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2000
Volume 23, Issue 14
  • ISSN: 1570-159X
  • E-ISSN: 1875-6190

Abstract

Introduction

Migraine is a prevalent and debilitating neurological disorder, with current therapies are frequently ineffective and have side effects. Recent studies in neuropharmacology present the serotonin 1B receptor (HTR1B) receptor as a viable avenue of migraine treatment since it influences pain and vasoconstriction.

Methods

This research broadly uses computational approaches to explain the 5-hydroxytryptamine receptor 1B (HTR1B) pathways in neuropharmacology for migraine treatment.

Results

Text mining results reveal 25 essential genes, and network pharmacology provides complex mechanisms among genes and proteins, revealing a sophisticated network consisting of 41 nodes and 361 edges. The protein structure and function were elucidated through high-resolution protein modelling and validation, yielding significant new information. The structure has a resolution of 2.05 Å and a C-score of 0.30. The virtual screening explored the best ligands, which had binding affinities ranging from -13.8 to -9.6 kcal/mol from a set of 25 molecules. Docking results indicated that FDA-approved ligands showed high binding affinities, ranging from -11.4 to -12.5 kcal/mol among other natural and synthetic libraries. The pharmacokinetic profiles of the potential drugs showed significant diversity in their solubility and lipophilicity qualities (F(2,6) = 15.13, = 0.004), suggesting different levels of safety and efficacy. MD simulation clarified the dynamic interactions between the protein and ligand at 100ns. The RMSD values were stable within the 6.0-7.5 Å range, indicating a consistent structure. RMSF values revealed areas of flexibility in the protein. The toxicity risk assessment of Xaliproden indicated modest risks.

Conclusion

This study provides a foundation for targeted HTR1B-based migraine therapies and highlights the value of informatics tools in accelerating drug discovery in neuropharmacology.

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References

  1. AgostoniE.C. BarbantiP. CalabresiP. ColomboB. CortelliP. FredianiF. GeppettiP. GrazziL. LeoneM. MartellettiP. PiniL.A. PrudenzanoM.P. SarchielliP. TedeschiG. RussoA. Current and emerging evidence-based treatment options in chronic migraine: A narrative review.J. Headache Pain20192019210.1186/s10194‑019‑1038‑4 31470791
    [Google Scholar]
  2. de TommasoM. VecchioE. QuitadamoS.G. CoppolaG. Di RenzoA. ParisiV. SilvestroM. RussoA. TedeschiG. Pain-related brain connectivity changes in migraine: A narrative review and proof of concept about possible novel treatments interference.Brain Sci.202111223410.3390/brainsci11020234 33668449
    [Google Scholar]
  3. BelyaevaI.I. SubbotinaA.G. EremenkoI.I. TarasovV.V. ChubarevV.N. SchiöthH.B. MwinyiJ. Pharmacogenetics in primary headache disorders.Front. Pharmacol.20221282021410.3389/fphar.2021.820214 35222013
    [Google Scholar]
  4. TanveerJ. BaigB. RubeenR. QureshiS.R.Q. Fatima-ShaK.K. BashirN. Unlocking the mysteries: Serotonin receptor networks explored.Serotonin - Neurotransmitter and Hormone of Brain, Bowels and BloodIntechOpen202410.5772/intechopen.1004061
    [Google Scholar]
  5. Lima NetoJ.X. Soares-RachettiV.P. AlbuquerqueE.L. ManzoniV. FulcoU.L. Outlining migrainous through dihydroergotamine–serotonin receptor interactions using quantum biochemistry.New J. Chem.20184242401241210.1039/C7NJ03645K
    [Google Scholar]
  6. SimonettaI. RioloR. TodaroF. TuttolomondoA. New insights on metabolic and genetic basis of migraine: Novel impact on management and therapeutical approach.Int. J. Mol. Sci.2022236301810.3390/ijms23063018 35328439
    [Google Scholar]
  7. Viudez-MartínezA. TorregrosaA.B. NavarreteF. García-GutiérrezM.S. Understanding the biological relationship between migraine and depression.Biomolecules202414216310.3390/biom14020163 38397400
    [Google Scholar]
  8. HallK.T. LoscalzoJ. KaptchukT.J. Systems pharmacogenomics- gene, disease, drug and placebo interactions: A case study in COMT.Pharmacogenomics201920752955110.2217/pgs‑2019‑0001 31124409
    [Google Scholar]
  9. SpiesM. HandschuhP.A. LanzenbergerR. KranzG.S. Sex and the serotonergic underpinnings of depression and migraine.Handb. Clin. Neurol.202017511714010.1016/B978‑0‑444‑64123‑6.00009‑6 33008520
    [Google Scholar]
  10. PietrobonD. Ion channels in migraine disorders.Curr. Opin. Physiol.201829810810.1016/j.cophys.2018.02.001
    [Google Scholar]
  11. EbahimzadehK. GholipourM. SamadianM. TaheriM. Ghafouri-FardS. A comprehensive review on the role of genetic factors in the pathogenesis of migraine.J. Mol. Neurosci.202171101987200610.1007/s12031‑020‑01788‑1 33447900
    [Google Scholar]
  12. BarnesN.M. NeumaierJ.F.J.T.B.S.R.S. Neumaier, neuronal 5-HT receptors and SERT.Tocris Scien. Rev. Ser.201134116
    [Google Scholar]
  13. FerrariM.D. GoadsbyP.J. RoonK.L. LiptonR.B. Triptans (serotonin, 5-HT1B/1D agonists) in migraine: Detailed results and methods of a meta-analysis of 53 trials.Cephalalgia2002228633658
    [Google Scholar]
  14. HaanesK.A. EdvinssonL. Pathophysiological mechanisms in migraine and the identification of new therapeutic targets.CNS Drugs201933652553710.1007/s40263‑019‑00630‑6 30989485
    [Google Scholar]
  15. GuerreroC. The Role of Purinergic, 5-Hydroxytryptaminergic and Glutamatergic Receptors in Rat Peripheral Trigeminal Nociception: Implications for Migraine Pain. Doctoral dissertations.FinlandUniversity of Eastern Finland2019
    [Google Scholar]
  16. BertelsZ.J. Mechanisms of Chronic Migraine and the Development of Novel Therapeutic Targets for This Disorder.University of Illinois at Chicago2021
    [Google Scholar]
  17. NancyJ. Molecular mimicry of anti-migraine drugs with neurotransmitters, dopamine (DA) and serotonin (5-HT) and its role in the treatment of migraine.Thesis University of Canterbury2019
    [Google Scholar]
  18. Frimpong-MansonK. OrtizY.T. McMahonL.R. WilkersonJ.L. Advances in understanding migraine pathophysiology: A bench to bedside review of research insights and therapeutics.Front. Mol. Neurosci.202417135528110.3389/fnmol.2024.1355281 38481473
    [Google Scholar]
  19. SolimanN. KersebaumD. LawnT. Improving neuropathic pain treatment–by rigorous stratification from bench to bedside.J. Neurochem.202316836993714 36852505
    [Google Scholar]
  20. AgamahF.E. MazanduG.K. HassanR. BopeC.D. ThomfordN.E. GhansahA. ChimusaE.R. Computational/in silico methods in drug target and lead prediction.Brief. Bioinform.20202151663167510.1093/bib/bbz103 31711157
    [Google Scholar]
  21. SadybekovA.V. KatritchV. Computational approaches streamlining drug discovery.Nature2023616795867368510.1038/s41586‑023‑05905‑z 37100941
    [Google Scholar]
  22. MaS. ZhengL. LinX. FengY. YangM. ShenL. Network pharmacology and metabolomics studies on antimigraine mechanisms of da chuan xiong Fang (DCXF).Evid. Based Complement. Alternat. Med.2021202111610.1155/2021/6665137 33995549
    [Google Scholar]
  23. ShuY. XuY. XiaoW. DengX. ZengY. ChenR. XiaoB. LongH. A conjoint analysis of epilepsy and migraine through network-and-pathway-based method.Ann. Palliat. Med.2020952642265310.21037/apm‑19‑690 32921083
    [Google Scholar]
  24. TuranliB. KaragozK. GulfidanG. SinhaR. MardinogluA. ArgaK.Y. A network-based cancer drug discovery: From integrated multi-omics approaches to precision medicine.Curr. Pharm. Des.201924323778379010.2174/1381612824666181106095959 30398107
    [Google Scholar]
  25. SudershanA. MahajanK. SinghK. DharM.K. KumarP. The complexities of migraine: A debate among migraine researchers: A review.Clin. Neurol. Neurosurg.202221410713610.1016/j.clineuro.2022.107136 35101780
    [Google Scholar]
  26. VgontzasA. RenthalW. Migraine-associated gene expression in cell types of the central and peripheral nervous system.Cephalalgia202040551752310.1177/0333102419877834 31660761
    [Google Scholar]
  27. DreisbachC. KoleckT.A. BourneP.E. BakkenS. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.Int. J. Med. Inform.2019125374610.1016/j.ijmedinf.2019.02.008 30914179
    [Google Scholar]
  28. ThanhC.D. MenC.V. KimH.M. KangJ.S. Network pharmacology-based investigation on therapeutic mechanisms of the Angelica dahurica Radix and Ligusticum chuanxiong rhizoma herb pair for anti-migraine effect.Plants20221117219610.3390/plants11172196 36079577
    [Google Scholar]
  29. LiptonR.B. MunjalS. AlamA. BuseD.C. FanningK.M. ReedM.L. SchwedtT.J. DodickD.W. Migraine in America symptoms and treatment (MAST) study: Baseline study methods, treatment patterns, and gender differences.Headache20185891408142610.1111/head.13407 30341895
    [Google Scholar]
  30. Falah AlshehriF. AlzahraniF.M. AlkhoshaibanA. Saad Al ShehriZ. Exploring the multi-gene regulatory molecular mechanism of Saudi Arabian flora against epilepsy based on data mining, network pharmacology and docking analysis.Saudi Pharm. J.202331910173210.1016/j.jsps.2023.101732 37638220
    [Google Scholar]
  31. Noor, F.; Tahir ul Qamar, M.; Ashfaq, U.A.; Albutti, A.; Alwashmi, A.S.S.; Aljasir, M.A. Network pharmacology approach for medicinal plants: Review and assessment.Pharmaceuticals202215557210.3390/ph15050572 35631398
    [Google Scholar]
  32. LiuM. FanG. ZhangD. ZhuM. ZhangH. Study on mechanism of jiawei chaiqin wendan decoction in treatment of vestibular migraine based on network pharmacology and molecular docking technology.Evid. Based Complement. Alternat. Med.2021202111210.1155/2021/5528403 34754315
    [Google Scholar]
  33. RezvantalabS. ImanpourA. SeifA. Graphene as a potential treatment for acute migraine: A computational study.Mater. Today Commun.20243810802410.1016/j.mtcomm.2024.108024
    [Google Scholar]
  34. JamirE. SarmaH. PriyadarsineeL. KiewhuoK. NagamaniS. SastryG.N. Polypharmacology guided drug repositioning approach for SARS-CoV2.PLoS One2023188e028989010.1371/journal.pone.0289890 37556478
    [Google Scholar]
  35. SzilágyiK. FlachnerB. HajdúI. SzaszkóM. DobiK. LőrinczZ. CsehS. DormánG. Rapid identification of potential drug candidates from multi-million compounds’ repositories. combination of 2D similarity search with 3D ligand/structure based methods and in vitro screening.Molecules20212618559310.3390/molecules26185593 34577064
    [Google Scholar]
  36. CostaG. CartaF. AmbrosioF.A. ArteseA. OrtusoF. MoracaF. RoccaR. RomeoI. LupiaA. MarucaA. BagettaD. CatalanoR. VulloD. AlcaroS. SupuranC.T. A computer-assisted discovery of novel potential anti-obesity compounds as selective carbonic anhydrase VA inhibitors.Eur. J. Med. Chem.201918111156510.1016/j.ejmech.2019.111565 31387062
    [Google Scholar]
  37. PatelA.R. Virtual screening in drug discovery.J. Vet. Pharmacol. Toxicol.202120219
    [Google Scholar]
  38. ZhangB. LiH. YuK. JinZ. Molecular docking-based computational platform for high-throughput virtual screening.CCF Trans. High Perform. Comput.20226374
    [Google Scholar]
  39. Shahid, F.; Noreen, ; Ali, R.; Badshah, S.L.; Jamal, S.B.; Ullah, R.; Bari, A.; Majid Mahmood, H.; Sohaib, M.; Akber Ansari, S. Identification of potential HCV inhibitors based on the interaction of epigallocatechin-3-gallate with viral envelope proteins.Molecules2021265125710.3390/molecules26051257 33652639
    [Google Scholar]
  40. FuY. ZhaoJ. ChenZ. Insights into the molecular mechanisms of protein-ligand interactions by molecular docking and molecular dynamics simulation: A case of oligopeptide binding protein.Comput. Math. Methods Med.20182018350251410.1155/2018/3502514
    [Google Scholar]
  41. KumarY. SinghH. PatelC.N. In silico prediction of potential inhibitors for the main protease of SARS-CoV-2 using molecular docking and dynamics simulation based drug-repurposing.J. Infect. Public Health20201391210122310.1016/j.jiph.2020.06.016 32561274
    [Google Scholar]
  42. BasakS. KumarA. RamseyS. GibbsE. KapoorA. FilizolaM. ChakrapaniS. High-resolution structures of multiple 5-HT3AR-setron complexes reveal a novel mechanism of competitive inhibition.eLife20209e5787010.7554/eLife.57870 33063666
    [Google Scholar]
  43. ZhouX. LingM. LinQ. TangS. WuJ. HuH. Effectiveness analysis of multiple initial states simulated annealing algorithm, a case study on the molecular docking tool autodock vina.IEEE/ACM Trans. Comput. Biol. Bioinformatics20232063830384110.1109/TCBB.2023.3323552 37831573
    [Google Scholar]
  44. Al-ShabibN.A. KhanJ.M. MalikA. AlsenaidyM.A. RehmanM.T. AlAjmiM.F. AlsenaidyA.M. HusainF.M. KhanR.H. Molecular insight into binding behavior of polyphenol (rutin) with beta lactoglobulin: Spectroscopic, molecular docking and MD simulation studies.J. Mol. Liq.201826951152010.1016/j.molliq.2018.07.122
    [Google Scholar]
  45. DarkoL.K.S. BroniE. AmuzuD.S.Y. WilsonM.D. ParryC.S. KwofieS.K. Computational study on potential novel anti-Ebola virus protein VP35 natural compounds.Biomedicines2021912179610.3390/biomedicines9121796 34944612
    [Google Scholar]
  46. ZhangL. XuJ. GuoJ. WangY. WangQ. Elucidation of pharmacological mechanism underlying the anti-alzheimer’s disease effects of Evodia rutaecarpa and discovery of novel lead molecules: An in silico study.Molecules20232815584610.3390/molecules28155846 37570816
    [Google Scholar]
  47. MendesJ.A. SalustianoE.J. PiresC.S. OliveiraT. BarcellosJ.C.F. CifuentesJ.M.C. CostaP.R.R. RennóM.N. BuarqueC.D. 11a-N-tosyl-5-carbapterocarpans: Synthesis, antineoplastic evaluation and, in silico prediction of ADMETox properties.Bioorg. Chem.20188058559010.1016/j.bioorg.2018.07.004 30036814
    [Google Scholar]
  48. da Silva, C.P.M.; das Neves, G.M.; Poser, G.L.; Eifler-Lima, V.L.; Rates, S.M.K. In silico prediction of ADMET/Drug-likeness properties of bioactive phloroglucinols from Hypericum Genus.Med. Chem.202319101002101710.2174/1573406419666230601092358 37259926
    [Google Scholar]
  49. XiongG. WuZ. YiJ. FuL. YangZ. HsiehC. YinM. ZengX. WuC. LuA. ChenX. HouT. CaoD. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties.Nucleic Acids Res.202149W1W5W1410.1093/nar/gkab255 33893803
    [Google Scholar]
  50. 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.1c06110 34990125
    [Google Scholar]
  51. SchneiderJ. KorshunovaK. Si ChaibZ. GiorgettiA. Alfonso-PrietoM. CarloniP. Ligand pose predictions for human G protein-coupled receptors: Insights from the Amber-based hybrid Molecular Mechanics/Coarse-Grained approach.J. Chem. Inf. Model.202060105103511610.1021/acs.jcim.0c00661 32786708
    [Google Scholar]
  52. RehakP. Molecular Dynamics Simulations of Material Assembly, Growth, and Transport.University of Illinois at Chicago2021
    [Google Scholar]
  53. LeungL. LiaoS. WuC. To probe the binding interactions between two FDA approved migraine drugs (ubrogepant and rimegepant) and calcitonin-gene related peptide receptor (CGRPR) using molecular dynamics simulations.ACS Chem. Neurosci.202112142629264210.1021/acschemneuro.1c00135 34184869
    [Google Scholar]
  54. SullivanH.J. TursiA. MooreK. CampbellA. FloydC. WuC. Binding interactions of ergotamine and dihydroergotamine to 5-hydroxytryptamine receptor 1B (5-HT1b) using molecular dynamics simulations and dynamic network analysis.J. Chem. Inf. Model.20206031749176510.1021/acs.jcim.9b01082 32078320
    [Google Scholar]
  55. ParedesS. CantilloS. CandidoK.D. KnezevicN.N. An association of serotonin with pain disorders and its modulation by estrogens.Int. J. Mol. Sci.20192022572910.3390/ijms20225729 31731606
    [Google Scholar]
  56. KörtésiT. SpekkerE. VécseiL. Exploring the tryptophan metabolic pathways in migraine-related mechanisms.Cells20221123379510.3390/cells11233795 36497053
    [Google Scholar]
  57. Vila-PueyoM. Targeted 5-HT1F therapies for migraine.Neurotherapeutics201815229130310.1007/s13311‑018‑0615‑6 29488143
    [Google Scholar]
  58. NegroA. KoverechA. MartellettiP. Serotonin receptor agonists in the acute treatment of migraine: A review on their therapeutic potential.J. Pain Res.20181151552610.2147/JPR.S132833 29563831
    [Google Scholar]
  59. MartikainenI.K. HagelbergN. JääskeläinenS.K. HietalaJ. PertovaaraA. Dopaminergic and serotonergic mechanisms in the modulation of pain: In vivo studies in human brain.Eur. J. Pharmacol.201883433734510.1016/j.ejphar.2018.07.038 30036531
    [Google Scholar]
  60. HaleemD.J. Targeting Serotonin1A receptors for treating chronic pain and depression.Curr. Neuropharmacol.201917121098110810.2174/1570159X17666190811161807 31418663
    [Google Scholar]
  61. PedronJ. BoudotC. HutterS. Bourgeade-DelmasS. StiglianiJ.L. Sournia-SaquetA. MoreauA. Boutet-RobinetE. PaloqueL. MothesE. LagetM. VendierL. PratvielG. WyllieS. FairlambA. AzasN. CourtiouxB. ValentinA. VerhaegheP. Novel 8-nitroquinolin-2(1H)-ones as NTR-bioactivated antikinetoplastid molecules: Synthesis, electrochemical and SAR study.Eur. J. Med. Chem.201815513515210.1016/j.ejmech.2018.06.001 29885575
    [Google Scholar]
  62. SchantellM. Structural magnetic Resonance Imaging as a Diagnostic Biomarker of HIV-associated Neurocognitive Disorders (HAND).Thesis, University of Nebraska Medical Center2020
    [Google Scholar]
  63. KumarS. TeliM.K. KimM. GPCR-IPL score: Multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation.Brief. Bioinform.2024252bbae10510.1093/bib/bbae105 38517694
    [Google Scholar]
  64. DehuryB. MishraS. PatiS. Structural insights into SARS‐CoV‐2 main protease conformational plasticity.J. Cell. Biochem.2023124686187610.1002/jcb.30409 37099673
    [Google Scholar]
  65. KhanM.F. AliA. RehmanH.M. Noor KhanS. HammadH.M. WaseemM. WuY. ClarkT.G. JabbarA. Exploring optimal drug targets through subtractive proteomics analysis and pangenomic insights for tailored drug design in tuberculosis.Sci. Rep.20241411090410.1038/s41598‑024‑61752‑6 38740859
    [Google Scholar]
  66. YousafM. IsmailS. UllahA. BibiS. Immuno-informatics profiling of monkeypox virus cell surface binding protein for designing a next generation multi-valent peptide-based vaccine.Front. Immunol.202213103592410.3389/fimmu.2022.1035924 36405737
    [Google Scholar]
  67. AksoydanB. DurdagiS. Virtual drug repurposing study for the CGRPR identifies pentagastrin and leuprorelin as putative candidates.J. Mol. Graph. Model.202211610825410.1016/j.jmgm.2022.108254 35803082
    [Google Scholar]
  68. JahanfarF. SadofskyL. MoriceA. D’AmicoM. Nebivolol as a potent TRPM8 channel blocker: A Drug-screening approach through automated patch clamping and ligand-based virtual screening.Membranes2022121095410.3390/membranes12100954 36295712
    [Google Scholar]
  69. RushendranR. VellapandianC. Unlocking the potential of luteolin: A natural migraine management approach through network pharmacology.J. Tradit. Complement. Med.202414661162110.1016/j.jtcme.2024.04.011
    [Google Scholar]
  70. ZilbergG. ParpounasA.K. WarrenA.L. FiorilloB. Structural insights into the unexpected agonism of tetracyclic antidepressants at serotonin receptors 5-HT1eR and 5-HT1FR.Sci. Adv.20241016eadk4855
    [Google Scholar]
  71. BojarskiA.J. Pharmacophore models for metabotropic 5-Ht receptor ligands.Curr. Top. Med. Chem.20066182000522026
    [Google Scholar]
  72. NaharL. TalukdarA.D. NathD. NathS. MehanA. IsmailF.M.D. SarkerS.D. Naturally occurring calanolides: Occurrence, biosynthesis, and pharmacological properties including therapeutic potential.Molecules20202521498310.3390/molecules25214983 33126458
    [Google Scholar]
  73. ShlapakovaP.S. DobryninaL.A. KalashnikovaL.A. GubanovaM.V. DanilovaM.S. GnedovskayaE.V. GrigorenkoA.P. GusevF.E. ManakhovA.D. RogaevE.I. Peripheral blood gene expression profiling reveals molecular pathways associated with cervical artery dissection.Int. J. Mol. Sci.20242510520510.3390/ijms25105205 38791244
    [Google Scholar]
  74. HoffmannJ. CharlesA. Glutamate and its receptors as therapeutic targets for migraine.Neurotherapeutics201815236137010.1007/s13311‑018‑0616‑5 29508147
    [Google Scholar]
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