Mathematical representation of electrophoretic mobility of basic drugs in ternary solvent buffers in capillary zone electrophoresis

Theoretical and empirical approaches to express the mobility of small ions in capillary electrophoresis

A discussion is given about the concepts of the ion mobility, the analyte property which governs migration and thus separation selectivity in CE. It deals with small organic and inorganic ions, not with charged polymers or large particles like colloids. The discussion is directed to two main concepts. (i) The first is based on physico-chemistry of ion conductance in solution, and distinguishes three types of mobility. The absolute mobility is the limiting mobility at zero ionic strength; it depends on the solvent and the temperature. It is obtained by extrapolation of the actual mobilities, those of the fully charged particles at finite ion concentration. The observed reduction of the absolute mobility with ionic concentration is related to an ion cloud, and is formulated by the established theories of ion conductance. It explains the actual mobility as function of (beside other factors) the ionic strength, the viscosity and relative permittivity of the solvent, the temperature, the relaxation time of solvent polarisation and the distance of closest approach between ion and counterion. The effective mobility, finally, is the mobility when association and dissociation equilibria play a role. Most important are acid-base reactions, but complexation, ion pairing and homo- and heteroconjugation were considered as well. (ii) The second approach treats mobility data with different mathematical methods, and formulates their dependence on variables like solvent composition with appropriate algorithms. These empirical methods mainly include least squares and neural network-based methods. The least square methods ranges from the simplest model, which uses only the molecular weight of the analyte, to more complicated model requiring three-dimensional structural descriptors of the solutes. Neural networks have been applied to model the mobility using different input variables and various architectures. Work comparing the accuracy of least squares and neural network methods was discussed; the results showed that the neural network method leads to a more accurate mobility calculation. However, the least squares methods could give some information to the factors affecting the mobility of the analytes. The resulting methods allow the prediction of mobilities under different experimental conditions with certain accuracy. It has been shown that using such models, it is possible to predict mobility of analytes after training the models by a minimum number of data to speed up the method development stage.

Calculation of the Viscosity of Binary Liquids at Various Temperatures Using Jouyban-Acree Model

Applicability of the Jouyban-Acree model for calculating absolute viscosity of binary liquid mixtures with respect to temperature and mixture composition is proposed. The correlation ability of the model is evaluated by employing viscosity data of 143 various aqueous and non-aqueous liquid mixtures at various temperatures collected from the literature. The results show that the model is able to correlate the data with an overall percentage deviation (PD) of 1.9+/-2.5%. In order to test the prediction capability of the model, three experimental viscosities from the highest and lowest temperatures along with the viscosities of neat liquids at all temperatures have been employed to train the model, then the viscosity values at other mixture compositions and temperatures were predicted and the overall PD obtained is 2.6+/-4.0%.

Surface Tension Calculation of Mixed Solvents with Respect to Solvent Composition and Temperature by Using Jouyban-Acree Model

Applicability of a solution model, i.e. Jouyban-Acree model (JAM), for calculating surface tension of binary and ternary solvents at various temperatures has been shown employing experimental surface tension data collected from the literature. The accuracy of the model was evaluated by calculating average percentage deviation (APD) between calculated and observed values. The obtained overall APD (+/-S.D.) for JAM using binary solvent data were 4.06 (+/-4.27) and 8.07 (+/-9.78)%, respectively for correlative and predictive analyses. The corresponding values for the best similar model from the literature were 8.86 (+/-6.40) and 37.10 (+/-27.65)% and the mean APD differences between JAM and previously published model were significant (p<0.003). The capability of JAM for correlating surface tension of ternary solvents at various temperatures was also shown and the overall APD was 1.39 (+/-0.37)%.

Electrophoretic behavior of alprenolol in mixed solvent electrolyte systems

The electrophoretic mobilities of alprenolol have been determined in a mixed solvent background electrolyte system containing sodium acetate (40 mM)+acetic acid (40 mM) as buffering agent and different volume fractions of water, methanol and ethanol using capillary electrophoresis. The mobility of alprenolol has been used to test the prediction capability of a model trained by previously reported mobility data of five beta-blocker drugs at the same electrophoretic conditions. The average percentage mean deviations (APMD) between experimental and predicted values were used as an accuracy criterion. The APMD (+/-SD) obtained for alprenolol data in binary/ternary solvent electrolyte system employing the mobility values in mono-solvent buffers was 4.37 (+/-3.50)% and the corresponding value for an ab initio prediction method was 7.65 (+/-4.30)%.