Publication detail

A Novel Computational Strategy for the pKa Estimation of Drugs by the Non-linear Regression of Multiwavelength Spectrophotometric pH-titration Data Exhibiting Small Changes in Spectra

Authors: Meloun Milan | Bordovská Sylva | Syrový Tomáš

Year: 2007

Type of publication: článek v odborném periodiku

Name of source: Journal of Physical Organic Chemistry

Publisher name: John Wiley & Sons Ltd.

Place: Chichester

Page from-to: 690-701

Titles:

Language	Name	Abstract	Keywords
cze	Nová počítačová strategie odhadu pKa léčiv nelineární regresí pH-absorbančních spekter, která vykazují velmi malé změny ve spektrech	Nová počítačová strategie odhadu pKa léčiv nelineární regresí pH-absorbančních spekter, která vykazují velmi malé změny ve spektrech. Změna absorbance dělená směrodatnou odchylkou absorbance představuje poměr signálu vůči chybě SER studovaného spektra. V prvním stupni je určen počet světlo-absorbujících částic a zbytková směrodatná odchylka absorbance užitého přístroje, která je pak užita k vyčíslení SER. Nelineární regresí programů SQUAD a SPECFIT32 je určen chemický model včetně disociačních konstant tří léčiv haemanthaminu pKa = 7.28(1) při I = 0.50, lisuridu pKa = 7.86(1) a losartanu pKa,1 = 3.60(1), pKa,2 = 4.73(1) at I = 0.01. Věrohodnost odhadů je posuzována na základě těsnosti regresního proložení vypočtených spekter experimentálními body. Predikční programy PALLAS a MARVIN našly dle strukturního vzorce hodnoty ve shodě s experimentálními.	Spektrofotometrická titrace, disociační konstanta, protonace, haemanthamin, lisurid, losartan, SPECFIT, SQUAD, INDICES, PALLAS, MARVIN
eng	A Novel Computational Strategy for the pKa Estimation of Drugs by the Non-linear Regression of Multiwavelength Spectrophotometric pH-titration Data Exhibiting Small Changes in Spectra	The new computational procedure for the protonation model building of a multiwavelength and multivariate spectra treatment is proposed for a special case of small changes in spectra. The absorbance change Δi for the ith spectrum divided with the instrumental standard deviation sinst(A) represents the signal-to-error ratio SER of the spectra studied. Determining 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 sinst(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 a temperature of 25oC 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 pKa was estimated by nonlinear regression of {pKa, I} data at 25oC: for haemanthamine pKa = 7.28(1) at I = 0.50, for lisuride pKa = 7.86(1) and for losartan pKa,1 = 3.60(1), pKa,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 pKa being based on the structural formulae of the drug compounds in agreement with the experimental value.	Spectrophotometric titration, dissociation constant, protonation, haemanthamine, lisuride, losartane, SPECFIT, SQUAD, INDICES, PALLAS, MARVIN

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