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Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time
The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we i...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5287291/ https://www.ncbi.nlm.nih.gov/pubmed/28117835 http://dx.doi.org/10.1038/ncomms14183 |
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author | Lee, Bai-Yu Clemens, Daniel L. Silva, Aleidy Dillon, Barbara Jane Masleša-Galić, Saša Nava, Susana Ding, Xianting Ho, Chih-Ming Horwitz, Marcus A. |
author_facet | Lee, Bai-Yu Clemens, Daniel L. Silva, Aleidy Dillon, Barbara Jane Masleša-Galić, Saša Nava, Susana Ding, Xianting Ho, Chih-Ming Horwitz, Marcus A. |
author_sort | Lee, Bai-Yu |
collection | PubMed |
description | The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis. |
format | Online Article Text |
id | pubmed-5287291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-52872912017-02-22 Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time Lee, Bai-Yu Clemens, Daniel L. Silva, Aleidy Dillon, Barbara Jane Masleša-Galić, Saša Nava, Susana Ding, Xianting Ho, Chih-Ming Horwitz, Marcus A. Nat Commun Article The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis. Nature Publishing Group 2017-01-24 /pmc/articles/PMC5287291/ /pubmed/28117835 http://dx.doi.org/10.1038/ncomms14183 Text en Copyright © 2017, The Author(s) 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 Lee, Bai-Yu Clemens, Daniel L. Silva, Aleidy Dillon, Barbara Jane Masleša-Galić, Saša Nava, Susana Ding, Xianting Ho, Chih-Ming Horwitz, Marcus A. Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
title | Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
title_full | Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
title_fullStr | Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
title_full_unstemmed | Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
title_short | Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
title_sort | drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5287291/ https://www.ncbi.nlm.nih.gov/pubmed/28117835 http://dx.doi.org/10.1038/ncomms14183 |
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