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Pharmacokinetic/Pharmacodynamic Modellingof GnRH Antagonist Degarelix: A Comparisonof the Non-linear Mixed-Effects Programs NONMEM and NLME

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In this paper, the two non-linear mixed-effects programs NONMEM and NLME were compared for their use in population pharmacokinetic/pharmacodynamic (PK/PD) modelling. We have described the first-order conditional estimation (FOCE) method as implemented in NONMEM and the alternating algorithm in NLME proposed by Lindstrom and Bates. The two programs were tested using clinical PK/PD data of a new gonadotropin-releasing hormone (GnRH) antagonist degarelix currently being developed for prostate cancer treatment. The pharmacokinetics of intravenous administered degarelix was analysed using a three compartment model while the pharmacodynamics was analysed using a turnover model with a pool compartment. The results indicated that the two algorithms produce consistent parameter estimates. The bias and precision of the two algorithms were further investigated using a parametric bootstrap procedure which showed that NONMEM produced more accurate results than NLME together with the nlmeODE package for this specific study.

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References

  1. L. Aarons M. O. Karlsson F. Mentre F. Rombout J. L. Steimer A. Peer Particlevan (2001) ArticleTitleRole of modelling and simulation in Phase I drug development Eur. J. Pharm. Sci 13 IssueID2 115–122 Occurrence Handle11297895

    PubMed  Google Scholar 

  2. L. Yuh S. Beal M. Davidian F. Harrison A. Hester K. Kowalski E. Vonesh R. Wolfinger (1994) ArticleTitlePopulation pharmacokinetic/pharmacodynamic methodology and applications: a bibliography Biometrics 50 IssueID2 566–575 Occurrence Handle8068854

    PubMed  Google Scholar 

  3. L. Aarons (1999) ArticleTitleSoftware for population pharmacokinetics and pharmacodynamics Clin. Pharmacokinet 36 IssueID4 255–264 Occurrence Handle10320948

    PubMed  Google Scholar 

  4. P. Maire X. Barbaut P. Girard A. Mallet R. W. Jelliffe T. Berod (1994) ArticleTitlePreliminary results of three methods for population pharmacokinetic analysis (NONMEM, NPML, NPEM) of amikacin in geriatric and general medicine patients Int. J. Biomed. Comput 36 IssueID1–2 139–141 Occurrence Handle7927854

    PubMed  Google Scholar 

  5. J. E. Bennett J. C. Wakefield (1996) ArticleTitleA comparison of a Bayesian population method with two methods as implemented in commercially available software J. Pharmacokinet. Biopharm 24 IssueID4 403–432 Occurrence Handle9044168

    PubMed  Google Scholar 

  6. D. J. Roe (1997) ArticleTitleComparison of population pharmacokinetic modeling methods using simulated data: results from the Population Modeling Workgroup Stat. Med 16 IssueID11 1241–1257 Occurrence Handle9194270

    PubMed  Google Scholar 

  7. C. E. Staatz S. E. Tett (2002) ArticleTitleComparison of two population pharmacokinetic programs, NONMEM and P-PHARM, for tacrolimus Eur. J. Clin. Pharmacol 58 IssueID9 597–605 Occurrence Handle12483452

    PubMed  Google Scholar 

  8. S. L. Beal L. B. Sheiner (1994) NONMEM User’s Guides NONMEM Project Group, University of California San Francisco

    Google Scholar 

  9. J. C. Pinheiro D. M. Bates (2000) Mixed-Effects Models in S and S-PLUS Springer-Verlag New York

    Google Scholar 

  10. C. W. Tornoe H. Agerso E. N. Jonsson H. Madsen H. A. Nielsen (2004) ArticleTitleNon-linear mixed-effects pharmacokinetic/pharmacodynamic modelling in NLME using differential equations Comput. Methods Programs Biomed 76 IssueID1 31–40 Occurrence Handle15313540

    PubMed  Google Scholar 

  11. R. W. Setzer. The odesolve Package version 0.5–8. Solvers for Ordinary Differential Equations. http://www.cran.r-project.org.

  12. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2004). http://www.R-project.org.

  13. M. J. Lindstrom D. M. Bates (1990) ArticleTitleNonlinear mixed effects models for repeated measures data Biometrics 46 IssueID3 673–687 Occurrence Handle2242409 Occurrence HandleMR1085815

    PubMed  MathSciNet  Google Scholar 

  14. A. Racine-Poon J. Wakefield (1998) ArticleTitleStatistical methods for population pharmacokinetic modelling Stat. Methods Med. Res 7 IssueID1 63–84 Occurrence Handle9533262

    PubMed  Google Scholar 

  15. L. Sheiner J. Wakefield (1999) ArticleTitlePopulation modelling in drug development Stat. Methods Med. Res 8 IssueID3 183–193 Occurrence Handle10636334

    PubMed  Google Scholar 

  16. S. L. Beal L. B. Sheiner (1982) ArticleTitleEstimating population kinetics Crit Rev. Biomed. Eng 8 IssueID3 195–222 Occurrence Handle6754254

    PubMed  Google Scholar 

  17. M. Davidian D. M. Giltinan (1995) Nonlinear Models for Repeated Measurement Data Chapman & Hall London

    Google Scholar 

  18. J. C. Pinheiro D. M. Bates (1995) ArticleTitleApproximations to the log likelihood function in the nonlinear mixed effects model J. Comput. Graph. Stat 4 IssueID1 12–35

    Google Scholar 

  19. R. Wolfinger (1993) ArticleTitleLaplace’s approximation for nonlinear mixed models Biometrika 80 791–795 Occurrence HandleMR1282787

    MathSciNet  Google Scholar 

  20. E. F. Vonesh (1996) ArticleTitleA note on the use of Laplace’s approximation for nonlinear mixed-effects models Biometrika 83 IssueID2 447–452

    Google Scholar 

  21. M. J. Lindstrom D. M. Bates (1988) ArticleTitleNewton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data J. Am. Stat. Assoc 83 IssueID404 1014–1022 Occurrence HandleMR997577

    MathSciNet  Google Scholar 

  22. G. Jiang J. Stalewski R. Galyean J. Dykert C. Schteingart P. Broqua A. Aebi M. L. Aubert G. Semple P. Robson K. Akinsanya R. Haigh P. Riviere J. Trojnar J. L. Junien J. E. Rivier (2001) ArticleTitleGnRH antagonists: a new generation of long acting analogues incorporating p-ureido-phenylalanines at positions 5 and 6 J. Med. Chem 44 IssueID3 453–467 Occurrence Handle11462984

    PubMed  Google Scholar 

  23. G. Pinieux Particlede M. E. Legrier F. Poirson-Bichat Y. Courty R. Bras-Goncalves A. M. Dutrillaux F. Nemati S. Oudard R. Lidereau P. Broqua J. L. Junien B. Dutrillaux M. F. Poupon (2001) ArticleTitleClinical and experimental progression of a new model of human prostate cancer and therapeutic approach Am. J. Pathol 159 IssueID2 753–764 Occurrence Handle11485933

    PubMed  Google Scholar 

  24. P. Broqua P. J. Riviere P. M. Conn J. E. Rivier M. L. Aubert J. L. Junien (2002) ArticleTitlePharmacological profile of a new, potent, and long-acting gonadotropin-releasing hormone antagonist: degarelix J. Pharmacol. Exp. Ther 301 IssueID1 95–102 Occurrence Handle11907162

    PubMed  Google Scholar 

  25. H. Agerso W. Koechling M. Knutsson R. Hjortkjaer M. O. Karlsson (2003) ArticleTitleThe dosing solution influence on the pharmacokinetics of degarelix, a new GnRH antagonist, after s.c. administration to beagle dogs Eur. J. Pharm. Sci 20 IssueID3 335–340 Occurrence Handle14592699

    PubMed  Google Scholar 

  26. C. W. Tornoe H. Agerso H. A. Nielsen H. Madsen E. N. Jonsson (2004) ArticleTitlePopulation Pharmacokinetic Modelling of a Subcutaneous Depot for GnRH Antagonist Degarelix Pharm. Res 21 IssueID4 574–584 Occurrence Handle15139513

    PubMed  Google Scholar 

  27. V. P. Shah K. K. Midha S. Dighe I. J. McGilveray J. P. Skelly A. Yacobi T. Layloff C. T. Viswanathan C. E. Cook R. D. McDowall (1991) ArticleTitleAnalytical methods validation: bioavailability, bioequivalence and pharmacokinetic studies. Conference report Eur. J. Drug Metab Pharmacokinet 16 IssueID4 249–255 Occurrence Handle1823867

    PubMed  Google Scholar 

  28. D. Klingmuller H. U. Schweikert (1992) ArticleTitleGonadotropin-releasing hormone: physiological and endocrinological aspects Recent Results Cancer Res 124 1–6

    Google Scholar 

  29. K. L. Parker and B. P. Schimmer. Pituitary Hormones and their Hypothalamic Releasing Factors. In Goodman & Gilman’s The Pharmacological Basis of Therapeutics. McGraw-Hill, London (2001).

  30. J. E. Griffin and J. D. Wilson. Disorders of the Testes and the Male Reproductive Tract. In Williams Textbook of Endocrinology. W. B. Saunders Company, London (1992).

  31. T. Cook W. P. Sheridan (2000) ArticleTitleDevelopment of GnRH antagonists for prostate cancer: new approaches to treatment The Oncologist 5 IssueID2 162–168 Occurrence Handle10794807

    PubMed  Google Scholar 

  32. A. J. Tilbrook I. J. Clarke (2001) ArticleTitleNegative feedback regulation of the secretion and actions of gonadotropin-releasing hormone in males Biol. Reprod 64 IssueID3 735–742 Occurrence Handle11207186

    PubMed  Google Scholar 

  33. L. Zhang S. L. Beal L. B. Sheiner (2003) ArticleTitleSimultaneous vs. Sequential Analysis for Population PK/PD Data I: Best-case Performance J. Pharmacokinet. Pharmacodyn 30 IssueID6 387–404 Occurrence Handle15000421

    PubMed  Google Scholar 

  34. E. N. Jonsson M. O. Karlsson (1999) ArticleTitleXpose–an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM Comput. Methods Programs Biomed 58 IssueID1 51–64 Occurrence Handle10195646

    PubMed  Google Scholar 

  35. A. Sharma W. F. Ebling W. J. Jusko (1998) ArticleTitlePrecursor-dependent indirect pharmacodynamic response model for tolerance and rebound phenomena J. Pharm. Sci 87 IssueID12 1577–1584 Occurrence Handle10189270

    PubMed  Google Scholar 

  36. M. Gardmark L. Brynne M. Hammarlund-Udenaes M. O. Karlsson (1999) ArticleTitleInterchangeability and predictive performance of empirical tolerance models Clin. Pharmacokinet 36 IssueID2 145–167 Occurrence Handle10092960

    PubMed  Google Scholar 

  37. B. Efron R. Tibshirani (1993) An Introduction to the Bootstrap Chapman & Hall London

    Google Scholar 

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Authors and Affiliations

  1. Experimental Medicine, Ferring Pharmaceuticals A/S, Kay Fiskers Plads 11, DK 2300, Copenhagen S, Denmark

    Christoffer W. Tornøe & Henrik Agersø

  2. Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark

    Henrik A. Nielsen & Henrik Madsen

  3. Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy, Uppsala University, Uppsala, Sweden

    E. Niclas Jonsson

Authors
  1. Christoffer W. Tornøe
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  2. Henrik Agersø
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  3. Henrik A. Nielsen
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  4. Henrik Madsen
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  5. E. Niclas Jonsson
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Correspondence to Christoffer W. Tornøe.

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Tornøe, C.W., Agersø, H., Nielsen, H.A. et al. Pharmacokinetic/Pharmacodynamic Modellingof GnRH Antagonist Degarelix: A Comparisonof the Non-linear Mixed-Effects Programs NONMEM and NLME. J Pharmacokinet Pharmacodyn 31, 441–461 (2004). https://doi.org/10.1007/s10928-005-5911-1

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