Partial derivative—Based sensitivity analysis of models describing target-mediated drug disposition

Sensitivity analysis is commonly used to characterize the effects of parameter perturbations on model output. One use for the approach is the optimization of an experimental design enabling estimation of model parameters with improved accuracy. The primary objective of this study is to conduct a sensitivity analysis of selected target-mediated pharmacokinetic models, ascertain the effect of parameter variations on model predictions, and identify influential model parameters. One linear model (Model 1, control) and 2 target-mediated models (Models 2 and 3) were evaluated over a range of dose levels. Simulations were conducted with model parameters being perturbed at the higher and lower ends from literature mean values. Profiles of free plasma drug concentrations and their partial derivatives with respect to each parameter vs time were analyzed. Perturbations resulted in altered outputs, the extent of which reflected parmater influence. The model outputs were highly sensitive to perturbations of linear disposition parameters in all 3 models. The equilibrium dissociation constant (KD) was less influential in Model 2 but was influential in the terminal phase in Model 3, highlighting the role ofKD in this region. An equation for Model 3 in support of the result forKD was derived. Changes in the initial receptor concentration [Rtot(0)] paralleled the observed effects of initial plasma volume (Vc) perturbations, with increased influence at higher values. Model 3 was also sensitive to the rates of receptor degradation and internalization. These results suggest that informed sampling may be essential to accurately estimate influential parameters of target-mediated models

Simultaneous versus sequential optimal design for pharmacokinetic-pharmacodynamic models with FO and FOCE considerations

We consider nested multiple response models which are used extensively in the area of pharmacometrics. Given the conditional nature of such models, differences in predicted responses are a consequence of different assumptions about how the models interact. As such, sequential versus simultaneous and First Order (FO) versus First Order Conditional Estimation (FOCE) techniques have been explored in the literature where it was found that the sequential and FO approaches can produce biased results. It is therefore of interest to determine any design consequences between the various methods and approximations. As optimal design for nonlinear mixed effects models is dependent upon initial parameter estimates and an approximation to the expected Fisher information matrix, it is necessary to incorporate any influence of nonlinearity (or parameter-effects curvature) into our exploration. Hence, sequential versus simultaneous design with FO and FOCE considerations are compared under low, typical and high degrees of nonlinearity. Additionally, predicted standard errors of parameters are also compared to empirical estimates formed via a simulation/estimation study in NONMEM. Initially, design theory for nested multiple response models is developed and approaches mentioned above are investigated by considering a pharmacokinetic–pharmacodynamic model found in the literature. We consider design for situations where all responses are continuous and extend this methodology to the case where a response may be a discrete random variable. In particular, for a binary response pharmacodynamic model, it is conjectured that such responses will offer little information about all parameters and hence a sequential optimization, in the form of product design optimality, may yield near optimal designs