Xenograft experiments: assessing consistency between a drug-driven and a biomarker-driven tumor growth inhibition model
M.L. Sardu (1), I. Poggesi (1,2), G. De Nicolao (1)
(1) Department of Electrical, Computer and Biomedical Engineering , University of Pavia, Italy, (2) Model-based Drug Development, Janssen Research & Development, Beerse, Belgium
Objectives: Integrating biomarker dynamics to describe the effect of antitumoral drugs provides a deeper insight in the mechanistic aspects of tumor progression [1][2]. When the dynamics of a selective biomarker, causally and quantitatively related to the inhibition of an associated tumor is considered [3], it is worth asking what structures should have a biomarker-to-tumor model and a drug-to-tumor one, in order to produce consistent predictions. In this work we resort to steady-state conditions to check the consistency between some PKPD models published in the literature [4].
Methods: All PKPD data used in this work, were simulated according to the models proposed in [4], using NONMEM version. 7.2. We focused the analysis on three models: i) drug-to-biomarker (model I), ii) drug-to-tumor model driven by the concentration in the effect compartment (model III), iii) tumor growth inhibition model driven from the effect of AKT biomarker (model IV). To assess whether model III and the cascade of model I with model IV describe compatible behaviors, a steady-state analysis was performed. In analogy with the method proposed in [5] [6], the so-called characteristic curves were computed.
Results: Steady-state behaviors described by the characteristic curves of model III and model IV differ especially for higher concentrations. In particular, model IV predicted higher tumor volumes than model I. In order to recover consistency between these models, a novel biomarker-to-tumor model was proposed. In the new model, tumor growth modulation induced by biomarker inhibition is compatible with that induced by drug-concentration. The new model eases the comparison and understanding of the relationships between parameters of drug-to-biomarker and biomarker-to-tumor submodels. This paves the way to more precise predictions of tumor growth inhibition resulting from different protocols, but also from administration of different drugs, provided that they act on the same causal pathway.
Conclusions:
With reference to xenograft experiments, we analysed the steady-state consistency between a drug-to tumor model and a biomarker-to-tumor one taken from [4]. Since a discernible discrepancy was highlighted, we proposed a novel biomarker-to-tumor model that ensures steady-state consistency. The proposed model was validated on both steady-state characteristic curves and simulated PK-PD experiments.
This work was supported by the DDMoRe project (www.ddmore.eu)
References:
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[5] Sardu M L, Russu A, Poggesi I, and De Nicolao G, Tumour growth inhibition in preclinical animal studies: Steady-state analysis of biomarker-driven models, (2013) PAGE 22, Abstr 2880 [www.page-meeting.org/?abstract=2880].
[6] Sardu M L, Poggesi I, and De Nicolao G, Steady-state equivalence of drug-driven and biomarker-driven models in tumor growth experiments, (2014), PAGE 23, Abstr 3186 [www.page-meeting.org/?abstract=3186]
