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2000
Volume 14, Issue 3
  • ISSN: 2211-5501
  • E-ISSN: 2211-551X

Abstract

Introduction

Biotechnology provides the biological data and molecular insights that drive Computer-Aided Drug Designing (CADD), which is an advanced computational technique used in drug discovery and development. It integrates biological, chemical, and computational tools to identify and optimize potential therapeutic compounds. Its connection with biotechnology is significant. The importance of the indole moiety in drug discovery emphasizes its privileged status in finding new drug molecules. Understanding the functions of indole alkaloids, as well as structure-activity relationships (SARs) of indole derivatives and receptor tyrosine kinases, such as the platelet-derived growth factor receptor (PDGFR), is critical for developing targeted therapies for various diseases like breast cancer. Rational drug design is found to be important in the drug development process. The aim of the current investigation is to find, explore, and optimize indole alkaloids against receptor tyrosine kinases as a promising avenue in drug discovery and development, particularly in the context of breast cancer treatment employing a computational approach.

Methods

ChemAxon Marvin Sketch 5.11.5 was used to create 2D structures of indole alkaloids. The physicochemical characteristics of indole alkaloids, as well as their toxicity, were predicted using Swiss ADME & pkCSM online web tools. Molecular docking technology was used to examine the ligand-receptor interactions of indole alkaloids with the target receptor (PDB: 5GRN) using various programs, including Autodock 1.1.2, MGL Tools 1.5.6, Discovery Studio Visualizer v20.1.0.19295, Procheck, Protparam tool, and PyMOL.

Results

All indole alkaloids and their derivatives were determined to be orally bioavailable, less toxic, and have acceptable pharmacokinetic properties according to studies. In comparison to the traditional medication Sunitinib, all indole alkaloids displayed higher docking scores.

Discussion

The indole alkaloids increase their potential as a novel therapy alternative for breast cancer and could facilitate more comprehensive , , chemical-based and pharma studies by medicinal chemists. As of now, our work is limited to investigation of indole analogues, which will lay down a strong foundation for medicinal chemists to explore indole alkaloids.

Conclusion

The increase in binding energy and the quantity of H-bonds created by indole alkaloids with interactions at distances below 3.40A provide a helpful starting point for isolating indole alkaloids that are most suitable for additional research. The application of indole alkaloids as a potential new cancer treatment candidate is supported by their pharmacokinetics and toxicological profile, which may aid medical chemists in conducting more in-depth and chemical and pharmacological studies.

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References

  1. HunterT. Signaling—2000 and beyond.Cell2000100111312710.1016/S0092‑8674(00)81688‑810647936
     [Google Scholar]
  2. SchlessingerJ. Cell signaling by receptor tyrosine kinases.Cell2000103221122510.1016/S0092‑8674(00)00114‑811057895
     [Google Scholar]
  3. HunterT. CooperJ.A. Protein-tyrosine kinases.Annu. Rev. Biochem.198554189793010.1146/annurev.bi.54.070185.0043412992362
     [Google Scholar]
  4. SawyersC. Rational therapeutic intervention in cancer: Kinases as drug targets.Curr. Opin. Genet. Dev.200212111111510.1016/S0959‑437X(01)00273‑811790564
     [Google Scholar]
  5. SchenkP.W. Snaar-JagalskaB.E. Signal perception and transduction: The role of protein kinases.Biochim. Biophys. Acta Mol. Cell Res.19991449112410.1016/S0167‑4889(98)00178‑510076047
     [Google Scholar]
  6. HeldinG.H. Dimerization of cell surface receptor tyrosine kinases.Cell19958021322310.1016/0092‑8674(95)90404‑27834741
     [Google Scholar]
  7. TomiguchiM. YamamotoY. Yamamoto-IbusukiM. Goto-YamaguchiL. FujikiY. FujiwaraS. SuetaA. HayashiM. TakeshitaT. InaoT. IwaseH. Fibroblast growth factor receptor‐1 protein expression is associated with prognosis in estrogen receptor‐positive/human epidermal growth factor receptor‐2‐negative primary breast cancer.Cancer Sci.2016107449149810.1111/cas.1289726801869
     [Google Scholar]
  8. PalmieriD. BronderJ.L. HerringJ.M. YonedaT. WeilR.J. StarkA.M. KurekR. Vega-ValleE. FeigenbaumL. HalversonD. VortmeyerA.O. SteinbergS.M. AldapeK. SteegP.S. Her-2 overexpression increases the metastatic outgrowth of breast cancer cells in the brain.Cancer Res.20076794190419810.1158/0008‑5472.CAN‑06‑331617483330
     [Google Scholar]
  9. LemmonM.A. SchlessingerJ. Cell signaling by receptor tyrosine kinases.Cell201014171117113410.1016/j.cell.2010.06.01120602996
     [Google Scholar]
  10. TempletonA.J. Diez-GonzalezL. AceO. Vera-BadilloF. ŠerugaB. JordánJ. AmirE. PandiellaA. OcañaA. Prognostic relevance of receptor tyrosine kinase expression in breast cancer: A meta-analysis.Cancer Treat. Rev.20144091048105510.1016/j.ctrv.2014.08.00325217796
     [Google Scholar]
  11. ShrivastavaA. RadziejewskiC. CampbellE. KovacL. McGlynnM. RyanT.E. DavisS. GoldfarbM.P. GlassD.J. LemkeG. YancopoulosG.D. An orphan receptor tyrosine kinase family whose members serve as nonintegrin collagen receptors.Mol. Cell199711253410.1016/S1097‑2765(00)80004‑09659900
     [Google Scholar]
  12. 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]
  13. LipinskiC.A. Lead- and drug-like compounds: The rule-of-five revolution.Drug Discov. Today. Technol.20041433734110.1016/j.ddtec.2004.11.00724981612
     [Google Scholar]
  14. KuntzI.D. BlaneyJ.M. OatleyS.J. LangridgeR. FerrinT.E. A geometric approach to macromolecule-ligand interactions.J. Mol. Biol.1982161226928810.1016/0022‑2836(82)90153‑X7154081
     [Google Scholar]
  15. MhaskeG.S. SenA.K. ShahA. KhisteR.H. DaleA.V. SenD.B. In silico identification of novel quinoline-3-carboxamide derivatives targeting platelet-derived growth factor receptor.Curr. Cancer Ther. Rev.202218213114210.2174/1573394718666220421111546
     [Google Scholar]
  16. HughesJ.P. ReesS. KalindjianS.B. PhilpottK.L. Principles of early drug discovery.Br. J. Pharmacol.201116261239124910.1111/j.1476‑5381.2010.01127.x21091654
     [Google Scholar]
  17. MondalD. AminS.A. MoinulM. DasK. JhaT. GayenS. How the structural properties of the indole derivatives are important in kinase targeted drug design?: A case study on tyrosine kinase inhibitors.Bioorg. Med. Chem.2022535311653410.1016/j.bmc.2021.11653434864496
     [Google Scholar]
  18. GaoG.R. Design, synthesis and biological evaluation of O-linked indoles as VEGFR-2 kinase inhibitors.Chin. Chem Lett..201526911651168
     [Google Scholar]
  19. GangjeeA. ZawareN. RaghavanS. DischB.C. Synthesis and biological activity of 5-chloro-N4-substituted phenyl-9H-pyrimido [4, 5-b] indole-2, 4-diamines as vascular endothelial growth factor receptor-2 inhibitors and antiangiogenic agents.Bioorg. Med. Chem.20132171857186410.1016/j.bmc.2013.01.04023434139
     [Google Scholar]
  20. FraleyME ArringtonKL HambaughSR HoffmanWF CunninghamAM YoungMB HungateRW TebbenAJ RutledgeRZ KendallRL HuckleWR McFallRC CollKE ThomasKA Discovery and evaluation of 3-(5-thien-3-ylpyridin-3-yl)-1H-indoles as a novel class of KDR kinase inhibitors.Bioorg Med Chem Lett200313182973297610.1016/s0960‑894x(03)00627‑912941314
     [Google Scholar]
  21. HondaT NagaharaH MogiH BanM AonoH KDR inhibitor with the intramolecular non-bonded interaction: conformation-activity relationships of novel indole-3-carboxamide derivatives.Bioorg Med Chem Lett20112161782178510.1016/j.bmcl.2011.01.06321324683
     [Google Scholar]
  22. BiscardiJS TiceDA ParsonsSJ c-Src, receptor tyrosine kinases, and human cancer.Adv Cancer Res1999766111910.1016/s0065‑230x(08)60774‑510218099
     [Google Scholar]
  23. IngleyE. Src family kinases: Regulation of their activities, levels and identification of new pathways.Biochim Biophys Acta200817841566510.1016/j.bbapap.2007.08.01217905674
     [Google Scholar]
  24. SusvaM MissbachM GreenJ Src inhibitors: drugs for the treatment of osteoporosis, cancer or both?Trends Pharmacol Sci2000211248949510.1016/s0165‑6147(00)01567‑411121839
     [Google Scholar]
  25. AllisonP. DemoryM.L. The Src pathway as a therapeutic strategy.Drug Discov. Today Ther. Strateg.200524313321
     [Google Scholar]
  26. SchmidtS. PreuL. LemckeT. TotzkeF. Dual IGF-1R/SRC inhibitors based on a N′-aroyl-2-(1H-indol-3-yl)-2-oxoacetohydrazide structure.Eur J Med Chem20114672759276910.1016/j.ejmech.2011.03.06521524503
     [Google Scholar]
  27. RaoVK ChhikaraBS ShiraziAN TiwariR ParangK KumarA 3-substitued indoles: one-pot synthesis and evaluation of anticancer and Src kinase inhibitory activities.Bioorg Med Chem Lett201121123511351410.1016/j.bmcl.2011.05.01021612925
     [Google Scholar]
  28. KinoshitaK OnoY EmuraT AsohK FuruichiN ItoT KawadaH TanakaS MorikamiK TsukaguchiT SakamotoH TsukudaT OikawaN Discovery of novel tetracyclic compounds as anaplastic lymphoma kinase inhibitors.Bioorg Med Chem Lett201121123788379310.1016/j.bmcl.2011.04.02021561771
     [Google Scholar]
  29. GaikwadR. 2-Phenylindole derivatives as anticancer agents: Synthesis and screening against murine melanoma, human lung and breast cancer cell lines.Synth. Commun.202249172258226910.1080/00397911.2019.1620282
     [Google Scholar]
  30. PrakashCR RajaS Indolinones as promising scaffold as kinase inhibitors: A review.Mini Rev Med Chem20121229811910.2174/13895571279899503922372601
     [Google Scholar]
  31. HartmannJT HaapM KoppHG LippHP Tyrosine kinase inhibitors - a review on pharmacology, metabolism and side effects.Curr Drug Metab200910547048110.2174/13892000978889797519689244
     [Google Scholar]
  32. MedinaV CalvoMB Díaz-PradoS EspadaJ Hedgehog signalling as a target in cancer stem cells.Clin Transl Oncol200911419920710.1007/s12094‑009‑0341‑y19380296
     [Google Scholar]
  33. WongK.K. Recent developments in anti-cancer agents targeting the Ras/Raf/MEK/ERK pathway.Recent Pat Anticancer Drug Discov.200941283510.2174/15748920978700246119149686
     [Google Scholar]
  34. LaiSL ChienAJ MoonRT Wnt/Fz signaling and the cytoskeleton: potential roles in tumorigenesis.Cell Res200919553254510.1038/cr.2009.4119365405
     [Google Scholar]
  35. McDonaldS.L. SilverA. The opposing roles of Wnt-5a in cancer.Br J Cancer.2009101220921410.1038/sj.bjc.660517419603030
     [Google Scholar]
  36. GuoX. WangX.F. Signaling cross-talk between TGF-β/BMP and other pathways.Cell Res.2009191718810.1038/cr.2008.30219002158
     [Google Scholar]
  37. TianM. SchiemannW.P. The TGF-β paradox in human cancer: An update.Future Oncol20095225927110.2217/14796694.5.2.25919284383
     [Google Scholar]
  38. AhmadI. KhalidH. PerveenA. ShehrozM. NishanU. RahmanF.U. Sheheryar MouraA.A. UllahR. AliE.A. ShahM. OjhaS.C. Identification of novel quinolone and quinazoline alkaloids as phosphodiesterase 10A inhibitors for Parkinson’s disease through a computational approach.ACS Omega2024914162621627810.1021/acsomega.3c1035138617664
     [Google Scholar]
  39. GorinMA Protein kinase Cε: An oncogene and emerging tumor biomarker.Mol. Cancer2022810.1186/1476‑4598‑8‑9
     [Google Scholar]
  40. Al-NemaM. GauravA. LeeV.S. GunasekaranB. LeeM.T. OkechukwuP. Identification of dual inhibitor of phosphodiesterase 1B/10A using structure-based drug design approach.J. Mol. Liq.202134234211748510.1016/j.molliq.2021.117485
     [Google Scholar]
  41. AbdelliI. HassaniF. Bekkel BrikciS. GhalemS. In silico study the inhibition of angiotensin converting enzyme 2 receptor of COVID-19 by Ammoides verticillata components harvested from Western Algeria.J. Biomol. Struct. Dyn.20213993263327610.1080/07391102.2020.176319932362217
     [Google Scholar]
  42. ShahM. KhanF. AhmadI. DengC.L. PerveenA. IqbalA. NishanU. ZamanA. UllahR. AliE.A. ChenK. Computer-aided identification of Mycobacterium tuberculosis resuscitation-promoting factor B (RpfB) inhibitors from Gymnema sylvestre natural products.Front. Pharmacol.20231414132522710.3389/fphar.2023.132522738094882
     [Google Scholar]
  43. MorrisG.M. HueyR. LindstromW. SannerM.F. BelewR.K. GoodsellD.S. OlsonA.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.J. Comput. Chem.200930162785279110.1002/jcc.2125619399780
     [Google Scholar]
  44. SannerM.F. OlsonA.J. Fast and robust computation of molecular surfaces.Proc. Natl. Acad. Sci. USA19979462756276110.1002/(SICI)1097‑0282(199603)38:3%3C305:AID‑BIP4%3E3.0.CO;2‑Y11038610
     [Google Scholar]
  45. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.2133419499576
     [Google Scholar]
  46. ShahM. Computational analysis of plant-derived terpenes as α -glucosidase inhibitors for the discovery of therapeutic agents against type 2 diabetes mellitus.S. Afr. J. Bot.202114346247310.1016/j.sajb.2021.09.030
     [Google Scholar]
  47. FriesnerR.A. BanksJ.L. MurphyR.B. HalgrenT.A. KlicicJ.J. MainzD.T. RepaskyM.P. KnollE.H. ShelleyM. PerryJ.K. ShawD.E. FrancisP. ShenkinP.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.J. Med. Chem.20044771739174910.1021/jm030643015027865
     [Google Scholar]
  48. ShahM. YaminR. AhmadI. WuG. JahangirZ. ShamimA. NawazH. NishanU. UllahR. AliEA. In-silico evaluation of natural alkaloids against the main protease and spike glycoprotein as potential therapeutic agents for SARS-CoV-2.PLoS ONE2024191e029476910.1371/journal.pone.0294769
     [Google Scholar]
  49. MoustakasD.T. LangP.T. PDB-ligand: A ligand database based on PDB for the automated and customized classification of ligand-binding structures.Nucleic Acids Res.20174638038510.1093/nar/gki059
     [Google Scholar]
  50. KatuwalS. In Silico study of coumarins: Wedelolactone as a potential inhibitor of the spike protein of the SARS‐CoV‐2 variants.J. Trop. Med.2023477174510.1155/2023/4771745
     [Google Scholar]
  51. HuangS.Y. ZouX. An iterative knowledge‐based scoring function for protein–protein recognition.Proteins200872255757910.1002/prot.2194918247354
     [Google Scholar]
  52. NazS. FarooqU. KhanS. SarwarR. MabkhotY.N. SaeedM. AlsayariA. MuhsinahA.B. Ul-HaqZ. Pharmacophore model-based virtual screening, docking, biological evaluation and molecular dynamics simulations for inhibitors discovery against α -tryptophan synthase from Mycobacterium tuberculosis.J. Biomol. Struct. Dyn.202139261062010.1080/07391102.2020.171525931937192
     [Google Scholar]
  53. MuhammedM.T. Aki-YalcinE. Pharmacophore modeling in drug discovery: Methodology and current status.J. Turk. Chem. Soc. Sect.20218374976210.18596/jotcsa.927426
     [Google Scholar]
  54. 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]
  55. ZhangL. ZhuH. OpreaT.I. GolbraikhA. TropshaA. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds.Pharm. Res.20082581902191410.1007/s11095‑008‑9609‑018553217
     [Google Scholar]
  56. EkinsS. WilliamsA.J. Finding promiscuous old drugs for new uses.Pharm. Res.201027577077110.1007/s11095‑011‑0486‑621607776
     [Google Scholar]
  57. FourchesD. MuratovE. TropshaA. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research.J. Chem. Inf. Model.20105071189120410.1021/ci100176x20572635
     [Google Scholar]
  58. IsaA. QSAR, docking and pharmacokinetic studies 2, 4-diphenyl indenol [1, 2-B] pyridinol derivatives targeting breast cancer receptors.J. Chem. Lett.2024154457
     [Google Scholar]
  59. HughesJ.D. BlaggJ. PriceD.A. BaileyS. DeCrescenzoG.A. DevrajR.V. EllsworthE. FobianY.M. GibbsM.E. GillesR.W. GreeneN. HuangE. Krieger-BurkeT. LoeselJ. WagerT. WhiteleyL. ZhangY. Physiochemical drug properties associated with in vivo toxicological outcomes.Bioorg. Med. Chem. Lett.200818174872487510.1016/j.bmcl.2008.07.07118691886
     [Google Scholar]
  60. GfellerD. GrosdidierA. WirthM. DainaA. Swiss Target Prediction: A web server for target prediction of bioactive small molecules.Nucleic Acids Res.2014Web Server issue42323810.1093/nar/gku29324792161
     [Google Scholar]
  61. DainaA. Swiss Target Prediction: Updated data and new features for efficient prediction of protein targets of small molecules.Nucleic Acids Res.20194735736410.1093/nar/gkz38231106366
     [Google Scholar]
  62. KumarV. SahaA. RoyK. Multi-target QSAR modeling for the identification of novel inhibitors against Alzheimer’s disease.Chemom. Intell. Lab. Syst.202323323310473410.1016/j.chemolab.2022.104734
     [Google Scholar]
  63. GenesteH. DrescherK. JakobC. LaplancheL. OchseM. TorrentM. Novel, potent, selective, and brain penetrant phosphodiesterase 10A inhibitors.Bioorg. Med. Chem. Lett.201929340641210.1016/j.bmcl.2018.12.02930587449
     [Google Scholar]
  64. KorbO. StützleT. ExnerT.E. Empirical scoring functions for advanced protein-ligand docking with PLANTS.J. Chem. Inf. Model.2009491849610.1021/ci800298z19125657
     [Google Scholar]
  65. BhardwajV.K. PurohitR. Computer simulation to identify selective inhibitor for human phosphodiesterase10A.J. Mol. Liq.202132832811541910.1016/j.molliq.2021.115419
     [Google Scholar]
  66. ParchaP.K. SarvagallaS. AshokC. SudharshanS.J. DyavaiahM. CoumarM.S. RajasekaranB. Repositioning antispasmodic drug Papaverine for the treatment of chronic myeloid leukemia.Pharmacol. Rep.202173261562810.1007/s43440‑020‑00196‑x33389727
     [Google Scholar]
  67. PajouheshH. LenzG.R. Medicinal chemical properties of successful central nervous system drugs.NeuroRx20052454155310.1602/neurorx.2.4.54116489364
     [Google Scholar]
  68. RasganiaJ. GavadiaR. JakharK. Facile synthesis, pharmacological and in silico analysis of succinimide derivatives: An approach towards drug discovery.J. Mol. Struct.20231274127413442410.1016/j.molstruc.2022.134424
     [Google Scholar]
  69. OtaT. KamadaY. HayashidaM. Iwao-KoizumiK. MurataS. KinoshitaK. Combination analysis in genetic polymorphisms of drug-metabolizing enzymes CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A5 in the Japanese population.Int. J. Med. Sci.2015121788210.7150/ijms.1026325552922
     [Google Scholar]
  70. PantaleãoS.Q. FernandesP.O. GonçalvesJ.E. MaltarolloV.G. HonorioK.M. Recent advances in the prediction of pharmacokinetics properties in drug design studies: A review.ChemMedChem202217120210054210.1002/cmdc.20210054234655454
     [Google Scholar]
  71. KadriA. AouadiK. In vitro antimicrobial and α-glucosidase inhibitory potential of enantiopure cycloalkylglycine derivatives: Insights into their in silico pharmacokinetic, druglikeness, and medicinal chemistry properties.J. Appl. Pharm. Sci.202010610711510.7324/JAPS.2020.10614
     [Google Scholar]
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High-Throughput Screening of Novel Indole Alkaloids as Potential Tyrosine Kinase Inhibitors for Breast Cancer Therapy
Curr. Biotechnol. 14, 181 (2025); https://doi.org/10.2174/0122115501376706250815074812
/content/journals/cbiot/10.2174/0122115501376706250815074812
/content/journals/cbiot/10.2174/0122115501376706250815074812
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  • Article Type:     Research Article
Keyword(s): breast cancer; H-bond; in silico study; Indole alkaloids; pharmacokinetics; receptor tyrosine kinase

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