An energy based model able to describe the effect of anticancer drugs on tumor growth and host body weight
N. Terranova (1,5), F. Del Bene (2), M. Germani (3), M. Rocchetti (4) and P. Magni (1)
(1) Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy, (2) Accelera srl, Nerviano, Italy, (3) PKMS, Ablynx nv, Zwijnarde, Belgium, (4) Independent Consultant,Via T. Grossi 13, Rho, Milan, Italy, (5) Current address: Merck Institute for Pharmacometrics, Merck Serono S.A., EPFL Innovation Park, Building I, CH-1015, Lausanne, Switzerland.
Objectives: Cachexia is a complication responsible for around 20% of cancer deaths. For this reason, in preclinical pharmacological models, the decrease in the net body weight time course is considered a fundamental toxicological parameter to be evaluated. In both settings the energy loss caused by the tumor growth within the body is considered one of the causes of this side effect. Models based on dynamic energy budget (DEB) theory for describing the dynamics of the tumor host interaction are currently available; however the effect of anticancer treatments should also be considered. For this purpose, a new PK/PD tumor-in-host DEB-based model that includes the Simeoni TGI model , able to describe the drug action on the tumor mass, was developed. The model parameters provide a quantitative measure of these effects.
Methods: Pharmacological experiments using Harlan Sprague Dawley mice were performed in Nerviano Medical Sciences labs. In these experiments the tumor and mice net weights of control and treated animals were recorded at different doses and schedules. The PK profiles were derived from separated studies. Model parameters were evaluated by using the nonlinear weighted least squared algorithm as implemented in Matlab 2007b.
Results: The model has been tested in different experiments showing good capability in describing tumor growth as well as host body weight time-course in untreated and treated animals. Due to the model complexity, the physiological parameters of the tumor-free model have been estimated based on the growth data of typical HSD mice, subsequently the tumor-related and the drug-related parameters were estimated using the physiological values previously obtained in control animals.
Conclusions: The proposed model presents a new and complete approach for the simultaneous assessment of the anticancer drug efficacy and its impact on the clinical status of the animal represented by its body weight, providing in addition an evaluation of the direct effect of the drug on the net body weight. The possibility of predicting the behavior of the tumor and body weight within the same experiment under different conditions may provide a useful tool for identifying the most promising treatment schedules in terms of efficacy and toxicity balance. Moreover, even if the model has been tested and identified by using data of xenograft mice, its translational application turns out to be an interesting challenge in further investigations.
This work was supported by the DDMoRe project (www.ddmore.eu).
 M. Simeoni, P. Magni, C. Cammia, G. De Nicolao, V. Croci, E. Pesenti, M. Germani, I. Poggesi, and M. Rocchetti. Predictive pharmacokinetic pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Research, no. 64, pp. 1094–11 101, 2004.