Current Medicinal Chemistry - Volume 33, Issue 6, 2026

The incidence of psychiatric disorders, such as post-traumatic stress disorder (PTSD), anxiety, and depression, has been steadily increasing, while current treatment approaches remain limited in efficacy. As a result, there is an urgent need to explore more effective therapeutic interventions. In recent years, MDMA (3,4-methylenedioxymethamphetamine)-assisted therapy (MDMA-AT) has emerged as a promising and innovative approach, demonstrating favorable clinical potential in the treatment of these disorders. Although preliminary studies have confirmed its therapeutic efficacy, a comprehensive and systematic analysis of the research trends and current limitations of MDMA-AT remains lacking.

This study employed a bibliometric approach to systematically retrieve and analyze research literature published between 1994 and 2023 on the application of MDMA in the treatment of PTSD, anxiety, and depression. Relevant data were obtained from three prominent databases: Web of Science Core Collection, PubMed, and Scopus. VOSviewer and Microsoft Excel were used to perform visual and quantitative analyses, focusing on publication trends, research hotspots, prolific authors, leading institutions, and international collaboration networks.

The findings indicated a substantial increase in MDMA-related research over the past decade. The United States has led the field in publication output, with the Multidisciplinary Association for Psychedelic Studies (MAPS) identified as the most productive institution. Key figures, such as Rick Doblin, have demonstrated high influence and centrality within the global research network. The research focus has gradually shifted from investigations of the neurotoxic properties of MDMA to explorations of its therapeutic mechanisms, safety profiles, and clinical applications.

This study provides a comprehensive synthesis of the past thirty years of research on MDMA-AT in the treatment of PTSD, anxiety, and depression, identifying major research trajectories and critical challenges in the field. While current findings highlight the therapeutic promise of MDMA and its translational potential, further research is needed to improve trial design, enhance sample diversity, and evaluate long-term effects in order to support the standardization and evidence-based integration of MDMA-assisted therapy into clinical practice.

Multiple myeloma (MM) is the second most common hematologic malignancy, accounting for approximately 10% of all hematological cases, with higher morbidity and mortality.

This study aimed to investigate the clonal evolutionary characteristics to identify novel prognostic biomarkers associated with extramedullary progression in MM.

We downloaded transcriptomic profiles and single-cell microarray (scRNA-seq) data from public databases. Then, we used the LASSO method to develop a prognostic signature and validated its efficacy using external MM cohorts. We evaluated the differences in the immune microenvironment and drug sensitivity (IC50) between the different risk score groups. scRNA-seq analysis identified key cell types through AUCell scores, cell communication, and differentiation trajectory analyses.

In total, 126 DEGs were identified as crucial genes associated with extramedullary and intramedullary MM. After LASSO analysis, seven signature genes were selected to develop a risk score model, and high-risk patients showed worse outcomes. Subsequently, the nomogram incorporating age, albumin, b2m, LDH, and RiskScore predicted 1-, 3-, and 5-year outcomes with high AUCs. Immune analyses showed that 25 immune cell types, 35 immune checkpoints, 27 chemokines, 20 MHC molecules, and 14 receptor-related genes differed significantly between the two risk groups. We also identified 116 drugs (roscovitine and JNK inhibitor VIII) with significantly different IC50 values between the two risk groups. CD4⁺ T cells exhibited the highest signature gene activity. CellChat analysis demonstrated enhanced communication between CD4⁺, NK, and CD8⁺ T cells.

Our study has proposed a risk score model based on seven identified signature genes for MM prognosis and revealed CD4⁺ T cells to be a major immune cell type associated with MM progression, contributing to personalized treatment decision-making and precise risk stratification of MM.

Gastric Cancer (GC) represents a highly prevalent and aggressive form of malignancy. Recent scholarly endeavors have implicated the RNA binding motif protein 15 (RBM15) gene in the onset and progression of cancer. The present study was designed to delve into the expression profile of RBM15 in GC, assess its prognostic significance, and explore its potential as a viable therapeutic target.

In this investigation, we conducted a comprehensive analysis of multiple facets related to the gene RBM15 in GC samples utilizing publicly accessible databases, including TCGA, GEPIA, TIMER, HPA, Kaplan-Meier Plotter, CPADS, and BEST. Specifically, we examined the mRNA expression levels, clinicopathological characteristics, and survival prognosis, conducted gene function enrichment assessments, analyzed mutation profiles, performed immune cell infiltration evaluations, and assessed drug sensitivity. Notably, all parameters employed in the online databases adhered to their respective default settings.

The expression of RBM15 mRNA is upregulated in GC and exhibits a correlation with a favorable prognosis, with an HR of 0.74 (95% CI = 0.62-0.88, p = 0.00054). Specifically, its expression is notably higher in tumor tissues compared to normal tissues. Furthermore, RBM15 expression is associated with differences in pTNM stage, gender, and racial disparities in survival outcomes. Functionally, RBM15 is implicated in DNA replication and cell cycle regulation. Mutations within the RBM15 gene are frequently observed in GC. Additionally, a positive correlation has been identified between RBM15 expression and immune cell infiltration. Drug sensitivity analyses suggest that RBM15 may represent potential therapeutic targets for GC treatment, further highlighting its pivotal role in the progression of GC.

RBM15 mRNA is upregulated in GC and significantly associated with a favorable prognosis. It correlates with clinicopathological features, is enriched in DNA replication processes, and is commonly mutated. Its expression also impacts immune cell infiltration and drug sensitivity, indicating its potential as a prognostic marker and therapeutic target in GC.

Ferula assa-foetida L. has traditionally been used to treat various diseases, including infections, asthma, stomach aches, and flatulence. Previous studies have highlighted its anti-inflammatory, anti-oxidative, anti-diabetic, neuroprotective, and nerve-stimulating properties.

This study aimed to evaluate the therapeutic effects and molecular mechanisms of action of the oleo-gum resin from Ferula assa-foetida L. in an animal model of diabetic neuropathy (DN).

The essential oil of oleo-gum resin from Ferula assa-foetida L. was analyzed using Gas Chromatography-Mass Spectrometric Analysis. Forty-two male Wistar rats were included in the study, with diabetes induced via streptozotocin (STZ) injection. The rats were randomly assigned to seven groups (n=6 per group) and treated with different doses of Ferula assa-foetida L. extract (100, 200 mg/kg/day) or oil (10, 20 mg/kg/day), alongside appropriate control groups. After a five-week treatment period, samples of dorsal root ganglia (DRG), pancreatic tissue, and blood were collected. Key parameters assessed included blood glucose and insulin levels, motor function tests, oxidative stress protein generation, pro-inflammatory cytokine gene expression, and histopathological analyses.

Treatment with various doses of Ferula assa-foetida L. extract or oil, as well as gabapentin, led to significant improvements. These included reduced blood sugar levels, increased insulin levels, and improved glycemic control. Motor function was enhanced, while the expression of pro-inflammatory cytokines and oxidative stress markers was significantly reduced.

These findings indicate a promising therapeutic approach for managing DN. Further studies are warranted to elucidate the underlying mechanisms of Ferula assa-foetida L.'s beneficial effects in DN.

Prior studies have associated vesicle-associated membrane protein-associated protein B (VAPB) with various cardiovascular diseases, yet its role in calcific Aortic Valve Disease (CAVD) is not fully understood. This study aims to elucidate the relationship between VAPB and aortic valve calcification at both tissue and cellular molecular levels, exploring potential regulatory mechanisms.

The expression levels of VAPB in calcified valve tissues were assessed using immunohistochemical analysis. Osteogenic differentiation and calcification levels of valve interstitial cells (VICs) were evaluated by alizarin red staining, calcium content quantification, and the detection of the osteogenic markers. Gene set enrichment analysis (GSEA) was used to identify downstream pathways regulated by VAPB, immunoblotting was used to detect the expression of key molecules of SMAD signaling, and immunoprecipitation was performed to detect interactions between VAPB and SMAD1/5/9.

VAPB expression was significantly upregulated in the aortic valve leaflets of patients with CAVD. VICs with VAPB overexpression exhibited a significant increase in calcium content (p < 0.001) and upregulation of osteogenic markers RUNX2, osteocalcin, and osteopontin (p <0. 01). Conversely, VAPB knockdown reduced osteogenic differentiation in VICs. Furthermore, VAPB overexpression led to the enhanced expression of p-SMAD1/5/9 through activation of the SMAD signaling pathway (p < 0.05), while inhibition of the SMAD pathway abrogated the pro-calcification effects of VAPB. Dual immunofluorescence staining demonstrated colocalization of VAPB with SMAD1/5/9, and immunoprecipitation confirmed an interaction between VAPB and SMAD1/5/9.

The findings indicate that VAPB promotes osteogenic differentiation in aortic valve interstitial cells by activating the SMAD signaling pathway.

Early and precise diagnosis is essential for effectively treating and managing pulmonary tuberculosis. The purpose of this research is to leverage artificial intelligence (AI), specifically convolutional neural networks (CNNs), to expedite the diagnosis of tuberculosis (TB) using chest X-ray (CXR) images.

Mycobacterium tuberculosis, an aerobic bacterium, is the causative agent of TB. The disease remains a global health challenge, particularly in densely populated countries. Early detection via chest X-rays is crucial, but limited medical expertise hampers timely diagnosis.

This study explores the application of CNNs, a highly efficient method, for automated TB detection, especially in areas with limited medical expertise.

Previously trained models, specifically VGG-16, VGG-19, ResNet 50, and Inception v3, were used to validate the data. Effective feature extraction and classification in medical image analysis, especially in TB diagnosis, is facilitated by the distinct design and capabilities that each model offers. VGG-16 and VGG-19 are very good at identifying minute distinctions and hierarchical characteristics from CXR images; on the other hand, ResNet 50 avoids overfitting while retaining both low and high-level features. The inception v3 model is quite useful for examining various complex patterns in a CXR image with its capacity to extract multi-scale features.

Inception v3 outperformed other models, attaining 97.60% accuracy without pre-processing and 98.78% with pre-processing.

The proposed model shows promising results as a tool for improving TB diagnosis, and reducing the global impact of the disease, but further validation with larger and more diverse datasets is needed.

Chronic Renal Failure (CRF) is a progressive disease that severely affects patients' quality of life, but its current treatment options remain limited. This study explores the potential mechanism of Eriobotryae Folium (EF) in treating CRF by targeting ferroptosis.

Active compounds and targets of EF were identified through multiple databases (TCMSP, SwissTargetPrediction, UniProt, GeneCards, DrugBank). Using Cytoscape and STRING, both a compound-target network and a PPI network were generated. GO and KEGG analyses were conducted to explore relevant biological functions and pathways. The binding affinity and stability between critical compounds and target proteins were investigated through molecular docking and Molecular Dynamics (MD) simulations.

Eighteen active compounds and 366 targets of EF were identified, along with 1,267 CRF-related and 1,673 ferroptosis-related targets, with 40 overlapping genes. PPI analysis highlighted AKT1, EGFR, HIF1A, SRC, and ESR1 as key targets. The KEGG analysis indicated MAPK and HIF-1 pathways as major regulatory pathways. Molecular docking suggested quercetin, ellagic acid, and oleanolic acid as potential active compounds, with EGFR and SRC as promising targets. MD simulations confirmed strong and stable binding, especially for EGFR-ellagic acid (-21.38 kcal/mol) and EGFR-oleanolic acid (-24.02 kcal/mol).

This study suggests that EF treats CRF by targeting ferroptosis-related pathways and key proteins, such as EGFR and AKT1. MAPK and HIF-1 signaling pathways further substantiate its significant role in disease regulation.

EF may regulate ferroptosis through multiple targets and pathways, offering potential therapeutic benefits for CRF. The findings offer foundational insights for subsequent research and therapeutic development.