pubmed.ncbi.nlm.nih.gov

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 - PubMed

. 2021 Nov;10(11):1396-1411.

doi: 10.1002/psp4.12710. Epub 2021 Oct 28.

Affiliations

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

Elena M Tosca et al. CPT Pharmacometrics Syst Pharmacol. 2021 Nov.

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.

© 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

PubMed Disclaimer

Conflict of interest statement

G.G., S.F., M.B., and M.C. are employed by Servier. E.M.T. and P.M. are employed by Università degli Studi di Pavia.

Figures

FIGURE 1
FIGURE 1

Schematic representation of the Dynamic Energy Budget‐tumor growth inhibition (DEB‐TGI) modeling framework. Energy is taken up from food and delivered to the reserves. Energy required by the somatic processes is obtained from reserves and assigned to host or to tumor through the partition fraction ku(t) on the basis of the gluttony coefficient μu. Due to the tumor energy request, host starts to degrade its structural biomass (tumor‐related cachexia). In case of cytostatic treatment, the energy flow to the tumor is reduced. Cytotoxic drug exerts a killing effect on proliferating tumor cells. The presence of tumor mass itself (tumor‐related anorexia) or toxic effect of drug treatment (drug‐related anorexia) may reduce the host energy intake

FIGURE 2
FIGURE 2

Representative individual time courses of the tumor and mice body weight profiles (solid lines) together with the corresponding observed data (dots) for control and single‐agent treated arms (arms A–D)

FIGURE 3
FIGURE 3

External visual predictive check plots stratified by group (1000 replicates of the dataset) relative to combination arms E and F: dashed lines represent the 90% confidence interval for the corresponding percentile predicted by the null‐interaction combination model, dots are individual observed data

FIGURE 4
FIGURE 4

External visual predictive check plots stratified by group (1000 replicates of the dataset) relative to combination arms E and F: dashed lines represent the 90% confidence interval for the corresponding percentile predicted by the combination model, dots are individual observed data

Similar articles

Cited by

References

    1. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non‐small cell lung cancer. Nature. 2018;553(7689):446‐454. - PubMed
    1. Duma N, Santana‐Davila R, Molina JR. Non‐small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc. 2019;94(8):1623‐1640. - PubMed
    1. Kobayashi S, Boggon TJ, Dayaram T, et al. EGFR mutation and resistance of non‐small‐cell lung cancer to gefitinib. N Engl J Med. 2005;352(8):786‐792. - PubMed
    1. Lin L, Bivona TG. Mechanisms of resistance to epidermal growth factor receptor inhibitors and novel therapeutic strategies to overcome resistance in NSCLC patients. Chemother Res Pract. 2012;2012(817297):1‐9. - PMC - PubMed
    1. Pao W, Chmielecki J. Rational, biologically based treatment of EGFR‐mutant non‐small‐cell lung cancer. Nat Rev Cancer. 2010;10(11):760‐774. - PMC - PubMed

MeSH terms

Substances