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: | , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6bfc02d0615b4d33ac88a7be2e3e419b |
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| Sumario: | 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. |
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