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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...

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Autores principales: Kakizoe, Yusuke, Koizumi, Yoshiki, Ikoma, Yukino, Ohashi, Hirofumi, Wakita, Takaji, Iwami, Shingo, Watashi, Koichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
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.
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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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