Correctly chosen d-optimal designs provide efficient experimental schemes when the aim of the investigation is to obtain precise estimates of parameters. In the current work, estimates of parameters refer to the enzyme kinetic parameters V(max) and K(m), but they also refer to the inhibition constant K(i). In general, this experimental approach is performed on a grid of values of the design variables. However, this approach may not be very efficient, in the sense that the parameter estimates (V(max), K(m), and K(i)) have unnecessarily high variances. For good estimates of parameters, the most efficient designs consist of clusters of replicates of a few sets of experimental conditions. The current study compares the application of such d-optimal designs with that of a conventional approach in assessing the competitive inhibitory potency of fluconazole and sertraline toward CYP2C9 and 2D6, respectively. In each instance, the parameter estimates, namely V(max), K(m), and K(i), were predicted well using the d-optimal design compared with those measured using the rich data sets, for both inhibitors. We show that d optimality can provide more efficient designs for estimating the model parameters, including K(i). We also show that real cost savings can be made by carefully planning studies that use the theory of optimal experimental design.