Current Alzheimer Research - Volume 22, Issue 11, 2025

Alzheimer’s disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development.

This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins.

Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with in vitro and in vivo experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets.

The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement.

Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer’s disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by synaptic dysfunction and the accumulation of amyloid plaques. The molecular mechanisms linking gene dysregulation, pathogenic variants, and protein interaction networks to these core pathologies remain incompletely understood. This study aimed to integrate transcriptomic data with mutation and structural modeling to uncover disease mechanisms and identify therapeutic targets.

We performed differential gene expression analysis on the GSE138260 microarray dataset using GEO2R to identify DEGs in AD brain tissue. Missense mutations in DEGs were retrieved from the Alzheimer’s Disease Variant Portal (ADVP). Protein-protein interaction networks were constructed using the STRING database to identify connections with the amyloid precursor protein (APP). Molecular dynamics simulations were conducted to evaluate the structural consequences of the BDNF V66M mutation.

A total of 1,588 DEGs were identified, including upregulation of immune-related genes and downregulation of neuroplasticity-associated genes (e.g., BDNF, GRIN2B, GRM8). PPI analysis revealed a core network centered on APP, including BDNF as a direct interactor. The V66M variant in BDNF, confirmed to be downregulated in AD brains, showed increased rigidity and localized flexibility in structural models.

The integration of transcriptomics and protein modeling revealed a critical link between BDNF dysfunction and APP interaction in AD. The V66M mutation was found to structurally alter BDNF, potentially disrupting its neuroprotective roles. The findings suggested that impaired BDNF signaling, driven by transcriptional repression and structural mutation, contributes to amyloid pathology and synaptic failure.

This multi-omics investigation has identified BDNF as a converging point of gene dysregulation and pathogenic mutation within an APP-centric network. Structural alterations induced by the V66M mutation may exacerbate amyloid accumulation and neuronal dysfunction, supporting therapeutic strategies aimed at enhancing BDNF signaling in AD.

The primary objective of this study was to examine changes in brain network architecture across multiple frequency bands using spectral analysis of both weighted and binarized functional connectivity networks. This cross-sectional observational study, conducted as a secondary analysis of a publicly available EEG dataset, analyzed spectral coherence measurements from 25 patients with Alzheimer’s disease (AD) and 25 age- and sex-matched healthy controls (HC). Nevertheless, the modest sample size and cultural homogeneity of the dataset may limit the statistical power and generalizability of the results. A data-driven thresholding approach was employed to generate binary networks, allowing a robust comparison of connectivity disruptions associated with AD.

Brain network features derived from the graph Laplacian, including weighted Fiedler value, spectral range, and Middle Eigenvalue, were analyzed across seven frequency layers: delta, theta, alpha1, alpha2, beta1, beta2, and gamma. For binary networks, the Fiedler value was calculated after thresholding. Statistical group comparisons between AD and HC were performed using t-tests (p < 0.05), and each feature was assessed based on the number of frequency bands showing significant differences.

Among all features, the weighted Fiedler value was the most discriminative, showing significant reductions in AD patients within the alpha2 and beta1 bands. In binary networks, the Fiedler value remained significantly lower in AD within the alpha2 band, confirming topological degradation even without edge weight information. Other spectral features showed similar trends, but did not reach statistical significance in the binary networks.

The consistent decline in Fiedler value across both weighted and binary networks indicates a global reduction in connectivity characteristic of AD. These spectral markers offer a quantitative and interpretable framework for understanding the progressive disconnection syndrome in AD.

This study demonstrates significant alterations in Laplacian spectral features of brain networks between the AD and HC groups across specific frequency bands. These exploratory findings indicate that the spectral features, particularly the Fiedler value, consistently differentiate AD patients from healthy controls across frequency bands, suggesting its potential as a biomarker. However, larger and longitudinal studies are needed to confirm its diagnostic and prognostic utility. The combined use of weighted and binarized connectivity matrices enhances analytical sensitivity and facilitates the application of spectral graph theory for the early detection and monitoring of AD.

Primary progressive aphasia (PPA) is a clinical syndrome characterized by progressive language impairment. Three subtypes have been identified: semantic (svPPA), nonfluent (nfPPA), and logopenic (lvPPA). Although clinical criteria exist to classify these subtypes, the specific ways in which semantic cognition is impaired across these variants have not yet been fully elucidated. This cross-sectional study aimed to analyze the effects of cognitive demand and imaginability on semantic cognition in patients with PPA.

Fifteen patients with PPA (five per variant) and 20 healthy controls completed a semantic association task comprising 20 items. The task included two levels of cognitive demand (low and high) and two types of concepts (concrete and abstract). Participants selected the word with the strongest semantic link to a probe word, based on synonymy, categorical relations, or shared features. Accuracy and reaction times were recorded and analyzed using nonparametric statistics.

All PPA groups performed significantly worse than controls, showing fewer correct responses and longer reaction times. svPPA patients exhibited the greatest impairment across all conditions. nfPPA patients performed similarly to controls with concrete concepts but showed deficits with abstract words. lvPPA patients experienced greater difficulty under high cognitive demand, particularly with abstract words, indicating impaired semantic control.

These findings suggest that svPPA is characterized by global impairment of conceptual knowledge, whereas nfPPA and lvPPA exhibit more selective deficits depending on concept type and cognitive demand.

The research herein highlights the importance of considering cognitive demand and imaginability when assessing semantic cognition in PPA.

Neuropsychiatric symptoms frequently occur in patients with Alzheimer´s disease (AD); apathy, depression, delusions, optical hallucinations, anxiety, and agitation often appear as first symptoms of AD, while auditory hallucinations have never been described as the first symptom. In this case report, we describe a case of a woman who had auditory hallucinations as the first symptom of AD.

An 85-year-old woman was admitted to our hospital suffering from imperative auditory hallucinations without subjective and minimal objective memory complaints. Further diagnostics with an MRI scan, neuropsychological tests, and an analysis of cerebral spinal fluid were accomplished. AD was confirmed during the hospital stay, suggesting auditory hallucinations as the first symptom of AD. She was temporarily treated with risperidone, which improved the hallucinations.

Auditory hallucinations in older age could be the first symptom of AD and even occur before cognitive decline.