Current status of neuroprotective and neuroregenerative strategies in multiple sclerosis: A systematic review

BACKGROUND

Immune-mediated demyelination and consequent degeneration of oligodendrocytes and axons are hallmark features of multiple sclerosis (MS). Remyelination declines in progressive MS, causing permanent axonal loss and irreversible disabilities. Strategies aimed at enhancing remyelination are critical to attenuate disease progression.

OBJECTIVE

We systematically reviewed recent advances in neuroprotective and regenerative therapies for MS, covering preclinical and clinical studies.

METHODS

We searched three biomedical databases using defined keywords. Two authors independently reviewed articles for inclusion based on pre-specified criteria. The data were extracted from each study and assessed for risk of bias.

RESULTS

Our search identified 7351 studies from 2014 to 2020, of which 221 met the defined criteria. These studies reported 262 interventions, wherein 92% were evaluated in animal models. These interventions comprised protein, RNA, lipid and cellular biologics, small molecules, inorganic compounds, and dietary and physiological interventions. Small molecules were the most highly represented strategy, followed by antibody therapies and stem cell transplantation.

CONCLUSION

While significant strides have been made to develop regenerative treatments for MS, the current evidence illustrates a skewed representation of the types of strategies that advance to clinical trials. Further examination is thus required to address current barriers to implementing experimental treatments in clinical settings.

A metric learning method for estimating myelin content based on T2-weighted MRI from a de- and re-myelination model of multiple sclerosis

Myelin plays a critical role in the pathogenesis of neurological disorders but is difficult to characterize in vivo using standard analysis methods. Our goal was to develop a novel analytical framework for estimating myelin content using T2-weighted magnetic resonance imaging (MRI) based on a de- and re-myelination model of multiple sclerosis. We examined 18 mice with lysolecithin induced demyelination and spontaneous remyelination in the ventral white matter of thoracic spinal cord. Cohorts of 6 mice underwent 9.4T MRI at days 7 (peak demyelination), 14 (ongoing recovery), and 28 (near complete recovery), as well as histological analysis of myelin and the associated cellularity at corresponding timepoints. Our MRI framework took an unsupervised learning approach, including tissue segmentation using a Gaussian Markov random field (GMRF), and myelin and cellularity feature estimation based on the Mahalanobis distance. For comparison, we also investigated 2 regression-based supervised learning approaches, one using our GMRF results, and another using a freely available generalized additive model (GAM). Results showed that GMRF segmentation was 73.2% accurate, and our unsupervised learning method achieved a correlation coefficient of 0.67 (top quartile: 0.78) with histological myelin, similar to 0.70 (top quartile: 0.78) obtained using supervised analyses. Further, the area under the receiver operator characteristic curve of our unsupervised myelin feature (0.883, 95% CI: 0.874–0.891) was significantly better than any of the supervised models in detecting white matter myelin as compared to histology. Collectively, metric learning using standard MRI may prove to be a new alternative method for estimating myelin content, which ultimately can improve our disease monitoring ability in a clinical setting.