Evaluation of a PK/PD DEB-based model for tumor-in-host growth kinetics under anticancer treatment
Elena Maria Tosca (1), Elisa Borella (1), Nadia Terranova (2), Maurizio Rocchetti, Paolo Magni(1)
(1) Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia, I-27100, Italy; (2) Merck Institute for Pharmacometrics, Merck Serono S.A., Lausanne, Switzerland.
Objectives: Mathematical models for describing the tumor growth in animals often neglect the relationship between tumor and host organism. To overcome this limitation, a more mechanistic model, based on energy balance between tumor and host, was developed . This PK/PD model, combining the Dynamic Energy Budget (DEB) theory  with the Simeoni tumor growth inhibition (TGI) model , describes both the dynamics of the tumor-host interaction and the effect of anticancer treatments. Here a slightly revised model formulation and a new implementation are proposed. Moreover, a comparative study on the tumor growth in control groups between the DEB-TGI model and the widely used Simeoni TGI model is presented.
Methods: Data used for model validation refer to xenograft experiments conducted on Harlan Sprague Dawley mice. Average data of tumor weight and mice net body weight were considered for the control and treated groups. The PKs were derived from separated studies. Monolix 4.3.3 was used for model identification, while Simulx was used to confirm the hypothesis emerged from a dynamic system analysis.
Results: First of all, the model was identified on different experimental datasets with the following strategy: 1) physiological parameters of the tumor-free model were estimated on growth data of typical HSD mice; 2) estimated values were used to find the initial value for the energy reserve at the beginning of the experiment; 3) once fixed the tumor-free model parameters and energy initial value, the tumor-related and the drug-related parameters were simultaneously estimated.
The mathematical analysis of the dynamic system showed that, as the Simeoni model, the DEB-TGI model predicts an exponential growth of the tumor in the early phases of its development. The exponential growth rate depends on several model parameters some of them related to the tumor cell lines and other to the host. We investigated also the relationship between the DEB-TGI model parameters and the decreasing of the tumor growth rate.
Conclusions: The tumor-in-host DEB-based model confirmed its good capability in describing tumor growth and host body growth even when an anticancer drug is administered. Moreover, the affinities emerged from the comparative analysis with the Simenoni model provide a possible biological interpretation of the assumptions underlying the Simeoni model unperturbed (control) growth curve.
Acknowledgements: This work was supported by the DDMoRe project (www.ddmore.eu).
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