GV-971 attenuates α-Synuclein aggregation and related pathology

The Brain-Gut-Bone Axis in Neurodegenerative Diseases: Insights, Challenges, and Future Prospects

Neurodegenerative diseases are global health challenges characterized by the progressive degeneration of nerve cells, leading to cognitive and motor impairments. The brain-gut-bone axis, a complex network that modulates multiple physiological systems, has gained increasing attention owing to its profound effects on the occurrence and development of neurodegenerative diseases. No comprehensive review has been conducted to clarify the triangular relationship involving the brain-gut-bone axis and its potential for innovative therapies for neurodegenerative disorders. In light of this, a new perspective is aimed to propose on the interplay between the brain, gut, and bone systems, highlighting the potential of their dynamic communication in neurodegenerative diseases, as they modulate multiple physiological systems, including the nervous, immune, endocrine, and metabolic systems. Therapeutic strategies for maintaining the balance of the axis, including brain health regulation, intestinal microbiota regulation, and improving skeletal health, are also explored. The intricate physiological interactions within the brain-gut-bone axis pose a challenge in the development of effective treatments that can comprehensively target this system. Furthermore, the safety of these treatments requires further evaluation. This review offers a novel insights and strategies for the prevention and treatment of neurodegenerative diseases, which have important implications for clinical practice and patient well-being.

GV-971 attenuates the progression of neuromyelitis optica in murine models and reverses alterations in gut microbiota and associated peripheral abnormalities

AIMS

Growing evidence suggests that an imbalanced gut microbiota composition plays a crucial role in the development of neuromyelitis optica spectrum disorders (NMOSD), an inflammatory demyelinating disease primarily affecting the optic nerves and central nervous system (CNS). In light of this, we explored the potential therapeutic benefits of GV-971 in NMOSD. GV-971 is a drug used for treating mild-to-moderate Alzheimer's disease, which targets the gut-brain axis and reduces neuroinflammation.

METHODS

To evaluate GV-971's effects, we employed the experimental autoimmune encephalomyelitis (EAE) mouse model to establish NMOSD animal models. This was achieved by injecting NMO-IgG into aged mice (11 months old) or using NMO-IgG along with complement injection and microbubble-enhanced low-frequency ultrasound (MELFUS) techniques in young mice (7 weeks old). We assessed the impact of GV-971 on incidence rate, clinical scores, body weight, and survival, with methylprednisolone serving as a positive control. In NMOSD models of young mice, we analyzed spinal cord samples through H&E staining, immunohistochemistry, and Luxol Fast Blue staining. Fecal samples collected at different time points underwent 16S rRNA gene sequencing, while plasma samples were analyzed using cytokine array and untargeted metabolomics analysis.

RESULTS

Our findings indicated that GV-971 significantly reduced the incidence of NMOSD, alleviated symptoms, and prolonged survival in NMOSD mouse models. The NMOSD model exhibited substantial neuroinflammation and injury, accompanied by imbalances in gut microbiota, peripheral inflammation, and metabolic disorders, suggesting a potentially vicious cycle that accelerates disease pathogenesis. Notably, GV-971 effectively reduces neuroinflammation and injury, and restores gut microbiota composition, as well as ameliorates peripheral inflammation and metabolic disorders.

CONCLUSIONS

GV-971 attenuates the progression of NMOSD in murine models and reduces neuroinflammation and injury, likely through its effects on remodeling gut microbiota and peripheral inflammation and metabolic disorders.

GV-971 attenuates α-Synuclein aggregation and related pathology

RATIONALE

Synucleinopathies, including Parkinson's disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB), share a distinct pathological feature, that is, a widespread accumulation of α-synuclein (α-syn) in the brain. There is a significant clinical unmet need for disease-modifying treatments for synucleinopathies. Recently, a seaweed-derived mixture of oligosaccharides sodium oligomannate, GV-971, was approved for Phase 2 clinical trials for PD. This study aimed to further evaluate the therapeutic effects of GV-971 on synucleinopathies using cellular and animal models and explore its associated molecular mechanisms.

METHODS

α-Syn aggregation was assessed, in vitro and ex vivo, by ThT assay. A dopaminergic neuron cell line, Prnp-SNCAA53T mice, and brain slices from PD and DLB patients were used to determine the efficacy of GV-971 in ameliorating α-syn pathology. Measurements of motor functions, including pole, cylinder, and rotarod tests, were conducted on Prnp-SNCAA53T mice 4 weeks after intragastric administration of GV-971 (200 mg day-1  kg-1 ).

RESULTS

GV-971 effectively prevented α-syn aggregation and even disassembled pre-aggregated α-syn fibrils, in vitro and ex vivo. In addition, GV-971 was able to rescue α-syn-induced neuronal damage and reduced release of extracellular vesicles (EVs), likely via modulating Alix expression. In the Prnp-SNCAA53T mouse model, when treated at the age of 5 months, GV-971 significantly decreased α-syn deposition in the cortex, midbrain, and cerebellum regions, along with ameliorating the motor dysfunctions.

CONCLUSIONS

Our results indicate that GV-971, when administered at a relatively early stage of the disease process, significantly reduced α-syn accumulation and aggregation in Prnp-SNCAA53T mice. Furthermore, GV-971 corrected α-syn-induced inhibition of EVs release in neurons, contributing to neuronal protection. Future studies are needed to further assess GV-971 as a promising disease-modifying therapy for PD and other synucleinopathies.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.