163
Views
0
CrossRef citations to date
0
Altmetric
Articles

In silico design of novel anticancer drugs with amino acid and carbohydrate building blocks to inhibit PIM kinases

&
Pages 526-540 | Received 26 Jul 2021, Accepted 22 Dec 2021, Published online: 09 Feb 2022

References

  • Bachmann M, Möröy T. The serine/threonine kinase Pim-1. Int. J. Biochem. Cell Biol. 2005;37:726–730.
  • Saris, C. J.; Domen, J.; & Berns, A. The pim-1 oncogene encodes two related protein-serine/threonine kinases by alternative initiation at AUG and CUG. EMBO J. 1991, 10, 655-664.
  • Eichmann A, Yuan L, Bréant C, et al. Developmental expression of pim kinases suggests functions also outside of the hematopoietic system. Oncogene. 2000;19:1215–1224.
  • Hoover DS, Wingett DG, Zhang J, et al. Pim-1 protein expression is regulated by its 5’-untranslated region and translation initiation factor elF-4E. Cell Growth and Differentiation-Publication American Association for Cancer Research. 1997;8:1371–1380.
  • Leverson JD, Koskinen PJ, Orrico FC, et al. Pim-1 kinase and p100 cooperate to enhance c-Myb activity. Mol. Cell. 1998;2:417–425.
  • Cuypers HT, Selten G, Quint W, et al. Murine leukemia virus-induced T-cell lymphomagenesis: integration of proviruses in a distinct chromosomal region. Cell. 1984;37:141–150.
  • Amaravadi R, Thompson CB. The survival kinases Akt and Pim as potential pharmacological targets. J. Clin. Investig. 2005;115:2618–2624.
  • Bachmann M, Kosan C, Xing PX, et al. The oncogenic serine/threonine kinase Pim-1 directly phosphorylates and activates the G2/M specific phosphatase Cdc25C. Int. J. Biochem. Cell Biol. 2006;38:430–443.
  • Chiang WF, Yen CY, Lin CN, et al. Up-regulation of a serine–threonine kinase proto-oncogene Pim-1 in oral squamous cell carcinoma. Int. J. Oral Maxillofac. Surg. 2006;35:740–745.
  • Amson R, Sigaux F, Przedborski S, et al. The human protooncogene product p33pim is expressed during fetal hematopoiesis and in diverse leukemias. Proc. Natl. Acad. Sci. U.S.A. 1989;86:8857–8861.
  • Dhanasekaran SM, Barrette TR, Ghosh D, et al. Delineation of prognostic biomarkers in prostate cancer. Nature. 2001;412:822–826.
  • Warfel NA, Sainz AG, Song JH, et al. PIM kinase inhibitors kill hypoxic tumor cells by reducing Nrf2 signaling and increasing reactive oxygen species. Mol. Cancer Ther. 2016;15:1637–1647.
  • Chauhan SS, Toth RK, Jensen CC, et al. Pim kinases alter mitochondrial dynamics and chemosensitivity in lung cancer. Oncogene. 2020;39:2597–2611.
  • Le BT, Kumarasiri M, Adams JR, et al. Targeting Pim kinases for cancer treatment: opportunities and challenges. Future Med. Chem. 2015;7:35–53.
  • Chen LS, Redkar S, Taverna P, et al. Mechanisms of cytotoxicity to Pim kinase inhibitor, SGI-1776, in acute myeloid leukemia. Blood, Am. J. Hematol. 2011;118:693–702.
  • Keeton EK, McEachern K, Dillman KS, et al. AZD1208, a potent and selective pan-Pim kinase inhibitor, demonstrates efficacy in preclinical models of acute myeloid leukemia. Blood. 2014;123:905–913.
  • Cortes J, Tamura K, DeAngelo DJ, et al. Phase I studies of AZD1208, a proviral integration Moloney virus kinase inhibitor in solid and haematological cancers. Br. J. Cancer. 2018;118:1425–1433.
  • Li W, Wan X, Zeng F, et al. More than just a GPCR ligand: structure-based discovery of thioridazine derivatives as Pim-1 kinase inhibitors. Med.Chem.Comm. 2014;5:507–511.
  • Li G, Zhang W, Xie Y, et al. Structure-based optimization of 10-DEBC derivatives as potent and selective Pim-1 kinase inhibitors. J Chem Inf Model. 2020;60:3287–3294.
  • Luszczak S, Kumar C, Sathyadevan VK, et al. Pim kinase inhibition: co-targeted therapeutic approaches in prostate cancer. Signal. Transduct. Target. Ther. 2020;5:1–10.
  • Mond JJ, Lees A, Snapper CM. T cell-independent antigens type 2. Annu. Rev. Immunol. 1995;13:655–692.
  • Geraci C, Consoli GM, Galante E, et al. Calix[4]arene Decorated with Four Tn Antigen Glycomimetic Units and P3CS Immunoadjuvant: Synthesis, Characterization, and Anticancer Immunological EvaluationCalix [4] arene decorated with four Tn antigen glycomimetic units and P3CS immunoadjuvant: synthesis, characterization, and anticancer immunological evaluation. Bioconjug. Chem. 2008;19:751–758.
  • Brooks CL, Schietinger A, Borisova SN, et al. Antibody recognition of a unique tumor-specific glycopeptide antigen. Proc. Natl. Acad. Sci. U.S.A. 2010;107:10056–10061.
  • Renaudet O, Dasgupta G, Bettahi I, et al. Linear and branched glyco-lipopeptide vaccines follow distinct cross-presentation pathways and generate different magnitudes of antitumor immunity. PLOS. ONE. 2010;5:11216.
  • Marqus S, Pirogova E, Piva TJ. Evaluation of the use of therapeutic peptides for cancer treatment. J. Biomed. Sci. 2017;24:1–15.
  • Boohaker RJ, Lee MW, Vishnubhotla P, et al. The use of therapeutic peptides to target and to kill cancer cells. Curr. Med. Chem. 2012;19:3794–3804.
  • Chanda D, Harohally NV. Revisiting amadori and heyns synthesis: critical percentage of acyclic form play the trick in addition to catalyst. Tetrahedron Lett. 2018;59:2983–2988.
  • Emens LA. Cancer vaccines: on the threshold of success. Expert. Opin. Emerg. Drugs. 2008;13:295–308.
  • Hu Z, Ott PA, Wu CJ. Towards personalized, tumour-specific, therapeutic vaccines for cancer. Nat. Rev. Immunol. 2018;18:168–182.
  • Kukol A. Molecular modeling of proteins (Vol. 443). Totowa (NJ): Humana Press; 2008.
  • Selassie C, Verma RP. History of quantitative structure–activity relationships. Burger’s Medicinal Chemistry and Drug Discovery. 2008;1: 1–48.
  • Åqvist J, Medina C, Samuelsson JE. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. Des. Sel. 1994;7:385–391.
  • Böhm HJ. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des. 1994;8:243–256.
  • Apostolakis J, Caflisch A. Computational ligand design. Comb. Chem. High Throughput Screen. 1999;2:91–104.
  • Talele TT, Khedkar SA, Rigby AC. Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr. Top. Med. Chem. 2010;10:127–141.
  • Vickers NJ. Animal communication: when i’m calling you, will you answer too? Curr. Biol. 2017;27:R713–R715.
  • (a) Becke AD. A new mixing of Hartree–Fock and local density-functional theories. J. Chem. Phys. 1993;98:1372–1377. (b) Perdew JP, Ernzerhof M, Burke K. Rationale for mixing exact exchange with density functional approximations. J. Chem. Phys. 1996;105:9982–9985. (c) Shao Y, Fusti-Molnar L, Jung Y, et al. Wavefunct. Inc., Irvine, CA, 2011.
  • Thomas G. Fundamentals of medicinal chemistry. John Wiley & Sons, West Sussex, U.K.; 2004.
  • Cramer J, Sager CP, Ernst B. Hydroxyl groups in synthetic and natural-product-derived therapeutics: a perspective on a common functional group. J. Med. Chem. 2019;62:8915–8930.
  • Seeliger D, de Groot BL. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des. 2010;24:417–422.
  • Páll S, Abraham MJ, Kutzner C, et al. Lecture Notes in Computer Science. Cham: Springer International publishing; 2015. p. 3–27
  • Kumari R, Kumar R, OpenSource Drug Discovery Consortium, et al. G_mmpbsa, A GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 2014;54:1951–1962.
  • Appell M, Willett JL, Momany FA. Dft study of α-and β-d-mannopyranose at the B3LYP/6-311++ G** level. Carbohydr. Res. 2005;340:459–468.
  • Elshakre ME, Noamaan MA, Moustafa H, et al. Density functional theory, chemical reactivity, pharmacological potential and molecular docking of dihydrothiouracil-indenopyridopyrimidines with human-DNA topoisomerase II. Int. J. Mol. Sci. 2020;21:1253.
  • Humphrey W, Dalke A, Schulten K. Vmd: visual molecular dynamics. J. Mol. Graph. 1996;14:33–38.
  • Systemes D. Biovia, discovery studio modeling environment. Release 4.5. San Diego (CA): Dassault Systemes; 2015.
  • Huey R, Morris GM. 2008. Using AutoDock 4 with AutoDocktools: a tutorial. The Scripps Research Institute; 54–56.
  • Trott O, Olson AJ. Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010;31:455–461.
  • Kufareva I, Abagyan R. Methods in Molecular Biology. Humana Press; 2011. p. 231–257.
  • Sapundzhi FI, Dzimbova TA. Computer modelling of the CB1 receptor by molecular operating environment. Bulg. Chem. Commun. 2018;50:15–19.
  • Morris GM, Huey R, Lindstrom W, et al. Autodock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009;30:2785–2791.
  • Mansourian M, Mahnam K, Madadkar-Sobhani A, et al. Insights into the human A 1 adenosine receptor from molecular dynamics simulation: structural study in the presence of lipid membrane. Med. Chem. Res. 2015;24:3645–3659.
  • Morris GM, Goodsell DS, Halliday RS, et al. Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 1998;19:1639–1662.
  • Marenich AV, Cramer CJ, Truhlar DG. Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J. Phys. Chem. B. 2009;113:6378–6396.
  • Zhao Y, Truhlar DG. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor. Chem. Acc. 2008;120:215–241.
  • Nguyen TT, Viet MH, Li MS. Effects of water models on binding affinity: evidence from all-atom simulation of binding of tamiflu to A/H5N1 neuraminidase. Sci. World J. 2014;2014:1–14.
  • Davydov AS. Solitons in molecular systems. Reidel: Dordrecht; 1985, p. 113.
  • Roychoudhury M. Umesh Yadava, Bindesh Kumar Shukla.
  • Berendsen HJC, Postma JPM, van Gunsteren WF, et al. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984;81:3684–3690.
  • Darden T, York D, Pedersen L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systemsParticle mesh Ewald: An N log (N) method for Ewald sums in large systems. J. Chem. Phys. 1993;98:10089–10092.
  • Yadav RK, Yadava U. Molecular dynamics simulation of DNA duplex, analog of PPT (polypurine tract), its conformation and hydration: a theoretical study. Med. Chem. Res. 2014;23:280–286.
  • Hess, B.; Bekker, H.; Berendsen, H. J.; & Fraaije, J. G. Lincs: a linear constraint solver for molecular simulations. J. Comput. Chem. 1997, 18, 1463-1472.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.