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Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma

Nuclear factor κB (NFκB) activation plays a crucial role in anti-apoptotic responses in response to the apoptotic signaling during tumor necrosis factor (TNFα) stimulation in Multiple Myeloma (MM). Although several drugs have been found effective for the treatment of MM by mainly inhibiting NFκB pat...

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Detalles Bibliográficos
Autores principales: Peng, Huiming, Wen, Jianguo, Li, Hongwei, Chang, Jeff, Zhou, Xiaobo
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051063/
https://www.ncbi.nlm.nih.gov/pubmed/21408099
http://dx.doi.org/10.1371/journal.pone.0014750
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author Peng, Huiming
Wen, Jianguo
Li, Hongwei
Chang, Jeff
Zhou, Xiaobo
author_facet Peng, Huiming
Wen, Jianguo
Li, Hongwei
Chang, Jeff
Zhou, Xiaobo
author_sort Peng, Huiming
collection PubMed
description Nuclear factor κB (NFκB) activation plays a crucial role in anti-apoptotic responses in response to the apoptotic signaling during tumor necrosis factor (TNFα) stimulation in Multiple Myeloma (MM). Although several drugs have been found effective for the treatment of MM by mainly inhibiting NFκB pathway, there are not any quantitative or qualitative results of comparison assessment on inhibition effect between different drugs either used alone or in combinations. Computational modeling is becoming increasingly indispensable for applied biological research mainly because it can provide strong quantitative predicting power. In this study, a novel computational pathway modeling approach is employed to comparably assess the inhibition effects of specific drugs used alone or in combinations on the NFκB pathway in MM and to predict the potential synergistic drug combinations.
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spelling pubmed-30510632011-03-15 Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma Peng, Huiming Wen, Jianguo Li, Hongwei Chang, Jeff Zhou, Xiaobo PLoS One Research Article Nuclear factor κB (NFκB) activation plays a crucial role in anti-apoptotic responses in response to the apoptotic signaling during tumor necrosis factor (TNFα) stimulation in Multiple Myeloma (MM). Although several drugs have been found effective for the treatment of MM by mainly inhibiting NFκB pathway, there are not any quantitative or qualitative results of comparison assessment on inhibition effect between different drugs either used alone or in combinations. Computational modeling is becoming increasingly indispensable for applied biological research mainly because it can provide strong quantitative predicting power. In this study, a novel computational pathway modeling approach is employed to comparably assess the inhibition effects of specific drugs used alone or in combinations on the NFκB pathway in MM and to predict the potential synergistic drug combinations. Public Library of Science 2011-03-07 /pmc/articles/PMC3051063/ /pubmed/21408099 http://dx.doi.org/10.1371/journal.pone.0014750 Text en Peng et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Peng, Huiming
Wen, Jianguo
Li, Hongwei
Chang, Jeff
Zhou, Xiaobo
Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma
title Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma
title_full Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma
title_fullStr Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma
title_full_unstemmed Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma
title_short Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma
title_sort drug inhibition profile prediction for nfκb pathway in multiple myeloma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051063/
https://www.ncbi.nlm.nih.gov/pubmed/21408099
http://dx.doi.org/10.1371/journal.pone.0014750
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