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

在治疗效果和不良事件方面，人们对药物的反应差别很大。遗传变异是许多药物变异的重要因素，旨在理解这一点的科学被称为药物遗传学。英国最近的一项研究使用了目前的药物遗传学处方指南，估计有近600万英国初级保健患者面临药物-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肝酶将其代谢成活性形式，以最大限度地发挥作用。然而，人们早就知道，大约30%的人在他们的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处方时，结果对一些人有效，对另一些人无效。现有的基于观察数据分析的方法很难将药物治疗的疾病的影响与药物本身的影响区分开来，这是一个被称为“指征混淆”的问题。例如，服用氯吡格雷的人比不服用的人更容易中风，但这并不意味着氯吡格雷真的会增加中风的风险。随机临床试验（RCTs）为基因亚群内的测试药物提供了可靠的结果估计，但通常是在没有多病的选择性患者队列中进行的，并且不能代表常规临床实践中治疗的患者。随机对照试验也往往规模太小，持续时间太短，无法确定不良事件或评估长期结果。该建议涉及开发和扩展基于遗传学的方法，以分析来自观察性电子病历的积累的大量数据，以便(a)发现影响治疗反应的遗传变异和患者特征，以及(b)量化个性化处方的人口效益。例如，使用截至2017年的20多万英国生物银行参与者的相关初级保健数据显示，如果遗传不利的CYPC219基因型患者能够通过剂量调整或切换到替代疗法来体验氯吡格雷的全部效果，则中风总数可能减少13.2%。我们的项目将建立在一个最近提出的决策框架上，该框架被称为“研究中的三角测量”（TWIST），由学术团队开发。这种方法试图使用一系列依赖于不同假设集的估计策略来解决一个研究问题。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。该方法是可靠的，已经通过了一家受人尊敬的科学杂志严格的同行评审过程。它显示出真正的希望，但需要进一步的研究投资来实现其全部潜力，这是我们建议的基础。我们的工作将主要是为了回答高血压和心血管疾病患者的最佳治疗问题，但具有广泛应用于整个疾病谱的潜力，包括多病患者。启用药物遗传学是英国政府在“我们未来的健康”研究中对500万人进行基因分型的项目的核心目标之一，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将在未来几年在国内和国际上得到广泛应用

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