References
- C. Lopez-Otin and L.M. Matrisian, Emerging roles of proteases in tumour suppression, Nat. Rev. Cancer 7 (2007), pp. 800–808.
- C.E. Brinckerhoff and L.M. Matrisian, Matrix metalloproteinases: A tail of a frog that became a prince, Nat. Rev. Mol. Cell Biol. 3 (2002), pp. 207–214.
- R.H. Holm, P. Kennepohl, and E.I. Solomon, Structural and functional aspects of metal sites in biology, Chem. Rev. 96 (1996), pp. 2239–2314.
- E.H. Olsen, B. Fadnes, I. Sylte, L.U. Hansen, and J.O. Winberg, Regulation of matrix metalloproteinase activity in health and disease, FEBS J. 278 (2011), pp. 28–45.
- J.F. Fisher and S. Mobashery, Recent advances in MMP inhibitor design, Cancer Metast. Rev. 25 (2006), pp. 115–136.
- P. Jain, C. Saravanan, and S.K. Singh, Sulphonamides: Deserving class as MMP inhibitors? Eur. J. Med. Chem. 60 (2013), pp. 89–100.
- N. Adhikari, A. Mukherjee, A. Saha, and T. Jha, Arylsulfonamides and selectivity of matrix metalloproteinase-2: An overview, Eur. J. Med. Chem. 129 (2017), pp. 72–109.
- M.G. Belvisi and K.M. Bottomley, The role of matrix metalloproteinases (MMPs) in the pathophysiology of chronic obstructive pulmonary disease (COPD): A therapeutic role for inhibitors of MMPs? Inflamm. Res. 52 (2003), pp. 95–100.
- G.H. Skrepnek and S.V. Skrepnek, Epidemiology, clinical and economic burden, and natural history of chronic obstructive pulmonary disease and asthma, Am. J. Manag. Care 10 (2004), pp. S129–S138.
- J. Hu, P.E. Van den Steen, Q.X. Sang, and G. Opdenakker, Matrix metalloproteinase inhibitors as therapy for inflammatory and vascular diseases, Nat. Rev. Drug Discov. 6 (2007), pp. 480–498.
- S. Chakrabarti and K.D. Patel, Matrix metalloproteinase-2 (MMP-2) and MMP-9 in pulmonary pathology, Exp. Lung Res. 31 (2005), pp. 599–621.
- V. Dive, C.F. Cheng, A. Yiotakis, and E.D. Sturrock, Inhibition of zinc metallopeptidases in cardiovascular disease-from unity to trinity, or duality? Curr. Pharm. Des. 15 (2009), pp. 3606–3621.
- K. Holmbeck, P. Bianco, J. Caterina, S. Yamada, M. Kromer, S.A. Kuznetsov, M. Mankani, P.G. Robey, A.R. Poole, I. Pidoux, J.M. Ward, and H. Birkedal-Hansen, MT1-MMP-deficient mice develop dwarfism, osteopenia, arthritis, and connective tissue disease due to inadequate collagen turnover, Cell 99 (1999), pp. 81–92.
- L.M. Coussens, B. Fingleton, and L.M. Matrisian, Matrix metalloproteinase inhibitors and cancer-trials and tribulations, Science 295 (2002), pp. 2387–2392.
- C. Gialeli, A.D. Theocharis, and N.K. Karamanos, Roles of matrix metalloproteinases in cancer progression and their pharmacological targeting, FEBS J. 278 (2011), pp. 16–27.
- K. Kessenbrock, V. Plaks, and Z. Werb, Matrix metalloproteinases: Regulators of the tumor microenviroment, Cell 141 (2011), pp. 52–67.
- 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.
- S.A. Amin, N. Adhikari, and T. Jha, Is dual inhibition of metalloenzymes HDAC-8 and MMP-2 a potential pharmacological target to combat hematological malignancies?, Pharmacol. Res. 122 (2017), pp. 8–19.
- R. Renkiewicz, L. Qiu, C. Lesch, X. Sun, R. Devalaraja, T. Cody, E. Kaldjian, H. Welgus, and V. Baragi, Broad-spectrum matrix metalloproteinase inhibitor marimastat-induced musculoskeletal side effects in rats, Arthritis Rheum. 48 (2003), pp. 1742–1749.
- I. Bertini, M. Fragai, and C. Luchinat, Intra- and interdomain flexibility in matrix metalloproteinases: Functional aspects and drug design, Curr. Pharm. Des. 15 (2009), pp. 3592–3605.
- F.J. Moy, P.K. Chanda, J. Chen, S. Cosmi, W. Edris, J.I. Levin, T.S. Rush, J. Wilhelm, and R. Powers, Impact of mobility on structure-based drug design for the MMPs, J. Am. Chem. Soc. 124 (2002), pp. 12658–12659.
- A.K. Halder, A. Saha, and T. Jha, Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors, J. Pharm. Pharmacol. 65 (2013), pp. 1541–1554.
- A.K. Halder, S. Mallick, D. Shikha, A. Saha, K.D. Saha, and T. Jha, Design of dual MMP-2/HDAC-8 inhibitors by pharmacophore mapping, molecular docking, synthesis and biological activity, RSC Adv. 5 (2015), pp. 72373–72386.
- 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.
- T. Jha, S. Basu, A.K. Halder, N. Adhikari, and S. Samanta, Possible anticancer agents: Synthesis, pharmacological activity, and molecular modeling studies on some 5-N-substituted-2-N-(substituted benzenesulphonyl)-L(+) glutamines, Med. Chem. Res. 26 (2017), pp. 1437–1458.
- N. Adhikari, S.A. Amin, A. Saha, and T. Jha, Understanding chemico-biological interactions of glutamate MMP-2 inhibitors through rigorous alignment-dependent 3D-QSAR analyses, ChemistrySelect 2 (2017), pp. 7888–7898.
- N. Adhikari, S.A. Amin, A. Saha, and T. Jha, Exploring in-house glutamate inhibitors of matrix metalloproteinase-2 through validated robust chemico-biological quantitative approaches, Struct. Chem. (2017), in press. DOI: 10.1007/s11224-017-1028-6.
- A. Mukherjee, N. Adhikari, and T. Jha, A pentanoic acid derivative targeting matrix metalloproteinase-2 (MMP-2) induces apoptosis in a chronic myeloid leukemia cell line, Eur. J. Med. Chem. 141 (2017), pp. 37–50.
- S. Begum and P.G.R. Achary, Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1), SAR QSAR Environ. Res. 26 (2015), pp. 343–361.
- C.W. Yap, PaDEL–descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474.
- SYBYL-X 2.0 Software, Tripos Inc., St. Louis. MO, 2012. Software available at https://www.certara.com.
- R.B. Darlington, Regression and Linear Models, McGraw-Hill, New York, 1990.
- The simple, user-friendly and reliable online standalone tools freely available at http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software-tools.
- O. Raevsky, A. Sapegin, and N. Zefirov, The QSAR discriminant-regression model, Mol. Inform. 13 (1994), pp. 412–418.
- S.A. Amin, N. Adhikari, T. Jha, and S. Gayen, First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington’s disease: A proposal to chemists!, Bioorg. Med. Chem. Lett. 26 (2016), pp. 5712–5718.
- A. Perez-Garrido, A.M. Helguera, F. Borges, M.N. Cordeiro, V. Rivero, and A.G. Escudero, Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models, J. Chem. Inf. Model. 51 (2011), pp. 2746–2759.
- M. Gálvez-Llompart, M.C. Recio, and R. García-Domenech, Topological virtual screening: A way to find new compounds active in ulcerative colitis by inhibiting NF-κB, Mol. Divers. 15 (2011), pp. 917–926.
- T. Fawcett, An introduction to ROC analysis, Patt. Recog. Lett. 27 (2006), pp. 861–874.
- A.E. Klon, J.F. Lowrie, and D.J. Diller, Improved naive Bayesian modelling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction, J. Chem. Inf. Model. 46 (2006), pp. 1945–1956.
- 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.
- Discovery Studio 3.0, Accelrys Inc, San Diego, CA, 2011. Software available at https://www.accelrys.com
- C. Zhang, C. Du, Z. Feng, J. Zhu, and Y. Li, Hologram quantitative structure activity relationship, docking, and molecular dynamics studies of inhibitors for CXCR4, Chem. Biol. Drug Des. 85 (2015), pp. 119–136.
- S. Yu, J. Yuan, J. Shi, X. Ruan, T. Zhang, Y. Wang, and Y. Du, HQSAR and topomer CoMFA for predicting melanocortin-4 receptor binding affinities of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides, Chemometr. Intell. Lab. Sys. 146 (2015), pp. 34–41.
- N. Adhikari, A.K. Halder, C. Mondal, and T. Jha, Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach, Med. Chem. Res. 22 (2013), pp. 6029–6045.
- E. Pourbasheer, R. Aalizadeh, T.S. Shokouhi, M.R. Ganjali, P. Norouzi, and J. Shadmanesh, 2D and 3D quantitative structure-activity relationship study of hepatitis C virus NS5B polymerase inhibitors by comparative molecular field analysis and comparative molecular similarity indices analysis methods, J. Chem. Inf. Model. 54 (2014), pp. 2902–2914.
- R.D. Cramer, D.E. Patterson, and J.D. Bunce, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc. 110 (1988), pp. 5959–5967.
- G. Klebe, U. Abraham, and T. Mietzner, Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity, J. Med. Chem. 37 (1994), pp. 4130–4146.
- B. Bush and R. Nachbar Jr, Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA, J. Comput. Aided. Mol. Des. 7 (1993), pp. 587–619.
- P. Tosco and T. Balle, Open3DQSAR: A new open–source software aimed at high-throughput chemometric analysis of molecular interaction fields, J. Mol. Model. 17 (2011), pp. 201–208.
- S.A. Amin, S. Bhargava, N. Adhikari, S. Gayen, and T. Jha, Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: A predictive approach combining comparative validated multiple molecular modelling techniques, J. Biomol. Struct. Dyn. (2017), in press. https://doi.org/10.1080/07391102.2017.1288659.
- A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
- A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77.
- V. Consonni, D. Ballabio, and R. Todeschini, Comments on the definition of the Q2 parameter for QSAR validation, J. Chem. Inf. Model. 49 (2009), pp. 1669–1678.
- V. Consonni, D. Ballabio, and R. Todeschini, Evaluation of model predictive ability by external validation techniques, J. Chemometr. 24 (2010), pp. 194–201.
- I. Svab, D. Alexandru, G. Vitos, and M.L. Flonta, Binding affinities for sulfonamide inhibitors with matrix metalloproteinase-2 using a linear response method, J. Cell. Mol. Med. 8 (2004), pp. 551–562.
- Y. Feng, J.J. Likos, L. Zhu, H. Woodward, G. Munie, J.J. McDonald, A.M. Stevens, C.P. Howard, G.A. De Crescenzo, D. Welsch, H.S. Shieh, and W.C. Stallings, Solution structure and backbone dynamics of the catalytic domain of matrix metalloproteinase-2 complexed with a hydroxamic acid inhibitor, Biochim. Biophys. Acta 1598 (2002), pp. 10–23.
- R.B. Aher and K. Roy, First report on exploring classification and regression based QSAR modelling of Plasmodium falciparum glycogen synthase kinase (PfGSK-3) inhibitors, SAR QSAR Env. Res. 26 (2015), pp. 959–976.
- R. Todeschini and V. Consonni, Molecular descriptors for chemoinformatics, 2 volumes, WILEY-VCH, Weinheim, Germany, 2009.
- A. Jamloki, C. Karthikeyan, N.S.H.N. Moorthy, and P. Trivedi, QSAR analysis of some 5-amino-2-mercapto-1,3,4-thiadiazole based inhibitors of matrix metalloproteinases and bacterial collagenase, Bioorg. Med. Chem. Lett. 16 (2006), pp. 3847–3854.
- J. Zheng, R. Wen, and D. Guillaume, Three-dimensional quantitative structure-activity relationship (CoMFA and CoMSIA) studies on galardin derivatives as gelatinase A (matrix metalloproteinase 2) inhibitors, J. Enz. Inhib. Med. Chem. 23 (2008), pp. 445–453.
- H. Zhu, H. Fang, X. Cheng, Q. Wang, L. Zhang, J. Feng, and W. Xu, 3D-QSAR study of pyrrolidine derivatives as matrix metalloproteinase-2 inhibitors, Med. Chem. Res. 18 (2009), pp. 683–701.
- 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.
- K.M. Turra, D.P. Rivelli, S.B. de Moraes Barros, and K.F.M. Pasqualoto, Predicting novel antitumor agents: 3D-pharmacophore mapping of b-N-biaryl ether sulfonamide-based hydroxamates as potentially MMP-2 inhibitors, Mol. Inform. 33 (2014), pp. 573–587.
- H.Y. Qiu, Z.C. Wang, P.F. Wang, X.Q. Yan, X.M. Wang, Y.H. Yang, and H.L. Zhu, Design, synthesis, evaluation and 3D-QSAR analysis of benzosulfonamide benzenesulfonates as potent and selective inhibitors of MMP-2, RSC Adv. 4 (2014), pp. 39214–39225.
- X.Q. Yan, Z.C. Wang, Z. Li, P.F. Wang, H.Y. Qiu, L.W. Chen, X.Y. Lu, P.C. Lv, and H.L. Zhu, Sulfonamide derivatives containing dihydropyrazole moieties selectively and potently inhibit MMP-2/MMP-9: Design, synthesis, inhibitory activity and 3D-QSAR analysis, Bioorg. Med. Chem. Lett. 25 (2015), pp. 4664–4671.
- M. Abbasi, F. Ramezani, M. Elyasi, H. Sadeghi-Aliabadi, and M. Amanlou, A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold, DARU J. Pharmaceut. Sci. 23 (2015), p. 29.
- J. Zhang, X.J. Wang, Z.K. Dong, S.Q. Wang, W.R. Xu, J.W. Fu, X.C. Cheng, and R.L. Wang, CoMFA/CoMSIA and molecular docking studies of novel matrix metalloproteinase-2 inhibitors based on L-tyrosine scaffold, Lett. Drug Des. Discov. 13 (2016), pp. 376–386.
- R. Kiyama, Y. Tamura, F. Watanabe, H. Tsuzuki, M. Ohtani, and M. Yodo, Homology modeling of gelatinase catalytic domains and docking simulations of novel sulfonamide inhibitors, J. Med. Chem. 42 (1999), pp. 1723–1738.