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Application of Hematological Toxicity Modeling in Clinical Development of Abexinostat (S-78454, PCI-24781), A New Histone Deacetylase Inhibitor

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Abstract

Purpose

A population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to describe the thrombocytopenia (dose-limiting toxicity) of abexinostat, a new histone deacetylase inhibitor. An optimal administration schedule of the drug was determined using a simulation-based approach.

Methods

Early PK and PK/PD data were analysed using a sequential population modeling approach (NONMEM 7), allowing for the description of a PK profile and platelet-count decrease after abexinostat administration with various administration schedules. Simulations of platelet count with several administration schedules over 3-week treatment cycles (ASC) and over a day (ASD) were computed to define the optimal schedule that limits the depth of thrombocytopenia.

Results

An intermediate PK/PD model accurately described the data. The administration of abexinostat during the first 4 days of each week in a 3-week cycle resulted in fewer adverse events (with no influence of ASD on platelet count profiles), and corresponded to the optimal treatment schedule. This administration schedule was clinically evaluated in a phase I clinical trial and allowed for the definition of a new maximum tolerated dose (MTD), leading to a nearly 30% higher dose-intensity than that of another previously tested schedule. Lastly, a final model was built using all of the available data.

Conclusions

The final model, characterizing the dose-effect and the dose-toxicity relationships, provides a useful modeling tool for clinical drug development.

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Abbreviations

ASC:

Administration schedule over a cycle (3-week treatment)

ASD:

Administration schedule over a day (e.g. once a day dosing, bid dosing, et cetera)

BASE:

Baseline platelet count (x10^9/L)

CIRC:

Compartment of circulating cells

EBE:

Empirical Bayesian Estimates

HDACi:

Histone deacetylase inhibitor

kel :

Constant rate of elimination

kprol :

Constant rate of proliferation

ktr :

Constant rate between transit compartments

MTT:

Maturation time from PROL to CIRC (h)

NPDE:

Normalized prediction distribution errors

PK/PD:

Pharmacokinetic/Pharmacodynamic

PROL:

Compartment of proliferative cells

SLOPE:

Coefficient of drug decrease (μg/mL)−1

TRAN:

Transit compartment

VPC:

Visual predictive check

γ:

Power factor for the feedback mechanism

References

  1. Batty N, Malouf GG, Issa JP. Histone deacetylase inhibitors as anti-neoplastic agents. Cancer Lett. 2009;280(2):192–200.

    Article  PubMed  CAS  Google Scholar 

  2. Marks P, Rifkind RA, Richon VM, Breslow R, Miller T, Kelly WK. Histone deacetylases and cancer: causes and therapies. Nat Rev Cancer. 2001;1(3):194–202.

    Article  PubMed  CAS  Google Scholar 

  3. Marks PA, Richon VM, Breslow R, Rifkind RA. Histone deacetylase inhibitors as new cancer drugs. Curr Opin Oncol. 2001;13(6):477–83.

    Article  PubMed  CAS  Google Scholar 

  4. Marks PA, Xu WS. Histone deacetylase inhibitors: potential in cancer therapy. J Cell Biochem. 2009;107(4):600–8.

    Article  PubMed  CAS  Google Scholar 

  5. Finnin MS, Donigian JR, Cohen A, Richon VM, Rifkind RA, Marks PA, et al. Structures of a histone deacetylase homologue bound to the TSA and SAHA inhibitors. Nature. 1999;401(6749):188–93.

    Article  PubMed  CAS  Google Scholar 

  6. Gui CY, Ngo L, Xu WS, Richon VM, Marks PA. Histone deacetylase (HDAC) inhibitor activation of p21WAF1 involves changes in promoter-associated proteins, including HDAC1. Proc Natl Acad Sci U S A. 2004;101(5):1241–6.

    Article  PubMed  CAS  Google Scholar 

  7. Stimson L, Wood V, Khan O, Fotheringham S, La Thangue NB. HDAC inhibitor-based therapies and haematological malignancy. Ann Oncol. 2009;20(8):1293–302.

    Article  PubMed  CAS  Google Scholar 

  8. Bishton MJ, Harrison SJ, Martin BP, McLaughlin N, James C, Josefsson EC, et al. Deciphering the molecular and biologic processes that mediate histone deacetylase inhibitor-induced thrombocytopenia. Blood. 2011;117(13):3658–68.

    Article  PubMed  CAS  Google Scholar 

  9. Giver CR, Jaye DL, Waller EK, Kaufman JL, Lonial S. Rapid recovery from panobinostat (LBH589)-induced thrombocytopenia in mice involves a rebound effect of bone marrow megakaryocytes. Leukemia. 2011;25(2):362–5.

    Article  PubMed  CAS  Google Scholar 

  10. Prince HM, Bishton MJ, Harrison SJ. Clinical studies of histone deacetylase inhibitors. Clin Cancer Res. 2009;15(12):3958–69.

    Article  PubMed  CAS  Google Scholar 

  11. Rubin EH, Agrawal NG, Friedman EJ, Scott P, Mazina KE, Sun L, et al. A study to determine the effects of food and multiple dosing on the pharmacokinetics of vorinostat given orally to patients with advanced cancer. Clin Cancer Res. 2006;12(23):7039–45.

    Article  PubMed  CAS  Google Scholar 

  12. Matsuoka H, Unami A, Fujimura T, Noto T, Takata Y, Yoshizawa K, et al. Mechanisms of HDAC inhibitor-induced thrombocytopenia. Eur J Pharmacol. 2007;571(2–3):88–96.

    Article  PubMed  CAS  Google Scholar 

  13. Horstmann E, McCabe MS, Grochow L, Yamamoto S, Rubinstein L, Budd T, et al. Risks and benefits of phase 1 oncology trials, 1991 through 2002. N Engl J Med. 2005;352(9):895–904.

    Article  PubMed  CAS  Google Scholar 

  14. Simon R, Freidlin B, Rubinstein L, Arbuck SG, Collins J, Christian MC. Accelerated titration designs for phase I clinical trials in oncology. J Natl Cancer Inst. 1997;89(15):1138–47.

    Article  PubMed  CAS  Google Scholar 

  15. Friberg LE, Freijs A, Sandstrom M, Karlsson MO. Semiphysiological model for the time course of leukocytes after varying schedules of 5-fluorouracil in rats. J Pharmacol Exp Ther. 2000;295(2):734–40.

    PubMed  CAS  Google Scholar 

  16. Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol. 2002;20(24):4713–21.

    Article  PubMed  Google Scholar 

  17. Panetta JC, Schaiquevich P, Santana VM, Stewart CF. Using pharmacokinetic and pharmacodynamic modeling and simulation to evaluate importance of schedule in topotecan therapy for pediatric neuroblastoma. Clin Cancer Res. 2008;14(1):318–25.

    Article  PubMed  CAS  Google Scholar 

  18. Schmitt A, Gladieff L, Laffont CM, Evrard A, Boyer JC, Lansiaux A, et al. Factors for hematopoietic toxicity of carboplatin: refining the targeting of carboplatin systemic exposure. J Clin Oncol. 2010;28(30):4568–74.

    Article  PubMed  CAS  Google Scholar 

  19. Soto E, Staab A, Tillmann C, Trommeshauser D, Fritsch H, Munzert G, et al. Semi-mechanistic population pharmacokinetic/pharmacodynamic model for neutropenia following therapy with the Plk-1 inhibitor BI 2536 and its application in clinical development. Cancer Chemother Pharmacol. 2010;66(4):785–95.

    Article  PubMed  CAS  Google Scholar 

  20. Zandvliet AS, Schellens JH, Dittrich C, Wanders J, Beijnen JH, Huitema AD. Population pharmacokinetic and pharmacodynamic analysis to support treatment optimization of combination chemotherapy with indisulam and carboplatin. Br J Clin Pharmacol. 2008;66(4):485–97.

    Article  PubMed  CAS  Google Scholar 

  21. Bender BC, Schaedeli-Stark F, Koch R, Joshi A, Chu YW, Rugo H, et al. A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer. Cancer Chemother Pharmacol. 2012;70(4):591–601.

    Article  PubMed  CAS  Google Scholar 

  22. Testart-Paillet D, Girard P, You B, Freyer G, Pobel C, Tranchand B. Contribution of modelling chemotherapy-induced hematological toxicity for clinical practice. Crit Rev Oncol Hematol. 2007;63(1):1–11.

    Article  PubMed  Google Scholar 

  23. Zhang L, Beal SL, Sheiner LB. Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. J Pharmacokinet Pharmacodyn. 2003;30(6):387–404.

    Article  PubMed  Google Scholar 

  24. Lavielle M, Bleakley K. Automatic data binning for improved visual diagnosis of pharmacometric models. J Pharmacokinet Pharmacodyn. 2011;38(6):861–71.

    Article  PubMed  Google Scholar 

  25. Brendel K, Comets E, Laffont C, Laveille C, Mentre F. Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide. Pharm Res. 2006;23(9):2036–49.

    Article  PubMed  CAS  Google Scholar 

  26. Brendel K, Comets E, Laffont C, Mentre F. Evaluation of different tests based on observations for external model evaluation of population analyses. J Pharmacokinet Pharmacodyn. 2010;37(1):49–65.

    Article  PubMed  Google Scholar 

  27. Quartino AL, Friberg LE, Karlsson MO. A simultaneous analysis of the time-course of leukocytes and neutrophils following docetaxel administration using a semi-mechanistic myelosuppression model. Invest New Drugs. 2012;30(2):833–45.

    Article  PubMed  CAS  Google Scholar 

  28. Sundman-Engberg B, Tidefelt U, Paul C. Toxicity of cytostatic drugs to normal bone marrow cells in vitro. Cancer Chemother Pharmacol. 1998;42(1):17–23.

    Article  PubMed  CAS  Google Scholar 

  29. Zandvliet AS, Karlsson MO, Schellens JH, Copalu W, Beijnen JH, Huitema AD. Two-stage model-based clinical trial design to optimize phase I development of novel anticancer agents. Invest New Drugs. 2010;28(1):61–75.

    Article  PubMed  CAS  Google Scholar 

  30. Calvert AH, Plummer R. The development of phase I cancer trial methodologies: the use of pharmacokinetic and pharmacodynamic end points sets the scene for phase 0 cancer clinical trials. Clin Cancer Res. 2008;14(12):3664–9.

    Article  PubMed  CAS  Google Scholar 

  31. Zohar S, Chevret S. The continual reassessment method: comparison of Bayesian stopping rules for dose-ranging studies. Stat Med. 2001;20(19):2827–43.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments and Disclosures

Quentin Chalret du Rieu and Sylvain Fouliard contributed equally to this work.

The authors would like to thank Pharmacyclics for providing data from the PCYC-401 and PCYC-402 clinical studies.

This work was incorporated into a Ph. D. project (Quentin Chalret du Rieu), granted by Institut de Recherches Internationales Servier.

Quentin Chalret du Rieu, Sylvain Fouliard, Anne Jacquet-Bescond, Renata Robert, Ioana Kloos, Stéphane Depil and Marylore Chenel are employed by Institut de Recherches Internationales Servier. The other authors indicated no potential conflicts of interest.

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Authors and Affiliations

  1. Clinical Pharmacokinetics Department, Institut de Recherches Internationales Servier, 50 rue Carnot, 92284, Suresnes Cedex, France

    Quentin Chalret du Rieu, Sylvain Fouliard & Marylore Chenel

  2. EA4553, Université Paul-Sabatier and Institut Claudius-Regaud, Toulouse, France

    Quentin Chalret du Rieu & Etienne Chatelut

  3. Oncology Business Unit, Institut de Recherches Internationales Servier, Suresnes, France

    Anne Jacquet-Bescond, Renata Robert, Ioana Kloos & Stéphane Depil

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  1. Quentin Chalret du Rieu
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  2. Sylvain Fouliard
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Correspondence to Marylore Chenel.

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Chalret du Rieu, Q., Fouliard, S., Jacquet-Bescond, A. et al. Application of Hematological Toxicity Modeling in Clinical Development of Abexinostat (S-78454, PCI-24781), A New Histone Deacetylase Inhibitor. Pharm Res 30, 2640–2653 (2013). https://doi.org/10.1007/s11095-013-1089-1

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