Quantitative decision-making in clinical drug development incorporating biomarkers

结合生物标志物的临床药物开发的定量决策

• 批准号: 2106296
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2106296
• 关键词: Quantitative; decision; making; clinical; drug

This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address important applied biomedical research questions in priority areas aligned with industry. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice. Project:Development of new drugs and therapies must pass three clinical milestones, Phase 1 (evaluation of safety), Phase 2 (initial evaluation of efficacy), and confirmatory Phase 3 trials. Each stage of this pipeline provides useful decision points to critically evaluate the accumulated data and make important decisions using sound quantitative methods. Given the high attrition rate, especially in early phases, it is paramount to make correct Go or No-Go decisions based on metrics relevant to that particular stage of development. Traditionally, these have relied on a hypothesis testing framework involving the use of statistical tests to achieve the relevant thresholds of significance based on p-values. However, given the high attrition rate especially in oncology, focusing on p-values alone can be counter-intuitive. Recently, alternative model-based approaches have been proposed based on using the upper and lower confidence intervals of the treatment effect such as the Target Value (TV) being the desired effect whereas the Lower Reference Value (LRV) representing the smallest clinically meaningful effect. Such methods provide a more robust categorisation of the strength of evidence of observed results into 3 outcomes: Go, No-Go decision and Consider zone. The proposed project will aim to formulate a generalisation of the decision-making criteria by further incorporating a biomarker that splits the patient population into two distinct subgroups. This will be achieved by simulation of a Phase II biomarker stratified oncology trial, whose operating characteristics will be studied in detail. This should improve quantitative decision-making using a variety of clinical endpoints and statistical designs across the drug development pipeline.

这一由 MRC 资助的博士培训合作伙伴关系 (DTP) 将尖端的分子和分析科学与数据分析中的创新计算方法结合在一起，使学生能够解决与行业相关的优先领域中的重要应用生物医学研究问题。这是一个为期 4 年的课程，第一年涉及一系列教学模块和两个基于实验室的研究项目，最终获得跨学科生物医学研究硕士学位。前两个学期包括精选的教学模块，使学生能够在多学科科学方面打下坚实的基础。学生还参加由分子、细胞和组织动力学、微生物学和感染、应用生物医学技术以及人工智能和数据科学领域的学术和行业专家主持的一系列大师班。在第三学期和夏季学期，学生在自己选择的实验室进行两个为期十一周的研究项目。项目：新药和新疗法的开发必须通过三个临床里程碑，即第一阶段（安全性评估）、第二阶段（疗效初步评估）和验证性第三阶段试验。该流程的每个阶段都提供有用的决策点，以批判性地评估积累的数据并使用合理的定量方法做出重要决策。鉴于高流失率，尤其是在早期阶段，根据与特定开发阶段相关的指标做出正确的进行或不进行决策至关重要。传统上，这些依赖于假设检验框架，涉及使用统计检验来实现基于 p 值的相关显着性阈值。然而，考虑到高流失率，尤其是在肿瘤学领域，仅关注 p 值可能是违反直觉的。最近，基于使用治疗效果的上限和下限置信区间提出了基于模型的替代方法，例如目标值（TV）是期望的效果，而下参考值（LRV）代表最小的临床有意义的效果。此类方法将观察结果的证据强度更可靠地分类为 3 个结果：继续、不继续决策和考虑区域。拟议的项目旨在通过进一步纳入将患者群体分为两个不同亚组的生物标志物来制定决策标准的概括。这将通过模拟 II 期生物标志物分层肿瘤学试验来实现，该试验的操作特征将得到详细研究。这应该可以利用整个药物开发流程中的各种临床终点和统计设计来改进定量决策。