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A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo

We present a computational modeling protocol that can accurately predict changes in both in vitro and in vivo gene expression levels in response to the application of various siRNA formulations. We describe how to use this Python-based pipeline to obtain crucial information, namely maximum silencing...

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Detalles Bibliográficos
Autores principales: Roh, Esther H., Sullivan, Millicent O., Epps, Thomas H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597118/
https://www.ncbi.nlm.nih.gov/pubmed/36313537
http://dx.doi.org/10.1016/j.xpro.2022.101723
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author Roh, Esther H.
Sullivan, Millicent O.
Epps, Thomas H.
author_facet Roh, Esther H.
Sullivan, Millicent O.
Epps, Thomas H.
author_sort Roh, Esther H.
collection PubMed
description We present a computational modeling protocol that can accurately predict changes in both in vitro and in vivo gene expression levels in response to the application of various siRNA formulations. We describe how to use this Python-based pipeline to obtain crucial information, namely maximum silencing level and duration of silencing, toward the design of therapeutically relevant dosing regimens. The protocol details the steps for running internalization rate fitting to produce predictions based on experimental measurements from a single time point. For complete details on the use and execution of this protocol, please refer to Roh et al., 2021.
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spelling pubmed-95971182022-10-27 A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo Roh, Esther H. Sullivan, Millicent O. Epps, Thomas H. STAR Protoc Protocol We present a computational modeling protocol that can accurately predict changes in both in vitro and in vivo gene expression levels in response to the application of various siRNA formulations. We describe how to use this Python-based pipeline to obtain crucial information, namely maximum silencing level and duration of silencing, toward the design of therapeutically relevant dosing regimens. The protocol details the steps for running internalization rate fitting to produce predictions based on experimental measurements from a single time point. For complete details on the use and execution of this protocol, please refer to Roh et al., 2021. Elsevier 2022-10-22 /pmc/articles/PMC9597118/ /pubmed/36313537 http://dx.doi.org/10.1016/j.xpro.2022.101723 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Roh, Esther H.
Sullivan, Millicent O.
Epps, Thomas H.
A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo
title A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo
title_full A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo
title_fullStr A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo
title_full_unstemmed A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo
title_short A kinetic modeling platform for predicting the efficacy of siRNA formulations in vitro and in vivo
title_sort kinetic modeling platform for predicting the efficacy of sirna formulations in vitro and in vivo
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597118/
https://www.ncbi.nlm.nih.gov/pubmed/36313537
http://dx.doi.org/10.1016/j.xpro.2022.101723
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