Quantitative structure-retention relationships , predicting

T. Baczek and R. Kaliszan, 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. 

A quantitative analysis of the structure-retention relationship can be derived by using the relative solubility of solutes in water. One parameter is the partition coefficient, log P, of the analyte measured as the octanol-water partition distribution. In early work, reversed-phase liquid chromatography was used to measure log P values for drug design. Log P values were later used to predict the retention times in reversed-phase liquid chromatography.The calculation of the molecular properties can be performed with the aid of computational chemical calculations. In this chapter, examples of these quantitative structure-retention relationships are described. 

Many attempts to correlate the analyte structure with its HPLC behavior have been made in the past [4-6], The Quantitative structure-retention relationships (QSRR) theory was introduced as a theoretical approach for the prediction of HPLC retention in combination with the Abraham and co-workers adaptation of the linear solvation energy relationship (LSER) theory to chromatographic retention [7,8],... 

Farkas, O., Heberger, K and Zenkevich, I.G. (2004) Quantitative structure-retention relationships XIV. Prediction of gas chromatographic retention indices for saturated 0-, N-, and S-heterocydic compounds. Chemom. Intell. Lab. Syst., 72, 173-184. 

Put, R., Perrin, C., Questier, E., Coomans, D., Massart, D.L. and Vander Heyden, Y. (2003) Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure—retention relationship studies. 

Applications. CART is not generally established yet, and as a consequence, not many applications for electrophoretic or similar data in the pharmaceutical held are found. Put et al. (52) apphed CART in a quantitative structure-retention relationship context on a retention data set of 83 structurally diverse drugs, in order to predict chromatographic retention. There were 266 molecular descriptors calculated and used as explanatory variables (X matrix). The considered response (y) was the retention factor of the compounds, predicted for a pure aqueous mobile phase. The total sum of squares of the response values about the mean of the node was applied as impurity measure. From all descriptors, three were selected to describe and predict the retention, and four terminal nodes were obtained (Fig. 13.11b). Arbitrarily, the drugs were then divided into hve retention classes. Each terminal node was then labeled with either one or two class names. The regression tree thus becomes a classihcation tree. From CV, it was concluded that only 9% serious misclassihcations were observed. 

A computer-assisted system for predicting retention of aromatic compounds has been investigated in reversed-phase liquid chromatography. The basic retention descriptions have been derived from the studies on quantitative structure-retention relationships. The system was constructed on a 16-bit microcomputer and then evaluated by comparing the retention data between measured and predicted values. The excellent agreement between both values were observed on an octadecyl-silioa stationsu y phase with acetonitrile and methanol aqueous mobile phase systems. This system has been modified to give us the information for optimal separation conditions in reversed-phase separation mode. The approach could also work well for any other reveraed— phase stationsury phases such as octyl, phenyl and ethyl silicas. 

N. E. Moustafa, Prediction of GC retention times of complex petroleum fractions based on quantitative structure-retention relationships, Chromatographia, 2008, 67, 85-91. 

Quantitative structure-retention relationships (QSRR) are helpful in elucidation retention mechanisms, for predicting retention indices and estimating some physicochemical properties. Gas chromatographic retention is a phenomenon that is mainly dependent on molecule-stationary phase interactions. Thus, each molecule, at least in theory, will exhibit unique retention characteristics based on its chemical, structural, and electronic properties. 

One of the primary application areas for SPR studies is in chromatography. Quantitative relationships between the molecular structures of solutes and their chromatographic retention have been extensively investigated. The field known as Quantitative Structure—Retention Relationships (QSRR) has resulted. Reasons for this interest include the desire to predict retention, investigations of the mechanism of interaaions between solute molecules and the stationary phase, and the attempt to focus on the physicochemical properties of the solute molecules that affect retention and why they have such an effect. Of the three main variables that affect chromatographic retention— solute structure, physicochemical properties of the mobile phase, and physicochemical properties of the stationary phase—the effeas of varying solute... 

This section again covers ab initio, density functional theory (DFT), semi-empirical and empirical calculations, molecular mechanics and molecular dynamics methods. Other interesting theoretical and computational chemistry techniques reported include quantitative structure-retention relationships (QSRR), and quantitative structure-property relationships (QSPR) for the prediction of various physicochemical properties. Again, all areas of theoretical and computational chemistry have continued to expand rapidly. These methods have been used to predict, support and also validate the majority of the observed experimental data. 

A major use of theoretical and computational methods is the prediction or validation of observed experimental data. New applications involving quantitative structure-property relationships (QSPR) and quantitative structure-retention relationships (QSRR) for the prediction of various physicochemical properties have also been reported. A series of allg ltributylphosphonium chloride ionic liquids has been synthesized and their experimental density and viscosity data interpreted using QSPRs and group contribution methods. A QSRR study has also been performed on a series of new phosphoramidic acid derivatives. Their retention factors Id ) were predicted using a model obtained from a set of previously-synthesized phosphoramidic acid derivatives. 

Katritzky, A.R., Ignatchenko, E.S., Barcock, R.A., Lobanov, V.S. and Karelson, M. (1994). Prediction of Gas Chromatographic Retention Times and Response Factors Using a General Quantitative Structure-Property Relationship Treatment. Anal.Chem.,66,1799-1807. 

In recent years, three-dimensional quantitative structure biological activity relationship methods known as comparative molecular field analysis (CoMFA) has been applied to construct a 3D-QSRR model for prediction of retention data. The CoMFA 3D-QSRR model is obtained by systematically sampling the steric and electrostatic fields surrounding a set of analyte molecules. Next, the differences in these fields are correlated to the corresponding differences in retention. The CoMFA model was successfully applied to HPLC retention data of polycyclic aromatic hydrocarbons [60]. 

Kaliszan, R., Noctor, T.A. and Wainer, I.W. (1992b). Quantitative Structure-Enantioselective Retention Relationships for the Chromatography of 1,4 Benzodiazepines on a Human Serum Albumin Based HPLC Chiral Stationary Phase An Approach to the Computational Prediction of Retention and Enantioselectivity. Chromatographia, 33,546-550. 

Kaliszan, R Noctor, TA. and Wainer, I.W. (1992) Quantitative structure-enantioselective retention relationships for the chromatography of 1,4-benzodiazepines on a human serum albumin based HPLC chiral stationary phase an approach to the computational prediction of retention and enantioselectivity. Chromatographia, 33, 546-550. 

The Q is put into QSAR by describing the structure of a compound in a quantitative way, the simplest examples of quantitative descriptors being the mass of the compound or the number of atoms present. When the compound is described using physical, as opposed to structural, properties the relationship becomes a PAR. Correlations of this type have been used in the perfumery industry to describe and predict the substantivity and retention of fragrance ingredients that is, the ability of a compound to stick to and remain bound to surfaces such as hair, skin or cloth (see Chapter 11 for more details). 

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