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. 2013 Jul 3;2(7):e51.
doi: 10.1038/psp.2013.26.

Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance

Affiliations

Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance

W Byon et al. CPT Pharmacometrics Syst Pharmacol. .

Abstract

This tutorial describes the development of a population pharmacokinetic (Pop PK) analysis guidance within Pfizer, which strives for improved consistency and efficiency, and a more systematic approach to model building. General recommendations from the Pfizer internal guidance and a suggested workflow for Pop PK model building are discussed. A description is also provided for mechanisms by which conflicting opinions were captured and resolved across the organization to arrive at the final guidance.

CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e51; doi:10.1038/psp.2013.26; advance online publication 3 July 2013

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Conflict of interest statement

W.B., M.K.S., P.C., M.A.T., S.R., H.D., J.D., A.R.‐G., K.S., and C.C. are all employees of Pfizer and received salaries and benefits commensurate with employment.

Figures

Figure 1
Figure 1
Table of content for the guidance.
Figure 2
Figure 2
Population pharmacokinetic modeling workflow. CI, confidence interval; $COV, covariance step; CWRES, conditional weighted residuals; FME, full model estimation; GOF, goodness of fit; IIV, interindividual variance; IOV, interoccasional variance; SCM, stepwise covariate modeling; VPC, visual predictive check.
Figure 3
Figure 3
Visual predictive check for (a) nontechnical and (b) technical audiences. Pred Corr, prediction corrected. Blue circles represent observed data. Red lines represent the percentiles of the observed data: solid red lines represent the median; dashed red lines represent the upper and lower percentiles; i.e., 10% and 90%, respectively. The black lines represent the median, upper, and lower percentiles of the simulated data in each bin (aggregating across simulated trials). The shaded regions summarize the percentiles within each bin for each simulated trial. For each simulated trial, the median, lower, and upper percentiles are calculated for each bin, and then a percentile range of these percentiles is shown as the shaded region.

References

    1. Beal, S.L. & Sheiner, L.B. NONMEM User's Guides. NONMEM Project Group (University of California, San Francisco, 1992).
    1. Perl‐speaks‐NONMEM. <http://psn.sourceforge.net/>.
    1. Jonsson, E.N. & Karlsson, M.O. Xpose–an S‐PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput. Methods Programs Biomed. 58, 51–64 (1999). - PubMed
    1. Ahn, J.E. , Karlsson, M.O. , Dunne, A. & Ludden, T.M. Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J. Pharmacokinet. Pharmacodyn. 35, 401–421 (2008). - PubMed
    1. Bergstrand, M. & Karlsson, M.O. Handling data below the limit of quantification in mixed effect models. AAPS J. 11, 371–380 (2009). - PMC - PubMed