Abstract
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 Vmax and Km, but they also refer to the inhibition constant Ki. 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 (Vmax, Km, and Ki) 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 Vmax, Km, and Ki, 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 Ki. We also show that real cost savings can be made by carefully planning studies that use the theory of optimal experimental design.
Footnotes
Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.
doi:10.1124/dmd.110.033142.
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ABBREVIATIONS:
- P450
- cytochrome P450
- DDI
- drug-drug interaction.
- Received March 11, 2010.
- Accepted April 16, 2010.
- Copyright © 2010 by The American Society for Pharmacology and Experimental Therapeutics
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