Current Medicinal Chemistry - Online First

The objective of this study is to obtain inhibitors against mTOR targets with virtual screening, dynamic simulation and bioactivity assessment. This pursuit aims to obtain a rapid and accurate method for the discovery of new mTOR inhibitors.

Firstly, the researchers obtained nearly 9000 compounds by using ROC-guided machine learning from a library of over 200000 compounds. Secondly, virtual screening was used to evaluate the affinity of 45 compounds. Further analysis was performed to identify 6 hit compounds. Simultaneously, MTT antitumor activity evaluation and kinase inhibition assays are conducted for the active compounds to discern the most promising candidates. Furthermore, AO staining and JC-1 assays are performed for the selected compounds. Simultaneously, MTT antitumor activity evaluation and kinase inhibition assays are conducted for the active compounds to discern the most promising candidates. Furthermore, AO staining, JC-1 and hemolytic toxicity evaluation assays are performed for the selected compounds.

The kinase assay demonstrates that these 6 compounds display greater sensitivity to mTOR than to PI3K. Among them, compounds AJ-292/12941271 and AG-205/12550019 show better activity against mTOR target than PI3K, with an IC50 of 2.55 and 4.48 μM, respectively. Additionally, the anti-proliferative activity of the six hit compounds was also considered. Compound AJ-292/12941271 shows the best anticancer activity against A549 cell lines with an IC50 value of 4.3 μM. Further analysis reveals that compound AJ-292/12941271 induces apoptosis in the A549 cell line in a concentration-dependent or time-dependent manner. Hemolytic toxicity evaluation suggests that the compound AJ-292/12941271 is safe for further in vivo study.

This research proposes that the fused method of ROC-based machine learning, virtual screening, and bioactivity evaluation could be used to discover novel mTOR inhibitors quickly and precisely.

Doxorubicin (DOX) is a chemotherapeutic agent widely used for the treatment of various cancers; however, its clinical use is limited by its cardiotoxicity. However, the underlying molecular mechanisms remain poorly understood, hindering the development of effective preventive and treatment strategies. This study aimed to identify core target genes and explore the mechanisms involved in DOX-induced cardiotoxicity by integrating microarray analysis, machine learning, and molecular docking.

Differential expression analysis was performed using microarray data from DOX-induced cardiotoxic samples and healthy controls. Multiple machine learning algorithms were applied to identify core target genes. The predictive performance of these genes was evaluated using receiver operating characteristic (ROC) curves. Molecular docking was conducted to evaluate the binding affinity of DOX to the target genes. Functional analysis was performed to investigate potential toxic mechanisms.

In total, 276 differentially expressed genes were identified between DOX-induced cardiotoxicity samples and controls. The support vector machine algorithm demonstrated the best performance, leading to the identification of five core target genes: RAP1A, CTLA4, OR2M1P, TRIM53, and LOC149837. The ROC curves confirmed the strong predictive power of these genes, with area under the curve values greater than 0.85. Molecular docking showed stable binding between DOX and the target genes. Functional analysis suggested that the Rap1 signaling pathway and immune system regulation may be involved in DOX-induced cardiotoxicity.

Traditional toxicological studies often rely on limited experimental approaches that do not fully capture the complexity of disease mechanisms. The integration of microarray analysis, machine learning, and molecular docking in this study offers a comprehensive framework for investigating the toxicological pathways of DOX-induced cardiotoxicity, thereby providing insights into therapeutic development and safety regulations.

By combining microarray analysis, machine learning, and molecular docking, we identified five key target genes associated with DOX-induced cardiotoxicity. Functional analysis further suggested the involvement of the Rap1 signaling pathway and immune system regulation in DOX-induced cardiotoxicity. These findings offer insights into the molecular mechanisms underlying DOX-induced cardiotoxicity and have implications for the development of protective strategies and therapeutic interventions.

Endoplasmic reticulum stress (ER stress) plays a crucial role in influencing the malignant behaviors of various tumors. Targeting the expression or degradation of transcription factors (TFs) offers a promising avenue for cancer treatment. However, a detailed understanding of how ER stress affects TF function and their interactions remains limited. This study aims to develop a prognostic model and identify TFs associated with ER stress in pancreatic ductal adenocarcinoma (PDAC).

We obtained gene expression profiles and corresponding clinical data from The Cancer Genome Atlas (TCGA). To develop a prognostic signature, we performed several analyses, including unsupervised clustering, enrichment analysis, immune infiltration assessment, as well as univariate, LASSO, and multivariate Cox regression analyses. Four transcription factors—STAT1, IRF6, NRF1, and RXRA—were incorporated into a risk model, which was subsequently validated using the GSE dataset. Additionally, we examined IRF6 through quantitative PCR, western blotting, flow cytometry, and immunohistochemistry in vitro using pancreatic cancer cell lines and a tissue microarray.

The high-risk group identified by the model exhibited significant associations with immune cell infiltration and poorer survival outcomes, though there was no significant correlation with tumor purity (p = 0.19). Furthermore, IRF6 downregulation in vitro was found to inhibit pancreatic cancer cell proliferation and promote apoptosis. IRF6 depletion also increased the expression of key molecules involved in ER stress at both the transcriptional and translational levels. Immunohistochemical analysis revealed marked differences in IRF6 expression between tumor and adjacent non-tumor tissues (59.29±29.88 vs. 95.22±40.80, p<0.001).

This study provides evidence that the constructed risk model can effectively predict prognosis in PDAC patients. Transcription factors related to ER stress, such as IRF6, show promise as both prognostic biomarkers and potential therapeutic targets for PDAC.

Osteosarcoma (OS) is one of the most common primary malignancies in children and adolescents. Disulfidptosis, a newly identified form of metabolically induced programmed cell death triggered by disulfide stress, has not yet been explored in OS.

We integrated data from public databases and applied a series of bioinformatics approaches, including clustering analysis to classify OS subtypes, and Cox and LASSO regression analysis to identify prognostic disulfidptosis-related genes (DRGs). Enrichment analysis was performed to explore the biological pathways associated with DRG-related molecular subtypes. The immune infiltration landscape was assessed to understand the tumor microenvironment in different risk subgroups. Additionally, drug sensitivity analysis was conducted to evaluate the potential clinical therapeutic strategies of the identified DRG score subgroups. The distribution of DRG expression across OS cell subtypes was further analyzed using single-cell RNA sequencing. In vitro assays, including Western blotting, qRT-PCR, and cell migration and invasion assays, were conducted to validate POLR1D expression and function in OS cells.

We established a DRG-based prognostic model that effectively stratifies OS patients into distinct risk groups with different survival outcomes. The model also revealed significant differences in immune cell infiltration between high and low DRG scores group, suggesting a link between disulfidptosis and the OS immune microenvironment. Drug sensitivity analysis indicated that the DRG signature could guide personalized therapeutic strategies. Single-cell RNA sequencing revealed heterogeneous expression of DRG signature across OS cell subtypes. Functional assays confirmed that POLR1D was aberrantly overexpressed in OS cells and promotes their migration and invasion, supporting its role as a potential oncogenic driver in OS.

Our study is the first to investigate the role of DRGs for risk stratification in OS, providing new insights and targets into OS pathogenesis.

Human oncostatin M (OSM) is a pleiotropic cytokine that regulates inflammatory and immune responses by binding to the heterodimer receptor complex OSM receptor beta (OSMRβ) and glycoprotein 130 (gp130). The distinct signaling pathways triggered by OSM are involved in multiple chronic inflammatory conditions, such as inflammatory bowel disease (IBD), rheumatoid arthritis (RA), and cancers, making the OSM-bound receptor complex a significant therapeutic target. Currently, no 3D structure of human OSM recognition complex is available, and thus, the molecular mechanisms underlying OSM signaling remain poorly understood.

In this study, for the first time, we proposed a full-length structural model of the human OSM-OSMRβ-gp130, generated using AlphaFold2 protein structure prediction and all-atom molecular dynamics (MD) simulation (~ 1.12 million atoms with explicit solvent), enabling investigation of the geometric and dynamic profiles of OSM-OSMRβ-gp130 structure at atomic-level.

Analysis of the simulation trajectory demonstrated that the structural rearrangements of the heterodimer receptors (i.e., OSMRβ and gp130) initiated by OSM binding mediated the signal transduction from the extracellular to the intracellular domains. In the representative conformation identified through clustering analysis, two main allosteric pathways contributed were found to mediate signal transduction from the allosteric region of OSM to the active sites of OSMRβ and gp130. Finally, two druggable binding sites located on OSM and gp130 were detected by dynamically monitoring pocket flexibility throughout the simulation. A comprehensive analysis of the OSM-OSMRβ-gp130 model was carried out with respect to OSM signaling.

The findings of this study not only enhance the mechanistic understanding of OSM binding to the heteromeric OSMRβ/gp130 but also identify druggable binding sites for structure-based design of small molecules to inhibit the intracellular signal transduction.

With the rapid emergence of drug-resistant strains of tuberculosis, resistance to current first-line and second-line anti-tuberculosis drugs is becoming increasingly prevalent. Consequently, the discovery of new lead compounds is essential to address this challenge. GyrB has emerged as a promising target for tuberculosis treatment due to its pivotal role in DNA replication and topology regulation in Mycobacterium tuberculosis.

In this study, a multi-conformational virtual screening approach, complemented by antibacterial activity assays, was utilized to identify novel GyrB inhibitors from the ChemDiv database.

Among the 27 compounds purchased, 10 exhibited significant inhibitory effects against the H37Rv strain, with 8 featuring novel core scaffolds. Notably, three compounds (V027-7669, V017-8710, and 5132-0213) demonstrated a minimum inhibitory concentration (MIC) of 8 μg/mL. Compounds V027-7669 and V017-8710, in particular, showed antibacterial activity against a multidrug-resistant tuberculosis strain, with MIC values of 32 μg/mL and 16 μg/mL, respectively. Molecular dynamics simulations revealed that both V027-7669 and V017-8710 bind stably to GyrB, which are primarily driven by nonpolar interactions. Furthermore, both of them occupy a novel sub-pocket formed by residues Val99, Gly106, Val123, Gly124, and Val125, where they establish hydrogen bonds with Val125.

Our study underscores the effectiveness of a multi-conformational virtual screening strategy in identifying novel GyrB inhibitors and suggests V027-7669 and V017-8710 as promising lead compounds for the development of treatments against multidrug-resistant tuberculosis.

Ultra-rare diseases (URDs) are defined based on point prevalence and are classified as conditions affecting fewer than 1 in 50,000 individuals, and they are more likely to exist among communities with higher consanguinity rates requiring evidence-based data.

In this multi-center study, we used next-generation sequencing to identify 30 pediatric patients with URDs. Along with the demographic information about their parents, clinical, laboratory, and radiological data was also obtained. Multinomial regression was carried out to assess statistical differences and determine associations using the Quality of Life of Childhood Epilepsy (QOLCE)-55 scale.

There were 19 male (63.33%) and 11 (36.67%) female patients. Their current age range was 2-15 years (mean=8.83 years). The majority were diagnosed with sodium channelopathy (64.51%). The average Quality of Life (QoL) score of all participants was 51.43 ± 9.01 (reference range 0-100) with quartiles Q1=40, Q2=43.5, and Q3=56.

We propose that URDs complicated by epilepsy can significantly impair the QoL of patients and their families.

The cyclin-dependent kinases (CDKs) play a crucial role in the normal progression of these stages. In tumor cells, CDKs are often highly expressed, leading to uncontrolled cell proliferation. Inhibiting the activity of CDKs in tumor cells can inhibit their growth and proliferation, thereby achieving anti-tumor effects. In recent years, many CDKs inhibitors have been developed, but due to side effects and drug resistance issues, only a few CDKs inhibitors have been approved by the FDA.

Publications on CDK-based dual-target inhibitors were reviewed using SciFinder and PubMed, excluding reviews, patents, and studies with irrelevant content.

The study outlines advancements in CDK-based dual-target inhibitors as antitumor agents, offering insights to support the development and application of more effective cancer therapies.

Dual-targeted anti-tumor drugs may have better therapeutic effects than single-targeted drugs, which may address drug resistance issues and overcome drug interactions and pharmacokinetic issues associated with combination therapy. As an important direction in cancer treatment, dual target inhibitors have broad development prospects. By continuing to explore and improve dual target therapies, it has potential to overcome many limitations of single target therapy and provide more effective and lasting treatment outcomes for cancer patients.

Hepatocellular carcinoma (HCC) is a life-threatening cancer with rising incidence and mortality rates. Identifying new prognostic biomarkers is crucial for improving HCC management.

This study investigates the role of Double PHD Fingers 1 (DPF1) in hepatocellular carcinoma (HCC), exploring its potential as a prognostic indicator and therapeutic target.

We analyzed DPF1 expression in 374 hepatocellular carcinoma (HCC) tissues and 50 normal tissues from the TCGA-HCC database, as well as in 240 HCC tissues and 202 normal tissues from the ICGC-HCC repository. We examined the correlation between DPF1 expression and clinical parameters, immune cell infiltration, drug response profiles, cancer stem cell (CSC) characteristics, and its diagnostic/prognostic potential using various bioinformatics tools and statistical analyses. Validation was performed using the ICGC and HPA databases, and qRT-PCR was used to confirm DPF1 expression in HCC cell lines.

DPF1 exhibited abnormal expression in HCC and several other malignancies. Elevated DPF1 levels were significantly associated with higher Alpha-fetoprotein (AFP) levels (p = 0.043) and poorer clinical outcomes, including diminished overall survival (OS) (p = 0.002), progression-free survival (PFS) (p = 0.018), and disease-specific survival (DSS) (p = 0.001). DPF1 expression was also linked to immune cell infiltration, immune checkpoint gene expression, drug sensitivity, and CSC characteristics. Notably, DPF1 was significantly overexpressed in HCC tissues and cell lines at both transcriptional and translational levels.

Our study reveals that DPF1 is a novel prognostic biomarker in HCC, with potential implications for immunotherapy and drug resistance. Elevated DPF1 expression is associated with adverse clinical outcomes and may serve as a target for future therapeutic interventions in HCC.

Hepatocellular carcinoma (HCC) has a poor prognosis due to late diagnosis and rapid progression, highlighting the need for a deeper understanding of its pathogenesis. Lysine crotonylation (Kcr), a unique post-translational modification, plays a crucial role in epigenetic regulation. However, the role of crotonylation-related genes (CRGs) in HCC remains poorly understood, necessitating an investigation of their prognostic and therapeutic relevance.

Transcriptomic and clinical data were obtained from TCGA and GEO databases. A CRG-based risk score was developed using Cox and LASSO regression analyses. To enhance survival prediction, a nomogram incorporating the risk score was constructed. Immune cell infiltration and drug sensitivity were assessed using CIBERSORT and 'OncoPredict.' Single-cell sequencing was employed to examine CRG expression within the HCC tumor microenvironment.

An 8-gene risk score model (HDAC2, ACADS, HDAC1, ENO1, PPARG, ACADL, ACSL6, and AGPAT5) was established, effectively stratifying patients into high- and low-risk groups in the training set. Cox regression and Kaplan-Meier analyses validated its prognostic value in the test set. The nomogram demonstrated enhanced prognostic accuracy for survival prediction. Differences in immune cell infiltration and immune checkpoint expression between risk groups highlighted the association between CRGs and the tumor immune microenvironment. Single-cell sequencing revealed that CRGs were highly expressed in key immune cells within the HCC microenvironment. Additionally, drug sensitivity analysis suggested that specific targeted therapies may be more effective in HCC patients.

Crotonylation-related gene signature demonstrates strong prognostic value in hepatocellular carcinoma (HCC), effectively stratifying patients into high- and low-risk groups and recapitulating known oncogenic roles of HDAC1/2, ENO1, PPARG, AGPAT5 and the protective functions of ACADS, ACADL, and ACSL6. It was found that crotonylation not only influences tumor cell metabolism and epigenetic regulation but also shapes the immune microenvironment, highlighted by distinct checkpoint expression, differential immune cell infiltration, and drug sensitivity profiles, which position our model as a promising tool for personalized therapeutic decision-making. However, clinical translation will require standardized, reproducible assays for crotonylation measurement and rigorous validation across diverse HCC etiologies (e.g., viral vs. non-viral), along with mechanistic and longitudinal studies to dissect causality versus correlation, assess off- target effects of crotonylation modulators, and confirm functional impacts on immune modulation before routine diagnostic or therapeutic use.

This study identifies a prognostic CRG signature for HCC and provides novel insights into personalized treatment strategies.