Linear solvation energy relationship model

Trone, M. D., Khaledi, M. G. Statistical evaluation of linear solvation energy relationship models used to characterize chemical selectivity in micellar electrokinetic chromatography. J. Chromatogr. A 2000, 886, 245-257. 

Kamlet-Taft Linear Solvation Energy Relationships. Most recent works on LSERs are based on a powerfiil predictive model, known as the Kamlet-Taft model (257), which has provided a framework for numerous studies into specific molecular thermodynamic properties of solvent—solute systems. This model is based on an equation having three conceptually expHcit terms (258). 

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) ... 

A. Wang and P.W. Carr, Comparative study of the linear solvation energy relationship, linear solvent strength theory, and typical conditions model for retention prediction in reversed-phase liquid chromatography. J. Chromatogr.A 965 (2002) 3-23. 

Canals, L, Portal, J. A., Roses, M., and Bosch, E., Retention of ionizable compounds on HPLC. Modeling retention for neutral and ionizable compounds by linear solvation energy relationships, Chromatographia, 56,431-437,2002. 

A linear solvation energy relationship (LSER) has been developed to predict the water-supercritical CO2 partition coefficients for a published collection of data. The independent variables in the model are empirically determined descriptors of the solute and solvent molecules. The LSER approach provides an average absolute relative deviation of 22% in the prediction of the water-supercritical CO2 partition coefficients for the six solutes considered. Results suggest that other types of equilibrium processes in supercritical fluids may be modeled using a LSER approach (Lagalante and Bruno, 1998). 

Let us now extend our molecular descriptor model introduced in Chapter 4 (Eqs. 4-26 and 4-27) to the aqueous activity coefficient. We should point out it is not our principal goal to derive an optimized tool for prediction of yw, but to develop further our understanding of how certain structural features determine a compound s partitioning behavior between aqueous and nonaqueous phases. Therefore, we will try to keep our model as simple as possible. For a more comprehensive treatment of this topic [i.e., of so-called linear solvation energy relationships (LSERs)] we refer to the literature (e.g., Kamlet et al., 1983 Abraham et al., 1990 Abraham, 1993 Abraham et al., 1994a and b Sherman et al., 1996). 

The so-called solvatochromic or linear solvation energy relationship (LSER) descriptors developed by Abraham and coworkers (Kamlet et al., 1983) have proved valuable in correlating a wide variety of biological endpoints and physicochemical properties, and two studies have utilized them to model BCF. Park and Lee (1993) found the following QSAR for the fish BCF values of a set of diverse chemicals ... 

Theoretical Linear Solvation Energy Relationship (TLSER) With the LSER descriptors of Kamlet and Taft in mind, Famini and Wilson developed QM-derived parameters to model terms in Eq. [18] and dubbed these the TLSER descriptors. Descriptor calculations are done with the MNDO Hamiltonian in MOPAC and AMP AC. MNDO has greater systematic errors than do AMI and PM3, but the errors tend to cancel out better in MNDO-derived correlation equations. A program called MADCAP was developed to facilitate descriptor calculation from MOPAC output files. 

Electric polarization, dipole moments and other related physical quantities, such as multipole moments and polarizabilities, constitute another group of both local and molecular descriptors, which can be defined either in terms of classical physics or quantum mechanics. They encode information about the charge distribution in molecules [Bbttcher et al, 1973]. They are particularly important in modelling solvation properties of compounds which depend on solute/solvent interactions and in fact are frequently used to represent the -> dipolarity/polarizability term in - linear solvation energy relationships. Moreover, they can be used to model the polar interactions which contribute to the determination of the -> lipophilicity of compounds. 

Improvements of the Pohtzer hydrophobic model were later proposed using additional quantum-chemical descriptors derived from the molecular electrostatic potential, dipole moment, and ionization energies. These descriptors were searched for to give the best estimations of the cavity term, polarity/dipolarizability term, and hydrogen-bond parameters defined in -> linear solvation energy relationships [Haeberlein and Brinck, 1997]. 

Once a decision of the chemical functionality or host structure is made and a sensing film is included in a sensor device, the next goal would be to model the sensor response of the film in the device. Sensor response to an analyte is a complex function of the partitioning of the target analytes based on the interactions within the film as well as the transport properties of the analyte in the sensor. The sensor responses for polymer-based sensors have been modeled by various approaches using (1) first principles techniques such as Hansen solubilities, (2) multivariate techniques such as QSAR to correlate sensor response with molecular descriptors, and (3) simulations and empirical formulations used to calculate the partition coefficient, such as linear solvation energy relationships, to provide a measure of selectivity and sensitivity of the material under consideration. 

There is no such clear-cut judgment about the statistical methods of modeling solubility. There are models as simple as the relationship between log Pand melting point (MP), established some time ago by Yalkowsky and coworkers, and the very complex linear solvation energy relationships (LSERs). The limitation of the simple Yalkowsky relationship is that it uses two variables, obtained with accuracy only by measurement, and thus the simple relationship turns out to be very complicated when calculated log P and MP are used. 

A modest data base for aqueous systems has beSen obtained by the use of these techniques. The data are reasonably reliable for systems with y values less then a couple thousand and not measured by the liquid-liquid chromatography technique. A reliable data base is required in the development of predictive techniques for y. Several predictive techniques are currently available the MOSCED (45) model has not yet been extended to aqueous systems. UNIFAC (46-48), which is really an outgrowth of ASOG (21,49) does include water, but with mixed results at best. Linear solvation energy relationships (LSER s) have been used to correlate ratios of y values for aqueous systems (50) and may be capable of some prediction. Nonetheless, a more extensive and accurate data base is what is really needed for correlation development... 

Solute-solvent interactions were largely studied and modeled by Linear Solvation Energy Relationships and the —> Hildebrand solubility parameter. 

AF Lagalante, TJ Bruno. Modeling the water-supercritical CO2 partition coefficients of organic solutes using a linear solvation energy relationship. J Phys Chem B 102 907-909. 

Since the sum of the forward and reverse rates = kf + k. ) determines the measured rate, as indicated in Eq. (1.1), whichever is the faster wiU dominate the process. With the exception noted above, k i j > lO s when kf and k are similar in magnitude and rises toward the relative diffusion limit as the imbalance between them increases that is, as Kj 1 or Kj 1 in Eq. (1.2) is approached. At such speeds, there is simply no hope of freezing the process, and worse, no way of isolating a minor tautomer, as on attempting isolation it would instantly be transformed into the major one. The classic way around this is to use the properties, for example, of model compounds, chosen that are as close electronically as possible to those of the minor tautomer. This is described in Chapter 12, along with certain pitfalls in their use which are often neglected. Another technique that can sometimes bypass the problem is to use linear solvation energy relationship (LSER) methods, which are described in some detail in Chapter 11. The reader is referred to both these chapters for further details. 

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