GV-971 attenuates α-Synuclein aggregation and related pathology

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.