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Articles

Cyto- and enzyme toxicities of ionic liquids modelled on the basis of VolSurf+ descriptors and their principal properties

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Pages 221-244 | Received 15 Jan 2016, Accepted 17 Feb 2016, Published online: 08 Mar 2016

References

  • R. Carlson and J.E. Carlson, Design and Optimisation in Organic Synthesis, Second revised and Enlarged Edition. Elsevier, Amsterdam, 2005.
  • F.P. Ballistreri, C.G. Fortuna, G. Musumarra, D. Pavone, and S. Scirè, Principal properties (PPs) as solvent descriptors for multivariate optimisation in organic synthesis: Specific PPs for ethers, Arkivoc part(xi) (2002), pp. 54–64.
  • R. Carlson, M.P. Prochazka, and T. Lundstedt, Principal properties for synthetic screening: Ketones and aldehydes, Acta Chem. Scand. B 42 (1988), pp. 145–156.
  • R. Carlson, M.P. Prochazka, and T. Lundstedt, Principal properties for synthetic screening: Amines, Acta Chem. Scand. B 42 (1988), pp. 157–165.
  • R. Carlson, T. Lundstedt, A. Nordahl, and M.P. Prochazka, Lewis acids in organic synthesis. Approach to a selection strategy for screening experiments, Acta Chem. Scand. B 40 (1986), pp. 522–533.
  • C.G. Fortuna, G. Musumarra, M. Nardi, A. Procopio, G. Sindona, and S. Scire, Principal properties (PPs) for lanthanide triflates as Lewis-acid catalysts, J. Chemom. 20 (2006), pp. 418–424.
  • M. Skagerberg, D. Bonelli, S. Clementi, G. Cruciani, and C. Ebert, Principal properties for aromatic substituents. A multivariate approach for design in QSAR, Quant. Struct.-Act. Relat. 8 (1989), pp. 32–38.
  • S. Hellberg, M. Sjöström, and S. Wold, The prediction of bradykinin potentiating potency of pentapeptides. An example of a peptide quantitative structure-activity relationship, Acta Chem. Scand. B 40 (1986), pp. 135–140.
  • S. Hellberg, M. Sjöström, and S. Wold, Peptide quantitative structure-activity relationships, a multivariate approach, J. Med. Chem. 30 (1987), pp. 1126–1135.
  • M. Skagerberg, M. Sjöström, and S. Wold, Dedicated principal properties for peptide QSARs: Theory and applications, J. Chemom. 4 (1990), pp. 241–253.
  • G. Cruciani, M. Baroni, E. Carosati, M. Clementi, R. Valigi, and S. Clementi, Peptide studies by means of principal properties of amino acids derived from MIF descriptors, J. Chemom. 18 (2004), pp. 146–155.
  • L. Caruso, G. Musumarra, and A.R. Katritzky, Classical and magnetic aromaticies as new descriptors for heteroaromatics in QSAR. 3. Principal properties for heteroaromatics, Quant. Struct.-Act. Relat. 12 (1993), pp. 146–151.
  • S. Clementi, G. Cruciani, P. Fifi, D. Riganelli, R. Valigi, and G. Musumarra, New set of principal properties for heteroaromatics obtained by GRID, Quant. Struct.-Act. Relat. 15 (1996), pp. 108–120.
  • M.P. Prochazka and R. Carlson, On the roles of Lewis catalysts and solvents in the Fischer indole sinthesys, Acta Chem. Scand. 43 (1989), pp. 651–659.
  • M.P. Prochazka and R. Carlson, One-pot Fischer indole synthesis, Acta Chem. Scand. 44 (1990), pp. 614–616.
  • E. Fischer and O. Hess, Synthese von Indolderivaten, Ber Dtsch. Chem. Ges. 17 (1884), pp. 559–568.
  • B. Robinson, The Fischer Indole Synthesis, Wiley, Chichester, 1982.
  • S. Kitagaki, F. Inagaki, and C. Mukai, [2+2+1] Cyclization of allenes, Chem. Soc. Rev. 43 (2014), pp. 2956–2978.
  • H.-P. Steinruck and P. Wasserscheid, Ionic liquids in catalysis, Catal. Lett. 145 (2015), pp. 380–397.
  • M. Smiglak, J.M. Pringle, X. Lu, L. Han, S. Zhang, H. Gao, D.R. MacFarlane, and R.D. Rogers, Ionic liquids for energy, materials, and medicine, Chem. Comm. 50 (2014), pp. 9228–9250.
  • K.S. Egorova and V.P. Ananikov, Toxicity of ionic liquids: Eco(cyto)toxicity as a complicated but unavoidable parameter for task-specific optimization, ChemSusChem 7 (2014), pp. 336–360.
  • UFT-Merck Ionic Liquids Biological Effects Database. Available at http://www.il-eco.uft.unibremen.de/ ?page=home&chent_id=&view=intro&lang=en. (accessed September 2014).
  • A. Paternò, F. D’Anna, G. Musumarra, R. Noto, and S. Scirè, A multivariate insight into ionic liquids toxicities, RSC Adv. 4 (2014), pp. 23985–24000.
  • K.R. Seddon, A. Stark, and M.-J. Torres, Influence of chloride, water, and organic solvents on the physical properties of ionic liquids, Pure Appl. Chem. 72 (2000), pp. 2275–2287.
  • G. Cruciani, P. Crivori, P.A. Carrupt, and B. Testa, Molecular fields in quantitative structure–permeation relationships: The VolSurf approach, J. Mol. Struct. Theochem. 503 (2000), pp. 17–30.
  • VolSurf+ v. 1.1.1. Available at http://www.moldiscovery.com/docs/vsplus/
  • GRID v. 20, Software from Molecular Discovery Ltd., Perugia, Italy.
  • P.J. Goodford, A computational procedure for determining energetically favourable binding sites on biologically important macromolecules, J. Med. Chem. 28 (1985), pp. 849–857.
  • D.N.A. Boobbyer, P.J. Goodford, P.M. Mcwhinnie, and R.C. Wade, New hydrogen-bond potential for use in determining energetically favourable binding sites on molecules on known structure, J. Med. Chem. 32 (1989), pp. 1083–1094.
  • R. Wade, K.J. Clerk, and P.J. Goodford, Further development of hydrogen bond functions for use in determining energetically favorable binding sites on molecules of known structure. 1. Ligand probe groups with the ability to form two hydrogen bonds, J. Med. Chem. 36 (1993), pp. 140–147.
  • P. Crivori, G. Cruciani, P.A. Carrupt, and B. Testa, Predicting blood−brain barrier permeation from three-dimensional molecular structure, J. Med. Chem. 43 (2000), pp. 2204–2216.
  • G. Cruciani, M. Meniconi, E. Carosati, I. Zamora, and R. Mannhold, VOLSURF: A tool for drug ADME-properties prediction, in Drug Bioavailability: Estimation of Solubility, Permeability, Absorption and Bioavailability, van de H. Waterbeemd, H. Lennernäs, and P. Artursson, eds., Wiley-VCH, 2003, pp. 416–419.
  • G. Berellini, G. Cruciani, and R. Mannhold, Pharmacophore, drug metabolism, and pharmacokinetics models on non-peptide AT1, AT2, and AT1/AT2 angiotensin II receptor antagonists, J. Med. Chem. 48 (2005), pp. 4389–4399.
  • V. Barresi, C. Bonaccorso, G. Consiglio, L. Goracci, N. Musso, G. Musumarra, C. Satriano, and C.G. Fortuna, Modeling, design and synthesis of new heteroaryl ethylenes active against the MCF-7 breast cancer cell-line, Mol. Biosys. 9 (2013), pp. 2426–2429.
  • C.G. Fortuna, V. Barresi, C. Bonaccorso, G. Consiglio, S. Failla, A. Trovato-Salinaro, and G. Musumarra, Design, synthesis and in vitro antitumour activity of new heteroaryl ethylenes, Eur. J. Med. Chem. 47 (2012), pp. 221–227.
  • C.G. Fortuna, V. Barresi, and G. Musumarra, Design, synthesis and biological evaluation of trans 2-(thiophen-2-yl)vinyl heteroaromatic iodides, Bioorg. Med. Chem. 18 (2010), pp. 4516–4523.
  • C.G. Fortuna, V. Barresi, G. Berellini, and G. Musumarra, Design and synthesis of trans 2-(furan-2-yl)vinyl heteroaromatic iodides with antitumour activity, Bioorg. Med. Chem. 16 (2008), pp. 4150–4159.
  • G. Forte, C.G. Fortuna, L. Salerno, M.N. Modica, M.A. Siracusa, V. Cardile, G. Romeo, A. Bulbarelli, E. Lonati, and V. Pittala, Antitumor properties of substituted (αE)-α-(1H-indol-3-ylmethylene)benzeneacetic acids or amides, Bioorg. Med. Chem. 21 (2013), pp. 5233–5245.
  • M. Barone, A. Santagati, A.C.E. Graziano, C.G. Fortuna, G. Ronsisvalle, and V. Cardile, Synthesis and biological evaluation of sulfonilamidothienopyrimidinone derivatives as novel anti-inflammatory agents, Med. Chem. 10 (2014), pp. 700–710.
  • L. Goracci, M. Ceccarelli, D. Bonelli, and G. Cruciani, Modeling phospholipidosis induction: Reliability and warnings, J. Chem. Inf. Model. 53 (2013), pp. 1436–1446.
  • L. Goracci, S. Buratta, L. Urbanelli, G. Ferrara, R. Di Guida, C. Emiliani, and S. Cross, Evaluating the risk of phospholipidosis using a new multidisciplinary pipeline approach, Eur J. Med. Chem. 92 (2015), pp. 49–63.
  • M. Salahinejad and J.B. Ghasemi, 3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches, Ecotoxicol. Environ. Saf. 105 (2014), pp. 128–134.
  • H. Shi, S. Tian, Y. Li, D. Li, H. Yu, X. Zhen, and T. Hou, Absorption, distribution, metabolism, excretion, and toxicity evaluation in drug discovery. 14. Prediction of human pregnane X receptor activators by using naive Bayesian classification technique, Chem. Res. Toxicol. 28 (2015), pp. 116–125.
  • A. Paternò, G. Bocci, L. Goracci, G. Musumarra, and S. Scirè, Modelling the aquatic toxicity of ionic liquids by means of VolSurf in silico descriptors, Musumarra, SAR QSAR Environ. Res. 27 (2016), pp. 1–15.
  • M.H. Fatemi and P. Izadiyan, Cytotoxicity estimation of ionic liquids based on their effective structural features, Chemosphere 84 (2011), pp. 553–563.
  • E. Borges de Melo, A structure–activity relationship study of the toxicity of ionic liquids using an adapted Ferreira-Kiralj hydrophobicity parameter, Phys. Chem. Chem. Phys. 17 (2015), pp. 4516–4523.
  • R.N. Das, K. Roy, and P.L.A. Popelier, Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines, Chemosphere 139 (2015), pp. 163–173.
  • C.D. Andersson, J.M. Hillgren, C. Lindgren, W. Qian, C. Akfur, L. Berg, F. Ekstrom, and A. Linusson, Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: A case study on acetylcholinesterase, J. Comput. Aided Mol. Des. 29 (2015), pp. 199–215.
  • J. Arning, S. Stolte, A. Böschen, F. Stock, W.R. Pitner, U. Welz-Biermann, B. Jastorff, and J. Ranke, Qualitative and quantitative structure activity relationships for the inhibitory effects of cationic head groups, functionalised side chains and anions of ionic liquids on acetylcholinesterase, Green Chem. 10 (2008), pp. 47–58.
  • F. Yan, S. Xia, Q. Wang, and P. Ma, Predicting toxicity of ionic liquids in acetylcholinesterase enzyme by the quantitative structure–activity relationship method using topological indexes, J. Chem. Eng. Data 57 (2012), pp. 2252–2257.
  • N. Basant, S. Gupta, and K.P. Singh, Predicting acetyl cholinesterase enzyme inhibition potential of ionic liquids using machine learning approaches: An aid to green chemicals designing, J. Mol. Liq. 209 (2015), pp. 404–412.
  • J.S. Torrecilla, J. Garcìa, E. Rojo, and F. Rodrìguez, Estimation of toxicity of ionic liquids in leukemia rat cell line and acetylcholinesterase enzyme by principal component analysis, neural networks and multiple lineal regressions, J. Hazard. Mater. 164 (2009), pp. 182–194.
  • B. Peric, J. Sierra, E. Martí, R. Cruanas, M.A. Garaua, J. Arning, U. Bottin-Weber, and S. Stolte, (Eco)toxicity and biodegradability of selected protic and aprotic ionic liquids, J Hazard. Mater. 261 (2013), pp. 99–105.
  • SIMCA v.13.0.3 MKS Umetrics AB, Malmo, Sweden, 2013.
  • S. Wold, K. Esbensen, and P. Geladi, Principal component analysis, Chemom. Intell. Lab. Syst 2 (1987), pp. 37–52.
  • G. Cruciani, F. Milletti, L. Storchi, G. Sforna, and L. Goracci, In silico pKa prediction and ADME profiling, Chem. Biodivers. 6 (2009), pp. 1812–1821.
  • F. Milletti, L. Storchi, L. Goracci, S. Bendels, B. Wagner, M. Kansy, and G. Cruciani, Extending pKa prediction accuracy: High-throughput pKa measurements to understand pKa modulation of new chemical series, Eur. J. Med. Chem. 45 (2010), pp. 4270–4279.
  • SYBYL-X v. 1.3, Tripos. Available at http://www.tripos.com.
  • S. Wold and M. Sjöström, SIMCA: A method for analyzing chemical data in terms of similarity and analogy, in Chemometrics: Theory and Application, B.R. Kowalski, eds., ACS Symposium Series, Washington, 1977, pp. 243–282.
  • S. Wold, M. Sjöström, and L. Eriksson, Partial Least Squares Projections to latent structures (PLS) in chemistry, in The Encyclopedia of Computational Chemistry, Schleyer P.v.R., eds., John Wiley & Sons, Chichester, 1998, pp. 2006–2020.
  • M. Marszalek, Z. Fei, D.-R. Zhu, R. Scopelliti, P.J. Dyson, S.M. Zakeeruddin, and M. Gratzel, Application of ionic liquids containing tricyanomethanide [C(CN)3]− or tetracyanoborate [B(CN)4]− anions in dye-sensitized solar cells, Inorg. Chem. 50 (2011), pp. 11561–11567.
  • Y. Yoshida, K. Muroi, A. Otsuka, G. Saito, M. Takahashi, and T. Yoko, 1-Ethyl-3-methylimidazolium based ionic liquids containing cyano groups: Synthesis, characterization, and crystal structure, Inorg. Chem. 43 (2004), pp. 1458–1462.
  • D. Kuang, P. Wang, S. Ito, S.M. Zakeeruddin, and M. Gratzel, Stable mesoscopic dye-sensitized solar cells based on tetracyanoborate ionic liquid electrolyte, J. Am. Chem. Soc. 128 (2006), pp. 7732–7733.
  • A. Romero, A. Santos, J. Tojo, and A. Rodríguez, Toxicity and biodegradability of imidazolium ionic liquids, J. Haz. Mat. 151 (2008), pp. 268–273.
  • S. Stolte, J. Arning, U. Bottin-Weber, M. Matzke, F. Stock, K. Thiele, M. Uerdingen, U. Welz-Biermann, B. Jastorff, and J. Ranke, Anion effects on the cytotoxicity of ionic liquids, Green Chem. 8 (2006), pp. 621–629.
  • R. Hayes, G.G. Warr, and R. Atkin, Structure and nanostructure in ionic liquids, Chem. Rev. 115 (2015), pp. 6357–6426.
  • L. Berg, C.D. Andersson, E. Artursson, A. Hornberg, A.K. Tunemalm, A. Linusson, and F. Ekstrom, Targeting acetylcholinesterase: Identification of chemical leads by high throughput screening, structure determination and molecular modeling, PLoS ONE 6(e26039) (2011), pp. 1–12.
  • F. Stock, J. Hoffmann, J. Ranke, R. Stormann, B. Ondruschka, and B. Jastoff, Effects of ionic liquids on the acethylcholinesterase-A structure-activity relationship consideration, Green Chem. 6 (2004), pp. 286–290.
  • R.G. Diaza, S. Manganelli, A. Esposito, A. Roncaglioni, A. Manganaro, and E. Benfenati, Comparison of in silico tools for evaluating rat oral acute toxicity, SAR QSAR Environ. Res. 26 (2015), pp. 1–27.
  • C.I. Cappelli, A. Cassano, A. Golbamaki, Y. Moggio, A. Lombardo, M. Colafranceschi, and E. Benfenati, Assessment of in silico models for acute aquatic toxicity towards fish under REACH regulation, SAR QSAR Environ. Res. 26 (2015), pp. 977–999.
  • 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 acetylcholinesterase, SAR QSAR Environ. Res. 26 (2015), pp. 449–460.

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