201
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Exploration of structural alerts and fingerprints for novel anticancer therapeutics: a robust classification-QSAR dependent structural analysis of drug-like MMP-9 inhibitors

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 299-319 | Received 22 Feb 2023, Accepted 27 Apr 2023, Published online: 12 May 2023

References

  • S. Mondal, N. Adhikari, S. Banerjee, S.A. Amin, and T. Jha, Matrix metalloproteinase-9 (MMP-9) and its inhibitors in cancer: A minireview, Eur. J. Med. Chem. 194 (2020), pp. 112260. doi:10.1016/j.ejmech.2020.112260.
  • H. Huang, Matrix metalloproteinase-9 (MMP-9) as a cancer biomarker and MMP-9 biosensors: Recent advances, Sensors 18 (2018), pp. 3249. doi:10.3390/s18103249.
  • K.M. Turra, D. Pineda Rivelli, S. Berlanga de Moraes Barros, and K.F.M. Pasqualoto, Constructing and validating 3D-pharmacophore models to a set of MMP-9 inhibitors for designing novel anti-melanoma agents, Mol. Inform. 35 (2016), pp. 238–252. doi:10.1002/minf.201600004.
  • M.S. Ayoup, M.A. Fouad, H. Abdel-Hamid, M.M. Abu-Serie, A. Noby, and M. Teleb, Battle tactics against MMP-9; discovery of novel non-hydroxamate MMP-9 inhibitors endowed with PI3K/AKT signaling attenuation and caspase 3/7 activation via Ugi bis-amide synthesis, Eur. J. Med. Chem. 186 (2020), pp. 111875. doi:10.1016/j.ejmech.2019.111875.
  • N. Adhikari, A.K. Halder, S. Mallick, A. Saha, K.D. Saha, and T. Jha, Robust design of some selective matrix metalloproteinase-2 inhibitors over matrix metalloproteinase-9 through in silico/fragment-based lead identification and de novo lead modification: Syntheses and biological assays, Bioorg. Med. Chem. 24 (2016), pp. 4291–4309.
  • H. Nagase, R. Visse, and G. Murphy, Structure and function of matrix metalloproteinases and TIMPs, Cardiovasc. Res. 69 (2006), pp. 562–573. doi:10.1016/j.cardiores.2005.12.002.
  • N.I. Solovyeva, S.V. Vinokurova, O.S. Ryzhakova, T.A. Gureeva, and I.V. Tsvetkova, Expression of gelatinases A and B and their endogenous regulators in immortal and transformed fibroblasts, Biochem. Supple. Series B Biomed. Chem.3 (2009), pp. 266–271.
  • N.E. Campbell, L. Kellenberger, J. Greenaway, R.A. Moorehead, N.M. Linnerth-Petrik, and J. Petrik, Extracellular matrix proteins and tumor angiogenesis, J. Oncol. 2010 (2010), pp. 586905. doi:10.1155/2010/586905.
  • Y. Liu, H. Liu, X. Luo, J. Deng, Y. Pan, and H. Liang, Overexpression of SMYD3 and matrix metalloproteinase-9 are associated with poor prognosis of patients with gastric cancer, Tumor Biol. 36 (2015), pp. 4377–4386. doi:10.1007/s13277-015-3077-z.
  • A. Gimeno, R. Beltrán-Debón, M. Mulero, G. Pujadas, and S. Garcia-Vallvé, Understanding the variability of the S1′ pocket to improve matrix metalloproteinase inhibitor selectivity profiles, Drug Discov. Today 25 (2020), pp. 38–57.
  • E. Bronisz and I. Kurkowska-Jastrzębska, Matrix metalloproteinase 9 in epilepsy: The role of neuroinflammation in seizure development, Mediators Inflamm. 2016 (2016), pp. 7369020. doi:10.1155/2016/7369020.
  • D. Pradiba, M. Aarthy, V. Shunmugapriya, S.K. Singh, and M. Vasanthi, Structural insights into the binding mode of flavonols with the active site of matrix metalloproteinase-9 through molecular docking and molecular dynamic simulations studies, J. Biomol. Struct. Dyn. 36 (2018), pp. 3718–3739.W. doi:10.1080/07391102.2017.1397058.
  • R. Visse and H. Nagase, Matrix metalloproteinases and tissue inhibitors of metalloproteinases: Structure, function, and biochemistry, Circ. Res. 92 (2003), pp. 827–839. doi:10.1161/01.RES.0000070112.80711.3D.
  • E. Nuti, T. Tuccinardi, and A. Rossello, Matrix metalloproteinase inhibitors: New challenges in the era of post broad-spectrum inhibitors, Curr. Pharm. Des. 13 (2007), pp. 2087–2100. doi:10.2174/138161207781039706.
  • D. Georgiadis and A. Yiotakis, Specific targeting of metzincin family members with small-molecule inhibitors: Progress toward a multifarious challenge, Bioorg. Med. Chem. 16 (2008), pp. 8781–8794. doi:10.1016/j.bmc.2008.08.058.
  • B.G. Rao, Recent developments in the design of specific matrix metalloproteinase inhibitors aided by structural and computational studies, Curr. Pharm. Des. 11 (2005), pp. 295–322. doi:10.2174/1381612053382115.
  • Available at https://clinicaltrials.gov (accessed January 20, 2023).
  • C.M. Overall and O. Kleifeld, Validating matrix metalloproteinases as drug targets and anti-targets for cancer therapy, Nat. Rev. Cancer 6 (2006), pp. 227–239. doi:10.1038/nrc1821.
  • S.K. Baidya, S. Banerjee, N. Adhikari, and T. Jha, Selective inhibitors of medium-size s1’ pocket matrix metalloproteinases: A stepping stone of future drug discovery, J. Med. Chem. 65 (2022), pp. 10709–10754.
  • V.M. Macaulay, K.J. O’Byrne, M.P. Saunders, J.P. Braybrooke, L. Long, F. Gleeson, C.S. Mason, A.L. Harris, P. Brown, and D.C. Talbot, Phase I study of intrapleural batimastat (BB-94), a matrix metalloproteinase inhibitor, in the treatment of malignant pleural effusions, Clin. Cancer Res. 5 (1999), pp. 513–520.
  • J.R. Molina, J.M. Reid, C. Erlichman, J.A. Sloan, A. Furth, S.L. Safgren, C.D. Lathia, and S.R. Alberts, A phase I and pharmacokinetic study of the selective, non-peptidic inhibitor of matrix metalloproteinase BAY 12-9566 in combination with etoposide and carboplatin, Anticancer Drugs 16 (2005), pp. 997–1002. doi:10.1097/01.cad.0000176504.86551.5c.
  • N.C. Levitt, F.A. Eskens, K.J. O’Byrne, D.J. Propper, L.J. Denis, S.J. Owen, L. Choi, J.A. Foekens, S. Wilner, J.M. Wood, M. Nakajima, D.C. Talbot, W.P. Steward, A.L. Harris, and J. Verweij, Phase I and pharmacological study of the oral matrix metalloproteinase inhibitor, MMI270 (CGS27023A), in patients with advanced solid cancer, Clin. Cancer Res. 7 (2001), pp. 1912–1922.
  • K. Abd-Elaziz, C. Voors-Pette 2, K.L. Wang, S. Pan, Y. Lee, J. Mao, Y. Li, B. Chien, D. Lau, and Z. Diamant, First-in-man safety, tolerability, and pharmacokinetics of a novel and highly selective inhibitor of matrix metalloproteinase-12, FP-025: Results from two randomized studies in healthy subjects, Clin. Drug Invest. 41 (2021), pp. 65–76. doi:10.1007/s40261-020-00981-9.
  • J.A. Jacobsen, J.L. Jourden, M.T. Miller, and S.M. Cohen, To bind zinc or not to bind zinc: An examination of innovative approaches to improved metalloproteinase inhibition, Biochim. Biophys. Acta Mol. Cell Res. 1803 (2010), pp. 72–94. doi:10.1016/j.bbamcr.2009.08.006.
  • S. Mondal, S. Banerjee, S.A. Amin, and T. Jha, Structural analysis of arylsulfonamide-based carboxylic acid derivatives: A QSAR study to identify the structural contributors toward their MMP-9 inhibition, Struct. Chem. 32 (2021), pp. 417–430.
  • D. Rathee, V. Lather, and H. Dureja, Pharmacophore modeling and 3D QSAR studies for prediction of matrix metalloproteinases inhibitory activity of hydroxamate derivatives, Biotechnol. Res. Innov. 1 (2017), pp. 112–122. doi:10.1016/j.biori.2017.10.002.
  • S. Kalva, D. Vinod, and L.M. Saleena, Field- and Gaussian-based 3D-QSAR studies on barbiturate analogs as MMP-9 inhibitors, Med. Chem. Res. 22 (2013), pp. 5303–5313. doi:10.1007/s00044-013-0479-6.
  • O. Nicolotti, M. Catto, I. Giangreco, M. Barletta, F. Leonetti, A. Stefanachi, L. Pisani, S. Cellamare, P. Tortorella, F. Loiodice, and A. Carotti, Design, synthesis and biological evaluation of 5-hydroxy, 5-substitutedpyrimidine-2,4,6-triones as potent inhibitors of gelatinases MMP-2 and MMP-9, Eur. J. Med. Chem. 58 (2012), pp. 368–376. doi:10.1016/j.ejmech.2012.09.036.
  • M. Fernandeza and J. Caballerob, QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-α-phenylsulfonylacetamide derivatives, Bioorg. Med. Chem. 15 (2007), pp. 6298–6310. doi:10.1016/j.bmc.2007.06.014.
  • R.P. Verma and C. Hansch, Matrix metalloproteinases (MMPs): Chemical-biological functions and (Q)SARs, Bioorg. Med. Chem. 15 (2007), pp. 2223–2268. doi:10.1016/j.bmc.2007.01.011.
  • S.P. Gupta and S. Kumaran, Quantitative structure-activity relationship studies on matrix metalloproteinase inhibitors: Piperazine, piperidine and diazepine hydroxamic acid analogs, Asian J. Biochem. 1 (2006), pp. 211–223. doi:10.3923/ajb.2006.211.223.
  • S.P. Gupta and S. Kumaran, Quantitative structure-activity relationship studies on benzodiazepine hydroxamic acid inhibitors of matrix metalloproteinases and tumor necrosis factor-α converting enzyme, Asian J. Biochem. 1 (2006), pp. 47–56.
  • S.P. Gupta and S. Kumaran, Quantitative structure-activity relationship studies onmatrixmetalloproteinase inhibitors: Hydroxamic acid analogs, Med. Chem. 2 (2006), pp. 243–250. doi:10.2174/157340606776930790.
  • M. Fernandez, J. Caballero, and A. Tundidor-Camba, Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors, Bioorg. Med. Chem. 14 (2006), pp. 4137–4150. doi:10.1016/j.bmc.2006.01.072.
  • R.P. Verma, A. Kurup, and C. Hansch, On the role of polarizability in QSAR, Bioorg. Med. Chem. 13 (2005), pp. 237–255. doi:10.1016/j.bmc.2004.09.039.
  • S.P. Gupta and S. Kumaran, Quantitative structure-activity relationship studies on matrix metalloproteinase inhibitors: Bicyclic heteroaryl hydroxamic acid analogs, Lett. Drug Des. Discov. 2 (2005), pp. 522–528. doi:10.2174/157018005774479096.
  • S.P. Gupta and S. Kumaran, A quantitative structure-activity relationship study on some series of anthranilic acid-based matrix metalloproteinase inhibitors, Bioorg. Med. Chem. 13 (2005), pp. 5454–5462. doi:10.1016/j.bmc.2005.05.055.
  • S.P. Gupta, V. Maheswaran, V. Pande, and D. Kumar, A comparative QSAR study on carbonic anhydrase and matrix metalloproteinase inhibition by sulfonylated amino acid hydroxamates, J. Enzyme Inhib. Med. Chem. 18 (2003), pp. 7–13. doi:10.1080/1475636021000049735.
  • D. Kumar and S.P. Gupta, A quantitative structure–activity relationship study on some matrix metalloproteinase and collagenase inhibitors, Bioorg. Med. Chem. 11 (2003), pp. 421–426. doi:10.1016/S0968-0896(02)00438-8.
  • K. Roy, D.K. Pal, A.U. De, and C. Sengupta, QSAR of matrix metalloproteinase inhibitor N-[(substituted phenyl)sulfonyl]-N-4-nitrobenzylglycine hydroxamates using LFER model, Drug Des. Discov. 17 (2001), pp. 315–323.
  • Available at https://www.bindingdb.org/bind/index.jsp (accessed December 20, 2022).
  • C.A. Lipinski, F. Lombardo, B.W. Dominy, and P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev. 23 (1997), pp. 3–25. doi:10.1016/S0169-409X(96)00423-1.
  • D.F. Veber, S.R. Johnson, H.Y. Cheng, B.R. Smith, K.W. Ward, and K.D. Kopple, Molecular properties that influence the oral bioavailability of drug candidates, J. Med. Chem. 45 (2002), pp. 2615–2623. doi:10.1021/jm020017n.
  • Discovery Studio 3.0 (DS 3.0), Accelrys Inc., CA, USA, 2015; software available at http://www.accelrys.com.
  • T. Jha, N. Adhikari, A. Saha, and S.A. Amin, Multiple molecular modelling studies on some derivatives and analogues of glutamic acid as matrix metalloproteinase-2 inhibitors, SAR QSAR Environ. Res. 29 (2018), pp. 43–68. doi:10.1080/1062936X.2017.1406986.
  • A.K. Romasanta, P. van der Sijde, I. Hellsten, R.E. Hubbard, G.M. Keseru, J. van Muijlwijk-Koezen, and I.J. de Esch, When fragments link: A bibliometric perspective on the development of fragment-based drug discovery, Drug Discov. Today 23 (2018), pp. 1596–1609. doi:10.1016/j.drudis.2018.05.004.
  • C.W. Murray, M.L. Verdonk, and D.C. Rees, Experiences in fragment-based drug discovery, Trends Pharmacol. Sci. 33 (2012), pp. 224–232. doi:10.1016/j.tips.2012.02.006.
  • L.L. Liu, J. Lu, Y. Lu, M.Y. Zheng, X.M. Luo, W.L. Zhu, H.L. Jiang, and K.X. Chen, Novel Bayesian classification models for predicting compounds blocking hERG potassium channels, Acta Pharmacol. Sinica 35 (2014), pp. 1093–1102. doi:10.1038/aps.2014.35.
  • R. Kundu, S. Banerjee, S.K. Baidya, N. Adhikari, and T. Jha, A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors, SAR QSAR Environ. Res. 33 (2022), pp. 861–883. doi:10.1080/1062936X.2022.2145353.
  • D. Rogers and M. Hahn, Extended-connectivityfingerprints, J. Chem. Inf. Model. 50 (2010), pp. 742–754. doi:10.1021/ci100050t.
  • G. Gini, T. Ferrari, A. Lombardo, A. Cassano, and E. Benfenati, A new QSAR model for acute fish toxicity based on mined structural alerts, J. Toxicol. Risk Assess. 5 (2019), pp. 016.
  • N. Adhikari, S. Banerjee, S.K. Baidya, B. Ghosh, and T. Jha, Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: Theoretical justification in light of experimental evidences, SAR QSAR Environ. Res. 32 (2021), pp. 473–493. doi:10.1080/1062936X.2021.1914721.
  • Available at http://sarpy.sourceforge.net/ (accessed January 19, 2023).
  • V. Yadav, S. Banerjee, S.K. Baidya, N. Adhikari, and T. Jha, Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: An attempt to identify promising structural features for potent HDAC2 inhibition, SAR QSAR Environ. Res. 33 (2022), pp. 1–22. doi:10.1080/1062936X.2021.2013317.
  • N.M. O’Boyle and R.A. Sayle, Comparing structural fingerprints using a literature-based similarity benchmark, J. Cheminform. 8 (2016), pp. 1–4. doi:10.1186/s13321-016-0148-0.
  • K. Roy, S. Kar, and R.N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic press, London, 2015.
  • S. Guti, S.K. Baidya, S. Banerjee, N. Adhikari, and T. Jha, A robust classification-dependent multi-molecular modelling study on some biphenyl sulphonamide based MMP-8 inhibitors, SAR QSAR Environ. Res. 32 (2021), pp. 835–861. doi:10.1080/1062936X.2021.1976831.
  • T. Fawcett, An introduction to ROC analysis, Pattern Recognit. Lett. 27 (2006), pp. 861–874. doi:10.1016/j.patrec.2005.10.010.
  • R.H. Scannevin, R. Alexander, T.M. Haarlander, S.L. Burke, M. Singer, C. Huo, Y.M. Zhang, D. Maguire, J. Spurlino, I. Deckman, and K.I. Carroll, Discovery of a highly selective chemical inhibitor of matrix metalloproteinase-9 (MMP-9) that allosterically inhibits zymogen activation, J. Biol. Chem. 292 (2017), pp. 17963–17974. doi:10.1074/jbc.M117.806075.
  • E. Nuti, D. Cuffaro, F. D’Andrea, L. Rosalia, L. Tepshi, M. Fabbi, G. Carbotti, S. Ferrini, S. Santamaria, C. Camodeca, and L. Ciccone, Sugar-based arylsulfonamide carboxylates as selective and water-soluble matrix metalloproteinase-12 inhibitors, ChemMedChem 11 (2016), pp. 1626–1637. doi:10.1002/cmdc.201600235.
  • A. Tochowicz, K. Maskos, R. Huber, R. Oltenfreiter, V. Dive, A. Yiotakis, M. Zanda, W. Bode, and P. Goettig, Crystal structures of MMP-9 complexes with five inhibitors: Contribution of the flexible Arg424 side-chain to selectivity, J. Mol. Biol. 371 (2007), pp. 989–1006. doi:10.1016/j.jmb.2007.05.068.
  • C. Camodeca, E. Nuti, L. Tepshi, S. Boero, T. Tuccinardi, E.A. Stura, A. Poggi, M.R. Zocchi, and A. Rossello, Discovery of a new selective inhibitor of A Disintegrin And Metalloprotease 10 (ADAM-10) able to reduce the shedding of NKG2D ligands in Hodgkin’s lymphoma cell models, Eur. J. Med. Chem. 111 (2016), pp. 193–201. doi:10.1016/j.ejmech.2016.01.053.
  • E. Nuti, D. Cuffaro, E. Bernardini, C. Camodeca, L. Panelli, S. Chaves, L. Ciccone, L. Tepshi, L. Vera, E. Orlandini, S. Nencetti, E.A. Stura, S.M. Santos, V. Dive, and A. Rossello, Development of thioaryl-based matrix metalloproteinase-12 inhibitors with alternative zinc-binding groups: Synthesis, potentiometric, NMR, and crystallographic studies, J. Med. Chem. 61 (2018), pp. 4421–4435. doi:10.1021/acs.jmedchem.8b00096.
  • L.R. de Souza Neto, J.T. Moreira-Filho, B.J. Neves, R.L.B.R. Maidana, A.C.R. Guimaraes, N. Furnham, C.H. Andrade, and F.P. Silva Jr, In silico strategies to support fragment-to-lead optimization in drug discovery, Front. Chem. 8 (2020), pp. 93. doi:10.3389/fchem.2020.00093.
  • I.J.P. de Esch, D.A. Erlanson, W. Jahnke, C.N. Johnson, and L. Walsh, Fragment-to-lead medicinal chemistry publications in 2020, J. Med. Chem. 65 (2020), pp. 84–99. doi:10.1021/acs.jmedchem.1c01803.

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.