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Volume 21, Issue 2
  • ISSN: 1573-4099
  • E-ISSN: 1875-6697

Abstract

Introduction

It has been reported that the extension of conjugation in chalcone scaffolds considerably enhanced the potency, selectivity, reversibility, and competitive mode of MAO-B inhibition. In this study, using the experimental results of IC values of fifteen halogenated conjugated dienone derivatives () against MAO-B, we developed a 3D-QSAR model.

Methods

Further, we created a 3D pharmacophore model in active compounds in the series. The built model selected three variables (G2U, RDF115m, RDF155m) among the 653 AlvaDesc molecular descriptors, with a r2 value of 0.87 and a Q2 for cross-validation equal to 0.82. The three variables were mostly associated with the direction of symmetry and the likelihood of discovering massive atoms at great distances. The evaluated molecules exhibited a good correlation between experimental and predicted data, indicating that the IC value of the structure was related to the interatomic distances of 15.5 Å between bromine and chloro substituents. Furthermore, the molecules in the series with the highest activity were those with enhanced second component symmetry directional index from the 3D representation, which included the structures MK5 and MK6.

Results

Additionally, a pharmacophore hypothesis was developed and validated using the decoy Schrodinger dataset, with an ROC score of 0.87 and an HHRR 1 fitness score that ranged from 2.783 to 3.00. The MK series exhibited a significant blood-brain barrier (BBB) permeability, according to exploratory analyses and projections, and almost all analogues were expected to have strong BBB permeability.

Conclusion

Further DFT research revealed that electrostatics were important in the interactions with MAO-B.

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References

  1. JonesD.N. RaghantiM.A. The role of monoamine oxidase enzymes in the pathophysiology of neurological disorders.J. Chem. Neuroanat.202111410195710.1016/j.jchemneu.2021.101957
     [Google Scholar]
  2. TripathiA.C. UpadhyayS. PaliwalS. SarafS.K. Privileged scaffolds as MAO inhibitors: Retrospect and prospects.Eur. J. Med. Chem.201814544549710.1016/j.ejmech.2018.01.003
     [Google Scholar]
  3. TanY.Y. JennerP. ChenS.D. Monoamine oxidase-b inhibitors for the treatment of parkinson’s disease: Past, present, and future.J. Parkinsons Dis.202212247749310.3233/JPD‑212976
     [Google Scholar]
  4. RamsayR.R. Molecular aspects of monoamine oxidase B.Prog. Neuropsychopharmacol. Biol. Psychiatry201669818910.1016/j.pnpbp.2016.02.005
     [Google Scholar]
  5. FerinoG. VilarS. MatosM.J. UriarteE. CadoniE. Monoamine oxidase inhibitors: Ten years of docking studies.Curr. Top. Med. Chem.2012122145216210.2174/156802612805220048
     [Google Scholar]
  6. MelladoM. GonzálezC. MellaJ. AguilarL.F. ViñaD. UriarteE. CuellarM. MatosM.J. Combined 3D-QSAR and docking analysis for the design and synthesis of chalcones as potent and selective monoamine oxidase B inhibitors.Bioorg. Chem.202110810468910.1016/j.bioorg.2021.104689
     [Google Scholar]
  7. AlborghettiM. NicolettiF. Different generations of type-b monoamine oxidase inhibitors in parkinson’s disease: From bench to bedside.Curr. Neuropharmacol.201917986187310.2174/1570159X16666180830100754
     [Google Scholar]
  8. BindaC. LiM. HubálekF. RestelliN. EdmondsonD.E. MatteviA. Insights into the mode of inhibition of human mitochondrial monoamine oxidase B from high-resolution crystal structures.Proc. Natl. Acad. Sci. USA2003100179750975510.1073/pnas.1633804100
     [Google Scholar]
  9. BindaC. HubálekF. LiM. HerzigY. SterlingJ. EdmondsonD.E. MatteviA. Binding of rasagiline-related inhibitors to human monoamine oxidases: A kinetic and crystallographic analysis.J. Med. Chem.200548268148815410.1021/jm0506266
     [Google Scholar]
  10. CarradoriS. SilvestriR. New frontiers in selective human MAO-B inhibitors.J. Med. Chem.201558176717673210.1021/jm501690r
     [Google Scholar]
  11. GuglielmiP. MathewB. SecciD. CarradoriS. Chalcones: Unearthing their therapeutic possibility as monoamine oxidase B inhibitors.Eur. J. Med. Chem.202020511265010.1016/j.ejmech.2020.112650
     [Google Scholar]
  12. Pérez-GonzálezA. Castañeda-ArriagaR. Guzmán-LópezE.G. Hernández-AyalaL.F. GalanoA. Chalcone derivatives with a high potential as multifunctional antioxidant neuroprotectors.ACS Omega2022743382543826810.1021/acsomega.2c05518
     [Google Scholar]
  13. OrhanI. GulcanH. Coumarins: Auspicious cholinesterase and monoamine oxidase inhibitors.Curr. Top. Med. Chem.201515171673168210.2174/1568026615666150427113103
     [Google Scholar]
  14. KoyiparambathV.P. Prayaga RajappanK. RangarajanT.M. Al-SehemiA.G. PanniparaM. BhaskarV. NairA.S. SudevanS.T. KumarS. MathewB. Deciphering the detailed structure–activity relationship of coumarins as Monoamine oxidase enzyme inhibitors—An updated review.Chem. Biol. Drug Des.202198465567310.1111/cbdd.13919
     [Google Scholar]
  15. MathewB. MathewG.E. PetzerJ.P. PetzerA. Structural exploration of synthetic chromones as selective MAO-B inhibitors: A mini review.Comb. Chem. High Throughput Screen.201720652253210.2174/1386207320666170227155517
     [Google Scholar]
  16. RangarajanT.M. MathewB. Recent updates on pyrazoline derivatives as promising candidates for neuropsychiatric and neurodegenerative disorders.Curr. Top. Med. Chem.202121302695271410.2174/1568026621999210902123132
     [Google Scholar]
  17. KumarS. NairA.S. AbdelgawadM.A. MathewB. Exploration of the detailed structure–activity relationships of isatin and their isomers as monoamine oxidase inhibitors.ACS Omega2022719162441625910.1021/acsomega.2c01470
     [Google Scholar]
  18. HagenowJ. HagenowS. GrauK. KhanfarM. HefkeL. ProschakE. StarkH. Reversible small molecule inhibitors of MAO A and MAO B with anilide motifs.Drug Des. Devel. Ther.20201437139310.2147/DDDT.S236586
     [Google Scholar]
  19. GuglielmiP. CarradoriS. D’AgostinoI. CampestreC. PetzerJ.P. An updated patent review on monoamine oxidase (MAO) inhibitors.Expert Opin. Ther. Pat.202232884988310.1080/13543776.2022.2083501
     [Google Scholar]
  20. Bhawna KumarA. BhatiaM. KapoorA. KumarP. KumarS. Monoamine oxidase inhibitors: A concise review with special emphasis on structure activity relationship studies.Eur. J. Med. Chem.202224211465510.1016/j.ejmech.2022.114655
     [Google Scholar]
  21. SudevanS.T. OhJ.M. AbdelgawadM.A. AbourehabM.A.S. RangarajanT.M. KumarS. AhmadI. PatelH. KimH. MathewB. Introduction of benzyloxy pharmacophore into aryl/heteroaryl chalcone motifs as a new class of monoamine oxidase B inhibitors.Sci. Rep.20221212240410.1038/s41598‑022‑26929‑x
     [Google Scholar]
  22. MathewB. OhJ.M. AbdelgawadM.A. KhamesA. GhoneimM.M. KumarS. NathL.R. SudevanS.T. ParambiD.G.T. AgoniC. SolimanM.E.S. KimH. Conjugated dienones from differently substituted cinnamaldehyde as highly potent monoamine oxidase-B inhibitors: Synthesis, biochemistry, and computational chemistry.ACS Omega2022798184819710.1021/acsomega.2c00397
     [Google Scholar]
  23. MauriA. AlvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints.Methods in Pharmacology and Toxicology.New York, NYSpringer US2020801820
     [Google Scholar]
  24. BertholdM.R. CebronN. DillF. GabrielT.R. KötterT. MeinlT. OhlP. ThielK. WiswedelB. KNIME - The Konstanz information miner.SIGKDD Explor.2009111263110.1145/1656274.1656280
     [Google Scholar]
  25. HanwellM.D. CurtisD.E. LonieD.C. VandermeerschT. ZurekE. HutchisonG.R. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform.J. Cheminform.2012411710.1186/1758‑2946‑4‑17
     [Google Scholar]
  26. DixonS.L. SmondyrevA.M. RaoS.N. PHASE: A novel approach to pharmacophore modeling and 3D database searching.Chem. Biol. Drug Des.200667537037210.1111/j.1747‑0285.2006.00384.x
     [Google Scholar]
  27. MathewB. AdeniyiA.A. DevS. JoyM. UcarG. MathewG.E. Singh-PillayA. SolimanM.E.S. Pharmacophore-based 3D-QSAR analysis of thienyl chalcones as a new class of human MAO-B inhibitors: Investigation of combined quantum chemical and molecular dynamics approach.J. Phys. Chem. B201712161186120310.1021/acs.jpcb.6b09451
     [Google Scholar]
  28. BindaC. WangJ. PisaniL. CacciaC. CarottiA. SalvatiP. EdmondsonD.E. MatteviA. Structures of human monoamine oxidase B complexes with selective noncovalent inhibitors: Safinamide and coumarin analogs.J. Med. Chem.200750235848585210.1021/jm070677y
     [Google Scholar]
  29. KumarS. OhJ.M. AbdelgawadM.A. AbourehabM.A.S. TengliA.K. SinghA.K. AhmadI. PatelH. MathewB. KimH. Development of isopropyl-tailed chalcones as a new class of selective MAO-B inhibitors for the treatment of parkinson’s disorder.ACS Omega2023876908691710.1021/acsomega.2c07694
     [Google Scholar]
  30. InadaY. OritaH. Efficiency of numerical basis sets for predicting the binding energies of hydrogen bonded complexes: Evidence of small basis set superposition error compared to Gaussian basis sets.J. Comput. Chem.200829222523210.1002/jcc.20782
     [Google Scholar]
  31. Ben Hadj AyedM. OsmaniT. IssaouiN. BerishaA. OujiaB. GhallaH. Structures and relative stabilities of Na+Nen (n = 1–16) clusters via pairwise and DFT calculations.Theor. Chem. Acc.201913878410.1007/s00214‑019‑2476‑4
     [Google Scholar]
  32. MarshK.N. COSMO-RS from quantum chemistry to fluid phase thermodynamics and drug design. By A. Klamt. Elsevier: Amsterdam, The Netherlands, 2005. 246 pp. $US 165. ISBN 0-444-51994-7.J. Chem. Eng. Data20065141480148010.1021/je0602317
     [Google Scholar]
  33. KlamtA. The COSMO and COSMO‐RS solvation models.Wiley Interdiscip. Rev. Comput. Mol. Sci.201881e133810.1002/wcms.1338
     [Google Scholar]
  34. BerishaA. Interactions between the aryldiazonium cations and graphene oxide: A DFT study.J. Chem.201920191510.1155/2019/5126071
     [Google Scholar]
  35. TodeschiniR. GramaticaP. The whim theory: New 3D molecular descriptors for Qsar in environmental modelling.SAR QSAR Environ. Res.199771-48911510.1080/10629369708039126
     [Google Scholar]
  36. TodeschiniR. ConsonniV. Handbook of Molecular Descriptors.John Wiley & Sons2008
     [Google Scholar]
  37. HemmerM.C. SteinhauerV. GasteigerJ. Deriving the 3D structure of organic molecules from their infrared spectra.Vib. Spectrosc.199919115116410.1016/S0924‑2031(99)00014‑4
     [Google Scholar]
  38. JarrayA. GerbaudV. HematiM. Polymer-plasticizer compatibility during coating formulation: A multi-scale investigation.Prog. Org. Coat.201610119520610.1016/j.porgcoat.2016.08.008
     [Google Scholar]
  39. BerishaA. The influence of the grafted aryl groups on the solvation properties of the graphyne and graphdiyne - A MD study.Open Chem.201917170371010.1515/chem‑2019‑0083
     [Google Scholar]
  40. OngariD. BoydP.G. KadiogluO. MaceA.K. KeskinS. SmitB. Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials.J. Chem. Theory Comput.201915138240110.1021/acs.jctc.8b00669
     [Google Scholar]
  41. TangC. FarhadianA. BerishaA. DeyabM.A. ChenJ. IravaniD. RahimiA. ZhangZ. LiangD. Novel biosurfactants for effective inhibition of gas hydrate agglomeration and corrosion in offshore oil and gas pipelines.ACS Sustain. Chem.& Eng.202311135336710.1021/acssuschemeng.2c05716
     [Google Scholar]
  42. El GaaydaJ. Ezzahra TitchouF. OukhribR. KarmalI. Abou OualidH. BerishaA. ZazouH. SwansonC. HamdaniM. Ait AkbourR. Removal of cationic dye from coloured water by adsorption onto hematite-humic acid composite: Experimental and theoretical studies.Separ. Purif. Tech.202228812060710.1016/j.seppur.2022.120607
     [Google Scholar]
  43. MehmetiV. HaliliJ. BerishaA. Which is better for Lindane pesticide adsorption, graphene or graphene oxide? An experimental and DFT study.J. Mol. Liq.202234711834510.1016/j.molliq.2021.118345
     [Google Scholar]
  44. DagdagO. BerishaA. MehmetiV. HaldharR. BerdimurodovE. HamedO. JodehS. LgazH. SherifE.S.M. EbensoE.E. Epoxy coating as effective anti-corrosive polymeric material for aluminum alloys: Formulation, electrochemical and computational approaches.J. Mol. Liq.202234611788610.1016/j.molliq.2021.117886
     [Google Scholar]
  45. BerishaA. PodvoricaF.I. VatajR. Corrosion inhibition study of mild steel in an aqueous hydrochloric acid solution using brilliant cresyl blue - A combined experimental and monte carlo study.Port. Electrochem. Acta202139539340110.4152/pea.2021390601
     [Google Scholar]
  46. DagdagO. BerishaA. SafiZ. HamedO. JodehS. VermaC. EbensoE.E. El HarfiA. DGEBA‐polyaminoamide as effective anti‐corrosive material for 15CDV6 steel in NaCl medium: Computational and experimental studies.J. Appl. Polym. Sci.202013784840210.1002/app.48402
     [Google Scholar]
  47. BerishaA. Chemically modified cnts as corrosion inhibitors.Carbon Allotropes.Chapter 10De Gruyter202222724010.1515/9783110782820‑010
     [Google Scholar]
  48. OuassA. GalaiM. OuakkiM. Ech-ChihbiE. KadiriL. HsissouR. EssaadaouiY. BerishaA. CherkaouiM. LebkiriA. RifiE.H. Poly(sodium acrylate) and Poly(acrylic acid sodium) as an eco-friendly corrosion inhibitor of mild steel in normal hydrochloric acid: Experimental, spectroscopic and theoretical approach.J. Appl. Electrochem.20215171009103210.1007/s10800‑021‑01556‑y
     [Google Scholar]
  49. DamejM. HsissouR. BerishaA. AzgaouK. SadikuM. BenmessaoudM. LabjarN. El hajjajiS. New epoxy resin as a corrosion inhibitor for the protection of carbon steel C38 in 1M HCl. experimental and theoretical studies (DFT, MC, and MD).J. Mol. Struct.2022125413242510.1016/j.molstruc.2022.132425
     [Google Scholar]
  50. MolhiA. HsissouR. DamejM. BerishaA. ThaçiV. BelafhailiA. BenmessaoudM. LabjarN. El HajjajiS. Contribution to the corrosion inhibition of C38 steel in 1 M hydrochloric acid medium by a new epoxy resin PGEPPP.Int. J. Corros. Scale Inhib.202110139941810.17675/2305‑6894‑2021‑10‑1‑23
     [Google Scholar]
  51. DagdagO. BerishaA. SafiZ. DagdagS. BerraniM. JodehS. VermaC. EbensoE.E. WazzanN. El HarfiA. Highly durable macromolecular epoxy resin as anticorrosive coating material for carbon steel in 3% NaCl: Computational supported experimental studies.J. Appl. Polym. Sci.2020137344900310.1002/app.49003
     [Google Scholar]
  52. DagdagO. HsissouR. El HarfiA. BerishaA. SafiZ. VermaC. EbensoE.E. Ebn TouhamiM. El GouriM. Fabrication of polymer based epoxy resin as effective anti-corrosive coating for steel: Computational modeling reinforced experimental studies.Surf. Interfaces20201810045410.1016/j.surfin.2020.100454
     [Google Scholar]
  53. HsissouR. AbboutS. SeghiriR. RehiouiM. BerishaA. ErramliH. AssouagM. ElharfiA. Evaluation of corrosion inhibition performance of phosphorus polymer for carbon steel in [1 M] HCl: Computational studies (DFT, MC and MD simulations).J. Mater. Res. Technol.2020932691270310.1016/j.jmrt.2020.01.002
     [Google Scholar]
  54. HsissouR. DagdagO. AbboutS. BenhibaF. BerradiM. El BouchtiM. BerishaA. HajjajiN. ElharfiA. Novel derivative epoxy resin TGETET as a corrosion inhibition of E24 carbon steel in 1.0 M HCl solution. Experimental and computational (DFT and MD simulations) methods.J. Mol. Liq.201928418219210.1016/j.molliq.2019.03.180
     [Google Scholar]
  55. RathiP.C. LudlowR.F. VerdonkM.L. Practical high-quality electrostatic potential surfaces for drug discovery using a graph-convolutional deep neural network.J. Med. Chem.202063168778879010.1021/acs.jmedchem.9b01129
     [Google Scholar]
  56. DoughertyD.A. The cation−π interaction.Acc. Chem. Res.201346488589310.1021/ar300265y
     [Google Scholar]
  57. PiresD.E.V. BlundellT.L. AscherD.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures.J. Med. Chem.20155894066407210.1021/acs.jmedchem.5b00104
     [Google Scholar]
  58. DainaA. ZoeteV. A BOILED‐egg to predict gastrointestinal absorption and brain penetration of small molecules.ChemMedChem201611111117112110.1002/cmdc.201600182
     [Google Scholar]
  59. LagorceD. DouguetD. MitevaM.A. VilloutreixB.O. Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors.Sci. Rep.2017714627710.1038/srep46277
     [Google Scholar]
  60. HouT.J. ZhangW. XiaK. QiaoX.B. XuX.J. ADME evaluation in drug discovery. 5. Correlation of caco-2 permeation with simple molecular properties.J. Chem. Inf. Comput. Sci.20044451585160010.1021/ci049884m
     [Google Scholar]
  61. GhafourianT. BarzegarjalaliM. DastmalchiS. KhavarikhorasaniT. HakimihaN. NokhodchiA. QSPR models for the prediction of apparent volume of distribution.Int. J. Pharm.20063191-2829710.1016/j.ijpharm.2006.03.043
     [Google Scholar]
  62. HasanahA.N. AbdurrahmanS. RuslinR. MustarichieR. Molecular docking studies and ADME-Tox prediction of phytocompounds from Merremia peltata as a potential anti-alopecia treatment.J. Adv. Pharm. Technol. Res.202112213213910.4103/japtr.JAPTR_222_20
     [Google Scholar]
  63. MuehlbacherM. SpitzerG.M. LiedlK.R. KornhuberJ. Qualitative prediction of blood–brain barrier permeability on a large and refined dataset.J. Comput. Aided Mol. Des.201125121095110610.1007/s10822‑011‑9478‑1
     [Google Scholar]
  64. ZhaoY.H. AbrahamM.H. IbrahimA. FishP.V. ColeS. LewisM.L. de GrootM.J. ReynoldsD.P. Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes.J. Chem. Inf. Model.200747117017510.1021/ci600312d
     [Google Scholar]
  65. SpitzC. PrimasN. TermeT. VanelleP. Nitro-containing self-immolative systems for biological applications.Pharmaceuticals20221511140410.3390/ph15111404
     [Google Scholar]
  66. NoriegaS. Cardoso-OrtizJ. López-LunaA. Cuevas-FloresM.D.R. Flores De La TorreJ.A. The diverse biological activity of recently synthesized nitro compounds.Pharmaceuticals202215671710.3390/ph15060717
     [Google Scholar]
  67. BastrakovM. StarosotnikovA. Recent progress in the synthesis of drugs and bioactive molecules incorporating nitro(het)arene core.Pharmaceuticals202215670510.3390/ph15060705
     [Google Scholar]
  68. OlenderD. ŻwawiakJ. ZaprutkoL. Multidirectional efficacy of biologically active nitro compounds included in medicines.Pharmaceuticals20181125410.3390/ph11020054
     [Google Scholar]
  69. NishiwakiN. A walk through recent nitro chemistry advances.Molecules20202516368010.3390/molecules25163680
     [Google Scholar]
  70. MahíaA. Peña-DíazS. NavarroS. José Galano-FrutosJ. PallarésI. PujolsJ. Díaz-de-VillegasM.D. GálvezJ.A. VenturaS. SanchoJ. Design, synthesis and structure-activity evaluation of novel 2-pyridone-based inhibitors of α-synuclein aggregation with potentially improved BBB permeability.Bioorg. Chem.202111710547210.1016/j.bioorg.2021.105472
     [Google Scholar]
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3D-QSAR, Pharmacophore Modeling, ADMET and DFT Studies of Halogenated Conjugated Dienones as Potent MAO-B Inhibitors
Curr. Computeraided Drug Des. 21, 179 (2025); https://doi.org/10.2174/0115734099307062240801053329
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  • Article Type:     Research Article
Keyword(s): 3D-QSAR; conjugated dienone; DFT; Monoamine oxidase; pharmacophore; ROC

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