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Required concentration index quantifies effective drug combinations against hepatitis C virus infection
Successful clinical drug development requires rational design of combination treatments based on preclinical data. Anti-hepatitis C virus (HCV) drugs exhibit significant diversity in antiviral effect. Dose-response assessments can be used to determine parameters profiling the diverse antiviral effec...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796629/ https://www.ncbi.nlm.nih.gov/pubmed/33422060 http://dx.doi.org/10.1186/s12976-020-00135-6 |
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author | Kakizoe, Yusuke Koizumi, Yoshiki Ikoma, Yukino Ohashi, Hirofumi Wakita, Takaji Iwami, Shingo Watashi, Koichi |
author_facet | Kakizoe, Yusuke Koizumi, Yoshiki Ikoma, Yukino Ohashi, Hirofumi Wakita, Takaji Iwami, Shingo Watashi, Koichi |
author_sort | Kakizoe, Yusuke |
collection | PubMed |
description | Successful clinical drug development requires rational design of combination treatments based on preclinical data. Anti-hepatitis C virus (HCV) drugs exhibit significant diversity in antiviral effect. Dose-response assessments can be used to determine parameters profiling the diverse antiviral effect during combination treatment. In the current study, a combined experimental and mathematical approaches were used to compare and score different combinations of anti-HCV treatments. A “required concentration index” was generated and used to rank the antiviral profile of possible double- and triple-drug combinations against HCV genotype 1b and 2a. Rankings varied based on target HCV genotype. Interestingly, multidrug (double and triple) treatment not only augmented antiviral activity, but also reduced genotype-specific efficacy, suggesting another advantage of multidrug treatment. The current study provides a quantitative method for profiling drug combinations against viral genotypes, to better inform clinical drug development. |
format | Online Article Text |
id | pubmed-7796629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77966292021-01-11 Required concentration index quantifies effective drug combinations against hepatitis C virus infection Kakizoe, Yusuke Koizumi, Yoshiki Ikoma, Yukino Ohashi, Hirofumi Wakita, Takaji Iwami, Shingo Watashi, Koichi Theor Biol Med Model Research Successful clinical drug development requires rational design of combination treatments based on preclinical data. Anti-hepatitis C virus (HCV) drugs exhibit significant diversity in antiviral effect. Dose-response assessments can be used to determine parameters profiling the diverse antiviral effect during combination treatment. In the current study, a combined experimental and mathematical approaches were used to compare and score different combinations of anti-HCV treatments. A “required concentration index” was generated and used to rank the antiviral profile of possible double- and triple-drug combinations against HCV genotype 1b and 2a. Rankings varied based on target HCV genotype. Interestingly, multidrug (double and triple) treatment not only augmented antiviral activity, but also reduced genotype-specific efficacy, suggesting another advantage of multidrug treatment. The current study provides a quantitative method for profiling drug combinations against viral genotypes, to better inform clinical drug development. BioMed Central 2021-01-09 /pmc/articles/PMC7796629/ /pubmed/33422060 http://dx.doi.org/10.1186/s12976-020-00135-6 Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, 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 data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Kakizoe, Yusuke Koizumi, Yoshiki Ikoma, Yukino Ohashi, Hirofumi Wakita, Takaji Iwami, Shingo Watashi, Koichi Required concentration index quantifies effective drug combinations against hepatitis C virus infection |
title | Required concentration index quantifies effective drug combinations against hepatitis C virus infection |
title_full | Required concentration index quantifies effective drug combinations against hepatitis C virus infection |
title_fullStr | Required concentration index quantifies effective drug combinations against hepatitis C virus infection |
title_full_unstemmed | Required concentration index quantifies effective drug combinations against hepatitis C virus infection |
title_short | Required concentration index quantifies effective drug combinations against hepatitis C virus infection |
title_sort | required concentration index quantifies effective drug combinations against hepatitis c virus infection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796629/ https://www.ncbi.nlm.nih.gov/pubmed/33422060 http://dx.doi.org/10.1186/s12976-020-00135-6 |
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