Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model

Systems pharmacology:Predicting efficacy of novel anti-cancer drugs in colorectal cancer While cancer drug development relies on experimental tumor models for testing, results observed in these systems often fail to translate clinically. Kirouac et al. demonstrate how computational systems modelling...

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Autores principales:	Daniel C. Kirouac, Gabriele Schaefer, Jocelyn Chan, Mark Merchant, Christine Orr, Shih-Min A. Huang, John Moffat, Lichuan Liu, Kapil Gadkar, Saroja Ramanujan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6bfc02d0615b4d33ac88a7be2e3e419b
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spelling	oai:doaj.org-article:6bfc02d0615b4d33ac88a7be2e3e419b2021-12-02T11:41:50ZClinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model10.1038/s41540-017-0016-12056-7189https://doaj.org/article/6bfc02d0615b4d33ac88a7be2e3e419b2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41540-017-0016-1https://doaj.org/toc/2056-7189Systems pharmacology:Predicting efficacy of novel anti-cancer drugs in colorectal cancer While cancer drug development relies on experimental tumor models for testing, results observed in these systems often fail to translate clinically. Kirouac et al. demonstrate how computational systems modelling can help bridge this divide. Focusing on a class of colorectal cancers with poor prognosis (those with a mutant form of the BRAF oncogene) they develop a mathematical model linking drug exposure, via cellular signal transduction, to tumor growth. By triangulating experimental data from multiple cell lines and mouse models, with results from three clinical trials of related drugs, the model accurately predicted tumor shrinkage observed in a first-in-human study of GDC-0994, an ERK inhibitor. Simulations were then used to explore strategies for increasing the activity of this class of drugs (MAPK pathway inhibitors) via combinations, alternate dosing regimens, and predictive biomarkers to guide future clinical studies. Extended to other cancer types and drugs, the approach could streamline early clinical development.Daniel C. KirouacGabriele SchaeferJocelyn ChanMark MerchantChristine OrrShih-Min A. HuangJohn MoffatLichuan LiuKapil GadkarSaroja RamanujanNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 3, Iss 1, Pp 1-17 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Daniel C. Kirouac Gabriele Schaefer Jocelyn Chan Mark Merchant Christine Orr Shih-Min A. Huang John Moffat Lichuan Liu Kapil Gadkar Saroja Ramanujan Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model
description	Systems pharmacology:Predicting efficacy of novel anti-cancer drugs in colorectal cancer While cancer drug development relies on experimental tumor models for testing, results observed in these systems often fail to translate clinically. Kirouac et al. demonstrate how computational systems modelling can help bridge this divide. Focusing on a class of colorectal cancers with poor prognosis (those with a mutant form of the BRAF oncogene) they develop a mathematical model linking drug exposure, via cellular signal transduction, to tumor growth. By triangulating experimental data from multiple cell lines and mouse models, with results from three clinical trials of related drugs, the model accurately predicted tumor shrinkage observed in a first-in-human study of GDC-0994, an ERK inhibitor. Simulations were then used to explore strategies for increasing the activity of this class of drugs (MAPK pathway inhibitors) via combinations, alternate dosing regimens, and predictive biomarkers to guide future clinical studies. Extended to other cancer types and drugs, the approach could streamline early clinical development.
format	article
author	Daniel C. Kirouac Gabriele Schaefer Jocelyn Chan Mark Merchant Christine Orr Shih-Min A. Huang John Moffat Lichuan Liu Kapil Gadkar Saroja Ramanujan
author_facet	Daniel C. Kirouac Gabriele Schaefer Jocelyn Chan Mark Merchant Christine Orr Shih-Min A. Huang John Moffat Lichuan Liu Kapil Gadkar Saroja Ramanujan
author_sort	Daniel C. Kirouac
title	Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model
title_short	Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model
title_full	Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model
title_fullStr	Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model
title_full_unstemmed	Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model
title_sort	clinical responses to erk inhibition in braf v600e-mutant colorectal cancer predicted using a computational model
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/6bfc02d0615b4d33ac88a7be2e3e419b
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Clinical responses to ERK inhibition in BRAF V600E-mutant colorectal cancer predicted using a computational model

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