Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking

Novel 2-Alkoxy-3-Cyanopyridine Derivatives as Cholinesterase Inhibitors: Synthesis, Biological Evaluation, and In Silico Investigations

Alzheimer's disease remains a major challenge in neuroscience and medicine. Cholinesterase inhibitors provide symptomatic relief but do not alter disease progression. While significant progress has been made in understanding its biology, there is an urgent need for effective therapies. In this study, a series of 2-alkoxy-3-cyanopyridine derivatives (1-7) were prepared and evaluated as inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). Among the compounds, 3 and 4 were identified as good inhibitors of AChE and BuChE with relatively low IC50 values. 3 inhibited AChE with an IC50 of 53.95 ± 4.29 µM, while 4 had a greater potency for BuChE with an IC50 of 31.79 ± 0.38 µM. Kinetic studies revealed that 3 and 4 are competitive inhibitors with Ki values of 14.23 ± 0.42 and 19.80 ± 3.38 µM for AChE and BuChE, respectively. In silico investigations, including docking studies, DFT calculations, and ADME/drug-likeness properties, were carried out to understand the mode of interaction of 3 and 4 toward the AChE and BuChE enzymes, as well as to determine their molecular geometry, chemical reactivity, and pharmacokinetic properties. This study highlights the potential of 3-cyanopyridine derivatives in the treatment of AD and provides a solid foundation for further optimization and exploration of their therapeutic applications.

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.

Multi-Target-Directed Cinnamic Acid Hybrids Targeting Alzheimer's Disease

Progressive cognitive decline in Alzheimer's disease (AD) is a growing challenge. Present therapies are based on acetylcholinesterase inhibition providing only temporary relief. Promising alternatives include butyrylcholinesterase (BuChE) inhibitors, multi-target ligands (MTDLs) that address the multi-factorial nature of AD, and compounds that target oxidative stress and inflammation. Cinnamate derivatives, known for their neuroprotective properties, show potential when combined with established AD agents, demonstrating improved efficacy. They are being positioned as potential AD therapeutic leads due to their ability to inhibit Aβ accumulation and provide neuroprotection. This article highlights the remarkable potential of cinnamic acid as a basic structure that is easily adaptable and combinable to different active groups in the struggle against Alzheimer's disease. Compounds with a methoxy substitution at the para-position of cinnamic acid display increased efficacy, whereas electron-withdrawing groups are generally more effective. The effect of the molecular volume is worthy of further investigation.

Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma

Objective

This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging.

Methods

The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set.

Results

Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001).

Conclusions

The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.