Vol. 293, Issue 3, 861-869, June 2000

Evaluation of a Minimal Experimental Design for Determination of Enzyme Kinetic Parameters and Inhibition Mechanism¹

Tarundeep Kakkar², Yvonne Pak and Michael Mayersohn

Department of Pharmacy Practice and Science and the Center for Toxicology, College of Pharmacy, The University of Arizona, Tucson, Arizona

The advent of combinatorial chemistry has led to a deluge of new chemical entities whose metabolic pathways need to be determined. A significant issue involves determination of the ability of new agents to inhibit the metabolism of existing drugs as well as its own susceptibility for altered metabolism. There is need to estimate the enzyme inhibition parameters and mechanism or mechanisms of inhibition with minimal experimental effort. We examined a minimal experimental design for obtaining reliable estimates of K_i (and V_max and K_m). Simulations have been applied to a variety of experimental scenarios. The least experimentally demanding case involved three substrate concentrations, [S], for the control and one substrate-inhibitor pair, [S]-[I]. The control and inhibitor data (with 20% coefficient of variance random error) were simultaneously fit to the full nonlinear competitive inhibition equation [simultaneous nonlinear regression (SNLR)]. Excellent estimates of the correct kinetic parameters were obtained. This approach is clearly limited by the a prior assumption of mechanism. Further simulations determined whether SNLR would permit assessment of the inhibition mechanism (competitive or noncompetitive). The minimal design examined three [S] (control) and three [S]-[I] pairs. This design was successful in identifying the correct model for 98 of 100 data sets (20% coefficient of variance random error). SNLR analysis of metabolite formation rate versus [S] permits a dramatic reduction in experimental effort while providing reliable estimates of K_i, K_m, and V_max along with an estimation of the mechanism of inhibition. The accuracy of the parameter estimates will be affected by the experimental variability of the system under investigation.