MAximizing Sle ThERapeutic PotentiaL by Application of Novel and Stratified approaches (MASTERPLANS)

系统性红斑狼疮（SLE）是一种慢性不治之症，由人体免疫系统攻击关节、皮肤、肾脏和大脑等器官和组织引起。在英国，每2000人中就有一人患有SLE。目前，治疗方法的选择是基于医生的经验和“试错”方法。许多药物需要至少6个月才能显示出最大的改善，在此期间，患者通常需要大剂量的类固醇。人们认识到，狼疮的长期并发症与持续的“抱怨”疾病活动和慢性类固醇使用有关。标准的免疫抑制剂如吗替麦考酚酯（MMF）在临床试验中的反应率为50-60%，更新的、更具靶向的生物疗法如利妥昔单抗、贝利木单抗和依帕珠单抗在试验中的反应率也为40- 60%。然而，临床经验和一些研究表明，某些患者对特定治疗反应良好。因此，分层方法的目标是允许医生最大限度地提高主要缓解率，同时避免/最小化慢性类固醇治疗，并根据我们对个体患者关键疾病过程的理解更好地调整治疗选择。我们的联盟将确定并应用于临床，预测对治疗的良好反应的因素，以使医生能够增加“最有效”疗法的早期使用。这种“分层”方法也将提高未来狼疮新疗法试验的成功率，迄今为止，这种新疗法的记录并不理想。为此，我们将联合收割机结合临床和实验室研究人员的专业知识，并将这些知识与制药行业的研究人员联系起来。我们的重点将是确定预测哪些患者在任何特定的狼疮治疗中表现非常好的因素。我们将首先关注少数药物。当我们证明这种方法效果良好时，我们将能够将这种方法扩展到目前正在开发的其他狼疮治疗方法。我们计划重新分析英国和国际上正在进行的一些大型研究的数据，以及重新分析以前狼疮临床试验的数据。从这些研究中，我们将寻找关键的预测因素;这些因素可能包括狼疮的类型，患者遗传的遗传标记和血液检查结果。为了更详细地研究这个问题，我们计划建立两个平行研究;一个是狼疮皮疹患者，另一个是肾脏受累患者。在这两项研究中，我们将采取活检来检查受影响的组织，并定期采集血液和尿液样本。这些样本将用于详细观察患者接受特定治疗后细胞、蛋白质和其他分子如何随时间变化。将这些详细信息与从大型研究中获得的信息相结合，我们的目标是更好地预测对治疗的良好反应水平。这些信息将用于帮助临床开发设备和/或计算机程序，以帮助为狼疮患者找到最合适和最有效的治疗选择。我们计划在临床试验中测试我们的结果，以检查这种方法是否真的对患者有更多的好处。与此同时，我们将研究狼疮对医疗保健系统的经济成本以及狼疮对个人和社会的成本。我们预计，在正确的时间用正确的药物治疗正确的患者将有助于更好地控制个体患者的狼疮，提高他们的生存率，并减少他们对类固醇治疗的需求。我们还预计，这种方法将显着提高狼疮患者的生活质量，同时也为医疗保健和福利系统提供财政节省。

Systemic Lupus Erythematosus (SLE) is a chronic incurable disease caused by a person's immune system attacking organs and tissues such as the joints, skin, kidneys and brain. SLE affects one in 2000 individuals in the UK. Currently, treatment is selected based on a doctor's experience and on a 'trial and error' approach. Many agents take at least 6 months to show maximum improvement during which patients often require large steroid doses. It is recognised that long-term complications of lupus are associated with both ongoing 'grumbling' disease activity and chronic steroid use. Standard immunosuppressives such as mycophenolate mofetil (MMF) have had response rates of 50-60% in clinical trials and newer, more targeted' biological therapies such as rituximab, belimumab and epratuzumab also report response rates in trials of 40-60%. Clinical experience and a number of studies have however suggested that there are certain patients who respond very well to particular treatments. The goals of a stratified approach therefore would be to allow doctors to maximise major response rates whilst avoiding / minimising chronic steroid therapy and aligning therapy selection better with our understanding of the key disease process in an individual patient. Our consortium will identify and apply in the clinic, factors that predict excellent response to therapy to allow doctors to increase the early use of 'most effective' therapies. This 'stratified' approach will also improve the success of future trials of new treatments for lupus which to date has had a suboptimal record. To do this we will combine expertise from clinical and laboratory-based investigators, and link these with researchers working in the pharmaceutical industry. Our focus will be to identify factors that predict which patients do extremely well on any particular lupus treatment. We will start by focusing on a small number of drugs. As we demonstrate that this approach works well, we will be able to expand this method to other lupus treatments which are currently in development. We plan to re-analyse data already available from a number of large studies ongoing in the UK and internationally as well as to re-analyse data from previous lupus clinical trials. From these studies we will look for key predictive factors; such factors may include the type of lupus, genetic markers that the patient inherited and results of blood tests . In order to examine this question in even more detail, we plan to set up two parallel studies; one in patients with skin rashes due to lupus and one in patients with kidney involvement. In both these studies we will take biopsies to examine the affected tissue and also take blood and urine samples on a regular basis. These samples will be used to look in detail at how cells, proteins and other molecules change over time after a patient has been treated with a particular therapy. Combining this detailed information with the information gained from the larger studies we aim to better predict excellent levels of response to treatment.This information will be used to help develop devices and/or computer programmes for the clinic to help find the most appropriate and effective treatment choices for patients with lupus. We plan to test our results in a clinical trial to examine whether this approach actually has more benefit for patients. Running alongside this, we will study the economic costs of lupus to the health care system as well as the costs of lupus to the individual and society. We anticipate that treating the right patient with the right drug at the right time will help control lupus better in individual patients, improve their survival rates and reduce their needs for need for steroid treatment. We also anticipate that this approach will significantly improve the quality of life of patients with lupus whilst also providing financial saving for the healthcare and benefits system.