A PK/PD model for tumor-in-host growth kinetics following administration of an antiangiogenic agent given alone or in combination regimens
Elena Maria Tosca (1), Maurizio Rocchetti (2), Paolo Magni (1)
(1) Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia, I-27100, Italy, (2) Consultant, Milano, Italy
Objectives: PK/PD models predicting the action of antiangiogenic drugs in tumor xenograft neglect tumor and host interactions[1,2]. Conversely, current tumor-in-host models[3,4], based on cell kill hypothesis, are inadequate to describe the effect of antiangiogenic compounds that act on the tumor vascularization. For this reason, a PK/PD tumor-in-host model able to describe the tumor growth inhibition (TGI) following cytostatic therapy, as opposed to a cytotoxic treatment, is here proposed.
Methods: Experiments: The experimental setting is the typical in vivo study performed on xenograft mice using different human carcinoma cell lines. Mice were treated with vehicle or Bevacizumab, either alone or in combination with cytotoxic compounds. Tumor and mice net body weight data were considered for control and treated groups. PK were derived from separated studies. Model: Untreated tumor growth was described by a tumor-in-host DEB-based model[3,4]. Assuming a reduction of the nutrient supply to the tumor, antiangiogenic action was implemented as an inhibitory effect on the energy fraction appropriated by the tumor from the host, while no direct drug effect on mice weight was included. A combination model was also developed under a ‘no-interaction’ assumption[5] to assess the effect of the combination of Bevacizumab with cytotoxic agents: tumor-in-host kinetics can be described by a joint model incorporating both antiangiogenic and cytotoxic DEB-TGI model. Monolix 2016R1 was used for model identification.
Results: The single agent antiangiogenic model was successfully identified on average and individual data from control and several Bevacizumab treated groups. In combination experiments, tumor-related and drug-related parameters for Bevacizumab and different cytotoxic compounds were collected from the single-agent arms. Then, taking into account inter-individual variability, tumor and mice body weight curves for the combination arms were simulated under ‘no-interaction’ assumption. Possible drug-drug interactions on tumor growth inhibition can be visually assessed by overlapping the observed to the predicted curves.
Conclusions: The antiagiogenic tumor-in-host model well describes tumor and mice body weight data following Bevacizumab treatment. The ‘no-interaction’ model, combined with a population approach, can provide quantitative indications about possible interaction between cytostatic and cytotoxic drugs, resulting a useful tool to optimize combination treatments during preclinical experiments.
References:
[1] Rocchetti M., Germani M., Del Bene F., Poggesi I, Magni P., Pesenti E., De Nicolao G. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts. Cancer Chemother Pharmacol. 2013; 71:1147-1157.
[2] Wilson S, Tod M, Ouerdani A, et al. Modeling and predicting optimal treatment scheduling between the anti-angiogenic drug sunitinib and irinotecan in preclinical settings. CPT: Pharmacometrics & Systems Pharmacology. 2015; 4(12):720-727.
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[5] Rocchetti M., Del Bene F., Germani M., Fiorentini F., Poggesi I, Pesenti E., Magni P., De Nicolao G. Testing additivity of anticancer agents in pre-clinical studies: A PK/PD modelling approach. Eur. J of Cancer, 45(18): 3336-3346,2009.