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Background: The autophagy lysosomal pathway (ALP) and the ubiquitin-proteasome system (UPS) are key proteostasis mechanisms in cells, which are dysfunctional in AD and linked to protein aggregation and neuronal death. Autophagy is over activated in Alzheimer's disease brain whereas UPS is severely impaired. Activating autophagy has received most attention, however recent evidence suggests that UPS can clear aggregate proteins and a potential therapeutic target for AD and protein misfolding diseases.
View Article and Find Full Text PDFBackground: Alzheimer's disease (AD) is the most common cause of dementia worldwide. It is characterized by dysfunction in the U1 small nuclear ribonucleoproteins (snRNPs) complex, which may precede TAU aggregation, enhancing premature polyadenylation, spliceosome dysfunction, and causing cell cycle reentry and death. Thus, we evaluated the effects of a synthetic single-stranded cDNA, called APT20TTMG, in induced pluripotent stem cells (iPSC) derived neurons from healthy and AD donors and in the Senescence Accelerated Mouse-Prone 8 (SAMP8) model.
View Article and Find Full Text PDFBackground: Immunotherapy of Alzheimer's disease (AD) is a promising approach to reducing the accumulation of beta-amyloid, a critical event in the onset of the disease. Targeting the group II metabotropic glutamate receptors, mGluR2 and mGluR3, could be important in controlling Aβ production, although their respective contribution remains unclear due to the lack of selective tools.
Method: 5xFAD mice were chronically treated by a brain penetrant camelid single domain antibody (VHH or nanobody) that is an activator of mGluR2.
Background: Our previous study identified that Sildenafil (a phosphodiesterase type 5 [PDE5] inhibitor) is a candidate repurposable drug for Alzheimer's Disease (AD) using in silico network medicine approach. However, the clinically meaningful size and mechanism-of-actions of sildenafil in potential prevention and treatment of AD remind unknown.
Method: We conducted new patient data analyses using both the MarketScan® Medicare with Supplemental database (n = 7.
Drug Development.
Alzheimers Dement
December 2024
The University of Texas Health Science Center at Houston, Houston, TX, USA.
Background: Developing drugs for treating Alzheimer's disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies.
Method: To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates RESULT: Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase 2/3 clinical trials.
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