Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure

References

• M.C. Haigis and D.A. Sinclair, Mammalian sirtuins: Biological insights and disease relevance, Annu. Rev. Pathol. 5 (2010), pp. 253–295.
   PubMed Web of Science ®Google Scholar
• A. Kumar and S. Chauhan, How much successful are the medicinal chemists in modulation of SIRT1: A critical review, Eur. J. Med. Chem. 119 (2016), pp. 45–69.
   PubMed Web of Science ®Google Scholar
• M. Pan, H. Yuan, M. Brent, E.C. Ding, and R. Marmorstein, SIRT1 contains N- and C-terminal regions that potentiate deacetylase activity, J. Biol. Chem. 278 (2012), pp. 2468–2476.
   Google Scholar
• L. Jin, W. Wei, Y. Jiang, H. Peng, J. Cai, C. Mao, H. Dai, W. Choy, J.E. Bemis, M.R. Jirousek, J.C. Milne, C.H. Westphal, and R.B. Perni, Crystal structures of human SIRT3 displaying substrate-induced conformational changes, J. Biol. Chem. 284 (2009), pp. 24394–24405.
   PubMed Web of Science ®Google Scholar
• A.M. Davenport, F.M. Huber, and A. Hoelz, Structural and functional analysis of human SIRT1, J. Mol. Biol. 426 (2014), pp. 526–541.
   PubMed Web of Science ®Google Scholar
• H. Dai, A.W. Case, T.V. Riera, T. Considine, J.E. Lee, Y. Hamuro, H. Zhao, Y. Jiang, S.M. Sweitzer, B. Pietrak, B. Schwartz, C.A. Blum, J.S. Disch, R. Caldwell, B. Szczepankiewicz, C. Oalmann, P.Y. Ng, B.H. White, R. Casaubon, R. Narayan, K. Koppetsch, F. Bourbonais, B. Wu, J. Wang, D. Qian, F. Jiang, C. Mao, M. Wang, E. Hu, J.C. Wu, R.B. Perni, G.P. Vlasuk, and J.L. Ellis, Crystallographic structure of a small molecule SIRT1 activator-enzyme complex, Nat. Commun. 6 (2015), pp. 7645–7655.
   PubMed Web of Science ®Google Scholar
• K.T. Howitz, K.J. Bitterman, H.Y. Cohen, D.W. Lamming, S. Lavu, J.G. Wood, R.E. Zipkin, P. Chung, A. Kisielewski, L.L. Zhang, B. Scherer, and D.A. Sinclair, Small molecule activators of sirtuins extend Saccharomyces cerevisiae lifespan, Nature 425 (2003), pp. 191–196.
   PubMed Web of Science ®Google Scholar
• C.B. Vu, J.E. Bemis, J.S. Disch, P.Y. Ng, J.J. Nunes, J.C. Milne, D.P. Carney, A.V. Lynch, J.J. Smith, S. Lavu, D. Phili, P.D. Lambert, D.J. Gagne, M.R. Jirousek, S. Schenk, J.M. Olefsky, and R.B. Perni, Discovery of imidazo[1,2-b]thiazole derivatives as novel SIRT1 activators, J. Med. Chem. 52 (2009), pp. 1275–1283.
   PubMed Web of Science ®Google Scholar
• J.E. Bemis, C.B. Vu, R. Xie, J.J. Nunes, P.Y. Ng, J.S. Disch, J.C. Milne, D.P. Carney, A.V. Lynch, L. Jin, J.J. Smith, S. Lavu, A. Iffland, M.R. Jirousek, and R.B. Perni, Discovery of oxazolo[4,5-b]pyridines and related heterocyclic analogs as novel SIRT1 activators, Bioorg. Med. Chem. Lett. 19 (2009), pp. 2350–2353.
   PubMed Web of Science ®Google Scholar
• C.M. Grozinger, E.D. Chao, H.E. Blackwell, D. Moazed, and S.L. Schreiber, Identification of a class of small molecule inhibitors of the sirtuins family of NAD+-dependent deacetylase by phenotypic screening, J. Biol. Chem. 276 (2001), pp. 38837–38843.
   PubMed Web of Science ®Google Scholar
• J.E. Bemis, J.S. Disch, P.Y. Ng, C. Oalmann, R.B. Perni, and C.B. Vu, Sirtuin modulating compounds, 7893086US Patent (2011).
   Google Scholar
• C. Oalmann, R.B. Perni, J.S. Disch, C.B. Vu, B. Szczepankiewicz, G. Gualtieri, R.L. Casaubon, and K. Koppetsch. Patent appl; PCT WO 2009/00058348, 2009.
   Google Scholar
• A. Cherkasov, E.N. Muratov, D. Fourches, A. Varnek, I.I. Baskin, M. Cronin, J. Dearden, P. Gramatica, Y.C. Martin, R. Todeschini, V. Consonni, V.E. Kuz’min, R. Cramer, R. Benigni, C. Yang, J. Rathman, L. Terfloth, J. Gasteiger, A. Richard, and A. Tropsha, QSAR modeling: Where have you been? Where are you going to? J. Med. Chem. 57 (2014), pp. 4977–5010.
   PubMed Web of Science ®Google Scholar
• H. Zhu, A. Tropsha, D. Fourches, A. Varnek, E. Papa, P. Gramatica, T. Oberg, P. Dao, A. Cherkasov, and I.V. Tetko, Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis, J. Chem. Inf. Model. 48 (2008), pp. 766–784.
   PubMed Web of Science ®Google Scholar
• V.E. Kuz’min, A.G. Artemenko, and E.N. Muratov, Hierarchical QSAR technology based on the Simplex representation of molecular structure, J. Comput. Aided Mol. Des. 22 (2008), pp. 403–421.
   PubMed Web of Science ®Google Scholar
• C.B. Vu, J.S. Disch, S.K. Springer, C.A. Blum, and R.B. Perni, Quinolines and related analogs as sirtuin modulators, Patent Appl., PCT WO 2009/134973, 2009.
   Google Scholar
• MarvinSketch 14.11.17.0. ChemAxon Ltd, Budapest, Hungary, 2014; software available at http://www.chemaxon.com.
   Google Scholar
• A. Pedretti, L. Villa, and G. Vistoli, VEGA: A versatile program to convert, handle and visualize molecular structure on WINDOWS-based PCs, J. Mol. Graph. 21 (2002), pp. 47–49.
   Google Scholar
• G. Hessler, O. Korb, P. Monecke, T. Stützle, and T.E. Exner, PharmACOphore: Multiple flexible ligand alignment based on ant colony optimization, J. Cheminform. 2010, 2 (Suppl. I), P17.
   Google Scholar
• D. Cao, M. Wang, X. Qiu, D. Liu, H. Jiang, N. Yang, and R.-M. Xu, Structural basis for allosteric, substrate dependent stimulation of SIRT1 activity by resveratrol, Genes Dev. 29 (2015), pp. 1316–1325.
   PubMed Web of Science ®Google Scholar
• V.E. Kuz’min, A.G. Artemenko, P.G. Polischuk, E.N. Muratov, A.I. Hromov, A.V. Liahovskiy, S.A. Andronati, and S.Y. Makan, Hierarchic system of QSAR models (1D–4D) on the base of simplex representation of molecular structure, J. Mol. Model. 11 (2005), pp. 457–467.
   PubMed Web of Science ®Google Scholar
• V.E. Kuz’min, A.G. Artemenko, E.N. Muratov, V.P. Lozitsky, A.S. Fedchuk, R.N. Lozitska, Y.A. Boschenko, and T.L. Gridina, The hierarchical QSAR technology for effective virtual screening and molecular design of the promising antiviral compounds, Antiviral. Res. 65 (2005), pp. A70–A71.
   Web of Science ®Google Scholar
• V.E. Kuz’min, A.G. Artemenko, V.P. Lozitsky, E.N. Muratov, A.S. Fedtchouk, N.S. Dyachenko, L.N. Nosach, T.L. Gridina, L.I. Shitikova, L.M. Mudrik, A.K. Mescheriakov, V.A. Chelombitko, A.I. Zheltvay, and J-J.E. Vanden, The analysis of structure–anticancer and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the basis of 4D QSAR models (simplex representation of molecular structure), Acta. Biochim. Pol. 49 (2002), pp. 157–168.
   PubMed Web of Science ®Google Scholar
• V.E. Kuz’min, About homo- and heterochirality of dissymmetrical tetrahedrons (chiral simplexes). Stereochemical tunnelling, Zh. Strucur. Khim. 36 (1995), pp. 873–878.
   Google Scholar
• W.L. Jolly, and W.B. Perry, Estimation of atomic charges by an electronegativity equalization procedure calibration with core binding energies, J. Am. Chem. Soc. 95 (1973), pp. 5442–5450.
   Web of Science ®Google Scholar
• R. Wang, Y. Fu, and L. Lai, A new atom-additive method for calculating partition coefficients, J. Chem. Inf. Comput. Sci. 37 (1997), pp. 615–621.
   Google Scholar
• B.V. Ioffe, Chemistry Refractometric Methods, 3rd ed., Himiya, Leningrad, 1983.
   Google Scholar
• H. Kubinyi, Evolutionary variable selection in regression and PLS analyses, J. Chemometr. 10 (1996), pp. 119–133.
   Web of Science ®Google Scholar
• E.N. Muratov, E.V. Varlamova, A.G. Artemenko, P.G. Polishchuk, L. Nikolaeva-Glomb, A.S. Galabov, and V.E. Kuz’min, QSAR analysis of poliovirus inhibition by dual combinations of antivirals, Struct. Chem. 24 (2013), pp. 1665–1679.
   Web of Science ®Google Scholar
• P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701.
   Google Scholar
• V.E. Kuzmina, E.N. Muratova, A.G. Artemenkoa, E.V. Varlamovab, L. Gorba, J. Wanga, and J. Leszczynskia, Consensus QSAR modeling of phosphor-containing chiral AChE inhibitors, QSAR Comb. Sci. 28 (2009), pp. 664–677.
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of Q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
   PubMed Web of Science ®Google Scholar
• P.P. Roy, S. Paul, I. Mitra, and K. Roy, On two novel parameters for validation of predictive QSAR models, Molecules 14 (2009), pp. 1660–1701.
   PubMed Web of Science ®Google Scholar
• L.M. Shi, H. Fang, W.D. Tong, J. Wu, R. Perkins, R.M. Blair, W.S. Branham, S.L. Dial, C.I. Moland, and D.M. Sheehan, QSAR models using a large diverse set of estrogens, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 186–195.
   PubMedGoogle Scholar
• G. Schüurmann, R.-U. Ebert, J. Chen, B. Wang, and R. Kuehne, External validation and prediction employing the predictive squared correlation coefficient-test set activity mean vs. training set activity mean, J. Chem. Inf. Model. 48 (2008), pp. 2140–2145.
   PubMed Web of Science ®Google Scholar
• P. Gramatica and A. Sangion, A historical excursus on the statistical validation parameters for QSAR models: A clarification concerning metrics and terminology, J. Chem. Inf. Model. 56 (2016), pp. 1127–1131.
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemometr. Intell. Lab. Syst. 145 (2015), pp. 22–29.
   Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring metrics for validation of QSPR models, Chemometr. Intell. Lab. Syst. 107 (2011), pp. 194–205.
   Web of Science ®Google Scholar
• J.B. Veselinovic, A.M. Veselinovic, A.P. Toropova, and A.A. Toropov, The Monte Carlo technique as a tool to predict LOAEL, Eur. J. Med. Chem. 116 (2016), pp. 71–75.
   PubMed Web of Science ®Google Scholar
• V.E. Kuz’min, A.G. Artemenko, E.N. Muratov, I.L. Volineckaya, V.A. Makarov, O.B. Riabova, P. Wutzler, and M. Schmidtke, Quantitative structure–activity relationship studies of [(biphenyloxy)propyl]isoxazole derivatives: Inhibitors of human rhinovirus 2 replication, J. Med. Chem. 50 (2007), pp. 4205–4213.
   PubMed Web of Science ®Google Scholar
• T. Asadollahi, S. Dadfarnia, A.M.H. Shabani, J.B. Ghasemi, and M. Sarkhosh, QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening, Molecules 16 (2011), pp. 1928–1955.
   PubMed Web of Science ®Google Scholar
• M. Meloun, J. Militky, M. Hill, and R.G. Brereton, Crucial problems in regression modelling and their solutions, Analyst 127 (2002), pp. 433–450.
   PubMed Web of Science ®Google Scholar
• V.E. Kuz’min, A.G. Artemenko, E.N. Muratov, P.G. Polischuk, L.N. Ognichenko, A.V. Liahovsky, A.I. Hromov, and E.V. Varlamova, Virtual screening and molecular design based on hierarchical QSAR technology, in Recent Advances in QSAR Studies, J.L.T. Puzyn, M. Cronin, eds., Springer, London, 2010, pp. 127–176.
   Google Scholar
• J. Jaworska, N. Nikolova-Jeliazkova, and T. Aldenberg, QSAR applicability domain estimation by projection of the training set in descriptor space: A review, Altern. Lab. Anim. 33 (2005), pp. 445–459.
   PubMed Web of Science ®Google Scholar
• Organisation for Economic Co-operation and Development, Guidance document on the validation of (quantitative) structure–activity relationship [(Q)SAR] models, ENV/JM/MONO(2007) 2, OECD, Paris, France, 2007 Available at http://www.oecd.org/dataoecd/55/35/38130292.pdf .
   Google Scholar
• K. Roy, P. Chakraborty, I. Mitra, P.K. Ojha, S. Kar, and R.N. Das, Some case studies on application of “” metrics for judging quality of QSAR predictions: Emphasis on scaling of response data, J. Comput. Chem. 34 (2013), pp. 1071–1082.
   PubMed Web of Science ®Google Scholar
• C.B. Vu, J.S. Disch, P.Y. Ng, C.A. Blum, and R.B. Perni, Benzimidazoles and related analogs as sirtuin modulators, US patent 20150057272 (2015), p. A1.
   Google Scholar
• C. Oalmann, J.S. Disch, P.Y. Ng, and R.B. Perni, Thiazolopyridine sirtuin modulating compounds, US patent 8492401 (2013), p. B2.
   Google Scholar