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Pharmacological disruption of mouse social approach behavior: relevance to negative symptoms of schizophrenia. | LitMetric

Pharmacological disruption of mouse social approach behavior: relevance to negative symptoms of schizophrenia.

Behav Brain Res

Neuroscience Research Unit, Pfizer Global Research, 700 Main Street, Cambridge, MA, USA.

Published: September 2013


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Article Abstract

Social withdrawal is one of several negative symptoms of schizophrenia, all of which are poorly treated by current therapies. One challenge in developing agents with efficacy against negative symptoms is the lack of suitable preclinical models. The social approach test was used as the basis for developing an assay to test emerging therapies for negative symptoms. NMDA antagonists and dopamine agonists have been used extensively to produce or disrupt behaviors thought to be rodent correlates of positive and cognitive symptoms of schizophrenia. The aim of these studies was to determine whether sociability of mice in the 3-chamber social approach test could be disrupted and whether this paradigm could have utility in predicting efficacy against negative symptoms. The criteria for such a model were: a lack of response to antipsychotics and attenuation by agents such as the glycine agonist, d-cycloserine, which has been shown to possess clinical efficacy against negative symptoms. Administration of the NMDA antagonists MK-801, PCP, or ketamine did not disrupt sociability. In contrast, Grin1 hypomorph mice displayed a social deficit which was not reversed by atypical antipsychotics or d-serine. d-Amphetamine disrupted sociability without stimulating locomotor activity and its effect was not reversed by antipsychotics. The GABAA inverse agonist, FG-7142, reduced sociability and this was reversed by the GABAA antagonist, flumazenil and dcycloserine, but not by clozapine, or the GABAA benzodiazepine anxiolytic, alprazolam. Based on our criteria, the GABAA model warrants further evaluation to confirm that this paradigm has utility as a preclinical model for predicting efficacy against negative symptoms of schizophrenia.

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http://dx.doi.org/10.1016/j.bbr.2013.06.017DOI Listing

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