Cognitive biomarkers of 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.

Depression is a common condition associated with very substantial health disabilities and limited treatment options. The major bottleneck in improving drug treatment is the lack of valid animal models; this hinders translation of new therapies from the bench into the clinic. Human cognitive biomarker models may be better placed to detect and understand neuropsychological processes in this uniquely human disorder. Such models could be invaluable in enabling better selection of novel antidepressant compounds for subsequent randomised clinical trials and in providing a likely indication of their overall therapeutic profile. Equally, valid human biomarkers might be helpful in identifying drugs with a significant risk of provoking depression during clinical use. There is now substantial evidence that negative biases in the processing of emotional information play a key role in the onset and maintenance of clinical mood disorders. We have shown that antidepressant treatment in healthy subjects produces biases in tasks of emotional processing that essentially are the opposite of those seen in depression. The aim of the present application is to validate these tasks as effective cognitive biomarkers of antidepressant action applicable to the assessment of the novel candidate compounds by assessing the following hypotheses 1) that early effects on emotional processing also occur with antidepressants acting via different mechanisms 2) that these cognitive biomarkers can distinguish agents which have antidepressant activity from those whose therapeutic effects are confined to anxiety disorders; 3) that our cognitive biomarkers are able to screen out ?false positives? by exploring the failed Nk1 antagonist aprepitant 4) that these biomarkers will be able to detect agents likely to cause depression as an adverse effect. These studies should provide validation of a cognitive biomarker approach for screening and understanding novel candidate agents in an area of high unmet need.