Personalized Response Indicators of SSRI Effectiveness in Major Depression

描述(申请人提供)：重度抑郁障碍(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。费用高的一个原因是，大多数患者在通过反复试验确定一种有益的药物之前，要忍受漫长的经验性抗抑郁药试验，最终不会成功。如果生物标记物能够在治疗的早期确定特定的抗抑郁药是否可能导致应答、缓解或治疗失败，护理将会得到改善。医生可以迅速改变治疗方法，使用生物标记物表明可能对患者有帮助的抗抑郁药。我们建议进行一项盲法和对照的可行性研究，根据抗抑郁药物治疗第一周的数据，评估一种预测结果的实用生物标记物。我们已经确定了选择性5-羟色胺再摄取抑制剂(SSRI)治疗过程中早期出现的定量脑电(QEEG)变化，这些变化似乎可以预测后期反应和缓解。在这项名为“严重抑郁中SSRI有效性的个性化反应指标”的项目中，我们建议从生物标记物的发现进入开发阶段，使用基于生物标记物状态分配的治疗方法进行前瞻性的对照试验。我们的具体目标是(1)检验使用基于qEEG的生物标记物引导的治疗模型来前瞻性分配抗抑郁药物治疗的实用性，以及(2)评估通过包括临床、社会人口学或遗传因素来提高该模型的预测准确性。我们将测试特定的假设：(1)无论生物标记物状态如何，qEEG-生物标记物引导的SSRI治疗将产生比治疗更好的应答率；(2)通过包括临床、社会人口和遗传预测因子，该模型的预测准确性将得到提高。共有172名成年MDD患者将接受为期一周的SSRI西替普兰(ESC)的药理学挑战，然后通过分层随机分配进行双盲治疗：生物标志物阳性(即预测ESC缓解)的受试者中，一半将继续接受ESC，另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，是临床合理的替代药物。同样，一半生物标志物阴性的受试者(即预测的ESC无应答)将继续接受ESC治疗，另一半将接受BUP。共同的主要结果将是在接受用于生物标记物(ESC)的SSRI的受试者中，在17个项目的汉密尔顿抑郁评定量表和30个项目的抑郁症状清单上的7周反应(50%改善)。我们将检验我们的假说(1)检验生物标记物引导治疗的结果，以及(2)检验纳入非生理学因素对预测模型的改进。数据安全监测委员会将监测生物标记物在预测治疗反应方面的效用，并将有权在生物标记物被证明是对ESC的反应或无反应的高度准确预测时提前停止试验。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：严重抑郁障碍(MDD)是一种常见的精神疾病，对世界各地的社会和个人患者来说成本很高，但根据当前最佳做法选择的治疗往往不能在最初尝试的药物治疗下恢复；这会导致长期的症状性痛苦、功能障碍、复发的风险增加，以及个人将完全放弃治疗努力的风险。如果生物标记物可以指导临床医生在治疗中进行选择，那么可能会有更好的结果。这个项目将检验基于定量脑电(QEEG)特征的生物标记物对结果的预测，这些特征在接触SSRI抗抑郁药物一周内发生变化；通过允许临床医生更有效地使用治疗方法，使用生理学生物标记物信息指导可能对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.