A novel computational strategy for the pKa estimation of drugs by non‐linear regression of multiwavelength spectrophotometric pH‐titration data exhibiting small spectral changes

A new computational procedure for the protonation model building of a multiwavelength and multivariate spectra treatment is proposed for the special case of small changes in spectra. The absorbance change Δ_i for the ith spectrum divided with the instrumental standard deviation s_inst(A) represents the signal‐to‐error ratio SER of the spectra studied. The determination of the number of chemical components in a mixture is the first important step for further quantitative analysis in all forms of spectral data treatment. Most index‐based methods of the factor analysis can always predict the correct number of components, and even the presence of a minor one, when the SER is higher than 10. The Wernimont–Kankare procedure in the program INDICES performs reliable determinations of the instrumental standard deviation of the spectrophotometer used s_inst(A), correctly predicts the number of light‐absorbing components present, and also solves ill‐defined problems with severe collinearity in spectra or very small changes in spectra. The mixed dissociation constants of three drugs, haemanthamine, lisuride, and losartan, including diprotic molecules at ionic strengths of I = 0.5 and 0.01 and at 25°C were determined using two different multiwavelength and multivariate treatments of the spectral data, SPECFIT32 and SQUAD(84) non‐linear regression analyses and INDICES factor analysis, even in the case of small absorbance changes in spectra. The dissociation constant pK_a was estimated by non‐linear regression of {pK_a, I} data at 25°C: for haemanthamine pK_a = 7.28(1) at I = 0.50, for lisuride pK_a = 7.86(1) and for losartan pK_a,1 = 3.60(1), pK_a,2 = 4.73(1) at I = 0.01. Goodness‐of‐fit tests for the various regression diagnostics enabled the reliability of the parameter estimates found to be proven. PALLAS and MARVIN predict pK_a being based on the structural formulae of the drug compounds in agreement with the experimental value. Copyright © 2007 John Wiley & Sons, Ltd.