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Predicting Drug Resistance Using Deep Mutational Scanning

Drug resistance is a major healthcare challenge, resulting in a continuous need to develop new inhibitors. The development of these inhibitors requires an understanding of the mechanisms of resistance for a critical mass of occurrences. Recent genome editing technologies based on high-throughput DNA...

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Detalles Bibliográficos
Autores principales: Pines, Gur, Fankhauser, Reilly G., Eckert, Carrie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248951/
https://www.ncbi.nlm.nih.gov/pubmed/32403408
http://dx.doi.org/10.3390/molecules25092265
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author Pines, Gur
Fankhauser, Reilly G.
Eckert, Carrie A.
author_facet Pines, Gur
Fankhauser, Reilly G.
Eckert, Carrie A.
author_sort Pines, Gur
collection PubMed
description Drug resistance is a major healthcare challenge, resulting in a continuous need to develop new inhibitors. The development of these inhibitors requires an understanding of the mechanisms of resistance for a critical mass of occurrences. Recent genome editing technologies based on high-throughput DNA synthesis and sequencing may help to predict mutations resulting in resistance by testing large mutagenesis libraries. Here we describe the rationale of this approach, with examples and relevance to drug development and resistance in malaria.
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spelling pubmed-72489512020-06-10 Predicting Drug Resistance Using Deep Mutational Scanning Pines, Gur Fankhauser, Reilly G. Eckert, Carrie A. Molecules Communication Drug resistance is a major healthcare challenge, resulting in a continuous need to develop new inhibitors. The development of these inhibitors requires an understanding of the mechanisms of resistance for a critical mass of occurrences. Recent genome editing technologies based on high-throughput DNA synthesis and sequencing may help to predict mutations resulting in resistance by testing large mutagenesis libraries. Here we describe the rationale of this approach, with examples and relevance to drug development and resistance in malaria. MDPI 2020-05-11 /pmc/articles/PMC7248951/ /pubmed/32403408 http://dx.doi.org/10.3390/molecules25092265 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Pines, Gur
Fankhauser, Reilly G.
Eckert, Carrie A.
Predicting Drug Resistance Using Deep Mutational Scanning
title Predicting Drug Resistance Using Deep Mutational Scanning
title_full Predicting Drug Resistance Using Deep Mutational Scanning
title_fullStr Predicting Drug Resistance Using Deep Mutational Scanning
title_full_unstemmed Predicting Drug Resistance Using Deep Mutational Scanning
title_short Predicting Drug Resistance Using Deep Mutational Scanning
title_sort predicting drug resistance using deep mutational scanning
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248951/
https://www.ncbi.nlm.nih.gov/pubmed/32403408
http://dx.doi.org/10.3390/molecules25092265
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