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RESEARCH ARTICLE
Contents Vol 74(11)

Method Optimisation in Hydrophilic-Interaction Liquid Chromatography by Design of Experiments Combined with Quantitative Structure–Retention Relationships*

Maryam Taraji A B C and Paul R. Haddad https://orcid.org/0000-0001-9579-7363 C D
+ Author Affiliations
- Author Affiliations

A The Australian Wine Research Institute, PO Box 197, Adelaide, SA 5064, Australia.

B Metabolomics Australia, PO Box 197, Adelaide, SA 5064, Australia.

C Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tas. 7001, Australia.

D Corresponding author. Email: paul.haddad@utas.edu.au

Australian Journal of Chemistry 74(11) 778-786 https://doi.org/10.1071/CH21102
Submitted: 30 April 2021  Accepted: 1 June 2021   Published: 5 July 2021

Abstract

Accurate prediction of the separation conditions for a set of target analytes with no retention data available is fundamental for routine analytical assays but remains a very challenging task. In this paper, a quality by design (QbD) optimisation workflow capable of discovering the optimal chromatographic conditions for separation of new compounds in hydrophilic-interaction liquid chromatography (HILIC) is introduced. This workflow features the application of quantitative structure−retention relationship (QSRR) methodology in conjunction with design of experiments (DoE) principles and was used to carry out a two-level full factorial DoE optimisation for a mixture of pharmaceutical analytes on zwitterionic, amide, amine, and bare silica HILIC stationary phases, with mobile phases containing varying acetonitrile content, mobile phase pH, and salt concentration. A dual-filtering approach that considers both retention time (tR) and structural similarity was used to identify the optimal set of analytes to train the QSRR in order to maximise prediction accuracy. Highly predictive retention models (average R2 of 0.98) were obtained and statistical analysis of the prediction performance of the QSRR models demonstrated their ability to predict the retention times of new compounds based solely on their molecular structures, with root-mean-square errors of prediction in the range 7.6–11.0 %. Further, the obtained retention data for pharmaceutical test compounds were used to compute their separation selectivity, which was used as input into a DoE optimiser in order to select the optimal separation conditions. Experimental separations performed under the chosen optimal working conditions showed good agreement with the theoretical predictions. To the best of our knowledge, this is the first study of a QbD optimisation workflow assisted with dual-filtering-based retention modelling to facilitate the method development process in HILIC.

Keywords: quality-by-design, separation optimisation, design of experiments, quantitative structure-retention relationships, prediction accuracy, dual-filtering, retention prediction, similarity searching, hydrophilic interaction liquid chromatography, nucleosides.


References

[1]  V. D’Atri, S. Fekete, A. Clarke, J. L. Veuthey, D. Guillarme, Anal. Chem. 2019, 91, 210.
         | Crossref | GoogleScholarGoogle Scholar | 30406984PubMed |

[2]  Q. Zhang, F. Q. Yang, L. Ge, Y. J. Hu, Z. N. Xia, J. Sep. Sci. 2017, 40, 49.
         | Crossref | GoogleScholarGoogle Scholar | 27717145PubMed |

[3]  K. Broeckhoven, G. Desmet, Anal. Chem. 2021, 93, 257.
         | Crossref | GoogleScholarGoogle Scholar | 33300336PubMed |

[4]  C. Galea, D. Mangelings, Y. Vander Heyden, Anal. Chim. Acta 2015, 886, 1.
         | Crossref | GoogleScholarGoogle Scholar | 26320631PubMed |

[5]  P. Jandera, Anal. Chim. Acta 2011, 692, 1.
         | Crossref | GoogleScholarGoogle Scholar | 21501708PubMed |

[6]  Y. Guo, S. Gaiki, J. Chromatogr. A 2011, 1218, 5920.
         | Crossref | GoogleScholarGoogle Scholar | 21737083PubMed |

[7]  P. R. Haddad, M. Taraji, R. Szucs, Anal. Chem. 2021, 93, 228.
         | Crossref | GoogleScholarGoogle Scholar | 33085452PubMed |

[8]  M. Taraji, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, Anal. Chim. Acta 2018, 1000, 20.
         | Crossref | GoogleScholarGoogle Scholar | 29289311PubMed |

[9]  R. Cela, E. Y. Ordonez, J. B. Quintana, R. Rodil, J. Chromatogr. A 2013, 1287, 2.
         | Crossref | GoogleScholarGoogle Scholar | 22901328PubMed |

[10]  S. Orlandini, S. Pinzauti, S. Furlanetto, Anal. Bioanal. Chem. 2013, 405, 443.
         | Crossref | GoogleScholarGoogle Scholar | 22941176PubMed |

[11]  F. G. Vogt, A. S. Kord, J. Pharm. Sci. 2011, 100, 797.
         | Crossref | GoogleScholarGoogle Scholar | 21280050PubMed |

[12]  E. Rozet, P. Lebrun, P. Hubert, B. Debrus, B. Boulanger, TrAC Trends Analyt. Chem. 2013, 42, 157.
         | Crossref | GoogleScholarGoogle Scholar |

[13]  L. V. Candioti, M. M. De Zan, M. S. Camara, H. C. Goicoechea, Talanta 2014, 124, 123.
         | Crossref | GoogleScholarGoogle Scholar | 24767454PubMed |

[14]  R. Kaliszan, Chem. Rev. 2007, 107, 3212.
         | Crossref | GoogleScholarGoogle Scholar | 17595149PubMed |

[15]  K. Heberger, J. Chromatogr. A 2007, 1158, 273.
         | Crossref | GoogleScholarGoogle Scholar | 17499256PubMed |

[16]  M. Taraji, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, J. Chromatogr. A 2017, 1486, 59.
         | Crossref | GoogleScholarGoogle Scholar | 28049585PubMed |

[17]  M. Talebi, G. Schuster, R. A. Shellie, R. Szucs, P. R. Haddad, J. Chromatogr. A 2015, 1424, 69.
         | Crossref | GoogleScholarGoogle Scholar | 26592563PubMed |

[18]  K. Muteki, J. E. Morgado, G. L. Reid, J. Wang, G. Xue, F. W. Riley, J. W. Harwood, D. T. Fortin, J. I. Miller, Ind. Eng. Chem. Res. 2013, 52, 12269.
         | Crossref | GoogleScholarGoogle Scholar |

[19]  C. Wang, M. J. Skibic, R. E. Higgs, I. A. Watson, H. Bui, J. Wang, J. M. Cintron, J. Chromatogr. A 2009, 1216, 5030.
         | Crossref | GoogleScholarGoogle Scholar | 19439313PubMed |

[20]  R. I. J. Amos, P. R. Haddad, R. Szucs, J. W. Dolan, C. A. Pohl, TrAC Trends Analyt. Chem. 2018, 105, 352.
         | Crossref | GoogleScholarGoogle Scholar |

[21]  M. Talebi, S. H. Park, M. Taraji, Y. Wen, R. I. J. Amos, P. R. Haddad, R. A. Shellie, R. Szucs, C. A. Pohl, J. W. Dolan, LC GC 2016, 34, 550.

[22]  S. H. Park, M. De Pra, P. R. Haddad, S. Grosse, C. A. Pohl, F. Steiner, J. Chromatogr. A 2020, 1609, 460508.
         | Crossref | GoogleScholarGoogle Scholar | 31530383PubMed |

[23]  S. H. Park, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, C. A. Pohl, J. W. Dolan, J. Chromatogr. A 2017, 1520, 107.
         | Crossref | GoogleScholarGoogle Scholar | 28916393PubMed |

[24]  R. Todeschini, V. Consonni, H. Xiang, J. Holliday, M. Buscema, P. Willett, J. Chem. Inf. Model. 2012, 52, 2884.
         | Crossref | GoogleScholarGoogle Scholar | 23078167PubMed |

[25]  M. Taraji, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, Anal. Chem. 2017, 89, 1870.
         | Crossref | GoogleScholarGoogle Scholar | 28208251PubMed |

[26]  Y. Wen, M. Talebi, R. I. J. Amos, R. Szucs, J. W. Dolan, C. A. Pohl, P. R. Haddad, J. Chromatogr. A 2018, 1541, 1.
         | Crossref | GoogleScholarGoogle Scholar | 29454529PubMed |

[27]  E. Tyteca, M. Talebi, R. I. J. Amos, S. H. Park, M. Taraji, Y. Wen, R. Szucs, C. A. Pohl, J. W. Dolan, P. R. Haddad, J. Chromatogr. A 2017, 1486, 50.
         | Crossref | GoogleScholarGoogle Scholar | 27720174PubMed |

[28]  M. Taraji, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, J. Chromatogr. A 2017, 1507, 53.
         | Crossref | GoogleScholarGoogle Scholar | 28587779PubMed |

[29]  P. Hemström, K. Irgum, J. Sep. Sci. 2006, 29, 1784.
         | Crossref | GoogleScholarGoogle Scholar | 16970185PubMed |

[30]  B. Buszewski, S. Noga, Anal. Bioanal. Chem. 2012, 402, 231.
         | Crossref | GoogleScholarGoogle Scholar | 21879300PubMed |

[31]  Y. Guo, Analyst 2015, 140, 6452.
         | Crossref | GoogleScholarGoogle Scholar | 26221630PubMed |

[32]  M. Taraji, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, Anal. Chem. 2017, 89, 1870.
         | Crossref | GoogleScholarGoogle Scholar | 28208251PubMed |

[33]  B. Dejaegher, D. Mangelings, Y. Vander Heyden, J. Sep. Sci. 2008, 31, 1438.
         | Crossref | GoogleScholarGoogle Scholar | 18461571PubMed |

[34]  D. B. Hibbert, J. Chromatogr. B 2012, 910, 2.
         | Crossref | GoogleScholarGoogle Scholar |

[35]  G. Greco, T. Letzel, J. Chromatogr. Sci. 2013, 51, 684.
         | Crossref | GoogleScholarGoogle Scholar | 23492984PubMed |

[36]  R. E. Bruns, I. S. Scarminio, B. B. Neto, Statistical Design – Chemometrics 2006 (Elsevier: Amsterdam).

[37]  Y. Wen, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, P. R. Haddad, Anal. Chem. 2018, 90, 9434.
         | Crossref | GoogleScholarGoogle Scholar | 29952550PubMed |

[38]  I. Molnar, J. Chromatogr. A 2002, 965, 175.
         | Crossref | GoogleScholarGoogle Scholar | 12236524PubMed |

[39]  P. Wiczling, L. Kubik, R. Kaliszan, Anal. Chem. 2015, 87, 7241.
         | Crossref | GoogleScholarGoogle Scholar | 26096131PubMed |

[40]  MODDE ver. 10 Software for Design of Experiments: User’s Guide and Tutorial 2013 (MKS: Sweden).

[41]  M. Á. Herrador, A. G. Asuero, A. G. González, Chemom. Intell. Lab. Syst. 2005, 79, 115.
         | Crossref | GoogleScholarGoogle Scholar |

[42]  M. Taraji, Quantitative Structure–Retention Relationships for Rapid Method Development in Hydrophilic-Interaction Liquid Chromatography of Pharmaceutical Compounds 2017, Ph.D. thesis, University of Tasmania.

[43]  R. Parr, W. Yang, Density-Functional Theory of Atoms and Molecules 1989 (Oxford University Press: New York, NY).

[44]  W. Koch, M. C. Holthausen, A Chemist’s Guide to Density Functional Theory, 2nd edn 2001 (Wiley-VCH: Weinheim).

[45]  T. A. Halgren, R. B. Nachbar, J. Comput. Chem. 1996, 17, 587.

[46]  T. A. Halgren, J. Comput. Chem. 1996, 17, 553.
         | Crossref | GoogleScholarGoogle Scholar |

[47]  T. A. Halgren, J. Comput. Chem. 1996, 17, 520.
         | Crossref | GoogleScholarGoogle Scholar |

[48]  T. A. Halgren, J. Comput. Chem. 1996, 17, 490.
         | Crossref | GoogleScholarGoogle Scholar |

[49]  M. J. Vainio, M. S. Johnson, J. Chem. Inf. Model. 2007, 47, 2462.
         | Crossref | GoogleScholarGoogle Scholar | 17892278PubMed |

[50]  J. J. Stewart, J. Mol. Model. 2013, 19, 1.
         | Crossref | GoogleScholarGoogle Scholar | 23187683PubMed |

[51]  J. J. P. Stewart, MOPAC 2012 (Stewart Computational Chemistry: Colorado Springs, CO).

[52]  M. J. T. Frisch, G. W. Schlegel, H. B. Scuseria, G. E. Robb, M. A. Cheeseman, J. R. Scalmani, G. Barone, V. Mennucci, B. Petersson, G. A. Nakatsuji, H. Caricato, M. Li, X. Hratchian, H. P. Izmaylov, A. F. Bloino, J. Zheng, G. Sonnenberg, J. L. Hada, M. Ehara, M. Toyota, K. Fukuda, R. Hasegawa, J. Ishida, M. Nakajima, T. Honda, Y. Kitao, O. Nakai, H. Vreven, T. Montgomery, Jr, J. A. Peralta, J. E. Ogliaro, F. Bearpark, M. Heyd, J. J. Brothers, E. Kudin, K. N. Staroverov, V. N. Kobayashi, R. Normand, J. Raghavachari, K. Rendell, A. Burant, J. C. Iyengar, S. S. Tomasi, J. Cossi, M. Rega, N. Millam, J. M. Klene, M. Knox, J. E. Cross, J. B. Bakken, V. Adamo, C. Jaramillo, J. Gomperts, R. Stratmann, R. E. Yazyev, O. Austin, A. J. Cammi, R. Pomelli, C. Ochterski, J. W. Martin, R. L. Morokuma, K. Zakrzewski, V. G. Voth, G. A. Salvador, P. Dannenberg, J. J. Dapprich, S. Daniels, A. D. Farkas, O. Foresman, J. B. Ortiz, J. V. Cioslowski, D. J. Fox, Gaussian 09, Revision A.02 2009 (Gaussian, Inc.: Wallingford, CT).

[53]  A. D. Becke, J. Chem. Phys. 1993, 98, 1372.
         | Crossref | GoogleScholarGoogle Scholar |

[54]  A. D. Becke, Phys. Rev. A 1988, 38, 3098.
         | Crossref | GoogleScholarGoogle Scholar |

[55]  C. Lee, W. Yang, R. G. Parr, Phys. Rev. B Condens. Matter 1988, 37, 785.
         | Crossref | GoogleScholarGoogle Scholar | 9944570PubMed |

[56]  P. J. Stephens, F. J. Devlin, C. F. Chabalowski, M. J. Frisch, J. Phys. Chem. 1994, 98, 11623.
         | Crossref | GoogleScholarGoogle Scholar |

[57]  M. J. Frisch, J. A. Pople, J. S. Binkley, J. Chem. Phys. 1984, 80, 3265.
         | Crossref | GoogleScholarGoogle Scholar |

[58]  J. Tomasi, B. Mennucci, R. Cammi, Chem. Rev. 2005, 105, 2999.
         | Crossref | GoogleScholarGoogle Scholar | 16092826PubMed |

[59]  Dragon ver. 6.0 2015 (Talete srl: Milano, Italy).

[60]  R. Todeschini, V. Consonni, Handbook of Molecular Descriptors 2000 (Wiley-WCH: Weinheim).

[61]  J. Huang, X. Fan, Mol. Pharm. 2011, 8, 600.
         | Crossref | GoogleScholarGoogle Scholar | 21370915PubMed |

[62]  S. Wold, M. Sjöström, L. Eriksson, Chemom. Intell. Lab. Syst. 2001, 58, 109.
         | Crossref | GoogleScholarGoogle Scholar |

[63]  J. H. Holland, Adaptation in Natural and Artificial Systems 1975 (University of Michigan Press: Ann Arbor, MI).

[64]  R. Leardi, A. Lupiáñez González, Chemom. Intell. Lab. Syst. 1998, 41, 195.
         | Crossref | GoogleScholarGoogle Scholar |

[65]  M. Taraji, P. R. Haddad, R. I. J. Amos, M. Talebi, R. Szucs, J. W. Dolan, C. A. Pohl, J. Chromatogr. A 2017, 1524, 298.
         | Crossref | GoogleScholarGoogle Scholar | 29037590PubMed |

[66]  V. Consonni, D. Ballabio, R. Todeschini, J. Chemometr. 2010, 24, 194.
         | Crossref | GoogleScholarGoogle Scholar |