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通过多波长分光光度法pH滴定数据的最小二乘非线性回归测定四种非甾体抗炎药的热力学解离常数。

The thermodynamic dissociation constants of four non-steroidal anti-inflammatory drugs by the least-squares nonlinear regression of multiwavelength spectrophotometric pH-titration data.

作者信息

Meloun Milan, Bordovská Sylva, Galla Lubomír

机构信息

Department of Analytical Chemistry, University of Pardubice, CZ 532 10 Pardubice, Czech Republic.

出版信息

J Pharm Biomed Anal. 2007 Nov 30;45(4):552-64. doi: 10.1016/j.jpba.2007.07.029. Epub 2007 Aug 3.

Abstract

The mixed dissociation constants of four non-steroidal anti-inflammatory drugs (NSAIDs) ibuprofen, diclofenac sodium, flurbiprofen and ketoprofen at various ionic strengths I of range 0.003-0.155, and at temperatures of 25 degrees C and 37 degrees C, were determined with the use of two different multiwavelength and multivariate treatments of spectral data, SPECFIT/32 and SQUAD(84) nonlinear regression analyses and INDICES factor analysis. The factor analysis in the INDICES program predicts the correct number of components, and even the presence of minor ones, when the data quality is high and the instrumental error is known. The thermodynamic dissociation constant pK(a)(T) was estimated by nonlinear regression of (pK(a), I) data at 25 degrees C and 37 degrees C. Goodness-of-fit tests for various regression diagnostics enabled the reliability of the parameter estimates found to be proven. PALLAS, MARVIN, SPARC, ACD/pK(a) and Pharma Algorithms predict pK(a) being based on the structural formulae of drug compounds in agreement with the experimental value. The best agreement seems to be between the ACD/pK(a) program and experimentally found values and with SPARC. PALLAS and MARVIN predicted pK(a,pred) values with larger bias errors in comparison with the experimental value for all four drugs.

摘要

采用两种不同的多波长和多变量光谱数据处理方法,即SPECFIT/32和SQUAD(84)非线性回归分析以及INDICES因子分析,测定了四种非甾体抗炎药(NSAIDs)布洛芬、双氯芬酸钠、氟比洛芬和酮洛芬在离子强度I为0.003 - 0.155范围内、温度为25℃和37℃时的混合解离常数。当数据质量高且仪器误差已知时,INDICES程序中的因子分析能够预测正确的组分数,甚至能检测到次要组分的存在。通过对25℃和37℃下的(pK(a),I)数据进行非线性回归,估算了热力学解离常数pK(a)(T)。各种回归诊断的拟合优度检验证实了所得到的参数估计的可靠性。PALLAS、MARVIN、SPARC、ACD/pK(a)和Pharma Algorithms根据药物化合物的结构式预测pK(a),其结果与实验值相符。ACD/pK(a)程序与实验值以及SPARC之间的一致性似乎最佳。与所有四种药物的实验值相比,PALLAS和MARVIN预测的pK(a,pred)值存在较大的偏差误差。

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