Current medicinal chemistry最新文献

Glioblastoma (GBM) is a malignant primary brain tumor with a poor prognosis and high recurrence rates. At present, the current treatments available for GBM patients can only prolong their overall survival and cannot provide a complete cure. Discovering an effective therapy against the disease is a challenge due to its recurrence and resistance to common available treatments for GBM. Several natural products have been documented to possess the potential to function as anticancer agents through diverse mechanisms. Astaxanthin (AXT) is an orange-red pigment that is a natural lipophilic and xanthophyll carotenoid derived mostly from microalgae. Numerous studies have examined that AXT impacts GBM cells in laboratory settings and animal models. This review aims to provide the latest information about the potential of astaxanthin as a novel therapeutic option for GBM. AXT has been targeted more on reactive oxygen species (ROS), and suppressed tumor growth in vitro and in vivo conditions. The available data suggests that AXT might serve as a key component in the development of innovative cancer therapies, especially for glioblastoma.

Introduction: Prolyl-specific oligopeptidase (POP), one of the brain's highly expressed enzymes, is an important target for the therapy of central nervous system disorders, notably autism spectrum disorder, schizophrenia, Parkinson's, Alzheimer's disease, and dementia.

Method: The current study was designed to investigate 2,4-bis(trifluoromethyl) benzaldehyde- based thiosemicarbazones as POP inhibitors to treat the above-mentioned disorders. A variety of techniques, such as nuclear magnetic resonance (NMR), mass spectrometry (MS), and Fourier-transform infrared spectroscopy (FTIR), were used for the structural confirmation of synthesized compounds. After in-vitro evaluation, all of these compounds were found to be prominent inhibitors of the POP enzyme (IC50= 10.14 - 41.73 μM).

Result: Compound 3a emerged as the most active compound (IC50 10.14 ± 0.72 μM) of the series. The kinetic study of the most active 3a (Ki =13.66 0.0012 μM) indicated competitive inhibition of the aforementioned enzyme.

Conclusion: Moreover, molecular docking depicted a noticeable role of thiosemicarbazide moiety in the binding of these molecules within the active site of the POP enzyme.

Background: Non-Nucleoside Reverse Transcriptases Inhibitors (NNRTIs) are among the most extensively studied enzymes for understanding the biology of Human Immunodeficiency Viruses (HIV) and designing inhibitors for managing HIV infections. Indolyl aryl sulfones (IASs), an underexplored class of potent NNRTIs, require further exploration for the development of newer drugs for HIV.

Aims: In this context, we synthesized a series of novels by Indolyl Aryl Sulfones with a hydrazone moiety at the carboxylate site of the indole nucleus. A 2D-QSAR model was developed to predict Reverse Transcriptase inhibitory activity against wild-type RT (WT-RT) enzyme.

Method: The model was successfully applied to predict the HIV-1 inhibitory activity of known Indolyl Aryl Sulfones. Considering the reliability, robustness, and reproducibility of the 2D-QSAR model, we made an in-silico prediction of the RT inhibition for our synthesized compounds (1-14).

Results: Molecular docking and dynamics simulations established our synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, as effective NNRTIs by stabilizing HIV reverse transcriptase's structure. Binding energy calculations revealed compound 28 as the strongest inhibitor (-43.21 ± 0.09 kcal/mol), followed by 23 (-40.94 ± 0.10 kcal/mol) and 24 (-39.18±0.08 kcal/mol), emphasizing their binding affinity towards HIV reverse transcriptase.

Conclusion: In summary, the synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, demonstrate significant potential as Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) against HIV. These results highlight the promising role of these compounds in developing novel NNRTIs for managing HIV infections.

Background: The development of effective anti-cancer medicines with low side effects is imperative as cancer continues to be a leading cause of death globally. By obstructing the survival and growth of cancer cells, small-molecule medications have made tremendous progress in the field of cancer research. Several bioactive heterocyclic compounds, including derivatives of piperidine and 2,3-dihydrobenzofuran, have shown great promise and are found in various anti-cancer medications. Cancer growth and metastasis are hindered by these small molecule inhibitors, which interfere with vital signals that drive cancer cell proliferation.

Objective: This study focuses on the synthesis and evaluation of novel Sulfonyl Piperidine Analogues containing 2,3-Dihydrobenzofuran-5-Carboxamide as potential anti-- cancer agents.

Methods: The synthesized compounds were characterized using spectroscopic techniques such as 1H NMR and ESI-MS. Protein-drug interaction studies, DFT analysis, and target prediction techniques were employed. The anti-cancer properties of the compounds were evaluated in vitro against MCF-7 cell lines. Compounds 5 and 7 were specifically investigated for their growth-inhibitory effects on MCF7 breast cancer cells.

Results: Compounds5 and 7 demonstrated strong binding affinity towards both mutated BRCA1 (PDB ID: 1N5O) and BRCA2 (PDB ID:8BR9). Furthermore, they displayed notable efficacy against MCF-7 cell lines.

Conclusion: Synthesized compounds displayed activity against MCF-7 cell lines, supporting findings from in-silico predictions. Further investigations are warranted to elucidate the mechanisms of action of these selected molecules against MCF-7 cell types.

Background: Both coronavirus disease 2019 (COVID-19) and idiopathic pulmonary fibrosis (IPF) could cause severe pulmonary injury and have extremely dismal prognoses with a high risk of mortality. Resveratrol (RSV), a natural polyphenol, has promising potential in the treatment of viral infection and pulmonary fibrosis.

Objective: The purpose of this research was to investigate the unclear mechanism of RSV as an anti-COVID-19 and IPF therapy.

Method: Utilizing relevant databases, the intersection of genes related to IPF, COVID-19, and possible RSV targets was discovered. Then the obtained targets were investigated using GO and KEGG analysis, TP and PPI network analysis. Furthermore, the binding affinities between core targets and RSV were calculated using molecular docking.

Results: The 1101 COVID-19 targets, 2166 IPF targets, and 341 RSV targets intersected with 21 overlapping targets. PPI network reveals the interactions among targets and TP network reveals interactions between targets and pathways. Five targets including JUN, CCL2, CXCL8, IL6, and SERPINE1 were identified as the core targets through two network analyses. GO analysis demonstrated chemotaxis, inflammatory response and angiogenesis were the significant pathophysiological processes. Combing TP network analysis and KEGG analysis, IL-17 signaling pathway was considered as the significant pathway. Except for JUN, molecular docking showed the binding energies of other four targets were lower than -5 kcal/mol indicating intimate interactions between RSV and other targets.

Conclusions: Our research elucidate the targets, pathways and pathophysiological processes of RSV involved in effects of anti-COVID-19 and IPF, suggesting RSV could be a therapeutic candidate for reducing infection and fibrosis.

Background: Atherosclerosis is a complex cardiovascular disease often associated with mitochondrial dysfunction, which can lead to various cellular and metabolic abnormalities. Within the mitochondrial genome, specific mutations have been implicated in contributing to mitochondrial dysfunction. Atherosclerosis-associated m.15059G>A mutation has been of particular interest due to its potential role in altering mitochondrial function and cellular health.

Objective: This study aims to investigate the role of the atherosclerosis-associated m.15059G>A mutation in the development of mitochondrial dysfunction in monocyte-- like cells.

Methods: Monocyte-like cytoplasmic hybrid cell line TC-HSMAM1, which contains the m.15059G>A mutation in mtDNA, was used. The MitoCas9 vector was utilized to eliminate mtDNA copies carrying the m.15059G>A mutation from TC-HSMAM1 cybrids. Mitochondrial membrane potential, generation of reactive oxygen species, and lipid peroxidation levels were assessed using flow cytometry. Cellular reduced glutathione levels were assessed using the confocal microscopy. The oxygen consumption rate was measured using polarographic oxygen respirometry.

Results: The elimination of the m.15059G>A mutation resulted in a significant increase in mitochondrial membrane potential and improved mitochondrial efficiency while also causing a decrease in the generation of reactive oxygen species, lipid peroxidation, as well as cellular bioenergetic parameters, such as proton leak and non-mitochondrial oxygen consumption. At the same time, no changes were found in the intracellular antioxidant system after the mitochondrial genome editing.

Conclusions: The presence of the m.15059G>A mutation contributes to mitochondrial dysfunction by reducing mitochondrial membrane potential, increasing the generation of reactive oxygen species and lipid peroxidation, and altering mitochondrial bioenergetics. Elimination of the mtDNA containing atherogenic mutation leads to an improvement in mitochondrial function.

Tuberculosis (TB) is a leading cause of death from a single infectious disease worldwide. Early and accurate diagnosis is advantageous for timely detection and prompt treatment, thereby reducing the risk of disease transmission, which is essential for effective TB control. Biomarkers provide a valuable resource for TB diagnosis. Proteomic technologies have emerged as a powerful tool in biomarker discovery. In this perspective, we explore how proteomic technologies contribute to the discovery of TB diagnostic biomarkers. We also address the challenges and discuss prospective methods to augment the performance of biomarkers in diagnosing TB.

Aim: We focused on the FOXN3 gene and selected its antisense transcripts (FOXN3-AS1) to investigate its potential involvement in acute myeloid leukemia (AML).

Background: Several integrated multi-omics datasets have expanded the horizons of the cancer landscape. With the emergence of new high-throughput technologies, a large number of non-coding RNAs have been confirmed to be involved in the pathogenesis of different types of hematological malignancies.

Methods: We conducted experimental validation using quantitative polymerase chain reaction (qPCR) with bone marrow specimens from AML patients. Then, Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves were used to substantiate the prognostic association between FOXN3-AS1 and AML patients within the TCGA database. Correlation between FOXN3-AS1 expression and gene mutation, immune, and immune function using Spearman correlation analysis. To explore the physical and functional interaction between FOXN3-AS1 and the DNMT1 protein, we utilized the RPISeq web tool from Iowa State University. Subsequently, we performed qPCR experiments to test the effect of 5AzaC (DNMT1 inhibitor) on FOXN3-AS1 expression AML cell lines (THP1 and OCI-AML3). We leveraged the "OncoPredict" R package in conjunction with the Genomics of Drug Sensitivity (GDSC) database to predict drug response in AML patients expressing FOXN3-AS1.

Results: We observed a significant upregulation of FOXN3-AS1 expression in AML patients compared to healthy controls using clinical samples. The TCGA database revealed an association between high FOXN3-AS1 expression and adverse prognosis. In our subsequent analysis, genes with poor prognostic implications in AML patients were exclusively identified in the FOXN3-AS1 high-expression group, further corroborating this relationship. AML patients with higher FOXN3-AS1 expression levels may respond less optimally to immunotherapy than patients with lower levels. Besides, we computationally predicted the interaction of FOXN3- AS1 and DNMT1 protein and experimentally confirmed that DNMT1i (GSK-3484862) affects the expression level of FOXN3-AS1. We also found that the chemotherapy drugs (5-Fluorouralic, Cisplatin, Dactolisib, Sapitinib, Temozolomide, Ulixertinib, Vinorelbine, Ruxolitinib, Osimertinib and Cisplatin) showed favorable responses in AML patients with high FOXN3-AS1 expression levels.

Conclusion: Our candidate approach identifies FOXN3-AS1 as a prognostic indicator of survival in AML with a potential immune-related role. The preliminary observations we made on FOXN3-AS1/DNMT1 crosstalk warrant more in-depth invested immunotherapeutic approaches in AML.

Aim: Silent information regulator two homologue one (SIRT1) is an emerging target for managing metabolic disorders. This study aimed to synthesize novel 5-(- substituted phenyl)-2-aryl benzimidazole derivatives and evaluate them for SIRT1 activation.

Methods: The compounds were designed according to the findings of the QSAR models framed in our previous studies. Molecular docking and dynamics studies were also performed to explore the interactions of designed compounds with the active site of the SIRT1 enzyme using AutoDock Vina and Schrödinger Maestro version 11.8.012, respectively. Compounds with good binding affinity were synthesized by Suzuki-Miyaura cross-coupling and spectrally characterized. The molecules were evaluated for their in vitro SIRT1 activation properties using a fluorescent screening kit. Based on the results of in vitro assay, a structure-activity relationship was established. SwissADME was employed to calculate the pharmacokinetics characteristics of the synthesized molecules.

Results: The molecular docking studies revealed that all the activators were effectively docked in the catalytic active site. All compounds demonstrated interactions with important amino acids like Glu230 and Arg446. In molecular dynamics simulations, the root mean square deviation (RMSD) of compound 5m and protein SIRT1 remained stable, i.e., below 3mm. Compound 5m, 4-(2-(3,4-dihydroxy-5-nitrophenyl)-1H-benzo[d]imidazol- 5-yl)benzaldehyde, was the most potent compound with an EC50 value of 0.006 mM (±0.001) and maximum activation of 240.5%. All the synthesized compounds had acceptable theoretical ADME profiles, and drug-likeness properties complied with Lipinski's rule.

Conclusion: According to the findings, synthesized compounds may be viable leads for SIRT1 activators and may be used to advance preclinical in vivo research utilizing animal models.

Drugs are commonly utilized to diagnose, cure, or prevent the occurrence of diseases, as well as to restore, alter, or change organic functions. Drug discovery is a time-consuming, costly, difficult, and inefficient process that yields very few medicinal breakthroughs. Drug research and design involves the capturing of structural information for biological targets and small molecules as well as various in silico methods, such as molecular docking and molecular dynamic simulation. This article proposes the idea of expediting computational drug development through a collaboration of scientists and universities, similar to the Human Genome Project using machine learning (ML) strategies. We envision an automated system where readily available or novel small molecules (chemical or plant-derived), as well as their biological targets, are uploaded to an online database, which is constantly updated. For this system to function, machine learning strategies have to be implemented, and high-quality datasets and high quality assurance of the ML models will be required. ML can be applied to all computational drug discovery fields, including hit discovery, target validation, lead optimization, drug repurposing, and data mining of small compounds and biomolecule structures. Researchers from various disciplines, such as bioengineers, bioinformaticians, geneticists, chemists, computer and software engineers, and pharmacists, are expected to collaborate to establish a solid workflow and certain parameters as well as constraints for a successful outcome. This automated system may help speed up the drug discovery process while also lowering the number of unsuccessful drug candidates. Additionally, this system will decrease the workload, especially in computational studies, and expedite the process of drug design. As a result, a drug may be manufactured in a relatively short time.