Jan-Georg Wojtyniak (1), Johanna Liebl (2) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) Department of Pharmacy - Center for Drug Research, University of Munich, Munich, Germany
Objectives: Despite intensive research in the field of cancer pathogenesis, today’s modern chemotherapy still relies on empirical values rather than a rational design [1]. Thus, the development of a novel mathematical semi-mechanistic cancer cell cycle model that allows the prediction of combination chemotherapy effects and different dosing schedules on cell cycle and tumor growth, was aspired.
Methods: The model was based on FACS in vitro and xenograft in vivo data by Ehrlich et al. [2], describing single and combination effects of the chemotherapeutics Irinotecan and Roscovitin on HUH7 and HUH7 CDK5 shRNA cells. Model development was done using NONMEM V7.3 [3] by testing several cell cycle phase compartments and transit compartments [4]. The quality of different models was assessed based on common statistical and graphical diagnostics. Also placebo, single compound treatment and Irinotecan in combination with Roscovitin treatment data were implemented in a stepwise procedure. The final model was evaluated by predicting prior excluded data. Comparison of simulated treatment regimens was realized through estimation of the tumor growth inhibition (TGI).
Results: The final model consisted of three main compartments corresponding to G1, S and G2/M phases, respectively. All in all seven transit compartments were implemented. Two of them concerned transit from G1 to S phase, while the remaining five were required to describe S to G2/M transit. For all treatments static effect parameters could be estimated. In vivo tumor growth under Irinotecan treatment was successfully predicted by only using the data of cycle distribution under treatment and placebo tumor growth. Combination therapy effects on tumor growth could be predicted using placebo and single compound effects. Comparing the simulated TGIs of treatment combinations revealed synergistic effects.
Conclusions: A novel mathematical semi-mechanistic cancer cell cycle model was developed and successfully used to predict single treatments as well as combination treatment effects of both common and new dosing schedules and drug combinations. This approach demonstrates that the model holds the capability to act as a pioneering tool for a rationalized and data based decision making in tumor therapy by providing striking pre-evaluations of newly developed chemotherapy protocols.
References:
[1] Pinto, A. C., Moreira, J. N. and Simões, S. Combination Chemotherapy in Cancer : Principles , Evaluation and Drug Delivery Strategies. Curr. Cancer Treat. 695–714.
[2] Ehrlich, S. M. et al. Targeting cyclin dependent kinase 5 in hepatocellular carcinoma–A novel therapeutic approach. J. Hepatol. 63, 102–13.
[3] Beal, S. L., Sheiner, L. B., Boeckmann, A. J., and Bauer, R. J. (eds) NONMEM 7.3.0 Users Guides. (1989–2013). ICON Development Solutions, Hanover, MD.
[4] Savic, R. M., Jonker, D. M., Kerbusch, T. and Karlsson, M. O. Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies. J.Pharmacokinet. Pharmacodyn. 34, 711–26.
Reference: PAGE 25 () Abstr 5823 [www.page-meeting.org/?abstract=5823]
Poster: Drug/Disease modeling - Oncology