Development of patient-centric digital biobank for world-leading chronic disease research: using AI to uniquely map genomic, clinical and longitudinal health data

开发以患者为中心的数字生物库，用于世界领先的慢性病研究：使用人工智能独特地绘制基因组、临床和纵向健康数据图谱

• 批准号: 67707
• 金额: $ 40.16万
• 依托单位: SANO GENETICS LIMITED
• 依托单位国家: 英国
• 项目类别: Study
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=67707
• 关键词: Development; patient; centric; digital; biobank

Public descriptionThe COVID-19 crisis has thrown into sharp focus the global inability to predict personalised disease progression i.e. why do some people suffer more severely than others or respond differently to treatments. The reason is complex and due to a combination of genetic, demographic, and phenotypic factors. For nearly every common and rare disease, there is no central repository nor tools/mechanisms for researchers to quickly access/harmonise/interrogate this data. This drastically reduces our ability to accurately identify those most at risk and to develop new medicines/treatments.To address this and tap into a fast-growing £50bn+ market(alliedmarketresearch:2018), we intend to build a patient-centric, chronic disease digital biobank of genomic, clinical, and longitudinal health data.As the market for personal genetic sequencing- both public and private (£19.2bn by 2025;19% CAGR-Allied Market Research:2017)- continues to grow, as does the volume of personal health data stored, which in turn opens up new opportunities for drug discovery, quicker access to target patient groups and reducing patient recruitment costs in medical research.Artificial Intelligence and Machine Learning in particular have the potential to dramatically speed up the discovery of novel biomarkers for patient stratification and novel drug targets using multiple data-types including genomic, clinical, and patient-generated longitudinal data as input.Medicines supported by genomics are 2-3x more likely to succeed, but accessing the right data is a major challenge. Using AI and Machine Learning approaches to statistical genetics, combined with big-data architectures, Sano Genetics, in partnership with Patients Know Best, C4X Discovery and BenevolentAI will develop the patient-centric digital biobank platform of genomic, clinical, and longitudinal health data (TRL6) that will allow users and to quickly identify phenotype-genotype patterns, in order to discover novel biomarkers to stratify patients and to discover novel drug targets and compounds.To help build the platform, we have chosen to initially focus on Ulcerative Colitis(a form of Inflammatory Bowel Disease)- a market worth £17.4bn by 2026, that affects over 8m globally and that has significant available data- before looking to scale the platform to address the same data-access/analysis challenge in other chronic conditions and emerging threats such as COVID-19, where our ability to predict the diseases impact and severity in patients has created significant challenges in planning, resources and policy.

公众警告COVID-19危机使全球无法预测个性化疾病进展的问题成为焦点，即为什么有些人比其他人遭受更严重的痛苦或对治疗的反应不同。原因是复杂的，是遗传、人口统计学和表型因素的综合作用。对于几乎每一种常见和罕见疾病，研究人员都没有中央存储库或工具/机制来快速访问/协调/查询这些数据。这大大降低了我们准确识别风险最大人群和开发新药/治疗方法的能力。为了解决这个问题并进入快速增长的500亿英镑以上的市场（alliedmarketresearch：2018），我们打算建立一个以患者为中心的慢性病数字生物库的基因组，临床和纵向健康数据。作为个人基因测序的市场-公共和私人（到2025年为192亿英镑; CAGR-Allied Market Research：2017年）-继续增长，存储的个人健康数据量也在增长，这反过来又为药物发现开辟了新的机会，更快地接触目标患者群体，降低医学研究中的患者招募成本。人工智能和机器学习尤其有潜力大大加快发现用于患者分层的新型生物标志物和使用多种数据类型（包括基因组，临床，由基因组学支持的药物成功的可能性高出2- 3倍，但访问正确的数据是一个重大挑战。使用人工智能和机器学习方法进行统计遗传学，结合大数据架构，佐野遗传学与Patients Know Best，C4 X Discovery和BenevolentAI合作，将开发以患者为中心的基因组，临床和纵向健康数据（TRL 6）数字生物库平台，使用户能够快速识别表型-基因型模式，为了发现新的生物标志物来对患者进行分层，并发现新的药物靶点和化合物。为了帮助建立平台，我们选择首先关注溃疡性结肠炎（一种炎症性肠病）-到2026年市场价值174亿英镑，影响全球超过800万人，并拥有大量可用数据-在寻求扩展平台以解决相同的数据访问/在其他慢性疾病和新出现的威胁（如COVID-19）中，我们预测疾病对患者的影响和严重程度的能力在规划、资源和政策方面带来了重大挑战。