QSRR

Quantitative structure-chemical reactivity relationships (QSRR). Chemical reactivities involve the formation and/or cleavage of chemical bonds. Examples of chemical reactivity data are equilibrium constants, rate constants, polarographic half wave potentials and oxidation-reduction potentials. 

Mechanistic. QSRR and those QSAR which involve enzyme reactivity can provide information about the sensitivity of a reaction to electrical effects, its electronic demand, the composition of the electrical effect and the sensitivity to steric effects. QSAR which involve binding to receptor sites can provide information about the nature of the receptor site. Other QSAR can shed light on the bioactivity-determining step. 

The earliest successful parameterization of electrical effects is that of Hammett6-8. Burkhardt reported the existence of QSRR two years before Hammett but did not develop a general relationship9. Hammett defined the am and ap constants using the ionization constants (Kx) of 3- and 4-substituted benzoic acids in water at 25 °C as the reference set and hydrogen as the reference substituent (i.e. K ) to which all others are compared. For hydrogen the values of the am and ap constants were defined as zero. 

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. 

The linear solvent strength (LSS) model combined with QSRR calculations has been applied for the prediction of retention in gradient RP-HPLC. It was established that total dipole moment (jd), electron excess charge of the most negatively charged atom (<5Mm) and water-accessible molecular surface area (Awas) exert the highest impact on the retention ... 

Fornal et al. [75] determined selectivity differences for bases in RP-HPLC under high pH conditions. They used quantitative structure retention relationships (QSRR) to model retention behavior. They reported that the stability of the columns they used (Waters XTerra MS, Zorbax Extend, Thermo BetaBasic) was limited with... 

Kaliszan, R. (2007) QSRR quantitative structure-(chromatographic) retention relationships. Chemical Reviews, 107, 3212-3246. 

TTie structural features are represented by molecular descriptors, which are numeric quantities related directly to the molecular structure rather than physicochemical properties. Examples of such descriptors include molecular weight, molecular connectivity indexes, molecular complexity (degree of substitution), atom counts and valencies, charge, molecular polarizability, moments of inertia, and surface area and volume. Once a set of descriptors has been developed and tested to remove interdependent/collinear variables, a linear regression equation is developed to correlate these variables with the retention parameter of interest, e.g., retention index, retention volume, or partition coefficient The final equation includes only those descriptors that ate statistically significant and provide the best fit to the data. For more details on QSRR and the development and use of molecular descriptors, the reader is referred to the literature [188,195,198,200-202 and references therein]. 

The authors presumed that the estimate must have been zero when phosphoric acid was used as the IPR but they did not test their guess. A theoretical analysis of the meaning of this parameter (Equation 9 in Reference 1) confirms that the value cannot be zero. A phosphate ion was used as an IPR [18] and was demonstrated to be effective [11,19-23], Unfortunately, the estimates of this variable obtained for different IPRs are not in agreement with the theoretical values. If one keeps in mind that enough processing of large quantities of data with a sufficient number of fitting parameters may lead to any deduction [24], it can be concluded that the use of QSRRs in IPC is still in its infancy. 

T. Baczek, R. Kaliszan, K. Novotan, and R Jandera, Comarpative characteristics of HPLC Columns based on quantitative structure-retention relationships (QSRR) and hydrophobic-subtraction model, /. Chromatogr. A 1075 (2005), 109-115. 

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],... 

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