Recent Progress in the pKa Estimation of Druglike Molecules by the Nonlinear Regression of Multiwavelength Spectrophotometric pH-Titration Data

Abstract

Recent developments in the computational diagnostic tools for the pKa estimation of druglike molecules carried out by the nonlinear regression of multiwavelength spectrophotometric pH-titration data are demonstrated on the protonation equilibria of silybin. The factor analysis of spectra predict the correct number of components when the signal-to-error ratio SER is higher than 10. The mixed dissociation constants of the drug silybin at ionic strength I = 0.03 and a temperature of 25∘C were determined using two different programs, SPECFIT32 and SQUAD(84). A proposed experimental and computational strategy for the determination of the dissociation constants is presented. The dissociation constant pKa was estimated by nonlinear regression of the {pKa,I} data at 25∘C with SQUAD (and SPECFIT); that is, pKa1 = 6.898(0.022) and 6.897(0.002); pKa2 = 8.666(0.021) and 8.667(0.012); pKa3 = 9.611(0.010) and 9.611(0.004); pKa4 = 11.501(0.008) and 11.501(0.007). While great progress has been achieved in terms of the reliability of the protonation model estimation, among the most efficient diagnostics of the nonlinear regression of multiwavelength pH-spectra are the goodness-of-fit test, Cattel's scree plot of the factor analysis, spectra deconvolution, the signal-to-error SER ratio analysis, and other tools of efficient spectra analysis.

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Copyright © 2010 Milan Meloun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.