Computer aided designing of novel pyrrolopyridine derivatives as JAK1 inhibitors

Keretsu, Seketoulie; Ghosh, Suparna; Cho, Seung Joo

doi:10.1038/s41598-021-02364-2

Cited by 12 publications

(14 citation statements)

References 36 publications

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“…JAK1 inhibitors including ruxolitinib, tofacitinib, filgotinib, peficitinib, and numerous additional second-generation inhibitors are now under investigation for the treatment of inflammatory and autoimmune illnesses. Because of limited potency, non-targeting, and off-target effects ( Keretsu et al, 2021a ), new JAK1 inhibitors with high potency and selectivity are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

et al. 2022

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JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

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Section: Introductionmentioning

confidence: 99%

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

et al. 2022

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“…Both drugs are effective with JAK1 or JAK2 depending on the binding interactions and binding position inward these proteins. Glu957 and Leu959 are important interactions in the hinge region of JAK1, 13 and this interaction is determined to be significant for the binding of inhibitors within the kinase protein. Therefore, Ruxolitinib strongly binds with JAK1 compared to Tofacitinib via the formation of two strong hydrogen bonds.…”

Section: Resultsmentioning

confidence: 99%

Synchrotron Fourier Transform Infrared Microscopy Spectra in Cellular Effects of Janus Kinase Inhibitors on Myelofibrosis Cancer Cells

et al. 2022

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Janus kinase (JAK) deregulation of the JAK/signal transducers and activators of transcription pathway leads to myelofibrosis that can be treated by JAK inhibitors including Ruxolitinib and Tofacitinib. Even though both inhibitors are effective against myelofibrosis, each of them has a different mode of action in the cells. Ruxolitinib is an inhibitor for selective JAK1/2, and Tofacitinib is an inhibitor for JAK3. This study evaluated the chemical fingerprints of TF-1 cells after JAK inhibitor treatments by the synchrotron Fourier transform infrared microspectroscopy (S-FTIR) spectrum. Tofacitinib and Ruxolitinib treatments in TF-1 cells were applied with a chemical fingerprint approach in S-FTIR spectroscopy and in vitro cytotoxicity in a cell-based assay. Principal component analysis or PCA was utilized to classify three cell treatments with three biochemical alteration absorbances of lipid vibration by the C–H stretching, protein amide I that appeared from the C=O stretching, and a P=O phosphodiester bond from nucleic acids. The results showed that the inhibition effect of Ruxolitinib on the TF-1 cell lines was two-fold higher than Tofacitinib. PCA distinguishes untreated and drug-treated cells by detecting cellular biochemical alteration. The loading plots identify that proteins and nucleic acids were the different main components in disparate cell treatments. Tofacitinib was distinct from the others in lipid and nucleic acid. The second derivative spectra of the three molecular components had decreased lipid production and accumulation, changes in secondary structures in proteins, and a high level of RNA overexpression in cell treatment. The JAK inhibitors caused different spectroscopic biomarkers of the modifications of secondary protein conformation, stimulated cell lipid accumulation, and phosphorylation from untreated cells. The alteration of cellular biochemical components suggests that FTIR is a potential tool to analyze specific patterns of drug cellular responses at the molecular level.

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“…The pIC 50 values were well distributed across the three log units (pIC 50 = 5.26 to 8.80). The entire dataset was categorized into low, medium, and high activity segments, as described here [ 31 , 32 ]. The 9 compounds were randomly selected from each segment as the test set compounds in such a way that the compounds would cover different activity ranges while maintaining their structural variations.…”

Section: Methodsmentioning

confidence: 99%

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3

Ghosh

Cho²

2022

IJMS

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Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein–ligand binding affinity by employing molecular mechanics Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure–activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC50 values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods.

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Computer aided designing of novel pyrrolopyridine derivatives as JAK1 inhibitors

Cited by 12 publications

References 36 publications

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

Synchrotron Fourier Transform Infrared Microscopy Spectra in Cellular Effects of Janus Kinase Inhibitors on Myelofibrosis Cancer Cells

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3

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