2013 - Glasgow - Scotland

PAGE 2013: Oncology
Camille Vong

Semi-mechanistic PKPD model of thrombocytopenia characterizing the effect of a new histone deacetylase inhibitor (HDACi) in development, in co-administration with doxorubicin.

C. Vong (1,2), Q. Chalret du Rieu (2), S. Fouliard (2), E. Roger (3), I. Kloos (3), S. Depil (3), M. Chenel (2), LE. Friberg (1)

(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; (2) Clinical Pharmacokinetic department and (3) Oncology business Unit, Institut de Recherches Internationales Servier, Suresnes, France

Objectives: Recent studies [1, 2] demonstrated that the combination of an HDAC inhibitor and DNA-damaging agents has synergistic effects to induce apoptosis. This observation is of potential clinical applicability although several dose-limiting toxicities need to be pre-assessed before dose optimization. The aim was to develop a PKPD model of thrombocytopenia to assess the combined effects of an HDACi in development and the cytotoxic agent doxorubicin on circulating platelets.    

Methods: 8 patients suffering from solid tumors received six 4-week cycles administration of oral twice-daily doses of HDACi given 4 hours apart and a fixed 15 minutes IV-infusion dose of doxorubicin, in a 3 out of 4 week regimen in an ongoing phase I dose-escalation study. 230 and 160 PK samples for HDACi and doxorubicin respectively, and 202 platelet counts were analyzed with FOCE-I in NONMEM 7.2. A PK model previously developed using internal data to describe HDACi's disposition and a literature PK model [3] characterizing the time course of doxorubin and its metabolite doxorubicinol were used in a sequential modeling approach, where individual Bayesian estimates of PK parameters were fixed in subsequent PD modeling. A semi-physiological model incorporating stem cell proliferation inhibition drug effect from HDACi [4] and structurally similar to the myelosuppression model described by Friberg et al. [5, 6] was further refined and the effect of doxorubicin was added. Patient baseline characteristics were modeled using the B2 method [7].

Results: An Emax and a power models describing respectively HDACi and doxorubicin for the drug effect on the proliferative cells were found to best characterize the platelet data. Incorporation of an interaction between the two drugs (INT), implemented in the concentration-effect model as Eff(HDACi) + Eff(DOXO) + INT x Eff(HDACi) x Eff(DOXO) was found not significant.  A mean transit time through the chain of non-proliferative cells of 104 hours, a feedback parameter of 0.239 and a platelet baseline value of 277×109 /L were estimated. Model evaluation using Visual Prediction checks showed that the resulting PKPD model adequately described the 80% PI of the data.

Conclusions: A PKPD model was developed that integrated the PK of HDACi and doxorubicin to describe their combined effects on the time-course of platelets. The thrombocytopenic effects were adequately predicted assuming an additive effect between the two drugs on the proliferative cells. Future refinements of the model are expected with additional dosing regimen data.

References:
[1]. Lopez et al. Combining PC-24781, a novel histone deacetylase inhibitor, with chemotherapy for the treatment of soft tissue sarcoma. Clin Cancer Res. 2009;15(10):3472-83.
[2]. Yang et al. Histone deacetylase inhibitor (HDACi) PCI-24781 potentiates cytotoxic effects of doxorubicin bone sarcoma cells. Cancer Chemother Pharmacol. 2011;67(2):439-46.
[3]. Callies S. et al. A population pharmacokinetic model for doxorubicin and doxorubicinol in the presence of a novel MDR modulator, zosuquidar trihydrochloride (LY335979). Cancer Chemother Pharmacol. 2003; 51(2): 107-118.
[4]. Chalret du Rieu et al. Semi-mechanistic thrombocytopenia model of a new histone deacetylase inhibitor (HDACi) in development, with a drug-induced apoptosis of megakaryocytes. PAGE 21 (2012) Abstr 2503 [www.page-meeting.org/?abstract=2503]
[5]. Friberg et al. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol. 2002;20(24):4713-21.
[6]. Friberg and Karlsson. Mechanistic models for myelosuppression. Invest New Drugs. 2003;21(2):183-94.
[7]. Dansirikul et al. Approaches to handling pharmacodynamic baseline responses. J Pharmacokinet Pharmacodyn. 2008;35(3):269-83.




Reference: PAGE 22 (2013) Abstr 2915 [www.page-meeting.org/?abstract=2915]
Poster: Oncology
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