Discovery of Novel Azetidine Amides as Potent Small-Molecule STAT3 Inhibitors

Brotherton‐Pleiss, Christine; Yue, Peibin; Zhu, Yinsong; Nakamura, Kayo; Chen, Wei‐Liang; Fu, Wen‐Zhen; Kubota, Casie; Chen, Jasmine; Alonso-Valenteen, Felix; Mikhael, Simoun; Medina-Kauwe, Lali K.; Tius, Marcus A.; Lopez-Tapia, Francisco; Turkson, James

doi:10.1021/acs.jmedchem.0c01705

Cited by 25 publications

(19 citation statements)

References 46 publications

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“…It is stabilized in this pocket via a bidentate H bond with Arg595. It is noteworthy that the pTyr705 site is known to be essential for inhibitor binding, and the nitro group of nifuroxazide represents the key binding moiety [33]. However, our data demonstrate that the ligand was able to occupy only two of the three pockets forming the binding cleft (Figure 1B).…”

Section: Molecular Docking Studymentioning

confidence: 70%

Nifuroxazide Mitigates Angiogenesis in Ehlrich’s Solid Carcinoma: Molecular Docking, Bioinformatic and Experimental Studies on Inhibition of Il-6/Jak2/Stat3 Signaling

et al. 2021

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Nifuroxazide is an antidiarrheal medication that has promising anticancer activity against diverse types of tumors. The present study tested the anticancer activity of nifuroxazide against Ehrlich’s mammary carcinoma grown in vivo. Furthermore, we investigated the effect of nifuroxazide on IL-6/jak2/STAT3 signaling and the possible impact on tumor angiogenesis. The biological study was supported by molecular docking and bioinformatic predictions for the possible effect of nifuroxazide on this signaling pathway. Female albino mice were injected with Ehrlich carcinoma cells to produce Ehrlich’s solid tumors (ESTs). The experimental groups were as follows: EST control, EST + nifuroxazide (5 mg/kg), and EST + nifuroxazide (10 mg/kg). Nifuroxazide was found to reduce tumor masses (730.83 ± 73.19 and 381.42 ± 109.69 mg vs. 1099.5 ± 310.83) and lessen tumor pathologies. Furthermore, nifuroxazide downregulated IL-6, TNF-α, NFk-β, angiostatin, and Jak2 proteins, and it also reduced tumoral VEGF, as indicated by ELISA and immunohistochemical analysis. Furthermore, nifuroxazide dose-dependently downregulated STAT3 phosphorylation (60% and 30% reductions, respectively). Collectively, the current experiment shed light on the antitumor activity of nifuroxazide against mammary solid carcinoma grown in vivo. The antitumor activity was at least partly mediated by inhibition of IL-6/Jak2/STAT3 signaling that affected angiogenesis (low VEGF and high angiostatin) in the EST. Therefore, nifuroxazide might be a promising antitumor medication if appropriate human studies will be conducted.

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Section: Molecular Docking Studymentioning

confidence: 70%

Nifuroxazide Mitigates Angiogenesis in Ehlrich’s Solid Carcinoma: Molecular Docking, Bioinformatic and Experimental Studies on Inhibition of Il-6/Jak2/Stat3 Signaling

et al. 2021

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“…Azetidine moieties are common fragments of pharmaceutical molecules . These molecules are strained and highly reactive which causes potential safety hazards during synthesis and storage. − Azetidine freebases are highly reactive and tend to oligomerize, which makes it difficult to use directly in synthesis .…”

Section: Resultsmentioning

confidence: 99%

Quantum Mechanical Methods for Thermal Hazard Risk Assessment in Early Phase Pharmaceutical Development

Sayyed

Kolis

Xia

2022

Org. Process Res. Dev.

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Quantum mechanical (QM) applications to predict heat of reaction (ΔH r ) and thermal stability of strained aminocarbocylic salts, diazo compounds, and nitroalkanes for early phase thermal hazard risk assessment is presented. We provide examples on the use of explicit solvation to predict accurate ΔH r . Based on the QM calculations, the criticality class of a coppercatalyzed C−N coupling reaction is determined according to Stoessel's reaction criticality class and the predictions are consistent with RC1 calorimetric experiment. We emphasize that to predict accurate ΔH r , it is important to consider the roles of reagents and solvents in QM calculations rather than simply considering the bond formation and bond breaking steps involved with reactants and products. Further, the use of predicted ΔH r for salt formation is applied to predict the thermal stability of bromoacetylene azetidine compounds to establish structure−stability relationship which would be useful to identify stable salt intermediates for safe reaction design. A strong correlation between ΔH r and the left limit of the DSC onset temperature (T init , °C) of the of exothermic peak is identified (T init = −2.85 ΔH r − 99.5). We propose that this model can be used as a prediction tool for novel azetidine salts to provide an estimate of thermal stability before synthesis. In this paper, for the first time we report molecular electrostatic potential (MESP) descriptor for the prediction of T init of diazo compounds. The deepest MESP minimum (V min ) on the diazo group is considered as a probe to quantify the variation in structural effects. A strong correlation between V min and T init is found which would provide a new way of interpreting the thermal stability of novel diazo molecules just based on chemical structure. Further, the applicability of V min is verified on another set of compounds (nitroalkanes) and a good correlation is obtained. The structure− stability relationships that involves V min can be a useful QM descriptor for thermal stability prediction of a variety of molecules.

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“…Many structures containing four-membered rings were generated using the previous method. Although four-membered ring structures, such as azetidine, have been used as building blocks in recent years, − these rings generally lack stability because of their strong distortion, which increases the difficulty of their synthesis. The molecules that were newly generated using our proposed method were similar to the source molecules, for which gene expression profiles were measured.…”

Section: Resultsmentioning

confidence: 99%

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

Kaitoh

Yamanishi

2021

J. Chem. Inf. Model.

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One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand−target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

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Discovery of Novel Azetidine Amides as Potent Small-Molecule STAT3 Inhibitors

Cited by 25 publications

References 46 publications

Nifuroxazide Mitigates Angiogenesis in Ehlrich’s Solid Carcinoma: Molecular Docking, Bioinformatic and Experimental Studies on Inhibition of Il-6/Jak2/Stat3 Signaling

Nifuroxazide Mitigates Angiogenesis in Ehlrich’s Solid Carcinoma: Molecular Docking, Bioinformatic and Experimental Studies on Inhibition of Il-6/Jak2/Stat3 Signaling

Quantum Mechanical Methods for Thermal Hazard Risk Assessment in Early Phase Pharmaceutical Development

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

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