Exploring the potential of SGLT2 inhibitors in Alzheimer’s disease: network pharmacology, molecular docking, and MD simulation study

Abstract Background Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) exhibit similar clinical characteristics and are increasingly prevalent in developed countries. Both conditions pose significant public health challenges, prompting research into effective treatments and preventive strategies. This study employs network pharmacology, molecular docking, molecular dynamics (MD) simulations, principal component analysis (PCA), free energy landscape (FEL), molecular mechanics generalized born surface area (MMGBSA) and absorption, distribution, metabolism, excretion, and toxicity (ADMET) and drug-likeness to explore the mechanisms by which sodium-glucose co-transporter-2 (SGLT2) inhibitors may treat AD. Results A comprehensive search identified 1153 targets for SGLT2 inhibitors via SwissTargetPrediction and SuperPred databases, alongside 716 targets for AD from UniProt. The protein–protein interaction (PPI) network highlighted 19 shared targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the Alzheimer disease signaling pathway (hsa05010), involving proteins such as GSK3B, APP, ADAM10, CAPN1, HSD17B10, and GAPDH, could mediate the effects of SGLT2 inhibitors. Molecular docking studies revealed that canagliflozin binds effectively to glyceraldehyde-3-phosphate dehydrogenase (GAPDH), forming hydrogen bonds with residues Arg11, Asn32, and Arg78, achieving a binding affinity of − 8.9 kcal/mol. Furthermore, 200 ns MD simulations assessed the stability of the canagliflozin–GAPDH complex, analyzing parameters like RMSD, RMSF and hydrogen bonding to elucidate binding interactions. PCA and FEL analyses revealed that canagliflozin occupies a more stable and energetically favorable conformational space, indicating superior dynamic stability compared with rivastigmine system. MMGBSA binding free energy analysis demonstrated that canagliflozin exhibited a significantly stronger binding affinity (− 43.63 ± 5.64 kcal/mol) compared with rivastigmine (− 26.38 ± 5.96 kcal/mol). Furthermore, canagliflozin possesses favorable drug-like features supporting its repurposing potential, further optimization and validation are required to enhance its therapeutic applicability in AD. Conclusion The results suggest that canagliflozin maintains stable interactions with GAPDH, potentially contributing to its potential binding affinity against AD.