Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

References

• M. Paul, A.P. Mehr, and R. Kreutz, Physiology of local renin-angiotensin systems, Physiol. Rev. 86 (2006), pp. 747–803.
   Google Scholar
• A.H.J. Danser, Local renin-angiotensin systems: The unanswered questions, Int. J. Biochem. Cell Biol. 35 (2003), pp. 759–768.
   PubMed Web of Science ®Google Scholar
• P. Pagliaro and C. Penna, Rethinking the renin-angiotensin system and its role in cardiovascular regulation, Cardiovasc. Drug Ther. 19 (2005), pp. 77–87.
   Google Scholar
• D. Coates, The angiotensin converting enzyme (ACE), Int. J. Biochem. Cell Biol. 35 (2003), pp. 769–773.
   PubMed Web of Science ®Google Scholar
• E.K. Jackson, Renin and angiotensin, in Goodman and Gilman’s the Pharmacologic Basis of Therapeutics, 10th ed., J.G. Hardman, L.E. Limberd and A.G. Gilman, eds., McGrawHill, New York, 2001, pp. 809–829.
   Google Scholar
• A. Kuoppala, K.A. Lindstedt, J. Saarinen, P.T. Kovanen, and J.O. Kokkonen, Inactivation of bradykinin by angiotensin-converting enzyme and by carboxypeptidase Nin human plasma, Am. J. Physiol. Heart Circ. Physiol. 278 (2000), pp. H1069–H1074.
   Google Scholar
• G. Novo, D. Guttilla, G. Fazio, D. Cooper, and S. Novo, The role of the renin-angiotensin system in atrial fibrillation and the therapeutic effects of ACE-Is and ARBS, Brit. J. Clin. Pharmaco. 66 (2008), pp. 345–351.
   Web of Science ®Google Scholar
• J.W. Wright, S. Mizutani, and J.W. Harding, Pathways involved in the transition from hypertension to hypertrophy to heart failure. Treatment strategies, Heart Fail Rev. 13 (2008), pp. 367–375.
   Google Scholar
• M. Butters, Systemic hypertension, in Current Medical Diagnosis and Treatment, S.J. McPhee, M.A. Papadakis and L.M. Tierney, eds., McGraw-Hill, New Jersey, 2008, pp. 371–399.
   Google Scholar
• A.J. Scheen, Prevention of type 2 diabetes mellitus through inhibition of the renin-angiotensin system, Drugs 64 (2004), pp. 2537–2565.
   PubMed Web of Science ®Google Scholar
• T. Matsusaka, J. Hymes, and I. Ichikawa, Angiotensin in progressive renal diseases: Theory and practice, J. Am. Soc. Nephrol. 7 (1996), pp. 2025–2043.
   PubMed Web of Science ®Google Scholar
• F.W. Asselbergs and W.H. Van Gilst, Angiotensin converting enzyme inhibition in cardiovascular risk populations: A practical approach to identify the patient who will benefit most, Curr. Opin. Cardiol. 22 (2007), pp. 267–272.
   PubMed Web of Science ®Google Scholar
• R. Latini, A.P. Maggioni, M. Flather, P. Sleight, and G. Tognoni, ACE inhibitor use in patients with myocardial infarction. Summary of evidence from clinical trials, Circulation 92 (1995), pp. 3132–3137.
   Google Scholar
• T.W. Kurtz and M. Pravenec, Antidiabetic mechanisms of angiotensin-converting enzyme inhibitors and angiotensin II receptor antagonists: Beyond the renin-angiotensin system, J. Hypertens. 22 (2004), pp. 2253–2261.
   Web of Science ®Google Scholar
• R.W. Nesta and S. Zarich, Acute myocardial infarction in diabetes mellitus: Lessons learned from ACE inhibition, Circulation 97 (1998), pp. 12–15.
   Google Scholar
• A.B. Fogo, New insights into the renin angiotensin system and hypertensive renal disease, Curr. Hypertens. Rep. 1 (1999), pp. 187–194.
   PubMedGoogle Scholar
• H. Yokoyama, O. Tomonaga, M. Hirayama, A. Ishii, M. Takeda, T. Babazono, U. Ujihara, C. Takahashi, and Y.Omori, Predictors of the progression of diabetic 0 nephropathy and the beneficial effect of angiotensin-converting enzyme inhibitors in NIDDM patients, Diabetologia 40 (1997), pp. 405–411.
   Google Scholar
• A. Tropsha and A. Golbraikh, Predictive QSAR modeling workflow, model applicability domains, and virtual screening, Curr. Pharm. Design 13 (2007), pp. 3494–3504.
   PubMed Web of Science ®Google Scholar
• J. Verma, V.M. Khedkar, and E.C. Coutinho, 3D-QSAR in drug design – A review, Curr, Top, Med, Chem, 10 (2010), pp. 95–115.
   Google Scholar
• A.Z. Dudek, T́. Arodz, and J. Gálvez, Computational methods in developing quantitative structure-activity relationships (QSAR): A review, Comb. Chem. High. T. Scr. 9 (2006), pp. 213–228.
   Google Scholar
• D. Fourches, E. Muratov, and A. Tropsha, Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research, J. Chem. Inf. Model. 50 (2010), pp. 1189–1204.
   PubMed Web of Science ®Google Scholar
• C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul, A practical overview of quantitative structure-activity relationship, EXCLI J. 8 (2009), pp. 74–88.
   Google Scholar
• M. Pérez González, C. Terán, L. Saíaz-Urra, and M.Teijeira, Variables selection methods in QSAR: An overview, Curr. Top. Med. Chem. 8 (2008), pp. 1606–1627.
   PubMed Web of Science ®Google Scholar
• P. Liu and W. Long, Current mathematical methods used in QSAR/QSPR studies, Int. J. Mol. Sci. 10 (2009), pp. 1978–1998.
   Web of Science ®Google Scholar
• H. Kubinyi, QSAR and 3D QSAR in drug design part 2: Applications and problems, Drug Discov. Today 2 (1997), pp. 538–546.
   Google Scholar
• R. Gozalbes, J.P. Doucet, and F. Derouin, Application of topological descriptions in QSAR and drug design: History and new trends, Curr. Drug Targets Infect. Disord. 1 (2002), pp. 93–102.
   Google Scholar
• A.U. Khan, Descriptors and their selection methods in QSAR analysis: Paradigm for drug design, Drug Discov Today 21 (2016), pp. 1291–1302.
   Google Scholar
• A.M. Veselinović, J.B. Veselinović, J.V. Živković, and G.M. Nikolić, Application of SMILES notation based optimal descriptors in drug discovery and design, Curr. Top. Med. Chem. 15 (2015), pp. 1768–1779.
   PubMed Web of Science ®Google Scholar
• J.B. Veselinović, A.M. Veselinović, 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.
   Web of Science ®Google Scholar
• A.P. Toropova, A.A. Toropov, J.B. Veselinović, and A.M. Veselinović, QSAR as a random event: A case of NOAEL, Environ. Sci. Pollut. R. 22 (2015), pp. 8264–8271.
   PubMed Web of Science ®Google Scholar
• 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.
   Google Scholar
• 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 SMILE S-based descriptors, SAR QSAR Environ Res 28 (2017), pp. 1–16.
   Google Scholar
• 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.
   Google Scholar
• D.B. Kitchen, H. Decornez, J.R. Furr, and J. Bajorath, Docking and scoring in virtual screening for drug discovery: Methods and applications, Nat. Rev. Drug Discov. 3 (2004), pp. 935–949.
   Google Scholar
• I. Halperin, B. Ma, H. Wolfson, and R. Nussinov, Principles of docking: An overview of search algorithms and a guide to scoring functions, Proteins 47 (2002), pp. 409–443.
   PubMed Web of Science ®Google Scholar
• J.B. Veselinović, G.M. Kocić, A. Pavic, J. Nikodinovic-Runic, L. Senerovic, G.M. Nikolić, and A.M. Veselinović, Selected 4-phenyl hydroxycoumarins: In vitro cytotoxicity, teratogenic effect on zebrafish (Danio rerio) embryos and molecular docking study, Chem-Biol. Interact. 231 (2015), pp. 167–174.
   Google Scholar
• J.T. Suh, J.W. Skiles, B.E. Williams, R.D. Youssefyeh, H. Jones, B. Loev, E.S. Neiss, A. Schwab, and W.S. Mann, Angiotensin converting enzyme inhibitors. New orally active antihypertensive (mercaptoalkanoyl)- and [(acylthio)alkanoyl]glycine derivatives, J. Med. Chem. 28 (1985), pp. 57–66.
   Google Scholar
• S.K. Paliwal, A. Pandey, and S. Paliwal, Quantitative structure activity relationship analysis of N[(acylthio)alkanoyl] and (mercaptoalkanoyl)- glycine derivatives as ACE inhibitors, Am. J. Drug Discov. Dev. 1 (2011), pp. 85–104.
   Google Scholar
• A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, and R. Fanelli, The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method, Struct. Chem. 24 (2013), pp. 1369–1381.
   Web of Science ®Google Scholar
• A.A. Toropov, R. Carbó-Dorca, and A.P. Toropova, Index of Ideality of Correlation: New possibilities to validate QSAR: A case study, Struct. Chem. 29 (2017), pp. 33–38.
   Web of Science ®Google Scholar
• A.P. Toropova and A.A. Toropov, The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Sci. Total Environ. 586 (2017), pp. 466–472.
   PubMed Web of Science ®Google Scholar
• 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 (2018), pp. 441–449.
   Web of Science ®Google Scholar
• R. Thomsen and M.H. Christensen, MolDock: A new technique for high-accuracy molecular docking, J. Med. Chem. 49 (2006), pp. 3315–3321.
   Web of Science ®Google Scholar
• R.A. Laskowski and M.B. Swindells, LigPlot+: Multiple ligand–protein interaction diagrams for drug discovery, J. Chem. Inf. Model. 51, (2011), pp. 2778–2786.
   PubMed Web of Science ®Google Scholar
• 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.
   Google Scholar
• 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. 38 (2018), pp. 590–608.
   Web of Science ®Google Scholar
• 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.
   Google Scholar