Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model
Park, S. H.; Haddad, P. R.; Talebi, M.et al.
2017 • In Journal of Chromatography. A, 1486, p. 68-75
Park, S. H.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Haddad, P. R.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Talebi, M.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Tyteca, Eva ; Université de Liège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Analyse, qual. et risques - Labo. de Chimie analytique
Amos, R. I. J.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Szucs, R.; Pfizer Global Research and Development, Sandwich, United Kingdom
Dolan, J. W.; LC Resources Inc., 1795 NW Wallace Rd., McMinnville, OR, United States
Pohl, C. A.; Thermo Fisher Scientific, Sunnyvale, CA, United States
Title :
Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model
[2] Heberger, K., Quantitative structure-(chromatographic) retention relationships. J. Chromatogr. A 1158 (2007), 273–305.
[3] Carlucci, G., D'Archivio, A.A., Maggi, M.A., Mazzeo, P., Ruggieri, F., Investigation of retention behaviour of non-steroidal anti-inflammatory drugs in high-performance liquid chromatography by using quantitative structure-retention relationships. Anal. Chim. Acta 601 (2007), 68–76.
[4] Ghasemi, J., Saaidpour, S., QSRR prediction of the chromatographic retention behavior of painkiller drugs. J. Chromatogr. Sci. 47 (2009), 156–163.
[5] Gorynski, K., Bojko, B., Nowaczyk, A., Bucinski, A., Pawliszyn, J., Kaliszan, R., Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds. Anal. Chim. Acta 797 (2013), 13–19.
[6] Kritikos, N., Tsantili-Kakoulidou, A., Loukas, Y.L., Dotsikas, Y., Liquid chromatography coupled to quadrupole-time of flight tandem mass spectrometry based quantitative structure-retention relationships of amino acid analogues derivatized via n-propyl chloroformate mediated reaction. J. Chromatogr. A 1403 (2015), 70–80.
[7] Mazza, C.B., Sukumar, N., Breneman, C.M., Cramer, S.M., Prediction of protein retention in ion-exchange systems using molecular descriptors obtained from crystal structure. Anal. Chem. 73 (2001), 5457–5461.
[8] Song, M., Breneman, C.M., Bi, J., Sukumar, N., Bennett, K.P., Cramer, S., Tugcu, N., Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J. Chem. Inf. Comput. Sci. 42 (2002), 1347–1357.
[9] Malmquist, G., Nilsson, U.H., Norrman, M., Skarp, U., Stromgren, M., Carredano, E., Electrostatic calculations and quantitative protein retention models for ion exchange chromatography. J. Chromatogr. A 1115 (2006), 164–186.
[10] Studzinska, S., Molikova, M., Kosobucki, P., Jandera, P., Buszewski, B., Study of the interactions of ionic liquids in IC by QSRR. Chromatographia 73 (2011), 35–44.
[11] Ukić, Š., Novak, M., Žuvela, P., Avdalović, N., Liu, Y., Buszewski, B., Bolanča, T., Development of gradient retention model in ion chromatography. Part I: conventional QSRR approach. Chromatographia 77 (2014), 985–996.
[12] Ukić, Š., Novak, M., Vlahović, A., Avdalović, N., Liu, Y., Buszewski, B., Bolanča, T., Development of gradient retention model in ion chromatography. Part II: artificial intelligence QSRR approach. Chromatographia 77 (2014), 997–1007.
[13] Gupta, V.K., Khani, H., Ahmadi-Roudi, B., Mirakhorli, S., Fereyduni, E., Agarwal, S., Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks. Talanta 83 (2011), 1014–1022.
[14] Yan, J., Cao, D.S., Guo, F.Q., Zhang, L.X., He, M., Huang, J.H., Xu, Q.S., Liang, Y.Z., Comparison of quantitative structure-retention relationship models on four stationary phases with different polarity for a diverse set of flavor compounds. J. Chromatogr. A 1223 (2012), 118–125.
[15] Fragkaki, A.G., Tsantili-Kakoulidou, A., Angelis, Y.S., Koupparis, M., Georgakopoulos, C., Gas chromatographic quantitative structure-retention relationships of trimethylsilylated anabolic androgenic steroids by multiple linear regression and partial least squares. J. Chromatogr. A 1216 (2009), 8404–8420.
[16] Fragkaki, A.G., Farmaki, E., Thomaidis, N., Tsantili-Kakoulidou, A., Angelis, Y.S., Koupparis, M., Georgakopoulos, C., Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. J. Chromatogr. A 1256 (2012), 232–239.
[17] Schefzick, S., Kibbey, C., Bradley, M.P., Prediction of HPLC conditions using QSPR techniques: an effective tool to improve combinatorial library design. J. Comb. Chem. 6 (2004), 916–927.
[18] Hancock, T., Put, R., Coomans, D., Vander Heyden, Y., Everingham, Y., A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemom. Intell. Lab. Syst. 76 (2005), 185–196.
[19] Goodarzi, M., Jensen, R., Vander Heyden, Y., QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions. J. Chromatogr. B 910 (2012), 84–94.
[20] Talebi, M., Schuster, G., Shellie, R.A., Szucs, R., Haddad, P.R., Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modeling of retention times in reversed-phase liquid chromatography. J. Chromatogr. A 1424 (2015), 69–76.
[21] Zuvela, P., Liu, J.J., Macur, K., Baczek, T., Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms. Anal. Chem. 87 (2015), 9876–9883.
[22] Law, B., Weir, S., Quantitative structure-retention relationships for secondary interactions in cation-exchange liquid chromatography. J. Chromatogr. A 657 (1993), 17–24.
[24] Morgan, P.E., Barlow, D.J., Hanna-Brown, M., Flanagan, R.J., Artificial neural network modeling of the retention of acidic analytes in strong anion-exchange HPLC: elucidation of structure-retention relationships. Chromatographia 75 (2012), 693–700.
[25] Ng, B.K., Shellie, R.A., Dicinoski, G.W., Bloomfield, C., Liu, Y., Pohl, C.A., Haddad, P.R., Methodology for porting retention prediction data from old to new columns and from conventional-scale to miniaturised ion chromatography systems. J. Chromatogr. A 1218 (2011), 5512–5519.
[26] Shellie, R.A., Ng, B.K., Dicinoski, G.W., Poynter, S.D.H., O'Reilly, J.W., Pohl, C.A., Haddad, P.R., Prediction of analyte retention for ion chromatography separations performed using elution profiles comprising multiple isocratic and gradient steps. Anal. Chem. 80 (2008), 2474–2482.
[27] Park, S.H., Shellie, R.A., Dicinoski, G.W., Schuster, G., Talebi, M., Haddad, P.R., Szucs, R., Dolan, J.W., Pohl, C.A., Enhanced methodology for porting ion chromatography retention data. J. Chromatogr. A 1436 (2016), 59–63.
[28] Leardi, R., Genetic algorithms in chemistry. J. Chromatogr. A 1158 (2007), 226–233.
[29] Tu, Y.K., Kellett, M., Clerehugh, V., Gilthorpe, M.S., Problems of correlations between explanatory variables in multiple regression analyses in the dental literature. Br. Dent. J. 199 (2005), 457–461.
[30] Hair, J.F.J., Anderson, R.E., Tatham, R.L., Black, W.C., Multivariate Data Analysis. 3rd ed., 1995, Macmillan, New York.
[31] He, L., Jurs, P.C., Assessing the reliability of a QSAR model's predictions. J. Mol. Graph. Model. 23 (2005), 503–523.
[32] Ghasemi, J., Saaidpour, S., QSRR prediction of the chromatographic retention behavior of painkiller drugs. J. Chromatogr. Sci. 47 (2009), 156–163.
[33] Tropsha, A., Gramatica, P., Gombar, V.K., The importance of being earnest: validation in the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 22 (2003), 69–77.
[34] Consonni, V., Ballabio, D., Todeschini, R., Comments on the definition of the Q2 parameter for QSAR validation. J. Chem. Inf. Model. 49 (2009), 1669–1678.
[35] Consonni, V., Ballabio, D., Todeschini, R., Evaluation of model predictive ability by external validation techniques. J. Chemometr. 24 (2010), 194–201.
[36] Baczek, T., Kaliszan, R., Combination of linear solvent strength model and quantitative structure-retention relationships as a comprehensive procedure of approximate prediction of retention in gradient liquid chromatography. J. Chromatogr. A 962 (2002), 41–55.
[37] Chirico, N., Gramatica, P., Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J. Chem. Inf. Model. 52 (2012), 2044–2058.
