Skip to content
2000
Volume 21, Issue 7
  • ISSN: 1573-4099
  • E-ISSN: 1875-6697

Abstract

Introduction

Butyrylcholinesterase (BChE) plays a pivotal role in the progression of Alzheimer's disease. Empirical research demonstrated a fundamental alteration in the role of BChE concerning the reduction of cholinergic neurotransmission within the brains of individuals at advanced stages of Alzheimer's.

Methods

This study focuses on developing potent inhibitors for Butyrylcholinesterase (BChE) in the context of Alzheimer's disease (AD) treatment. Building upon previous research, a series of 44 aromatic tertiary amine-based compounds was investigated. Starting with ADME-Tox studies, the pharmacokinetic and pharmacodynamic properties of the compounds were analyzed to select promising candidates for BChE inhibition, which is a crucial factor in AD pathology.

Results

Molecular docking analyses identified compound M18 as the most promising candidate, and further compounds (X9 and X10) were proposed based on M18's chemical structure. These compounds displayed superior properties in terms of binding energies and hydrogen bonds in comparison to M18.

Conclusion

The Molecular Dynamics (MD) simulations, which are over a 500 ns timeframe, confirmed the conformational stability of compounds X9 and X10, compared to M18. Overall, the stated results suggest that the proposed compounds, including X9 and X10 specifically, have a significant potential as candidates for BChE inhibition. This presents a promising avenue for therapeutic intervention in Alzheimer's disease.

Loading

Article metrics loading...

/content/journals/cad/10.2174/0115734099302980240722074437
2024-07-29
2026-02-01
Loading full text...

Full text loading...

References

  1. BishtT. SundramS. MalviyaR. PandeyA. Herbal components for the treatment of alzheimer’s disease.Nat. Prod. J.2023137e23012321300110.2174/2210315513666230123111541
    [Google Scholar]
  2. KanchiP.K. DasmahapatraA.K. Polyproline chains destabilize the Alzheimer’s amyloid-β protofibrils: A molecular dynamics simulation study.J. Mol. Graph. Model.20199310745610.1016/j.jmgm.2019.10745631581064
    [Google Scholar]
  3. AbbasiH. FereidoonnezhadM. Vilazodone-tacrine hybrids as potential anti-alzheimer agents: QSAR, molecular docking, and molecular dynamic (MD).Simulation Studies2022121588607
    [Google Scholar]
  4. ElangovanN.D. DhanabalanA.K. KandimallaR. SankarganeshD. Screening of potential drug for Alzheimer ’ s disease: A computational study with GSK-3 β inhibition through virtual screening, docking, and molecular dynamics simulation.J. Biomol. Struct. Dyn.20200011510.1080/07391102.2020.180536232779973
    [Google Scholar]
  5. SelvarajM. SadasivamK. JothimaniM. MuthusamyK. A comprehensive computational perspective in drug discovery for alzheimer’s disease.Comb. Chem. High Throughput Screen.202326122113212310.2174/138620732566622060614291035670352
    [Google Scholar]
  6. LiuT. CaoL. ZhangT. FuH. Molecular docking studies, anti-Alzheimer ’ s disease, antidiabetic, and anti-acute myeloid leukemia potentials of narcissoside.Arch. Physiol. Biochem.20200011110.1080/13813455.2020.182848333075241
    [Google Scholar]
  7. SinghK. YadavD. ChauhanP.S. MishraM. JinJ.O. Novel therapeutics for the treatment of alzheimer’s and parkinson’s disease.Curr. Pharm. Des.202026775576310.2174/138161282666620010716105131914906
    [Google Scholar]
  8. KumarV. SahaA. RoyK. Multi-target QSAR modeling for the identification of novel inhibitors against Alzheimer’s disease.Chemom. Intell. Lab. Syst.202323310473410.1016/j.chemolab.2022.104734
    [Google Scholar]
  9. KumarA. Current and novel therapeutic molecules and targets in Alzheimer's disease.J. Formos. Med. Assoc.2016115131010.1016/j.jfma.2015.04.001
    [Google Scholar]
  10. LuX. QinN. LiuY. DuC. FengF. LiuW. ChenY. SunH. Design, synthesis, and biological evaluation of aromatic tertiary amine derivatives as selective butyrylcholinesterase inhibitors for the treatment of Alzheimer’s disease.Eur. J. Med. Chem.2022243August11472910.1016/j.ejmech.2022.11472936084535
    [Google Scholar]
  11. UzairuS.M. TijaniY. GadakaM.A. ModuB. WatafuaM. AhmadH.A. ZakariyaU.A. IbrahimA. DajaA. ZannaH. SallauA.B. Kinetics and computational study of butyrylcholinesterase inhibition by methylrosmarinate: Relevance to Alzheimer’s disease treatment.Heliyon202289e1061310.1016/j.heliyon.2022.e1061336148271
    [Google Scholar]
  12. QaraA. OuabaneM. SekkateC. ChtitaS. Activity of aromatic tertiary amine derivatives as selective butyrylcholinesterase inhibitors for the treatment of Alzheimer ’ s disease.2D and 3D QSAR Studies202318
    [Google Scholar]
  13. RemyaR.S. RamalakshmiN. NaliniC.N. NiraimathiV. AmuthalakshmiS. Design synthesis and in vitro evaluation of tacrine-flavone hybrids as multifunctional cholinesterase inhibitors for alzheimer’s disease.Curr. Computeraided Drug Des.202218427129210.2174/157340991866622080415375435927818
    [Google Scholar]
  14. RasoolM. UllahH. HussainA. AsifM. NawazF. Natural products as bioactive agents in the prevention of dementia.CNS Neurol. Disord. Drug Targets202322446647610.2174/187152732166622042208583535466886
    [Google Scholar]
  15. GulcanH.O. KosarM. The hybrid compounds as multi-target ligands for the treatment of alzheimer’s disease: Considerations on donepezil.Curr. Top. Med. Chem.202222539540710.2174/156802662166621111115362634766890
    [Google Scholar]
  16. KošakU. BrusB. KnezD. ŠinkR. ŽakeljS. TronteljJ. PišlarA. ŠlencJ. GobecM. ŽivinM. TratnjekL. PeršeM. SałatK. PodkowaA. FilipekB. NachonF. BrazzolottoX. WięckowskaA. MalawskaB. StojanJ. RaščanI.M. KosJ. CoquelleN. ColletierJ.P. GobecS. Development of an in-vivo active reversible butyrylcholinesterase inhibitor.Sci. Rep.2016613949510.1038/srep3949528000737
    [Google Scholar]
  17. BaammiS. El AllaliA. DaoudR. Potent VEGFR-2 inhibitors for resistant breast cancer: A comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives.Front. Mol. Biosci.202310November128865210.3389/fmolb.2023.128865238074087
    [Google Scholar]
  18. LianZ. SangC. LiN. ZhaiH. BaiW. 3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma.Front. Pharmacol.202314112489510.3389/fphar.2023.112489536895941
    [Google Scholar]
  19. BelghaliaE. OuabaneM. El BahiS. RehmanH.M. SbaiA. LakhlifiT. BouachrineM. In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME/Tox, and molecular dynamics.J. Biomol. Struct. Dyn.20230011910.1080/07391102.2023.225046037656159
    [Google Scholar]
  20. El KhatabiK. AanouzI. El-mernissiR. KhaldanA. AjanaM.A. BouachrineM. LakhlifiT. 3d-qsar and molecular docking studies of p-aminobenzoic acid derivatives to explore the features requirements of alzheimer inhibitors.Orbital: The Electronic Journal of Chemistry202012417218110.17807/orbital.v12i4.1467
    [Google Scholar]
  21. En-NahliF. HajjiH. OuabaneM. Aziz AJANAM. SekatteC. LakhlifiT. BouachrineM. ADMET profiling and molecular docking of pyrazole and pyrazolines derivatives as antimicrobial agents.Arab. J. Chem.2023161110526210.1016/j.arabjc.2023.105262
    [Google Scholar]
  22. El KhatabiK. El-MernissiR. AanouzI. AjanaM.A. LakhlifiT. KhanA. WeiD.Q. BouachrineM. Identification of novel acetylcholinesterase inhibitors through 3D-QSAR, molecular docking, and molecular dynamics simulation targeting Alzheimer’s disease.J. Mol. Model.2021271030210.1007/s00894‑021‑04928‑534581863
    [Google Scholar]
  23. OuabaneM. ZakiK. TabtiK. AlaqarbehM. SbaiA. SekkateC. BouachrineM. LakhlifiT. Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations.Comput. Biol. Med.202416910788010.1016/j.compbiomed.2023.10788038211383
    [Google Scholar]
  24. BaammiS. El AllaliA. DaoudR. Unleashing Nature’s potential: A computational approach to discovering novel VEGFR-2 inhibitors from African natural compound using virtual screening, ADMET analysis, molecular dynamics, and MMPBSA calculations.Front. Mol. Biosci.202310September122764310.3389/fmolb.2023.122764337800126
    [Google Scholar]
  25. TabtiK. SbaiA. MaghatH. LakhlifiT. BouachrineM. Discovery of novel indoleamine 2,3-dioxygenase-1 (IDO-1) inhibitors: pharmacophore-based 3D-QSAR, Gaussian field-based 3D-QSAR, docking, and binding free energy studies.Struct. Chem.20231012345678910.1007/s11224‑023‑02213‑0
    [Google Scholar]
  26. AbdessadakO. OuabaneM. Aziz AjanaM. LakhlifiT. BouachrineM. Chemical reactivity and regioselectivity investigation for the formation of 3,5-disubstituted isoxazole via cycloaddition [2 + 3] and antitrypanosomal activity prediction.Comput. Theor. Chem.2024123311447810.1016/j.comptc.2024.114478
    [Google Scholar]
  27. ElbouhiM. OuabaneM. TabtiK. BadaouiH. AbdessadakO. El AlaouyM.A. ElkamelK. LakhlifiT. SbaiA. AjanaM.A. BouachrineM. Computational evaluation of 1,2,3-triazole-based VEGFR-2 inhibitors: Anti-angiogenesis potential and pharmacokinetic assessment.J. Biomol. Struct. Dyn.202411110.1080/07391102.2023.230168638193897
    [Google Scholar]
  28. HajjiH. In silico investigation on the beneficial effects of medicinal plants on diabetes and obesity: Molecular docking, molecular dynamic simulations, and admet studies.Biointerface Res. Appl. Chem.20211256933694910.33263/BRIAC125.69336949
    [Google Scholar]
  29. AjalaA. UzairuA. ShallangwaG.A. AbechiS.E. Virtual screening, molecular docking simulation and ADMET prediction of some selected natural products as potential inhibitors of NLRP3 inflammasomes as drug candidates for Alzheimer disease.Biocatal. Agric. Biotechnol.20234810261510.1016/j.bcab.2023.102615
    [Google Scholar]
  30. OuabaneM. ZakiK. AlaqarbehM. GuendouziA. SekkateC. SbaiA. BouachrineM. LakhlifiT. Exploring structure–toxicity relationships in nitrobenzene and derivatives: A multifaceted biochemical investigation using 3d–qspr, hqspr, molecular docking, and md simulation.ChemistrySelect2024915e20230458810.1002/slct.202304588
    [Google Scholar]
  31. OuabaneM. TabtiK. HajjiH. ElbouhiM. KhaldanA. ElkamelK. SbaiA. Aziz AJANAM. SekkateC. BouachrineM. LakhlifiT. Structure-odor relationship in pyrazines and derivatives: A physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics.Arab. J. Chem.2023161110520710.1016/j.arabjc.2023.105207
    [Google Scholar]
  32. KhaldanA. BouamraneS. El-mernissiR. OuabaneM. AlaqarbehM. MaghatH. Aziz AjanaM. SekkatC. BouachrineM. LakhlifiT. SbaiA. Design of new α-glucosidase inhibitors through a combination of 3D-QSAR, ADMET screening, molecular docking, molecular dynamics simulations and quantum studies.Arab. J. Chem.202417310565610.1016/j.arabjc.2024.105656
    [Google Scholar]
  33. OuabaneM. AlaqarbehM. HajjiH. TabtiK. AjanaM.A. SbaiA. SekkateC. LakhlifiT. BouachrineM. Quality control of coumarins, furocoumarins and polymethoxyflavones in citrus essential oils: In silico analysis.ChemistrySelect202495e20230303710.1002/slct.202303037
    [Google Scholar]
  34. LuX. Expansion of the scaffold diversity for the development of highly selective butyrylcholinesterase (BChE) inhibitors: Discovery of new hits through the pharmacophore model generation, virtual screening and molecular dynamics simulation.Bioorg. Chem.201985201811712710.1016/j.bioorg.2018.12.023
    [Google Scholar]
  35. TabtiK. BaammiS. SbaiA. MaghatH. LakhlifiT. BouachrineM. Molecular modeling study of pyrrolidine derivatives as novel myeloid cell leukemia-1 inhibitors through combined 3D-QSAR, molecular docking, ADME/Tox and MD simulation techniques.J. Biomol. Struct. Dyn.20234123137981381410.1080/07391102.2023.218303236841617
    [Google Scholar]
  36. AbrahamM.J. MurtolaT. SchulzR. PállS. SmithJ.C. HessB. LindahlE. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  37. LindahlE. AbrahamM. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS.J. Chem. Phys.20201531313411010.1063/5.0018516
    [Google Scholar]
  38. BjelkmarP. LarssonP. CuendetM.A. HessB. LindahlE. Implementation of the charmm force field in GROMACS: Analysis of protein stability effects from correction maps, virtual interaction sites, and water models.J. Chem. Theory Comput.20106245946610.1021/ct900549r26617301
    [Google Scholar]
  39. ZoeteV. CuendetM.A. GrosdidierA. MichielinO. SwissParam: A fast force field generation tool for small organic molecules.J. Comput. Chem.201132112359236810.1002/jcc.2181621541964
    [Google Scholar]
  40. Valdés-tresancoM. S. Valdés-tresancoM. E. ValienteP. A. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS.J. Chem. Theory Comput.20211710
    [Google Scholar]
  41. YaoH. LiuJ. XuM. JiJ. DaiQ. YouZ. Discussion on molecular dynamics (MD) simulations of the asphalt materials.Adv. Colloid Interface Sci.202229910256510.1016/j.cis.2021.102565
    [Google Scholar]
  42. SainiR.K. ShuaibS. GoyalD. GoyalB. Insights into the inhibitory mechanism of a resveratrol and clioquinol hybrid against Aβ 42 aggregation and protofibril destabilization: A molecular dynamics simulation study.J. Biomol. Struct. Dyn.201937123183319710.1080/07391102.2018.151147530582723
    [Google Scholar]
  43. AhmadS.S. SinhaM. AhmadK. KhalidM. ChoiI. Study of caspase 8 inhibition for the management of Alzheimer’s disease: A molecular docking and dynamics simulation.Molecules2020259207110.3390/molecules2509207132365525
    [Google Scholar]
  44. ChennaiH.Y. BelaidiS. BourougaaL. OuassafM. SinhaL. SamadiA. ChtitaS. Identification of potent acetylcholinesterase inhibitors as new candidates for alzheimer disease via virtual screening, molecular docking, dynamic simulation, and molecular mechanics–poisson–boltzmann surface area calculations.Molecules2024296123210.3390/molecules2906123238542869
    [Google Scholar]
  45. SoufiH. MoussaouiM. BaammiS. BaassiM. SalahM. DaoudR. El AllaliA. BelghitiM.E. MoutaabbidM. BelaaouadS. Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors.J. Biomol. Struct. Dyn.202311510.1080/07391102.2023.223831437485860
    [Google Scholar]
  46. TabtiK. AbdessadakO. SbaiA. MaghatH. BouachrineM. LakhlifiT. Design and development of novel spiro-oxindoles as potent antiproliferative agents using quantitative structure activity based Monte Carlo method, docking molecular, molecular dynamics, free energy calculations, and pharmacokinetics/toxicity studies.J. Mol. Struct.2023128413540410.1016/j.molstruc.2023.135404
    [Google Scholar]
  47. CavaniM. RiofríoW.A. ArciniegaM. Molecular dynamics and MM-PBSA analysis of the SARS-CoV-2 gamma variant in complex with the hACE-2 receptor.Molecules2022277237010.3390/molecules2707237035408761
    [Google Scholar]
  48. PaissoniC. SpiliotopoulosD. MuscoG. SpitaleriA. GMXPBSA 2.1: A GROMACS tool to perform MM/PBSA and computational alanine scanning.Comput. Phys. Commun.201518610510710.1016/j.cpc.2014.09.010
    [Google Scholar]
  49. SundarS. ThangamaniL. ManivelG. KumarP. PiramanayagamS. Molecular docking, molecular dynamics and MM/PBSA studies of FDA approved drugs for protein kinase a of Mycobacterium tuberculosis; Application insights of drug repurposing.Informatics in Medicine Unlocked201916May10021010.1016/j.imu.2019.100210
    [Google Scholar]
  50. JäntschiL. Detecting extreme values with order statistics in samples from continuous distributions.Mathematics20208221610.3390/math8020216
    [Google Scholar]
  51. ModellingL.R. Quant. Struct.-Act. Relationsh.20132111
    [Google Scholar]
  52. JäntschiL. Formulas, algorithms and examples for binomial distributed data confidence interval calculation: Excess risk, relative risk and odds ratio.Mathematics2021919250610.3390/math9192506
    [Google Scholar]
  53. JäntschiL. Introducing structural symmetry and asymmetry implications in development of recent pharmacy and medicine.Symmetry2022148167410.3390/sym14081674
    [Google Scholar]
  54. DistributedB. ConfidenceD. CalculationI. Binomial Distributed Data Confidence Interval Calculation: Formulas, Algorithms and ExamplesSymmetry20221461104
    [Google Scholar]
/content/journals/cad/10.2174/0115734099302980240722074437
Loading
/content/journals/cad/10.2174/0115734099302980240722074437
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.


  • Article Type:
    Research Article
Keyword(s): AD pathology; ADME-Tox; aromatic tertiary amine; BChE inhibitors; docking; MD simulation
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test