Extending the Triangulation Within a Study (TWIST) framework to improve real-world evaluation of genetically driven medication response

扩展研究内三角测量 (TWIST) 框架，以改善对遗传驱动药物反应的现实评估

• 批准号: MR/X011372/1
• 负责人: Jack Bowden
• 金额: $ 63.21万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX011372%2F1
• 关键词: Extending; Triangulation; Within; Study; TWIST

People vary greatly in their responses to medicines, both for therapeutic effects and adverse events. Genetic variation is an important contributor to such variation in many drugs and the science aimed at understanding this is termed pharmacogenetics. A recent UK study using current pharmacogenetic prescribing guidelines estimated that nearly six million UK primary care patients risk drug-gene interactions. For example, Clopidogrel is the most commonly used drug in the UK to reduce the risk of stroke. It requires the CYP2C19 liver enzyme to metabolise it into an active form so that works to its fullest extent. However, it has long been known that about 30% of the population have genetic variants in their CYP2C19 gene region which impacts their ability to metabolise it. When prescribed in a primary care setting it consequently works well for some and not for others.Existing methods based on the analysis of observational data can struggle to disentangle the effects of the disease for which the medication was given from the effects of drug itself, a problem termed `confounding by indication'. For example, people who take Clopidogrel are more likely to experience a stroke than those who do not, but this does not mean Clopidogrel truly increases stroke risk. Randomised clinical trials (RCTs) provide robust estimates of outcomes to tested drugs within genetic subgroups, but are typically carried out in selective patient cohorts that are free from multimorbidity and not representative of those treated in routine clinical practice. RCTs are also often too small and too short in duration to identify adverse events or assess longer-term outcomes. This proposal involves developing and extending genetics-based methods to analyse the accumulating wealth of data from observational electronic medical records to (a) discover genetic variants and patient characteristics that influence response to treatment and (b) to quantify the population benefits of personalised prescribing. For example, using data from over 200,000 UK Biobank participants with linked primary care data up to 2017 revealed a potential 13.2% reduction in the total number of strokes is possible if those with a genetically unfavourable CYPC219 genotype could experience the full effect of Clopidogrel, through either dose modification or switching to an alternative therapy. Our project will build on a recently proposed decision framework termed `Triangulation Within A Study (TWIST) developed by the academic team. This method seeks to address a research question using a range of estimation strategies that are reliant on different sets of assumptions. Statistical tests and expert knowledge is then used to decide how best to combine the estimates to provide the most efficient and reliable estimate. The method is sound, having passed a rigorous peer review process for a respected scientific journal. It shows real promise but requires further research investment to realise its full potential, which is the basis of our proposal.Our work will be primarily motivated by answering questions about the optimal treatment of patients with hypertension and cardiovascular disease, but has the potential to be applied widely across a whole spectrum of diseases, including patients with multimorbidity.Enabling pharmacogenetics is one of the core aims of the UK Government's project to genotype 5 million people in the 'Our Future Health' study and there are many large-scale databases internationally linking genotyping to electronic clinical records. Therefore the methods and tools we will develop will have wide application locally and internationally in future years

人们对药物的反应差异很大，无论是治疗效果还是不良反应。遗传变异是许多药物中这种变异的重要因素，旨在了解这一点的科学被称为药物遗传学。英国最近一项使用当前药物遗传处方指南的研究估计，近600万英国初级保健患者存在药物-基因相互作用的风险。例如，氯吡格雷是英国最常用的降低中风风险的药物。它需要CYP2C19肝酶将其代谢成一种活性形式，以便最大限度地发挥作用。然而，人们早就知道，大约30%的人群在他们的CYP2C19基因区域有遗传变异，这会影响他们代谢它的能力。当在初级保健环境中开药时，它对一些人很有效，对另一些人则不起作用。现有的基于观察数据分析的方法很难将给药的疾病的影响与药物本身的影响分开，这一问题被称为“适应症混淆”。例如，服用氯吡格雷的人比没有服用氯吡格雷的人更有可能中风，但这并不意味着氯吡格雷确实增加了中风的风险。随机临床试验(RCT)对基因亚组内测试药物的结果提供了强有力的估计，但通常是在没有多发病的选择性患者队列中进行的，并且不能代表那些在常规临床实践中接受治疗的患者。随机对照试验的规模往往太小，持续时间太短，无法识别不良事件或评估较长期的结果。这项建议涉及开发和推广基于遗传学的方法，以分析从观察性电子病历中积累的丰富数据，以(A)发现影响治疗反应的基因变异和患者特征，以及(B)量化个性化处方对人群的好处。例如，使用截至2017年的20多万名英国生物库参与者的相关初级保健数据显示，如果那些具有遗传不利的CYPC219基因的人可以通过调整剂量或切换到替代疗法来体验氯吡格雷的全部效果，中风总数可能减少13.2%。我们的项目将建立在学术团队最近提出的名为“研究中的三角测量”(TWIST)的决策框架的基础上。这种方法寻求使用一系列依赖于不同假设集的估计策略来解决研究问题。然后，使用统计测试和专家知识来决定如何最好地组合这些估计，以提供最有效和最可靠的估计。这种方法是可靠的，已经通过了一家受人尊敬的科学期刊的严格同行审查程序。它展示了真正的前景，但需要进一步的研究投资来实现其全部潜力，这是我们提议的基础。我们的工作将主要通过回答高血压和心血管疾病患者的最佳治疗问题来推动，但有可能被广泛应用于包括多发性疾病患者在内的所有疾病。启用药物遗传学是英国政府项目的核心目标之一，该项目将500万人的基因分型纳入《我们的未来健康》研究，国际上有许多大型数据库将基因分型与电子临床记录联系起来。因此，我们将开发的方法和工具在未来几年将在国内和国际上得到广泛应用