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Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection

As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to pre...

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Autores principales: Faquetti, Maria L., Grisoni, Francesca, Schneider, Petra, Schneider, Gisbert, Burden, Andrea M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096754/
https://www.ncbi.nlm.nih.gov/pubmed/35551258
http://dx.doi.org/10.1038/s41598-022-11879-1
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author Faquetti, Maria L.
Grisoni, Francesca
Schneider, Petra
Schneider, Gisbert
Burden, Andrea M.
author_facet Faquetti, Maria L.
Grisoni, Francesca
Schneider, Petra
Schneider, Gisbert
Burden, Andrea M.
author_sort Faquetti, Maria L.
collection PubMed
description As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.
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spelling pubmed-90967542022-05-12 Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection Faquetti, Maria L. Grisoni, Francesca Schneider, Petra Schneider, Gisbert Burden, Andrea M. Sci Rep Article As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing. Nature Publishing Group UK 2022-05-12 /pmc/articles/PMC9096754/ /pubmed/35551258 http://dx.doi.org/10.1038/s41598-022-11879-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 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/) .
spellingShingle Article
Faquetti, Maria L.
Grisoni, Francesca
Schneider, Petra
Schneider, Gisbert
Burden, Andrea M.
Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
title Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
title_full Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
title_fullStr Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
title_full_unstemmed Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
title_short Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
title_sort identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096754/
https://www.ncbi.nlm.nih.gov/pubmed/35551258
http://dx.doi.org/10.1038/s41598-022-11879-1
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