Physicochemical parameters, retention prediction

As the logarithm of 1-octanol-water partition coefficient (log P) describes the hydrophobicity of molecules and the retention of solutes in RP-HPLC depends on the hydrophobicity, a strong correlation can be expected between the log V value and the retention of solutes in RP-HPLC. Besides log P, a considerable number of physicochemical parameters have been tested for their capacity to predict retention in RP-HPLC. Thus, Snyder s polarity index, fraction of positively and negatively charged surface area, molecular bulkiness, nonpolar surface area, electron donor and acceptor capacity, various ster-ical parameters, and the energy of highest occupied molecular orbit have all been included in QSRR calculations. 

Much effort has been devoted to the development of reliable calculation methods for the prediction of the retention behaviour of analyses with well-known chemical structure and physicochemical parameters. Calculations can facilitate the rapid optimization of the separation process, reducing the number of preliminary experiments required for optimization. It has been earlier recognized that only one physicochemical parameter is not sufficient for the prediction of the retention of analyte in any RP-HPLC system. One of the most popular multivariate models for the calculation of the retention parameters of analyte is the linear solvation energy relationship (LSER) ... 

Quinones et al. (2000) reported the successful use of neural networks to predict the half-life of a series of 30 antihistamines. The input for the network was derived from the output of CODES, a routine that generates descriptors for a structure based on atom nature, bonding, and connectivity. Attempts to correlate the half-life with the physicochemical parameters log Kow, pKa, molecular weight, molar refractivity, molar volume, parachor, and polarity were unsuccessful. In a subsequent study by Quinones-Torrelo et al. (2001), the authors correlated the half-life of 18 antihistamines with their retention in a biopartitioning micellar chromatography system with a resultant correlation coefficient (R2adj) value of 0.89. The correlation is explained in that the retention in this system is dependent on hydrophobic, electronic, and steric properties, which are also important in determining half-life. 

Structure-based prediction software predicts retention times or important physicochemical processes based on chemical structures. Application databases store chromatographic methods for later retrieval and adaptation to new samples with similar structures and physicochemical parameters. 

A second approach is based on physicochemical parameters, used in ACD/LC Simulator. The prediction of RP retention times based on physicochemical parameters assumes that the primary retention mechanism is hydrophobicity of the compound as a function of its ionic form at a given pH. The general approach is given as... 

M. McBrien, E. Kolovanov, and R. Taylor, Highly accurate prediction of retention times in standard gradient chromatographic systems based on physicochemical parameters, March 9-14, 2003, Pittsburgh conference, Orlando, FL. 

Besides MS detection, identification of unknown peaks in GC routine analysis of environmental samples can be aided by the use of correlations between physicochemical parameters and structure of the analytes to predict the retention times. The correlation between the boiling points and the retention times of chloro- and bromo-benzenes and of some chloro- and nitro-substituted phenols was investigated for nonpolar capillary columns and allowed tentative identification of many compounds belonging to these analogous series. ... 

That is to say, prediction of retention in reversed-phase LC can be made, based on the premise that relationships exist between the physicochemical parameters representing the molecular properties of the solute suc as structure, shape and/or electronic states etc., and its retention, if suc parameters are available. The baisic concept is shown in equation-1 ... 

The accuracy of this system is dependent on the correlation coefficient of a retention description obtained from studies of QSRR, therefore, the selection of descriptors is the most basic and important task to construct RPS. This selection could be done with statistical framework, even if such description is not clearly derived from theories. The retention description obtained from QSRR studies is more effective for a rapid and accurate prediction of retention than that derived from theoretical models, because the former is simple and does not require introduction of a number of physicochemical parameters (they are often not clearly known and are very difficult and time-consuming to determine) for the latter case. By contrast, the consideration of physical meanings of descriptors derived from QSRR studies gave the overview of retention mechanisms in reversed-phase LC (7-10). That is to say, hydrophobicity, size and shape of alkyl-benzenes and PAHs are dominate factors controlling their retention. 

Physicochemical profiling has gained considerable importance in the last years as most companies realized that inappropriate physicochemical properties could lead to compound withdrawal later in development. The basic physicochemical parameters of interest for drugability prediction are solubility and permeability, the two components of the Biopharmaceutical Classification Scheme. However, these two fundamental parameters are in turn influenced by other physicochemical parameters worth considering, particularly in the lead optimization phase. For example, permeability is influeneed by lipophilicity (induces membrane retention) and pH (ioniz-able compounds), solubility is influenced by pH (ionizable compounds), and dissolution rate is linked to particle size, polymorphism, and wettability. 

Since the geometry and flow profile (parabolic) of FFF channels is well-defined, rather exact theoretical predictions of retention and plate height can often be made. The correlation of these experimental parameters with various physicochemical properties of component species (such as molecular weight, charge, and size) is therefore possible (11, 13). 

In spite of widespread applications, the exact mechanism of retention in reversed-phase chromatography is still controversial. Various theoretical models of retention for RPC were suggested, such as the model using the Hildebrand solubility parameter theory [32,51-53], or the model supported by the concept of molecular connectivity [54], models based on the solvophobic theory [55,56) or on the molecular statistical theory [57j. Unfortunately, sophisticated models introduce a number of physicochemical constants, which are often not known or are difficult and time-consuming to determine, so that such models are not very suitable for rapid prediction of retention data. 

Buydens, L., Massart, D.L. and Geerlings, P. (1983b). Prediction of Gas Chromatographic Retention Indexes with Topological, Physicochemical, and Quantum Chemical Parameters. Anal. Chem.,55,738-744. 

Buydens L, Geerlings P, Massart DL. Prediction of gas chromatographic retention indices with topological, physicochemical and quantum chemical parameters. Anal Chem 1983 55 738-744. 

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