| Literature DB >> 30793996 |
Konstantin A Demin1,2, Maxim Sysoev3,4, Maria V Chernysh2, Anna K Savva5, Mamiko Koshiba6, Edina A Wappler-Guzzetta7, Cai Song8,9, Murilo S De Abreu10, Brian Leonard11, Matthew O Parker12, Brian H Harvey13, Li Tian14, Eero Vasar14, Tatyana Strekalova15,16,17, Tamara G Amstislavskaya18, Andrey D Volgin7,18, Erik T Alpyshov19, Dongmei Wang19, Allan V Kalueff19,20,21,22,23,24,25,26.
Abstract
INTRODUCTION: Depression is a highly debilitating psychiatric disorder that affects the global population and causes severe disabilities and suicide. Depression pathogenesis remains poorly understood, and the disorder is often treatment-resistant and recurrent, necessitating the development of novel therapies, models and concepts in this field. Areas covered: Animal models are indispensable for translational biological psychiatry, and markedly advance the study of depression. Novel approaches continuously emerge that may help untangle the disorder heterogeneity and unclear categories of disease classification systems. Some of these approaches include widening the spectrum of model species used for translational research, using a broader range of test paradigms, exploring new pathogenic pathways and biomarkers, and focusing more closely on processes beyond neural cells (e.g. glial, inflammatory and metabolic deficits). Expert opinion: Dividing the core symptoms into easily translatable, evolutionarily conserved phenotypes is an effective way to reevaluate current depression modeling. Conceptually novel approaches based on the endophenotype paradigm, cross-species trait genetics and 'domain interplay concept', as well as using a wider spectrum of model organisms and target systems will enhance experimental modeling of depression and antidepressant drug discovery.Entities:
Keywords: Depression; animal modeling; endophenotype; major depressive disorder; pathogenesis
Year: 2019 PMID: 30793996 DOI: 10.1080/17460441.2019.1575360
Source DB: PubMed Journal: Expert Opin Drug Discov ISSN: 1746-0441 Impact factor: 6.098