Evaluating the performances of quantitative structure-retention relationship models with different sets of molecular descriptors and databases for high-performance liquid chromatography predictions

Wang, Chunlei; Skibic, Michael J.; Higgs, Richard E.; Watson, Ian; Bui, Hai H.; Wang, Jibo; Cintrón, José M.

doi:10.1016/j.chroma.2009.04.064

Cited by 24 publications

(7 citation statements)

References 52 publications

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“…PLS is a regression technique that works with two matrices, X (independent variables) and Y (dependent variables), and has two objectives to well approximate X and Y and to model the relationship between them . This methodology has advantages over MLR because it works with intercorrelated variables . The relation between the chemical descriptors and the dependent data is described as a linear model in the space described by the latent variables .…”

Section: Methodsmentioning

confidence: 99%

Synthesis and QSRR Study for a Series of Phosphoramidic Acid Derivatives

Petric

Crisan

Crişan

et al. 2013

Heteroatom Chemistry

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New phosphoramidic acid derivatives were synthesized by three different methods in the liquid-liquid and liquid-solid systems. The highest yields were obtained when the liquid-solid system was used. Moreover, for these compounds, the retention factor was predicted, using a robust model obtained for a set of phosphoramidic acid derivatives, which were previously synthesized in our laboratory. For these compounds, the retention factor (k ) was determined by means of the high performance liquid chromatography technique, using a Nucleosil C18 column as a stationary phase and methanol as a mobile phase. The stepwise multiple linear regression (MLR) and partial least square (PLS) methodology were used to investigate the correlation between the retention factor and a number of molecular descriptors for the mentioned compounds. In both procedures, a considerably large number of molecular descriptors have been used. The statistical qualities of MLR and PLS equations have been assessed on the basis of several parameters such as squares of the correlation coefficient (R 2 = 0.985), standard error of estimate (SEE = 0.149), Fischer test (F = 257.528), and the cumulative sum of squares of the correlation coefficient R 2 Y(CUM) = 0.984, the cu-Correspondence to: Luminita Crisan; mulative fraction of the total variation of the Y values that can be predicted for all the extracted principal components by cross-validation Q 2 (CUM) = 0.971, respectively, and by the Y-randomization test. The MLR and PLS equations can be useful for the estimation and comparison of the retention factor, for new synthesized phosphoramidic acid derivatives, using the selected molecular descriptors. C 2013 Wiley Periodicals, Inc. Heteroatom Chem 24:138-145, 2013; View this article online at wileyonlinelibrary.com.

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Section: Methodsmentioning

confidence: 99%

Synthesis and QSRR Study for a Series of Phosphoramidic Acid Derivatives

Petric

Crisan

Crişan

et al. 2013

Heteroatom Chemistry

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“…[14,15] However, owing to the frequently poor predictive ability of QSRRs, use is often made of the insertion of strategies to improve accuracy, such as molecular descriptor optimisers, feature selection tools, and training subset selectors. [16][17][18][19] Previous studies from our group have shown the very significant advantages gained by careful selection of the set of compounds used to train the QSRR. [7,8,[20][21][22][23] In particular, we have shown that filtering a database of compounds with known molecular descriptors and known retention times to identify a subset of the most relevant training compounds can greatly improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Taraji

Haddad

2021

Aust. J. Chem.

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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 structureÀretention 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 (t R ) 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 R 2 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.

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“…The linear solvation energy relationship (LSER) theory is most suitable for investigating these models because it allows one to compare various sorbents [2][3][4]. Many-parameter correlation models are currently widely used; they allow the prediction of the retention characteristics of the compounds synthesized for the first time under the conditions of HPLC [5][6][7][8][9][10][11][12][13][14][15][16]. The use of manyparameter correlation equations also allows the prediction of the physicochemical parameters (descriptors) of molecules and the potential biological activity of compounds [17].…”

Section: Introductionmentioning

confidence: 99%