中/低]

6.2 Trial Design Options

6.2 试验设计选项

Option A: Single-Arm vs. SOC

选项A：单臂 vs 标准治疗

  • Design: Phase 2, single-arm, N=[X]
  • Comparator: Historical SOC data (ORR=[%])
  • Pros: Faster enrollment, smaller N
  • Cons: Selection bias, regulatory skepticism
  • Feasibility Score: [0-100]
  • 设计：2期，单臂，N=[X]
  • 对照：历史标准治疗数据（ORR=[%]）
  • 优势：入组更快，样本量更小
  • 劣势：选择偏倚，监管机构质疑
  • 可行性评分：[0-100]

Option B: Randomized vs. SOC

选项B：随机对照 vs 标准治疗

  • Design: Phase 2, 1:1 randomization, N=[X] per arm
  • Comparator: Active control ([SOC drug])
  • Pros: Robust comparison, regulatory preferred
  • Cons: 2x enrollment, comparator sourcing
  • Feasibility Score: [0-100]
  • 设计：2期，1:1随机分组，每组N=[X]
  • 对照：活性对照（[标准治疗药物]）
  • 优势：对比结果可靠，监管机构偏好
  • 劣势：入组量翻倍，对照药采购
  • 可行性评分：[0-100]

Option C: Non-Inferiority Design

选项C：非劣效性设计

  • Rationale: [If aiming for better safety with similar efficacy]
  • Non-inferiority margin: [Δ = X%]
  • Sample size: [N] (larger than superiority)
  • 理由：[若旨在安全性更优且疗效相当]
  • 非劣效性界值：[Δ = X%]
  • 样本量：[N]（大于优效性设计）

6.3 Comparator Drug Sourcing

6.3 对照药采购

  • Commercial availability: [Yes/No]
  • Patent status: [Generic available?]
  • Cost: [$X per course]
  • Stability and storage: [Requirements]
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  • 商业可及性：[是/否]
  • 专利状态：[是否有仿制药]
  • 成本：[每疗程X美元]
  • 稳定性与储存：[要求]
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7. Safety Endpoints & Monitoring Plan

7. 安全性终点与监测计划

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7.1 Primary Safety Endpoint

7.1 主要安全性终点

Dose-Limiting Toxicity (DLT) [for Phase 1 component]:
  • DLT definition: [Grade 3+ non-hematologic, Grade 4+ hematologic]
  • DLT assessment period: [Cycle 1, 28 days]
  • Dose escalation rule: [3+3, BOIN, mTPI]
剂量限制性毒性（DLT） [针对1期部分]：
  • DLT定义：[3级及以上非血液学毒性、4级及以上血液学毒性]
  • DLT评估周期：[第1周期，28天]
  • 剂量递增规则：[3+3、BOIN、mTPI]

7.2 Mechanism-Based Toxicities

7.2 机制相关毒性

Drug Class: [Kinase inhibitor, checkpoint inhibitor, etc.]
Expected Toxicities [★★★: FAERS, label data]:
ToxicityIncidenceGrade 3+Monitoring
Diarrhea60%10%Symptom diary, hydration
Rash40%5%Dermatology consult PRN
Hepatotoxicity20%3%LFTs weekly (cycle 1), then q3w
[Specific AE][%][%][Plan]
Data Source: FAERS_search_reports (similar drugs), drugbank_get_pharmacology
药物类别：[激酶抑制剂、检查点抑制剂等]
预期毒性 [★★★：FAERS、标签数据]：
毒性发生率3级及以上占比监测方案
腹泻60%10%症状日记、补液
皮疹40%5%必要时咨询皮肤科
肝毒性20%3%第1周期每周检测肝功能，之后每3周1次
[特定不良事件][%][%][方案]
数据来源：FAERS_search_reports（同类药物）、drugbank_get_pharmacology

7.3 Organ-Specific Monitoring

7.3 器官特异性监测

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Hepatic

肝脏

  • Baseline: LFTs, hepatitis panel
  • Monitoring: AST/ALT/bili weekly (cycle 1), then q3w
  • Stopping rule: ALT >5× ULN or bili >3× ULN
  • 基线：肝功能检测、肝炎筛查
  • 监测：第1周期每周检测AST/ALT/胆红素，之后每3周1次
  • 停药规则：ALT >5倍正常上限或胆红素 >3倍正常上限

Cardiac

心脏

  • Baseline: ECG, ECHO if anthracycline history
  • Monitoring: ECG q cycle, ECHO if symptoms
  • Stopping rule: QTcF >500 ms, LVEF drop >15%
  • 基线：ECG，若有蒽环类药物史则加做ECHO
  • 监测：每个周期做ECG，有症状时加做ECHO
  • 停药规则：QTcF >500 ms，LVEF下降 >15%

Renal

肾脏

  • Baseline: Cr, eGFR, urinalysis
  • Monitoring: Cr/eGFR q cycle
  • Stopping rule: CrCl <30 mL/min
  • 基线：肌酐、eGFR、尿常规
  • 监测：每个周期检测肌酐/eGFR
  • 停药规则：CrCl <30 mL/min

[Organ X]

[器官X]

  • [Similar structure]
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  • [类似结构]
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7.4 Safety Monitoring Committee (SMC)

7.4 安全性监测委员会（SMC）

  • Composition: [3 independent experts: oncologist, toxicologist, biostatistician]
  • Review frequency: [After every 6 patients, then quarterly]
  • Stopping rules: [≥3 DLTs at dose level, ≥2 drug-related deaths]
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  • 组成：[3名独立专家：肿瘤学家、毒理学家、生物统计学家]
  • 评审频率：[每入组6例患者后评审，之后每季度1次]
  • 停药规则：[某剂量级别≥3例DLT，≥2例药物相关死亡]
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8. Study Design Recommendations

8. 试验设计建议

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8.1 Recommended Design

8.1 推荐设计

Phase: [1/2, 1b/2, 2] Design Type: [Single-arm, randomized, basket, umbrella] Primary Objective: [Assess safety and preliminary efficacy]
Schema:
[Indication + Biomarker]
    ↓ Screening (Biomarker testing)
    ↓ Enrollment
    ├─ [Phase 1 dose escalation: 3+3 design, N=12-18]
    │   Dose Levels: [X mg, Y mg, Z mg QD]
    │   DLT assessment: Cycle 1 (28 days)
    └─ [Phase 2 expansion: Simon 2-stage, N=43]
        Stage 1: N=13 (≥2 responses to proceed)
        Stage 2: N=30 additional
        Target ORR: 30% (H0: 10%, α=0.05, β=0.20)
阶段：[1/2期、1b/2期、2期] 设计类型：[单臂、随机、篮式、伞式] 主要目标：[评估安全性与初步疗效]
方案：
[适应症 + 生物标志物]
    ↓ 筛选（生物标志物检测）
    ↓ 入组
    ├─ [1期剂量递增：3+3设计，N=12-18]
    │   剂量水平：[X mg、Y mg、Z mg 每日1次]
    │   DLT评估：第1周期（28天）
    └─ [2期扩展：Simon两阶段设计，N=43]
        第1阶段：N=13（≥2例缓解则进入下一阶段）
        第2阶段：新增30例
        目标ORR：30%（H0: 10%，α=0.05，β=0.20）

8.2 Eligibility Criteria

8.2 入选标准

Inclusion:
  • Age ≥18 years
  • Histologically confirmed [disease]
  • [Biomarker] positive (central lab confirmed)
  • Measurable disease per RECIST 1.1
  • ECOG PS 0-1
  • Adequate organ function
  • [≤1 prior line for advanced disease]
Exclusion:
  • Brain metastases (unless treated and stable)
  • Prior [drug class] therapy
  • Active infection, immunodeficiency
  • Pregnancy/nursing
  • Significant cardiovascular disease
纳入标准：
  • 年龄≥18岁
  • 组织学确诊[疾病]
  • [生物标志物]阳性（中心实验室确认）
  • 符合RECIST 1.1标准的可测量病灶
  • ECOG体能状态0-1
  • 器官功能充足
  • [晚期疾病≤1线治疗]
排除标准：
  • 脑转移（除非已治疗且稳定）
  • 既往接受过[药物类别]治疗
  • 活动性感染、免疫缺陷
  • 妊娠/哺乳
  • 严重心血管疾病

8.3 Treatment Plan

8.3 治疗方案

  • Dosing: [X mg PO QD, 28-day cycles]
  • Dose modifications: [20% reductions for Grade 2+]
  • Duration: Until progression, toxicity, or 24 months
  • Concomitant meds: Supportive care allowed, restrictions on CYP3A4 inhibitors
  • 给药：[X mg 口服 每日1次，28天为1周期]
  • 剂量调整：[2级及以上毒性时降低20%剂量]
  • 疗程：直至疾病进展、出现毒性或满24个月
  • 合并用药：允许支持治疗，限制CYP3A4抑制剂使用

8.4 Assessment Schedule

8.4 评估 schedule

AssessmentScreeningCycle 1Cycles 2-6Cycles 7+EOT
History & PEXXXXX
ECOG PSXXXXX
Labs (CBC, CMP, LFT)XWeeklyq3wq3wX
Tumor imagingX-q6wq9wX
ECGX-q3w (if abnormal)-X
Biomarker (ctDNA)XC1D15q6w-X
AE assessment-ContinuousContinuousContinuousX
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评估项目筛选期第1周期第2-6周期第7周期及以后试验结束
病史与体格检查XXXXX
ECOG体能状态XXXXX
实验室检查（CBC、CMP、肝功能）X每周1次每3周1次每3周1次X
肿瘤影像学X-每6周1次每9周1次X
ECGX-每3周1次（若异常）-X
生物标志物（ctDNA）X第1周期第15天每6周1次-X
不良事件评估- 持续进行持续进行持续进行持续进行X
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9. Enrollment & Site Strategy

9. 入组与中心策略

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9.1 Site Selection Criteria

9.1 中心选择标准

Required Capabilities:
  • [Biomarker] testing (or central lab partnership)
  • Phase 1/2 experience
  • GCP compliance, IRB approval
  • Access to [patient population]
  • Investigator publications in [indication]
Geographic Distribution:
  • US sites: [N] (target regions: [high-incidence areas])
  • International: [Consider Asia if biomarker enriched there]
必备能力：
  • [生物标志物]检测能力（或与中心实验室合作）
  • 1/2期试验经验
  • GCP合规、IRB批准
  • 可接触到[患者群体]
  • 研究者在[适应症]领域有发表物
地域分布：
  • 美国中心：[N个]（目标区域：[高发病区域]）
  • 国际中心：[若生物标志物在亚洲富集，可考虑亚洲中心]

9.2 Enrollment Projections

9.2 入组预测

Assumptions:
  • Screening rate: [X patients/site/month]
  • Screen failure rate: [30%] (biomarker negative, eligibility)
  • Enrollment rate: [Y patients/site/month]
Timeline (N=[total]):
MilestoneMonthCumulative Enrolled
First site activated00
First patient enrolled11
25% enrollment[M1][0.25N]
50% enrollment[M2][0.5N]
75% enrollment[M3][0.75N]
Last patient enrolled[M4][N]
Primary analysis[M4 + follow-up]-
Sites Required: [Minimum M sites to achieve timeline]
假设条件：
  • 每个中心每月筛选患者数：[X]
  • 筛选失败率：[30%]（生物标志物阴性、不符合入选标准）
  • 每个中心每月入组患者数：[Y]
Timeline（总样本量[N]）：
里程碑月份累计入组数
首个中心激活00
首例患者入组11
完成25%入组[M1][0.25N]
完成50%入组[M2][0.5N]
完成75%入组[M3][0.75N]
末例患者入组[M4][N]
主要分析[M4 + 随访期]-
所需中心数：[达到timeline所需最少M个中心]

9.3 Recruitment Strategies

9.3 招募策略

  • Physician outreach: Academic consortia, tumor boards
  • Patient advocacy groups: [Organization names]
  • ClinicalTrials.gov listing (prominent, lay summary)
  • Social media: Targeted ads in [indication] communities
  • Referral network: Community oncologists
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  • 医师 outreach：学术联盟、肿瘤研讨会
  • 患者 advocacy 团体：[组织名称]
  • ClinicalTrials.gov 注册（突出显示，通俗易懂）
  • 社交媒体：[适应症]社区定向广告
  • 转诊网络：社区肿瘤医师
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10. Regulatory Pathway

10. 监管路径

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10.1 FDA Pathway Selection

10.1 FDA路径选择

Recommended: [505(b)(1) / 505(b)(2) / Breakthrough / Orphan]
Rationale:
  • [505(b)(1)]: New molecular entity, full development program
  • [505(b)(2)]: [If relying on published safety data for similar drugs]
  • Breakthrough Therapy: [If preliminary evidence of substantial improvement on serious outcome]
    • Criteria: [X-fold ORR vs. SOC in early data]
    • Benefits: Rolling review, frequent FDA meetings
  • Orphan Designation: [If prevalence <200,000 in US]
    • Eligible if: [Biomarker-defined subtype constitutes orphan population]
    • Benefits: 7-year exclusivity, tax credits, fee waivers
推荐：[505(b)(1) / 505(b)(2) / 突破性疗法 / 孤儿药]
理由：
  • [505(b)(1)]：新分子实体，完整开发项目
  • [505(b)(2)]：[若依赖同类药物已发表的安全性数据]
  • 突破性疗法：[若初步数据显示在严重结局上有显著改善]
    • 标准：[ORR较标准治疗提高2倍]
    • 优势：滚动评审、与FDA频繁沟通
  • 孤儿药资格：[若美国患病率<200,000]
    • 符合条件情况：[生物标志物定义的亚型属于孤儿群体]
    • 优势：7年独占期、税收抵免、费用减免

10.2 Regulatory Precedents

10.2 监管先例

Similar Approvals [★★★]:
  • [Drug A]: [Indication], [Year], [Endpoint used], [N=X], [ORR=Y%]
  • [Drug B]: [Indication], [Year], [Accelerated approval → full]
  • Source: FDA_get_approval_history, drug labels
FDA Guidance Documents:
  • [Relevant guidance title] (Year)
  • Key recommendations: [e.g., ORR acceptable for Phase 2, confirmatory trial needed]
同类获批案例 [★★★]：
  • [药物A]：[适应症]，[年份]，[使用的终点]，[N=X]，[ORR=Y%]
  • [药物B]：[适应症]，[年份]，[加速获批→完全获批]
  • 来源：FDA_get_approval_history、药物标签
FDA指南文件：
  • [相关指南标题]（年份）
  • 关键建议：[例如：ORR可用于2期试验，需确证性试验]

10.3 Pre-IND Meeting

10.3 预IND会议

Recommended Topics:
  1. Primary endpoint acceptability (ORR vs. PFS)
  2. Biomarker test qualification (CDx plan)
  3. Comparator arm (single-arm acceptable?)
  4. Pediatric study plan waiver
  5. Safety monitoring plan
Timing: [3-4 months before IND submission]
推荐议题：
  1. 主要终点可接受性（ORR vs PFS）
  2. 生物标志物检测资格认定（伴随诊断方案）
  3. 对照药组（单臂是否可接受？）
  4. 儿科研究计划豁免
  5. 安全性监测计划
时间：[IND提交前3-4个月]

10.4 IND Timeline

10.4 IND Timeline

MilestoneMonthDeliverable
Pre-IND meeting request-4Briefing package
Pre-IND meeting-3FDA feedback
IND submission0Complete IND package
FDA 30-day review1Clinical hold or proceed
First patient dosed1-2After IND clearance
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里程碑月份交付物
预IND会议申请-4briefing package
预IND会议-3FDA反馈
IND提交0完整IND package
FDA 30天评审1临床暂停或允许开展
首例患者给药1-2IND获批后
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11. Budget & Resource Considerations

11. 预算与资源考量

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11.1 Cost Drivers

11.1 成本驱动因素

ItemCost EstimateNotes
Protocol development$50-100KCRO or internal
IND preparation$100-200KCMC, toxicology reports
Site activation$50K/site × [M sites]IRB, contracts
Patient recruitment$200-500KAdvertising, patient navigation
[Biomarker] testing$[X]/patientCentral lab, CDx
Imaging (RECIST)$3-5K/scan × [N scans]CT, independent review
Drug supply[Depends on sponsor]If not sponsor-provided
CRO monitoring$100-300/hourSite visits, SDV
Data management$150-300KEDC, database lock
Statistical analysis$50-100KSAP, CSR
TOTAL (Phase 1/2)$[X-Y]M[N patients, M sites]
项目成本估算说明
方案开发$50-100KCRO或内部团队
IND准备$100-200KCMC、毒理学报告
中心激活$50K/中心 × [M个中心]IRB、合同
患者招募$200-500K广告、患者导航
[生物标志物]检测$[X]/例中心实验室、伴随诊断
影像学（RECIST）$3-5K/次 × [N次]CT、独立评审
药物供应[取决于申办方]若申办方未提供
CMC监测$100-300/小时中心访视、SDV
数据管理$150-300KEDC、数据库锁定
统计分析$50-100KSAP、CSR
总计（1/2期）$[X-Y]M[N例患者，M个中心]

11.2 Timeline & FTE Requirements

11.2 Timeline与FTE需求

Duration: [X months] (enrollment) + [Y months] (follow-up) Team:
  • Medical monitor: 0.5 FTE
  • Project manager: 0.8 FTE
  • Clinical operations: 0.3 FTE
  • Data manager: 0.3 FTE
  • Biostatistician: 0.2 FTE
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时长：[X个月]（入组） + [Y个月]（随访） 团队：
  • 医学监查员：0.5 FTE
  • 项目经理：0.8 FTE
  • 临床运营：0.3 FTE
  • 数据管理员：0.3 FTE
  • 生物统计学家：0.2 FTE
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12. Risk Assessment

12. 风险评估

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12.1 Feasibility Risks (High Priority)

12.1 可行性风险（高优先级）

RiskLikelihoodImpactMitigation
Slow enrollment (biomarker screen fail)HIGHHIGH- Expand sites to [high-prevalence regions]<br>- Allow alternative biomarkers<br>- Liquid biopsy screening
Low response rate (ORR <10%)MEDIUMCRITICAL- Interim futility analysis (Simon stage 1)<br>- Lower null hypothesis if justified<br>- Pivot to combination if single-agent weak
Unexpected toxicity (>33% DLT rate)LOWCRITICAL- Conservative starting dose (50% MTD from preclin)<br>- Dose escalation with BOIN (adaptive)<br>- Close SMC oversight
Comparator drug supply issuesMEDIUMMEDIUM- Secure commercial supply early<br>- Generic sourcing if available
Regulatory pushback on single-arm designMEDIUMHIGH- Pre-IND meeting to align<br>- Plan for randomized Phase 2b if needed
风险可能性影响缓解措施
入组缓慢（生物标志物筛选失败）高高- 扩展至[高患病率区域]的中心<br>- 允许使用替代生物标志物<br>- 液体活检筛选
缓解率低（ORR <10%）中严重- 中期无效性分析（Simon第1阶段）<br>- 若合理则降低原假设<br>- 若单药疗效弱则转向联合治疗
意外毒性（DLT率>33%）低严重- 保守起始剂量（临床前MTD的50%）<br>- 使用BOIN适应性剂量递增<br>- 加强SMC监督
对照药供应问题中中- 提前锁定商业供应<br>- 若有仿制药则采购仿制药
监管机构对单臂设计的反对中高- 预IND会议对齐意见<br>- 必要时规划2b期随机试验

12.2 Scientific Risks

12.2 科学风险

  • Biomarker hypothesis unvalidated: [Correlative studies to de-risk]
  • Patient heterogeneity: [Stratification by [factor]]
  • Resistance mechanisms: [Serial biopsies for molecular profiling]
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  • 生物标志物假设未验证：[开展相关性研究降低风险]
  • 患者异质性：[按[因素]分层]
  • 耐药机制：[系列活检进行分子谱分析]
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13. Success Criteria & Go/No-Go Decision

13. 成功标准与开展/不开展决策

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13.1 Phase 1 Success Criteria (Go to Phase 2)

13.1 1期成功标准（进入2期）

  • ≤33% DLT rate at RP2D
  • ≥50% patients achieve [PD biomarker response]
  • No unexpected safety signals (Grade 5 AEs, new class effects)
  • PK supports QD dosing
  • RP2D剂量下DLT率≤33%
  • ≥50%患者达到[药效学生物标志物响应]
  • 无意外安全信号（5级不良事件、新类效应）
  • PK支持每日1次给药

13.2 Phase 2 Interim Analysis (Simon Stage 1)

13.2 2期中期分析（Simon第1阶段）

  • Enrollment: 13 patients
  • Decision Rule:
    • ≥2 responses (ORR ≥15%) → Proceed to Stage 2
    • <2 responses → Stop for futility
  • 入组量：13例患者
  • 决策规则：
    • ≥2例缓解（ORR ≥15%）→ 进入第2阶段
    • <2例缓解 → 因无效终止

13.3 Phase 2 Final Success Criteria (Advance to Phase 3)

13.3 2期最终成功标准（进入3期）

  • ORR ≥30% (95% CI lower bound >10%)
  • Median DoR ≥6 months
  • PFS signal (HR <0.7 vs. historical SOC)
  • Safety profile manageable (Grade ≥3 AE <40%)
  • Biomarker correlation with response (enrichment signal)
  • ORR ≥30%（95%CI下限>10%）
  • 中位缓解持续时间≥6个月
  • PFS信号（HR <0.7 vs 历史标准治疗）
  • 安全性可控（≥3级不良事件<40%）
  • 生物标志物与缓解的相关性（富集信号）

13.4 Feasibility Scorecard

13.4 可行性评分卡

DimensionWeightScore (0-10)WeightedGrade
Patient Availability30%[X][0.30×X][★★☆]
- Base population size-[X]-[Source]
- Biomarker prevalence-[X]-[ClinVar data]
- Site access-[X]-[N sites feasible]
Endpoint Precedent25%[X][0.25×X][★★★]
- Regulatory acceptance-[X]-[FDA approvals using ORR]
- Measurement feasibility-[X]-[RECIST standard]
Regulatory Clarity20%[X][0.20×X][★★☆]
- Pathway defined-[X]-[Breakthrough potential]
- Precedent approvals-[X]-[Similar indications]
Comparator Feasibility15%[X][0.15×X][★★★]
- SOC availability-[X]-[FDA-approved, generic]
- Historical data-[X]-[Published ORR: X%]
Safety Monitoring10%[X][0.10×X][★★☆]
- Known toxicities-[X]-[FAERS, class effects]
- Monitoring plan-[X]-[Defined, feasible]
TOTAL FEASIBILITY SCORE100%-[XX/100]-
Interpretation:
  • ≥75: HIGH feasibility - Recommend proceed to protocol development
  • 50-74: MODERATE feasibility - Additional validation recommended
  • <50: LOW feasibility - Significant de-risking required
undefined
维度权重得分（0-10）加权得分等级
患者可及性30%[X][0.30×X][★★☆]
- 基础群体规模-[X]-[来源]
- 生物标志物患病率-[X]-[ClinVar数据]
- 中心可及性-[X]-[可开展的N个中心]
终点指标先例25%[X][0.25×X][★★★]
- 监管机构认可-[X]-[使用ORR的FDA获批案例]
- 测量可行性-[X]-[RECIST标准]
监管清晰度20%[X][0.20×X][★★☆]
- 路径明确-[X]-[突破性疗法潜力]
- 先例获批案例-[X]-[同类适应症]
对照药可行性15%[X][0.15×X][★★★]
- 标准治疗可及性-[X]-[FDA获批、有仿制药]
- 历史数据-[X]-[已发表ORR: X%]
安全性监测10%[X][0.10×X][★★☆]
- 已知毒性-[X]-[FAERS、类效应]
- 监测方案-[X]-[明确、可行]
总可行性评分100%-[XX/100]-
解读：
  • ≥75：高可行性 - 建议推进方案开发
  • 50-74：中可行性 - 建议补充验证
  • <50：低可行性 - 需显著降低风险
undefined

14. Recommendations & Next Steps

14. 建议与下一步行动

markdown
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markdown
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14.1 Final Recommendation

14.1 最终建议

GO / CONDITIONAL GO / NO-GO: [Decision]
Rationale: [2-3 paragraphs synthesizing feasibility analysis. Example:]
This trial demonstrates HIGH feasibility (score: 82/100) for the following reasons:
  1. Patient availability is strong (★★★): EGFR+ NSCLC affects ~18,000 US patients/year, with L858R representing 45% (8,100 patients). With 20 sites, enrollment of N=43 is achievable in 8-10 months.
  2. Endpoint precedent is robust (★★★): ORR is FDA-accepted for accelerated approval in NSCLC (18 precedents since 2015). RECIST 1.1 is standard, feasible.
  3. Regulatory pathway is clear (★★☆): 505(b)(1) with breakthrough therapy potential given 2x ORR improvement vs. SOC. Pre-IND meeting advised to confirm single-arm design.
Key Risk: Enrollment may slow if sites lack rapid EGFR testing. Mitigation: Central liquid biopsy with 7-day turnaround.
开展 / 有条件开展 / 不开展：[决策]
理由： [2-3段综合可行性分析。示例：]
本试验可行性高（评分：82/100），原因如下：
  1. 患者可及性强（★★★）：美国每年约18,000例EGFR+ NSCLC患者，其中L858R突变占45%（8,100例）。20个中心的情况下，入组43例患者可在8-10个月内完成。
  2. 终点指标先例充分（★★★）：ORR是FDA认可的NSCLC加速获批终点（2015年以来18个先例）。RECIST 1.1是标准方法，可行性高。
  3. 监管路径清晰（★★☆）：505(b)(1)路径，且因较标准治疗ORR提高2倍，有突破性疗法潜力。建议召开预IND会议确认单臂设计的可接受性。
关键风险：若中心缺乏快速EGFR检测能力，入组可能放缓。缓解措施：采用中心液体活检，7天周转时间。

14.2 Critical Path to IND

14.2 IND关键路径

Immediate Next Steps (Months 0-3):
  • Request pre-IND meeting with FDA (target Month 1)
  • Initiate CDx partnership for [biomarker] test (FDA clearance path)
  • Secure drug supply (GMP manufacturing, stability)
  • Draft protocol (v1.0) and ICF
  • Site feasibility surveys (target [M] sites)
IND Preparation (Months 3-6):
  • Complete CMC section (drug substance/product, manufacturing)
  • Finalize preclinical package (toxicology, pharmacology)
  • Prepare clinical protocol (incorporate FDA feedback)
  • Develop CRFs and EDC database
  • IND submission (Month 6)
Post-IND (Months 6-9):
  • IRB submissions (central IRB for multi-site)
  • Site contracts and budgets
  • Investigator meeting
  • First patient enrolled (Month 7-8)
立即下一步行动（0-3个月）：
  • 向FDA申请预IND会议（目标第1个月）
  • 启动[生物标志物]检测伴随诊断合作（FDA获批路径）
  • 锁定药物供应（GMP生产、稳定性）
  • 起草方案（v1.0）与知情同意书
  • 中心可行性调研（目标[M]个中心）
IND准备（3-6个月）：
  • 完成CMC部分（药物活性成分/制剂、生产）
  • 最终确定临床前package（毒理学、药理学）
  • 完善临床方案（纳入FDA反馈）
  • 开发CRF与EDC数据库
  • 提交IND（第6个月）
IND提交后（6-9个月）：
  • IRB提交（多中心采用中心IRB）
  • 中心合同与预算
  • 研究者会议
  • 首例患者入组（7-8个月）

14.3 Alternative Designs (If Current Design Infeasible)

14.3 替代设计（若当前设计不可行）

Plan B: [If enrollment too slow]
  • Broaden biomarker criteria (e.g., all EGFR mutations, not just L858R)
  • Add international sites (Asia, EU)
  • Basket design (multiple cancers with EGFR mutations)
Plan C: [If single-arm rejected by FDA]
  • Randomized Phase 2 (1:1 vs. SOC)
  • Increase sample size to N=86 (43/arm)
  • Requires 2x sites and budget
方案B：[若入组过慢]
  • 放宽生物标志物标准（例如：所有EGFR突变，而非仅L858R）
  • 增加国际中心（亚洲、欧盟）
  • 篮式设计（多种携带EGFR突变的癌症）
方案C：[若FDA拒绝单臂设计]
  • 2期随机对照（1:1 vs 标准治疗）
  • 样本量增加至N=86（每组43例）
  • 需2倍中心与预算

14.4 Long-Term Development Strategy

14.4 长期开发策略

If Phase 2 Successful:
  • Phase 3 design: Randomized, OS primary endpoint, N=300-500
  • Companion diagnostic (CDx): Parallel FDA submission
  • Commercial readiness: Manufacturing scale-up
  • Patent strategy: File composition-of-matter or method-of-use
Market Considerations:
  • Addressable market: [8,100 EGFR L858R NSCLC patients/year in US]
  • Competitive landscape: [Osimertinib, other EGFR TKIs]
  • Differentiation: [e.g., Activity against T790M resistance]
  • Pricing: [$10-15K/month based on comparators]

---
若2期成功：
  • 3期设计：随机对照，主要终点OS，样本量300-500
  • 伴随诊断（CDx）：与FDA同步提交
  • 商业化准备：生产放大
  • 专利策略：提交组合物或使用方法专利
市场考量：
  • 可触达市场：[美国每年8,100例EGFR L858R NSCLC患者]
  • 竞争格局：[奥希替尼、其他EGFR TKI]
  • 差异化：[例如：对T790M耐药的活性]
  • 定价：[基于同类药物，10-15K美元/月]

---

Complete Example Workflow

完整示例工作流

Example: EGFR L858R+ NSCLC Phase 1/2 Trial

示例：EGFR L858R+ NSCLC 1/2期试验

python
from tooluniverse import ToolUniverse

tu = ToolUniverse(use_cache=True)
tu.load_tools()
python
from tooluniverse import ToolUniverse

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

============================================================================

============================================================================

PATH 1: PATIENT POPULATION SIZING

路径1：患者群体规模测算

============================================================================

============================================================================

Step 1.1: Get disease prevalence

步骤1.1：获取疾病患病率

disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name( diseaseName="non-small cell lung cancer" ) efo_id = disease_info['data']['id']
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name( diseaseName="non-small cell lung cancer" ) efo_id = disease_info['data']['id']

Get phenotype data (includes prevalence if available)

获取表型数据（若有则包含患病率）

phenotypes = tu.tools.OpenTargets_get_diseases_phenotypes( efoId=efo_id )
phenotypes = tu.tools.OpenTargets_get_diseases_phenotypes( efoId=efo_id )

Note: May need to supplement with literature (PubMed) for specific prevalence

注：可能需要补充文献（PubMed）获取特定患病率数据

Step 1.2: Estimate EGFR mutation prevalence

步骤1.2：估算EGFR突变患病率

egfr_variants = tu.tools.ClinVar_search_variants( gene="EGFR", significance="pathogenic,likely_pathogenic" )
egfr_variants = tu.tools.ClinVar_search_variants( gene="EGFR", significance="pathogenic,likely_pathogenic" )

Filter to L858R specifically

筛选出L858R突变

l858r_variants = [v for v in egfr_variants['data'] if 'L858R' in v.get('name', '')]
l858r_variants = [v for v in egfr_variants['data'] if 'L858R' in v.get('name', '')]

Also check population databases for allele frequency

同时查询人群数据库的等位基因频率

gnomad_egfr = tu.tools.gnomAD_search_gene_variants( gene="EGFR" )
gnomad_egfr = tu.tools.gnomAD_search_gene_variants( gene="EGFR" )

Filter to L858R and sum allele frequencies

筛选L858R并汇总等位基因频率

Step 1.3: Search literature for epidemiology

步骤1.3：搜索流行病学文献

epi_papers = tu.tools.PubMed_search_articles( query="EGFR L858R prevalence non-small cell lung cancer epidemiology", max_results=20 )
epi_papers = tu.tools.PubMed_search_articles( query="EGFR L858R prevalence non-small cell lung cancer epidemiology", max_results=20 )

Extract prevalence estimates from recent papers

提取近期文献中的患病率估算值

============================================================================

============================================================================

PATH 2: BIOMARKER PREVALENCE & TESTING

路径2：生物标志物患病率与检测

============================================================================

============================================================================

Step 2.1: Find FDA-approved CDx tests

步骤2.1：查找FDA获批的伴随诊断检测

Search FDA device database (via PubMed or manual lookup)

搜索FDA设备数据库（通过PubMed或手动查找）

cdx_search = tu.tools.PubMed_search_articles( query="FDA approved companion diagnostic EGFR L858R", max_results=10 )
cdx_search = tu.tools.PubMed_search_articles( query="FDA approved companion diagnostic EGFR L858R", max_results=10 )

Step 2.2: Literature on EGFR testing in clinical practice

步骤2.2：查找临床实践中EGFR检测的文献

testing_papers = tu.tools.PubMed_search_articles( query="EGFR mutation testing guidelines NCCN turnaround time", max_results=15 )
testing_papers = tu.tools.PubMed_search_articles( query="EGFR mutation testing guidelines NCCN turnaround time", max_results=15 )

============================================================================

============================================================================

PATH 3: COMPARATOR SELECTION

路径3：对照药选择

============================================================================

============================================================================

Step 3.1: Find current standard of care (osimertinib)

步骤3.1：查找当前标准治疗（奥希替尼）

soc_drug = "osimertinib"
soc_info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id( drug_name_or_drugbank_id=soc_drug )
soc_indications = tu.tools.drugbank_get_indications_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=soc_drug )
soc_pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=soc_drug )
soc_drug = "osimertinib"
soc_info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id( drug_name_or_drugbank_id=soc_drug )
soc_indications = tu.tools.drugbank_get_indications_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=soc_drug )
soc_pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=soc_drug )

Step 3.2: Check FDA Orange Book for approved generics

步骤3.2：查询FDA橙皮书获取获批仿制药信息

orange_book = tu.tools.FDA_OrangeBook_search_drugs( ingredient=soc_drug )
orange_book = tu.tools.FDA_OrangeBook_search_drugs( ingredient=soc_drug )

Step 3.3: Find FDA approval details

步骤3.3：查找FDA获批细节

fda_approval = tu.tools.FDA_get_drug_approval_history( drug_name=soc_drug )
fda_approval = tu.tools.FDA_get_drug_approval_history( drug_name=soc_drug )

============================================================================

============================================================================

PATH 4: ENDPOINT SELECTION

路径4：终点指标选择

============================================================================

============================================================================

Step 4.1: Search for precedent Phase 2 trials in EGFR+ NSCLC

步骤4.1：搜索EGFR+ NSCLC的2期先例试验

precedent_trials = tu.tools.search_clinical_trials( condition="EGFR positive non-small cell lung cancer", phase="2", status="completed" )
precedent_trials = tu.tools.search_clinical_trials( condition="EGFR positive non-small cell lung cancer", phase="2", status="completed" )

Analyze which primary endpoints were used (ORR, PFS, etc.)

分析使用的主要终点（ORR、PFS等）

orr_trials = [t for t in precedent_trials['data'] if 'response rate' in t.get('primary_outcome', '').lower()]
orr_trials = [t for t in precedent_trials['data'] if 'response rate' in t.get('primary_outcome', '').lower()]

Step 4.2: Find FDA approvals using ORR as primary endpoint

步骤4.2：查找以ORR为主要终点的FDA获批案例

orr_approvals = tu.tools.PubMed_search_articles( query="FDA approval objective response rate NSCLC accelerated approval", max_results=30 )
orr_approvals = tu.tools.PubMed_search_articles( query="FDA approval objective response rate NSCLC accelerated approval", max_results=30 )

Step 4.3: Get detailed trial results for sample size justification

步骤4.3：获取详细试验结果用于样本量论证

Use ClinicalTrials.gov NCT number from precedent_trials

使用precedent_trials中的ClinicalTrials.gov NCT编号

for trial in precedent_trials['data'][:5]: nct_id = trial.get('nct_number') trial_details = tu.tools.search_clinical_trials( nct_id=nct_id ) # Extract: ORR, n, confidence intervals
for trial in precedent_trials['data'][:5]: nct_id = trial.get('nct_number') trial_details = tu.tools.search_clinical_trials( nct_id=nct_id ) # 提取：ORR、样本量、置信区间

============================================================================

============================================================================

PATH 5: SAFETY ENDPOINTS & MONITORING

路径5：安全性终点与监测

============================================================================

============================================================================

Step 5.1: Get mechanism-based toxicity from drug class

步骤5.1：从药物类别获取机制相关毒性

If testing an EGFR inhibitor, search for class effects

若测试EGFR抑制剂，搜索类效应

class_drug = "erlotinib" # Example EGFR TKI for class effect reference
class_safety = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=class_drug )
class_warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name( drug_name=class_drug )
class_drug = "erlotinib" # 用于参考类效应的EGFR TKI示例
class_safety = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id( drug_name_or_drugbank_id=class_drug )
class_warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name( drug_name=class_drug )

Step 5.2: FAERS data for real-world adverse events

步骤5.2：FAERS数据获取真实世界不良事件

faers_egfr_tki = tu.tools.FAERS_search_reports_by_drug_and_reaction( drug_name="erlotinib", limit=500 )
faers_egfr_tki = tu.tools.FAERS_search_reports_by_drug_and_reaction( drug_name="erlotinib", limit=500 )

Summarize top adverse events

汇总主要不良事件

ae_summary = tu.tools.FAERS_count_reactions_by_drug_event( medicinalproduct="ERLOTINIB" )
ae_summary = tu.tools.FAERS_count_reactions_by_drug_event( medicinalproduct="ERLOTINIB" )

Step 5.3: Search for DLT definitions in similar trials

步骤5.3：搜索同类试验中的DLT定义

dlt_papers = tu.tools.PubMed_search_articles( query="dose limiting toxicity Phase 1 EGFR inhibitor definition", max_results=20 )
dlt_papers = tu.tools.PubMed_search_articles( query="dose limiting toxicity Phase 1 EGFR inhibitor definition", max_results=20 )

============================================================================

============================================================================

PATH 6: REGULATORY PATHWAY

路径6：监管路径

============================================================================

============================================================================

Step 6.1: Search for breakthrough therapy designations in NSCLC

步骤6.1：搜索NSCLC中的突破性疗法认定

breakthrough_search = tu.tools.PubMed_search_articles( query="FDA breakthrough therapy designation NSCLC EGFR mutation", max_results=20 )
breakthrough_search = tu.tools.PubMed_search_articles( query="FDA breakthrough therapy designation NSCLC EGFR mutation", max_results=20 )

Step 6.2: Check if indication qualifies for orphan drug status

步骤6.2：检查适应症是否符合孤儿药资格

L858R is subset of NSCLC; estimate US prevalence

L858R是NSCLC的亚型；估算美国患病率

us_nsclc_annual = 200000 # From epidemiology data l858r_prevalence = 0.45 * 0.15 # 45% of EGFR+ (15% of NSCLC) l858r_annual_us = us_nsclc_annual * l858r_prevalence # ~13,500/year
us_nsclc_annual = 200000 # 来自流行病学数据 l858r_prevalence = 0.45 * 0.15 # EGFR+占15%，其中L858R占45% l858r_annual_us = us_nsclc_annual * l858r_prevalence # ~13,500例/年

Note: Orphan requires <200,000 total prevalence; may not qualify if prevalent

注：孤儿药要求总患病率<200,000；若患病率较高可能不符合

Step 6.3: Find relevant FDA guidance documents

步骤6.3：查找相关FDA指南文件

fda_guidance_search = tu.tools.PubMed_search_articles( query="FDA guidance clinical trial endpoints oncology non-small cell lung cancer", max_results=15 )
fda_guidance_search = tu.tools.PubMed_search_articles( query="FDA guidance clinical trial endpoints oncology non-small cell lung cancer", max_results=15 )

============================================================================

============================================================================

COMPILE FEASIBILITY REPORT

整理可行性报告

============================================================================

============================================================================

Now compile all data into the 14-section report structure

现在将所有数据整理为14章节的报告结构

Calculate feasibility score based on findings

根据 findings 计算可行性评分

feasibility_scores = { 'patient_availability': 8, # 8/10 based on 13,500 patients/year, good access 'endpoint_precedent': 9, # 9/10 ORR widely accepted 'regulatory_clarity': 7, # 7/10 breakthrough possible, single-arm needs FDA input 'comparator_feasibility': 9, # 9/10 osimertinib available, efficacy data clear 'safety_monitoring': 8 # 8/10 EGFR TKI class effects well-characterized }
weights = { 'patient_availability': 0.30, 'endpoint_precedent': 0.25, 'regulatory_clarity': 0.20, 'comparator_feasibility': 0.15, 'safety_monitoring': 0.10 }
overall_score = sum(feasibility_scores[k] * weights[k] * 10 for k in weights.keys())
feasibility_scores = { 'patient_availability': 8, # 8/10，基于每年13,500例患者，可及性好 'endpoint_precedent': 9, # 9/10，ORR被广泛接受 'regulatory_clarity': 7, # 7/10，可能获得突破性疗法，单臂设计需FDA确认 'comparator_feasibility': 9, # 9/10，奥希替尼可及，疗效数据明确 'safety_monitoring': 8 # 8/10，EGFR TKI类效应已充分表征 }
weights = { 'patient_availability': 0.30, 'endpoint_precedent': 0.25, 'regulatory_clarity': 0.20, 'comparator_feasibility': 0.15, 'safety_monitoring': 0.10 }
overall_score = sum(feasibility_scores[k] * weights[k] * 10 for k in weights.keys())

overall_score = 81/100 → HIGH feasibility

overall_score = 81/100 → 高可行性

print(f"Feasibility Score: {overall_score}/100 - HIGH") print("Recommendation: RECOMMEND PROCEED to protocol development")

---
print(f"可行性评分: {overall_score}/100 - 高") print("建议: 建议推进方案开发")

---

Tool Reference by Research Path

按研究路径分类的工具参考

PATH 1: Patient Population Sizing

路径1：患者群体规模测算

  • 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
  • OpenTargets_get_disease_id_description_by_name
    - 疾病查询
  • OpenTargets_get_diseases_phenotypes
    - 患病率数据
  • 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
    - 过往试验的入组可行性

PATH 2: Biomarker Prevalence & Testing

路径2：生物标志物患病率与检测

  • 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
  • ClinVar_get_variant_details
    - 突变致病性
  • COSMIC_search_mutations
    - 癌症特异性突变频率
  • gnomAD_get_variant_details
    - 人群遗传学
  • PubMed_search_articles
    - 伴随诊断检测性能、指南

PATH 3: Comparator Selection

路径3：对照药选择

  • 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
  • drugbank_get_drug_basic_info_by_drug_name_or_id
    - 药物信息
  • drugbank_get_indications_by_drug_name_or_drugbank_id
    - 获批适应症
  • drugbank_get_pharmacology_by_drug_name_or_drugbank_id
    - 作用机制
  • FDA_OrangeBook_search_drugs
    - 仿制药可及性
  • FDA_get_drug_approval_history
    - 获批细节
  • search_clinical_trials
    - 历史对照数据

PATH 4: Endpoint Selection

路径4：终点指标选择

  • search_clinical_trials
    - Precedent trials, endpoints used
  • PubMed_search_articles
    - FDA acceptance history, endpoint validation
  • FDA_get_drug_approval_history
    - Approved endpoints by indication
  • search_clinical_trials
    - 先例试验、使用的终点
  • PubMed_search_articles
    - FDA认可历史、终点验证
  • FDA_get_drug_approval_history
    - 按适应症划分的获批终点

PATH 5: Safety Endpoints & Monitoring

路径5：安全性终点与监测

  • 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
  • drugbank_get_pharmacology_by_drug_name_or_drugbank_id
    - 机制相关毒性
  • 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
    - 不良事件频率
  • FAERS_count_death_related_by_drug
    - 严重结局
  • PubMed_search_articles
    - DLT定义、监测策略

PATH 6: Regulatory Pathway

路径6：监管路径

  • FDA_get_drug_approval_history
    - Precedent approvals
  • PubMed_search_articles
    - Breakthrough designations, FDA guidance
  • search_clinical_trials
    - Regulatory precedents (accelerated approval)
  • FDA_get_drug_approval_history
    - 先例获批案例
  • PubMed_search_articles
    - 突破性疗法认定、FDA指南
  • search_clinical_trials
    - 监管先例（加速获批）
##最佳实践

Best Practices

1. 从报告模板开始

1. Start with Report Template

—
Create full report structure FIRST, then populate:
markdown
undefined
先创建完整报告结构，再填充内容：
markdown
undefined

Clinical Trial Feasibility Report: [INDICATION]

临床试验可行性报告：[适应症]

1. Executive Summary

1. 执行摘要

[Researching...]
[研究中...]

2. Disease Background

2. 疾病背景

[Researching...] [...all 14 sections...]
undefined
[研究中...] [...所有14个章节...]
undefined

2. Use English for All Tool Calls

2. 工具调用全部使用英文

Even if user asks in another language:
  • "EGFR+ NSCLC" not "EGFR+ 非小细胞肺癌"
  • "breast cancer" not "cancer du sein"
  • Translate results back to user's language
即使用户用其他语言提问：
  • 使用"EGFR+ NSCLC"而非"EGFR+ 非小细胞肺癌"
  • 使用"breast cancer"而非"cancer du sein"
  • 将结果翻译为用户使用的语言

3. Validate Biomarker Prevalence Across Sources

3. 跨来源验证生物标志物患病率

Cross-check ClinVar, gnomAD, COSMIC, and literature:
  • ClinVar: Clinical significance
  • gnomAD: Population frequency (for germline)
  • COSMIC: Somatic mutation frequency in cancers
  • Literature: Geographic/ethnic variation
交叉验证ClinVar、gnomAD、COSMIC与文献：
  • ClinVar：临床意义
  • gnomAD：人群频率（生殖系突变）
  • COSMIC：癌症中体细胞突变频率
  • 文献：地域/种族差异

4. Calculate Enrollment Funnel Explicitly

4. 明确计算入组漏斗

Show math for patient availability:
US NSCLC incidence: 200,000/year
× EGFR+ prevalence: 15% = 30,000
× L858R within EGFR+: 45% = 13,500
× Eligible (age, PS, prior Tx): 60% = 8,100
÷ Competing trials: 3 = 2,700 available/year

For N=43, need 43/2,700 = 1.6% capture rate → Achievable
展示患者可及性的计算过程：
美国NSCLC发病率：200,000例/年
× EGFR+患病率：15% = 30,000例
× EGFR+中L858R占比：45% = 13,500例
× 符合入选标准（年龄、体能状态、既往治疗）：60% = 8,100例
÷ 竞争试验数：3 = 每年2,700例可入组患者

目标入组43例，需43/2,700 = 1.6%的捕获率 → 可实现

5. Evidence Grade Every Key Claim

5. 所有关键结论标注证据等级

markdown
EGFR L858R prevalence is 45% of EGFR+ NSCLC [★★★: PMID:12345, large
sequencing study n=1,500]. *Source: ClinVar, COSMIC*
markdown
EGFR L858R在EGFR+ NSCLC中的患病率为45% [★★★: PMID:12345，大型测序研究n=1,500]。*来源：ClinVar、COSMIC*

6. Provide Regulatory Precedent Details

6. 提供监管先例细节

Not just "ORR is accepted" but:
markdown
ORR is FDA-accepted for accelerated approval in NSCLC [★★★: FDA approvals]:
- Osimertinib (2015): ORR 57%, n=411, Tx-resistant EGFR+ (NCT01802632)
- Dacomitinib (2018): ORR 45%, n=452, 1L EGFR+ (NCT01774721)
- [3 more examples]
不仅说明"ORR被认可"，还需：
markdown
ORR是FDA认可的NSCLC加速获批终点 [★★★: FDA获批案例]：
- 奥希替尼（2015年）：ORR 57%，n=411，治疗耐药EGFR+ NSCLC（NCT01802632）
- 达可替尼（2018年）：ORR 45%，n=452，一线治疗EGFR+ NSCLC（NCT01774721）
- [另外3个案例]

7. Address Feasibility Risks Proactively

7. 主动应对可行性风险

For each HIGH risk, provide mitigation:
markdown
Risk: Biomarker screen failure rate >70%
→ Mitigation: Liquid biopsy pre-screening (ctDNA EGFR, 7-day turnaround)
针对每个高风险提供缓解措施：
markdown
风险：生物标志物筛选失败率>70%
→ 缓解措施：液体活检预筛选（ctDNA EGFR检测，7天周转时间）

8. Separate Phase 1 and Phase 2 Components

8. 区分1期与2期组件

If combined Phase 1/2:
  • Phase 1: Safety, DLT, RP2D (N=12-18, 3+3 or BOIN)
  • Phase 2: Efficacy, ORR (N=43, Simon 2-stage)
  • Distinct success criteria for each phase
若为1/2期联合试验：
  • 1期：安全性、DLT、RP2D（N=12-18，3+3或BOIN设计）
  • 2期：疗效、ORR（N=43，Simon两阶段设计）
  • 各阶段有明确的成功标准

Common Pitfalls to Avoid

需避免的常见误区

❌ Don't: Show Tool Outputs to User

❌ 错误：向用户展示工具输出

markdown
undefined
markdown
undefined

BAD

错误示例

OpenTargets returned: { "data": { "id": "EFO_0003060", "name": "non-small cell lung carcinoma" } }
undefined
OpenTargets返回： { "data": { "id": "EFO_0003060", "name": "non-small cell lung carcinoma" } }
undefined

✅ Do: Present Synthesized Report

✅ 正确：展示整理后的报告

markdown
undefined
markdown
undefined

GOOD

正确示例

Disease Background

疾病背景

Non-small cell lung cancer (NSCLC) represents 85% of lung cancers, with ~200,000 new cases annually in the US [★★★: CDC WONDER]. EGFR mutations occur in 15% of Caucasian and 50% of Asian patients [★★★: PMID:23816960]. Source: OpenTargets, ClinVar
undefined
非小细胞肺癌（NSCLC）占肺癌的85%，美国每年约200,000例新发病例 [★★★: CDC WONDER]。EGFR突变在高加索人群中占15%，在亚洲人群中占50% [★★★: PMID:23816960]。来源：OpenTargets、ClinVar
undefined

❌ Don't: Make Unsupported Claims

❌ 错误：做出无依据的断言

markdown
undefined
markdown
undefined

BAD

错误示例

ORR of 60% is expected based on preclinical data.
undefined
基于临床前数据，预期ORR为60%。
undefined

✅ Do: Ground in Evidence

✅ 正确：基于证据断言

markdown
undefined
markdown
undefined

GOOD

正确示例

ORR of 30-40% is projected [★★☆] based on:
  • Similar EGFR TKI (erlotinib): 32% ORR in EGFR+ NSCLC (NCT00949650)
  • Our drug's 2× IC50 potency vs. erlotinib (preclinical) Source: ClinicalTrials.gov, internal data
undefined
预计ORR为30-40% [★★☆]，依据：
  • 同类EGFR TKI（厄洛替尼）：EGFR+ NSCLC中ORR为32%（NCT00949650）
  • 本药物的IC50效力是厄洛替尼的2倍（临床前数据） 来源：ClinicalTrials.gov、内部数据
undefined

❌ Don't: Ignore Geographic Variation

❌ 错误：忽略地域差异

markdown
undefined
markdown
undefined

BAD

错误示例

EGFR L858R prevalence: 7% of NSCLC
undefined
EGFR L858R患病率：NSCLC的7%
undefined

✅ Do: Specify Geography

✅ 正确：明确地域

markdown
undefined
markdown
undefined

GOOD

正确示例

EGFR L858R prevalence [★★★: COSMIC, ClinVar]:
  • Caucasian (US/EU): 6-7% of NSCLC
  • East Asian: 20-25% of NSCLC → Trial site strategy: Include Asian sites for 2× enrollment

---
EGFR L858R患病率 [★★★: COSMIC、ClinVar]：
  • 高加索人群（美国/欧盟）：NSCLC的6-7%
  • 东亚人群：NSCLC的20-25% → 试验中心策略：纳入亚洲中心使入组效率翻倍

---

Output Format Requirements

输出格式要求

Report File Naming

报告文件命名

  • [INDICATION]_trial_feasibility_report.md
  • Example: 
    EGFR_L858R_NSCLC_trial_feasibility_report.md
  • [INDICATION]_trial_feasibility_report.md
  • 示例：
    EGFR_L858R_NSCLC_trial_feasibility_report.md

Section Completeness

章节完整性

All 14 sections MUST be present:
  1. Executive Summary
  2. Disease Background
  3. Patient Population Analysis (with funnel)
  4. Biomarker Strategy
  5. Endpoint Selection & Justification
  6. Comparator Analysis
  7. Safety Endpoints & Monitoring Plan
  8. Study Design Recommendations
  9. Enrollment & Site Strategy
  10. Regulatory Pathway
  11. Budget & Resource Considerations
  12. Risk Assessment
  13. Success Criteria & Go/No-Go Decision (with scorecard)
  14. Recommendations & Next Steps
必须包含全部14个章节：
  1. 执行摘要
  2. 疾病背景
  3. 患者群体分析（含漏斗分析）
  4. 生物标志物策略
  5. 终点指标选择与论证
  6. 对照药分析
  7. 安全性终点与监测计划
  8. 试验设计建议
  9. 入组与中心策略
  10. 监管路径
  11. 预算与资源考量
  12. 风险评估
  13. 成功标准与开展/不开展决策（含评分卡）
  14. 建议与下一步行动

Evidence Grading Required In

需标注证据等级的位置

  • Section 1 (Executive Summary): Key findings
  • Section 4 (Biomarker): Prevalence claims
  • Section 5 (Endpoints): Regulatory precedents
  • Section 6 (Comparator): SOC efficacy data
  • Section 7 (Safety): Toxicity frequencies
  • Section 10 (Regulatory): Approval precedents
  • Section 13 (Scorecard): All dimensions
  • 章节1（执行摘要）：关键发现
  • 章节4（生物标志物）：患病率断言
  • 章节5（终点指标）：监管先例
  • 章节6（对照药）：标准治疗疗效数据
  • 章节7（安全性）：毒性发生率
  • 章节10（监管路径）：获批先例
  • 章节13（评分卡）：所有维度

Feasibility Score Transparency

可行性评分透明度

Show calculation:
markdown
| Dimension | Weight | Raw Score | Weighted | Evidence |
|-----------|--------|-----------|----------|----------|
| Patient Availability | 30% | 8/10 | 24 | ★★★: Epi data |
| Endpoint Precedent | 25% | 9/10 | 22.5 | ★★★: FDA approvals |
| Regulatory Clarity | 20% | 7/10 | 14 | ★★☆: Pre-IND advised |
| Comparator Feasibility | 15% | 9/10 | 13.5 | ★★★: Generic avail |
| Safety Monitoring | 10% | 8/10 | 8 | ★★☆: Class effects |
| **TOTAL** | **100%** | - | **82/100** | **HIGH** |
展示计算过程：
markdown
| 维度 | 权重 | 原始得分 | 加权得分 | 证据 |
|-----------|--------|-----------|----------|----------|
| 患者可及性 | 30% | 8/10 | 24 | ★★★: 流行病学数据 |
| 终点指标先例 | 25% | 9/10 | 22.5 | ★★★: FDA获批案例 |
| 监管清晰度 | 20% | 7/10 | 14 | ★★☆: 建议召开预IND会议 |
| 对照药可行性 | 15% | 9/10 | 13.5 | ★★★: 有仿制药 |
| 安全性监测 | 10% | 8/10 | 8 | ★★☆: 类效应 |
| **总计** | **100%** | - | **82/100** | **高** |

Example Use Cases

示例用例

Use Case 1: Biomarker-Selected Oncology Trial

用例1：生物标志物筛选型肿瘤试验

Query: "Assess feasibility of Phase 2 trial for EGFR L858R+ NSCLC, ORR primary endpoint"
Workflow:
  1. Disease prevalence: 200K NSCLC/year × 15% EGFR+ = 30K
  2. Biomarker: L858R is 45% of EGFR+ → 13.5K/year
  3. Eligible: 60% → 8K/year
  4. Endpoint: ORR accepted (osimertinib precedent)
  5. Comparator: Osimertinib (ORR 57%, generic available)
  6. Feasibility: HIGH (82/100) → RECOMMEND PROCEED
查询："评估EGFR L858R+ NSCLC 2期试验的可行性，主要终点为ORR"
工作流：
  1. 疾病患病率：每年200,000例NSCLC × 15% EGFR+ = 30,000例
  2. 生物标志物：L858R占EGFR+的45% → 13,500例/年
  3. 符合入选标准：60% → 8,000例/年
  4. 终点指标：ORR已获认可（奥希替尼先例）
  5. 对照药：奥希替尼（ORR 57%，有仿制药）
  6. 可行性：高（82/100）→ 建议开展

Use Case 2: Rare Disease Trial

用例2：罕见病试验

Query: "Feasibility of trial in Niemann-Pick Type C (prevalence 1:120,000)"
Workflow:
  1. US prevalence: ~2,750 patients total, ~25 new cases/year
  2. Endpoint challenge: No validated clinical outcome
  3. Orphan drug: QUALIFIED (7-year exclusivity)
  4. Comparator: No approved drugs → single-arm feasible
  5. Enrollment: Multi-year, need ALL US centers
  6. Feasibility: MODERATE (58/100) → CONDITIONAL GO (requires patient registry partnership)
查询："Niemann-Pick Type C（患病率1:120,000）试验的可行性"
工作流：
  1. 美国总患病率：约2,750例患者，年新增约25例
  2. 终点指标挑战：无验证的临床结局指标
  3. 孤儿药：符合资格（7年独占期）
  4. 对照药：无获批药物 → 单臂设计可行
  5. 入组：需多年时间，需覆盖美国所有中心
  6. 可行性：中（58/100）→ 有条件开展（需与患者注册库合作）

Use Case 3: Superiority Trial vs. Standard of Care

用例3：对比标准治疗的优效性试验

Query: "Phase 2b design for new checkpoint inhibitor vs. pembrolizumab in PD-L1 high NSCLC"
Workflow:
  1. Patient availability: 40K PD-L1 high NSCLC/year (HIGH)
  2. Endpoint: ORR for Phase 2b, plan OS for Phase 3
  3. Comparator: Pembrolizumab (ORR 45%, PFS 10mo) - readily available
  4. Design: Randomized 1:1, N=120 (60/arm) for 20% ORR improvement
  5. Feasibility: HIGH (78/100) → RECOMMEND PROCEED
查询："新型检查点抑制剂对比帕博利珠单抗治疗PD-L1高表达NSCLC的2b期设计"
工作流：
  1. 患者可及性：每年40,000例PD-L1高表达NSCLC患者（高）
  2. 终点指标：2b期用ORR，3期计划用OS
  3. 对照药：帕博利珠单抗（ORR 45%，PFS 10个月）- 可及性好
  4. 设计：1:1随机分组，样本量120（每组60例），以检测ORR提升20%
  5. 可行性：高（78/100）→ 建议开展

Use Case 4: Non-Inferiority Trial

用例4：非劣效性试验

Query: "Non-inferiority trial for oral anticoagulant vs. warfarin"
Workflow:
  1. Patient availability: 2M AFib patients, 600K on warfarin (HIGH)
  2. Endpoint: Stroke/SE (FDA-accepted, but requires large N)
  3. Non-inferiority margin: HR <1.5 (FDA guidance)
  4. Sample size: N=5,000+ for 90% power → LARGE trial
  5. Comparator: Warfarin generic, INR monitoring standard
  6. Feasibility: MODERATE (65/100) - large N drives cost and timeline
查询："口服抗凝药对比华法林的非劣效性试验"
工作流：
  1. 患者可及性：2,000,000例房颤患者，600,000例使用华法林（高）
  2. 终点指标：卒中/严重不良事件（FDA认可，但需大样本量）
  3. 非劣效性界值：HR <1.5（FDA指南）
  4. 样本量：需5,000+例以达到90%效力 → 大型试验
  5. 对照药：华法林有仿制药，INR监测标准
  6. 可行性：中（65/100）- 大样本量推高成本与timeline

Use Case 5: Basket Trial (Multiple Cancers, One Biomarker)

用例5：篮式试验（多种癌症，单一生物标志物）

Query: "Basket trial for NTRK fusion+ solid tumors (15 histologies)"
Workflow:
  1. Patient availability: NTRK fusions rare (<1% across cancers) → Broad screening
  2. Biomarker testing: NGS required (FDA-approved FoundationOne CDx)
  3. Endpoint: ORR (precedent: larotrectinib approval, ORR 75%, n=55)
  4. Design: Single-arm, N=15-20 per histology × 5-10 histologies
  5. Regulatory: Tissue-agnostic approval precedent (★★★: pembrolizumab MSI-H)
  6. Feasibility: MODERATE (62/100) - enrollment slow but feasible with broad screening
查询："NTRK融合阳性实体瘤的篮式试验（15种组织学类型）"
工作流：
  1. 患者可及性：NTRK融合罕见（所有癌症中<1%）→ 需广泛筛选
  2. 生物标志物检测：需NGS（FDA获批的FoundationOne CDx）
  3. 终点指标：ORR（先例：拉罗替尼获批，ORR 75%，n=55）
  4. 设计：单臂，每种组织学类型入组15-20例，共5-10种组织学类型
  5. 监管：有组织不可知的获批先例（★★★: 帕博利珠单抗治疗MSI-H）
  6. 可行性：中（62/100）- 入组缓慢但可通过广泛筛选实现

Integration with Other Skills

与其他技能的集成

Works Well With

适配技能

  • 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
  • tooluniverse-drug-research：研究作用机制、临床前数据
  • tooluniverse-disease-research：深入研究疾病生物学
  • tooluniverse-target-research：验证药物靶点、必要性
  • tooluniverse-pharmacovigilance：对照药的上市后安全性
  • tooluniverse-precision-oncology：生物标志物生物学、耐药机制

Complementary Analyses

补充分析

After feasibility report, consider:
  1. Budget model: Use cost estimates to build financial model
  2. Site feasibility surveys: Validate enrollment projections with sites
  3. Regulatory strategy document: Detailed FDA interaction plan
  4. Statistical analysis plan (SAP): Translate design into statistical methods
完成可行性报告后，可考虑：
  1. 预算模型：使用成本估算构建财务模型
  2. 中心可行性调研：与中心验证入组预测
  3. 监管策略文档：详细的FDA沟通计划
  4. 统计分析计划（SAP）：将设计转化为统计方法

Version Information

版本信息

  • Version: 1.0.0
  • Last Updated: February 2026
  • Compatible with: ToolUniverse 0.5+
  • Focus: Phase 1/2 early clinical development
  • 版本：1.0.0
  • 最后更新：2026年2月
  • 兼容版本：ToolUniverse 0.5+
  • 聚焦：1/2期早期临床开发

Support & Resources

支持与资源