Key Regulators of Alzheimer’s Disease: Network Biology and In-Silico Analysis with AChE and Glutamate Inhibitors

Key Regulators of Alzheimer’s Disease: Network Biology and In-Silico Analysis with AChE and Glutamate Inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Key Regulators of Alzheimer’s Disease: Network Biology and In-Silico Analysis with AChE and Glutamate Inhibitors Sayantan Das This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8638941/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder driven by both genetic and environmental factors, with hallmark features including amyloid-β plaques and neurofibrillary tangles. Despite advances in therapeutics, current treatments remain palliative, underscoring the urgent need for novel targets and multipathway interventions. This study employed a systems biology approach to identify central regulatory proteins in AD through protein−protein interaction (PPI) networks. Using six major biomedical databases, 85 overlapping AD-related genes were identified, and a primary PPI network was constructed and analyzed using CytoScape. Centrality metric (CytoNCA) and hub (CytoHubba) analyses led to the identification of seven key regulators: APP, BDNF, APOE, VEGFA, PSEN1, NOTCH1, and CASP1. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed their involvement in key neurobiological functions including axon development, signaling receptor binding, and neurodegenerative pathways. These targets were further evaluated through molecular docking against four FDA-approved AD drugs i.e., donepezil, galantamine, rivastigmine, and memantine using AutoDock Vina. Notably, BDNF showed the strongest binding affinity across all the compounds, especially with donepezil, whereas APP exhibited the weakest interactions. This multilevel computational study reveals critical molecular targets in AD and explores their potential responsiveness to existing therapeutics, supporting drug repurposing strategies. Computational Neuroscience Bioinformatics Alzheimer’s disease Cholinesterase inhibitors Glutamate inhibitors Molecular docking In-Silico studies Network biology Drug repurposing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Alzheimer’s disease (AD) is a neurodegenerative disorder known for damaging neurons in the brain and is characterized by memory loss, cognitive decline, and behavioural impairments [ 1 ]. Dementia, a more advanced manifestation of AD, currently affects over 55 million people worldwide, and this number is projected to triple by 2060 [ 2 ]. The hallmark pathological features of AD include the accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles formed by the tau protein, as well as the involvement of microglia and astrocytes [ 3 , 4 ]. Owing to the multifactorial nature of the disease, early-stage (prodromal) diagnosis and treatment remain clinically challenging. Currently, there is no cure for AD; existing treatments only offer symptomatic relief [ 5 ]. Hence, there is an urgent need for effective therapeutic agents capable of halting or reversing AD progression. Studies have shown that Aβ plaques and neurofibrillary tangles are present in approximately 85% of clinically diagnosed AD patients, suggesting that these features are central to disease pathology [ 6 – 8 ]. Several genetic mutations are associated with AD pathogenesis [ 9 ], including mutations in Amyloid precursor protein (APP), Presenilin 1 and 2 (PSEN1/2), β-secretase 1 (BACE1), Microtubule-Associated Protein Tau (MAPT), and Apolipoprotein E (APOE)-ε4 [ 10 – 12 ]. Aβ is a toxic peptide fragment of APP, that is produced via proteolytic cleavage by β- and γ-secretases [ 13 , 14 ], and deposited in the brain as plaques. Mutations in the PSEN1/2 genes (which encode γ-secretase) and the BACE1 gene (which encodes β-secretase) lead to this abnormal cleavage process [ 10 , 15 ]. Similarly, the MAPT gene, which is associated with tau pathology, can give rise to paired helical filaments (PHFs), neurofibrillary tangles (NFTs), and hyperphosphorylated tau [ 16 ]. APOE, a cholesterol transporter, has three isoforms―ε2, ε3, and ε4, with APOE-ε4 being the most significant genetic risk factor for AD [ 17 ]. Individuals carrying a single ε4 allele (heterozygotes) have a 2–3 times greater risk of developing AD, whereas those with two ε4 alleles (homozygotes) have a 12–15 times greater risk [ 18 ]. In addition to FDA-approved monoclonal antibodies [ 19 ], current treatments also include cholinesterase inhibitors (donepezil, rivastigmine, galantamine) and glutamate pathway modulators (memantine). Acetylcholine is a neurotransmitter essential for cognition, memory, synaptic transmission, neuromodulation, and the immune response [ 20 ]. It is degraded by the enzyme acetylcholinesterase, and its reduction contributes to cognitive decline. On the other hand, glutamate, an excitatory neurotransmitter, chronically activates N-methyl-D-aspartate receptors (NMDARs), leading to Ca²⁺ influx upon removal of the Mg²⁺ block during depolarization [ 21 ]. Excessive intracellular Ca²⁺ activates enzymes that produce reactive oxygen species, contributing to neurodegeneration. Cholinesterase and glutamate inhibitors function by binding to their respective enzymes or receptors to prevent these pathological processes, as illustrated in Fig. 1 . Network biology is an interdisciplinary field that combines computational techniques with biological sciences. Modeling biological systems as interconnected networks rather than isolated components helps in deciphering complex biological functions across multiple levels (genes, proteins, cells, tissues, and organs). In such networks, nodes represent biomolecules (e.g., amino acid residues, proteins, or cells), and edges depict their physical, functional, or chemical interactions. Protein−protein interaction (PPI) data can be applied on a large scale to construct networks that reflect these associations [ 22 ]. For this purpose, CytoScape serves as a comprehensive tool in network biology for the analysis and visualization of biomolecular interaction networks, integrating high-throughput expression data and other molecular states into a unified framework [ 23 ]. Despite extensive research, the molecular mechanisms underlying AD remain incompletely understood, particularly concerning key regulatory proteins and their potential therapeutic inhibitors. Although, many drugs have been repurposed [ 24 ] and may act on multiple targets [ 25 ], AD remains incurable, largely because of polygenic nature, and no molecule has yet been proven to comprehensively solve this problem. This gap highlights the importance of systems-level approaches to identify new intervention points and understand disease progression more comprehensively. This study aims to identify central protein regulators involved in the progression of AD by integrating protein−protein interaction networks with computational analysis. The therapeutic potential of cholinesterase and glutamate inhibitors against these key targets was further evaluated via molecular docking and simulation techniques. 2. Materials and Methods/Methodology 2.1. Extraction of overlapping genes from various databases For the present study, several biological databases were utilized to identify the overlapping genes from all the databases. Using “Alzheimer’s disease” as a search term, AD-related genes (gene symbols and descriptions) were mined from six existing databases, the DisGeNET database ( https://www.disgenet.org/ ) , the DrugBank database ( https://go.drugbank.com/ ) , the Ensembl database ( https://ensemblgenomes.org/ ) , the GeneCards database ( https://www.genecards.org/ ) , the OMIM database ( https://www.omim.org/ ) , and the TTD database ( https://db.idrblab.net/ttd/ ). In search of overlapping genes, a traditional method, the “VLOOKUP” formula in Microsoft Excel was used to identify the genes common to all the databases. This was done by comparing one database at a time with the remaining ones on the basis of gene symbols, and the process was repeated until a single consolidated sheet was obtained. 2.2. Network construction and analysis A PPI network was constructed using STRING database (version 12, https://string-db.org/ ), which displays multiple interactions between the selected genes. The search of species was restricted to “ Homo sapiens ” to construct the PPI network. The generated network was then visualized using CytoScape (version 3.10.3), an open-source software that provides visualization, analysis, modeling and integration of interaction networks. The importance of each node was evaluated by calculating mean of four centrality parameters and assessed using CytoNCA in CytoScape, these parameters include “Degree centrality (DC)”, “Closeness centrality (CC)”, “Betweenness centrality (BC)”, and “EigenVector centrality (EvC)”. DC represents the number of other nodes directly connected to given node, and CC represents the shortest path of other nodes with given node. BC is calculated by considering several nodes and calculating the number of shortest paths linked to the considered nodes. EvC reflects relative scores to all nodes in a network on the basis that connections to high-scoring nodes contribute to the score of a specific node more than equal connections to low-scoring nodes. The top 10 genes with the highest mean values were selected from this process for further studies. After centrality measurements, CytoHubba was applied to the three parameters separately in CytoScape software, which involves degree, closeness, and betweenness with the criteria of showing the top ten nodes. 2.3. Screening of target genes To identify overlapping or target genes, genes that appeared in all four abovementioned processes were considered and 10 genes were taken from each process. The overlapping genes were identified using VENNY version 2.1, https://bioinfogp.cnb.csic.es/tools/venny/ ) and considered for further analysis. Moreover, secondary PPI network was constructed with target genes, followed by gene enrichment analysis and docking studies with acetylcholinesterase and glutamate inhibitors. 2.4. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses The target genes were uploaded to the ShinyGO database (version 0.82) ( http://bioinformatics.sdstate.edu/go/ ) , a web-server that is used to produce enriched GO terms and KEGG pathways on the basis of gene or protein lists. A threshold of FDR < 0.05 and species restricted to “Humans” were applied along with the pathway to show 20 and pathway sizes between 2 and 2000, and charts were plotted on the same platform for data visualization and analysis. 2.5. Molecular docking and simulation The target proteins were docked with four FDA approved compounds, which include acetylcholinesterase inhibitors, and glutamate inhibitors, and their information is given in Table 1 . Molecular docking was performed using AutoDock Vina (version 4.2.6) to illustrate the binding activity and mechanism between identified targets, and, acetylcholinesterase and glutamate inhibitors. The 3D structures of the ligand molecules were downloaded in SDF format from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) , and the SDF format was converted into pdbqt format using Discovery Studio Visualizer 2021 software ( https://discover.3ds.com/discovery-studio-visualizer-download ). The protein structures of the targets were downloaded from the RCSB database ( https://www.rcsb.org/ ) in PDB format, and the docking sites were predicted using DeepSite ( https://www.playmolecule.com/deepsite/ ) and are presented in Table 2 . Discovery Studio Visualizer was used to remove water and ligand molecules, and AutoDock Tools were used to add hydrogen atoms and charges while all other parameters were left in default settings, the modified protein structures were saved in pdbqt format. Finally, AutoDock Vina was used to perform the molecular docking analysis. The binding affinities (kcal/mol) were predicted by docking scores, with the lowest score representing the strongest affinity. The docking results were visualized using Discovery Studio Visualizer 2021 software to analyze the best binding pocket. Table 1: Information on four FDA-approved drug molecules collected from the PubChem database (CID, chemical ID; MF, molecular formula; MW, molecular weight) CID Molecule name MF MW Structure 3152 Donepezil C 24 H 29 NO 3 379.5 g/mol 9651 Galantamine C 17 H 21 NO 3 287.35 g/mol 77991 Rivastigmine C 14 H 22 N 2 O 2 250.34 g/mol 4054 Memantine C 12 H 21 N 179.3 g/mol Table 2 Protein information and DeepSite prediction Protein PDB ID Description DeepSite Prediction X Y Z Score APP 1IYT Solution structure of the Alzheimer's disease amyloid beta-peptide (1–42) 3.8 -0.0 -5.8 0.47 BDNF 1BND Structure of the brain-derived neurotrophic factor(slash) neurotrophin 3 heterodimer 20.4 -12.3 38.7 1.00 APOE 2L7B NMR Structure of full length ApoE3 3.7 -10.6 4.7 0.99 VEGFA 1VPF Structure of human vascular endothelial growth factor 35.0 10.0 12.4 0.94 NOTCH1 1YYH Crystal structure of the human Notch 1 ankyrin domain 27.6 33.7 -1.2 0.90 CASP1 1RWK Crystal structure of human caspase-1 in complex with 3-(2-mercapto-acetylamino)-4-oxo-pentanoic acid 41.5 53.0 -1.3 0.97 PSEN1 2KR6 Solution structure of presenilin-1 CTF subunit 3.5 76.7 10.7 0.99 3. Results 3.1. Construction of a common gene PPI network In sum, 15,708 AD-related genes were extracted from the six databases, 85 of which were overlapping genes, and these genes were determined manually using VLOOKUP formula in MS Excel after the duplicate gene symbols were removed. A PPI network was subsequently constructed to describe the multiple interactions between different genes identified in the previous process. In a network, each protein is represented as circular nodes and the lines between two proteins represent edges. Thus, as the number of lines increases, strong interactions occur. A total of 85 nodes and 536 edges were obtained in the first network from the STRING database (Fig. 2 ), which were further visualized and analyzed via Cytoscape. 3.2. PPI network analysis From CytoNCA, the mean values of 85 AD genes, with respect to the DC, CC, BC, and EvC parameters, were obtained, and the top 10 nodes with the highest mean values were screened and sorted according to their mean values (higher to lower) (Table 3 ). These parameters are very important for identifying the significance of particular nodes in the network. The higher the mean value is, the greater the importance of that particular node. As a result, APP had the highest mean centrality value, followed by BDNF, APOE, VEGFA, CSTB, ADAM10, NOTCH1, CASP1, PSEN1, and ACE. Furthermore, 10 hub genes were also identified using CytoHubba, while selecting parameters such as degree (Fig. 3 A), closeness (Fig. 3 B), and betweenness (Fig. 3 C), and arranged in a rank wise manner in the clockwise direction. The rank was maintained by the size of the circle; a larger circle represents a higher rank. Surprisingly, APP, APOE, BDNF, and VEGFA had the highest ranks in all three parameters. Table 3 Centrality means of the top 10 genes Gene DC CC BC EvC Mean APP 44 0.6614173 1075.3599 0.2749554 280.0740682 BDNF 38 0.6363636 1067.5322 0.23041376 276.5997443 APOE 41 0.6511628 769.1726 0.2734267 202.7742974 VEGFA 33 0.60431653 593.82007 0.19633302 156.9051799 CSTB 19 0.5283019 311.4442 0.12551706 82.77450474 ADAM10 19 0.5283019 278.20917 0.14023627 74.46942704 NOTCH1 25 0.5562914 263.93576 0.17393363 72.41649626 CASP1 24 0.5637584 255.20116 0.15443727 69.97983892 PSEN1 30 0.5915493 209.61992 0.22315016 60.10865487 ACE 21 0.5283019 187.96638 0.13246998 52.40678797 3.3. Target gene screening and subnetwork formation The results from the previous steps generated four different outputs, each containing 10 genes/nodes. These results were then compared using VENNY 2.1 to identify the genes with greatest degree of overlap (Fig. 4 A). As a result, seven genes were identified, including APOE, VEGFA, NOTCH1, CASP1, PSEN1, APP, and BDNF. Moreover, a secondary PPI network was produced using target genes as described previously (Fig. 4 B). The sub-network contains 7 nodes with a total of 19 edges, where APP, APOE, PSEN1, NOTCH1, and BDNF are strongly connected with each other. Overall, APP had the greatest interaction with APOE, PSEN1, NOTCH1, and BDNF, followed by PSEN1, NOTCH1, and APOE. 3.4. Gene enrichment analysis To further explain the multiple mechanisms of the seven identified genes, GO enrichment and KEGG pathway analyses were conducted. The important GO terms included biological process, cellular component, and molecular functions. The analyzed biological processes were dominated by axonogenesis, axon development, cell morphogenesis involved in neuron differentiation, neuron projection morphogenesis, plasma membrane−bound cell projection morphogenesis, cell projection morphogenesis, cell part morphogenesis, positive regulation of multicellular organismal processes, positive regulation of signal transduction, positive regulation of cell communication and positive regulation of signaling, and all target genes accounted for the above mentioned biological processes (Fig. 5 A). However, the analyzed cell component was dominated by secretory vesicle (Fig. 5 B) and the molecular function was dominated by signaling receptor binding (Fig. 5 C). Secretory vesicles include 5 genes and 6 genes involved in signaling receptor binding. KEGG pathway analysis revealed 3 random genes associated with AD, pathways associated with neurodegeneration and human papillomavirus infection (Fig. 5 D). 3.5. Molecular docking analysis The top 7 targets and FDA-approved drugs were subjected to molecular docking using AutoDock Vina 4.2.6. The lowest binding energy indicates a strong binding affinity between the ligand and the receptor. In this study, the binding energies between all of the receptors and ligands were negative, and ranged from − 3.72 kcal/mol to -10.16 kcal/mol (Fig. 6 ). The findings suggest that BDNF has the overall strongest binding affinity with donepezil, galantamine, rivastigmine, and memantine; whereas the docking score of APP is lowest with drugs among all other targets. The binding energy of donepezil with BDNF was the strongest, followed by that of VEGFA > CASP1 > PSEN1 > NOTCH1 > APOE > APP (Fig. 7 ). However, galantamine effectively binds with BDNF, obtaining a docking score of -7.63 kcal/mol, and the lowest score is obtained with APP (-4.52 kcal/mol) as presented in Fig. 8 . On the basis of the binding energies between targets and rivastigmine, the targets are ranked from lowest to highest are BDNF, PSEN1, APOE, VEGFA, CASP1, NOTCH1, and APP (Fig. 9 ). Furthermore, the highest binding affinity of memantine is for PSEN1 (-7.85 kcal/mol) and the lowest binding affinity is for with APP (-4.73 kcal/mol), as shown in Fig. 10 . 4. Discussion AD is increasingly understood not as a disease with a single cause, but as a complex network of interacting molecular failures spanning synaptic dysfunction [ 26 ], mitochondrial imbalance [ 27 ], neuroinflammation [ 28 ], and vascular deterioration [ 29 ]. The present study steps into this intricate landscape with a systems biology approach, identifying seven critical regulators (APP, BDNF, APOE, VEGFA, PSEN1, NOTCH1, and CASP1) and exploring their molecular affinities with cholinergic and glutamatergic drugs via in-silico docking. This dual-layered strategy highlights not only the molecular nodes central to the pathogenesis of AD but also the therapeutic versatility of established drugs such as donepezil, memantine, galantamine, and rivastigmine. The strong binding affinity of donepezil for BDNF, in particular, opens an intriguing therapeutic avenue. BDNF, a key modulator of synaptic plasticity and memory encoding, is consistently downregulated in AD brains and cerebrospinal fluid, contributing to synaptic failure and hippocampal atrophy [ 30 ]. A study confirmed that AChE inhibitors can modulate BDNF expression through N-methyl-d-aspartate receptor (NMDAR)-blockade, independent of their cholinesterase activity [ 31 ]. This suggests a hidden neurotrophic potential in drugs originally designed only to address acetylcholine depletion. Additionally, the interaction between BDNF and NMDA receptor signaling cascades, creates a bridge between the cholinergic and glutamatergic systems [ 32 ]. By binding to BDNF and possibly stabilizing its function, donepezil may reinforce synaptic resilience beyond its canonical scope. In contrast, APP, the canonical hallmark of AD, showed weak docking interactions, which aligns with the historical difficulty of pharmacologically modulating this protein. APP is primarily a transmembrane structural protein with minimal surface-accessible binding pockets, rendering it a poor direct drug target [ 33 ], however, attention has shifted to its cleavage products, especially soluble Aβ oligomers, as neurotoxic agents. These proteins are largely generated through PSEN1/2-mediated γ-secretase activity [ 34 ], placing PSEN1/2 at the therapeutic frontline. Interestingly, Memantine, although it is primarily an NMDA receptor antagonist, demonstrated meaningful affinity for PSEN1 in this study. This observation supported by recent findings showing that NMDA modulation can indirectly alter γ-secretase activity and mitigate PSEN1-driven amyloidogenesis, likely via intracellular calcium regulation and synaptic stabilization [ 35 ]. PSEN has also been implicated in mitochondrial fragmentation [ 36 ] and synaptic vesicle cycling [ 37 ], indicating that drugs that interact with this gene could impact many neurodegenerative processes. In support of the polypharmacological narrative, VEGFA has emerged as a strong interactor with multiple cholinergic drugs. VEGFA is best known for its role in vascular permeability and angiogenesis [ 38 ], but it also contributes to neuroprotection through enhanced cerebral blood flow [ 39 ], neurogenesis [ 40 ], and antiapoptotic signaling [ 41 ] in astrocytes and endothelial cells. The involvement of VEGFA in AD has been substantiated by evidence of its upregulation in early disease stages and depletion during later progression [ 42 , 43 ], indicating a biphasic compensatory role. Recent evidence suggests that AChE inhibitors may increase VEGFA expression and function by promoting nitric oxide production via endothelial nitric oxide synthase activation [ 44 , 45 ]. The affinity of donepezil for VEGFA observed in docking studies could thus reflect not only symptomatic management but also vasoprotective effects that might alter disease progression. APOE, which has long been established as the strongest genetic risk factor for late-onset AD through its ε4 allele, presents a more nuanced pharmacological profile. While its docking affinity was moderate, its functional implications remain profound. APOE modulates lipid metabolism, synaptic pruning, and amyloid clearance, and its isoforms directly affect blood−brain barrier integrity [ 46 ] and tau phosphorylation [ 47 ]. Although not traditionally viewed as druggable, allosteric modulators and gene editing approaches targeting APOE isoform balance are now under development [ 48 ]. Moreover, pharmacogenomic insights suggest that the APOE genotype may influence patient response to AChE inhibitors [ 49 – 51 ], underscoring the need for stratified treatment models. Among the most compelling targets identified are CASP1, a proinflammatory protease that activates the NLRP3 inflammasome and drives pyroptotic cell death in neurons and glia [ 52 ]. The strong docking interactions of donepezil and rivastigmine with CASP1 suggest a possible anti-inflammatory role for these drugs, which is consistent with recent discoveries showing their capacity to inhibit inflammasome priming and cytokine release via α7 nicotinic acetylcholine receptor signaling [ 53 , 54 ]. CASP1 not only mediates IL-1β maturation but also influences microglial polarization and neurovascular integrity [ 55 ], making it a multidimensional target in AD. Suppression of CASP1 activity could slow disease progression by dampening neuroinflammatory loops that accelerate tauopathy and synaptic degeneration. The role of NOTCH1, a highly conserved signaling molecule involved in neurogenesis, stem cell maintenance, and angiogenesis [ 56 ], further enriches the therapeutic landscape. The observed affinity of memantine and galantamine for NOTCH1 supports recent models in which NMDA receptor modulation indirectly influences Notch signaling, affecting dendritic spine morphology, and neural regeneration [ 57 , 58 ]. Moreover, NOTCH1 cross-talks with PSEN1 via the γ-secretase complex [ 59 ], meaning that drugs affecting one may modulate the other, amplifying therapeutic effects through shared signaling nodes. Epigenetic regulation of NOTCH1 has also been implicated in tau pathology and neurogenic dysfunction, adding layers of potential intervention [ 60 ]. The combined evidence of docking across multiple genes underscores a paradigm shift in AD pharmacology from single-target inhibition toward multitarget engagement. This is not merely a theoretical exercise; real-world drug discovery is now embracing hybrid molecules such as bis(7)-tacrine, combining AChE inhibition with NMDA antagonism [ 61 ], and others that bridge neuroinflammation with neurotrophic support [ 62 ]. These drugs aim not only to slow symptoms but also to reshape the underlying molecular networks driving degeneration. This aligns with clinical findings that combination therapies outperform monotherapies in preserving cognition and delaying institutionalization in AD patients [ 63 , 64 ]. Notably, drugs such as donepezil and galantamine may also regulate neurotransmitter levels through indirect mechanisms, such as the inhibition of glutamate-induced neurotoxicity [ 65 ] and enhancement of GABAergic tone [ 66 ]. These effects have been linked to the modulation of calcium channels and voltage-gated potassium channels, suggesting broader electrophysiological influences beyond the synapse [ 67 , 68 ]. The convergence of cholinergic, glutamatergic, and neurotrophic signaling is therefore not incidental but mechanistically central to how these drugs provide cognitive and structural neuroprotection. Taken together, the results of this study not only identify key AD regulators but also provide a credible framework for repurposing and improving existing therapeutics on the basis of network biology. This study highlights how computational docking and centrality analysis can guide hypothesis-driven exploration of therapeutic targets. The finding that canonical drugs such as donepezil and memantine have high affinity for regulators such as BDNF, VEGFA, NOTCH1, and CASP1 suggests untapped therapeutic potential. These interactions, although in silico, resonate with in vivo observations of improved neurogenesis, vascular function, and reduced inflammatory marker levels in treated patients. While experimental validation remains crucial, the concordance between molecular docking and emerging mechanistic insights underscores the power of integrated bioinformatics in advancing Alzheimer’s therapeutics. 5. Conclusion This study underscores the importance of a systems-level approach to decipher the molecular complexity of Alzheimer’s. By integrating multi-database gene mining, PPI network analysis, and molecular docking, seven central regulatory genes, includes APP, APOE, BDNF, VEGFA, PSEN1, CASP1, and NOTCH1 that may serve as potential drug targets, were successfully identified. Enrichment analysis revealed their involvement in crucial biological processes such as axon development and signal transduction. Docking studies with FDA-approved drugs have demonstrated varying degrees of affinity, with BDNF consistently exhibiting strong interactions across all ligands, particularly with donepezil. Moreover, the superior binding of memantine with PSEN1 supports its continued use as a glutamate pathway modulator. Interestingly, APP, although central to AD pathology, displayed comparatively low binding affinities, indicating that disease causality may not always translate into therapeutic tractability. These findings not only validate current treatment strategies but also provide a foundation for future drug repurposing and multitarget drug development. As Alzheimer’s disease continues to impose a global health burden, this research highlights the promise of computational biology in uncovering novel intervention points, ultimately aiming to bridge the gap between molecular insight and therapeutic innovation. Declarations Ethical Approval: Not applicable Funding: None Availability of data and materials: No data was used for the research described in the article Clinical Trial Number: Not applicable References Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM (2021) Alzheimer's disease. 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09:16:59","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66868,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/8361d69d5cfcd3acae7fe860.png"},{"id":100767897,"identity":"e912b86d-61c0-4e19-b7f3-89b6ccb7da0e","added_by":"auto","created_at":"2026-01-21 09:17:09","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177376,"visible":true,"origin":"","legend":"","description":"","filename":"rs86389410structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/c97e8a38b2ca2a69941b143d.xml"},{"id":100767944,"identity":"e3164e08-fecc-4f40-baf5-5eb43618e58e","added_by":"auto","created_at":"2026-01-21 09:18:18","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190055,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/b89894ea059c1a1c43b88e36.html"},{"id":100767769,"identity":"6b188c0f-c99f-4913-ab7a-c2e71d04a9d1","added_by":"auto","created_at":"2026-01-21 09:16:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80457,"visible":true,"origin":"","legend":"\u003cp\u003eThis image illustrates the synaptic mechanisms targeted by FDA-approved drugs for Alzheimer's disease. (A) Cholinergic synapse, where an action potential triggers acetylcholine (ACh) release from the presynaptic neuron. ACh binds to nicotinic receptors on postsynaptic neurons, promoting signal transmission. Acetylcholinesterase (AChE) rapidly breaks down ACh into choline and acetate. Drugs such as donepezil, galantamine, and rivastigmine inhibit AChE, increasing synaptic ACh levels and improving cognitive function. (B) depicts the glutamatergic synapse, where glutamate is released and binds to NMDA receptors, allowing Na⁺ and Ca²⁺ influx. Excessive glutamate can cause excitotoxicity, contributing to neuronal damage. Memantine acts as an NMDA receptor antagonist, blocking abnormal Ca²⁺ influx while preserving normal neurotransmission. Together, these mechanisms represent key therapeutic strategies in Alzheimer’s the management of AD through the modulation of cholinergic and glutamatergic pathways.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/770b892e6f7f8c7df2169e04.png"},{"id":100767841,"identity":"cc96022d-0d31-4432-9d56-6575972c2784","added_by":"auto","created_at":"2026-01-21 09:16:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1309647,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of 85 AD-related genes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/99af1b7cf880bdfa6779ab48.png"},{"id":100767644,"identity":"64c4bfd7-9537-4ffa-b3a1-86e6def0013b","added_by":"auto","created_at":"2026-01-21 09:15:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":310941,"visible":true,"origin":"","legend":"\u003cp\u003eThe top 10 nodes identified using Cytohubba, based on (A) degree, (B) closeness, and (C) betweenness\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/c0ce2131c51b088246f0b2bb.png"},{"id":100767709,"identity":"896f8879-0e67-4691-a94e-625763da2236","added_by":"auto","created_at":"2026-01-21 09:15:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":673681,"visible":true,"origin":"","legend":"\u003cp\u003eTarget acquisition and sub-PPI network diagram of the overlapping genes (A)Venn diagram of the 7 overlapping genes (B)PPI network of the 7 targets.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/3e944b27d89a78726668d12b.png"},{"id":100767837,"identity":"f36984dd-7f7e-4fbc-ad76-34d3fc23a6f2","added_by":"auto","created_at":"2026-01-21 09:16:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":376634,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG pathway analyses. The number of genes in each category is represented here. (A)Biological processes (B)Cellular component (C)Molecular function (D)KEGG pathway\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/26e956901fd682ae0924c350.png"},{"id":100767713,"identity":"84997537-5f13-452c-b340-90d04b51c870","added_by":"auto","created_at":"2026-01-21 09:15:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":374083,"visible":true,"origin":"","legend":"\u003cp\u003eAutoDock results, and the lowest binding energies (kcal/mol) between the targets and FDA-approved drugs\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/e14fedc2775ad435167c83bd.png"},{"id":100767965,"identity":"36a01dc8-98ae-4536-8b08-b7f23eb14595","added_by":"auto","created_at":"2026-01-21 09:18:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":999563,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D docking conformations of donepezil with (A)APP, (B)APOE, (C)BDNF, (D)PSEN1, (E)CASP1, (F)NOTCH1, and (G)VEGFA\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/ae02840f1bb6abbcdce5b543.png"},{"id":100767851,"identity":"85ab4a2b-f480-48fb-b478-cd6c3a950de1","added_by":"auto","created_at":"2026-01-21 09:16:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1044627,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D docking conformations of galantamine with (A)APP, (B)APOE, (C)BDNF, (D)PSEN1, (E)CASP1, (F)NOTCH1, and (G)VEGFA\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/1ce0c7f764d019844c2e6c09.png"},{"id":100767835,"identity":"51bf5683-a5af-4a76-a9ad-678940739e2d","added_by":"auto","created_at":"2026-01-21 09:16:22","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":974969,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D docking conformations of rivastigmine with (A)APP, (B)APOE, (C)BDNF, (D)PSEN1, (E)CASP1, (F)NOTCH1, and (G)VEGFA.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/934b5ba3cb797c3eb10b743a.png"},{"id":100767842,"identity":"3977e3c0-b68d-4d80-93ee-3173c20f112e","added_by":"auto","created_at":"2026-01-21 09:16:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1006798,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D docking conformations of memantine with (A)APP, (B)APOE, (C)BDNF, (D)PSEN1, (E)CASP1, (F)NOTCH1, and (G)VEGFA.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/1abda61b740e9d88a0ce2d4b.png"},{"id":100798022,"identity":"5107442d-4f49-464a-86f6-3bdf771c8e79","added_by":"auto","created_at":"2026-01-21 13:52:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7436309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8638941/v1/bbe48fe3-2382-4ea4-a7b5-575a93c6caef.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eKey Regulators of Alzheimer’s Disease: Network Biology and In-Silico Analysis with AChE and Glutamate Inhibitors\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a neurodegenerative disorder known for damaging neurons in the brain and is characterized by memory loss, cognitive decline, and behavioural impairments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Dementia, a more advanced manifestation of AD, currently affects over 55\u0026nbsp;million people worldwide, and this number is projected to triple by 2060 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The hallmark pathological features of AD include the accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles formed by the tau protein, as well as the involvement of microglia and astrocytes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Owing to the multifactorial nature of the disease, early-stage (prodromal) diagnosis and treatment remain clinically challenging. Currently, there is no cure for AD; existing treatments only offer symptomatic relief [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hence, there is an urgent need for effective therapeutic agents capable of halting or reversing AD progression. Studies have shown that Aβ plaques and neurofibrillary tangles are present in approximately 85% of clinically diagnosed AD patients, suggesting that these features are central to disease pathology [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral genetic mutations are associated with AD pathogenesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], including mutations in Amyloid precursor protein (APP), Presenilin 1 and 2 (PSEN1/2), β-secretase 1 (BACE1), Microtubule-Associated Protein Tau (MAPT), and Apolipoprotein E (APOE)-ε4 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Aβ is a toxic peptide fragment of APP, that is produced via proteolytic cleavage by β- and γ-secretases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and deposited in the brain as plaques. Mutations in the PSEN1/2 genes (which encode γ-secretase) and the BACE1 gene (which encodes β-secretase) lead to this abnormal cleavage process [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, the MAPT gene, which is associated with tau pathology, can give rise to paired helical filaments (PHFs), neurofibrillary tangles (NFTs), and hyperphosphorylated tau [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. APOE, a cholesterol transporter, has three isoforms―ε2, ε3, and ε4, with APOE-ε4 being the most significant genetic risk factor for AD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Individuals carrying a single ε4 allele (heterozygotes) have a 2\u0026ndash;3 times greater risk of developing AD, whereas those with two ε4 alleles (homozygotes) have a 12\u0026ndash;15 times greater risk [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to FDA-approved monoclonal antibodies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], current treatments also include cholinesterase inhibitors (donepezil, rivastigmine, galantamine) and glutamate pathway modulators (memantine). Acetylcholine is a neurotransmitter essential for cognition, memory, synaptic transmission, neuromodulation, and the immune response [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It is degraded by the enzyme acetylcholinesterase, and its reduction contributes to cognitive decline. On the other hand, glutamate, an excitatory neurotransmitter, chronically activates N-methyl-D-aspartate receptors (NMDARs), leading to Ca\u0026sup2;⁺ influx upon removal of the Mg\u0026sup2;⁺ block during depolarization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Excessive intracellular Ca\u0026sup2;⁺ activates enzymes that produce reactive oxygen species, contributing to neurodegeneration. Cholinesterase and glutamate inhibitors function by binding to their respective enzymes or receptors to prevent these pathological processes, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eNetwork biology is an interdisciplinary field that combines computational techniques with biological sciences. Modeling biological systems as interconnected networks rather than isolated components helps in deciphering complex biological functions across multiple levels (genes, proteins, cells, tissues, and organs). In such networks, nodes represent biomolecules (e.g., amino acid residues, proteins, or cells), and edges depict their physical, functional, or chemical interactions. Protein\u0026minus;protein interaction (PPI) data can be applied on a large scale to construct networks that reflect these associations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For this purpose, CytoScape serves as a comprehensive tool in network biology for the analysis and visualization of biomolecular interaction networks, integrating high-throughput expression data and other molecular states into a unified framework [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite extensive research, the molecular mechanisms underlying AD remain incompletely understood, particularly concerning key regulatory proteins and their potential therapeutic inhibitors. Although, many drugs have been repurposed [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and may act on multiple targets [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], AD remains incurable, largely because of polygenic nature, and no molecule has yet been proven to comprehensively solve this problem. This gap highlights the importance of systems-level approaches to identify new intervention points and understand disease progression more comprehensively. This study aims to identify central protein regulators involved in the progression of AD by integrating protein\u0026minus;protein interaction networks with computational analysis. The therapeutic potential of cholinesterase and glutamate inhibitors against these key targets was further evaluated via molecular docking and simulation techniques.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods/Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Extraction of overlapping genes from various databases\u003c/h2\u003e \u003cp\u003eFor the present study, several biological databases were utilized to identify the overlapping genes from all the databases. Using \u0026ldquo;Alzheimer\u0026rsquo;s disease\u0026rdquo; as a search term, AD-related genes (gene symbols and descriptions) were mined from six existing databases, the DisGeNET database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the Ensembl database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ensemblgenomes.org/\u003c/span\u003e\u003cspan address=\"https://ensemblgenomes.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the OMIM database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omim.org/\u003c/span\u003e\u003cspan address=\"https://www.omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and the TTD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://db.idrblab.net/ttd/\u003c/span\u003e\u003cspan address=\"https://db.idrblab.net/ttd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e In search of overlapping genes, a traditional method, the \u0026ldquo;VLOOKUP\u0026rdquo; formula in Microsoft Excel was used to identify the genes common to all the databases. This was done by comparing one database at a time with the remaining ones on the basis of gene symbols, and the process was repeated until a single consolidated sheet was obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Network construction and analysis\u003c/h2\u003e \u003cp\u003eA PPI network was constructed using STRING database (version 12, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which displays multiple interactions between the selected genes. The search of species was restricted to \u0026ldquo;\u003cem\u003eHomo sapiens\u003c/em\u003e\u0026rdquo; to construct the PPI network. The generated network was then visualized using CytoScape (version 3.10.3), an open-source software that provides visualization, analysis, modeling and integration of interaction networks. The importance of each node was evaluated by calculating mean of four centrality parameters and assessed using CytoNCA in CytoScape, these parameters include \u0026ldquo;Degree centrality (DC)\u0026rdquo;, \u0026ldquo;Closeness centrality (CC)\u0026rdquo;, \u0026ldquo;Betweenness centrality (BC)\u0026rdquo;, and \u0026ldquo;EigenVector centrality (EvC)\u0026rdquo;. DC represents the number of other nodes directly connected to given node, and CC represents the shortest path of other nodes with given node. BC is calculated by considering several nodes and calculating the number of shortest paths linked to the considered nodes. EvC reflects relative scores to all nodes in a network on the basis that connections to high-scoring nodes contribute to the score of a specific node more than equal connections to low-scoring nodes. The top 10 genes with the highest mean values were selected from this process for further studies. After centrality measurements, CytoHubba was applied to the three parameters separately in CytoScape software, which involves degree, closeness, and betweenness with the criteria of showing the top ten nodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Screening of target genes\u003c/h2\u003e \u003cp\u003eTo identify overlapping or target genes, genes that appeared in all four abovementioned processes were considered and 10 genes were taken from each process. The overlapping genes were identified using VENNY version 2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and considered for further analysis. Moreover, secondary PPI network was constructed with target genes, followed by gene enrichment analysis and docking studies with acetylcholinesterase and glutamate inhibitors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses\u003c/h2\u003e \u003cp\u003eThe target genes were uploaded to the ShinyGO database (version 0.82) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, a web-server that is used to produce enriched GO terms and KEGG pathways on the basis of gene or protein lists. A threshold of FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and species restricted to \u0026ldquo;Humans\u0026rdquo; were applied along with the pathway to show 20 and pathway sizes between 2 and 2000, and charts were plotted on the same platform for data visualization and analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Molecular docking and simulation\u003c/h2\u003e \u003cp\u003eThe target proteins were docked with four FDA approved compounds, which include acetylcholinesterase inhibitors, and glutamate inhibitors, and their information is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Molecular docking was performed using AutoDock Vina (version 4.2.6) to illustrate the binding activity and mechanism between identified targets, and, acetylcholinesterase and glutamate inhibitors. The 3D structures of the ligand molecules were downloaded in SDF format from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and the SDF format was converted into pdbqt format using Discovery Studio Visualizer 2021 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.3ds.com/discovery-studio-visualizer-download\u003c/span\u003e\u003cspan address=\"https://discover.3ds.com/discovery-studio-visualizer-download\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The protein structures of the targets were downloaded from the RCSB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e in PDB format, and the docking sites were predicted using DeepSite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.playmolecule.com/deepsite/\u003c/span\u003e\u003cspan address=\"https://www.playmolecule.com/deepsite/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Discovery Studio Visualizer was used to remove water and ligand molecules, and AutoDock Tools were used to add hydrogen atoms and charges while all other parameters were left in default settings, the modified protein structures were saved in pdbqt format. Finally, AutoDock Vina was used to perform the molecular docking analysis. The binding affinities (kcal/mol) were predicted by docking scores, with the lowest score representing the strongest affinity. The docking results were visualized using Discovery Studio Visualizer 2021 software to analyze the best binding pocket.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eInformation on four FDA-approved drug molecules\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecollected from the PubChem database (CID, chemical ID; MF, molecular formula; MW, molecular weight)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecule name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eDonepezil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003ca href=\"https://pubchem.ncbi.nlm.nih.gov/#query=C24H29NO3\"\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e29\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e379.5 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e9651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eGalantamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003ca href=\"https://pubchem.ncbi.nlm.nih.gov/#query=C17H21NO3\"\u003eC\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e21\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e287.35 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cimg 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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e77991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eRivastigmine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003ca href=\"https://pubchem.ncbi.nlm.nih.gov/#query=C14H22N2O2\"\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e250.34 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cimg 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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eMemantine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003ca href=\"https://pubchem.ncbi.nlm.nih.gov/#query=C12H21N\"\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e21\u003c/sub\u003eN\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e179.3 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cimg 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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProtein information and DeepSite prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eDeepSite Prediction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1IYT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolution structure of the Alzheimer's disease amyloid beta-peptide (1\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1BND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructure of the brain-derived neurotrophic factor(slash) neurotrophin 3 heterodimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2L7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNMR Structure of full length ApoE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1VPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructure of human vascular endothelial growth factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOTCH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1YYH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrystal structure of the human Notch 1 ankyrin domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1RWK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrystal structure of human caspase-1 in complex with 3-(2-mercapto-acetylamino)-4-oxo-pentanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSEN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2KR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolution structure of presenilin-1 CTF subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Construction of a common gene PPI network\u003c/h2\u003e \u003cp\u003eIn sum, 15,708 AD-related genes were extracted from the six databases, 85 of which were overlapping genes, and these genes were determined manually using VLOOKUP formula in MS Excel after the duplicate gene symbols were removed. A PPI network was subsequently constructed to describe the multiple interactions between different genes identified in the previous process. In a network, each protein is represented as circular nodes and the lines between two proteins represent edges. Thus, as the number of lines increases, strong interactions occur. A total of 85 nodes and 536 edges were obtained in the first network from the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which were further visualized and analyzed via Cytoscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. PPI network analysis\u003c/h2\u003e \u003cp\u003eFrom CytoNCA, the mean values of 85 AD genes, with respect to the DC, CC, BC, and EvC parameters, were obtained, and the top 10 nodes with the highest mean values were screened and sorted according to their mean values (higher to lower) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These parameters are very important for identifying the significance of particular nodes in the network. The higher the mean value is, the greater the importance of that particular node. As a result, APP had the highest mean centrality value, followed by BDNF, APOE, VEGFA, CSTB, ADAM10, NOTCH1, CASP1, PSEN1, and ACE. Furthermore, 10 hub genes were also identified using CytoHubba, while selecting parameters such as degree (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), closeness (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and betweenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and arranged in a rank wise manner in the clockwise direction. The rank was maintained by the size of the circle; a larger circle represents a higher rank. Surprisingly, APP, APOE, BDNF, and VEGFA had the highest ranks in all three parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCentrality means of the top 10 genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEvC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6614173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1075.3599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2749554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e280.0740682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6363636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1067.5322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23041376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e276.5997443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6511628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e769.1726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2734267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e202.7742974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60431653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e593.82007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19633302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e156.9051799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5283019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e311.4442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12551706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.77450474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADAM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5283019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e278.20917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14023627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.46942704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOTCH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5562914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e263.93576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17393363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.41649626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5637584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255.20116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15443727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.97983892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSEN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5915493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e209.61992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22315016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.10865487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5283019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187.96638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13246998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.40678797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Target gene screening and subnetwork formation\u003c/h2\u003e \u003cp\u003eThe results from the previous steps generated four different outputs, each containing 10 genes/nodes. These results were then compared using VENNY 2.1 to identify the genes with greatest degree of overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As a result, seven genes were identified, including APOE, VEGFA, NOTCH1, CASP1, PSEN1, APP, and BDNF. Moreover, a secondary PPI network was produced using target genes as described previously (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The sub-network contains 7 nodes with a total of 19 edges, where APP, APOE, PSEN1, NOTCH1, and BDNF are strongly connected with each other. Overall, APP had the greatest interaction with APOE, PSEN1, NOTCH1, and BDNF, followed by PSEN1, NOTCH1, and APOE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Gene enrichment analysis\u003c/h2\u003e \u003cp\u003eTo further explain the multiple mechanisms of the seven identified genes, GO enrichment and KEGG pathway analyses were conducted. The important GO terms included biological process, cellular component, and molecular functions. The analyzed biological processes were dominated by axonogenesis, axon development, cell morphogenesis involved in neuron differentiation, neuron projection morphogenesis, plasma membrane\u0026minus;bound cell projection morphogenesis, cell projection morphogenesis, cell part morphogenesis, positive regulation of multicellular organismal processes, positive regulation of signal transduction, positive regulation of cell communication and positive regulation of signaling, and all target genes accounted for the above mentioned biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). However, the analyzed cell component was dominated by secretory vesicle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and the molecular function was dominated by signaling receptor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Secretory vesicles include 5 genes and 6 genes involved in signaling receptor binding. KEGG pathway analysis revealed 3 random genes associated with AD, pathways associated with neurodegeneration and human papillomavirus infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Molecular docking analysis\u003c/h2\u003e \u003cp\u003eThe top 7 targets and FDA-approved drugs were subjected to molecular docking using AutoDock Vina 4.2.6. The lowest binding energy indicates a strong binding affinity between the ligand and the receptor. In this study, the binding energies between all of the receptors and ligands were negative, and ranged from \u0026minus;\u0026thinsp;3.72 kcal/mol to -10.16 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The findings suggest that BDNF has the overall strongest binding affinity with donepezil, galantamine, rivastigmine, and memantine; whereas the docking score of APP is lowest with drugs among all other targets. The binding energy of donepezil with BDNF was the strongest, followed by that of VEGFA\u0026thinsp;\u0026gt;\u0026thinsp;CASP1\u0026thinsp;\u0026gt;\u0026thinsp;PSEN1\u0026thinsp;\u0026gt;\u0026thinsp;NOTCH1\u0026thinsp;\u0026gt;\u0026thinsp;APOE\u0026thinsp;\u0026gt;\u0026thinsp;APP (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, galantamine effectively binds with BDNF, obtaining a docking score of -7.63 kcal/mol, and the lowest score is obtained with APP (-4.52 kcal/mol) as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. On the basis of the binding energies between targets and rivastigmine, the targets are ranked from lowest to highest are BDNF, PSEN1, APOE, VEGFA, CASP1, NOTCH1, and APP (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Furthermore, the highest binding affinity of memantine is for PSEN1 (-7.85 kcal/mol) and the lowest binding affinity is for with APP (-4.73 kcal/mol), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAD is increasingly understood not as a disease with a single cause, but as a complex network of interacting molecular failures spanning synaptic dysfunction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], mitochondrial imbalance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], neuroinflammation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and vascular deterioration [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The present study steps into this intricate landscape with a systems biology approach, identifying seven critical regulators (APP, BDNF, APOE, VEGFA, PSEN1, NOTCH1, and CASP1) and exploring their molecular affinities with cholinergic and glutamatergic drugs via in-silico docking. This dual-layered strategy highlights not only the molecular nodes central to the pathogenesis of AD but also the therapeutic versatility of established drugs such as donepezil, memantine, galantamine, and rivastigmine.\u003c/p\u003e \u003cp\u003eThe strong binding affinity of donepezil for BDNF, in particular, opens an intriguing therapeutic avenue. BDNF, a key modulator of synaptic plasticity and memory encoding, is consistently downregulated in AD brains and cerebrospinal fluid, contributing to synaptic failure and hippocampal atrophy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A study confirmed that AChE inhibitors can modulate BDNF expression through N-methyl-d-aspartate receptor (NMDAR)-blockade, independent of their cholinesterase activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This suggests a hidden neurotrophic potential in drugs originally designed only to address acetylcholine depletion. Additionally, the interaction between BDNF and NMDA receptor signaling cascades, creates a bridge between the cholinergic and glutamatergic systems [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. By binding to BDNF and possibly stabilizing its function, donepezil may reinforce synaptic resilience beyond its canonical scope.\u003c/p\u003e \u003cp\u003eIn contrast, APP, the canonical hallmark of AD, showed weak docking interactions, which aligns with the historical difficulty of pharmacologically modulating this protein. APP is primarily a transmembrane structural protein with minimal surface-accessible binding pockets, rendering it a poor direct drug target [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], however, attention has shifted to its cleavage products, especially soluble Aβ oligomers, as neurotoxic agents. These proteins are largely generated through PSEN1/2-mediated γ-secretase activity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], placing PSEN1/2 at the therapeutic frontline. Interestingly, Memantine, although it is primarily an NMDA receptor antagonist, demonstrated meaningful affinity for PSEN1 in this study. This observation supported by recent findings showing that NMDA modulation can indirectly alter γ-secretase activity and mitigate PSEN1-driven amyloidogenesis, likely via intracellular calcium regulation and synaptic stabilization [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. PSEN has also been implicated in mitochondrial fragmentation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and synaptic vesicle cycling [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], indicating that drugs that interact with this gene could impact many neurodegenerative processes.\u003c/p\u003e \u003cp\u003eIn support of the polypharmacological narrative, VEGFA has emerged as a strong interactor with multiple cholinergic drugs. VEGFA is best known for its role in vascular permeability and angiogenesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], but it also contributes to neuroprotection through enhanced cerebral blood flow [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], neurogenesis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and antiapoptotic signaling [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] in astrocytes and endothelial cells. The involvement of VEGFA in AD has been substantiated by evidence of its upregulation in early disease stages and depletion during later progression [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], indicating a biphasic compensatory role. Recent evidence suggests that AChE inhibitors may increase VEGFA expression and function by promoting nitric oxide production via endothelial nitric oxide synthase activation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The affinity of donepezil for VEGFA observed in docking studies could thus reflect not only symptomatic management but also vasoprotective effects that might alter disease progression.\u003c/p\u003e \u003cp\u003eAPOE, which has long been established as the strongest genetic risk factor for late-onset AD through its ε4 allele, presents a more nuanced pharmacological profile. While its docking affinity was moderate, its functional implications remain profound. APOE modulates lipid metabolism, synaptic pruning, and amyloid clearance, and its isoforms directly affect blood\u0026minus;brain barrier integrity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and tau phosphorylation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Although not traditionally viewed as druggable, allosteric modulators and gene editing approaches targeting APOE isoform balance are now under development [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, pharmacogenomic insights suggest that the APOE genotype may influence patient response to AChE inhibitors [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], underscoring the need for stratified treatment models.\u003c/p\u003e \u003cp\u003eAmong the most compelling targets identified are CASP1, a proinflammatory protease that activates the NLRP3 inflammasome and drives pyroptotic cell death in neurons and glia [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The strong docking interactions of donepezil and rivastigmine with CASP1 suggest a possible anti-inflammatory role for these drugs, which is consistent with recent discoveries showing their capacity to inhibit inflammasome priming and cytokine release via α7 nicotinic acetylcholine receptor signaling [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. CASP1 not only mediates IL-1β maturation but also influences microglial polarization and neurovascular integrity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], making it a multidimensional target in AD. Suppression of CASP1 activity could slow disease progression by dampening neuroinflammatory loops that accelerate tauopathy and synaptic degeneration.\u003c/p\u003e \u003cp\u003eThe role of NOTCH1, a highly conserved signaling molecule involved in neurogenesis, stem cell maintenance, and angiogenesis [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], further enriches the therapeutic landscape. The observed affinity of memantine and galantamine for NOTCH1 supports recent models in which NMDA receptor modulation indirectly influences Notch signaling, affecting dendritic spine morphology, and neural regeneration [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Moreover, NOTCH1 cross-talks with PSEN1 via the γ-secretase complex [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], meaning that drugs affecting one may modulate the other, amplifying therapeutic effects through shared signaling nodes. Epigenetic regulation of NOTCH1 has also been implicated in tau pathology and neurogenic dysfunction, adding layers of potential intervention [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe combined evidence of docking across multiple genes underscores a paradigm shift in AD pharmacology from single-target inhibition toward multitarget engagement. This is not merely a theoretical exercise; real-world drug discovery is now embracing hybrid molecules such as bis(7)-tacrine, combining AChE inhibition with NMDA antagonism [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and others that bridge neuroinflammation with neurotrophic support [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These drugs aim not only to slow symptoms but also to reshape the underlying molecular networks driving degeneration. This aligns with clinical findings that combination therapies outperform monotherapies in preserving cognition and delaying institutionalization in AD patients [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, drugs such as donepezil and galantamine may also regulate neurotransmitter levels through indirect mechanisms, such as the inhibition of glutamate-induced neurotoxicity [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] and enhancement of GABAergic tone [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These effects have been linked to the modulation of calcium channels and voltage-gated potassium channels, suggesting broader electrophysiological influences beyond the synapse [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The convergence of cholinergic, glutamatergic, and neurotrophic signaling is therefore not incidental but mechanistically central to how these drugs provide cognitive and structural neuroprotection.\u003c/p\u003e \u003cp\u003eTaken together, the results of this study not only identify key AD regulators but also provide a credible framework for repurposing and improving existing therapeutics on the basis of network biology. This study highlights how computational docking and centrality analysis can guide hypothesis-driven exploration of therapeutic targets. The finding that canonical drugs such as donepezil and memantine have high affinity for regulators such as BDNF, VEGFA, NOTCH1, and CASP1 suggests untapped therapeutic potential. These interactions, although in silico, resonate with in vivo observations of improved neurogenesis, vascular function, and reduced inflammatory marker levels in treated patients. While experimental validation remains crucial, the concordance between molecular docking and emerging mechanistic insights underscores the power of integrated bioinformatics in advancing Alzheimer\u0026rsquo;s therapeutics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study underscores the importance of a systems-level approach to decipher the molecular complexity of Alzheimer\u0026rsquo;s. By integrating multi-database gene mining, PPI network analysis, and molecular docking, seven central regulatory genes, includes APP, APOE, BDNF, VEGFA, PSEN1, CASP1, and NOTCH1 that may serve as potential drug targets, were successfully identified. Enrichment analysis revealed their involvement in crucial biological processes such as axon development and signal transduction. Docking studies with FDA-approved drugs have demonstrated varying degrees of affinity, with BDNF consistently exhibiting strong interactions across all ligands, particularly with donepezil. Moreover, the superior binding of memantine with PSEN1 supports its continued use as a glutamate pathway modulator. Interestingly, APP, although central to AD pathology, displayed comparatively low binding affinities, indicating that disease causality may not always translate into therapeutic tractability. These findings not only validate current treatment strategies but also provide a foundation for future drug repurposing and multitarget drug development. As Alzheimer\u0026rsquo;s disease continues to impose a global health burden, this research highlights the promise of computational biology in uncovering novel intervention points, ultimately aiming to bridge the gap between molecular insight and therapeutic innovation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical Approval:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e \u003cp\u003eAvailability of data and materials: No data was used for the research described in the article\u003c/p\u003e \u003cp\u003eClinical Trial Number: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u0026eacute;telat G, Teunissen CE, Cummings J, van der Flier WM (2021) Alzheimer's disease. 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Life (Basel Switzerland) 13(8):1655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/life13081655\u003c/span\u003e\u003cspan address=\"10.3390/life13081655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, Cholinesterase inhibitors, Glutamate inhibitors, Molecular docking, In-Silico studies, Network biology, Drug repurposing","lastPublishedDoi":"10.21203/rs.3.rs-8638941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8638941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a complex, progressive neurodegenerative disorder driven by both genetic and environmental factors, with hallmark features including amyloid-β plaques and neurofibrillary tangles. Despite advances in therapeutics, current treatments remain palliative, underscoring the urgent need for novel targets and multipathway interventions. This study employed a systems biology approach to identify central regulatory proteins in AD through protein\u0026minus;protein interaction (PPI) networks. Using six major biomedical databases, 85 overlapping AD-related genes were identified, and a primary PPI network was constructed and analyzed using CytoScape. Centrality metric (CytoNCA) and hub (CytoHubba) analyses led to the identification of seven key regulators: APP, BDNF, APOE, VEGFA, PSEN1, NOTCH1, and CASP1. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed their involvement in key neurobiological functions including axon development, signaling receptor binding, and neurodegenerative pathways. These targets were further evaluated through molecular docking against four FDA-approved AD drugs i.e., donepezil, galantamine, rivastigmine, and memantine using AutoDock Vina. Notably, BDNF showed the strongest binding affinity across all the compounds, especially with donepezil, whereas APP exhibited the weakest interactions. This multilevel computational study reveals critical molecular targets in AD and explores their potential responsiveness to existing therapeutics, supporting drug repurposing strategies.\u003c/p\u003e","manuscriptTitle":"Key Regulators of Alzheimer’s Disease: Network Biology and In-Silico Analysis with AChE and Glutamate Inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 08:37:35","doi":"10.21203/rs.3.rs-8638941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a56ed81-17f1-4b11-a266-d0110eaeea42","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61366129,"name":"Computational Neuroscience"},{"id":61366130,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2026-01-21T08:37:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 08:37:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8638941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8638941","identity":"rs-8638941","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}