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Transcriptomic profiles of tissues from rats treated with anticancer drug combinations
To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinatio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326153/ https://www.ncbi.nlm.nih.gov/pubmed/30620345 http://dx.doi.org/10.1038/sdata.2018.306 |
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author | Davis, Myrtle Knight, Elaine Eldridge, Sandy R. Li, Jianying Bushel, Pierre R. |
author_facet | Davis, Myrtle Knight, Elaine Eldridge, Sandy R. Li, Jianying Bushel, Pierre R. |
author_sort | Davis, Myrtle |
collection | PubMed |
description | To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinations is similar to predicting synergistic efficacy, and is dependent upon building hypotheses from molecular mechanisms of drug toxicity. The key objective of this initiative was to generate and make publicly available key high-content data sets for mechanistic hypothesis generation as it pertains to a unique toxicity profile of a drug pair for several anticancer drug combinations. The expectation is that tissue-based genomic information that are derived from target tissues will also facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach. |
format | Online Article Text |
id | pubmed-6326153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-63261532019-01-10 Transcriptomic profiles of tissues from rats treated with anticancer drug combinations Davis, Myrtle Knight, Elaine Eldridge, Sandy R. Li, Jianying Bushel, Pierre R. Sci Data Data Descriptor To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinations is similar to predicting synergistic efficacy, and is dependent upon building hypotheses from molecular mechanisms of drug toxicity. The key objective of this initiative was to generate and make publicly available key high-content data sets for mechanistic hypothesis generation as it pertains to a unique toxicity profile of a drug pair for several anticancer drug combinations. The expectation is that tissue-based genomic information that are derived from target tissues will also facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach. Nature Publishing Group 2019-01-08 /pmc/articles/PMC6326153/ /pubmed/30620345 http://dx.doi.org/10.1038/sdata.2018.306 Text en Copyright © 2019, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Davis, Myrtle Knight, Elaine Eldridge, Sandy R. Li, Jianying Bushel, Pierre R. Transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
title | Transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
title_full | Transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
title_fullStr | Transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
title_full_unstemmed | Transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
title_short | Transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
title_sort | transcriptomic profiles of tissues from rats treated with anticancer drug combinations |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326153/ https://www.ncbi.nlm.nih.gov/pubmed/30620345 http://dx.doi.org/10.1038/sdata.2018.306 |
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