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In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index

In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in...

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Autores principales: Kashif, M., Andersson, C., Hassan, S., Karlsson, H., Senkowski, W., Fryknäs, M., Nygren, P., Larsson, R., Gustafsson, M.G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585751/
https://www.ncbi.nlm.nih.gov/pubmed/26392291
http://dx.doi.org/10.1038/srep14118
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author Kashif, M.
Andersson, C.
Hassan, S.
Karlsson, H.
Senkowski, W.
Fryknäs, M.
Nygren, P.
Larsson, R.
Gustafsson, M.G.
author_facet Kashif, M.
Andersson, C.
Hassan, S.
Karlsson, H.
Senkowski, W.
Fryknäs, M.
Nygren, P.
Larsson, R.
Gustafsson, M.G.
author_sort Kashif, M.
collection PubMed
description In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases.
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spelling pubmed-45857512015-09-29 In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index Kashif, M. Andersson, C. Hassan, S. Karlsson, H. Senkowski, W. Fryknäs, M. Nygren, P. Larsson, R. Gustafsson, M.G. Sci Rep Article In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases. Nature Publishing Group 2015-09-22 /pmc/articles/PMC4585751/ /pubmed/26392291 http://dx.doi.org/10.1038/srep14118 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Kashif, M.
Andersson, C.
Hassan, S.
Karlsson, H.
Senkowski, W.
Fryknäs, M.
Nygren, P.
Larsson, R.
Gustafsson, M.G.
In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
title In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
title_full In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
title_fullStr In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
title_full_unstemmed In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
title_short In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
title_sort in vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585751/
https://www.ncbi.nlm.nih.gov/pubmed/26392291
http://dx.doi.org/10.1038/srep14118
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