Objectives: A biomarker - in the context of mechanism-based PK-PD modelling - is a measurement that defines quantitatively a process on the causal path between drug administration and clinical outcome [1]. The aim of this work is to investigate mathematical models that link biomarker modulation (due to the action of anticancer compounds) to tumour growth inhibition in preclinical experimental models. A major goal is the derivation of tumour growth inhibition models that are biomarker-driven rather than directly linked to drug pharmacokinetics. Being dependent on measurements which are likely to be more directly related to tumour response, this model formulation should provide more accurate predictions of the antitumor treatment effects. Methods: To describe mathematically tumour growth we propose a biomarker-driven version of the TGI Simeoni model [2,3], herein named B-Simeoni, where the input is not represented by the drug concentration but depends on the drug-induced biomarker modulation. Different alternative formulations of the B-Simeoni model were considered. Constraints on the potency parameter were derived to ensure consistency of the outcomes of Simeoni and B-Simeoni models. This was done by equating the steady-state tumour volumes predicted following constant drug concentrations. The specific biomarker inhibition needed to maintain a certain constant tumour volume was mathematically determined. NONMEM (vers. VI) was used to analyze and simulate data sets. Results: To assess the applicability of the modeling approach in a population context, simulated data were analyzed. Parameter estimates were fully satisfactory both on the side of data fitting and CV values. Moreover, the B-Simeoni model was tested on tumor growth inhibition data taken from the literature [4]. Also in this case, identification was successful in terms of both data fitting and CV values. Conclusions: Building on the Simeoni TGI model, different mathematical models linking tumor growth inhibition and biomarker modulation have been proposed. The steady-state relationship that links tumor volume to drug concentration and biomarker inhibition was devised. This made it possible to express the potency parameter of the newly proposed B-Simeoni model as a function of the potency parameter of the standard Simeoni model, thus reducing unnecessary redundancy. Both experimental individual data and simulated population ones confirmed model suitability. References: [1] M. Danhof et al. Pharm Res, 22: 1432-7 (2005). [2] M. Simeoni et al. Cancer Research, 64: 1094-1101 (2004). [3] P. Magni et al. Mathematical Biosciences, 200: 127-151 (2006). [4] L. Salphati et al. DMD, 38: 1436-1442 (2010).

Biomarker-driven models of tumor growth inhibition in preclinical animal studies

SARDU, MARIA LUISA;RUSSU, ALBERTO;DE NICOLAO, GIUSEPPE;POGGESI, ITALO
2012

Abstract

Objectives: A biomarker - in the context of mechanism-based PK-PD modelling - is a measurement that defines quantitatively a process on the causal path between drug administration and clinical outcome [1]. The aim of this work is to investigate mathematical models that link biomarker modulation (due to the action of anticancer compounds) to tumour growth inhibition in preclinical experimental models. A major goal is the derivation of tumour growth inhibition models that are biomarker-driven rather than directly linked to drug pharmacokinetics. Being dependent on measurements which are likely to be more directly related to tumour response, this model formulation should provide more accurate predictions of the antitumor treatment effects. Methods: To describe mathematically tumour growth we propose a biomarker-driven version of the TGI Simeoni model [2,3], herein named B-Simeoni, where the input is not represented by the drug concentration but depends on the drug-induced biomarker modulation. Different alternative formulations of the B-Simeoni model were considered. Constraints on the potency parameter were derived to ensure consistency of the outcomes of Simeoni and B-Simeoni models. This was done by equating the steady-state tumour volumes predicted following constant drug concentrations. The specific biomarker inhibition needed to maintain a certain constant tumour volume was mathematically determined. NONMEM (vers. VI) was used to analyze and simulate data sets. Results: To assess the applicability of the modeling approach in a population context, simulated data were analyzed. Parameter estimates were fully satisfactory both on the side of data fitting and CV values. Moreover, the B-Simeoni model was tested on tumor growth inhibition data taken from the literature [4]. Also in this case, identification was successful in terms of both data fitting and CV values. Conclusions: Building on the Simeoni TGI model, different mathematical models linking tumor growth inhibition and biomarker modulation have been proposed. The steady-state relationship that links tumor volume to drug concentration and biomarker inhibition was devised. This made it possible to express the potency parameter of the newly proposed B-Simeoni model as a function of the potency parameter of the standard Simeoni model, thus reducing unnecessary redundancy. Both experimental individual data and simulated population ones confirmed model suitability. References: [1] M. Danhof et al. Pharm Res, 22: 1432-7 (2005). [2] M. Simeoni et al. Cancer Research, 64: 1094-1101 (2004). [3] P. Magni et al. Mathematical Biosciences, 200: 127-151 (2006). [4] L. Salphati et al. DMD, 38: 1436-1442 (2010).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1007185
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