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Inflamed brain: Targeting immune changes and inflammation for treatment of depression
Author(s) -
Sakamoto Shinji,
Zhu Xiaolei,
Hasegawa Yuto,
Karma Sadik,
Obayashi Mizuho,
Alway Emily,
Kamiya Atsushi
Publication year - 2021
Publication title -
psychiatry and clinical neurosciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.609
H-Index - 74
eISSN - 1440-1819
pISSN - 1323-1316
DOI - 10.1111/pcn.13286
Subject(s) - inflammation , medicine , depression (economics) , immune system , clinical trial , pathophysiology , disease , drug development , animal models of depression , mechanism (biology) , major depressive disorder , bioinformatics , neuroscience , immunology , drug , antidepressant , psychology , psychiatry , cognition , anxiety , biology , philosophy , epistemology , economics , macroeconomics
Although there are a number of clinically effective treatments for depression, many patients exhibit treatment resistance. Recent clinical and preclinical studies reveal that peripheral and brain immune changes and inflammation are involved in the pathophysiology of depression. This ‘Inflamed Brain’ research provides critical clues for understanding of disease pathophysiology and many candidate molecules that are potentially useful for identifying novel drug targets for the treatment of depression. In this review, we will present clinical evidence on the role of inflammation in the pathophysiology of depression. We will also summarize current clinical trials which test drugs targeting inflammation for the treatment of patients with depression. Furthermore, we will briefly provide preclinical evidence demonstrating altered immune system function and inflammation in stress‐induced animal models and will discuss the future potential of inflammation‐related drug targets. Collectively, inflammatory signatures identified in clinical and preclinical studies may allow us to stratify depressive patients based on biotypes, contributing to the development of novel mechanism‐based interventions that target specific patient populations.