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Determining Immune System Suppression versus CNS Protection for Pharmacological Interventions in Autoimmune Demyelination
Published on: September 12, 2016
Effectiveness of Native and Enhanced Extracellular Vesicle Preparations Assessed in Animal Models of Multiple
Melania Magercu1, Violeta Ristoiu1, Bogdan Amuzescu1
1Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania.
Abstract:
Extracellular vesicle (EV) preparations have emerged as potentially useful therapeutic tools for various inflammatory or neurodegenerative CNS disorders. Compared to mesenchymal stem cell preparations, they feature enhanced blood-brain barrier permeability and access to injured brain tissue, and better quality assessment and dosing options. A particular case where such therapies may prove beneficial is multiple sclerosis (MS), an autoimmune disease likely triggered by cross-reactivity with some viral antigens, producing an extended demyelinating encephalomyelitis with multiple patchy lesions. Several animal models of MS have been developed, including experimental autoimmune encephalomyelitis (EAE) via injection of myelin oligodendrocyte glycoprotein (MOG) 35-55, inoculation with Theiler's murine encephalomyelitis virus (TMEV), and the Cuprizone model. Via literature search we retrieved 24 experimental studies on rodent (rat or mouse) MS models with therapeutic administration of native or enhanced EV preparations suitable for this meta-analysis. We found statistically significant mean differences between experimental and control groups for clinical scores (normalized to a 0-5 scale) (-1.21, 95% confidence interval (CI) [-1.49,-0.93] (native EVs); -1.67, CI [-2.28,-1.05] (enhanced EVs)), for pathological scores of inflammation and demyelination, and for several molecular markers of inflammation and myelin repair. These results point to the need for clinical translation of the encouraging preliminary results obtained using such MS animal models.

