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• W2896938587 abstract "There are two important gaps of knowledge in depression treatment, namely the lack of biomarkers predicting response to antidepressants and the limited knowledge of the molecular mechanisms underlying clinical improvement. However, individually tailored treatment strategies and individualized prescription are greatly needed given the huge socio-economic burden of depression, the latency until clinical improvement can be observed and the response variability to a particular compound. Still, individual patient-level antidepressant treatment outcomes are highly unpredictable. In contrast to other therapeutic areas and despite tremendous efforts during the past years, the genomics era so far has failed to provide biological or genetic predictors of clinical utility for routine use in depression treatment. Specifically, we suggest to 1) shift the focus from the group patterns to individual outcomes, 2) use dimensional classifications such as Research Domain Criteria, 3) envision better planning and improved connections between pre-clinical and clinical studies within translational research units. In contrast to studies in patients, animal models enable both searches for peripheral biosignatures predicting treatment response and in depth-analyses of the neurobiological pathways shaping individual antidepressant response in the brain. While there is a considerable number of animal models available aiming at mimicking disease-like conditions such as those seen in depressive disorder, only a limited number of preclinical or truly translational investigations is dedicated to the issue of heterogeneity seen in response to antidepressant treatment. In this mini-review, we provide an overview on the current state of knowledge and propose a framework for successful translational studies into antidepressant treatment response." @default.
• W2896938587 created "2018-10-26" @default.
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• W2896938587 date "2018-10-22" @default.
• W2896938587 modified "2023-10-14" @default.
• W2896938587 title "Understanding and Predicting Antidepressant Response: Using Animal Models to Move Toward Precision Psychiatry" @default.
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• W2896938587 doi "https://doi.org/10.3389/fpsyt.2018.00512" @default.
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