Computational Repurposing of Janus Kinase Inhibitors as Potential Therapeutic Candidates for Alzheimer's Disease.

Background and

Objectives

Alzheimer's disease (AD) is the most common neurodegenerative disorder, and current therapies provide only limited symptomatic relief without effectively slowing its progression. Increasing evidence suggests that aberrant activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling cascade contributes to AD-associated neuroinflammation. This study investigated the therapeutic potential and molecular mechanisms of JAK inhibitors in AD using integrated bioinformatics and network pharmacology approaches. Materials and

Methods

Potential anti-AD targets of JAK inhibitors were identified using the SwissTargetPrediction and GeneCards databases. Functional enrichment, protein-protein interaction (PPI) analysis, transcriptomic validation using public datasets, regulatory network construction, molecular docking, normal mode analysis (NMA), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction were performed to investigate the potential mechanisms of action of these drugs in AD.

Results

Our analysis identified 163 shared targets between JAK inhibitors and AD. Enrichment analysis revealed that these genes were primarily involved in protein phosphorylation and were enriched in key signaling pathways, including the neurotrophin, phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling pathways. PPI analysis identified AKT1, BCL2, SRC, STAT3, and TNF as five highly ranked hub targets across multiple topological algorithms. Transcriptomic validation confirmed significantly higher expression of these targets in the prefrontal cortex of individuals with AD compared with normal subjects. Molecular docking indicated that pacritinib and momelotinib showed relatively favorable predicted interactions with the hub proteins, while NMA revealed differences in the predicted flexibility of the docked complexes. Furthermore, ADMET prediction showed that pacritinib possesses favorable pharmacokinetic properties for the treatment of AD.

Conclusions

Collectively, these findings provide mechanistic insights into the potential effects of JAK inhibitors in AD and identify pacritinib as a computationally prioritized candidate that warrants experimental validation in appropriate AD models. However, as this study is based solely on computational analyses without wet-lab validation, the findings should be considered hypothesis-generating in silico evidence, and the potential safety concerns of pacritinib require further investigation.