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

CPT: Pharmacometrics and Systems Pharmacology

Volumn 2, Issue 7, 2013, Pages

  Byon, W ^a   Smith, M K ^b   Chan, P ^b   Tortorici, M A ^a   Riley, S ^a   Dai, H ^a   Dong, J ^a   Ruiz Garcia, A ^a   Sweeney, K ^a   Cronenberger, C ^a  

^a PFIZER INC   (United States)

^b PFIZER GLOBAL RESEARCH AND DEVELOPMENT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLEARANCE; DECISION MAKING; HUMAN; LIMIT OF QUANTITATION; MODEL; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; WORKFLOW;

EID: 84881167230     PISSN: None     EISSN: 21638306     Source Type: Journal    
DOI: 10.1038/psp.2013.26     Document Type: Article

References (44)

1. Beal, S.L. & Sheiner, L.B. NONMEM User's Guides. NONMEM Project Group (University of California, San Francisco, 1992).
2. Perl-speaks-NONMEM. .
3. 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). (Pubitemid 29049052)
4. 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).
5. Bergstrand, M. & Karlsson, M.O. Handling data below the limit of quantification in mixed effect models. AAPS J. 11, 371-380 (2009).
6. Byon, W., Fletcher, C.V. & Brundage, R.C. Impact of censoring data below an arbitrary quantification limit on structural model misspecification. J. Pharmacokinet. Pharmacodyn. 35, 101-116 (2008). (Pubitemid 351420764)
7. Duval, V. & Karlsson, M.O. Impact of omission or replacement of data below the limit of quantification on parameter estimates in a two-compartment model. Pharm. Res. 19, 1835-1840 (2002). (Pubitemid 36016030)
8. Hing, J.P., Woolfrey, S.G., Greenslade, D. & Wright, P.M. Analysis of toxicokinetic data using NONMEM: impact of quantification limit and replacement strategies for censored data. J. Pharmacokinet. Pharmacodyn. 28, 465-479 (2001). (Pubitemid 33151425)
9. Beal, S.L. Ways to fit a PK model with some data below the quantification limit. J. Pharmacokinet. Pharmacodyn. 28, 481-504 (2001). (Pubitemid 33151426)
10. Bergstrand, M., Hooker, A.C. & Karlsson, M.O. Visual predictive checks for censored and categorical data. 2009, p 18, Abstr 1604.
11. Wade, J.R., Beal, S.L. & Sambol, N.C. Interaction between structural, statistical, and covariate models in population pharmacokinetic analysis. J. Pharmacokinet. Biopharm. 22, 165-177 (1994). (Pubitemid 24288140)
12. Savic, R.M. & Karlsson, M.O. Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions. AAPS J. 11, 558-569 (2009).
13. Gastonguay, M. A full model estimation approach for covariate effects: inference based on clinical importance and estimation precision. AAPS J... 6, Abstract W4354 (2004).
14. Mandema, J.W., Verotta, D. & Sheiner, L.B. Building population pharmacokinetic-pharmacodynamic models. I. Models for covariate effects. J. Pharmacokinet. Biopharm. 20, 511-528 (1992). (Pubitemid 23034413)
15. Jonsson, E.N. & Karlsson, M.O. Automated covariate model building within NONMEM. Pharm. Res. 15, 1463-1468 (1998). (Pubitemid 28427937)
16. Wahlby, U., Jonsson, E.N. & Karlsson, M.O. Comparison of stepwise covariate model building strategies in population pharmacokinetic- pharmacodynamic analysis. AAPS PharmSci. 4, E27 (2002).
17. Lindbom, L., Pihlgren, P. & Jonsson, E.N. PsN-Toolkit-A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput. Methods Programs Biomed. 79, 241-257 (2005). (Pubitemid 41196689)
18. U.S. Department of Health and Human Services, Food and Drug Administration. Guidance for industry. population pharmacokinetics. (1999).
19. European Medicines Agency. Committee for Medicinal Products for Human Use. Guideline on reporting the results of populationa pharmacokinetic analyses. .
20. Wahlby, U., Jonsson, E.N. & Karlsson, M.O. Assessment of actual significance levels for covariate effects in NONMEM. J. Pharmacokinet. Pharmacodyn. 28, 231-252 (2001). (Pubitemid 32643366)
21. Karlsson, M.O. & Savic, R.M. Diagnosing model diagnostics. Clin. Pharmacol. Ther. 82, 17-20 (2007). (Pubitemid 46944205)
22. Stram, D.O. & Lee, J.W. Variance components testing in the longitudinal mixed effects model. Biometrics 50, 1171-1177 (1994).
23. Efron, B. Bootstrap methods: another look at the jackknife. The Annal of Statistics 7, 1-26 (1979).
24. Efron, B. & Tibhirani, R.J. An Introduction to the Bootstrap. (Chapman and Hall/CRC London, UK, 1994).
25. Ette, E.I., Williams, P.J., Kim, Y.H., Lane, J.R., Liu, M.J. & Capparelli, E.V. Model appropriateness and population pharmacokinetic modeling. J. Clin. Pharmacol. 43, 610-623 (2003). (Pubitemid 36613120)
26. Holford, N. The visual predictive check-superiority to standard diagnostic (Rorschach) plots. 2005, p 14, Abstr 738.
27. Karlsson, M.O. & Holford, N. A tutorial on visual predictive checks. 2008, p 17, Abstr 1434.
28. Yano, Y., Beal, S.L. & Sheiner, L.B. Evaluating pharmacokinetic/ pharmacodynamic models using the posterior predictive check. J. Pharmacokinet. Pharmacodyn. 28, 171-192 (2001).
29. Bergstrand, M., Hooker, A.C., Wallin, J.E. & Karlsson, M.O. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 13, 143-151 (2011).
30. Menon-Andersen, D. et al. Essential pharmacokinetic information for drug dosage decisions: a concise visual presentation in the drug label. Clin. Pharmacol. Ther. 90, 471-474 (2011).
31. Sheiner, L.B. & Benet, L.Z. Premarketing observational studies of population pharmacokinetics of new drugs. Clin. Pharmacol. Ther. 38, 481-487 (1985). (Pubitemid 16193885)
32. Lalonde, R.L. et al. Model-based drug development. Clin. Pharmacol. Ther. 82, 21-32 (2007). (Pubitemid 46950619)
33. Lee, J.Y., et al. Impact of pharmacometric analyses on new drug approval and labelling decisions: a review of 198 submissions between 2000 and 2008. Clin. Pharmacokinet. 50, 627-635 (2011).
34. Kiang, T.K., Sherwin, C.M., Spigarelli, M.G. & Ensom, M.H. Fundamentals of population pharmacokinetic modelling: modelling and software. Clin. Pharmacokinet. 51, 515-525 (2012).
35. Bonate, P.L. et al. Guidelines for the quality control of population pharmacokineticpharmacodynamic analyses: an industry perspective. AAPS J. 14, 749-758 (2012).
36. Grasela, T. The application of systematic analysis for identifying and addressing the needs of the pharmacometric process, 2007, p 16, Abstr 1131. .
37. Mould, D.R. & Upton, R.N. Basic concepts in population modeling, simulation, and modelbased drug development. CPT: Pharmacomet. Syst. Pharmacol. 1, e6 (2012).
38. Karlsson, M.O., Jonsson, E.N., Wiltse, C.G. & Wade, J.R. Assumption testing in population pharmacokinetic models: illustrated with an analysis of moxonidine data from congestive heart failure patients. J. Pharmacokinet. Biopharm. 26, 207-246 (1998). (Pubitemid 28496599)
39. Sherwin, J.Y., et al. Fundamentals of population pharmacokinetic modelling: validation methods. Clin. Pharmacokinet. 51, 573-590 (2012).
40. Edholm, M. Modeling and simulation examples that failed to meet regulator's expectations. European Medicines Agency-European Federation of Pharmaceutical Industries and Associations Modelling and Simulation Workshop, London, UK,. November 2011.
41. Jönsson, S., Henningsson, A., Edholm, M., & Salmonson, T. Contribution of modeling and simulation studies in the regulatory review: a European regulatory perspective. In Clinical Trial Simulations: Applications and Trends. AAPS Series. Ch. 2, 15-36 (Springer, NY, 2011).
42. Romero, K., et al. Pharmacometrics as a discipline is entering the "industrialization" phase: standards, automation, knowledge sharing, and training are critical for future success. J. Clin. Pharmacol. 50, 9S-19S (2010).
43. Manolis, E., Rohou, S., Hemmings, R., Salmonson, T., Karlsson, M. & Milligan, P.A. The role of modeling and simulation in development and registration of medicinal products: output from the EFPIA/EMA Modeling and Simulation Workshop. CPT: Pharmacomet. Syst. Pharmacol. 2, e31 (2013).
44. Kowalski, K.G. & Hutmacher, M.M. Efficient screening of covariates in population models using Wald's approximation to the likelihood ratio test. J. Pharmacokinet. Pharmacodyn. 28, 253-275 (2001). (Pubitemid 32643367)