Pharmacodynamic parameters: Influence on dose -response relationship and estimation

Overall drug response is controlled by pharmacodynamic (PD) phase and pharmacokinetic (PK) phase. Over the last twenty years, much greater emphasis has been placed in PK phase because its outcome is much easier to measure and model compared to that of PD. In fact, PD and its parameters play an important role in controlling drug response. This document consists of three studies. The first study demonstrates through computer simulations using STELLA (High Performance System) the manner in which the main PD parameters influence the dose response relationship. A one compartment PK model linked to a sigmoid Emax model through an effect compartment was used. The results show that as the signioidicity constant increases the duration of effect gets shorter. This parameter also impacts the magnitude of the response where its effect depends on the drug concentration and its ratio to the concentration at 50% of the maximum effect (EC50). Also, it was found that as the EC50 increases, the response from a given concentration gets smaller and the duration of effect gets shorter. When an effect compartment is necessary to model drug action, the effect compartment characteristics become more prominent as ke0 decreases. Thus the delay in response gets larger, the magnitude of response from a given dose gets smaller and the duration of action gets longer as keo decreases. The second study was designed to investigate the effect of different sources of variability, dose, PK and PD parameters, on drug response through computer simulations using STELLA. The different sources of variability were studied separately and in combination using a one-compartment PK model linked to sigmoid Emax and linear PD models. The results show that in presence of similar amount of variability, the response is much more sensitive to variability in PD parameters than variability in PK parameters. It is concluded that variability in PD parameters are clinically important and must be taken into account in order to use the drug effectively and safely. The third study was designed to investigate the optimum sampling design for a PD modeling study through computer simulation using an inhibitory Sigmoid Emax model in NONMEM (Non-linear Mixed Effect Modeling). The bias and precision of parameter estimates were used to judge the performance of various studied designs. The effects of population size and level of inter-individual variability were further studied using the most optimum design. The experimental design for the determination of the equilibrium rate constant associated with an effect compartment was also studied. The most optimum design for determination of PD parameters in the absence of an effect compartment was found to be the one with the following sampling windows: 0.1–0.5, 0.5–1 and 1–2 EC50 units. However, in the presence of high inter-individual variability (60%) estimates of variability parameters, using the most optimum design, were biased and imprecise. More precise estimates of the parameters were obtained with a larger population. The most optimum design for the equilibrium rate constant was found to be the one in which two samples were taken per individual, but it gave poor estimates of the variability parameter