D. Hovdal (1)
(1) Modelling & Simulation, iMed DMPK CVGI, AstraZeneca R&D Mölndal, Sweden
Objectives: To determine the in vivo potency of drug induced reduction in plasma thyroxine, T4, levels for three different drugs in rat and to explore how in vivo potency correlates with in vitro data.
Methods: A three day toxicological study with a two days washout period in rat was performed. Vehicle and three different compounds were tested at two or three dose levels. The study was designed to monitor the onset and extent of T4 reduction, as well as the return of T4 levels to baseline during washout. In all blood samples collected, the drug exposure and T4 levels were measured. A pharmacokinetic model was developed for each compound. Since all treatment groups share the systems parameters related to the turnover of T4, one PD model in form of a standard turnover model was applied to all T4 data. To identify the effects of the different compounds, the drug induced changes in T4 levels were driven by the individual pharmacokinetic profiles and unique in vivo potency of T4 reduction (IC50) was used for each compound. All analysis was performed using the NMLE module in Phoenix.
Results: The pharmacokinetics of the three drugs could be described by oral one or two compartment models with modified absorption. A drift in the baseline levels of T4 was observed and accounted for in the turnover model. The drug induced changes in T4 levels were successfully modeled by applying an Imax function on the production rate of T4. In contrast to consider one compound at a time, the simultaneous fit of the model to all T4 data allowed determination of the systems parameters of T4 turnover. As a result the potency of the different compound could be determined; despite administration of insufficient dose ranges to appropriately define the full inhibitory function of each compound. The derived in vivo potencies confirmed the ranking the compounds obtained by in vitro data.
Conclusions: Population PKPD modeling of preclinical data allows generation of more robust models (takes into account all available information) and allows conclusions to be drawn when the available information of each data set is insufficient for individual analysis.
Reference: PAGE 22 () Abstr 2889 [www.page-meeting.org/?abstract=2889]
Poster: Endocrine