2007 - København - Denmark

PAGE 2007: Applications
Kim Stuyckens

Modeling Drug Effects and Resistance Development on Tumor Growth Dynamics

Kim Stuyckens, Stefaan Rossenu, Peter King, Janine Arts, Juan Jose Perez-Ruixo.

Johnson & Johnson Pharmaceutical Research & Development. Beerse. Belgium.

Objective: The objective of this study is to develop a semi-mechanistic model to quantify tumor growth dynamics, the anticancer drug effects and the development of resistance.

Methods: U87 human glioblastome cell lines were implanted subcutaneously into mice. One week after tumor inoculation, 317 mice bearing a palpable tumor were selected and randomized into control and treated groups, which included an oral anticancer drug treatment at doses ranging from X to 40X mg/m2 and given as continuous (once daily or once weekly) or intermittent dosing (daily for 7 consecutive days on a 14 days cycle or daily for 3 consecutive days on a 7 days cycle). A total of 1699 measurements of tumor volumes were modeled using NONMEM. An exponential growth model described the tumor dynamics in nontreated animals. In treated animals, the tumor growth rate of sensitive cells was decreased by a factor proportional to both drug concentration and number of proliferating sensitive tumor cells as previously described1. A transit compartmental system was used to model the process of cell death, which occurs at later times. In addition, sensitive tumor cells that became resistant were less sensitive to drug concentration and follow an exponential growth model, similarly to what has been reported earlier2. In absence of pharmacokinetic data, a "kinetics of drug action" model, as described by Jaqmin et al3, was used to characterize the time course of tumor growth. The model was evaluated using visual predictive check.

Results: Typical value (%CV) of exponential tumor growth rate was 0.22 day-1 (11%). The drug effect on sensitive cells is 7 times higher than the effect on resistant cells. Damaged cells died after approximately 9 days and the resistance rate was estimated to be equal to 0.03 day-1. Visual predictive check confirmed that the model developed was suitable to describe the tumor cells dynamics in presence of anticancer treatment.

Conclusions: The integration of tumor growth data using modeling approach allows to characterize the dynamics of the tumor growth, to quantify the drug potency and to describe the development of drug resistant cells. This model can be used prospectively to optimize the design of future preclinical studies.

[1]. Simeoni, M et al. Clinical Cancer Research 2004; 64, 1094-1101.
[2]. Chung, P et al. Antimicrobial Agents Chemother. 2006; 50: 2957-2965.
[3]. Jacqmin, P et al. J. Pharmacokinetics and Pharmacodynamics. 2007; 34: 57-85.

Reference: PAGE 16 (2007) Abstr 1185 [www.page-meeting.org/?abstract=1185]
Oral Presentation: Applications
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