Case Study 3: Criticality of High-Quality Curve Fitting—“Getting a K m,app ” Isn’t as Simple as It May Seem

In this chapter, we illustrate the criticality of proper fitting of enzyme kinetic data. Simple techniques are provided to arrive at meaningful kinetic parameters, illustrated using an example, nonmonotonic data set. In the initial analysis of this data set, derived Km and Vmax parameters incorporated into PBPK models resulted in outcomes that did not adequately describe clinical data. This prompted a re-review of the in vitro data set and curve-fitting procedures. During this review, it was found that the 3-parameter model was fitted on data that was improperly unweighted. Reanalysis of the data using a weighted model returned a better fit and resulted in kinetic parameters better aligning with clinical data. Tools and techniques used to identify and compare kinetic models of this data set are provided, including various replots, visual inspection, examination of residuals, and the Akaike information criterion.