Personalized Response Indicators of SSRI Effectiveness in Major Depression

描述（由申请人提供）：重性抑郁症（MDD）是一种常见的精神疾病，对社会和患者个体都有很高的成本。高成本的一个原因是，在通过试错法确定有益的药物之前，大多数患者经历了漫长的、最终不成功的经验性抗抑郁药试验。如果生物标志物可以在治疗过程的早期确定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，使用一种生物标志物表明可能对患者有帮助的抗抑郁药。我们提出了一个盲法和对照的可行性研究，以评估一个实用的生物标志物预测结果的基础上，从第一周的抗抑郁药治疗的数据。我们已经确定了定量脑电图（QEEG）的变化，出现在早期的治疗过程中与选择性5-羟色胺再摄取抑制剂（SSRIs），似乎预测以后的反应和缓解。在这个项目中，题为PRISE-MD（“SSRI有效性在抑郁症中的个性化反应指标”），我们建议从生物标志物发现转移到开发阶段，采用基于生物标志物状态分配的治疗进行前瞻性对照试验。我们的具体目标是（1）使用基于QEEG的生物标志物指导治疗模型检查前瞻性分配抗抑郁治疗的效用，以及（2）通过纳入临床，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治疗将产生比治疗更好的应答率，无论生物标志物状态如何;（2）通过纳入临床、社会人口统计学和遗传预测因子，模型的预测准确性将得到提高。共有172名MDD成人患者将接受为期一周的SSRI艾司西酞普兰（ESC）药理学挑战，然后通过分层随机化分配至双盲治疗组：一半生物标志物阳性的受试者（即，预计与ESC缓解）将继续ESC，一半将接受安非他酮XL（BUP），一种代表性的非SSRI抗抑郁药，这是一种临床合理的替代品。类似地，具有阴性生物标志物的一半受试者（即，预测ESC无应答）将继续ESC治疗，一半将接受BUP治疗。共同主要结局将是接受用于生物标志物（ESC）的SSRI的受试者在17项汉密尔顿抑郁评定量表和30项抑郁症状量表上的7周缓解（改善50%）。我们将（1）通过检查生物标志物指导治疗的结果，（2）通过检查纳入非生理因素的预测模型的改善来检验我们的假设。数据安全性监查委员会将监测生物标志物在预测治疗应答方面的效用，如果生物标志物被证明是ESC应答或无应答的高度准确预测因子，则有权提前停止试验。将我们的模型应用于治疗选择的初步评估将支持MDD个性化护理的进一步发展。 公共卫生相关性：重度抑郁症（MDD）是一种常见的精神疾病，对全世界的社会和患者个体都有很高的成本，但根据目前的最佳实践选择的治疗方法通常不会导致最初尝试药物的康复;这会导致长期的症状痛苦，功能障碍，复发风险增加，以及个人完全放弃治疗的风险。如果生物标志物可以指导临床医生选择治疗方法，可能会有更好的结果。该项目将基于暴露于SSRI抗抑郁药物一周期间发生变化的定量脑电图（QEEG）特征来检查生物标志物预测结果;通过允许临床医生更有效地使用治疗，使用生理生物标志物信息进行指导可能对MDD的管理产生重大影响。

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.