Estimation of Ki in a competitive enzyme-inhibition model: comparisons among three methods of data analysis (original) (raw)

Drug metabolism and disposition: the biological fate of chemicals, 1999

Abstract

There are a variety of methods available to calculate the inhibition constant (Ki) that characterizes substrate inhibition by a competitive inhibitor. Linearized versions of the Michaelis-Menten equation (e.g., Lineweaver-Burk, Dixon, etc.) are frequently used, but they often produce substantial errors in parameter estimation. This study was conducted to compare three methods of analysis for the estimation of Ki: simultaneous nonlinear regression (SNLR); nonsimultaneous, nonlinear regression, "KM,app" method; and the Dixon method. Metabolite formation rates were simulated for a competitive inhibition model with random error (corresponding to 10% coefficient of variation). These rates were generated for a control (i.e., no inhibitor) and five inhibitor concentrations with six substrate concentrations per inhibitor and control. The KM/Ki ratios ranged from less than 0.1 to greater than 600. A total of 3 data sets for each of three KM/Ki ratios were generated (i.e., 108 rates...

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