Skip to content
2000
Volume 21, Issue 8
  • ISSN: 1573-4064
  • E-ISSN: 1875-6638

Abstract

Introduction

Histamine Type I Receptor Antagonists (H1 blockers) are widely used to mitigate histamine-induced inflammation, particularly in allergic reactions. Histamine, a biogenic amine found in endothelial cells, vascular smooth muscle, bronchial smooth muscle, and the hypothalamus, plays a key role in these responses. H1 blockers are essential components of cough syrups and flu medications. They are classified into two generations: first-generation H1 blockers, which are sedating and associated with numerous side effects, and second-generation blockers, which are non-sedating, generally less toxic, but may still exhibit cross-reactivity with other receptors.

Methods

In this study, a comprehensive database of compounds was utilized, with fexofenadine serving as a benchmark to discover compounds with potentially superior efficacy and reduced side effect profiles. In particular, multidimensional K-means clustering, a machine-learning technique, was applied to identify compounds with chemical structures similar to fexofenadine.

Results

Utilizing computational prediction of pharmacokinetic profile and molecular docking experiments, the action of these drugs on the H1 receptor was assessed. Furthermore, the cross-reactivity of antihistamines was investigated by conducting a structure-based pharmacophore feature analysis of the docked poses of highly toxic antihistamines with various receptors.

Conclusion

By identifying and proposing the removal of common toxic features, this study aims to facilitate the development of antihistamines with reduced adverse effects.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/mc/10.2174/0115734064355393250121062539
2025-01-27
2025-12-24

  • From This Site

    /content/journals/mc/10.2174/0115734064355393250121062539
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. QianH. ShuC. XiaoL. WangG. Histamine and histamine receptors: Roles in major depressive disorder.Front. Psychiatry20221382559110.3389/fpsyt.2022.82559136213905
     [Google Scholar]
  2. PatelR.H. MohiuddinS.S. Biochemistry, Histamine.StatPearls2021
     [Google Scholar]
  3. SannaM.D. GaleottiN. Central neuronal functions of histamine H4 receptors.Oncotarget201788125561255710.18632/oncotarget.1528928199959
     [Google Scholar]
  4. SteinhoffM. BuddenkotteJ. LernerE.A. Role of mast cells and basophils in pruritus.Immunol. Rev.2018282124826410.1111/imr.1263529431207
     [Google Scholar]
  5. BonnekohH. ScheffelJ. KambeN. KrauseK. The role of mast cells in autoinflammation.Immunol. Rev.2018282126527510.1111/imr.1263329431217
     [Google Scholar]
  6. DileepanK.N. RaveendranV.V. SharmaR. AbrahamH. BaruaR. SinghV. SharmaR. SharmaM. Mast cell-mediated immune regulation in health and disease.Front. Med.202310121332010.3389/fmed.2023.121332037663654
     [Google Scholar]
  7. BrancoA.C.C.C. YoshikawaF.S.Y. PietrobonA.J. SatoM.N. Role of histamine in modulating the immune response and inflammation.Mediators Inflamm.20182018952407510.1155/2018/9524075
     [Google Scholar]
  8. ShulpekovaY.O. NechaevV.M. PopovaI.R. DeevaT.A. KopylovA.T. MalsagovaK.A. KayshevaA.L. IvashkinV.T. Food intolerance: The role of histamine.Nutrients2021139320710.3390/nu1309320734579083
     [Google Scholar]
  9. WangW. YuH. PanY. ShaoS. Combined treatment With H1 and H4 receptor antagonists improves th2 inflammatory responses in the nasal mucosa of allergic rhinitis rats.Am. J. Rhinol. Allergy202135680981610.1177/1945892421100260433726554
     [Google Scholar]
  10. MahdyA.M. WebsterN.R. Histamine and antihistamines.Anaesth. Intensive Care Med.201718421021510.1016/j.mpaic.2017.01.007
     [Google Scholar]
  11. ObaraI. TelezhkinV. AlrashdiI. ChazotP.L. Histamine, histamine receptors, and neuropathic pain relief.Br. J. Pharmacol.2020177358059910.1111/bph.1469631046146
     [Google Scholar]
  12. InceM. RuetherP. Histamine and antihistamines.Anaesth. Intensive Care Med.2021221174975510.1016/j.mpaic.2021.07.025
     [Google Scholar]
  13. LintonS. HossenbaccusL. EllisA.K. Evidence-based use of antihistamines for treatment of allergic conditions.Ann. Allergy Asthma Immunol.2023131441242010.1016/j.anai.2023.07.019
     [Google Scholar]
  14. AkimotoH. UesawaY. HishinumaS. Molecular determinants of the kinetic binding properties of antihistamines at the histamine H1 receptors.Int. J. Mol. Sci.2021225240010.3390/ijms2205240033673686
     [Google Scholar]
  15. BernsteinD.I. SchoenwetterW.F. NathanR.A. StormsW. AhlbrandtR. MasonJ. Efficacy and safety of fexofenadine hydrochloride for treatment of seasonal allergic rhinitis.Ann. Allergy Asthma Immunol.199779544344810.1016/S1081‑1206(10)63041‑49396979
     [Google Scholar]
  16. MeltzerE.O. RosarioN.A. Van BeverH. LucioL. Fexofenadine: Review of safety, efficacy and unmet needs in children with allergic rhinitis.Allergy Asthma Clin. Immunol.202117111310.1186/s13223‑021‑00614‑634727966
     [Google Scholar]
  17. LipkusA.H. A proof of the triangle inequality for the Tanimoto distance.J. Math. Chem.1999261/326326510.1023/A:1019154432472
     [Google Scholar]
  18. BallesterP.J. Multidisciplinary Digital Publishing Institute.2019Available from: [https://www.mdpi.com/]
    [Google Scholar]
  19. SasaharaK. ShibataM. SasabeH. SuzukiT. TakeuchiK. UmeharaK. KashiyamaE. Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.Drug Metab. Pharmacokinet.20213910040110.1016/j.dmpk.2021.10040134089983
     [Google Scholar]
  20. EnvironmentM.O. MontrealM.O.E. Molecular operating environment.2019Available from: [https://www.chemcomp.com/en/Products.htm]
    [Google Scholar]
  21. EibeI.W. WittenI.H. FrankE. TriggL. HallM. HolmesG. CunninghamS.J. Future Directions for Intelligent Systems and Information SciencesIn: Proceeding of ICONIP1999
     [Google Scholar]
  22. InselbergA. Parallel Coordinates: Visual Multidimensional Geometry and its Applications.New YorkSpringer2009
     [Google Scholar]
  23. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.2017714271710.1038/srep4271728256516
     [Google Scholar]
  24. ShimamuraT. ShiroishiM. WeyandS. TsujimotoH. WinterG. KatritchV. AbagyanR. CherezovV. LiuW. HanG.W. KobayashiT. StevensR.C. IwataS. Structure of the human histamine H1 receptor complex with doxepin.Nature20114757354657010.1038/nature1023621697825
     [Google Scholar]
  25. SalentinS. SchreiberS. HauptV.J. AdasmeM.F. SchroederM. PLIP: Fully automated protein–ligand interaction profiler.Nucleic Acids Res.201543W1W443W44710.1093/nar/gkv31525873628
     [Google Scholar]
  26. Larenas-LinnemannD. Comparing antihistamines in chronic spontaneous urticaria: Possible future directions.J. Allergy Clin. Immunol. Pract.2021962272227310.1016/j.jaip.2021.02.02434112475
     [Google Scholar]
  27. KalpakliogluF. BacciogluA. Efficacy and safety of H1-antihistamines: An update.Antiinflamm. Antiallergy Agents Med. Chem.2012113230237
     [Google Scholar]
  28. WolberG. LangerT. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters.J. Chem. Inf. Model.200545116016910.1021/ci049885e15667141
     [Google Scholar]
  29. SteindlT.M. SchusterD. WolberG. LaggnerC. LangerT. High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening.J. Comput. Aided Mol. Des.2007201270371510.1007/s10822‑006‑9066‑y17009092
     [Google Scholar]
  30. DaddamJ.R. SreenivasuluB. PeddannaK. UmamaheshK. Designing, docking and molecular dynamics simulation studies of novel cloperastine analogues as anti-allergic agents: Homology modeling and active site prediction for the human histamine H1 receptor.RSC Advances20201084745475410.1039/C9RA09245E35495246
     [Google Scholar]
/content/journals/mc/10.2174/0115734064355393250121062539
Loading
Integrating Machine Learning and Pharmacophore Features for Enhanced Prediction of H1 Receptor Blockers
Med. Chem. 21, 891 (2025); https://doi.org/10.2174/0115734064355393250121062539
/content/journals/mc/10.2174/0115734064355393250121062539
/content/journals/mc/10.2174/0115734064355393250121062539
Loading

Data & Media loading...

Supplements

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

file format download Download

  • Article Type:     Research Article
Keyword(s): H1 blockers; machine learning; monoaminergic amines; multidimensional k-means clustering; neurophysiology; vasoactive mediators

Most Cited Most Cited RSS feed

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