Personalized Response Indicators of SSRI Effectiveness in Major Depression

描述（由申请人提供）：重度抑郁症（MDD）是一种常见的精神疾病，对社会和患者个人都有很高的成本。高成本的一个原因是，大多数患者在通过反复试验确定一种有益的药物之前，都要经历漫长而最终不成功的抗抑郁药物试验。如果一种生物标志物能够在治疗过程的早期确定一种特定的抗抑郁药是否可能导致反应、缓解或治疗失败，那么护理将得到改善。医生可以迅速改变治疗方法，使用生物标志物显示可能对患者有帮助的抗抑郁药。我们提出了一项盲法和对照可行性研究，以评估一种实用的生物标志物，根据抗抑郁药物治疗第一周的数据预测结果。我们已经确定定量脑电图（QEEG）的变化出现在选择性血清素再摄取抑制剂（SSRIs）治疗过程的早期，似乎可以预测后来的反应和缓解。在这个名为PRISE-MD（“重度抑郁症中SSRI有效性的个性化反应指标”）的项目中，我们建议通过一项前瞻性对照试验，使用基于生物标志物状态分配的治疗方法，从生物标志物发现进入开发阶段。我们的具体目标是：(1)检查使用基于qeeg的生物标志物指导治疗模型前瞻性分配抗抑郁药物治疗的效用，以及(2)通过包括临床，社会人口统计学或遗传因素来评估模型预测准确性的增强。我们将检验特定的假设：(1)QEEG-生物标志物引导的SSRI治疗将产生更好的反应率，而不考虑生物标志物的状态；(2)通过纳入临床、社会人口统计学和遗传预测因素，模型的预测准确性将得到提高。共有172名成年重度抑郁症患者将接受为期一周的SSRI艾司西酞普兰（ESC）药理学挑战，然后通过分层随机分配进行双盲治疗：一半生物标志物阳性（即预计使用ESC缓解）的受试者将继续使用ESC，一半将接受安非他酮XL (BUP)，这是一种具有代表性的非SSRI抗抑郁药，是临床合理的替代方案。同样，一半生物标志物阴性（即预测ESC无反应）的受试者将继续使用ESC，一半将接受BUP。共同主要结果将是接受SSRI用于生物标志物（ESC）的受试者在7周内对17项汉密尔顿抑郁评定量表和30项抑郁症状量表的反应（改善50%）。我们将检验我们的假设(1)通过检查生物标志物引导治疗的结果，(2)通过检查纳入非生理因素的预测模型的改进。数据安全监测委员会将监测生物标志物在预测治疗反应方面的效用，如果生物标志物被证明是对ESC反应或无反应的高度准确的预测者，将有权提前停止试验。将我们的模型应用于治疗选择的初步评估将支持重度抑郁症个性化护理的进一步发展。

DESCRIPTION (provided by applicant): Major depressive disorder (MDD) is a common psychiatric illness with high cost to society and individual patients. One reason for the high cost is that most patients endure lengthy and ultimately unsuccessful empiric antidepressant trials before a beneficial medication is identified by trial-and-error. Care would be improved if a biomarker could determine, early in the course of treatment, whether a particular antidepressant would likely lead to response, remission, or treatment failure. Physicians could rapidly change treatments to an antidepressant which the biomarker indicated would be likely to help the patient. We propose a blinded and controlled feasibility study to evaluate a practical biomarker for predicting outcome based on data from the first week of antidepressant treatment. We have identified quantitative electroencephalographic (QEEG) changes that emerge early in the course of treatment with selective serotonin reuptake inhibitors (SSRIs) that appear to predict later response and remission. In this project, entitled PRISE-MD ("Personalized Response Indicators of SSRI Effectiveness in Major Depression"), we propose to move from biomarker discovery to the development phase with a prospective, controlled trial using treatments assigned based on biomarker status. Our specific aims are (1) to examine the utility of prospectively assigning antidepressant treatment using a QEEG-based biomarker guided treatment model, and (2) to evaluate the enhancement to the predictive accuracy of the model by including clinical, socio-demographic, or genetic factors. We will test specific hypotheses: (1) QEEG- biomarker-guided SSRI treatment will yield better rates of response than treatment irrespective of biomarker status; and (2) the predictive accuracy of the model will be enhanced by including clinical, socio-demographic, and genetic predictors. A total of 172 adults with MDD will receive a one-week pharmacologic challenge with the SSRI escitalopram (ESC), and then be assigned to double-blind treatment via stratified randomization: half of subjects with a positive biomarker (i.e., predicted to remit with ESC) will continue on ESC, and half will receive bupropion XL (BUP), a representative non-SSRI antidepressant which is a clinically-reasonable alternative. Similarly, half of subjects with a negative biomarker (i.e., predicted non-response with ESC) will continue with ESC and half will receive BUP. The co-primary outcomes will be 7-week response (50% improvement) on the 17-item Hamilton Depression Rating Scale and the 30-item Inventory of Depressive Symptomatology in subjects receiving the SSRI used for the biomarker (ESC). We will test our hypotheses (1) by examining outcomes from biomarker-guided treatment, and (2) by examining improvement in the prediction model from incorporating non-physiologic factors. A Data Safety Monitoring Board will monitor the utility of the biomarker in predicting treatment response and will have authority to halt the trial early if the biomarker proves to be a highly accurate predictor of response or non-response to ESC. This preliminary evaluation of applying our model to treatment selection will support further developments in the personalization of care for MDD. Public Health Relevance: Major depressive disorder (MDD) is a common psychiatric illness with high cost to society and individual patients worldwide, yet treatments chosen under current best practices frequently do not lead to recovery with the initial medication tried; this yields prolonged symptomatic suffering, functional disability, increased risk of relapse, and risk that individuals will abandon treatment efforts altogether. Better outcomes might be possible if a biomarker could guide clinicians in selecting among treatments. This project will examine biomarker predictions of outcome based on quantitative electroencephalographic (QEEG) features that change during a week of exposure to an SSRI antidepressant medication; by allowing clinicians to use treatments more effectively, the use of physiologic biomarker information for guidance could have a significant impact on the management of MDD.