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

JAK inhibitor selectivity: new opportunities, better drugs?

Cytokines function as communication tools of the immune system, serving critical functions in many biological responses and shaping the immune response. When cytokine production or their biological activity goes awry, the homeostatic balance of the immune response is altered, leading to the development of several pathologies such as autoimmune and inflammatory disorders. Cytokines bind to specific receptors on cells, triggering the activation of intracellular enzymes known as Janus kinases (JAKs). The JAK family comprises four members, JAK1, JAK2, JAK3 and tyrosine kinase 2, which are critical for intracellular cytokine signalling. Since the mid-2010s multiple JAK inhibitors have been approved for inflammatory and haematological indications. Currently, approved JAK inhibitors have demonstrated clinical efficacy; however, improved selectivity for specific JAKs is likely to enhance safety profiles, and different strategies have been used to accomplish enhanced JAK selectivity. In this update, we discuss the background of JAK inhibitors, current approved indications and adverse effects, along with new developments in this field. We address the issue of JAK selectivity and its relevance in terms of efficacy, and describe new modalities of JAK targeting, as well as new aspects of JAK inhibitor action.

Non-kinase off-target inhibitory activities of clinically-relevant kinase inhibitors

Protein kinases are responsible for a myriad of cellular functions, such as cell cycle, apoptosis, and proliferation. Because of this, kinases make excellent targets for therapeutics. During the process to identify clinical kinase inhibitor candidates, kinase selectivity profiles of lead inhibitors are typically obtained. Such kinome selectivity screening could identify crucial kinase anti-targets that might contribute to drug toxicity and/or reveal additional kinase targets that potentially contribute to the efficacy of the compound via kinase polypharmacology. In addition to kinome panel screening, practitioners also obtain the inhibition profiles of a few non-kinase targets, such as ion-channels and select GPCR targets to identify compounds that might possess potential liabilities. Often ignored is the possibility that identified kinase inhibitors might also inhibit or bind to the other proteins (greater than 20,000) in the cell that are not kinases, which may be relevant to toxicity or even additional mode of drug action. This review highlights various inhibitors, which have been approved by the FDA or are currently undergoing clinical trials, that also inhibit other non-kinase targets. The binding poses of the drugs in the binding sites of the target kinases and off-targets are analyzed to understand if the same features of the compounds are critical for the polypharmacology.

Cardiovascular Risk Management in Patients Treated With Janus Kinase Inhibitors

ABSTRACT

The Janus kinase-signal transducer and activator of transcription 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 because of a potential risk of venous thromboembolism 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 venous thromboembolism 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 venous thromboembolism 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.

Differential properties of Janus kinase inhibitors in the treatment of immune-mediated inflammatory diseases

Janus kinases (JAKs) are a family of cytosolic tyrosine kinases that regulate cytokine signal transduction, including cytokines involved in a range of inflammatory diseases, such as RA, psoriasis, atopic dermatitis and IBD. Several small-molecule JAK inhibitors (JAKis) are now approved for the treatment of various immune-mediated inflammatory diseases. There are, however, key differences between these agents that could potentially translate into unique clinical profiles. Each JAKi has a unique chemical structure, resulting in a distinctive mode of binding within the catalytic cleft of the target JAK, and giving rise to distinct pharmacological characteristics. In addition, the available agents have differing selectivity for JAK isoforms, as well as off-target effects against non-JAKs. Other differences include effects on haematological parameters, DNA damage repair, reproductive toxicity and metabolism/elimination. Here we review the pharmacological profiles of the JAKis abrocitinib, baricitinib, filgotinib, peficitinib, tofacitinib and upadacitinib.

Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer's disease

INTRODUCTION

Janus kinase (JAK) inhibitors were recently identified as promising drug candidates for repurposing in Alzheimer's disease (AD) due to their capacity to suppress inflammation via modulation of JAK/STAT signaling pathways. Besides interaction with primary therapeutic targets, JAK inhibitor drugs frequently interact with unintended, often unknown, biological off-targets, leading to associated effects. Nevertheless, the relevance of JAK inhibitors' off-target interactions in the context of AD remains unclear.

METHODS

Putative off-targets of baricitinib and tofacitinib were predicted using a machine learning (ML) approach. After screening scientific literature, off-targets were filtered based on their relevance to AD. Targets that had not been previously identified as off-targets of baricitinib or tofacitinib were subsequently tested using biochemical or cell-based assays. From those, active concentrations were compared to bioavailable concentrations in the brain predicted by physiologically based pharmacokinetic (PBPK) modeling.

RESULTS

With the aid of ML and in vitro activity assays, we identified two enzymes previously unknown to be inhibited by baricitinib, namely casein kinase 2 subunit alpha 2 (CK2-α2) and dual leucine zipper kinase (MAP3K12), both with binding constant (K d) values of 5.8 μM. Predicted maximum concentrations of baricitinib in brain tissue using PBPK modeling range from 1.3 to 23 nM, which is two to three orders of magnitude below the corresponding binding constant.

CONCLUSION

In this study, we extended the list of baricitinib off-targets that are potentially relevant for AD progression and predicted drug distribution in the brain. The results suggest a low likelihood of successful repurposing in AD due to low brain permeability, even at the maximum recommended daily dose. While additional research is needed to evaluate the potential impact of the off-target interaction on AD, the combined approach of ML-based target prediction, in vitro confirmation, and PBPK modeling may help prioritize drugs with a high likelihood of being effectively repurposed for AD.

Highlights

This study explored JAK inhibitors' off-targets in AD using a multidisciplinary approach.We combined machine learning, in vitro tests, and PBPK modelling to predict and validate new off-target interactions of tofacitinib and baricitinib in AD.Previously unknown inhibition of two enzymes (CK2-a2 and MAP3K12) by baricitinib were confirmed using in vitro experiments.Our PBPK model indicates that baricitinib low brain permeability limits AD repurposing.The proposed multidisciplinary approach optimizes drug repurposing efforts in AD research.

Computer-Aided Drug Design of Novel Derivatives of 2-Amino-7,9-dihydro-8H-purin-8-one as Potent Pan-Janus JAK3 Inhibitors

Rheumatoid arthritis (RA) remains one of the most prevalent autoimmune diseases worldwide. Janus kinase 3 (JAK3) is an essential enzyme for treating autoimmune diseases, including RA. Molecular modeling techniques play a crucial role in the search for new drugs by reducing time delays. In this study, the 3D-QSAR approach is employed to predict new JAK3 inhibitors. Two robust models, both field-based with R2 = 0.93, R = 0.96, and Q2 = 87, and atom-based with R2 = 0.94, R = 0.97, and Q2 = 86, yielded good results by identifying groups that may readily direct their interaction. A reliable pharmacophore model, DHRRR1, was provided in this work to enable the clear characterization of chemical features, leading to the design of 13 inhibitors with their pIC50 values. The DHRRR1 model yielded a validation result with a ROC value of 0.87. Five promising inhibitors were selected for further study based on an ADMET analysis of their pharmacokinetic properties and covalent docking (CovDock). Compared to the FDA-approved drug tofacitinib, the pharmaceutical features, binding affinity and stability of the inhibitors were analyzed through CovDock, 300 ns molecular dynamics simulations, free energy binding calculations and ADMET predictions. The results show that the inhibitors have strong binding affinity, stability and favorable pharmaceutical properties. The newly predicted molecules, as JAK3 inhibitors for the treatment of RA, are promising candidates for use as drugs.

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

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.