Transforming clinical research and development through the exploitation of patient level data sources

通过利用患者级数据源改变临床研究和开发

• 批准号: 105729
• 金额: $ 42.22万
• 依托单位: EXPLORISTICS LTD
• 依托单位国家: 英国
• 项目类别: Study
• 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=105729
• 关键词: Transforming; clinical; research; development; through

With over 90% of new medical treatments undergoing clinical trials failing to reach the market and the cost of developing each new successful drug now estimated to be around US$3 billion, there is an urgent need for the life science industry to reduce their study failure rates to achieve a better return on investment and bring down the cost of medical interventions. Whilst some failure is down to the novel drugs themselves being unsafe or ineffective, some failure is a result of poor design of the clinical trials used to test the effects of new drug candidates. Poor study design is completely avoidable with effective prospective clinical trial planning.Our aim is to reduce clinical trial failure by improving study design and accelerating the process of bringing new treatments to market for patients. To achieve this, this project will identify patient-level data sources and use them to create a digital library for key therapeutic areas such as cancer, respiratory and rare diseases for use within an intelligent software information ecosystem, extending the capability of our existing clinical study planning tool, KERUS Cloud. KERUS Cloud already optimizes clinical study design by allowing researchers to generate study simulations to understand how different factors can affect a study's outcome, so that the best design is chosen. This software has already been shown to improve a real study's success when designed with this tool by 40%. A library of information that is based on real patient data will improve this process further by allowing data for different disease types to also be fed into the simulations generated by KERUS Cloud, making them the most realistic study simulations yet possible. Following on from this, a later development stage will involve adding a machine learning element to the ecosystem that can compare simulations with real outcomes to identify the key factors that account for the variability seen in real trials. This information will then be used by our KERUS Cloud software in designing the next study, improving its chances of success. However, the project outlined here will identify suitable data sources and generate the algorithms required to prepare and clean the data for use in creation of the digital data libraries to be used by KERUS Cloud to inform study simulations. This approach of harnessing real patient-level data and machine learning to inform trial design will revolutionize the process, powering real trials for success.

由于90%以上正在进行临床试验的新药未能上市，每一种新药的开发成本目前估计约为30亿美元，生命科学行业迫切需要降低研究失败率，以实现更好的投资回报，并降低医疗干预成本。虽然一些失败是由于新药本身不安全或无效，但也有一些失败是用于测试新药候选效果的临床试验设计不当的结果。通过有效的前瞻性临床试验计划，不良的研究设计是完全可以避免的。我们的目标是通过改进研究设计和加快将新疗法推向市场的进程来减少临床试验失败。为了实现这一目标，该项目将识别患者级别的数据源，并使用它们为癌症、呼吸道和罕见疾病等关键治疗领域创建数字图书馆，供智能软件信息生态系统使用，从而扩展我们现有临床研究规划工具KERUS Cloud的能力。KERUS Cloud已经通过允许研究人员生成研究模拟以了解不同因素如何影响研究结果来优化临床研究设计，从而选择最佳设计。这个软件已经被证明，当使用这个工具设计时，可以将实际研究的成功率提高40%。基于真实患者数据的信息库将进一步改进这一过程，允许不同疾病类型的数据也被输入到KERUS Cloud生成的模拟中，使它们成为迄今最真实的研究模拟。在此之后，后续的开发阶段将涉及向生态系统添加一个机器学习元素，该元素可以将模拟与实际结果进行比较，以确定导致实际试验中看到的可变性的关键因素。这些信息将被我们的KERUS Cloud软件用于设计下一项研究，从而提高其成功的机会。然而，这里概述的项目将确定合适的数据源，并生成准备和清理数据所需的算法，以用于创建KERUS Cloud用于为研究模拟提供信息的数字数据库。这种利用真实患者水平的数据和机器学习来指导试验设计的方法将彻底改变这一过程，为真正的试验成功提供动力。