ArticleScientific reports2022
Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- MDI1228, a topical pan-JAK inhibitor, disrupts dermal fibroblast-T cell chemokine crosstalk to resolve allergic contact and atopic dermatitis.Frontiers in immunology · 2026Article
- Decision Tree for Prediction of Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- JAK inhibitor selectivity: new opportunities, better drugs?Nature reviews. Rheumatology · 2024Review
- Non-kinase off-target inhibitory activities of clinically-relevant kinase inhibitors.European journal of medicinal chemistry · 2024Review
- Differential properties of Janus kinase inhibitors in the treatment of immune-mediated inflammatory diseases.Rheumatology (Oxford, England) · 2024Review
- Computer-Aided Drug Design of Novel Derivatives of 2-Amino-7,9-dihydro-8H-purin-8-one as Potent Pan-Janus JAK3 Inhibitors.Molecules (Basel, Switzerland) · 2023Article
- CoGT: Ensemble Machine Learning Method and Its Application on JAK Inhibitor Discovery.ACS omega · 2023Article
- Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer's disease.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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