174
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Computational study on subfamilies of piperidine derivatives: QSAR modelling, model external verification, the inter-subset similarity determination, and structure-based drug designing

, , &
Pages 433-462 | Received 19 Dec 2020, Accepted 14 Feb 2021, Published online: 07 May 2021

References

  • G. Xue, A. Zippelius, A. Wicki, M. Mandala, F. Tang, D. Massi, and B.A. Hemmings, Integrated Akt/PKB signaling in immunomodulation and its potential role in cancer immunotherapy, J. Natl. Cancer Inst. 107 (2015), pp. 1–10. doi:10.1093/jnci/djv171.
  • S. Sestito, G. Nesi, S. Daniele, A. Martelli, M. Digiacomo, A. Borghini, D. Pietra, V. Calderone, A. Lapucci, M. Falasca, P. Parrella, A. Notarangelo, M.C. Breschi, M. Macchia, C. Martini, and S. Rapposelli, Design and synthesis of 2-oxindole based multi-targeted inhibitors of PDK1/Akt signaling pathway for the treatment of glioblastoma multiforme, Eur. J. Med. Chem. 105 (2015), pp. 274–288. doi:10.1016/j.ejmech.2015.10.020.
  • B.D. Manning and L.C. Cantley, AKT/PKB signaling: Navigating downstream, Cell 129 (2007), pp. 1261–1274. doi:10.1016/j.cell.2007.06.009.
  • F. Ardito, M. Giuliani, D. Perrone, G. Troiano, and L. Lo Muzio, The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (review), Int. J. Mol. Med. 40 (2017), pp. 271–280. doi:10.3892/ijmm.2017.3036.
  • B. Sepehri, M. Rezaei, and R. Ghavami, The in silico identification of potent anti-cancer agents by targeting the ATP binding site of the N-domain of HSP90, SAR QSAR Environ. Res. 29 (2018), pp. 551–565. doi:10.1080/1062936X.2018.1494626.
  • X. Dong, W. Zhan, M. Zhao, J. Che, X. Dai, Y. Wu, L. Xu, Y. Zhou, Y. Zhao, T. Tian, G. Cheng, Z. Jin, J. Li, Y. Shao, Q. He, B. Yang, Q. Weng, and Y. Hu, Discovery of 3,4,6-trisubstituted piperidine derivatives as orally active, low hERG blocking Akt inhibitors via conformational restriction and structure-based design, J. Med. Chem. 62 (2019), pp. 7264–7288. doi:10.1021/acs.jmedchem.9b00891.
  • W. Zhan, J. Che, L. Xu, Y. Wu, X. Hu, Y. Zhou, G. Cheng, Y. Hu, X. Dong, and J. Li, Discovery of pyrazole-thiophene derivatives as highly potent, orally active Akt inhibitors, Eur. J. Med. Chem. 180 (2019), pp. 72–85. doi:10.1016/j.ejmech.2019.07.017.
  • W. Zhan, L. Xu, X. Dong, J. Dong, X. Yi, X. Ma, N. Qiu, J. Li, B. Yang, Y. Zhou, and Y. Hu, Design synthesis and biological evaluation of pyrazol-furan carboxamide analogues as novel Akt kinase inhibitors, Eur. J. Med. Chem. 117 (2016), pp. 47–58. doi:10.1016/j.ejmech.2016.03.074.
  • P. Liu, H. Cheng, T.M. Roberts, and J.J. Zhao, Targeting the phosphoinositide 3-kinase pathway in cancer, Nat. Rev. Drug. Discov. 8 (2009), pp. 627–644. doi:10.1038/nrd2926.
  • A. Hallas-Potts, J.C. Dawson, and C.S. Herrington, Ovarian cancer cell lines derived from non-serous carcinomas migrate and invade more aggressively than those derived from high-grade serous carcinomas, Sci. Rep. 9 (2019), pp. 1–10. doi:10.1038/s41598-019-41941-4.
  • T.M. Yeung, S.C. Gandhi, J.L. Wilding, R. Muschel, and W.F. Bodmer, Cancer stem cells from colorectal cancer-derived cell lines, Proc. Natl. Acad. Sci. U. S. A. 107 (2010), pp. 3722–3727. doi:10.1073/pnas.0915135107.
  • M. Dumble, M.C. Crouthamel, S.Y. Zhang, M. Schaber, D. Levy, K. Robell, Q. Liu, D.J. Figueroa, E.A. Minthorn, M.A. Seefeld, M.B. Rouse, S.K. Rabindran, D.A. Heerding, and R. Kumar, Discovery of novel AKT inhibitors with enhanced anti-tumor effects in combination with the MEK inhibitor, PLoS One 9 (2014), pp. 1–11. doi:10.1371/journal.pone.0100880.
  • A. Spencer, S.S. Yoon, S.J. Harrison, S. Morris, D. Smith, S.J. Freedman, R. Brigandi, A. Oliff, J.B. Opalinska, and C. Chen, The novel AKT inhibitor afuresertib shows favorable safety, pharmacokinetics, and clinical activity in multiple myeloma, Blood 124 (2014), pp. 2190–2195. doi:10.1182/blood-2014-03-559963.
  • Z. Fang, Y. Song, P. Zhan, Q. Zhang, and X. Liu, Conformational restriction: An effective tactic in ‘follow-on’-based‘ drug discovery, Future Med. Chem. 6 (2014), pp. 885–901. doi:10.4155/fmc.14.50.
  • B. Sepehri and R. Ghavami, Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4-amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides, SAR QSAR Environ. Res. 30 (2019), pp. 21–38. doi:10.1080/1062936X.2018.1545695.
  • S. Chang, Z. Zhang, X. Zhuang, J. Luo, X. Cao, H. Li, Z. Tu, X. Lu, X. Ren, and K. Ding, New thiazole carboxamides as potent inhibitors of Akt kinases, Bioorganic Med. Chem. Lett. 22 (2012), pp. 1208–1212. doi:10.1016/j.bmcl.2011.11.080.
  • HyperChem Version 8.0, Hypercube, Inc. 2007; software available at http://www.hyper.com.
  • F. Sadeghi, A. Afkhami, T. Madrakian, and R. Ghavami, Computational study to select the capable anthracycline derivatives through an overview of drug structure-specificity and cancer cell line-specificity, Chem. Pap. 22 (2021), pp. 523–538. doi:10.1007/s11696-020-01321-z.
  • DRAGON Version 5.5, R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, TALETE SRL: Milano, Italy, 2007; software available at http://www.talete.mi.it.
  • V. Rastija and M. Medic-Saric, QSAR modeling of anthocyanins, anthocyanidins and catechins as inhibitors of lipid peroxidation using three-dimensional descriptors, Med. Chem. Res. 18 (2009), pp. 579–588. doi:10.1007/s00044-008-9151-y.
  • H. Lodhi and Y. Yamanishi, Chemoinformatics and advanced machine learning perspectives: Complex computational methods and collaborative techniques, Hershey New York, Ch. 5: Structure-activity relationships by autocorrelation descriptors and genetic algorithms (2011), pp. 60–94.
  • MATLAB Version 9.0, math work. Inc., Natick, MA, USA, 2016; software available at http://www.mathworks.com.
  • G.R. Famini, C.A. Penski, and L.Y. Wilson, Using theoretical descriptors in quantitative structure activity relationships: Some physicochemical properties, J. Phys. Org. Chem. 5 (1992), pp. 395–408. doi:10.1002/poc.610050704.
  • R. Todeschini, D. Ballabio, and F. Grisoni, Beware of unreliable Q2! A comparative study of regression metrics for predictivity assessment of QSAR models, J. Chem. Inf. Model. 56 (2016), pp. 1905–1913. doi:10.1021/acs.jcim.6b00277.
  • P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci 26 (2007), pp. 694–701. doi:10.1002/qsar.200610151.
  • A. Tropsha, Best practices for QSAR model development, validation, and exploitation, Mol. Inform. 29 (2010), pp. 476–488. doi:10.1002/minf.201000061.
  • J. Qin, J. Ji, R. Deng, J. Tang, F. Yang, G.K. Feng, W.D. Chen, X.Q. Wu, X.J. Qian, K. Ding, and X.F. Zhu, DC120, a novel AKT inhibitor, preferentially suppresses nasopharyngeal carcinoma cancer stem-like cells by downregulating Sox2, Oncotarget 6 (2015), pp. 6944–6958. doi:10.18632/oncotarget.3128.
  • K. Kawai, Y. Karuo, A. Tarui, K. Sato, and M. Omote, Effect of structural descriptors on the design of cyclin dependent kinase inhibitors using similarity-based molecular evolution, Mol. Inform. 39 (2020). doi:10.1002/minf.201900126.
  • J.L. Velazquez-Libera, J. Caballero, A.P. Toropova, and A.A. Toropov, Estimation of 2D autocorrelation descriptors and 2D Monte Carlo descriptors as a tool to build up predictive models for acetylcholinesterase (AChE) inhibitory activity, Chemom. Intell. Lab. Syst. 184 (2019), pp. 14–21. doi:10.1016/j.chemolab.2018.11.008.
  • R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Weinheim, Germany: John Wiley & Sons, Volume 11 of Methods and Principles in Medicinal Chemistry, 2008, pp. 1–688.
  • L. Saiz-Urra, M. Perez Gonzalez, and M. Teijeira, 2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma, Bioorg. Med. Chem. 15 (2007), pp. 3565–3571. doi:10.1016/j.bmc.2007.02.032.
  • M.T. Scotti, V. Emerenciano, M.J.P. Ferreira, L. Scotti, R. Stefani, M.S. Da Silva, and F.J.B. Mendonca, Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family, Molecules 17 (2012), pp. 4684–4702. doi:10.3390/molecules17044684.
  • M. Beglari, N. Goudarzi, D. Shahsavani, M. Arab Chamjangali, and Z. Mozafari, Combination of radial distribution functions as structural descriptors with ligand-receptor interaction information in the QSAR study of some 4-anilinoquinazoline derivatives as potent EGFR inhibitors, Struct. Chem. 31 (2020), pp. 1481–1491. doi:10.1007/s11224-020-01505-z.
  • J.H. Schuur, P. Selzer, and J. Gasteiger, The coding of the three-dimensional structure of molecules by molecular transforms and its application to structure-spectra correlations and studies of biological activity, J. Chem. Inf. Comput. Sci. 36 (1996), pp. 334–344. doi:10.1021/ci950164c.
  • O. Devinyak, D. Havrylyuk, and R. Lesyk, 3D-MoRSE descriptors explained, J. Mol. Graph. Model. 54 (2014), pp. 194–203. doi:10.1016/j.jmgm.2014.10.006.
  • V. Consonni, R. Todeschini, and M. Pavan, Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors, J. Chem. Inf. Comput. Sci. 42 (2002), pp. 682–692. doi:10.1021/ci015504a.

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.