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，无论是治疗作用还是不良事件。遗传变异是许多药物中这种差异的重要原因，旨在理解这是药物遗传学的科学。最近使用当前药物遗传学开处方指南的英国研究估计，将近600万英国初级保健患者有药物相互作用。例如，氯吡格雷是英国最常用的药物，可降低中风风险。它要求CYP2C19肝酶将其代谢为活性形式，以使其最大程度地发挥作用。然而，长期以来，众所周知，大约30％的人口在其CYP2C19基因区域中具有遗传变异，这会影响其代谢的能力。因此，在初级保健设置中处方时，它对某些人而不是对其他人的效果很好。基于观察数据的分析的存在方法可能难以消除药物本身的作用给药的疾病的影响，这一问题称为“通过适应性混淆”。例如，服用氯吡格雷的人比没有中风的人更有可能经历中风，但这并不意味着氯吡格雷真正增加了中风风险。随机临床试验（RCT）为遗传亚组中测试药物的结果提供了可靠的估计，但通常是在没有多种疾病且不代表常规临床实践治疗的选择性患者同类中进行的。 RCT通常也太小，持续时间太短，无法确定不良事件或评估长期结局。该建议涉及开发和扩展基于遗传学的方法，以分析从观察性电子病历到（a）发现遗传变异和患者特征的积累的数据，这些数据影响了对治疗的反应以及（b）以量化个性化处方的人口益处。例如，使用来自2017年至2017年链接的初级保健数据的超过200,000名英国生物银行参与者的数据显示，如果患有遗传不利的CYPC219基因型的人可以通过剂量修饰或转换为替代治疗，则可能会减少13.2％的中风数量。我们的项目将建立在最近提出的决策框架的基础上，该决策框架被称为“三角剖分”（Twist），这是由学术团队开发的研究（Twist）。该方法旨在使用一系列依赖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结合估计以提供最有效，最可靠的估计。该方法是合理的，已经通过了严格的同伴审查过程来获得尊敬的科学期刊。它表现出了真正的希望，但需要进一步的研究才能实现其全部潜力，这是我们提案的基础。我们的工作将通过回答有关高血压和心血管疾病的最佳治疗的问题来激发其动机。未来的健康研究和许多大规模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将在未来几年中在本地和国际上具有广泛的应用