Biological Predictors of Response to Antidepressants

描述（由申请人提供）：药物治疗是治疗重度抑郁症（MDD）最常见的干预措施。第一种抗抑郁药的反应率可低至50-60%1，而临床更有意义的缓解率通常仅在20% -35%1之间。生物试验的发展将使临床医生能够选择正确的抗抑郁药物类别，通过缩短缓解时间，将显著降低与重度抑郁症相关的发病率和死亡率。我们建议评估潜在的生物学试验，以预测选择性5 -羟色胺再摄取抑制剂（SSRI）治疗时MDD的缓解，以及个体患者是否更可能对SSRI或选择性去甲肾上腺素再摄取抑制剂（SNRI）有反应，这是治疗MDD的两种最常见的药物。我们已经证明抑郁症患者的5-羟色胺1A （5-HT1A）结合电位高于对照组。此外，在一项自然治疗研究中，我们发现5-HT1A结合电位较高的重度抑郁症患者不太可能接受社区治疗。最近有研究表明，中脑（而非纹状体）的基线5-羟色胺转运体（5-HTT）可预测对ssri治疗的反应。在我们的自然研究中，我们表明，与非递质相比，递质具有更高的区域特异性5-HTT结合潜力。在我们的初步研究中，治疗方案没有得到控制，而且患者太少，无法确定缓解是否取决于抗抑郁药的种类。在这项建议中，我们建议进行预处理正电子发射断层扫描（PET）扫描，并让所有患者接受SSRI的标准化治疗方案，然后在SSRI非缓解者中接受SNRI。艾司西酞普兰是SSRI而地西帕明是SNRI的选择因为在我们给药的剂量下，它们对各自的转运体具有高度选择性。我们假设在中脑、杏仁核、丘脑和壳核背侧具有高突触前和突触后5-HT1A结合电位和低5-HT1A结合电位的患者不会转变为SSRI，而会转变为SNRI。最后，我们将生成一个基于脑成像结果测量的缓解预测模型。我们的总体目标是通过使用治疗前5-HT1A受体和5-HTT的定量数据来指导抗抑郁药物的治疗选择，减少与寻找有效抗抑郁药物相关的试验和错误。

DESCRIPTION (provided by applicant): Pharmacotherapeutic treatments are the most common intervention in the treatment of major depressive disorder (MDD). Response rates to the first antidepressant can be as low as 50-60%1 while clinically more meaningful remission rates are typically only between 20%2-35%1. Development of biological tests that would enable clinicians to select the correct class of antidepressant would significantly reduce morbidity and mortality associated with MDD by reducing the time to remission. We propose to evaluate potential biological tests that can predict remission from MDD when treated with a selective serotonin reuptake inhibitor (SSRI) and whether an individual patient is more likely to respond to a SSRI or a selective norepinephrine reuptake inhibitor (SNRI), the 2 most common classes of medication for MDD. We have shown that depressed patients have higher serotonin 1A (5-HT1A) binding potential than controls. Additionally, in a naturalistic treatment study we found MDD patients with higher 5-HT1A binding potential were less likely to remit to community based treatment. It has recently been shown that baseline serotonin transporter (5-HTT) availability in the midbrain, but not striatum3, predicts response to treatment with a SSRI.4 In our naturalistic study we show that remitters have higher 5-HTT binding potential in a regionally specific manner compared to non-remitters. The treatment protocol was not controlled in our pilot study and there were too few patients to determine if remission depended on the class of antidepressant. In this proposal, we propose to perform pretreatment positron emission tomography (PET) scans and have all patients receive a standardized treatment protocol of a SSRI followed by a SNRI in SSRI non-remitters. Escitalopram is the SSRI and desipramine the SNRI of choice because at the doses we will administer, they are highly selective for the respective transporters. We hypothesize that patients with high pre and postsynaptic 5-HT1A binding potential and low 5-HTT binding potential in the midbrain, amygdala, thalamus, and dorsal putamen will not remit to a SSRI and will remit to a SNRI. Finally, we will generate a predictive model of remission based on brain imaging outcome measures. Our overall goal is to reduce the trial and error associated with finding an effective antidepressant by using data from pre-treatment quantification of 5-HT1A receptors and 5-HTT to guide antidepressant treatment selection.