Current Medicinal Chemistry - Online First

The disability and mortality related to Parkinson's disease (PD), a neurodegenerative disease, are increasing globally at a faster rate than other neurological disorders. With no permanent cure for PD, there is an urgent need to develop novel and effective anti-PD drugs.

Targeting monoamine oxidases (MAO), which catalyze the breakdown of neurotransmitters, is one way to treat neurodegenerative diseases. In this context, an initial molecular docking of twenty designed sulfonyl derivatives of benzimidazole against monoamine oxidase B (MAO-B) associated with PD was conducted using AutoDock Vina.

The results were compared with those of the conventional inhibitors, selegiline and rasagiline. Based on the docking score, the in-silico pharmacokinetic properties (ADME), drug-likeness, and toxicity profiles of the newly synthesized molecules were examined using SwissADME, PreADMET, ProTox-3.0, vNN, and ADMETlab web tools. Then, twelve potential derivatives were synthesized and characterized by IR, ¹H-NMR, ¹³C-NMR, ¹⁹F-NMR (for some compounds), and mass spectrometry. Derivatives 2cj and 1bj were the two molecules having the best binding affinity of -11.9 and -11.8 kcal/mol, respectively, against MAO-B, exhibiting a higher binding affinity compared to that of some commercially available drugs. A 50 ns MD simulation run was performed to observe the stability of the top two docked complexes, MAO-B-2cj and MAO-B-1bj, in order to further validate the efficacy of those two substances. Moreover, the MM-PBSA method was used to calculate the final, binding free energy of the simulated (MAO-B-2cj) complex.

This study indicates that the binding affinity of most of the hits was superior to that of known MAO inhibitors; therefore, these newly synthesized benzimidazole derivatives may be developed into essential drug candidates for the treatment of PD.

Prosopis juliflora has been employed in many traditional treatments. As evidenced by our earlier research, Prosopis juliflora leaf methanol extract (PJME) has a promising future in the fight against lung cancer. It may also be used in conjunction with other treatments to effectively manage lung cancer. Aims and objective: The main objective of this study was to explore the potential of PJME to inhibit lung cancer in A549 cells, along with its underlying mechanisms of action.

The antiproliferative effects were determined using MTT and LDH tests. Apoptosis-inducing capacity was evaluated using the DAPI staining, caspase-3 test, cytochrome C assay, PARP cleavage, and qRT-PCR. To investigate the mechanism of action of PJME in lung cancer, the levels of ROS, MMP, GSH, MDA, and specific ferroptosis indicators were measured.

The experimental data of the current study indicated that exposure of A549 cells to PJME reduced cell viability and increased cellular cytotoxicity. The apoptosis-inducing ability of PJME in A549 cells was validated by enhanced nuclear condensation, level of the caspase-3, cytochrome C, and PARP release. In addition, qRT-PCR investigations verified that the administration of PJME led to a decrease in the expression of anti-apoptotic gene Bcl2 while enhancing the mRNA level of pro-apoptotic genes, such as Bax and caspase-3, in A549 cells.

The study also found that PJME has the ability to activate ferroptosis pathways, as evidenced by elevated reactive oxygen species (ROS) generation, changes in the levels of antioxidant markers (MDA and GSH), and decreased expression of SLC7A11 and GPX4. The results of the present study clearly showed that PJME inhibited the proliferation of A549 cells and induced ferroptosis by reducing the expression of the important targets SLC7A11 and GPX4. Further research is necessary to fully understand the clinical efficacy of PJME before it can be investigated as supplemental or adjuvant therapy for lung cancer.

PANoptosis plays an important role in many inflammatory diseases. However, there are no reports on the association between PANoptosis and CD.

This study used five machine learning algorithms - least absolute shrinkage and selection operator, support vector machine, random forest, decision tree and Gaussian mixture models - to construct CD’s PANoptosis signature. Unsupervised hierarchical clustering analysis was used to identify PANoptosis-associated subgroups of CD. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to compare the PANoptosis-associated subgroups of CD among the potential biological mechanisms. Single sample GSEA was used to assess immune microenvironmental differences among the subgroups. The potential role of PANoptosis in CD was further explored using single-cell RNA-Seq (scRNA-Seq) for PANoptosis scoring, differential analysis, pseudotime analysis, cellular communication analysis and weighted gene co-expression network analysis (WGCNA) analysis.

CD’s PANoptosis signature consisted of seven genes: CEACAM6, CHP2, PIK3R1, CASP10, PSMB1, PSMB8 and UBC. The PANoptosis signature in multiple cohorts had a strong ability to recognise CD. The levels of immune cell infiltration and the vigour of the immune responses significantly varied between the two subpopulations of CD associated with PANoptosis. Multiple lines of evidence from the GO, KEGG, GSEA, GSVA, scRNA-Seq and WGCNA analyses suggested that I-kappaB kinase/NF-kappaB signalling, mitogen-activated protein kinase (MAPK), leukocyte activation and leukocyte migration were mechanisms closely associated with PANoptosis in CD.

This study is the first to construct a PANoptosis signature with excellent efficacy in recognising CD. PANoptosis may mediate the process of CD through inflammatory and immune mechanisms, such as NF- kappaB, MAPK and leukocyte migration.

Tyrosine kinase inhibitors (TKIs) target certain cell signalling pathways, and have become a promising class of medications for the treatment of cancer in recent years. Because of their distinct structure and adaptable chemistry, pyrazolines have drawn a lot of interest from organic and medicinal chemists. Their exceptional TKI activity has prompted them to investigate chemotherapy for cancer.

We aim to develop agents that inhibit tyrosine kinases highly effective with the least amount of harm possible, perhaps improving the course of cancer treatment.

This review compiled current information from recent literature sources, including in vitro, in vivo, approved medications, active clinical trials, and the structure-activity relationships (SAR) linked to various pyrazoline analogues used as small-molecule Tyrosine Kinase Inhibitors in cancer treatment.

This study focuses on SAR inside the pyrazoline ring and its derivatives as TKIs, and it emphasizes current developments, including patents, authorized medications, and compounds in clinical trials.

By enhancing our understanding of these compounds, our goal is to aid in making the roles of pharmacologists, scientists, and researchers who are designing and developing next-generation anticancer drugs with pyrazoline scaffolds easier. The future holds immense potential for the continued evolution of pyrazoline-based therapies, offering renewed hope in the ongoing battle against cancer.

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder that negatively affects female reproductive capacity. Although the association between autophagy and PCOS is known, there are few detailed studies on the association between autophagy-related genes and PCOS.

Publicly available gene expression datasets (GSE102293, GSE138518, GSE34526, GSE114419, GSE137684, GSE155489) were used in a comprehensive analysis to identify a role for autophagy in PCOS. Batch effects were mitigated using the sva package, followed by WGCNA (weighted gene correlation network analysis) and ssGSEA (single sample gene set enrichment analysis) to identify autophagy-related genes. Recursive feature elimination (RFE) and LASSO COX methods were used to identify important hub genes, and their correlation with immune cell activity was assessed using ssGSEA and Pearson correlation analysis.

High autophagy scores were observed in PCOS samples, and the dark green gene module with the highest autophagy correlation was identified. The differential analysis identified a total of 169 up-regulated genes versus 2 down-regulated genes in the PCOS samples, which were intersected by taking the intersection with the deep green module genes and resulted in 121 key genes. Subsequently, 6 hub genes (MMP25, CSF3R, SLPI, MMP9, CLEC4E, and SIGLEC10) were further identified based on RFE and LASSO algorithms. Diagnostic efficacy based on ROC curves showed six autophagy-associated hub genes with AUC values as high as 0.959 and 0.896 in the training and validation sets, respectively. Finally, we observed that these hub genes are strongly associated with immune function, especially chronic inflammation and aberrant immune activation pathways.

In this study, we identified autophagy genes closely related to PCOS and constructed a gene model with high diagnostic accuracy. These findings not only provided potential new biomarkers for the diagnosis of PCOS but also revealed the key role of autophagy in the pathogenesis of PCOS.

This study seeks to develop a prognostic risk signature for head and neck squamous cell carcinoma (HNSCC) based on cholesterol-related genes (CholRG), aiming to enhance prognostic accuracy in clinical practice.

HNSCC poses significant challenges due to its aggressive behavior and limited response to standard treatments, resulting in elevated morbidity and mortality rates.In order to improve prognostic prediction in HNSCC, our study is inspired by the realization that cholesterol metabolism plays a critical role in accelerating the progression of cancer. To this end, we are developing a unique risk signature using CholRG.

The aim of this study was to create a CholRG-based risk signature to predict HNSCC prognosis, aiding in clinical decision-making accurately.

The TCGA HNSCC dataset, along with GSE41613 and GSE65858, was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. A CholRG-based risk signature was then developed and validated across various independent HNSCC cohorts. Moreover, a nomogram model incorporating CholRG-based risk signature was established. Additionally, functional enrichment analysis was conducted, and the immune landscapes of the high- and low-risk groups were compared. Finally, in vitro experiments were performed using lipid-based transfection to deliver siRNAs targeting ACAT1 to SCC1 and SCC23 cell lines, further examining the effects of ACAT1 knockdown on these cells.

Utilizing RNA-seq, microarray, and clinical data from public databases, we constructed and validated a CholRG-based risk signature that includes key genes such as ACAT1, CYP19A1, CYP27A1, FAXDC2, INSIG2, PRKAA2, and SEC14L2, which can effectively predict the clinical outcome of HNSCC. Additionally, our findings were reinforced by a nomogram model that integrates the risk score with clinical variables for more clinically practical prognostic assessment. In addition, patients at high risk show hypoxia and increased oncogenic pathways such as mTORC1 signaling, as well as a suppressed immune microenvironment marked by a reduction in the infiltration of important immune cells. Notably, in vitro experiments showed that ACAT1 depletion significantly suppressed the proliferation, colony formation, and invasion capabilities of HNSCC cells, confirming ACAT1's role in promoting malignancy.

Collectively, our study not only underscores the importance of cholesterol metabolism in HNSCC pathogenesis but also highlights the CholRG-based risk signature as a promising tool for enhancing prognostic accuracy and personalizing therapeutic strategies.

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.

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

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.

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.

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.

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.

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

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.

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.

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.