Assessment of dosing impact on intra-individual variability in estimation of parameters for basic indirect response models.

The application of D-optimization and the assessment of bias and precision of parameter estimates for four basic pharmacodynamic (PD) indirect response (IDR) models for ascending doses was examined using simulated data. While D-optimization provided four sampling times, each IDR model was used to generate eight data points per dose level. The PD parameters were: input rate constant (k (in)), disposition rate constant (k (out)), capacity constant (I (max) or S (max)), and sensitivity constant (IC (50) or SC (50)). A monoexponential pharmacokinetic function was applied with single doses increased by a factor of 10 to generate responses that vary from weak to fully saturable. For each dose, 100 replications of response data were simulated using independent normally distributed errors of CV = 20%. The original IDR model was fitted and PD parameters estimated. Histograms and descriptive statistics were generated. All parameters exhibited asymmetric distributions with positive coefficients of skewness except for I (max). Higher doses resulted in unbiased estimates of all PD parameters. The precision of parameter estimates improved with increasing doses except for IC (50) and SC (50) indicating that a single dose experimental design cannot be corrected by increasing dose in order to improve precision of estimates of IC (50) or SC (50). Highest variability was for IC (50) and SC (50) parameters. This study provides new insights into optimum study designs and recovery of parameters for basic IDR models.