Skip to main content
Log in

Basic pharmacodynamic models for agents that alter the lifespan distribution of natural cells

  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

A new class of basic indirect pharmacodynamic models for agents that alter the loss of natural cells based on a lifespan concept are presented. The lifespan indirect response (LIDR) models assume that cells (R) are produced at a constant rate (k in ), survive during a certain duration T R , and finally are lost. The rate of cell loss is equal to the production rate but is delayed by T R . A therapeutic agent can increase or decrease the baseline cell lifespan to a new cell lifespan, T D , by temporally changing the proportion of cells belonging to the two modes of the lifespan distribution. Therefore, the change of lifespan at time t is described according to the Hill function, H(C(t)), with capacity (E max ) and sensitivity (EC 50), and the pharmacokinetic function C(t). A one-compartment cell model was examined through simulations to describe the role of pharmacokinetics, pharmacodynamics and cell properties for the cases where the drug increases (T D  > T R ) or decreases (T D  < T R ) the cell lifespan. The area under the effect curve (AUCE) and explicit solutions of LIDR models for large doses were derived. The applicability of the model was further illustrated using the effects of recombinant human erythropoietin (rHuEPO) on reticulocytes. The cases of both stimulation of the proliferation of bone marrow progenitor cells and the increase of reticulocyte lifespans were used to describe mean data from healthy subjects who received single subcutaneous doses of rHuEPO ranging from 20 to 160 kIU. rHuEPO is about 4.5-fold less potent in increasing reticulocyte survival than in stimulating the precursor production. A maximum increase of 4.1 days in the mean reticulocyte lifespan was estimated and the effect duration on the lifespan distribution was dose dependent. LIDR models share similar properties with basic indirect response models describing drug stimulation or inhibition of the response loss rate with the exception of the presence of a lag time and a dose independent peak time. The current concept can be applied to describe the pharmacodynamic effects of agents affecting survival of hematopoietic cell populations yielding realistic physiological parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Dayneka NL, Garg V, Jusko WJ (1993) Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 21: 457–478

    Article  CAS  PubMed  Google Scholar 

  2. Mager DE, Wyska E, Jusko WJ (2003) Diversity of mechanism-based pharmacodynamic models. Drug Metabol Disp 31: 510–519

    Article  CAS  Google Scholar 

  3. Krzyzanski W, Ramakrishnan R, Jusko WJ (1993) Basic pharmacodynamic models for agents that alter production of natural cells. J Pharmacokinet Biopharm 21: 457–478

    Article  Google Scholar 

  4. Ramakrishnan R, Cheung WK, Wacholtz MC, Minton N, Jusko WJ (2004) Pharmacokinetic and pharmacodynamic modeling of recombinant human erythropoietin after single and multiple doses in healthy volunteers. J Clin Pharmacol 44: 991–1002

    Article  CAS  PubMed  Google Scholar 

  5. Krzyzanski W, Jusko WJ, Wacholtz MC, Minton N, Cheung WK (2005) Pharmacokinetic and pharmacodynamic modeling of recombinant human erythropoietin after multiple subcutaneous doses in healthy subjects. Eur J Pharm Sci 26: 295–306

    Article  CAS  PubMed  Google Scholar 

  6. Samtani MN, Perez-Ruixo JJ, Brown K, Cerneus D, Molloy C (2007) Pharmacokinetic and pharmacodynamic model of pegylated thrombopoietin mimetic peptide in healthy subjects: comparison of verification procedures for assessing model predictability. In: Abstracts of the XVI annual meeting of the population approach group in Europe. Abstr. 1220 [www.page-meeting.org/?abstract=1220]

  7. Lacombe C, Mayeux P (1998) Biology of erythropoietin. Haematologica 83: 724–732

    CAS  PubMed  Google Scholar 

  8. Polenakovic M, Sikole A (1996) Is erythropoietin a survival factor for red blood cells?. J Am Soc Nephrol 7: 1178–1182

    CAS  PubMed  Google Scholar 

  9. Trial J, Rice L, Alfrey CP (2001) Erythropoietin withdrawal alters interactions between young red blood cells, splenic endothelial cells, and macrophages: an in vitro model of neocytolysis. J Invest Med 49: 335–345

    Article  CAS  Google Scholar 

  10. Al-Huniti NH, Widness JA, Schmidt RL, Veng-Pedersen P (2005) Pharmacodynamic analysis of changes in reticulocyte subtype distribution in phlebotomy-induced stress erythropoiesis. J Pharmacokinet Pharmacodyn 32: 359–376

    Article  PubMed  Google Scholar 

  11. Krzyzanski W, Perez-Ruixo JJ (2007) An assessment of recombinant human erythropoietin effect on reticulocyte production rate and lifespan distribution in healthy subjects. Pharm Res 24: 758–771

    Article  CAS  PubMed  Google Scholar 

  12. Harker LA, Roskos LK, Marzec UM, Carter RA, Cherry JK, Sundell B, Cheung EN, Terry D, Sheridan W (2000) Effects of megakaryocyte growth and development factor on platelet production, platelet life span, and platelet function in healthy human volunteers. Blood 95: 2514–2522

    CAS  PubMed  Google Scholar 

  13. Roskos LK, Lum P, Lockbaum P, Schwab G, Yang B-B (2006) Pharmacokinetic/pharmacodynamic modeling of pegfilgrastim in healthy subjects. J Clin Pharmacol 46: 747–757

    Article  CAS  PubMed  Google Scholar 

  14. Krzyzanski W, Woo S, Jusko WJ (2006) Pharmacodynamic models for agents that alter production of natural cells with various distributions of lifespans. J Pharamacokinet Pharmacodyn 33: 125–166

    Article  CAS  Google Scholar 

  15. Freise KJ, Widness JA, Schmidt RL, Veng-Pedersen P (2007) Pharmacodynamic analysis of time-variant cellular disposition: reticulocyte disposition changes in phlebotomized sheep. J Pharamacokinet Pharmacodyn 34: 519–547

    Article  CAS  Google Scholar 

  16. D’Argenio DZ, Schumitzky A (1997) ADAPT II user’s guide. Biomedical Simulations Resource, Los Angeles

    Google Scholar 

  17. McKenzie SB (1996) Textbook of hematology. Williams & Wilkins, Baltimore, pp 101–102

    Google Scholar 

  18. Cheung WK, Goon BL, Guilfoyle MC, Wacholtz MC (1998) Pharmacokinetics and pharmacodynamics of recombinant human erythropoietin after single and multiple subcutaneous doses to healthy subjects. Clin Pharmacol Ther 64: 412–423

    Article  CAS  PubMed  Google Scholar 

  19. Major A, Bauer C, Breymanann C, Huch A, Huch R (1994) rh-Erythropoietin stimulates immature reticulocyte release in man. Br J Haematol 87: 605–608

    Article  CAS  PubMed  Google Scholar 

  20. Perez-Ruixo JJ, Kimko HC, Chow AT, Piotrovsky V, Krzyzanski W, Jusko WJ (2005) Population cell life span models for effects of drugs following indirect mechanisms of action. J Pharmacokinet Pharmacodyn 32: 767–793

    Article  PubMed  Google Scholar 

  21. Olsson-Gisleskog P, Jacqmin P, Perez-Ruixo JJ (2007) Population pharmacokinetics meta-analysis of recombinant human erythropoietin in healthy subjects. Clin Pharmacokinet 46: 159–173

    Article  CAS  PubMed  Google Scholar 

  22. Belair J, Mackey MC, Mahaffy JM (1995) Age-structured and two-delay models for erythropoiesis. Math Biosci 128: 317–346

    Article  CAS  PubMed  Google Scholar 

  23. Uehlinger DE, Gotch FA, Sheiner LB (1992) A pharmacodynamic model of erythropoietin therapy for uremic anemia. Clin Pharmacol Ther 51: 76–89

    CAS  PubMed  Google Scholar 

  24. Krzyzanski W, Jusko WJ (2002) Multiple-pool cell lifespan model of hematologic effects of anticancer agents. J Pharmacokinet Pharmacodyn 29: 311–337

    Article  CAS  PubMed  Google Scholar 

  25. Ramakrishnan R, Cheung WK, Farrell F, Kelley M, Jolliffe L, Jusko WJ (2003) Pharmacokinetic and pharmacodynamic modeling of recombinant human erythropoietin after intravenous and subcutaneous single dose administrations in cynomolgus monkeys. J Pharmacol Exp Ther 306: 324–331

    Article  CAS  PubMed  Google Scholar 

  26. Woo S, Krzyzanski W, Jusko WJ (2006) Pharmacokientic and pharmacodynamic modeling of recombinant human erythropoietin after intravenous and subcutaneous administration in rats. J Pharmacol Exp Ther 319: 1297–1306

    Article  CAS  PubMed  Google Scholar 

  27. Holford NHG (2005) PK/PD models for red blood cell responses to erythropoietic stimulation with and without chemotherapy and iron supplements. In: Symposium annual meeting, Orlando, FL, USA. American Society for Clinical Pharmacology and Therapeutics

  28. Gieschke P, Chanu P, Charoin J-E, Pannier A, Reigner B (2006) Modelling of haematological responses to erythropoiesis-stimulating agents in healthy volunteers and patients. In: Fifth international symposium on measurement and kinetics of in vivo drug effects, Noordwijkerhout, The Netherlands

  29. Chapel SH, Veng-Pedersen P, Schmidt RL, Widness JA (2000) A pharmacodynamic analysis of erythropoietin-stimulated reticulocyte response in phlebotomized sheep. J Pharmacol Exp Ther 295: 346–351

    CAS  PubMed  Google Scholar 

  30. Veng-Pedersen P, Chapel SH, Schmidt RL, Widness JA (2002) An integrated pharmacodynamic analysis of erythropoietin, reticulocyte, and hemoglobin responses in acute anemia. Pharm Res 19: 1630–1635

    Article  CAS  PubMed  Google Scholar 

  31. Friberg LE, Freijs A, Sandstrom M, Karlsson MO (2000) Semiphysiological model for the time course of leukocytes after varying schedules of 5-fluorouracil in rats. J Pharamcol Exp Ther 295: 734–740

    CAS  Google Scholar 

  32. Perez-Ruixo JJ, Krzyzanski W, Hing J (2008) Pharmacodynamic analysis of recombinant human erythropoietin effect on reticulocyte production rate and age distribution in healthy subjects. Clin Pharmacokinet 46: 399–415

    Article  Google Scholar 

  33. Sharma A, Jusko WJ (1996) Characterization of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 24: 611–634

    Article  CAS  PubMed  Google Scholar 

  34. Krzyzanski W, Matsushima JN, Matsushima JN, Jusko WJ (2006) Assessment of dosing impact on intra-individual variability in estimation of parameters for basic indirect response models. J Pharmacokinet Pharamcodyn 33: 635–655

    Article  Google Scholar 

  35. Wyska E, Mager DE, Krzyzanski W (2003) Methods of estimation IC50 and SC50 parameters for indirect response models from single dose data. J Pharm Sci 92: 1438–1454

    Article  CAS  PubMed  Google Scholar 

  36. Krzyzanski W, Jusko WJ (1997) Application of moment analysis to the sigmoid effect model for drug administered intravenously. Pharm Res 14: 949–952

    Article  CAS  PubMed  Google Scholar 

  37. Mandema JW (1995) Population pharmacokinetics and pharmacodynamics. In: Welling PG, Tse FLS (eds) Pharmacokinetics: regulatory, industrial academic perspectives. Marcel Dekker, New York, pp 411–450

    Google Scholar 

  38. Egan TD, Lemmens HJ, Fiset P, Hermann DJ, Muir KT, Stanski DR, Shafer SL (1993) The pharmacokinetics of the new short-acting opioid remifentanil (GI87084B) in healthy adult male volunteers. Anesthesiology 79: 881–892

    Article  CAS  PubMed  Google Scholar 

  39. Glader B (2004) Destruction of erythrocytes. In: Greer JP, Rodgers GM, Forster J, Paraskevans F, Lukens JN, Glader B (eds) Wintrobe’s clinical hematology. 11. Lippincot Williams and Wilkins, Philadelphia

    Google Scholar 

  40. Perez Ruixo JJ (2006) Optimizing the design of phase I studies of erythropoietin receptor agonist through mechanism-based PK/PD modeling and simulation. In: Abstracts of the annual meeting of the population approach group in Europe, p. 15, Abstr. 1024 [www.page-meeting.org/?abstract=1024]

  41. Perez-Ruixo JJ, De Ridder F, Kimko H, Samtani M, Cox E, Mohanty S, Vermeulen A (2007) Simulation in clinical drug development. In: Bertau M, Mosekilde E, Westerhoff H (eds) Biosimulation in drug development. Wiley-VCH, Weinheim, pp 1–24

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wojciech Krzyzanski.

Additional information

This study was supported by Johnson & Johnson Pharmaceutical Research & Development, A Division of Janssen Pharmaceutica, NV, Beerse, Belgium, and in part by the National Institute of General Medical Sciences, National Institutes of Health Grant GM 57980.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Krzyzanski, W., Perez-Ruixo, J.J. & Vermeulen, A. Basic pharmacodynamic models for agents that alter the lifespan distribution of natural cells. J Pharmacokinet Pharmacodyn 35, 349–377 (2008). https://doi.org/10.1007/s10928-008-9092-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-008-9092-6

Keywords

Navigation