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Articles

Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method

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Pages 145-159 | Received 04 Nov 2018, Published online: 19 Feb 2019

References

  • Q. Dang, S.R. Kasibhatla, K.R. Reddy, T. Jiang, M.R. Reddy, S.C. Potter, J.M. Fujitaki, P.D.V. Poelje, J. Huang, W.N. Lipscomb, and M.D. Erion, Discovery of potent and specific fructose-1,6-bisphosphatase inhibitors and a series of orally-bioavailable phosphoramidase-sensitive prodrugs for the treatment of type 2 diabetes, J. Am. Chem. Soc. 129 (2007), pp. 15491–15502.
  • S. Heng, K.R. Gryncel, and E.R. Kantrowitz, A library of novel allosteric inhibitors against fructose 1,6-bisphosphatase, Bioorg. Med. Chem. 17 (2009), pp. 3916–3922.
  • J. Bie, S. Liu, J. Zhou, B. Xu, and Z. Shen, Design, synthesis and biological evaluation of 7-nitro-1H-indole-2-carboxylic acid derivatives as allosteric inhibitors of fructose-1,6-bisphosphatase, Bioorg. Med. Chem. 22 (2014), pp. 1850–1864.
  • Q. Dang, B.S. Brown, Y. Liu, R.M. Rydzewski, E.D. Robinson, P.D.V. Poelje, M.R. Reddy, and M.D. Erion, Fructose-1,6-bisphosphatase inhibitors. 1. Purine phosphonic acids as novel AMP mimics, J. Med. Chem. 52 (2009), pp. 2880–2898.
  • J. Bie, S. Liu, Z. Li, Y. Mu, B. Xu, and Z. Shen, Discovery of novel indole derivatives as allosteric inhibitors of fructose-1,6-bisphosphatase, Eur. J. Med. Chem. 90 (2015), pp. 394–405.
  • M. Hao, X. Zhang, H. Ren, Y. Li, S. Zhang, F. Luo, M. Ji, G. Li, and L. Yang, In silico identification of structure requirement for novel thiazole and oxazole derivatives as potent fructose 1,6-bisphosphatase inhibitors, Int. J. Mol. Sci. 12 (2011), pp. 8161–8180.
  • T.W.V. Geldern, C. Lai, R.J. Gum, M. Daly, C. Sun, E.H. Fryb, and C.A. Zapaterob, Benzoxazolebenzenesulfonamides are novel allosteric inhibitors of fructose-1,6-bisphosphatase with a distinct binding mode, Bioorg. Med. Chem. Lett. 16 (2006), pp. 1811–1815.
  • Q. Dang, S.R. Kasibthatla, T. Jiang, F. Taplin, T.Gibson, S.C. Potter, P.D.V. Poelje, and M.D. Erion, Oxazolephosphonic acids as fructose 1,6-bisphosphatase inhibitors with potent glucose-lowering activity, Med. Chem. Commun. 2 (2011), pp. 287–290.
  • Q. Dang, S.R. Kasibhatla, W. Xiao, Y. Liu, J. DaRe, F. Taplin, K.R. Reddy, G.R. Scarlato, T. Gibson, P.D.V. Poelje, S.C. Potter, and M.D. Erion, Fructose-1,6-bisphosphatase inhibitors. 2. Design, synthesis, and structure-activity relationship of a series of phosphonic acid containing benzimidazoles that function as 50-adenosinemonophosphate (AMP) mimics, J. Med. Chem. 53 (2010), pp. 441–451.
  • 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.
  • A.A. Toropov and A.P. Toropova, CORAL, software available at http://www.insilico.eu/coral.
  • K. Nesmerak, A.A. Toropov, A.P. Toropova, P. Kohoutova, and K. Waisser, SMILES-based quantitative structure property relationships for half-wave potential of N-benzylsalicylthioamides, Eur. J. Med. Chem. 67 (2013), pp. 111–114.
  • R. Gaikwad, S. Ghorai, Sk.A. Amin, N. Adhikari, T. Patel, K. Das, T. Jha, and S. Gayen, Monte Carlo based modelling approach for designing and predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cell line MCF7, Toxicol. Vitro 52 (2018), pp. 23–32.
  • P. Hebeisen, W. Haap, B. Kuhn, P. Mohr, H.P. Wessel, U. Zutter, S. Kirchner, A. Ruf, J. Benz, C. Joseph, R.A. Sánchez, M. Gubler, B. Schott, A. Benardeau E. Tozzo, and E. Kitas, Orally active aminopyridines as inhibitors of tetrameric fructose-1,6-bisphosphatase, Bioorg. Med. Chem. Lett. 21 (2011), pp. 3237–3242.
  • E. Kitas, P. Mohr, B. Kuhn, P. Hebeisen, H.P. Wessel, W. Haap, A. Ruf, J. Benz, C. Joseph, W. Huber, R.A. Sanchez, A. Paehler, A. Benardeau, M. Gubler, B. Schott, and E. Tozzo, Sulfonylureidothiazoles as fructose-1,6-bisphosphatase inhibitors for the treatment of Type-2 diabetes, Bioorg. Med. Chem. Lett. 20 (2010), pp. 594–599.
  • A.M. Veselinović, A. Toropov, A. Toropova, D.S. Đorđević, and J.B. Veselinović, Design and development of novel antibiotics based on FtsZ inhibition - in silico studies, New J. Chem. 42 (2018), pp. 10976–10982.
  • V. Prachayasittikul, A. Worachartcheewan, A.P. Toropova, A.A. Toropov, N. Schaduangrat, V. Prachayasittikul, and C. Nantasenamat, Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors, SAR QSAR Environ. Res. 28 (2017), pp. 1–16.
  • J.B. Veselinović, G.M. Nikolić, N.V. Trutić, J.V. Živković, and A.M. Veselinović, Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase, SAR QSAR Environ. Res. 26 (2015), pp. 449–460.
  • P. Kumar and A. Kumar, Monte Carlo method based QSAR studies of Mer kinase inhibitors in compliance with OECD principles, Drug Res. 68 (2017), pp. 189–195.
  • M. Gobbi, M.B. Mariya, A. Toropova, A.A. Toropov, and M. Salmona, Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds, Toxicol. Lett. 250–251 (2016), pp. 42–46.
  • J.V. Živković, N.V. Trutić, J.B. Veselinović, G.M. Nikolić, and A.M. Veselinović, Monte Carlo method based QSAR modelling of male imide derivatives as glycogen synthase kinase-3β inhibitors, Comput. Biol. Med. 64 (2015), pp. 276–286.
  • P.G.R. Acharya, Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2, SAR QSAR Environ. Res. 25 (2014), pp. 73–90.
  • A. Rescifina, G. Floresta, A. Marrazzo, C. Parenti, O. Prezzavento, G. Nastasi, M. Dichiara, and E. Amata, Development of a sigma-2 receptor affinity filter through a Monte Carlo based QSAR analysis, Eur. J. Pharma. Sci. 106 (2017), pp. 94–101.
  • D. Sokolović, D. Aleksić, V. Milenković, S. Karaleić, D. Mitić, J. Kocić, B. Mekić, J.B. Veselinović, and A.M. Veselinović, QSAR modeling of bis-quinolinium and bis-isoquinolinium compounds as acetylcholine esterase inhibitors based on the Monte Carlo method—the implication for myasthenia gravis treatment, Med. Chem. Res. 25 (2016), pp. 2989–2998.
  • J.B. Veselinovic, A.A. Toropov, A.P. Toropova, G.M. Nikolic, and A.M. Veselinovic, Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins, Arch. Pharm. Chem. Life Sci. 348 (2015), pp. 62–67.
  • P. Kumar, A. Kumar, J. Sindhu, and S. Lal, QSAR Models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on Monte Carlo method, Drug Res. (2018), doi: 10.1055/a-0652-5290 [ Epub ahead of print].
  • K. Nesměrák, A.A. Toropov, A.P. Toropova, T.E. Bolelli, and I. Yildiz, QSAR of antimycobacterial activity of benzoxazoles by optimal SMILES-based descriptors, Med. Chem. Res. 26 (2017), pp. 3203–3208.
  • 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.
  • K. Bouhedjar, S. Manganelli, G. Gini, A.A. Toropov, A.P. Toropova, S.A. Mokhnache, and D. Messadi, QSAR modeling useful in anti-cancer drug discovery: Prediction of V600EBRAF-dependent P-ERK using Monte Carlo method, J. Med. Chem. Toxicol. 2 (2017), pp. 34–39.
  • A. Rescifina, G. Floresta, A. Marrazzo, C. Parenti, O. Prezzavento, G. Nastasi, M. Dichiara, and E. Amata, Sigma-2 receptor ligands QSAR model dataset, Data in Brief 13 (2017), pp. 514–535.
  • L. Simon, A. Imane, K.K. Srinivasan, L. Pathak, and I. Daoud, In silico drug-designing studies on flavanoids as anticolon cancer agents: Pharmacophore mapping, molecular docking, and Monte Carlo method-based QSAR modeling, Interdiscip. Sci. Comput. Life Sci. 9 (2017), pp. 445–458.
  • V. Stoičkov, D. Stojanović, I. Tasić, S. Šarić, D. Radenković, P. Babović, D. Sokolović and A.M. Veselinović, QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method, Struct Chem. 29 (2017), pp. 441–449.
  • S. Bhargava, N. Adhikari, S.A. Amin, K. Das, S. Gayen, and T. Jha, Hydroxyethylamine derivatives as HIV-1 protease inhibitors: A predictive QSAR modelling study based on Monte Carlo optimization, SAR QSAR Environ. Res. 28 (2017), pp. 973–990.
  • A.A. Toropov, A.P. Toropova, and E. Benfenati, QSAR modelling for mutagenic potency of heteroaromatic amines by optimal SMILES-based descriptors, Chem. Biol. Drug. Des. 77 (2009), pp. 301–312.
  • E. Benfenati, A.A. Toropov, A.P. Toropova, A. Manganaro, and R.G. Diaza, CORAL software: QSAR for anticancer agents, Chem. Biol. Drug Des. 77 (2011), pp. 471–476.
  • A. Kumar and S. Chauhan, QSAR differential model for prediction of SIRT1 modulation using Monte Carlo method, Drug Res. 67 (2016), pp. 156–162.
  • J. Shamsara, Ezqsar: An R package for developing QSAR models directly from structures, Open Med. Chem. J. 11 (2017), pp. 212–221.
  • P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring rm1 metrics for validation of QSPR models, Chemom. Intell. Lab. Syst. 107 (2011), pp. 194–205.
  • A. Kumar and S. Chauhan, Use of the Monte Carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors, Arch. Pharm. Chem. Life Sci. 350 (2017), pp 1–9.
  • P.K. Ojha and K. Roy, Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection, Chemom. Intell. Lab. Syst. 109 (2011), pp. 146–161.
  • K. Roy, R.N. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab. Syst. 152 (2016), pp. 18–33.
  • K. Roy, MLR Plus Validation; software available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/.
  • A. Kumar and S. Chauhan, Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors, SAR QSAR Environ. Res. 28 (2017), pp. 179–197.
  • A.A. Toropov, A.P. Toropova, F. Como, and E. Benfenati, Quantitative structure–activity relationship models for bee toxicity, Toxicol. Environ. Chem. 99 (2016), pp. 1–12.
  • M. Golubović, M. Lazarević, D. Zlatanović, D. Krtinić, V. Stoičkov, B. Mladenović, D.J. Milić, D. Sokolović, and A.M. Veselinović, The anesthetic action of some polyhalogenated ethers −Monte Carlo method based QSAR study, Comput. Biol. Chem. 75 (2018), pp. 32–38.
  • A. Worachartcheewana, C. Nantasenamata, C.I.N. Ayudhyab and V. Prachayasittikul, QSAR study of H1N1 neuraminidase inhibitors from influenza A virus, Lett. Drug Design Discov. 11 (2014), pp. 420–427.
  • O. Trott and A.J. Olson, AutoDockVina: Improving the speed and accuracy of docking with a newscoring function, efficient optimization, and multithreading, J. Comput. Chem. 31 (2010), pp. 455–461.
  • S. Bhargava, T. Patel, R. Gaikwad, U.K. Patiland, and S. Gayen, Identification of structural requirements and prediction of inhibitory activity of natural flavonoids against Zika virus through molecular docking and Monte Carlo based QSAR simulation, Nat. Prod. Res. 15 (2017), pp. 1–7.
  • Discovery Studio Visualizer, Accelrys Inc, San Diego, CA, 2013.

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