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Unified model of short- and long-term HIV viral rebound for clinical trial planning

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants s...

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Autores principales: Conway, Jessica M., Meily, Paige, Li, Jonathan Z., Perelson, Alan S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086917/
https://www.ncbi.nlm.nih.gov/pubmed/33849338
http://dx.doi.org/10.1098/rsif.2020.1015
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author Conway, Jessica M.
Meily, Paige
Li, Jonathan Z.
Perelson, Alan S.
author_facet Conway, Jessica M.
Meily, Paige
Li, Jonathan Z.
Perelson, Alan S.
author_sort Conway, Jessica M.
collection PubMed
description Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants show viral rebound within at most a few months of therapy suspension, but the remaining 10%, showed viral rebound some months, or years, after ART suspension. Some may even never rebound. We investigate and compare branching process models aimed at gaining insight into these viral dynamics. Specifically, we provide a theory that explains both short- and long-term viral rebounds, and post-treatment control, via a multitype branching process with time-inhomogeneous rates, validated with data from Li et al. (Li et al. 2016 AIDS 30, 343–353. (doi:10.1097/QAD.0000000000000953)). We discuss the associated biological interpretation and implications of our best-fit model. To test the effectiveness of an experimental intervention in delaying or preventing rebound, the standard practice is to suspend therapy and monitor the study participants for rebound. We close with a discussion of an important application of our modelling in the design of such clinical trials.
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spelling pubmed-80869172021-05-21 Unified model of short- and long-term HIV viral rebound for clinical trial planning Conway, Jessica M. Meily, Paige Li, Jonathan Z. Perelson, Alan S. J R Soc Interface Life Sciences–Mathematics interface Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants show viral rebound within at most a few months of therapy suspension, but the remaining 10%, showed viral rebound some months, or years, after ART suspension. Some may even never rebound. We investigate and compare branching process models aimed at gaining insight into these viral dynamics. Specifically, we provide a theory that explains both short- and long-term viral rebounds, and post-treatment control, via a multitype branching process with time-inhomogeneous rates, validated with data from Li et al. (Li et al. 2016 AIDS 30, 343–353. (doi:10.1097/QAD.0000000000000953)). We discuss the associated biological interpretation and implications of our best-fit model. To test the effectiveness of an experimental intervention in delaying or preventing rebound, the standard practice is to suspend therapy and monitor the study participants for rebound. We close with a discussion of an important application of our modelling in the design of such clinical trials. The Royal Society 2021-04-14 /pmc/articles/PMC8086917/ /pubmed/33849338 http://dx.doi.org/10.1098/rsif.2020.1015 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Mathematics interface
Conway, Jessica M.
Meily, Paige
Li, Jonathan Z.
Perelson, Alan S.
Unified model of short- and long-term HIV viral rebound for clinical trial planning
title Unified model of short- and long-term HIV viral rebound for clinical trial planning
title_full Unified model of short- and long-term HIV viral rebound for clinical trial planning
title_fullStr Unified model of short- and long-term HIV viral rebound for clinical trial planning
title_full_unstemmed Unified model of short- and long-term HIV viral rebound for clinical trial planning
title_short Unified model of short- and long-term HIV viral rebound for clinical trial planning
title_sort unified model of short- and long-term hiv viral rebound for clinical trial planning
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086917/
https://www.ncbi.nlm.nih.gov/pubmed/33849338
http://dx.doi.org/10.1098/rsif.2020.1015
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