Current Medicinal Chemistry - Volume 32, Issue 24, 2025

The by-product of naturally occurring rock, soil with different agricultural and industrial processes contaminated groundwater with a toxic metalloid- Arsenic (As³⁺), which results in different toxicities within the human body and in developing fetus.

The present study emphasizes evaluating the presence of oxidative stress and excessive generation of reactive oxygen species (ROS) resulting in mitochondrial dysfunction and caspase activation followed by apoptosis due to arsenic-induced neurotoxicity along with epigenetic modifications at different molecular targets.

Published articles available on PubMed and Scopus were studied and summarized.

The precise mechanism causing arsenic-induced neurotoxicity at a critical stage of brain development is still unknown, while increased oxidative stress led to mitochondrial dysfunctions which are known to play a prominent role in this. AMPK acts as a metabolic checkpoint and restores ATP levels through a different anabolic pathway in energy starvation. At the same time, arsenic-induced AMPK activation leads to autophagy and neuronal cell death.

This review summarized the molecular mechanisms involved in arsenic-induced neurotoxicity, which can help to develop suitable future ameliorative and therapeutic strategies.

This study aimed to determine the molecular markers related to gefitinib sensitivity for guiding the prognosis of lung adenocarcinoma (LUAD) and providing new evidence for promoting the precise treatment of LUAD.

Lung adenocarcinoma (LUAD) is a prevalent lung cancer subtype with inferior survival outcomes. However, gefitinib is the first molecular targeted drug approved by Food and Drug Administration (FDA) to treat advanced LUAD. Gefitinib sensitivity-related gene targets for LUAD are rarely studied.

This study was designed to probe the potential molecular markers related to the sensitivity of gefitinib in LUAD.

The gene expression profiles of LUAD cells in the Genomics of Drug Sensitivity in Cancer (GDSC) database were used for Weighted Gene Co-expression Network Analysis (WGCNA) to select the modules most related to gefitinib sensitivity. The Cancer Genome Atlas (TCGA) database was used to compare the expression of LUAD and para-cancerous tissues. Differentially expressed genes (DEGs) were then filtered and intersected with the highly linked genes in the module relevant to gefitinib sensitivity. Univariate Cox regression analysis was conducted to identify prognostically related genes to LUAD. The correlation between genes and drug IC50 was calculated by Spearman correlation analysis. Quantitative RT-PCR and immunofluorescence detection validated hub genes FAM13B and PFKP expressions.

Among the 10 modules divided by WGCNA, the module with the most significant positive correlation and the most significant negative correlation with gefitinib sensitivity were found. FAM13B, PFKP, FGD3, RNASE1, MUC16, GJB5, and GJB3 were hub genes related to gefitinib sensitivity in LUAD. Significantly, the low expressed FAM13 in LUAD tissues positively correlated with immune response. At the same time, overexpressed PFKP in the LUAD cohort was related to an unfavorable prognosis, cell proliferation, and cell cycle. We also found that FAM13B and PFKP expressions were enhanced in LUAD cell lines.

This study identified 7 critical genes related to gefitinib sensitivity in LUAD. Functionally, genes positively correlated with gefitinib sensitivity might regulate the progression of LUAD through the immune, cell cycle, and metabolic pathways and showed potential effects in predicting sensitivity to different drugs. These findings help offer a theoretical direction for personalized treatment of LUAD.

Patients with glioma have limited treatment options and experience poor prognoses. Therefore, it is urgently needed to explore new diagnostic and therapeutic targets.

This study aimed to investigate the relevance of WSC domain-containing 2 (WSCD2) expression to glioma, clinicopathological characteristics, tumor-infiltrating immune cells (TILs), and patient prognosis.

We analyzed WSCD2 mRNA expression in glioma tissues and patient survival using the Gene Expression Profiling Interactive Analysis database. Furthermore, the relationship between the expressions of WSCD2 mRNA and TILs in gliomas was evaluated utilizing the Tumor Immune Estimation Resource database. Lastly, we employed multiplex immunohistochemistry to detect the protein expressions of WSCD2 and TILs in glioma tissues.

WSCD2 mRNA expression in glioma tissues was lower than that in tissues of benign brain disease. High WSCD2 mRNA expression was also significantly associated with a favorable outcome. Additionally, WSCD2 mRNA expression was correlated with TIL expression in glioma; however, no such relationship was detected between the protein expressions of WSCD2 and TILs in glioma tissues. Cox regression multivariate analysis and Kaplan-Meier survival analysis showed that WSCD2 expression in glioma tissues could be an independent prognostic factor.

This study highlights the correlation between WSCD2 expression and TILs and demonstrates the prognostic significance of WSCD2 in glioma. Furthermore, our results suggest that WSCD2 may be a potential immunotherapy target in glioma.

Reactive oxygen species (ROS) are potential targets for treating malignant tumors.

The aim of this study was to probe into the mechanisms of disease development and treatment in lung adenocarcinoma (LUAD).

This study investigated the impact of ROS on the progression of LUAD at different transcriptomic levels and analyzed key molecules involved in the regulation of LUAD.

Single-cell RNA-seq (scRNA-seq) data of LUAD were clustered and annotated to determine cell types. Scissor cells based on LUAD bulk transcriptome and epithelial scRNA-seq data were used to classify subsets associated with ROS phenotypes. Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analyses were performed between the Scissor-positive and Scissor-negative epithelial cells to select key differentially expressed genes (DEGs) for developing a ROS-related signature.

The ROS score was significantly negatively correlated with the overall survival (OS) of LUAD. Seven cell types from the LUAD tissues were identified. The ROS-related gene signature was significantly correlated with metabolism, tumor microenvironment (TME) indicators, and the half-maximal inhibitory concentration (IC50) values of 10 drugs. The gene signature was verified as an independent indicator for LUAD prognosis.

The current study provided novel insights into the impact of ROS on LUAD pathology at both single-cell and bulk-tissue levels, facilitating the prognostic evaluation and drug therapy development for patients with LUAD.

Breast cancer is the most common form of cancer in women and is the leading cause of cancer-related deaths among women globally. In this study, we aimed to synthesize a series of tropane derivatives to investigate their Hsp90 inhibitory activity as well as their cytotoxic impact on breast cancer cells (MCF-7 and MDA-MB-231).

Novel fused-tropane derivatives were created and produced as inhibitors of Hsp90, taking inspiration from XL888, a tropane medication used for treating cancer. The target compounds were screened in vitro to determine their ability to inhibit the activity of Hsp90.

All tropane derivatives displayed a good submicromolar inhibition of Hsp90 with IC50 values ranging from 52.64 to 76.05 nM, relative to XL888 reference medication (IC50 = 27.78 nM). Among all the compounds examined, tropane derivative 5 exhibited the highest level of Hsp90 inhibitory action, with an IC50 value of 52.64 nM. Furthermore, the cytotoxic activity of all compounds was evaluated against two breast cancer cell lines, namely MCF-7 and MDA-MB-231. Tropane derivative 5 exhibited greater potency than doxorubicin against both cell lines. In addition, it demonstrated a safety profile significantly superior to that of doxorubicin when tested on normal human cells (WI-38 cells), thereby confirming its exceptional level of safety. The western blotting analysis demonstrated a 2.4-fold reduction in Hsp90 expression in MCF-7 cells. Furthermore, the molecular docking analysis has provided additional evidence for the capacity of compound 5 to effectively bind with the target Hsp90 enzyme.

We have succeeded in synthesizing novel tropane hybrids exhibiting significant anti-Hsp90 action, similar to XL888 analogues.

Infections linked to orthopedic trauma are common complications that place a significant strain on the healthcare system. Immediate identification of the infection and its severity is essential for providing effective treatment.

C-reactive Protein (CRP) is a commonly used inflammatory marker in orthopedic surgery and has proven to be a valuable biomarker for diagnosing and monitoring infections. Specifically, CRP aids in the early identification of postoperative infections. This research work has focused on developing a highly sensitive CRP biosensor using iron oxide nanomaterial-modified dielectric sensors.

Gold Urchin (GU)-conjugated aptamers and antibodies were used as probes and attached to the electrode via amine linkers. The aptamer-GU-antibody-modified electrode detected CRP at concentrations as low as 1 pg/mL, with an R² value of 0.9942. Furthermore, CRP-spiked serum exhibited an increase in current response at all concentrations of CRP, indicating selective detection of CRP. Additionally, control experiments using complementary sequences of the aptamer, relevant proteins, and non-immune antibodies did not enhance the current responses, confirming the specific identification of CRP.

The sensing strategy has enabled the detection of CRP at its lowest levels, facilitating the identification of infections during orthopedic surgery and subsequent treatment.

Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs, thus uncovering new indications and repositioning them. Therefore, it is of great importance to develop efficient and accurate DTI prediction algorithms.

Current algorithms usually represent drugs as extracted features, which are learned by convolutional neural networks (CNNs) from its linear representation, or utilize graph neural networks (GNNs) to learn its graph representation. However, these methods either lose information or fail to capture the structural information of the drug. To address this issue, a novel molecule secondary structure representation network (MSSRN) is proposed to learn drug characterization more accurately. Firstly, the network performs relational graph convolutional networks (R-GCNs) on the drug's molecular graph and integrates drug sequence convolutions to learn the sequential information. Secondly, inspired by the attention mechanism, spatial importance weights of the drug sequence are calculated to guide R-GCNs to learn the topological information of the drug.

A drug-target affinity model, called MSSRN-DTA, was then constructed by using MSSRN to learn drug structure and CNN to learn protein sequence.

The effectiveness of the proposed method is verified by comparing it with other alternative methods and baseline models on two benchmark datasets.