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

Faquetti, Maria Luisa; Grisoni, Francesca; Schneider, Petra; Schneider, Gisbert; Burden, Andrea M.

doi:10.1038/s41598-022-11879-1

Cited by 10 publications

(10 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, no prior research has investigated the exploitation of Oxaliplatin in terms of off-targets at a larger scale. Several researchers have reported the use of off-target computational drug assessment techniques for drugs such as irinotecan, celecoxib, ofloxacin, and tamoxifen (34)(35)(36).…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: The Molecular Signature Associated with Oxaliplatin Induced Peripheral Neuropathy in Colorectal Cancer

Modekurty

2024

Preprint

View full text Add to dashboard Cite

Background: Oxaliplatin is the standard treatment option for colorectal cancer (CRC) which is one of the most prevalent forms of cancer. However, patients suffer either treatment discontinuation or adverse post-treatment life quality due to Oxaliplatin-induced peripheral neuropathy. Methods: Our study has comprehensively explored the molecular mechanisms underlying Oxaliplatin-induced peripheral neuropathy via an extensive literature survey and identified multiple genes that may contribute to neuropathy and neurotoxicity. In addition to that, the publicly available bulk transcriptomic data of Illumina HiSeq 2500 platform, comprising the CRC tissue of 18 individuals' tumor and adjacent normal tissue was processed to identify differentially expressed genes (DEGs). Moreover, the single-cell RNA sequencing data of 10X genomics comprising normal and tumor tissues was subjected to analysis using Seurat and sctype R packages to uncover the cancer cells associated DEGs. Functional and pathway enrichment analysis was conducted using the Genecodis4 web-based tool. Next, RNA-seq data of CRC cell lines treated with Oxaliplatin compared with normal individuals, was also processed and DEGs were determined to validate the inhibition of a curated list of neuropathy-associated genes. Results: From literature and database searches, a total of 1367 genes, including ion channel genes, normal sensory neuron-associated genes, and axon-excitability-related genes were collected that are either reported to be or may be contributing to neuropathy among cancer survivors upon oxaliplatin administration. The bulk transcriptomic data analysis revealed 715 DEGs and single-cell analysis uncovered 2,854 DEGs. Identified upregulated genes from single-cell data analysis, such as LGALS4, SPINK4, TFF3, REG4, and REG1A were found to be associated with tumor proliferation via epithelial-mesenchymal transitions, oxidative stress, dysregulated immune system, and inflammation which can be utilized as potential targets to devise novel therapeutic strategies for CRC treatment. Furthermore, many proteins involved in axon-excitability (NGF, SOD1, ROBO1, CNTNAP2, CNTNAP2, and KCNMB1), normal sensory neuron (SOX10, APOE, SST, S1PR1, and KCND3), voltage-gated sodium (SCNN1B, SCNN1G, SCNN1A, SCN1B, and SCN2B), calcium (CACNA2D2, CACNA1A, CACNA1C, CACNA1E, and CACNA1F), and potassium channels (KCND3, KCNMB1, KCNMA1, KCNJ2, and KCNN4) showed downregulation due to the oxaliplatin administration in CRC cell lines and their inhibition may have led to neuropathy which needs further validations. Conclusion: Our study uncovers the downregulation of multiple genes upon oxaliplatin administration leading to neuropathy development and also elucidates potential therapeutic targets for better prognosis of CRC.

show abstract

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: The Molecular Signature Associated with Oxaliplatin Induced Peripheral Neuropathy in Colorectal Cancer

Modekurty

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Several recent studies − have investigated using machine learning techniques to address JAK-related problems. However, these endeavors either involve a combination of the aforementioned methods or do not directly predict JAK types but rather do have an effect on the exploration of JAK inhibitors.…”

Section: Resultsmentioning

confidence: 99%

CoGT: Ensemble Machine Learning Method and Its Application on JAK Inhibitor Discovery

Gao

Zhang

et al. 2023

ACS Omega

View full text Add to dashboard Cite

The discovery of new drug candidates to inhibit an intended target is a complex and resource-consuming process. A machine learning (ML) method for predicting drug−target interactions (DTI) is a potential solution to improve the efficiency. However, traditional ML approaches have limitations in accuracy. In this study, we developed a novel ensemble model CoGT for DTI prediction using multilayer perceptron (MLP), which integrated graph-based models to extract non-Euclidean molecular structures and large pretrained models, specifically chemBERTa, to process simplified molecular input line entry systems (SMILES). The performance of CoGT was evaluated using compounds inhibiting four Janus kinases (JAKs). Results showed that the large pretrained model, chemBERTa, was better than other conventional ML models in predicting DTI across multiple evaluation metrics, while the graph neural network (GNN) was effective for prediction on imbalanced data sets. To take full advantage of the strengths of these different models, we developed an ensemble model, CoGT, which outperformed other individual ML models in predicting compounds' inhibition on different isoforms of JAKs. Our data suggest that the ensemble model CoGT has the potential to accelerate the process of drug discovery.

show abstract

“…9,11 To date, there are no clear off-target drug effects that explain the potential increased risk of thrombosis with JAKi. 20 JAKi selectivity furthermore appears dose-dependent with greater potential for off-target effects at higher doses. 11 In contrast, deucravacitinib is an allosteric inhibitor that binds to the regulatory domain of TYK2 in a highly selective manner with little to no activity against JAK1-JAK3.…”

Section: The Advent Of Jak Inhibitorsmentioning

confidence: 99%

Cardiovascular Risk Management in Patients Treated With Janus Kinase Inhibitors

Shah,

Femia

et al. 2024

Journal of Cardiovascular Pharmacology

View full text Add to dashboard Cite

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway plays a critical role in the pathogenesis of many immune-mediated inflammatory diseases (IMIDs). Although Janus kinase inhibitors (JAKi) are an effective treatment for several IMIDs, they have come under scrutiny as a class due to a potential risk of venous thromboembolism (VTE) and cardiovascular (CV) events, specifically noted with the oral JAKi, tofacitinib, as reported in the ORAL Surveillance Trial of a high CV risk rheumatoid arthritis population. This trial resulted in a black box warning from the Food and Drug Administration and European Medicines Agency regarding risk of VTE and CV events that was extended across several types of JAKi (including topical ruxolitinib) when treating IMIDs, leading to considerable controversy. Included is an up-to-date review of the current and rapidly evolving literature on CV risk in patients with IMIDs on JAKi therapy, including identification of potential risk factors for future VTE and CV events on JAKi therapy. We suggest a comprehensive, multimodal, and systematic approach for evaluation of CV risk in patients considering taking JAKi and emphasize that cardiologists play an important role in risk stratification and mitigation for patients with high CV risk factors or on long-term JAKi therapies.

show abstract

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

Cited by 10 publications

References 81 publications

WITHDRAWN: The Molecular Signature Associated with Oxaliplatin Induced Peripheral Neuropathy in Colorectal Cancer

WITHDRAWN: The Molecular Signature Associated with Oxaliplatin Induced Peripheral Neuropathy in Colorectal Cancer

CoGT: Ensemble Machine Learning Method and Its Application on JAK Inhibitor Discovery

Cardiovascular Risk Management in Patients Treated With Janus Kinase Inhibitors

Contact Info

Product

Resources

About