Modeling restoration of gefitinib efficacy by co‐administration of MET inhibitors in an EGFR inhibitor‐resistant NSCLC xenograft model: A tumor‐in‐host DEB‐based approach

Abstract

MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non‐small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor‐in‐host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly‐targeted combination therapies. The population DEB‐based tumor growth inhibition (TGI) model well‐described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib‐resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB‐TGI model allowed to capture gefitinib anticancer activity enhanced by the co‐administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model‐based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB‐based tumor‐in‐host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor‐in‐host DEB‐TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

“…In contrast, a multitude of mathematical modeling approaches, which describe the anticancer treatment effect on 2D in vitro cell cultures and xenograft animals, have been developed [ 217 ], proving an impressive proof-of-concept of the M&S potential to improve the power of preclinical cancer models. In particular, mathematical models quantitatively linking the drug concentration time curve to TGI are of extremely relevant value [ 223 , 224 , 225 , 226 , 227 ]. Among them, the Simeoni TGI model [ 223 ] has been applied by several international research groups on a huge panel of xenograft studies as well as in vitro data [ 228 ] involving a multitude of different cancer cell lines and anticancer agents, becoming a reference in the field.…”

Model‐based prediction of effective target exposure for MEN1611 in combination with trastuzumab in HER2‐positive advanced or metastatic breast cancer patients

“…[14][15][16][17] These approaches are generally based on pharmacokineticpharmacodynamic (PK-PD) modeling of tumor growth inhibition (TGI) observed in xenograft experiments after the administration of tested compounds in monotherapy [18][19][20][21][22][23] or combination regimens. [24][25][26][27] Their growing success is due to their ability to extract, summarize, and integrate results from in vivo experiments, in order to quantify drug activity, compare drug candidates, identify drug-drug interaction, and, most importantly, translate preclinical results into the clinical setting. Indeed, in some cases, high correlations between model-derived metrics of systemic exposure leading to TGI in xenograft models and clinically active exposures or doses in patients with cancer have been demonstrated.…”

“…Several approaches to model preclinical data of oncology drugs are already available and successfully applied during the drug development process 14–17 . These approaches are generally based on pharmacokinetic‐pharmacodynamic (PK‐PD) modeling of tumor growth inhibition (TGI) observed in xenograft experiments after the administration of tested compounds in monotherapy 18–23 or combination regimens 24–27 . Their growing success is due to their ability to extract, summarize, and integrate results from in vivo experiments, in order to quantify drug activity, compare drug candidates, identify drug–drug interaction, and, most importantly, translate preclinical results into the clinical setting.…”

A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory

“…The incorporation of such aspects in a single model allows to investigate body weight loss due to tumor progression and treatment and, at the same time, to obtain unbiased estimates of drug anticancer efficacy. The DEB-based modeling approach was successfully applied on several in vivo preclinical studies that had been performed to assess the efficacy of the investigated compounds and that involved different host species (mice and rats), tumor cell lines, type of anticancer agents and experimental settings, including combination regimens [ 25 ]. In addition, metrics with a relevant biological meaning were derived through a mathematical analysis of the tumor-in-host DEB-TGI model in the specific formulation defined for cytotoxic agents [ 15 ].…”