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Abstract

Population pharmacokinetics is the study of sources and correlates of variability in drug exposure and response. The study of population pharmacokinetics represents an important aspect of drug development and plays a key role in finding the right dose to inform product labeling decisions. Application of novel mathematical and statistical tools to the study of population pharmacokinetics has revolutionized the drug development process. Pharmacostatistical models composed on pharmacokinetic, pharmacodynamic, disease progression, trial design aspects, and econometrics are widely used in decision-making at every stage of drug development. Nonlinear mixed-effects modeling methodology enables the analysis of sparsely collected pharmacokinetic and pharmacodynamic data from large-scale late-stage clinical trials to understand drug exposure–response relationships. Regulatory authorities such as the US FDA and EMEA have supported and worked with pharmaceutical industry to bring about a successful culture of change in drug development, which has evolved into a concept called model-based drug development (MBDD). MBDD uses modeling and simulation to implement a “learn and confirm” paradigm. This chapter is intended to provide the reader with a basic understanding of the various methods involved in population pharmacokinetics with an emphasis on the current gold standard of nonlinear mixed-effects modeling methodology.

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References

  1. Aarons L (1991) Population pharmacokinetics: theory and practice. Br J Clin Pharmacol 32:669–670

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Ette EI, Williams PJ, Ahmad A (2007) In: Williams PJ, Ette EI (eds) Pharmacometrics: the science of quantitative pharmacology, John Wiley & Sons Inc, Hoboken, pp 265–285

    Google Scholar 

  3. Sheiner LB, Beal SL (1980) Evaluation of methods for estimating population pharmacokinetics parameters. I. Michaelis-Menten model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 8:553–571

    Article  CAS  PubMed  Google Scholar 

  4. Bonate PL (2011) Pharmacokinetic and pharmacodynamic modeling and simulation, 2nd edn. Springer Science+Business Media, LLC, New York, NY, USA

    Google Scholar 

  5. Giltinan MD, David M (1885) Nonlinear models for repeated measurement data (Chapman & Hall/CRC monographs on statistics & applied probability). Springer, New York, NY, USA

    Google Scholar 

  6. Sheiner LB (1997) Learning versus confirming in clinical drug development. Clin Pharmacol Ther 61:275–291

    Article  CAS  PubMed  Google Scholar 

  7. Barrett JS, Fossler MJ, Cadieu KD et al (2008) Pharmacometrics: a multidisciplinary field to facilitate critical thinking in drug development and translational research settings. J Clin Pharmacol 48:632–649

    Article  PubMed  Google Scholar 

  8. Lalonde RL, Kowalski KG, Hutmacher MM et al (2007) Model-based drug development. Clin Pharmacol Ther 82:21–32

    Article  CAS  PubMed  Google Scholar 

  9. Ross S (2014) A first course in probability, 9th edn. Pearson Education Limited, Harlow, England

    Google Scholar 

  10. Sheiner LB (1984) The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev 15:153–171

    Article  CAS  PubMed  Google Scholar 

  11. Sheiner LB, Beal SL (1981) Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data. J Pharmacokinet Biopharm 9:635–651

    Article  CAS  PubMed  Google Scholar 

  12. Sheiner LB, Beal SL (1983) Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 11:303–319

    Article  CAS  PubMed  Google Scholar 

  13. The US FDA (1999) www.fda.gov. [Online]. http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm064982.htm

  14. Mould D, Upton R (2012) Basic concepts in population modeling, simulation, and model-based drug development. CPT Pharmacometrics Syst Pharmacol 1:e6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Savic RM, Karlsson MO (2009) Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions. AAPS J 11:558–569

    Article  PubMed  PubMed Central  Google Scholar 

  16. Florian JA, Tornøe CW, Brundage R et al (2011) Population pharmacokinetic and concentration—QTc models for moxifloxacin: pooled analysis of 20 thorough QT studies. J Clin Pharmacol 51:1152–1162

    Article  CAS  PubMed  Google Scholar 

  17. Gibiansky L, Gibiansky E, Bauer R (2012) Comparison of Nonmem 7.2 estimation methods and parallel processing efficiency on a target-mediated drug disposition model. J Pharmacokinet Pharmacodyn 39:17–35

    Article  PubMed  Google Scholar 

  18. Beal SL, Sheiner LB (1992) NONMEM users guide- part VII. Conditional estimation methods. University of California, San Francisco

    Google Scholar 

  19. Bauer RJ (2013) NONMEM 7 technical guide. ICON Development Solutions Ellicott City, Maryland

    Google Scholar 

  20. Wang Y (2007) Derivation of various NONMEM estimation methods. J Pharmacokinet Pharmacodyn 34:575–593

    Article  PubMed  Google Scholar 

  21. Bertrand J, Céline M, Laffont CM et al (2011) Development of a complex parent-metabolite joint population pharmacokinetic model. AAPS J 13:390–404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Anderson BJ, McKee D, Holford NH (1997) Size, myths and the clinical pharmacokinetics of analgesia in paediatric patients. Clin Pharmacokinet 33:313–327

    Article  CAS  PubMed  Google Scholar 

  23. Holford NH (1996) A size standard for pharmacokinetics. Clin Pharmacokinet 30:329–332

    Article  CAS  PubMed  Google Scholar 

  24. Mandema JW, Verotta D, Sheiner LB (1992) Building population pharmacokinetic– pharmacodynamic models. I. Models for covariate effects. J Pharmacokinet Biopharm 20:511–528

    Article  CAS  PubMed  Google Scholar 

  25. Kowalski KG, Hutmacher MM (2001) Efficient screening of covariates in population models using Wald’s approximation to the likelihood ratio test. J Pharmacokinet Pharmacodyn 28:253–275

    Article  CAS  PubMed  Google Scholar 

  26. Wählby U, Jonsson EN, Karlsson MO (2002) Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis. AAPS PharmSci 4:E27

    Article  PubMed  Google Scholar 

  27. Gastonguay M (2004) A full model estimation approach for covariate effects: inference. AAPS meeting abstract W4354

    Google Scholar 

  28. Gastonguay M (2011) Full covariate models as an alternative to methods relying on statistical significance for inferences about covariate effects: a review of methodology and 42 case studies. PAGE Abstract

    Google Scholar 

  29. Lindbom L, Ribbing J, Jonsson EN (2004) Perl-speaks-NONMEM (PsN) – a Perl module for NONMEM related programming. Comput Methods Programs Biomed 75:85–94

    Article  PubMed  Google Scholar 

  30. Boeckmann AJ, Sheiner LB, Beal SL (2003) NONMEM user guide- part V, NONMEM Project Group, University of California, San Francisco.

    Google Scholar 

  31. Byon W, Smith MK, Chan P et al (2013) Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance. CPT Pharmacometrics Syst Pharmacol 2:e51

    Article  CAS  PubMed  Google Scholar 

  32. Frame B (2007) Mixture modeling with NONMEM V. Pharmacometrics: the science of quantitative pharmacology. Science+Business Media, LLC, New York, NY, USA. pp 723–757

    Google Scholar 

  33. Carlsson KC, Savic RM, Hooker AB et al (2009) Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making. AAPS J 11:148–154

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jonsson EN, Karlsson MO (1999) Xpose – an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed 58:51–64

    Article  CAS  PubMed  Google Scholar 

  35. R: a language and environment for statistical. R (2014) http://www.R-project.org/

  36. Hooker AC, Staatz CE, Karlsson MO (2007) Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method. Pharm Res 24:2187–2197

    Article  CAS  PubMed  Google Scholar 

  37. Comets E, Brendel K, Mentré F (2008) Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R. Comput Methods Programs Biomed 90:154–166

    Article  PubMed  Google Scholar 

  38. Jonsson NE, Hooker A. xpose.sourceforge.net/bestiarium_v1.0.pdf. xpose.sourceforge.net. [Online] xpose.sourceforge.net

    Google Scholar 

  39. Karlsson MO, Holford NH. (2008) A tutorial on visual predictive checks. PAGE 17 abstract: 1434.

    Google Scholar 

  40. Wang DD, Zhang S (2012) Standardized visual predictive check versus visual predictive check for model evaluation. J Clin Pharmacol 52:39–54

    Article  PubMed  Google Scholar 

  41. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO (2011) Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J 13:143–151

    Article  PubMed  PubMed Central  Google Scholar 

  42. Colby E, Bair E (2013) Cross-validation for nonlinear mixed effects models. J Pharmacokinet Pharmacodyn 40:243–252

    Article  PubMed  PubMed Central  Google Scholar 

  43. Feng Y, Pollock BG, Coley K et al (2008) Population pharmacokinetic analysis for risperidone using highly sparse sampling measurements from the CATIE study. Br J Clin Pharmacol 66:629–639

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Leysen JE, Gommeren W, Eens A et al (1988) Biochemical profile of risperidone, a new antipsychotic. J Pharmacol Exp Ther 247:661–670

    CAS  PubMed  Google Scholar 

  45. Fang J, Bourin M, Baker GB (1999) Metabolism of risperidone to 9-hydroxyrisperidone by human cytochromes P450 2D6 and 3A4. Naunyn Schmiedebergs Arch Pharmacol 359:147–151

    Article  CAS  PubMed  Google Scholar 

  46. Sherwin CM, Saldaña SN, Bies RR et al (2012) Population pharmacokinetic modeling of risperidone and 9-hydroxyrisperidone to estimate CYP2D6 subpopulations in children and adolescents. Ther Drug Monit 34:535–544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Thyssen A, Vermeulen A, Fuseau E et al (2010) Population pharmacokinetics of oral risperidone in children, adolescents and adults with psychiatric disorders. Clin Pharmacokinet 49:465–478

    Article  CAS  PubMed  Google Scholar 

  48. Ismail Z, Wessels AM, Uchida H et al (2012) Age and sex impact clozapine plasma concentrations in inpatients and outpatients with schizophrenia. Am J Geriatr Psychiatry 20:53–60

    Google Scholar 

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Acknowledgment

The author would like to express his sincere gratitude and thanks to Mr. Raj Thatavarthi and Mr. Karthik Lingineni at GVK Biosciences Pvt., Ltd., for helping in procuring literature and plots.

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Correspondence to Ayyappa Chaturvedula PhD .

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Chaturvedula, A. (2016). Population Pharmacokinetics. In: Jann, M., Penzak, S., Cohen, L. (eds) Applied Clinical Pharmacokinetics and Pharmacodynamics of Psychopharmacological Agents. Adis, Cham. https://doi.org/10.1007/978-3-319-27883-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-27883-4_4

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