Evaluating the IPPSE method for PKPD analysis
Brigitte D. Lacroix(1,2), Lena E. Friberg(1) and Mats O. Karlsson(1)
(1)Department of Pharmaceutical Biosciences, Uppsala University, Sweden;(2)Pharmacometrics, Global Exploratory Development, UCB Pharma SA, Belgium
Background: To develop PKPD models based on previously determined individual PK parameter (IPP) estimates is a common alternative to the simultaneous (SIM) analysis of PK and PD data. In the IPP analysis, individual PK parameters are fixed, which is equivalent to assume that they are estimated without error. The IPPSE method is similar to the IPP method but takes into account that individual parameters are estimated with imprecision (SE).
Objectives: To compare the IPPSE with the IPP and SIM methods.
Methods: Data sets (n=200) with various study designs were simulated according to a one-compartment PK model and direct Emax PD model. The study design of each dataset (number of subjects, number and sampling times of PK and PD observations, and nominal population parameters) was randomly selected using Latin hypercube sampling as described by Zhang et al. [1].
The same PK and PD models were fitted in NONMEM 7 to the simulated observations using the SIM, IPP and IPPSE methods. The uncertainty around individual estimated parameters was provided as a default output by NONMEM 7.
We compared the performance of the 3 methods with respects to estimation precision and bias, computation time and NONMEM estimation status, as a function of the number of PK and PD observations, shrinkage, and degree of uncertainty in the individual (empirical Bayes) PK estimates.
Results: Estimates of bias and precision for IPP and SIM agreed with those of Zhang et al. [1]. Estimated precision and bias for the IPPSE method were similar to that of SIM, while IPP had higher bias and imprecision. Similar results were obtained when removing the variability in Emax in the PD model in order to reduce the over‑parameterization. Moreover, in comparison with the SIM method, nearly as much computational run time was saved with the IPPSE method (50 to 60% according to the PD model tested - full or reduced) as with the IPP method (70%).
Conclusions: The IPPSE method seems to be a promising alternative for PKPD analysis, combining the advantages of the SIM (higher precision and lower bias of parameter estimates) and the IPP (shorter run time) methods.
References:
[1] Zhang L., Beal S.L. and Sheiner L.B. Simultaneous vs. sequential analysis for population PK/PD data I: Best-case performance. JPKPD 30, 387-404 (2003).
