Current Neuropharmacology - Volume 23, Issue 14, 2025

Parkinson's disease (PD) presents a complex challenge in neurodegenerative disorders, necessitating innovative therapeutic approaches. This review article elucidates the therapeutic potential of traditional herbal formulations alongside computational methods in PD research. Through comprehensive examination, we explore their mechanisms of action, synergistic effects, and implications for PD management. Furthermore, we discuss the significance of computational techniques such as molecular docking, molecular dynamics simulations, pharmacophore modeling, and network pharmacology. Our analysis underscores the integration of traditional wisdom with modern scientific inquiry, paving the way for nuanced interventions in PD.

Neurodegenerative diseases represent a prevalent category of age-associated diseases. As human lifespans extend and societies become increasingly aged, neurodegenerative diseases pose a growing threat to public health. The lack of effective therapeutic drugs for both common and rare neurodegenerative diseases amplifies the medical challenges they present. Current treatments for these diseases primarily offer symptomatic relief rather than a cure, underscoring the pressing need to develop efficacious therapeutic interventions. Drug repositioning, an innovative and data-driven approach to research and development, proposes the re-evaluation of existing drugs for potential application in new therapeutic areas. Fueled by rapid advancements in artificial intelligence and the burgeoning accumulation of medical data, drug repositioning has emerged as a promising pathway for drug discovery. This review comprehensively examines drug repositioning for neurodegenerative diseases through the lens of translational informatics, encompassing data sources, computational models, and clinical applications. Initially, we systematized drug repositioning-related databases and online platforms, focusing on data resource management and standardization. Subsequently, we classify computational models for drug repositioning from the perspectives of drug-drug, drug-target, and drug-disease interactions into categories such as machine learning, deep learning, and network-based approaches. Lastly, we highlight computational models presently utilized in neurodegenerative disease research and identify databases that hold potential for future drug repositioning efforts. In the artificial intelligence era, drug repositioning, as a data-driven strategy, offers a promising avenue for developing treatments suited to the complex and multifaceted nature of neurodegenerative diseases. These advancements could furnish patients with more rapid, cost-effective therapeutic options.

Neurological disorders are marked by neurodegeneration, leading to impaired cognition, psychosis, and mood alterations. These symptoms are typically associated with functional changes in both emotional and cognitive processes, which are often correlated with anatomical variations in the brain. Hence, brain structural magnetic resonance imaging (MRI) data have become a critical focus in research, particularly for predictive modeling. The involvement of large MRI data consortia, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), has facilitated numerous MRI-based classification studies utilizing advanced artificial intelligence models. Among these, convolutional neural networks (CNNs) and non-convolutional artificial neural networks (NC-ANNs) have been prominently employed for brain image processing tasks. These deep learning models have shown significant promise in enhancing the predictive performance for the diagnosis of neurological disorders, with a particular emphasis on Alzheimer's disease (AD). This review aimed to provide a comprehensive summary of these deep learning studies, critically evaluating their methodologies and outcomes. By categorizing the studies into various sub-fields, we aimed to highlight the strengths and limitations of using MRI-based deep learning approaches for diagnosing brain disorders. Furthermore, we discussed the potential implications of these advancements in clinical practice, considering the challenges and future directions for improving diagnostic accuracy and patient outcomes. Through this detailed analysis, we seek to contribute to the ongoing efforts in harnessing AI for better understanding and management of AD.

The review focuses on the ways that ontologies are revolutionising precision medicine in their effort to understand neurodegenerative illnesses. Ontologies, which are structured frameworks that outline the relationships between concepts in a certain field, offer a crucial foundation for combining different biological data. Novel insights into the construction of a precision medicine approach to treat neurodegenerative diseases (NDDs) are given by growing advancements in the area of pharmacogenomics. Affected parts of the central nervous system may develop neurological disorders, including Alzheimer's, Parkinson's, autism spectrum, and attention-deficit/hyperactivity disorder. These models allow for standard and helpful data marking, which is needed for cross-disciplinary study and teamwork. With case studies, you can see how ontologies have been used to find biomarkers, understand how sicknesses work, and make models for predicting how drugs will work and how the disease will get worse. For example, problems with data quality, meaning variety, and the need for constant changes to reflect the growing body of scientific knowledge are discussed in this review. It also looks at how semantic data can be mixed with cutting-edge computer methods such as artificial intelligence and machine learning to make brain disease diagnostic and prediction models more exact and accurate. These collaborative networks aim to identify patients at risk, identify patients in the preclinical or early stages of illness, and develop tailored preventative interventions to enhance patient quality of life and prognosis. They also seek to identify new, robust, and effective methods for these patient identification tasks. To this end, the current study has been considered to examine the essential components that may be part of precise and tailored therapy plans used for neurodegenerative illnesses.

Alzheimer’s disease (AD), a neurodegenerative condition, continues to pose significant challenges to modern medicine due to the limited efficacy offered by current therapeutic modalities. With the complex pathophysiology of AD, which includes tau protein accumulation, amyloid-β plaque formation, neuroinflammation, and synaptic dysfunction, novel drug-targeting sites must be identified. This study presents a thorough evaluation of novel drug targeting sites, with a focus on these pathological characteristics as promising therapeutic targets while providing an explanation of their role in the course of the disease. We investigate in detail how neurotoxicity, resulting in synapse failure and cognitive impairment, is caused by tau proteins and amyloid plaques. In addition, the article discusses the increasing evidence that synaptic dysfunction is a major factor in the disease's progression, as well as the significance of neuroinflammation in the pathophysiology of the condition. The review also covers new drug sites such as amyloid-β plaques, tau proteins, and the inhibition of neuroinflammation mediators, in addition to traditional drug sites, including cholinergic and glutamatergic therapeutic targets. Lastly, we discuss the role of translational informatics involving data modeling, predictive analytics, explainable artificial intelligence (AI), and multimodal approaches for the management and prediction of AD. This article will serve as a guide for future research efforts in the fields of neuroscience, neuropharmacology, drug delivery sciences, and translational informatics.

Microglia are the innate immune cells of the brain. Recent single cell and nucleus sequencing along with other omics technologies are leading the way for new discoveries related to microglial function and diversity. The Nogo-signaling system is a prime target for investigation with these tools as it has previously been neglected in microglia. The Nogo-signaling system consists of approximately 20 proteins, including ligands, receptors, co-receptors, and endogenous inhibitors known for their neuronal plasticity restricting properties via RhoA and ROCK1/ROCK2 activation, and have recently been implicated in microglial function. Here, we explore expression patterns of Nogo-family genes in the mouse and human brain. In mice, we focus on brain cell type enrichment, patterns of expression in microglia from embryonic stages to adulthood, sex differences, and changes in expression in acute and chronic inflammatory contexts from publicly available RNAseq and RiboTag translational profiling datasets. We identified differential expression of Nogo-family genes across age, sex, and disease/injury in mice. To analyze human microglia, we utilize a new tool, the CZ CellxGene Discover, to aggregate 21 single cell sequencing datasets of human brain cells in Alzheimer’s (AD) and control patients. In humans, LINGO1 is highly enriched in human AD microglia, a previously undescribed finding. We used The Alzheimer’s Cell Atlas (TACA) to further verify if this enrichment correlates to disease state, severity of human AD diagnosis, or sex of patients. The current work provides a comprehensive analysis of Nogo-family genes in microglia and identifies LINGO1 as a potential therapeutic target for AD.

Alzheimer's disease is a neurodegenerative disease that impairs cognitive function. The incidence of Alzheimer's disease increases with the increase in the elderly population. Although the clear pathogenesis of Alzheimer's disease is not yet known, the formation of amyloid plaques and tau fibrils, diminished acetylcholine levels, and increased inflammation can be observed in patients. Alzheimer's disease, whose pathogenesis is not fully demonstrated, cannot be treated radically. Since it has been observed that only pharmacological treatment alone isn’t sufficient, alternative approaches have become essential. Among these approaches, nanocarriers greatly facilitate the transport of drugs since the blood-brain barrier is an important obstacle to the penetration of drugs into the brain. Photosensitizers trigger activation after exposure to near-infrared radiation light of a suitable wavelength or laser light, resulting in the selective destruction of Aβ plaques. Photodynamic therapy and photothermal therapy have been investigated for their potential to inhibit Aβ plaques through photosensitizers. By ThT fluorescence measurements, TAS-loaded Ce6 micelles show inhibiting Aβ monomers from formation Aβ aggregates and degradation of protofibrills to small fragments. By using these photosensitizers, near-infrared radiation fluorescence imaging can be used as a theranostic. In this review, potential treatment options for photodynamic therapy and photothermal therapy for Alzheimer's disease are summarised, and a simultaneous or combined approach is discussed, taking into account potential nanotheranostics.