Quantitative structure-mobility relationships

Jalali-Heravi, M., Shen, Y., Hassanisadi, M., and Khaledi, M. G. (2005). Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the Offord model and artificial neural networks. Electrophoresis 26,1874—1885. 

Timerbaev, A.R., Semenova, O.P. and Petrukhin, O. M. (2002) Migration behavior of metal complexes in capillary zone electrophoresis. Interpretation in terms of quantitative structure-mobility relationships. J. Chromat., 943, 263-274. 

Aleksic et al. [47] estimated the hydrophobicity of miconazole and other antimycotic drugs by a planar chromatographic method. The retention behavior of the drugs have been determined by TLC by using the binary mobile phases acetone-n-hexane, methanol toluene, and methyl ethyl ketone toluene containing different amounts of organic modifier. Hydrophobicity was established from the linear relationships between the solute RM values and the concentration of organic modifier. Calculated values of RMO and CO were considered for application in quantitative structure activity relationship studies of the antimycotics. 

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. 

The retention and the selectivity of separation in RP and NP chromatography depend primarily on the chemistry of the stationary phase and the mobile phase, which control the polarity of the separation systems. There is no generally accepted definition of polarity, but it is agreed that it includes various selective contributions of dipole-dipole, proton-donor, proton-acceptor, tt-tt electron, or electrostatic interactions. Linear Free-Energy Relationships (LFER) widely used to charactaize chemical and biochemical processes were successfiiUy apphed in liquid chromatography to describe quantitative structure-retention relationships (QSRR) and to characterize the stmctural contributions to the retention and selectivity, using multiple linear correlation, such as Eq. 

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... 

In summary, designing low melting, low viscosity ionic liquids is a challenging task because several molecular features contribute. Additionally some molecular features, like ion pairing, enhance the mobility of the ions only over a selected range before their influence show a reversed effect. Therefore, semiemperical and quantitative structure-property relationship (QSPR) approaches seem to be a good choice to estimate melting points or... 

The octanol-water partition coefficient scale may not be the best tool for hydrophobicity evaluation. The ability of MLC for hydrophobicity measurement and some studies on quantitative structure retention activity relationships (QSAR) are described in Chapter 9. Chapters 10 and 11 contain selected examples of applications in the analysis of a variety of samples, especially pharmaceutical preparations and physiological fluids, some of them are taken from the authors own experience. Details on the nature of the sample, stationary phase, mobile phase composition, detection wavelength, and figures of merit, are tabulated at the end of each of these... 

Eppler et al. [103] viewed these results as having a potential relationship to salt-activated enzyme preparations, particularly in relation to the mobility of enzyme-bound water. Specifically, the authors examined both water mobility [as measured by T2-derived correlation times, (tc)D20] and NaF-activated enzyme activity and observed a linear relationship. This suggests that the salt-activated enzymes contain a more mobile water population than salt-free enzymes, which facilitates a more aqueous-like local environment and dramatically increases enzyme activity through increased flexibility. Therefore, enzyme activation appears to correlate with the properties of enzyme-associated water. Once again, the physicochemical properties of water dictate enzyme structure, function, and dynamics. Hence, salt activation has proven to be a useful technique in activating enzymes for use in organic solvents and has provided a quantitative tool to better understand the role of water in enzymatic catalysis in dehydrated media. 

---