MedChemComm最新文献

Interest has been generated in VEGFR-2 and c-MET as potential receptors for the treatment of different malignancies. Using aryl pyridine derivatives with 1,3-diphenylurea attached, a number of promising dual VEGFR-2 and c-MET inhibitors were developed and synthesized. Regarding the molecular target, compounds 2d, 2f, 2j, 2k, and 2n had potent IC₅₀ values of 65, 24, 150, 170, and 18 nM against c-MET, respectively. Additionally, they had potent IC₅₀ values of 310, 35, 290, 320, and 24 nM against VEGFR-2, respectively. Regarding cytotoxicity, compounds 2d, 2f, 2j, 2k and 2n exhibited potent cytotoxicity against MCF-7 with IC₅₀ values in the range 0.76–21.5 μM, and they showed promising cytotoxic activity against PC-3 with IC₅₀ values in the range 1.85–3.42 μM compared to cabozantinib (IC₅₀ = 1.06 μM against MCF-7 and 2.01 μM against PC-3). Regarding cell death, compound 2n caused cell death in MCF-7 cells by 87.34-fold; it induced total apoptosis by 33.19% (8.04% for late apoptosis, 25.15% for early apoptosis), stopping their growth in the G₂/M phase, affecting the expression of apoptosis-related genes P53, Bax, caspases 3 and 9 and the anti-apoptotic gene, Bcl-2. In vivo study illustrated the anticancer activity of compound 2n by reduction of tumor mass and volume, and the tumor inhibition ratio reached 56.1% with an improvement of hematological parameters. Accordingly, compound 2n can be further developed as a selective target-oriented chemotherapeutic against breast cancer.

Neuropathic pain is a type of chronic pain, usually caused by nerve damage, that responds poorly to traditional pain therapies. The N-type calcium channel (Ca_V2.2) is a well-validated pharmacological target to treat this condition. In order to further improve the inhibition of the N-type calcium channel relative to previously described inhibitors, and also address their problematic instability in blood plasma, the development of N-sulfonylphenoxazines as new calcium channel inhibitors was pursued. A series of N-sulfonylphenoxazines bearing ammonium side chains were synthesised and tested for their ability to inhibit both Ca_V2.2 and Ca_V3.2 (T-type) neuronal ion channels. Compounds with low micromolar activity in Ca_V2.2 were identified, equivalent to the most effective reported for this class of bioactive, and calculations based on their physical and chemical characteristics suggest that the best performing compounds have a high likelihood of being able to penetrate the blood–brain barrier. Representative N-sulfonylphenoxazines were tested for their stability in rat plasma and were found to be much more resilient than the previously reported N-acyl analogues. These compounds were also found to be relatively stable in an in vitro liver microsome metabolism model, the first time that this has been investigated for this class of compound. Finally, molecular modelling of the Ca_V2.2 channel was used to gain an understanding of the mode of action of these inhibitors at a molecular level. They appear to bind in a part of the channel, in and above its selectivity filter, in a way that hinders its ability to undergo the conformational changes required to open and allow calcium ions to pass through.

Correction for ‘Highly potent and selective phosphatidylinositol 4-kinase IIIβ inhibitors as broad-spectrum anti-rhinoviral agents’ by Avinash G. Vishakantegowda et al., RSC Med. Chem., 2024, 15, 704–719, https://doi.org/10.1039/D3MD00630A.

The plasma protein binding (PPB) of a drug plays a key role in both its pharmacokinetic and pharmacodynamic properties. During lead optimisation, medium and high throughput methods for the early determination of PPB can provide important information about potential PKPD profile within a chemotype or between different chemotype series. Diffusion ordered spectroscopy (DOSY) is an NMR spectroscopic technique that measures the diffusion of a molecule through the magnetic field gradient, according to its molecular size/weight. Here, we describe the use of DOSY for a rapid and straightforward method to evaluate the PPB of drug molecules, using their binding to bovine serum albumin (BSA) as a model.

Hybrid-based design has gained significant interest in the development of novel active substances with anti-inflammatory properties. In this study, two series of new pyrazole–pyridazine-based hybrids, 5a–f and 6a–f, were designed and synthesized. Molecules containing pyrazole and pyridazine pharmacophores in a single molecule, each with a unique mechanism of action and different pharmacological characteristics, are believed to exert higher biological activity. The cell viability of all compounds was evaluated using MTT assay in LPS-induced RAW264.7 macrophages. In vitro COX-1 and COX-2 inhibition assays were performed for the investigation of the anti-inflammatory activity of target compounds. Trimethoxy derivatives 5f and 6f were the most active candidates, demonstrating higher COX-2 inhibitory action than celecoxib, with IC₅₀ values of 1.50 and 1.15 μM, respectively. Bromo derivative 6e demonstrated a COX-2 inhibitory activity comparable to celecoxib. Further, the ability of compounds 5f, 6e, and 6f to inhibit the generation of specific pro-inflammatory cytokines and mediators, including nitric oxide (NO), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and prostaglandin-E2 (PGE-2), in RAW264.7 macrophages stimulated by LPS was also estimated. Compounds 5f and 6f demonstrated the most potent activity. Morover, according to the investigation using molecular modeling studies, derivatives 5f and 6f showed respectable binding affinity towards the COX-2 active site compared to the reference ligand. Moreover, the ADME parameters, physicochemical characteristics, pharmacokinetic characteristics, and l of the most potent compounds were also computed.

Cancer cell proliferation and metastasis are known to be dependent on angiogenesis which is regulated by several parameters including copper availability. Tetradentate monoquinoline (TDMQ) ligands constitute a series of chelators tailored to regulate copper homeostasis due to their specificity for copper(II) with respect to Cu(I) or other biometals like iron or zinc. One of these chelators, TDMQ20 efficiently inhibits both proliferation and migration of several human cancer cell lines, better than the reference drug 5-fluorouracil, and with higher selectivity indexes with respect to non-cancer human cells. The biological activity of TDMQ20 may be driven by the coordination chemistry of copper, and the ability of this chelator to restore copper homeostasis and its subsequent redox properties. The anticancer mechanism of action of TDMQ20 involves intracellular production of reactive oxygen species, drastic mitochondrial damages and induction of tumor cell apoptosis. These data support the selection of TDMQ20 as drug-candidate against several human cancers.

The phosphoantigen (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP) is an established activator of Vγ9/Vδ2 T cells and stimulates downstream effector functions including cytotoxicity and cytokine production. In order to improve its drug-like properties, we herein report the design, synthesis, serum stability, in vitro metabolism, and biological evaluation of a new class of symmetrical phosphonodiamidate prodrugs of methylene and difluoromethylene monophosphonate derivatives of HMBPP. These prodrugs, termed phosphonodiamidate ProPAgens, were synthesized in good yields, exhibited excellent serum stability (>7 h), and their in vitro metabolism was shown to be initiated by carboxypeptidase Y. These phosphonodiamidate ProPAgens triggered potent activation of Vγ9/Vδ2 T cells, which translated into efficient Vγ9/Vδ2 T cell-mediated eradication of bladder cancer cells in vitro. Together, these findings showcase the potential of these phosphonodiamidate ProPAgens as Vγ9/Vδ2 T cell modulators that could be further developed as novel cancer immunotherapeutic agents.

Molecular machine learning algorithms are becoming increasingly powerful at predicting the potency of potential drug candidates to guide molecular discovery, lead series prioritization, and structural optimization. However, a substantial amount of inhibition data is bounded and inaccessible to traditional regression algorithms. Here, we develop a novel molecular pairing approach to process this data. This creates a new classification task of predicting which one of two paired molecules is more potent. This novel classification task can be accurately solved by various, established molecular machine learning algorithms, including XGBoost and Chemprop. Across 230 ChEMBL IC₅₀ datasets, both tree-based and neural network-based “DeltaClassifiers” show improvements over traditional regression approaches in correctly classifying molecular potency improvements. The Chemprop-based deep DeltaClassifier outperformed all here evaluated regression approaches for paired molecules with shared and with distinct scaffolds, highlighting the promise of this approach for molecular optimization and scaffold-hopping.

To investigate atropisomers of non-steroidal glucocorticoid receptor modulator GSK866, a virtual library of substituted benzoic acid analogues was enumerated. Compounds from this library were subjected to a torsion angle scan using Spartan'20 to calculate the torsion rotation energy barrier which identified compounds predicted to be stable as atropisomers. After synthesis of the library, analysis showed that compounds 13 and 14 existed as stable atropisomers 13a, 13b, 14a and 14b, in agreement with the earlier calculations. Screening in a glucocorticoid receptor cellular assay showed that one compound from each atropisomer pair was significantly more potent than the other. Docking in a public structure of the glucocorticoid receptor (PBD code 3E7C) enabled the stereochemistry of the two most potent compounds 13a and 14b to be assigned as (R_a) and (S_a), respectively.

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein–ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.