Current Medicinal Chemistry - Volume 31, Issue 17, 2024

Background: The indiscriminate use of antibiotics brings an alarming reality: in 2050, bacterial resistance could be the main cause of death in the world, resulting in the death of 10 million people, according to the World Health Organization (WHO). In this sense, to combat bacterial resistance, several natural substances, including chalcones, have been described in relation to antibacterial, representing a potential tool for the discovery of new antibacterial drugs. Objective: The objective of this study is to perform a bibliographic survey and discuss the main contributions in the literature about the antibacterial potential of chalcones in the last 5 years. Methods: A search was carried out in the main repositories, for which the publications of the last 5 years were investigated and discussed. Unprecedented in this review, in addition to the bibliographic survey, molecular docking studies were carried out to exemplify the applicability of using one of the molecular targets for the design of new entities with antibacterial activity. Results: In the last 5 years, antibacterial activities were reported for several types of chalcones, for which activities were observed for both gram-positive and gram-negative bacteria with high potency, including MIC values in the nanomolar range. Molecular docking simulations demonstrated important intermolecular interactions between chalcones and residues from the enzymatic cavity of the enzyme DNA gyrase, one of the validated molecular targets in the development of new antibacterial agents. Conclusion: The data presented demonstrate the potential of using chalcones in drug development programs with antibacterial properties, which may be useful to combat resistance, a worldwide public health problem.

Schiff bases represent a valuable class of organic compounds, synthesized via condensation of primary amines with ketones or aldehydes. They are renowned for possessing innumerable applications in agricultural chemistry, organic synthesis, chemical and biological sensing, coating, polymer and resin industries, catalysis, coordination chemistry, and drug designing. Schiff bases contain imine or azomethine (-C=N-) functional groups which are important pharmacophores for the design and synthesis of lead bioactive compounds. In medicinal chemistry, Schiff bases have attracted immense attention due to their diverse biological activities. This review aims to encompass the recent developments on the antimicrobial activities of Schiff bases. The article summarizes the antibacterial, antifungal, antiviral, antimalarial, and antileishmanial activities of Schiff bases reported since 2011.

Existing evidence indicates that environmental factors might contribute up to 50% of the variance in autism spectrum disorder (ASD) risk. This structured narrative review offers a comprehensive synthesis of current knowledge on environmental risk factors in ASD, including evaluation of conflicting evidence, exploration of underlying mechanisms, and suggestions for future research directions. Analysis of diverse epidemiological investigations indicates that certain environmental factors, including advanced parental age, preterm birth, delivery complications, and exposure to toxic metals, drugs, air pollutants, and endocrine-disrupting chemicals, are linked to an increased ASD risk through various mechanisms such as oxidative stress, inflammation, hypoxia, and its consequences, changes in neurotransmitters, disruption of signaling pathways and some others. On the other hand, pregnancy-related factors such as maternal diabetes, maternal obesity, and caesarian section show a weaker association with ASD risk. At the same time, other environmental factors, such as vaccination, maternal smoking, or alcohol consumption, are not linked to the risk of ASD. Regarding nutritional elements data are inconclusive. These findings highlight the significance of environmental factors in ASD etiology and emphasize that more focused research is needed to target the risk factors of ASD. Environmental interventions targeting modifiable risk factors might offer promising avenues for ASD prevention and treatment.

Background: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. Objective: Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. Methods: We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. Results: The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. Conclusion: The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

Aims: We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD). Background: STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD. Objective: The objective of this study is to investigate the molecular subtypes and prognostic model for STAD. Methods: A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature. Results: We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets. Conclusion: This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

Background: Acute myocardial infarction (AMI) is an age-dependent cardiovascular disease in which cell aging, immunity, and inflammatory factors alter the course; however, cell aging-immune/inflammation signatures in AMI have not been investigated. Methods: Based on the GEO database to obtain microRNA (miRNA) sequencing, mRNA sequencing and single-cell sequencing data, and utilizing the Seurat package to identify AMI-associated cellular subpopulations. Subsequently, differentially expressed miRNAs and mRNAs were screened to establish a network of competing endogenous RNAs (ceRNAs). Senescence and immunity scores were calculated by single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithms, and the Hmisc package was used to screen for genes with the highest correlation with senescence and immunity scores. Finally, protein-protein interaction (PPI) and molecular docking analyses were performed to predict potential therapeutic agents for the treatment of AMI. Results: Four cell types (Macrophage, Fibroblast, Endothelial cells, CD8 T cells) were identified in AMI, and CD8 T cells exhibited the lowest cell aging activity. A ceRNA network of miRNAs- mNRA interactions was established based on the overlapping genes in differentially expressed miRNAs (DEmiRNAs) target genes and differentially expressed mRNAs (DEmRNAs). Twenty-four marker genes of CD8 T cells were observed. LIMD2 was identified as cell aging- immune/inflammation-related hub gene in AMI. This study also identified a potential therapeutic network of DB03276-LIMD2-AMI, which showed excellent and stable binding status between DB03276-LIMD2. Conclusion: This study identified LIMD2 as a cell aging-immune/inflammation-related hub gene. The understanding of the pathogenesis and therapeutic mechanisms of AMI was enriched by the ceRNA network and DB03276-LIMD2-LAMI therapeutic network.

Background: Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis. Methods: Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy. Results: We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility. Conclusion: To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Background: Head and neck squamous cell carcinoma (HNSC) is the seventh most common cancer worldwide. Although there are several options for the treatment of HNSC, there is still a lack of better biomarkers to accurately predict the response to treatment and thus be more able to correctly treat the therapeutic modality. Methods: First, we typed cases from the TCGA-HNSC cohort into subtypes by a Bayesian non-negative matrix factorization (BayesNMF)-based consensus clustering approach. Subsequently, genomic and proteomic data from HNSC cell lines were integrated to identify biomarkers of response to targeted therapies and immunotherapies. Finally, associations between HNSC subtypes and CD8 T-cell-associated effector molecules, common immune checkpoint genes, were compared to assess the potential of HNSC subtypes as clinically predictive immune checkpoint blockade therapy. Results: The 500 HNSC cases from TCGA were put through a consensus clustering approach to identify six HNSC expression subtypes. In addition, subtypes with unique proteomics and dependency profiles were defined based on HNSC cell line histology and proteomics data. Subtype 4 (S4) exhibits hyperproliferative and hyperimmune properties, and S4-associated cell lines show specific vulnerability to ADAT2, EIF5AL1, and PAK2. PD-L1 and CASP1 inhibitors have therapeutic potential in S4, and we have also demonstrated that S4 is more responsive to immune checkpoint blockade therapy. Conclusion: Overall, our HNSC typing approach identified robust tumor-expressing subtypes, and data from multiple screens also revealed subtype-specific biology and vulnerabilities. These HNSC expression subtypes and their biomarkers will help develop more effective therapeutic strategies.

Aim: We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment. Background: Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD. Objective: To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment. Methods: Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package. Results: Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment. Conclusion: We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

Aims: We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC). Background: Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC. Objective: We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC. Methods: We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram. Results: Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy. Conclusion: We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.