Quantitative structure-activity relationship study on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a 1,2,4-Triazol-3-yl group as a ZBG

Afsar  Jahan; Brij Kishore  Sharma; Vishnu Dutt  Sharma

doi:10.30574/gscbps.2021.16.1.0199

Authors

Afsar Jahan Department of Chemistry, Government College Bundi-323 001 (Rajasthan), India.
Brij Kishore Sharma Department of Chemistry, Government College Bundi-323 001 (Rajasthan), India.
Vishnu Dutt Sharma Department of Shalya Tantra, Dr. SR Rajasthan Ayurved University Jodhpur-342 037 (Rajasthan), India.

DOI:

https://doi.org/10.30574/gscbps.2021.16.1.0199

Keywords:

QSAR, MMP-13 inhibitory activity, Combinatorial protocol in multiple linear regression (CP-MLR) analysis, PLS analysis, Dragon descriptors, Fused pyrimidines, Zinc binding group (ZBG)

Abstract

QSAR study has been carried out on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a1,2,4-triazol-3-yl group as a ZBG in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the number of Sulfur atoms (descriptor nS), Balaban mean square distance index (descriptor MSD), molecular electrotopological variation (descriptor DELS), structural information content index of neighborhood symmetry of 2^nd and 3^rd order (descriptors SIC2 and SIC3), average valence connectivity index chi-4 (descriptor X4Av) in addition to 1^st order Galvez topological charge index (descriptor JGI1) and global topological charge index (descriptor JGT) played a pivotal role in rationalization of MMP-13 inhibition activity of titled compounds. Atomic properties such as mass and volume in terms of atomic properties weighted descriptors MATS5m and MATS3v, and certain atom centred fragments such as CH2RX (descriptor C-006), X--CX--X (descriptor C-044), H attached to heteroatom (descriptor H-050) and H attached to C0(sp3) with 1X attached to next C (descriptor H-052) are also predominant to explain MMP-13 inhibition actions of fused pyrimidines.

PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high-quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

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References

Wieland HA, Michaelis M, Kirschbaum BJ, Rudolphi KA. Osteoarthritis – an untreatable disease? Nature Reviews Drug Discovery. 2005; 4: 331-344.

Hochberg MC, Altman RD, Brandt KD, Clark BM, Dieppe PA, Griffin MR, Moskowitz RW, Schnitzer TJ. Guidelines for the medical management of osteoarthritis. Part I. Osteoarthritis of the hip. American College of Rheumatology. Arthritis and Rheumatology. 1995; 38: 1535-1540.

Hochberg MC, Altman RD, Brandt KD, Clark BM, Dieppe PA, Griffin MR, Moskowitz RW, Schnitzer TJ. Guidelines for the medical management of osteoarthritis. Part II. Osteoarthritis of the knee. American College of Rheumatology. Arthritis and Rheumatology. 1995; 38: 1541-1546.

Recommendations for the medical management of osteoarthritis of the hip and knee: 2000 update. (2000). American College of Rheumatology Subcommittee on Osteoarthritis Guidelines. Arthritis and Rheumatology. 2000; 43: 1905-1915.

Hochberg MC, Altman RD, April KT, Benkhalti M, Guyatt G, McGowan J, Towheed T, Welch V, Wells G, Tugwell P. American College of Rheumatology 2012 recommendations for the use of nonpharmacologic and pharmacologic therapies in osteoarthritis of the hand, hip, and knee. Arthritis Care and Research. 2012; 64: 465-474.

FitzGerald GA. Coxibs and cardiovascular disease. New England Journal of Medicine. 2004; 351: 1709-1711.

Steinmeyer J, Konttinen YT. Oral treatment options for degenerative joint disease–present and future. Advanced Drug Delivery Reviews. 2006; 58: 168-211.

Cooper C, Adachi JD, Bardin T, Berenbaum F, Flamion B, Jonsson H, Kanis JA, Pelousse F, Lems WF, Pelletier JP, Martel-Pelletier J, Reiter S, Reginster JY, Rizzoli R, Bruyere O. How to define responders in osteoarthritis. Current Medical Research and Opinion. 2013; 29: 719-729.

Davies PS, Graham SM, MacFarlane RJ, Leonidou A, Mantalaris A, Tsiridis E. Disease-modifying osteoarthritis drugs: in vitro and in vivo data on the development of DMOADs under investigation. Expert Opinion on Investigational Drugs. 2013; 22: 423-441.

Knäuper V, López-Otin C, Smith B, Knight G, Murphy G. Biochemical characterization of human collagenase-3. Journal of Biological Chemistry. 1996; 271: 1544-1550.

Mitchell PG, Magna HA, Reeves LM, Lopresti-Morrow LL, Yocum SA, Rosner PJ, Geoghegan KF, Hambor JE. Cloning, expression, and type II collagenolytic activity of matrixmetalloproteinase-13 from human osteoarthritic cartilage. Journal of Clinical Investigation. 1996; 97: 761-768.

Yocum SA, Lopresti-Morrow LL, Reeves LM, Mitchell PG. MMP-13 and MMP-1expression in tissues of normal articular joints. Annals of the New York Academy of Sciences. 1999; 878: 583-586.

Neuhold LA, Killar L, Zhao W, Sung ML, Warner L, Kulik J, Turner J, Wu W, Billinghurst C, Meijers T, Poole AR, Babij P, DeGennaro LJ. Postnatal expression in hyaline cartilage of constitutively active human collagenase-3 (MMP-13) induces osteoarthritis in mice. Journal of Clinical Investigations. 2001; 107: 35-44.

Billinghurst RC, Dahlberg L, Ionescu M, Reiner A, Bourne R, Rorabeck C, Mitchell P, Hambor J, Diekmann O, Tschesche H, Chen J, Van Wart H, Poole AR. Enhanced cleavage of type II collagen by collagenases in osteoarthritic articular cartilage. Journal of Clinical Investigations. 1997; 99: 1534-1545.

Sabatini M, Lesur C, Thomas M, Chomel A, Anract P, de Nanteuil G, Pastoureau P. Effect of inhibition of matrix metalloproteinases on cartilage loss in vitro and in a Guinea pig model of osteoarthritis. Arthritis and Rheumatology. 2005;52: 171-180.

Hoekstra R, Eskens FALM, Verweij J. Matrixmetalloproteinase inhibitors: current developments and future perspectives. Oncologist. 2001; 6: 415-427.

Reiter LA, Robinson RP, McClure KF, Jones CS, Reese MR, Mitchell PG, Otterness IG, Bliven ML, Liras J, Cortina SR, Donahue KM, Eskra JD, Griffiths RJ, Lame ME, Lopez-Anaya A, Martinelli GJ, McGahee SM, Yocum SA, Lopresti-Morrow LL, Tobiassen LM, Vaughn-Bowser ML. Pyran-containing sulfonamide hydroxamic acids: potent MMP inhibitors that spare MMP-1. Bioorganic and Medicinal Chemistry Letters. 2004; 14:3389-3395.

Peterson JT. The importance of estimating the therapeutic index in the development of matrix metalloproteinase inhibitors. Cardiovascular Research. 2006; 69: 677-687.

Nara H, Sato K, Kaieda A, Oki H, Kuno H, Santou T, Kanzaki N, Terauchi J, Uchikawa O, Kori M. Design, synthesis, and biological activity of novel, potent, and highly selective fusedpyrimidine-2-carboxamide-4-one-based matrix metalloproteinase (MMP)-13 zinc-binding inhibitors. Bioorganic and Medicinal Chemistry. 2016; 24:6149-6165.

Puerta DT, Cohen SM. A bioinorganic perspective on matrix metalloproteinase inhibition. Current Topics in Medicinal Chemistry. 2004; 4: 1551-1573.

Breuer E, Frant J, Reich R. Recent non-hydroxamate matrix metalloproteinase inhibitors. Expert Opinion on Therapeutic Patents. 2005;15: 253-269.

Puerta DT, Griffin MO, Lewis JA, Romero-Perez D, Garcia R, Villarreal FJ, Cohen SM. Heterocyclic zinc-binding groups for use in next-generation matrix metalloproteinase inhibitors: potency, toxicity, and reactivity. Journal of Biological Inorganic Chemistry. 2006;11:131-138.

Nara H, Sato K, Naito T, Mototani H, Oki H, Yamamoto Y, Kuno H, Santou T, Kanzaki N, Terauchi J, Uchikawa O, Kori M. Thieno[2,3-d]pyrimidine-2-carboxamides bearing a carboxybenzene group at 5-position: Highly potent, selective, and orally available MMP-13 inhibitors interacting with the S1″ binding site. Bioorganic and Medicinal Chemistry. 2014;22: 5487-5505.

Ruminski PG, Massa M, Strohbach J, Hanau CE, Schmidt M, Scholten JA, Fletcher TR, Hamper BC, Carroll JN, Shieh HS, Caspers N, Collins B, Grapperhaus M, Palmquist KE, Collins J, Baldus JE, Hitchcock J, Kleine HP, Rogers MD, McDonald J, Munie GE, Messing DM, Portolan S, Whiteley LO, Sunyer T, Schnute ME. Discovery of N-(4-fluoro-3-methoxybenzyl)-6-(2-(((2S,5R)-5-(hydroxymethyl)-1,4-dioxan-2-yl) methyl)-2H-tetrazol-5-yl)-2-methylpyrimidine-4-carboxamide. A highly selective and orally bioavailable matrix metalloproteinase-13 inhibitor for the potential treatment of osteoarthritis. Journal of Medicinal Chemistry. 2016;59: 313-327.

Nara H, Sato K, Naito T, Mototani H, Oki H, Yamamoto Y, Kuno H, Santou T, Kanzaki N, Terauchi J, Uchikawa O, Kori M. Discovery of novel, highly potent, and selective quinazoline-2-carboxamide-based matrix metalloproteinase (MMP)-13 inhibitors without a zinc binding group using a structure-based design approach. Journal of Medicinal Chemistry. 2014;57: 8886-8902.

Fields GB. New strategies for targeting matrix metalloproteinases. Matrix Biology. 2015;44-46: 239-246.

Vandenbroucke RE, Libert C. Is there new hope for therapeutic matrix metalloproteinase inhibition? Nature Reviews Drug Discovery. 2014;13: 904-927.

Cathcart JM, Cao J. MMP Inhibitors: Past, present and future. Frontiers in Biosciences Landmark Ed. 2015;20: 1164-1178.

Wang K, Xu J, Hunter DJ, Ding C. Investigational drugs for the treatment of osteoarthritis. Expert Opinion on Investigational Drugs. 2015;24:1539-1556.

Li NG, Shi ZH, Tang YP, Wang ZJ, Song SL, Qian LH, Qian DW, Duan JA. New hope for the treatment of osteoarthritis through selective inhibition of MMP-13. Current Medicinal Chemistry. 2011; 18: 977-1001.

Jacobsen JA, Major Jourden JL, Miller MT, Cohen SM. To bind zinc or not to bind zinc: an examination of innovative approaches to improved metalloproteinase inhibition. Biochimica et Biophysica Acta Molecular Cell Research. 2005; 1803: 72-94.

Devel L, Czarny B, Beau F, Georgiadis D, Stura E, Dive V. Third generation of matrix metalloprotease inhibitors: Gain in selectivity by targeting the depth of the S1′ cavity. Biochimie. 2010;92:1501-1508.

Terauchi J, Kuno H, Nara H, Oki H and Sato K. Preparation of heterocyclic amides as MMP-13 inhibitors for treating osteoarthritis and rheumatoid arthritis. 2005;WO2005105760A1.

Nara H, Kaieda A, Sato K, Terauchi J. Preparation of heterocyclic amide compounds as matrix metalloproteinase inhibitors.WO2007049820A1. 2007.

Gege C, Chevrier C, Schneider M, Bluhm H, Hochguertel M, Deng H, Gallagher BM, Sucholeiki I, Taveras AG. Preparation of heterotricyclic metalloproteaseinhibitors.WO2008063667A1.2008.

Pochetti G, Montanari R, Gege C, Chevrier C, Taveras AG, Mazza F. Extra binding region induced by non-zinc chelatingin hibitors into the S1′ subsite of matrix metalloproteinase 8 (MMP-8). Journal of Medicinal Chemistry. 2009; 52: 1040-1049.

Schnute ME, Ruminski PG, Hanau CE, Strohbach JW, Carroll JN and Sample K. Hetero bicyclic carboxamide derivatives, processes for preparing them, pharmaceutical compositions containing them, and their uses as inhibitors of matrix metalloproteinaseenzymes.WO2008149191A1.2008.

Nara H, Kaieda A, Sato K,Naito T, Mototani H, Oki H, Yamamoto Y, Kuno H, Santou T, Kanzaki N, Terauchi J, Uchikawa O, Kori M. Discovery of novel, highly potent, and selective matrix metalloproteinase (MMP)-13 inhibitors with a 1,2,4-Triazol-3-yl moiety as a zinc binding group using a structure-based design approach. Journal of Medicinal Chemistry. 2017; 60: 608-626.

Chemdraw ultra 6.0 and Chem3D ultra, Cambridge Soft Corporation, Cambridge, USA.http://www.cambridgesoft.com

Dragon software (version 1.11-2001) by Todeschini R.; Consonni V. Milano, Italy.http://www.talete.mi.it/dragon.htm

Prabhakar YS. A combinatorial approach to the variable selection in multiple linear regression: Analysis of Selwood et al. Data Set-a case study. QSAR and Combinatorial Science. 2003; 22: 583-595.

Sharma S, Prabhakar YS, Singh P, Sharma BK. QSAR study about ATP- sensitive potassium channel activation of cromakalim analogues using CP-MLR approach. European Journal of Medicinal Chemistry. 2008; 43:2354-2360.

Sharma S, Sharma BK, Sharma SK, Singh P, Prabhakar YS. Topological descriptors in modeling the agonistic activity of human A3 adenosine receptor ligands: The derivatives of 2-Chloro-N6-substituted-4'-thioadenosine-5'-uronamide. European Journal of Medicinal Chemistry. 2009; 44: 1377-1382.

Sharma BK, Singh P, Sarbhai K, Prabhakar YS. A quantitative structure-activity relationship study on serotonin 5-HT6 receptor ligands: Indolyl and piperidinyl sulphonamides. SAR and QSAR in Environmental Research. 2010; 21: 369-388.

Sharma BK, Pilania P, Sarbhai K, Singh P, Prabhakar YS. Chemometric descriptors in modeling the carbonic anhydrase inhibition activity of sulfonamide and sulfamate derivatives. Molecular Diversity. 2010; 14: 371-384.

Wold S. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics. 1978; 20: 397-405.

Kettaneh N, Berglund A, Wold S. PCA and PLS with very large data sets. Computational Statistics and Data Analysis. 2005; 48: 69-85.

Stahle L, Wold S. Multivariate data analysis and experimental design, in Biomedical Research, Progress in Medicinal Chemistry, Ellis GP and West WB, eds., Elsevier Science Publishers, BV, The Netherlands. 1998;25: 291-338.

Topliss JG, Edwards RP. Chance factors in studies of quantitative structure-activity relationships. Journal of Medicinal Chemistry. 1979; 22: 1238-1244.

Katritzky AR, Dobchev DA, Slavov S, Karelson M. Legitimate utilization of large descriptor pools for QSAR/QSPR models. Journal of Chemical Information and Modeling. 2008; 48:2207-2213.

So S-S, Karplus M. Three-dimensional quantitative structure activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations. Journal of Medicinal Chemistry. 1997; 40:4347-4359.

Prabhakar YS, Solomon VR, Rawal RK, Gupta MK, Katti SB. CP-MLR/PLS directed structure–activity modeling of the HIV-1RT inhibitory activity of 2,3-diaryl-1,3-thiazolidin-4-ones. QSAR and Combinatorial Science. 2004; 23: 234-244.

Akaike H. Information theory and an extension of the minimum likelihood principle. In: Petrov BN and Csaki F. (Eds.). Second international symposium on information theory. Budapest: Akademiai Kiado. 1973; 267-281.

Akaike H. A new look at the statistical identification model. IEEE Transactions on Automatic Control,AC-19. 1974; 716-723.

Kubinyi H. Variable selection in QSAR studies. I. An evolutionary algorithm. Quantitative Structure-Activity Relationship. 1994; 13: 285-294.

Kubinyi H. Variable selection in QSAR studies. II. A highly efficient combination of systematic search and evolution. Quantitative Structure-Activity Relationship. 1994; 13: 393-401.

Friedman J. In Technical Report No. 102. Laboratory for Computational Statistics, Stanford University: Stanford. 1990.

Gramatica P. Principles of QSAR models validation: internal and external. QSAR and Combinatorial Science. 26: 694-701.