I-23 Maria Matoses Osborne

Development of a PBPK model for the anticancer drug Vivia009 and its main metabolite administered as a new delivery system in the Rat

Matoses-Osborne M 1,2, Tudela C2, Garrido MJ1, Bennett TA2, Caveda L2, Ballesteros J2, Trocóniz IF1

(1) Pharmacometrics, School of Pharmacy; University of Navarra, Pamplona, Spain, (2) Vivia Biotech S.L., Madrid, Spain

Objectives: Vivia009 is a patented new drug indication that targets B cell population overproliferation in leukemic patients. In order to prevent side effects and to increase drug efficacy in the lymph nodes, a new delivery system (NDS) has been developed and its pharmacokinetic properties are currently being studied. The aim of this study was to develop an integrated PBPK model capable to describe simultaneously the biodistribution of Vivia009 and its main metabolite, after the parent’s drug administration as a free solution or as an NDS.

Methods: Fifty-one rats divided in two groups were treated with 0.75 mg/kg of either Vivia009 or Vivia009-NDS given as a bolus. At time points 0.08, 0.16, 0.25, 0.5, 1, 2, 4, 6 and 24 hours, three animals were sacrificed. Samples of plasma, axillary lymph node, brain, spleen and bone marrow were extracted and analysed to quantify Vivia009 and its metabolite. NDS’s composition was a mixture of Vivia009, PLGA, polivinil alcohol, poloxamer 188, tween 80 and sucrose, its size varied between 1.2-1.5 µm and the release profile could be characterized from 0-24 h. Analyses were performed with NONMEM version VII using the naïve pool data approach.

Results: A PBPK model was built for Vivia009 and its main metabolite after free drug administration assuming a perfusion limited distribution, and using available data from the literature regarding blood flows, and tissue volumes (1,2). A permeability surface factor was estimated for lymph node, and distinction between vascular and intracellular compartments was required for the case of spleen. Additional model parameters estimated during the fitting were drug and metabolite clearance, apparent fraction of drug metabolised, and tissue to plasma partition coefficients. The latter ones were also calculated using the ratio between AUCtissue/AUCblood (3) resulting similar to the model estimates. Based on the results of the release profile, 30% of the dose of Vivia009-NDS was dissolved before the bolus administration and behaves as a free drug (4). For the formulated fraction of the dose, it was assumed a rapid capture by the macrophages and distribution through the lymphatic system to the tissues.

Conclusions: A PBPK model has been developed to describe simultaneously drug and metabolite distributions obtained from several studies carried out with different drug formulations.

References:
[1] Kawai R, Lemaire M, Steimer JL, Bruelisauer A, Niederberger W, Rowland M. Physiologically based pharmacokinetic study on a cyclosporin derivative, SDZ IMM 125. J Pharmacokinet Biopharm 1994 Oct;22(5):327-365. 1.
[2] Kang HJK, Wientjes MG, Au JLS. Physiologically based pharmacokinetic models of 2′, 3′-dideoxyinosine. Pharm Res 1997;14(3):337-344.
[3] Shah DK, Balthasar JP. Physiologically based pharmacokinetic model for topotecan in mice. J Pharmacokinet Pharmacodyn 2011 Feb;38(1):121-142.
[4] Moreno D, Zalba S, Colom H, Troconiz IF, Tros de Ilarduya C, Garrido MJ. Biopharmaceutic and pharmacodynamic modeling of the in vitro antiproliferative effect of new controlled delivery systems of cisplatin. Eur J Pharm Sci 2009 Jun 28;37(3-4):341-350.

Reference: PAGE 21 (2012) Abstr 2363 [www.page-meeting.org/?abstract=2363]

Poster: Oncology

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