Estimation of K(i) in a competitive enzyme-inhibition model: Comparisons among three methods of data analysis

There are a variety of methods available to calculate the inhibition constant (K(i)) 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 K(i): simultaneous nonlinear regression (SNLR); nonsimultaneous, nonlinear regression, 'K(M,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 K(M)/K(i) ratios ranged from less than 0.1 to greater than 600. A total of 3 data sets for each of three K(M)/K(i) ratios were generated (i.e., 108 rates/data set per K(M)/K(i) ratio). The mean inhibition and control data were fit simultaneously (SNLR method) using the full competitive enzyme-inhibition equation. In the K(M,app) method, the mean inhibition and control data were fit separately to the Michaelis-Menten equation. The SNLR approach was the most robust, fastest, and easiest to implement. The K(M,app) method gave good estimates of K(i) but was more time consuming. Both methods gave good recoveries of K(M) and V(MAX) values. The Dixon method gave widely ranging and inaccurate estimates of K(i). For reliable estimation of K(i) values, the SNLR method is preferred.