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Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission

Eradication of malaria requires a novel type of drug that blocks transmission from the human to the mosquito host, but selection of such a drug is hampered by a lack of translational models. Experimental mosquito infections yield infection intensities that are substantially higher than observed in n...

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Autores principales: Dechering, Koen J., Duerr, Hans-Peter, Koolen, Karin M. J., Gemert, Geert-Jan van, Bousema, Teun, Burrows, Jeremy, Leroy, Didier, Sauerwein, Robert W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732164/
https://www.ncbi.nlm.nih.gov/pubmed/29247222
http://dx.doi.org/10.1038/s41598-017-16671-0
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author Dechering, Koen J.
Duerr, Hans-Peter
Koolen, Karin M. J.
Gemert, Geert-Jan van
Bousema, Teun
Burrows, Jeremy
Leroy, Didier
Sauerwein, Robert W.
author_facet Dechering, Koen J.
Duerr, Hans-Peter
Koolen, Karin M. J.
Gemert, Geert-Jan van
Bousema, Teun
Burrows, Jeremy
Leroy, Didier
Sauerwein, Robert W.
author_sort Dechering, Koen J.
collection PubMed
description Eradication of malaria requires a novel type of drug that blocks transmission from the human to the mosquito host, but selection of such a drug is hampered by a lack of translational models. Experimental mosquito infections yield infection intensities that are substantially higher than observed in natural infections and, as a consequence, underestimate the drug effect on the proportion of mosquitoes that become infected. Here we introduce a novel experimental and computational method to adequately describe drug efficacy at natural parasite densities. Parameters of a beta-binomial infection model were established and validated using a large number of experimental mosquito infections at different parasite densities. Analyses of 15 experimental and marketed drugs revealed a class-specific ability to block parasite transmission. Our results highlight the parasite’s elongation factor EF2, PI4 kinase and the ATP4 sodium channel as key targets for interruption of transmission, and compounds DDD107498 and KAE609 as most advanced drug candidates.
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spelling pubmed-57321642017-12-21 Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission Dechering, Koen J. Duerr, Hans-Peter Koolen, Karin M. J. Gemert, Geert-Jan van Bousema, Teun Burrows, Jeremy Leroy, Didier Sauerwein, Robert W. Sci Rep Article Eradication of malaria requires a novel type of drug that blocks transmission from the human to the mosquito host, but selection of such a drug is hampered by a lack of translational models. Experimental mosquito infections yield infection intensities that are substantially higher than observed in natural infections and, as a consequence, underestimate the drug effect on the proportion of mosquitoes that become infected. Here we introduce a novel experimental and computational method to adequately describe drug efficacy at natural parasite densities. Parameters of a beta-binomial infection model were established and validated using a large number of experimental mosquito infections at different parasite densities. Analyses of 15 experimental and marketed drugs revealed a class-specific ability to block parasite transmission. Our results highlight the parasite’s elongation factor EF2, PI4 kinase and the ATP4 sodium channel as key targets for interruption of transmission, and compounds DDD107498 and KAE609 as most advanced drug candidates. Nature Publishing Group UK 2017-12-15 /pmc/articles/PMC5732164/ /pubmed/29247222 http://dx.doi.org/10.1038/s41598-017-16671-0 Text en © The Author(s) 2017 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Dechering, Koen J.
Duerr, Hans-Peter
Koolen, Karin M. J.
Gemert, Geert-Jan van
Bousema, Teun
Burrows, Jeremy
Leroy, Didier
Sauerwein, Robert W.
Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission
title Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission
title_full Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission
title_fullStr Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission
title_full_unstemmed Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission
title_short Modelling mosquito infection at natural parasite densities identifies drugs targeting EF2, PI4K or ATP4 as key candidates for interrupting malaria transmission
title_sort modelling mosquito infection at natural parasite densities identifies drugs targeting ef2, pi4k or atp4 as key candidates for interrupting malaria transmission
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732164/
https://www.ncbi.nlm.nih.gov/pubmed/29247222
http://dx.doi.org/10.1038/s41598-017-16671-0
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