Pharmacometrics Modelling-Based Dose-Finding and Synthetic Control Arms Established Using Longitudinal Analyses - Numerical analysis - Mathematical sc

使用纵向分析建立基于药理学模型的剂量寻找和合成控制臂 - 数值分析 - 数学 sc

• 批准号: 2444835
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444835
• 关键词: Pharmacometrics; Modelling; Based; Dose; Finding

Advancements in the field of oncology are rapidly changing the life expectancy of patients across many cancers. Therapeutic interventions are gradually shifting, from short intermittent cycles of cytotoxic drugs administered to large populations until progression or death, to the life-long chronic treatment of combinations of targeted and/or immunomodulating molecules in smaller populations. These advances raise several challenges in oncology drug development that will be tackled in this project: 1. Do pharmacometrics based dose findings beyond the Maximum Tolerated Dose (MTD), balancing longer-term efficacy and safety, provide a feasible and cost-effective approach to clinical drug development in oncology? Using a systems modelling estimation and optimisation based approach several dose-finding strategies in oncology drug development will be explored. This will be based on virtual development programmes, but the settings will reflect what is currently seen across oncology. The traditional MTD-paradigm will be compared with more extensive dose-ranging, longer-term efficacy and safety evaluations, with decision-making based on the longitudinal analysis. This will be tied in with an analysis of costs to evaluate the feasibility and added value. 2. Can longitudinal analyses of historical clinical trial data and real-world datasets be leveraged to establish synthetic control arms for phase I/II trials to increase efficiency and minimise costs in oncology clinical drug development? Several internal AZ datasets and external databases will be identified and used for longitudinal modelling, linking drug exposure, biomarkers, genetics and other information to relevant clinical trial outcomes. These efforts will be aligned with ongoing development projects and the focus will be on the application of existing modelling and statistical approaches, facilitation decision-making, and preventing negative pivotal trials.

肿瘤学领域的进步正在迅速改变许多癌症患者的预期寿命。治疗干预措施正在逐渐转变，从对大量人群施用细胞毒性药物的短间歇周期直至进展或死亡，转变为在较小人群中使用靶向和/或免疫调节分子组合的终身慢性治疗。这些进展为肿瘤药物开发提出了几个挑战，这些挑战将在本项目中解决：基于药物计量学的最大耐受剂量（MTD）以外的剂量发现，平衡长期疗效和安全性，是否为肿瘤临床药物开发提供了一种可行且具有成本效益的方法？使用系统建模估计和优化为基础的方法，在肿瘤药物开发几个剂量发现策略将被探索。这将基于虚拟开发项目，但设置将反映目前在肿瘤学领域所看到的情况。传统的mtd模式将与更广泛的剂量范围、更长期的疗效和安全性评估进行比较，决策将基于纵向分析。这将与成本分析联系在一起，以评估可行性和附加值。2. 是否可以利用历史临床试验数据和真实世界数据集的纵向分析来建立I/II期试验的合成控制臂，以提高肿瘤临床药物开发的效率并最大限度地降低成本？将确定几个内部AZ数据集和外部数据库，并将其用于纵向建模，将药物暴露、生物标志物、遗传学和其他信息与相关临床试验结果联系起来。这些努力将与正在进行的发展项目保持一致，重点是应用现有的建模和统计方法，促进决策，防止不利的关键试验。