Table of Contents
SRX Pharmacology
Volume 2010, Article ID 481497, 10 pages
http://dx.doi.org/10.3814/2010/481497
Research Article

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

1Department of Analytical Chemistry, University of Pardubice, Pardubice 532 10, Czech Republic
2Chemistry Department, Faculty of Sciences, K. N. Toosi University of Technology (KNTU), 16167 Tehran, Iran

Received 19 October 2009; Revised 3 December 2009; Accepted 21 December 2009

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.

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 25C 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 25C 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.