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2000
Volume 32, Issue 25
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Alzheimer's disease (AD) stands as the predominant contributor to dementia cases. The ongoing developments in our understanding of its pathogenesis have sparked the interest of researchers, driving them to explore innovative treatment approaches. Existing therapies incorporating cholinesterase inhibitors and/or NMDA antagonists have shown limited improvement in alleviating symptoms. This, in turn, highlights the urgency for the pursuit of more effective therapeutic options. Given the annual rise in the number of individuals affected by dementia, it is imperative to allocate resources and efforts towards the exploration of novel therapeutic options. This review aims to provide a comprehensive overview of the AD-related hypotheses, along with the computational approaches employed in research within each hypothesis. In this comprehensive review, the authors shed light on using various computational tools, including diverse case studies, in the pursuit of finding efficacious treatments for AD. The development of more sophisticated diagnostic techniques is crucial, enabling early detection and intervention in the battle against this challenging condition. The potential treatments investigated in this analysis are poised to assume ever more significant functions in both preventing and treating AD, ultimately enhancing the management of the condition and the overall well-being of individuals affected by AD.

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References

  1. KnopmanD.S. AmievaH. PetersenR.C. ChételatG. HoltzmanD.M. HymanB.T. NixonR.A. JonesD.T. Alzheimer disease.Nat. Rev. Dis. Primers2021713310.1038/s41572‑021‑00269‑y33986301
     [Google Scholar]
  2. DeTureM.A. DicksonD.W. The neuropathological diagnosis of Alzheimer’s disease.Mol. Neurodegener.20191413210.1186/s13024‑019‑0333‑531375134
     [Google Scholar]
  3. de CastroA.A. SoaresF.V. PereiraA.F. PoliselD.A. CaetanoM.S. LealD.H.S. da CunhaE.F.F. NepovimovaE. KucaK. RamalhoT.C. Non-conventional compounds with potential therapeutic effects against Alzheimer’s disease.Expert Rev. Neurother.201919537539510.1080/14737175.2019.160882330999771
     [Google Scholar]
  4. ChenZ.R. HuangJ.B. YangS.L. HongF.F. Role of cholinergic signaling in Alzheimer’s disease.Molecules2022276181610.3390/molecules2706181635335180
     [Google Scholar]
  5. ScheltensP. De StrooperB. KivipeltoM. HolstegeH. ChételatG. TeunissenC.E. CummingsJ. van der FlierW.M. Alzheimer’s disease.Lancet2021397102841577159010.1016/S0140‑6736(20)32205‑433667416
     [Google Scholar]
  6. YiannopoulouK.G. PapageorgiouS.G. Current and future treatments in alzheimer disease: An update.J. Cent. Nerv. Syst. Dis.20201210.1177/117957352090739732165850
     [Google Scholar]
  7. TatulianS.A. Challenges and hopes for Alzheimer’s disease.Drug Discov. Today20222741027104310.1016/j.drudis.2022.01.01635121174
     [Google Scholar]
  8. DuboisB. HampelH. FeldmanH.H. ScheltensP. AisenP. AndrieuS. BakardjianH. BenaliH. BertramL. BlennowK. BroichK. CavedoE. CrutchS. DartiguesJ.F. DuyckaertsC. EpelbaumS. FrisoniG.B. GauthierS. GenthonR. GouwA.A. HabertM.O. HoltzmanD.M. KivipeltoM. ListaS. MolinuevoJ.L. O’BryantS.E. RabinoviciG.D. RoweC. SallowayS. SchneiderL.S. SperlingR. TeichmannM. CarrilloM.C. CummingsJ. JackC.R.Jr. Proceedings of the meeting of the international working group (IWG) and the American alzheimer’s association on “the preclinical state of AD”; July 23, 2015; wshington DC, USA. preclinical alzheimer’s disease: Definition, natural history, and diagnostic criteria.Alzheimers Dement.201612329232310.1016/j.jalz.2016.02.00227012484
     [Google Scholar]
  9. Alzheimer’s disease facts and figures.Alzheimers Dement.202117332740610.1002/alz.1232833756057
     [Google Scholar]
  10. HenekaM.T. CarsonM.J. KhouryJ.E. LandrethG.E. BrosseronF. FeinsteinD.L. JacobsA.H. Wyss-CorayT. VitoricaJ. RansohoffR.M. HerrupK. FrautschyS.A. FinsenB. BrownG.C. VerkhratskyA. YamanakaK. KoistinahoJ. LatzE. HalleA. PetzoldG.C. TownT. MorganD. ShinoharaM.L. PerryV.H. HolmesC. BazanN.G. BrooksD.J. HunotS. JosephB. DeigendeschN. GaraschukO. BoddekeE. DinarelloC.A. BreitnerJ.C. ColeG.M. GolenbockD.T. KummerM.P. Neuroinflammation in Alzheimer’s disease.Lancet Neurol.201514438840510.1016/S1474‑4422(15)70016‑525792098
     [Google Scholar]
  11. LiH.M. YuS.P. FanT.Y. ZhongY. GuT. WuW.Y. ZhaoC. ChenZ. ChenM. LiN.G. WangX.L. Design, synthesis, and biological activity evaluation of BACE1 inhibitors with antioxidant activity.Drug Dev. Res.202081220621410.1002/ddr.2158531397505
     [Google Scholar]
  12. NascimentoI.J.S. de AquinoT.M. da Silva-JúniorE.F. The new era of drug discovery: The power of computer-aided drug design (CADD).Lett. Drug Des. Discov.2022191195195510.2174/1570180819666220405225817
     [Google Scholar]
  13. TerstappenG.C. ReggianiA. In silico research in drug discovery.Trends Pharmacol. Sci.2001221232610.1016/S0165‑6147(00)01584‑411165668
     [Google Scholar]
  14. LonsdaleR. RanaghanK.E. MulhollandA.J. Computational enzymology.Chem. Commun201046142354237210.1039/b925647d20309456
     [Google Scholar]
  15. NemukhinA.V. GrigorenkoB.L. LushchekinaS.V. VarfolomeevS.D. Quantum chemical modelling in the research of molecular mechanisms of enzymatic catalysis.Russ. Chem. Rev.201281111011102510.1070/RC2012v081n11ABEH004311
     [Google Scholar]
  16. RamalhoT.C. de CastroA.A. LealD.H.S. TeixeiraJ.P. da CunhaE.F.F. KucaK. Assessing the therapeutic and toxicological profile of novel acetylcholinesterase reactivators: Value of in silico and in vitro Data.Curr. Med. Chem.202330364149416610.2174/092986733099922101410461036239718
     [Google Scholar]
  17. OliveiraS.S.C. CorreiaC.A. SantosV.S. da CunhaE.F.F. de CastroA.A. RamalhoT.C. DevereuxM. McCannM. BranquinhaM.H. SantosA.L.S. Silver(I) and copper(ii) 1,10-phenanthroline-5,6-dione complexes as promising antivirulence strategy against leishmania: Focus on Gp63 (Leishmanolysin).Trop. Med. Infect. Dis.20238734810.3390/tropicalmed807034837505644
     [Google Scholar]
  18. KucaK. MusilekK. JunD. NepovimovaE. SoukupO. KorabecnyJ. FrançaT.C.C. de CastroA.A. KrejcarO. da CunhaE.F.F. RamalhoT.C. Oxime K074 – in vitro and in silico reactivation of acetylcholinesterase inhibited by nerve agents and pesticides.Toxin Rev.202039215716610.1080/15569543.2018.1485702
     [Google Scholar]
  19. AssisL.C. de CastroA.A. de JesusJ.P.A. da CunhaE.F.F. NepovimovaE. KrejcarO. KucaK. RamalhoT.C. La PortaF.A. Theoretical insights into the effect of halogenated substituent on the electronic structure and spectroscopic properties of the favipiravir tautomeric forms and its implications for the treatment of COVID-19.RSC Adv.20211156352283524410.1039/D1RA06309J35493173
     [Google Scholar]
  20. de JesusJ.P.A. AssisL.C. de CastroA.A. da CunhaE.F.F. NepovimovaE. KucaK. de Castro RamalhoT. de Almeida La PortaF. Effect of drug metabolism in the treatment of SARS-CoV-2 from an entirely computational perspective.Sci. Rep.20211111999810.1038/s41598‑021‑99451‑134620963
     [Google Scholar]
  21. de CastroA.A. AssisL.C. da CunhaE.F.F. RamalhoT.C. La PortaF.A. New in silico insights into the application of (hydroxy)chloroquine with macrolide antibiotic co-crystals against the SARS-CoV-2 virus.COVID20222
     [Google Scholar]
  22. da SilvaA.P. de AngeloR.M. de PaulaH. HonórioK.M. da SilvaA.B.F. Drug design of new 5-HT6 antagonists: A QSAR study of arylsulfonamide derivatives.Struct. Chem.20203141585159710.1007/s11224‑020‑01513‑z
     [Google Scholar]
  23. ChiariL.P.A. da SilvaA.P. de OliveiraA.A. LipinskiC.F. HonórioK.M. da SilvaA.B.F. Drug design of new sigma-1 antagonists against neuropathic pain: A QSAR study using partial least squares and artificial neural networks.J. Mol. Struct.2021122312915610.1016/j.molstruc.2020.129156
     [Google Scholar]
  24. PantaleaoS.Q. FujiiD.G.V. MaltarolloV.G. da C SilvaD. TrossiniG.H.G. WeberK.C. ScottL.P.B. HonorioK.M. The role of QSAR and virtual screening studies in type 2 diabetes drug discovery.Med. Chem.201713870672028530546
     [Google Scholar]
  25. de CastroA.A. CaetanoM.S. SilvaT.C. ManciniD.T. RochaE.P. da CunhaE.F.F. RamalhoT.C. Molecular docking, metal substitution and hydrolysis reaction of chiral substrates of phosphotriesterase.Comb. Chem. High Throughput Screen.201619433434410.2174/138620731966616032511384427012528
     [Google Scholar]
  26. CastroA.A. de PrandiI.G. KucaK. RamalhoT.C. Organophosphate-degrading enzymes: Molecular basis and perspectives for enzymatic bioremediation of agrochemicals.Agrotechnical Sci.20174147148210.1590/1413‑70542017415000417
     [Google Scholar]
  27. de CastroA.A. AssisL.C. SilvaD.R. CorrêaS. AssisT.M. GajoG.C. SoaresF.V. RamalhoT.C. Computational enzymology for degradation of chemical warfare agents: promising technologies for remediation processes.AIMS Microbiol.20173110813510.3934/microbiol.2017.1.10831294152
     [Google Scholar]
  28. SoaresF.V. De CastroA.A. PereiraA.F. LealD.H.S. ManciniD.T. KrejcarO. RamalhoT.C. Da CunhaE.F.F. KucaK. Theoretical studies applied to the evaluation of the DFPase bioremediation potential against chemical warfare agents intoxication.Int. J. Mol. Sci.2018194125710.3390/ijms1904125729690585
     [Google Scholar]
  29. PereiraA.F. de CastroA.A. SoaresF.V. Soares LealD.H. da CunhaE.F.F. ManciniD.T. RamalhoT.C. Development of technologies applied to the biodegradation of warfare nerve agents: Theoretical evidence for asymmetric homogeneous catalysis.Chem. Biol. Interact.201930832333110.1016/j.cbi.2019.06.00731173750
     [Google Scholar]
  30. PoliselD.A. de CastroA.A. ManciniD.T. da CunhaE.F.F. FrançaT.C.C. RamalhoT.C. KucaK. Slight difference in the isomeric oximes K206 and K203 makes huge difference for the reactivation of organophosphorus-inhibited AChE: Theoretical and experimental aspects.Chem. Biol. Interact.201930910867110.1016/j.cbi.2019.05.03731207225
     [Google Scholar]
  31. de CastroA.A. SoaresF.V. PereiraA.F. SilvaT.C. SilvaD.R. ManciniD.T. CaetanoM.S. da CunhaE.F.F. RamalhoT.C. Asymmetric biodegradation of the nerve agents Sarin and VX by human dUTPase: Chemometrics, molecular docking and hybrid QM/MM calculations.J. Biomol. Struct. Dyn.20193782154216410.1080/07391102.2018.147875130044197
     [Google Scholar]
  32. van der KampM.W. MulhollandA.J. Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology.Biochemistry201352162708272810.1021/bi400215w23557014
     [Google Scholar]
  33. SharmaH. RajuB. NarendraG. MotiwaleM. SharmaB. VermaH. SilakariO. QM/MM studies on enzyme catalysis and insight into designing of new inhibitors by ONIOM approach: Recent update.ChemistrySelect202381e20220331910.1002/slct.202203319
     [Google Scholar]
  34. HuangS.Y. ZouX. Advances and challenges in protein-ligand docking.Int. J. Mol. Sci.20101183016303410.3390/ijms1108301621152288
     [Google Scholar]
  35. JonesG. WillettP. GlenR.C. LeachA.R. TaylorR. Development and validation of a genetic algorithm for flexible docking.J. Mol. Biol.1997267372774810.1006/jmbi.1996.08979126849
     [Google Scholar]
  36. GoodsellD.S. OlsonA.J. Automated docking of substrates to proteins by simulated annealing.Proteins19908319520210.1002/prot.3400803022281083
     [Google Scholar]
  37. GoodsellD.S. MorrisG.M. OlsonA.J. Automated docking of flexible ligands: Applications of autodock.J. Mol. Recognit.1996911510.1002/(SICI)1099‑1352(199601)9:1<1::AID‑JMR241>3.0.CO;2‑68723313
     [Google Scholar]
  38. RareyM. KramerB. LengauerT. KlebeG. A fast flexible docking method using an incremental construction algorithm.J. Mol. Biol.1996261347048910.1006/jmbi.1996.04778780787
     [Google Scholar]
  39. ThomsenR. ChristensenM.H. MolDock: A new technique for high-accuracy molecular docking.J. Med. Chem.200649113315332110.1021/jm051197e16722650
     [Google Scholar]
  40. KitchenD.B. DecornezH. FurrJ.R. BajorathJ. Docking and scoring in virtual screening for drug discovery: Methods and applications.Nat. Rev. Drug Discov.200431193594910.1038/nrd154915520816
     [Google Scholar]
  41. Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron.20189961129114310.1016/j.neuron.2018.08.01130236283
     [Google Scholar]
  42. DurrantJ.D. McCammonJ.A. Molecular dynamics simulations and drug discovery.BMC Biol.2011917110.1186/1741‑7007‑9‑7122035460
     [Google Scholar]
  43. Vidal-LimonA. Aguilar-ToaláJ.E. LiceagaA.M. Integration of molecular docking analysis and molecular dynamics simulations for studying food proteins and bioactive peptides.J. Agric. Food Chem.202270493494310.1021/acs.jafc.1c0611034990125
     [Google Scholar]
  44. FilipeH.A.L. LouraL.M.S. Molecular dynamics simulations: Advances and applications.Molecules2022277210510.3390/molecules2707210535408504
     [Google Scholar]
  45. WuX. XuL.Y. LiE.M. DongG. Application of molecular dynamics simulation in biomedicine.Chem. Biol. Drug Des.202299578980010.1111/cbdd.1403835293126
     [Google Scholar]
  46. FuH. ChenH. BlazhynskaM. Goulard Coderc de LacamE. SzczepaniakF. PavlovaA. ShaoX. GumbartJ.C. DehezF. RouxB. CaiW. ChipotC. Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations.Nat. Protoc.20221741114114110.1038/s41596‑021‑00676‑135277695
     [Google Scholar]
  47. WangM. IncecikA. YangC. GuptaM.K. KrólczykG. AndriukaitisD. LiZ. A critical review on molecular dynamics applied to structure fracture and failure analysis.Eng. Anal. Bound. Elem.202315041342210.1016/j.enganabound.2023.02.028
     [Google Scholar]
  48. GuimarãesA.P. RamalhoT.C. FrançaT.C.C. Preventing the return of smallpox: Molecular modeling studies on thymidylate kinase from Variola virus.J. Biomol. Struct. Dyn.201432101601161210.1080/07391102.2013.83057823998201
     [Google Scholar]
  49. KarplusM. McCammonJ.A. Molecular dynamics simulations of biomolecules.Nat. Struct. Biol.20029964665210.1038/nsb0902‑64612198485
     [Google Scholar]
  50. AlonsoH. BliznyukA.A. GreadyJ.E. Combining docking and molecular dynamic simulations in drug design.Med. Res. Rev.200626553156810.1002/med.2006716758486
     [Google Scholar]
  51. WeinerP.K. KollmanP.A. AMBER : Assisted model building with energy refinement. A general program for modeling molecules and their interactions.J. Comput. Chem.19812328730310.1002/jcc.540020311
     [Google Scholar]
  52. BrooksB.R. BruccoleriR.E. OlafsonB.D. StatesD.J. SwaminathanS. KarplusM. CHARMM : A program for macromolecular energy, minimization, and dynamics calculations.J. Comput. Chem.19834218721710.1002/jcc.540040211
     [Google Scholar]
  53. KaléL. SkeelR. BhandarkarM. BrunnerR. GursoyA. KrawetzN. PhillipsJ. ShinozakiA. VaradarajanK. SchultenK. NAMD2: Greater scalability for parallel molecular dynamics.J. Comput. Phys.1999151128331210.1006/jcph.1999.6201
     [Google Scholar]
  54. PhillipsJ.C. BraunR. WangW. GumbartJ. TajkhorshidE. VillaE. ChipotC. SkeelR.D. KaléL. SchultenK. Scalable molecular dynamics with NAMD.J. Comput. Chem.200526161781180210.1002/jcc.2028916222654
     [Google Scholar]
  55. ScottW.R.P. HünenbergerP.H. TironiI.G. MarkA.E. BilleterS.R. FennenJ. TordaA.E. HuberT. KrügerP. van GunsterenW.F. The GROMOS biomolecular simulation program package.J. Phys. Chem. A1999103193596360710.1021/jp984217f
     [Google Scholar]
  56. RamalhoT.C. de CastroA.A. SilvaD.R. SilvaM.C. FrancaT.C.C. BennionB.J. KucaK. Computational enzymology and organophosphorus degrading enzymes: Promising approaches toward remediation technologies of warfare agents and pesticides.Curr. Med. Chem.201623101041106110.2174/092986732366616022211350426898655
     [Google Scholar]
  57. JorgensenW.L. Foundations of biomolecular modeling.Cell201315561199120210.1016/j.cell.2013.11.02324315087
     [Google Scholar]
  58. da Silva GonçalvesA. FrançaT.C.C. CaetanoM.S. RamalhoT.C. Reactivation steps by 2-PAM of tabun-inhibited human acetylcholinesterase: Reducing the computational cost in hybrid QM/MM methods.J. Biomol. Struct. Dyn.201432230130710.1080/07391102.2013.76536123527625
     [Google Scholar]
  59. BrunkE. RothlisbergerU. Mixed quantum mechanical/molecular mechanical molecular dynamics simulations of biological systems in ground and electronically excited states.Chem. Rev.2015115126217626310.1021/cr500628b25880693
     [Google Scholar]
  60. WarshelA. LevittM. Theoretical studies of enzymic reactions: Dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme.J. Mol. Biol.1976103222724910.1016/0022‑2836(76)90311‑9985660
     [Google Scholar]
  61. MalaspinaT. CoutinhoK. CanutoS. Ab initio calculation of hydrogen bonds in liquids: A sequential Monte Carlo quantum mechanics study of pyridine in water.J. Chem. Phys.200211741692169910.1063/1.1485963
     [Google Scholar]
  62. AbdolmalekiA. GhasemiF. GhasemiJ.B. Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications.Chem. Biol. Drug Des.201789225726810.1111/cbdd.1282528205401
     [Google Scholar]
  63. GiordanoD. BiancanielloC. ArgenioM.A. FacchianoA. Drug design by pharmacophore and virtual screening approach.Pharmaceuticals202215564610.3390/ph1505064635631472
     [Google Scholar]
  64. JiangY. GaoH. Pharmacophore-based drug design for potential AChE inhibitors from traditional chinese medicine database.Bioorg. Chem.20187640041410.1016/j.bioorg.2017.12.01529258018
     [Google Scholar]
  65. MaiaE.H.B. AssisL.C. de OliveiraT.A. da SilvaA.M. TarantoA.G. Structure-based virtual screening: from classical to artificial intelligence.Front Chem.2020834310.3389/fchem.2020.0034332411671
     [Google Scholar]
  66. LiontaE. SpyrouG. VassilatisD. CourniaZ. Structure-based virtual screening for drug discovery: Principles, applications and recent advances.Curr. Top. Med. Chem.201414161923193810.2174/156802661466614092912444525262799
     [Google Scholar]
  67. IttnerL.M. KeY.D. DelerueF. BiM. GladbachA. van EerselJ. WölfingH. ChiengB.C. ChristieM.J. NapierI.A. EckertA. StaufenbielM. HardemanE. GötzJ. Dendritic function of tau mediates amyloid-beta toxicity in Alzheimer’s disease mouse models.Cell2010142338739710.1016/j.cell.2010.06.03620655099
     [Google Scholar]
  68. CorbettA. PickettJ. BurnsA. CorcoranJ. DunnettS.B. EdisonP. HaganJ.J. HolmesC. JonesE. KatonaC. KearnsI. KehoeP. MudherA. PassmoreA. ShepherdN. WalshF. BallardC. Drug repositioning for Alzheimer’s disease.Nat. Rev. Drug Discov.2012111183384610.1038/nrd386923123941
     [Google Scholar]
  69. BallardC. CorbettA. SharpS. Aligning the evidence with practice: NICE guidelines for drug treatment of Alzheimer’s disease.Expert Rev. Neurother.201111332732910.1586/ern.11.1321375436
     [Google Scholar]
  70. LiuP.P. XieY. MengX.Y. KangJ.S. History and progress of hypotheses and clinical trials for Alzheimer’s disease.Signal Transduct. Target. Ther.2019412910.1038/s41392‑019‑0063‑831637009
     [Google Scholar]
  71. ContestabileA. The history of the cholinergic hypothesis.Behav. Brain Res.2011221233434010.1016/j.bbr.2009.12.04420060018
     [Google Scholar]
  72. DaviesP. MaloneyA.J. Selective loss of central cholinergic neurons in Alzheimer’s disease.Lancet19763088000140310.1016/S0140‑6736(76)91936‑X63862
     [Google Scholar]
  73. HörnbergA. TunemalmA.K. EkströmF. Crystal structures of acetylcholinesterase in complex with organophosphorus compounds suggest that the acyl pocket modulates the aging reaction by precluding the formation of the trigonal bipyramidal transition state.Biochemistry200746164815482510.1021/bi062136117402711
     [Google Scholar]
  74. BowenD.M. SmithC.B. WhiteP. DavisonA.N. Neurotransmitter-related enzymes and indices of hypoxia in senile dementia and other abiotrophies.Brain197699345949610.1093/brain/99.3.45911871
     [Google Scholar]
  75. KarS. IssaA.M. SetoD. AuldD.S. CollierB. QuirionR. Amyloid beta-peptide inhibits high-affinity choline uptake and acetylcholine release in rat hippocampal slices.J. Neurochem.19987052179218710.1046/j.1471‑4159.1998.70052179.x9572306
     [Google Scholar]
  76. AuldD.S. KarS. QuirionR. β-Amyloid peptides as direct cholinergic neuromodulators: A missing link?Trends Neurosci.1998211434910.1016/S0166‑2236(97)01144‑29464686
     [Google Scholar]
  77. NordbergA. AlafuzoffI. WinbladB. Nicotinic and muscarinic subtypes in the human brain: Changes with aging and dementia.J. Neurosci. Res.199231110311110.1002/jnr.4903101151613816
     [Google Scholar]
  78. BarageS.H. SonawaneK.D. Amyloid cascade hypothesis: Pathogenesis and therapeutic strategies in Alzheimer’s disease.Neuropeptides20155211810.1016/j.npep.2015.06.00826149638
     [Google Scholar]
  79. FranklinM.C. RudolphM.J. GinterC. CassidyM.S. CheungJ. Structures of paraoxon-inhibited human acetylcholinesterase reveal perturbations of the acyl loop and the dimer interface.Proteins20168491246125610.1002/prot.2507327191504
     [Google Scholar]
  80. ReitzC. BrayneC. MayeuxR. Epidemiology of Alzheimer disease.Nat. Rev. Neurol.20117313715210.1038/nrneurol.2011.221304480
     [Google Scholar]
  81. LleóA. GreenbergS.M. GrowdonJ.H. Current pharmacotherapy for Alzheimer’s disease.Annu. Rev. Med.200657151353310.1146/annurev.med.57.121304.13144216409164
     [Google Scholar]
  82. HebertL.E. ScherrP.A. BieniasJ.L. BennettD.A. EvansD.A. Alzheimer disease in the US population: prevalence estimates using the 2000 census.Arch. Neurol.20036081119112210.1001/archneur.60.8.111912925369
     [Google Scholar]
  83. DoodyR.S. StevensJ.C. BeckC. DubinskyR.M. KayeJ.A. GwytherL. MohsR.C. ThalL.J. WhitehouseP.J. DeKoskyS.T. CummingsJ.L. Practice parameter: management of dementia (an evidence-based review). Report of the quality standards subcommittee of the american academy of neurology.Neurology20015691154116610.1212/WNL.56.9.115411342679
     [Google Scholar]
  84. GiacobiniE. Cholinesterases: new roles in brain function and in Alzheimer’s disease.Neurochem. Res.2003283/451552210.1023/A:102286922265212675140
     [Google Scholar]
  85. GiacobiniE. Long-term stabilizing effect of cholinesterase inhibitors in the therapy of Alzheimer’ disease.J. Neural Transm. Suppl.2002626218118710.1007/978‑3‑7091‑6139‑5_1712456062
     [Google Scholar]
  86. RogersS.L. FarlowM.R. DoodyR.S. MohsR. FriedhoffL.T. Donepezil Study Group.A 24-week, double-blind, placebo-controlled trial of donepezil in patients with Alzheimer’s disease.Neurology199850113614510.1212/WNL.50.1.1369443470
     [Google Scholar]
  87. Corey-BloomJ. AnandR. VeachJ. A randomized trial evaluating the efficacy and safety of ENA 713 (rivastigmine tartrate), a new acetylcholinesterase inhibitor, in patients with mild to moderately severe Alzheimer's disease.Int. J. Geriatr. Psychiatry1998125565
     [Google Scholar]
  88. TariotP.N. SolomonP.R. MorrisJ.C. KershawP. LilienfeldS. DingC. A 5-month, randomized, placebo- controlled trial of galantamine in AD.Neurology200054122269227610.1212/WNL.54.12.226910881251
     [Google Scholar]
  89. CummingsJ.L. Cholinesterase inhibitors: A new class of psychotropic compounds.Am. J. Psychiatry2000157141510.1176/ajp.157.1.410618007
     [Google Scholar]
  90. RyanJ. ScaliJ. CarriereI. RitchieK. AncelinM.L. Hormonal treatment, mild cognitive impairment and Alzheimer’s disease.Int. Psychogeriatr.2008201475610.1017/S104161020700648518072983
     [Google Scholar]
  91. GauthierS. JubyA. DalzielW. RéhelB. SchecterR. EXPLORE investigators. Effects of rivastigmine on common symptomatology of Alzheimer’s disease (EXPLORE).Curr. Med. Res. Opin.20102651149116010.1185/0300799100368888820230208
     [Google Scholar]
  92. LockhartI.A. MitchellS.A. KellyS. Safety and tolerability of donepezil, rivastigmine and galantamine for patients with Alzheimer’s disease: Systematic review of the ‘real-world’ evidence.Dement. Geriatr. Cogn. Disord.200928547849210.1159/00025557819893314
     [Google Scholar]
  93. HoltzmanD.M. MorrisJ.C. GoateA.M. Alzheimer’s disease: The challenge of the second century.Sci. Transl. Med.201137777sr110.1126/scitranslmed.300236921471435
     [Google Scholar]
  94. GiacobiniE. Cholinesterase inhibitors stabilize Alzheimer’s disease.Ann. N. Y. Acad. Sci.2000920132132710.1111/j.1749‑6632.2000.tb06942.x11193171
     [Google Scholar]
  95. CrismonM.L. Tacrine: First drug approved for Alzheimer’s disease.Ann. Pharmacother.199428674475110.1177/1060028094028006127919566
     [Google Scholar]
  96. MehtaM. AdemA. SabbaghM. New acetylcholinesterase inhibitors for Alzheimer’s disease.Int. J. Alzheimer’s Dis.201210.1155/2012/728983
     [Google Scholar]
  97. CacabelosR. Donepezil in Alzheimer’s disease: From conventional trials to pharmacogenetics.Neuropsychiatr. Dis. Treat.20073330333319300564
     [Google Scholar]
  98. BirksJ.S. HarveyR.J. Donepezil for dementia due to Alzheimer’s disease.Cochrane Libr.201820186CD00119010.1002/14651858.CD001190.pub329923184
     [Google Scholar]
  99. BallardC.G. Advances in the treatment of Alzheimer’s disease: benefits of dual cholinesterase inhibition.Eur. Neurol.2002471647010.1159/00004795211803198
     [Google Scholar]
  100. GaoH. JiangY. ZhanJ. SunY. Pharmacophore-based drug design of AChE and BChE dual inhibitors as potential anti-Alzheimer’s disease agents.Bioorg. Chem.202111410514910.1016/j.bioorg.2021.10514934252860
     [Google Scholar]
  101. IrwinJ.J. ShoichetB.K. ZINC-a free database of commercially available compounds for virtual screening.J. Chem. Inf. Model.200545117718210.1021/ci049714+15667143
     [Google Scholar]
  102. JiangY. GaoH. Pharmacophore-based drug design for the identification of novel butyrylcholinesterase inhibitors against Alzheimer’s disease.Phytomedicine20195427829010.1016/j.phymed.2018.09.19930668379
     [Google Scholar]
  103. JoubertJ. KappE. Discovery of 9-phenylacridinediones as highly selective butyrylcholinesterase inhibitors through structure-based virtual screening.Bioorg. Med. Chem. Lett.202030912707510.1016/j.bmcl.2020.12707532165042
     [Google Scholar]
  104. CodonyS. PontC. Griñán-FerréC. Di Pede-MattatelliA. Calvó-TusellC. FeixasF. OsunaS. Jarné-FerrerJ. NaldiM. BartoliniM. LozaM.I. BreaJ. PérezB. BartraC. SanfeliuC. Juárez-JiménezJ. MorisseauC. HammockB.D. PallàsM. VázquezS. Muñoz-TorreroD. Discovery and in vivo proof of concept of a highly potent dual inhibitor of soluble epoxide hydrolase and acetylcholinesterase for the treatment of Alzheimer’s disease.J. Med. Chem.20226564909492510.1021/acs.jmedchem.1c0215035271276
     [Google Scholar]
  105. Ahmed Ali AbdusalamA. VikneswaranM. Novel acetylcholinesterase inhibitors identified from zinc database using docking-based virtual screening for Alzheimer’s disease.ChemistrySelect20205123593359910.1002/slct.201904177
     [Google Scholar]
  106. SonM. ParkC. RampoguS. ZebA. LeeK.W. Discovery of novel acetylcholinesterase inhibitors as potential candidates for the treatment of Alzheimer’s disease.Int. J. Mol. Sci.2019204100010.3390/ijms2004100030823604
     [Google Scholar]
  107. Fatiha MuhammadE. KumarA. WahabH.A. ZhangK.Y.J. Identification of 1,2,4-triazolylthioethanone scaffold for the design of new acetylcholinesterase inhibitors.Mol. Inform.2021408210002010.1002/minf.20210002034060234
     [Google Scholar]
  108. ReissA.B. ArainH.A. SteckerM.M. SiegartN.M. KasselmanL.J. Amyloid toxicity in Alzheimer’s disease.Rev. Neurosci.201829661362710.1515/revneuro‑2017‑006329447116
     [Google Scholar]
  109. ImbimboB.P. IppatiS. WatlingM. ImbimboC. Role of monomeric amyloid-β in cognitive performance in Alzheimer’s disease: Insights from clinical trials with secretase inhibitors and monoclonal antibodies.Pharmacol. Res.202318710663110.1016/j.phrs.2022.10663136586644
     [Google Scholar]
  110. UddinM.S. KabirM.T. RahmanM.S. BehlT. JeandetP. AshrafG.M. NajdaA. Bin-JumahM.N. El-SeediH.R. Abdel-DaimM.M. Revisiting the amyloid cascade hypothesis: From anti-aβ therapeutics to auspicious new ways for Alzheimer’s disease.Int. J. Mol. Sci.20202116585810.3390/ijms2116585832824102
     [Google Scholar]
  111. ChenG. XuT. YanY. ZhouY. JiangY. MelcherK. XuH.E. Amyloid beta: Structure, biology and structure-based therapeutic development.Acta Pharmacol. Sin.20173891205123510.1038/aps.2017.2828713158
     [Google Scholar]
  112. PapadopoulosN. SuelvesN. PerrinF. VadukulD.M. VrancxC. ConstantinescuS.N. Kienlen-CampardP. Structural determinant of β-amyloid formation: from transmembrane protein dimerization to β-amyloid aggregates.Biomedicines20221011275310.3390/biomedicines1011275336359274
     [Google Scholar]
  113. HampelH. HardyJ. BlennowK. ChenC. PerryG. KimS.H. VillemagneV.L. AisenP. VendruscoloM. IwatsuboT. MastersC.L. ChoM. LannfeltL. CummingsJ.L. VergalloA. The amyloid-β pathway in Alzheimer’s disease.Mol. Psychiatry202126105481550310.1038/s41380‑021‑01249‑034456336
     [Google Scholar]
  114. GulisanoW. MaugeriD. BaltronsM.A. FàM. AmatoA. PalmeriA. D’AdamioL. GrassiC. DevanandD.P. HonigL.S. PuzzoD. ArancioO. Role of amyloid-β and tau proteins in Alzheimer’s disease: Confuting the amyloid cascade.J. Alzheimers Dis.201864s1S611S63110.3233/JAD‑17993529865055
     [Google Scholar]
  115. ShakerB. AhmadS. LeeJ. JungC. NaD. In silico methods and tools for drug discovery.Comput. Biol. Med.202113710485110.1016/j.compbiomed.2021.10485134520990
     [Google Scholar]
  116. YounK. ParkJ.H. LeeS. LeeS. LeeJ. YunE.Y. JeongW.S. JunM. BACE1 inhibition by genistein: Biological evaluation, kinetic analysis, and molecular docking simulation.J. Med. Food201821441642010.1089/jmf.2017.406829444415
     [Google Scholar]
  117. EganM.F. KostJ. VossT. MukaiY. AisenP.S. CummingsJ.L. TariotP.N. VellasB. van DyckC.H. BoadaM. ZhangY. LiW. FurtekC. MahoneyE. Harper MozleyL. MoY. SurC. MichelsonD. Randomized trial of verubecestat for prodromal Alzheimer’s disease.N. Engl. J. Med.2019380151408142010.1056/NEJMoa181284030970186
     [Google Scholar]
  118. SaravananK. SivanandamM. HundayG. MathiyalaganL. KumaradhasP. Investigation of intermolecular interactions and stability of verubecestat in the active site of BACE1: Development of first model from QM/MM-based charge density and MD analysis.J. Biomol. Struct. Dyn.20193792339235410.1080/07391102.2018.147966130044206
     [Google Scholar]
  119. AliM.A. VureeS. GoudH. HussainT. NayarisseriA. SinghS.K. Identification of high-affinity small molecules targeting gamma secretase for the treatment of Alzheimer’s disease.Curr. Top. Med. Chem.201919131173118710.2174/156802661966619061715532631244427
     [Google Scholar]
  120. JabirN.R. RehmanM.T. AlsolamiK. ShakilS. ZughaibiT.A. AlserihiR.F. KhanM.S. AlAjmiM.F. TabrezS. Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: In pursuit of Alzheimer’s treatment.Ann. Med.20215312332234410.1080/07853890.2021.200912434889159
     [Google Scholar]
  121. JabirN.R. ShakilS. TabrezS. KhanM.S. RehmanM.T. AhmedB.A. In silico screening of glycogen synthase kinase-3β targeted ligands against acetylcholinesterase and its probable relevance to Alzheimer’s disease.J. Biomol. Struct. Dyn.202139145083509210.1080/07391102.2020.178479632588759
     [Google Scholar]
  122. JabirN.R. RehmanM.T. TabrezS. AlserihiR.F. AlAjmiM.F. KhanM.S. HusainF.M. AhmedB.A. Identification of butyrylcholinesterase and monoamine oxidase B targeted ligands and their putative application in Alzheimer’s treatment: A computational strategy.Curr. Pharm. Des.202127202425243410.2174/138161282766621022612324033634754
     [Google Scholar]
  123. ChawlaP.A. >In Silico design and evaluation of triazine based 4-thiazolidinone (tbt) analogues as anti Alzheimer’s agents through bace 1 inhibition.Biomed. J. Sci. Tech. Res.2022463374333744110.26717/BJSTR.2022.46.007352
     [Google Scholar]
  124. FrostB. JacksR.L. DiamondM.I. Propagation of tau misfolding from the outside to the inside of a cell.J. Biol. Chem.200928419128451285210.1074/jbc.M80875920019282288
     [Google Scholar]
  125. MedeirosR. Baglietto-VargasD. LaFerlaF.M. The role of tau in Alzheimer’s disease and related disorders.CNS Neurosci. Ther.201117551452410.1111/j.1755‑5949.2010.00177.x20553310
     [Google Scholar]
  126. BraakH. BraakE. Neuropathological stageing of Alzheimer-related changes.Acta Neuropathol.199182423925910.1007/BF003088091759558
     [Google Scholar]
  127. HuttonM. LendonC.L. RizzuP. BakerM. FroelichS. HouldenH. Pickering-BrownS. ChakravertyS. IsaacsA. GroverA. HackettJ. AdamsonJ. LincolnS. DicksonD. DaviesP. PetersenR.C. StevensM. de GraaffE. WautersE. van BarenJ. HillebrandM. JoosseM. KwonJ.M. NowotnyP. CheL.K. NortonJ. MorrisJ.C. ReedL.A. TrojanowskiJ. BasunH. LannfeltL. NeystatM. FahnS. DarkF. TannenbergT. DoddP.R. HaywardN. KwokJ.B.J. SchofieldP.R. AndreadisA. SnowdenJ. CraufurdD. NearyD. OwenF. OostraB.A. HardyJ. GoateA. van SwietenJ. MannD. LynchT. HeutinkP. Association of missense and 5′-splice-site mutations in tau with the inherited dementia FTDP-17.Nature1998393668670270510.1038/315089641683
     [Google Scholar]
  128. SantaCruzK. LewisJ. SpiresT. PaulsonJ. KotilinekL. IngelssonM. GuimaraesA. DeTureM. RamsdenM. McGowanE. ForsterC. YueM. OrneJ. JanusC. MariashA. KuskowskiM. HymanB. HuttonM. AsheK.H. Tau suppression in a neurodegenerative mouse model improves memory function.Science2005309573347648110.1126/science.111369416020737
     [Google Scholar]
  129. ClavagueraF. BolmontT. CrowtherR.A. AbramowskiD. FrankS. ProbstA. FraserG. StalderA.K. BeibelM. StaufenbielM. JuckerM. GoedertM. TolnayM. Transmission and spreading of tauopathy in transgenic mouse brain.Nat. Cell Biol.200911790991310.1038/ncb190119503072
     [Google Scholar]
  130. BallatoreC. LeeV.M.Y. TrojanowskiJ.Q. Tau-mediated neurodegeneration in Alzheimer’s disease and related disorders.Nat. Rev. Neurosci.20078966367210.1038/nrn219417684513
     [Google Scholar]
  131. BakotaL. BrandtR. Tau biology and tau-directed therapies for Alzheimer’s disease.Drugs201676330131310.1007/s40265‑015‑0529‑026729186
     [Google Scholar]
  132. PradeepkiranJ.A. ReddyP.H. Structure based design and molecular docking studies for phosphorylated tau inhibitors in Alzheimer’s disease.Cells20198326010.3390/cells803026030893872
     [Google Scholar]
  133. PradeepkiranJ.A. MunikumarM. ReddyA.P. ReddyP.H. Protective effects of a small molecule inhibitor ligand against hyperphosphorylated tau-induced mitochondrial and synaptic toxicities in Alzheimer disease.Hum. Mol. Genet.202131224426110.1093/hmg/ddab24434432046
     [Google Scholar]
  134. VilarS. CozzaG. MoroS. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery.Curr. Top. Med. Chem.20088181555157210.2174/15680260878678662419075767
     [Google Scholar]
  135. SchmidtkeP. Le GuillouxV. MaupetitJ. TufféryP. fpocket: Online tools for protein ensemble pocket detection and tracking.Nucleic Acids Res.201038Web ServerW582W58910.1093/nar/gkq38320478829
     [Google Scholar]
  136. ShuklaR. SinghT.R. Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer’s disease.J. Biomol. Struct. Dyn.202038124826210.1080/07391102.2019.157194730688165
     [Google Scholar]
  137. TrudlerD. FarfaraD. FrenkelD. Toll-like receptors expression and signaling in glia cells in neuro-amyloidogenic diseases: Towards future therapeutic application.Mediators Inflamm2010
     [Google Scholar]
  138. OdfalkK.F. BieniekK.F. HoppS.C. Microglia: Friend and foe in tauopathy.Prog. Neurobiol.202221610230610.1016/j.pneurobio.2022.10230635714860
     [Google Scholar]
  139. CaiZ. HussainM.D. YanL.J. Microglia, neuroinflammation, and beta-amyloid protein in Alzheimer’s disease.Int. J. Neurosci.2014124530732110.3109/00207454.2013.83351023930978
     [Google Scholar]
  140. KraftA.D. HarryG.J. Features of microglia and neuroinflammation relevant to environmental exposure and neurotoxicity.Int. J. Environ. Res. Public Health2011872980301810.3390/ijerph807298021845170
     [Google Scholar]
  141. ColonnaM. ButovskyO. Microglia function in the central nervous system during health and neurodegeneration.Annu. Rev. Immunol.201735144146810.1146/annurev‑immunol‑051116‑05235828226226
     [Google Scholar]
  142. GaoS. TanH. LiD. Oridonin suppresses gastric cancer SGC -7901 cell proliferation by targeting the TNF -alpha/androgen receptor/ TGF -beta signalling pathway axis.J. Cell. Mol. Med.202327182661267410.1111/jcmm.1784137431884
     [Google Scholar]
  143. GaoS. GangJ. YuM. XinG. TanH. Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer.BMC Cancer202121179110.1186/s12885‑021‑08520‑134238253
     [Google Scholar]
  144. GaoS. TanH. GangJ. Inhibition of hepatocellular carcinoma cell proliferation through regulation of the cell cycle, age-rage, and leptin signaling pathways by a compound formulation comprised of andrographolide, wogonin, and oroxylin a derived from andrographis paniculata(Burm.f.) Nees.J. Ethnopharmacol.202432911800110.1016/j.jep.2024.11800138467318
     [Google Scholar]
  145. BakL.K. SchousboeA. WaagepetersenH.S. The glutamate/GABA-glutamine cycle: Aspects of transport, neurotransmitter homeostasis and ammonia transfer.J. Neurochem.200698364165310.1111/j.1471‑4159.2006.03913.x16787421
     [Google Scholar]
  146. González-ReyesR.E. Nava-MesaM.O. Vargas-SánchezK. Ariza-SalamancaD. Mora-MuñozL. Involvement of astrocytes in Alzheimer’s disease from a neuroinflammatory and oxidative stress perspective.Front. Mol. Neurosci.20171042710.3389/fnmol.2017.0042729311817
     [Google Scholar]
  147. MaruszakA. ŻekanowskiC. Mitochondrial dysfunction and Alzheimer’s disease.Prog. Neuropsychopharmacol. Biol. Psychiatry201135232033010.1016/j.pnpbp.2010.07.00420624441
     [Google Scholar]
  148. Rubio-PerezJ.M. Morillas-RuizJ.M. A review: Inflammatory process in Alzheimer’s disease, role of cytokines.ScientificWorldJournal2012201211510.1100/2012/75635722566778
     [Google Scholar]
  149. DiSabatoD.J. QuanN. GodboutJ.P. Neuroinflammation: The devil is in the details.J. Neurochem.2016139S2Suppl. 213615310.1111/jnc.1360726990767
     [Google Scholar]
  150. BlennowK. DavidssonP. WallinA. EkmanR. Chromogranin A in cerebrospinal fluid: A biochemical marker for synaptic degeneration in Alzheimer’s disease?Dementia1995663063118563783
     [Google Scholar]
  151. ClaytonK. DelpechJ.C. HerronS. IwaharaN. EricssonM. SaitoT. SaidoT.C. IkezuS. IkezuT. Plaque associated microglia hyper-secrete extracellular vesicles and accelerate tau propagation in a humanized APP mouse model.Mol. Neurodegener.20211611810.1186/s13024‑021‑00440‑933752701
     [Google Scholar]
  152. KarchC.M. GoateA.M. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis.Biol. Psychiatry2015771435110.1016/j.biopsych.2014.05.00624951455
     [Google Scholar]
  153. NavarroV. Sanchez-MejiasE. JimenezS. Muñoz-CastroC. Sanchez-VaroR. DavilaJ.C. VizueteM. GutierrezA. VitoricaJ. Microglia in Alzheimer’s disease: Activated, dysfunctional or degenerative.Front. Aging Neurosci.20181014010.3389/fnagi.2018.0014029867449
     [Google Scholar]
  154. DoornK.J. GoudriaanA. Blits-HuizingaC. BolJ.G.J.M. RozemullerA.J. HooglandP.V.J.M. LucassenP.J. DrukarchB. van de BergW.D.J. van DamA.M. Increased amoeboid microglial density in the olfactory bulb of Parkinson’s and Alzheimer’s patients.Brain Pathol.201424215216510.1111/bpa.1208824033473
     [Google Scholar]
  155. TischerJ. KruegerM. MuellerW. StaszewskiO. PrinzM. StreitW.J. BechmannI. Inhomogeneous distribution of Iba-1 characterizes microglial pathology in Alzheimer’s disease.Glia20166491562157210.1002/glia.2302427404378
     [Google Scholar]
  156. StreitW.J. BraakH. XueQ.S. BechmannI. Dystrophic (senescent) rather than activated microglial cells are associated with tau pathology and likely precede neurodegeneration in Alzheimer’s disease.Acta Neuropathol.2009118447548510.1007/s00401‑009‑0556‑619513731
     [Google Scholar]
  157. Sanchez-MejiasE. NavarroV. JimenezS. Sanchez-MicoM. Sanchez-VaroR. Nuñez-DiazC. Trujillo-EstradaL. DavilaJ.C. VizueteM. GutierrezA. VitoricaJ. Soluble phospho-tau from Alzheimer’s disease hippocampus drives microglial degeneration.Acta Neuropathol.2016132689791610.1007/s00401‑016‑1630‑527743026
     [Google Scholar]
  158. LengF. EdisonP. Neuroinflammation and microglial activation in Alzheimer disease: Where do we go from here?Nat. Rev. Neurol.202117315717210.1038/s41582‑020‑00435‑y33318676
     [Google Scholar]
  159. LeitnerG.R. WenzelT.J. MarshallN. GatesE.J. KlegerisA. Targeting toll-like receptor 4 to modulate neuroinflammation in central nervous system disorders.Expert Opin. Ther. Targets2019231086588210.1080/14728222.2019.167641631580163
     [Google Scholar]
  160. ZaffaroniL. PeriF. Recent advances on Toll-like receptor 4 modulation: New therapeutic perspectives.Future Med. Chem.201810446147610.4155/fmc‑2017‑017229380635
     [Google Scholar]
  161. Pérez-RegidorL. Guzmán-CaldenteyJ. OberhauserN. PunzónC. BaloghB. PedroJ.R. FalomirE. NurissoA. MátyusP. MenéndezJ.C. de AndrésB. FresnoM. Martín-SantamaríaS. Small molecules as toll-like receptor 4 modulators drug and in-house computational repurposing.Biomedicines2022109232610.3390/biomedicines1009232636140427
     [Google Scholar]
  162. ZussoM. LunardiV. FranceschiniD. PagettaA. LoR. StifaniS. FrigoA.C. GiustiP. MoroS. Ciprofloxacin and levofloxacin attenuate microglia inflammatory response via TLR4/NF-kB pathway.J. Neuroinflammation201916114810.1186/s12974‑019‑1538‑931319868
     [Google Scholar]
  163. ZhangY. LiangX. BaoX. XiaoW. ChenG. Toll- like receptor 4 (TLR4) inhibitors: Current research and prospective.Eur. J. Med. Chem.202223511429110.1016/j.ejmech.2022.11429135307617
     [Google Scholar]
  164. VermaR.K. ChawlaP. PandeyM. ChoudhuryH. MayurenJ. BhattamisraS.K. GorainB. RajaM.A.G. AmjadM.W. Obaidur RahmanS. An insight into the role of artificial intelligence in the early diagnosis of Alzheimer’s disease.CNS Neurol. Disord. Drug Targets2022211090191210.2174/187152732066621051201450533982657
     [Google Scholar]
  165. ChawlaP.A. KumarA. NehraB. SinghD. KumarD. Recent advances in the development of nitrogen-containing heterocyclic anti-Alzheimer’s agents.Curr. Top. Med. Chem.202323131277130610.2174/156802662366622101915250236278462
     [Google Scholar]
/content/journals/cmc/10.2174/0109298673318143240823064403
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Molecular Modeling Insights on the Pharmaceuticals and Hypotheses of Alzheimer’s Disease
Curr. Med. Chem. 32, 5110 (2025); https://doi.org/10.2174/0109298673318143240823064403
/content/journals/cmc/10.2174/0109298673318143240823064403
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  • Article Type:     Review Article
Keyword(s): AD-related hypotheses; Alzheimer’s disease; amyloid-beta; computational approaches; neuroinflammation; tau protein

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