The aim of this work was to explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of small peptides in capillary zone electrophoresis (CZE). The data set consists of electrophoretic mobilities, measured at pH 2.5, for 125 peptides ranging in size between 2 and 14 amino acids. Among the existing empirical models, the Offord model (i.e., mu identical with Q/M(2/3)) gave the best correlation for the data set. A quantitative structure-mobility relationship (QSMR) was developed using the Offord's charge-over-mass term (Q/M(2/3)) as one descriptor combined with the corrected steric substituent constant (E(s, c)) and molar refractivity (MR) descriptors to account for the steric effects and bulkiness of the amino acid side chains. The multilinear regression (MLR) of the data set showed an improvement in the predictive ability of the model over the simple Offord's relationship. A 3-4-1 back propagation artificial neural networks (BP-ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. The improved correlations by the BP-ANN analysis suggest the existence of nonlinear characteristic in the mobility-charge relationships.
Draize rabbit eye test scores, as modified maximum average score (MMAS), for 68 pure bulk liquids were adjusted by the liquid-saturated vapor pressure P. These 68 adjusted scores, as log (MMAS/P), were shown to be completely equivalent to eye irritation thresholds (EIT), expressed as log (1/EIT), for 23 compounds in humans. Thus, for the first time the Draize eye test in rabbits for pure bulk liquids is shown to be perfectly compatible with eye irritation thresholds in humans. The total data set for 91 compounds was analyzed by the general solvation equation of Abraham. Values of log (MMAS/P) or log (1/EIT) could be fitted to a five-parameter equation with R2 = 0.936, SD = 0.433, AD = 0.000, and AAD = 0.340 over a range of 9.6 log units. When divided into a training set of 45 compounds, the corresponding equation could be used to predict the remaining 46 compounds in a test set with AD = -0.037 and AAD = 0.345 log units. Thus, the 91-compound equation can now be used to predict further EIT values to around 0.4 log units. It is suggested that the mechanism of action in the Draize test and in the human EIT involves passive transfer of the compound to a biophase that is quite polar, is a strong hydrogen bond base, a moderate hydrogen bond acid, and quite hydrophobic. The biophase does not resemble water or plasma, but resembles an organic solvent such as N-methylformamide.
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