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Basic Pharmacodynamic Models for Agents That Alter Production of Natural Cells

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Abstract

Basic indirect pharmacodynamic models for agents which alter the generation of natural cells based on a life-span concept are introduced. It is assumed that cells (R) are produced at a constant rate (k in ), survive for a specific duration T R , and then are lost. The rate of cell loss must equal the production rate but is delayed by T R , A therapeutic agent can stimulate or inhibit the production rate according to the Hill function: 1 ±H(C(t)) where H(C(t)) contains capacity (S max ) and sensitivity (SC 50 ) constants and C(t) is a pharmacokinetic function. Thus an operative model is dR/dt=k in · [1± H(C(t))]-kin ·[1 ± H(C(t-TR))] with the baseline condition R0 = k in · T R . One- and two-compartment catenary cell models were examined by simulation to describe the role of pharmacokinetics and cell properties. The area under the effect curve (AUCE) was derived. The models were applied to literature data to describe the stimulatory effects of single doses of hematopoietic growth factors such as granulocyte colony-stimulating factor (G-CSF) on neutrophils, thrombopoietin (TPO) on platelets, and erythropoietin (EPO) on reticulocytes in blood. The models described experimental data adequately and provided cell life-spans and SC50 values. The proposed cell production/loss models can be readily used to analyze the pharmacodynamics of agents which alter cell production yielding realistic physiological parameters.

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REFERENCES

  1. H. E. Wichmann and M. Loeffler. Mathematical Modeling of Cell Proliferation: Stem Cell Regulation in Hemopoiesis, Vol. 1, CRC Press, Boca Raton, FL, 1985.

    Google Scholar 

  2. M. Loeffler, K. Pantel, H. Wulff, and H. E. Wichmann. A mathematical model of erythropoiesis in mice and rats, Part 1: Structure of the model. Cell Tissue Kinet. 22:13-30 (1989).

    CAS  PubMed  Google Scholar 

  3. H. E. Wichmann, M. Loeffler, K. Pantel, and H. Wulff. A mathematical model of erythropoiesis in mice and rats, Part 2: Stimulated erythropoiesis. Cell Tissue Kinet. 22:31-49 (1989).

    CAS  PubMed  Google Scholar 

  4. H. Wulff, H. E. Wichmann, K. Pantel, and M. Loeffler. A mathematical model of erythropoiesis in mice and rats, Part 3: Suppressed erythropoiesis. Cell Tissue Kinet. 22:51-61 (1989).

    CAS  PubMed  Google Scholar 

  5. K. Pantel, and M. Loeffler, B. Bungart, and H. E. Wichmann. A mathematical model of erythropoiesis in mice and rats, Part 4: Differences between bone marrow and spleen. Cell Tissue Kinet. 23:283-297 (1990).

    CAS  PubMed  Google Scholar 

  6. S. Schmitz, H. Franke, J. Brusis, and H. E. Wichmann. Quantification of the cell kinetic effects of G-CSF using a model of human granulopoiesis. Exp. Hematol. 21:755-760 (1993).

    CAS  PubMed  Google Scholar 

  7. H. E. Wichmann, M. D. Gerhardts, H. Spechtmeyer, and R. Gross. Mathematical model of thrombopoiesis in rats. Cell Tissue Kinet. 12:551-567 (1979).

    CAS  PubMed  Google Scholar 

  8. L. B. Sheiner, D. R. Stanski, S. Vozeh, R. D. Miller, and J. Ham. Simultaneous modeling of pharmacokinetics and pharmacodynamics: Application to d-tubocurarine. Clin. Pharmacol. Ther. 25:358-371 (1979).

    CAS  PubMed  Google Scholar 

  9. N. L. Dayneka, V. Garg, and W. J. Jusko. Comparison of four basic models of indirect pharmacodynamic responses. J. Pharmacokin. Biopharm. 21:457-478 (1993).

    Article  CAS  Google Scholar 

  10. F. Bressolle, M. Audran, R. Gareau, T. N. Pham, and R. Gomeni. Comparison of direct and indirect population pharmacodynamic model: Application to recombinant human erythropoietin in athletes. J. Pharmacokin. Biopharm. 25:263-275 (1997).

    Article  CAS  Google Scholar 

  11. D. Z. D'Argenio and A. Shumitzky. ADAPT II User's Guide, Biomedical Simulations Resource, University of Southern California, Los Angeles, 1988.

    Google Scholar 

  12. G. D. Demetri and J. D. Griffin. Granulocyte colony-stimulating factor and its receptor. Blood 78:2791 (1991).

    CAS  PubMed  Google Scholar 

  13. N. Stute, V. M. Santana, J. H. Rodman, M. J. Schell, N. J. Ihle, and W. E. Evans. Pharmacokinetics of subcutaneous recombinant human granulocyte colony-stimulating factor. Blood 79:2849-2854 (1992).

    CAS  PubMed  Google Scholar 

  14. T. Kuwabara, S. Kobayashi, and Y. Sugiyama. Pharmacokinetics and pharmacodynamics of a recombinant human granulocyte colony-stimulating factor. Drug Metab. Rev. 28:625-658 (1996).

    Article  CAS  PubMed  Google Scholar 

  15. N. Hayashi, H. Kinoshita, E. Yukawa, and S. Higuchi. Pharmacokinetic and pharmacodynamic analysis of subcutaneous recombinant human granulocyte colony stimulating factor (lenograstim) administration. J. Clin. Pharmacol. 39:583-592 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. N. Kubota, T. Orita, K. Hattori, M. Oh-eda, N. Ochi, and T. Yamazaki. Structural characterization of natural and recombinant human granulocyte colony-stimulating factors. J. Biochem. 107:486-492 (1990).

    CAS  PubMed  Google Scholar 

  17. G. S. Chatta, T. H. Price, R. C. Allen, and D. C. Dale. Effects of in vivo recombinant methionyl human granulocyte colony-stimulating factor on the neutrophil response and peripheral blood colony-forming cells in healthy young and elderly adult volunteers. Blood 84:2923-2929 (1994).

    CAS  PubMed  Google Scholar 

  18. L. K. Roskos, E. N. Cheung, M. Vincent, M. A. Foote, and G. Morstyn. Pharmacology of filgrastim (r-metHuG-CSF), In G. Morstyn, T. M. Dexter, and M. A. Foote (eds.),-Filgrastim (r-metHug-CSF) in Clinical Practice, Marcel Dekker, New York, 1998.

    Google Scholar 

  19. K. Kaushansky. Thrombopoietin: The primary regulator of platelet production. Blood 86:419-431 (1995).

    CAS  PubMed  Google Scholar 

  20. M. Chang, Y. Suen, G. Meng, J. S. Buzby, J. Bussel, V. Shen, C. van de Ven, and M. S. Cairo. Differential mechanisms in the regulations of endogenous levels of thrombopoietin and interleukin-11 during thrombocytopenia; insights into the regulation of platelet production. Blood 89:2782-2788 (1997).

    Google Scholar 

  21. P. J. Fielder, A. L. Gurney, E. Stefaniach, M. Marian, M. W. Moore, K. Carver-Moore, and F. J. de Sauvage. Regulation of thrombopoietin levels by c-mpl-mediated binding to platelets. Blood 87:2154-2161 (1996).

    CAS  PubMed  Google Scholar 

  22. S. Vadhan-Raj, L. J. Murray, C. Bueso-Ramos, S. Patel, S. P. Reddy, W. K. Hoots, T. Johnston, N. E. Papadopolous, W. N. Hittelman, D. A. Johnston, T. A. Yang, V. E. Paton, R. L. Cohen, S. D. Hellmann, R. S. Benjamin, and H. E. Broxmeyer. Stimulation of megakaryocyte and platelet production by a single dose of recombinant human thrombopoietin in patients with cancer. Ann. Intern. Med. 126:673-681 (1997).

    Article  CAS  PubMed  Google Scholar 

  23. W. K. Cheung, B. L. Goon, M. C. Guilfoyle, and M. C. Wacholtz. Pharmacokinetics and pharmacodynamics of recombinant human erythropoietin after single and multiple subcutaneous doses to healthy subjects. Clin. Pharmacol. Ther. 64:412-423 (1998).

    Article  CAS  PubMed  Google Scholar 

  24. R. Berkow (ed.). The Merck Manual of Diagnosis and Therapy, 14th ed., Merck Sharp & Dohme Research Laboratories, Rahway, NJ, 1982.

    Google Scholar 

  25. A. Sharma and W. J. Jusko. Characterization of four basic models of indirect pharmacodynamic responses. J. Pharmacokin. Biopharm. 24:611-634 (1996).

    Article  CAS  Google Scholar 

  26. E. A. Murphy and M. E. Francis. The estimation of blood platelets survival: I. General principles of the study of cell survival. Thrombos. Diathes. Haemorrh. 22:281-295 (1969).

    CAS  Google Scholar 

  27. W. Krzyzanski and W. J. Jusko. Mathematical formalism for the properties of four basic models of indirect pharmacodynamic responses. J. Pharmacokin. Biopharm. 25:107-123 (1997).

    Article  CAS  Google Scholar 

  28. J. V. S. Gobburu and W. J. Jusko. Role of dosage regimen in controlling indirect pharmacodynamic responses, Adv. Drug Delivery Rev. 33:221-233 (1998).

    Article  CAS  Google Scholar 

  29. W. Krzyzanski and W. J. Jusko. Integrated functions for four basic models of indirect pharmacodynamic response. J. Pharm. Sci. 87:67-72 (1998).

    Article  CAS  PubMed  Google Scholar 

  30. W. Krzyzanski and W. J. Jusko. Application of moment analysis to the sigmoid effect model for drug administered intravenously. Pharm. Res. 14:949-952 (1997).

    Article  CAS  PubMed  Google Scholar 

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Krzyzanski, W., Ramakrishnan, R. & Jusko, W.J. Basic Pharmacodynamic Models for Agents That Alter Production of Natural Cells. J Pharmacokinet Pharmacodyn 27, 467–489 (1999). https://doi.org/10.1023/A:1023249813106

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