Cognitive biomarkers of antidepressant drug efficacy

抗抑郁药物疗效的认知生物标志物

• 批准号: G0801432/1
• 负责人: Catherine Harmer
• 金额: $ 47.76万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0801432%2F1
• 关键词: Cognitive; biomarkers; antidepressant; drug; efficacy

Clinical depression is a common and extremely disabling condition. Although drug treatments for depression exist, these do not work for a substantial proportion of patients and for some they may only reduce the symptoms rather than allowing a full recovery. One of the problems with developing new more effective treatments is that the drugs need to be tested in clinical trials with depressed patients, which takes a lot of time and is very costly. In addition, a significant number of drugs may tested in this way are found, in fact, to be ineffective. One way of improving the development of new drugs is to have models which are able to predict the likely success of new treatments. At the moment this is carried out with animal models, but these often fail to capture human aspects of depression which involves negative thoughts and beliefs. We have developed some models for use with healthy volunteers which are sensitive to antidepressant drug action and which examine how people deal with emotional information. This work suggests that antidepressants increase how we attend to, recognise and remember positive emotional information such as happy facial expressions of emotion. The current application aims to explore this in more detail and confirm whether these effects are seen with different kinds of antidepressant drug treatments. This will help us understand whether these emotional changes occur irrespective of the precise way in which the drug affects the chemicals in the brain and also to what extent these models can predict whether a drug will be good for depression or for anxiety symptoms. We will also test whether these models could have predicted drug treatments which have failed to work in depression (though did work in the animal models) and finally whether these models can help identify drugs which may increase the likelihood of depression and which may therefore need to be used with caution in people at high risk of developing depression. These studies will help us understand what makes a good treatment for depression. Also this will develop the use of these models for the screening and detection of novel treatments which have particular promise for depression, therefore reducing the low hit rate of successful treatments in the clinic and speeding up the drug discovery process.

临床抑郁症是一种常见的极度致残的疾病。尽管存在治疗抑郁症的药物疗法，但这些疗法对相当大比例的患者并不起作用，对一些人来说，它们可能只会减轻症状，而不是让患者完全康复。开发更有效的新疗法的问题之一是，这些药物需要在抑郁症患者的临床试验中进行测试，这需要大量时间和非常昂贵的费用。此外，有相当数量的药物可能通过这种方式进行测试，实际上被发现是无效的。改进新药开发的一种方法是建立能够预测新药可能成功的模型。目前，这是在动物模型中进行的，但这些模型往往无法捕捉到人类抑郁的方面，这涉及到消极的想法和信念。我们开发了一些模型，供健康志愿者使用，这些模型对抗抑郁药物的作用很敏感，并检查了人们如何处理情绪信息。这项研究表明，抗抑郁药物可以提高我们对积极情绪信息的关注、识别和记忆能力，比如快乐的面部表情。目前的申请旨在更详细地探索这一点，并确认这些影响是否在不同类型的抗抑郁药物治疗中出现。这将帮助我们了解这些情绪变化是否发生，而不是药物影响大脑中化学物质的确切方式，以及这些模型在多大程度上可以预测一种药物对抑郁或焦虑症状有好处。我们还将测试这些模型是否可以预测对抑郁症无效的药物治疗(尽管在动物模型中确实有效)，最后这些模型是否可以帮助识别可能增加抑郁症可能性的药物，因此可能需要谨慎使用在抑郁症高危人群中。这些研究将帮助我们了解什么是治疗抑郁症的好方法。这也将开发使用这些模型来筛选和检测对抑郁症具有特殊前景的新治疗方法，从而降低临床上成功治疗的低命中率，并加快药物发现过程。