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Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis

BACKGROUND: Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be m...

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Autores principales: Vesga, Juan F., Lienhardt, Christian, Nsengiyumva, Placide, Campbell, Jonathon R., Oxlade, Olivia, den Boon, Saskia, Falzon, Dennis, Schwartzman, Kevin, Churchyard, Gavin, Arinaminpathy, Nimalan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115962/
https://www.ncbi.nlm.nih.gov/pubmed/35581650
http://dx.doi.org/10.1186/s12916-022-02378-1
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author Vesga, Juan F.
Lienhardt, Christian
Nsengiyumva, Placide
Campbell, Jonathon R.
Oxlade, Olivia
den Boon, Saskia
Falzon, Dennis
Schwartzman, Kevin
Churchyard, Gavin
Arinaminpathy, Nimalan
author_facet Vesga, Juan F.
Lienhardt, Christian
Nsengiyumva, Placide
Campbell, Jonathon R.
Oxlade, Olivia
den Boon, Saskia
Falzon, Dennis
Schwartzman, Kevin
Churchyard, Gavin
Arinaminpathy, Nimalan
author_sort Vesga, Juan F.
collection PubMed
description BACKGROUND: Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. METHODS: Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. RESULTS: Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. CONCLUSIONS: As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02378-1.
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spelling pubmed-91159622022-05-19 Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis Vesga, Juan F. Lienhardt, Christian Nsengiyumva, Placide Campbell, Jonathon R. Oxlade, Olivia den Boon, Saskia Falzon, Dennis Schwartzman, Kevin Churchyard, Gavin Arinaminpathy, Nimalan BMC Med Research Article BACKGROUND: Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. METHODS: Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. RESULTS: Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. CONCLUSIONS: As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02378-1. BioMed Central 2022-05-18 /pmc/articles/PMC9115962/ /pubmed/35581650 http://dx.doi.org/10.1186/s12916-022-02378-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Article
Vesga, Juan F.
Lienhardt, Christian
Nsengiyumva, Placide
Campbell, Jonathon R.
Oxlade, Olivia
den Boon, Saskia
Falzon, Dennis
Schwartzman, Kevin
Churchyard, Gavin
Arinaminpathy, Nimalan
Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title_full Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title_fullStr Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title_full_unstemmed Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title_short Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title_sort prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115962/
https://www.ncbi.nlm.nih.gov/pubmed/35581650
http://dx.doi.org/10.1186/s12916-022-02378-1
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