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 inhibitor...
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Autor*in:	Elena M. Tosca [verfasserIn] Glenn Gauderat [verfasserIn] Sylvain Fouliard [verfasserIn] Mike Burbridge [verfasserIn] Marylore Chenel [verfasserIn] Paolo Magni [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: CPT: Pharmacometrics & Systems Pharmacology - Wiley, 2013, 10(2021), 11, Seite 1396-1411
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:11 ; pages:1396-1411

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/psp4.12710

Katalog-ID:	DOAJ068561539

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520			\|a 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.
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700	0		\|a Marylore Chenel \|e verfasserin \|4 aut
700	0		\|a Paolo Magni \|e verfasserin \|4 aut
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allfields	10.1002/psp4.12710 doi (DE-627)DOAJ068561539 (DE-599)DOAJaa833859fc9044a780546843b278ac28 DE-627 ger DE-627 rakwb eng RM1-950 Elena M. Tosca verfasserin aut 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 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Therapeutics. Pharmacology Glenn Gauderat verfasserin aut Sylvain Fouliard verfasserin aut Mike Burbridge verfasserin aut Marylore Chenel verfasserin aut Paolo Magni verfasserin aut In CPT: Pharmacometrics & Systems Pharmacology Wiley, 2013 10(2021), 11, Seite 1396-1411 (DE-627)733752829 (DE-600)2697010-7 21638306 nnns volume:10 year:2021 number:11 pages:1396-1411 https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/article/aa833859fc9044a780546843b278ac28 kostenfrei https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/toc/2163-8306 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 11 1396-1411
spelling	10.1002/psp4.12710 doi (DE-627)DOAJ068561539 (DE-599)DOAJaa833859fc9044a780546843b278ac28 DE-627 ger DE-627 rakwb eng RM1-950 Elena M. Tosca verfasserin aut 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 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Therapeutics. Pharmacology Glenn Gauderat verfasserin aut Sylvain Fouliard verfasserin aut Mike Burbridge verfasserin aut Marylore Chenel verfasserin aut Paolo Magni verfasserin aut In CPT: Pharmacometrics & Systems Pharmacology Wiley, 2013 10(2021), 11, Seite 1396-1411 (DE-627)733752829 (DE-600)2697010-7 21638306 nnns volume:10 year:2021 number:11 pages:1396-1411 https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/article/aa833859fc9044a780546843b278ac28 kostenfrei https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/toc/2163-8306 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 11 1396-1411
allfields_unstemmed	10.1002/psp4.12710 doi (DE-627)DOAJ068561539 (DE-599)DOAJaa833859fc9044a780546843b278ac28 DE-627 ger DE-627 rakwb eng RM1-950 Elena M. Tosca verfasserin aut 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 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Therapeutics. Pharmacology Glenn Gauderat verfasserin aut Sylvain Fouliard verfasserin aut Mike Burbridge verfasserin aut Marylore Chenel verfasserin aut Paolo Magni verfasserin aut In CPT: Pharmacometrics & Systems Pharmacology Wiley, 2013 10(2021), 11, Seite 1396-1411 (DE-627)733752829 (DE-600)2697010-7 21638306 nnns volume:10 year:2021 number:11 pages:1396-1411 https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/article/aa833859fc9044a780546843b278ac28 kostenfrei https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/toc/2163-8306 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 11 1396-1411
allfieldsGer	10.1002/psp4.12710 doi (DE-627)DOAJ068561539 (DE-599)DOAJaa833859fc9044a780546843b278ac28 DE-627 ger DE-627 rakwb eng RM1-950 Elena M. Tosca verfasserin aut 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 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Therapeutics. Pharmacology Glenn Gauderat verfasserin aut Sylvain Fouliard verfasserin aut Mike Burbridge verfasserin aut Marylore Chenel verfasserin aut Paolo Magni verfasserin aut In CPT: Pharmacometrics & Systems Pharmacology Wiley, 2013 10(2021), 11, Seite 1396-1411 (DE-627)733752829 (DE-600)2697010-7 21638306 nnns volume:10 year:2021 number:11 pages:1396-1411 https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/article/aa833859fc9044a780546843b278ac28 kostenfrei https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/toc/2163-8306 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 11 1396-1411
allfieldsSound	10.1002/psp4.12710 doi (DE-627)DOAJ068561539 (DE-599)DOAJaa833859fc9044a780546843b278ac28 DE-627 ger DE-627 rakwb eng RM1-950 Elena M. Tosca verfasserin aut 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 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Therapeutics. Pharmacology Glenn Gauderat verfasserin aut Sylvain Fouliard verfasserin aut Mike Burbridge verfasserin aut Marylore Chenel verfasserin aut Paolo Magni verfasserin aut In CPT: Pharmacometrics & Systems Pharmacology Wiley, 2013 10(2021), 11, Seite 1396-1411 (DE-627)733752829 (DE-600)2697010-7 21638306 nnns volume:10 year:2021 number:11 pages:1396-1411 https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/article/aa833859fc9044a780546843b278ac28 kostenfrei https://doi.org/10.1002/psp4.12710 kostenfrei https://doaj.org/toc/2163-8306 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 11 1396-1411
language	English
source	In CPT: Pharmacometrics & Systems Pharmacology 10(2021), 11, Seite 1396-1411 volume:10 year:2021 number:11 pages:1396-1411
sourceStr	In CPT: Pharmacometrics & Systems Pharmacology 10(2021), 11, Seite 1396-1411 volume:10 year:2021 number:11 pages:1396-1411
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Therapeutics. Pharmacology
isfreeaccess_bool	true
container_title	CPT: Pharmacometrics & Systems Pharmacology
authorswithroles_txt_mv	Elena M. Tosca @@aut@@ Glenn Gauderat @@aut@@ Sylvain Fouliard @@aut@@ Mike Burbridge @@aut@@ Marylore Chenel @@aut@@ Paolo Magni @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	733752829
id	DOAJ068561539
language_de	englisch
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author	Elena M. Tosca
spellingShingle	Elena M. Tosca misc RM1-950 misc Therapeutics. Pharmacology 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
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topic_title	RM1-950 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
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title	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
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title_full	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
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author_browse	Elena M. Tosca Glenn Gauderat Sylvain Fouliard Mike Burbridge Marylore Chenel Paolo Magni
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title_sort	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
callnumber	RM1-950
title_auth	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	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	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
url	https://doi.org/10.1002/psp4.12710 https://doaj.org/article/aa833859fc9044a780546843b278ac28 https://doaj.org/toc/2163-8306
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Tosca</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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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

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