Surrogate Outcomes: Modelling Uptake in Clinical Trials and Deriving From Multiple Biomarkers

阿尔茨海默病（AD）是一种慢性、进行性神经退行性疾病，是痴呆的头号病因。随着全球人口年龄的持续增长，更多的人将受到这一疾病的影响。AD的威胁是一个全球性危机，研究需要解决治疗问题，并最终解决预防问题。目前，还没有针对阿尔茨海默病的疾病修饰疗法（DMT），但对支撑AD的病理生理学的表征表明了潜在DMT的可能靶点。然而，试验主要集中在通过抗AB单克隆抗体靶向淀粉样蛋白B（AB）途径。然而，目前的研究表明，越来越需要针对非淀粉样蛋白AD病理学的治疗与抗淀粉样蛋白治疗相结合。使用替代结果可以替代真实的临床结果，如患者的感觉，功能或死亡率。替代结果并不直接衡量治疗本身的临床获益，而是用于预测临床获益。临床上，生物标志物已被用作许多其他疾病药物治疗疗效的替代结果，但目前还没有明确的生物标志物可替代阿尔茨海默病药物治疗疗效。使用UKHRA的数据对替代结果的使用趋势进行建模，并使用自然语言处理/机器学习技术自动提取元数据。先进的统计和分析方法，以改善和了解替代结果评估的过程。以及替代结果方法的使用。2.使用来自公开访问的数据存储库的数据作为一种资源，使用已建立的统计方法（如基于信息论的方法）评估潜在的替代结果，以获得更可行的结果。将来自可穿戴设备的联合收割机数据与高危人群的临床结果相结合，并通过机器学习技术进行降维，以创建高度预测个人生活质量结果的多变量生物标志物。4.期待着收集数据和多维度的新方法。

Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder and the number one cause of dementia. As the global age of the population continues to increase, a greater number of people will be impacted by the disease. The threat of AD is a global crisis and research needs to address treatment and ultimately, prevention. At present, there are no disease-modifying therapies (DMT) for Alzheimer's disease but characterisation of the pathophysiology underpinning AD suggests possible targets for potential DMTs. However, trials have mainly focused on targeting the amyloid-B (AB) pathway via anti-AB monoclonal antibodies. Yet, present research depicts the increasing need for treatments targeting non-amyloid AD pathologies in combination with anti-amyloid therapies.The use of surrogate outcomes allows for a substitution for true clinical outcomes such as how a patient feels, functions or mortality. A surrogate outcome does not directly measure the clinical benefit of the therapy in and of itself, but is used to predict that clinical benefit.Clinically, biomarkers have been used as surrogate outcomes for the efficacy of drug treatments in many other diseases, yet, there are currently no biomarkers that are definite surrogates of drug treatment efficacy in Alzheimer's disease.Therefore, this project aims to: 1. Model trends in use of surrogate outcomes using data from the UKHRA and extracting meta-data automatically using natural language processing/machine learning techniques. Advanced statistics and analytical methods to improve and learn about the process of surrogate outcome evaluations. As well as the uptake of surrogate outcome methods. 2. Use data from publicly accessible data repositories as a resource to evaluate potential surrogate outcomes for a more feasible outcome using established statistical methods such as those based on information theory.3. Combine data from wearables and clinical outcomes for at risk individuals and use dimension reduction via machine learning techniques, to enable creation of multivariable biomarkers that are highly predictive of the outcome of individuals' quality of life.4. Looking forward to new ways of gathering data and in multiple dimensions.