Dose and sample time optimization of drug candidate screening experiments
Joakim Nyberg, Erik Sjögren, Hans Lennernäs, Andrew Hooker
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
Objectives: The estimation of the metabolic stability, i.e. the metabolic clearance, is of importance to decide if a new molecular entity will be suitable as a new drug. The current "standard" method assumes a mono-exponential decay model of the clearance. This can be a good approximation for most drug candidates but some drugs have a non-linear elimination and therefore a Michaelis-Menten model is more suitable.
To increase the efficiency in various stages in drug development, optimal experimental design has been used . This approach has mostly been used to optimize sample times but it is also possible to optimize other design variables . Further, when optimizing more than one continuous design variable, the simultaneous optimization approach should be considered .
The aim of this exercise is to determine a more optimal dose and sampling scheme that could be used for estimating the metabolic clearance for drug candidates with linear or non-linear elimination.
Methods: An analytic solution to a one compartment PK model with nonlinear elimination was used . For the design to be optimal over numerous drug candidates, a modified ED-optimal design with penalty was performed in PopED v2 . Briefly, the ED parameters' prior was a multivariate nonparametric distribution of 76 Vmax and Km values collected from SIMCYP . The penalty function was formulated to normalize the influence of each set of parameter values in the prior on the optimal design. The design for a new drug candidate comprised 15 elementary designs (groups) with one sample and one dose for each elementary design. The samples were limited to 0-40 min and the doses were limited to 0-100uM. An upper and a lower LOQ for the concentrations where set to 0.1uM and 100uM respectively. A proportional residual variability was assumed fixed to 7.5%.
Results: Expected CV's for the modified ED-design with penalty gave, in all cases, at least a 60% improvement in expected model parameter CVs. If a standard ED-design was used the most informative parameter values tended to over influence the design, resulting in design deficiencies compared to the standard design for some types of drug candidates.
Conclusions: A method for improving the estimation of metabolic clearance for new drug candidates has been implemented. Further this method assumes a more accurate model. However, the choice of the penalty function is important to make the design robust for new candidates.
 Mentré, F., A. Mallet, and D. Baccar, Optimal design in random-effects regression models. Biometrika, 1997. 84(2): p. 429-442.
 Foracchia, M., et al., POPED, a software for optimal experiment design in population kinetics. Comput Methods Programs Biomed, 2004. 74(1): p. 29-46.
 Nyberg, J., M.O. Karlsson, and A. Hooker, Sequential versus simultaneous optimal experimental design on dose and sample times. PAGE 16 (2007) Abstr 1160 [http://www.page-meeting.org/?abstract=1160].
 Beal, S.L., Computation of the explicit solution to the Michaelis-Menten equation. J Pharmacokinet Biopharm, 1983. 11(6): p. 641-57.
 PopED, version 2.07 (2008) http://poped.sf.net/.
 Proctor, N.J., G.T. Tucker, and A. Rostami-Hodjegan, Predicting drug clearance from recombinantly expressed CYPs: intersystem extrapolation factors. Xenobiotica, 2004. 34(2): p. 151-78.