Combined antitumoral effects of pretubulysin and methotrexate

Designing a novel drug-drug conjugate as a prodrug for breast cancer therapy: in silico insights

Breast cancer (BC) poses a significant global health threat, necessitating innovative therapeutic approaches. The ribosomal s6 kinase 2 (RSK2) has emerged as a promising target due to its roles in cell proliferation and survival. This study proposes a drug-drug conjugate prodrug comprising Methotrexate (hydrophobic) and Capecitabine (hydrophilic) for BC treatment. In silico approaches, including Molecular Docking, Molecular Dynamics Simulations, MM-PBSA, ADME, and DFT calculations were employed to evaluate the prodrug's potential. The designed MET-CAP ligand exhibits a robust docking score (-8.980 kcal/mol), superior binding affinity (-53.16 kcal/mol), and stable dynamic behavior (0.62 nm) compared to native ligands. The DFT results reveal intramolecular charge transfer in MET-CAP (HLG = 0.09 eV), indicating its potential as a BC inhibitor. ADME analysis suggests satisfactory pharmaceutically relevant properties. The results indicate that the conjugated MET-CAP ligand exhibits favorable binding characteristics, stability, and pharmaceutically relevant properties, making it a potential RSK2 inhibitor for BC therapy. The multifaceted approach provides insights into binding interactions, stability, and pharmacokinetic properties, laying the foundation for further experimental validation and potential clinical development.

Predicting combined antibacterial activity of sulfapyridine and its transformation products during sulfapyridine degradation

Antibiotics have strong antibacterial activity, even trace antibiotics can greatly inhibit the pollutant degradation efficiency. In order to effectively improve the pollutant degradation efficiency, it was hence of great significance to explore sulfapyridine (SPY) degradation and the mechanism of antibacterial activity. This study selected SPY as the research object, of which the trend of SPY concentration through hydrogen peroxide (H₂O₂), potassium peroxydisulfate (PDS) and sodium percarbonate (SPC) and resultant antibacterial activity at pre-oxidation was examined. The combined antibacterial activity (CAA) of SPY and its transformation products (TPs) was further analyzed. The SPY degradation efficiency reached more than 90 %. However, the degradation efficiency of antibacterial activity was between 40-60 %, and the mixture's antibacterial activity was difficult to be removed. The antibacterial activity of TP3, TP6 and TP7 was higher than that of SPY. TP1, and TP8 and TP10 were more prone to synergistic reaction with other TPs. The antibacterial activity of binary mixture gradually changed from synergism to antagonism as binary mixture concentration increased. The results provided a theoretical basis for the efficient degradation of antibacterial activity of the SPY mixture solution.

Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication

Cell mechanics is essential to cell development and function, and its dynamics evolution reflects the physiological state of cells. Here, we investigate the dynamical mechanical properties of single cells under various drug conditions, and present two mathematical approaches to quantitatively characterizing the cell physiological state. It is demonstrated that the cellular mechanical properties upon the drug action increase over time and tend to saturate, and can be mathematically characterized by a linear time-invariant dynamical model. It is shown that the transition matrices of dynamical cell systems significantly improve the classification accuracies of the cells under different drug actions. Furthermore, it is revealed that there exists a positive linear correlation between the cytoskeleton density and the cellular mechanical properties, and the physiological state of a cell in terms of its cytoskeleton density can be predicted from its mechanical properties by a linear regression model. This study builds a relationship between the cellular mechanical properties and the cellular physiological state, adding information for evaluating drug efficacy.

Characterization of the Lipid Metabolism in Bladder Cancer to Guide Clinical Therapy

Background

Bladder cancer is one of the most common malignancies of the urinary system with an unfavorable prognosis. More and more studies have suggested that lipid metabolism could influence the progression and treatment of tumors. However, there are few studies exploring the relationship between lipid metabolism and bladder cancer. This study aimed to explore the roles that lipid metabolism-related genes play in patients with bladder cancer.

Methods

TCGA_BLCA cohort and GSE13507 cohort were included in this study, and transcriptional and somatic mutation profiles of 309 lipid metabolism-related genes were analyzed to discover the critical lipid metabolism-related genes in the incurrence and progression of bladder cancer. Furthermore, the TCGA_BLCA cohort was randomly divided into training set and validation set, and the GSE13507 cohort was served as an external independent validation set. We performed the LASSO regression and multivariate Cox regression in training set to develop a prognostic signature and further verified this signature in TCGA_BLCA validation set and GSE13507 external validation set. Finally, we systematically investigated the association between this signature and tumor microenvironment, drug response, and potential functions and then verified the differential expression status of signature genes in the protein level by immunohistochemistry.

Results

A novel 6-lipidmetabolism-related gene signature was identified and validated, and this risk score model could predict the prognosis of patients with bladder cancer. In addition, the prognostic model was tightly related to immune cell infiltration and tumor mutation burden. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) showed that mTOR signaling pathway, G2M checkpoint, fatty acid metabolism, and hypoxia were enriched in patients in the high-risk score groups. Furthermore, 3 therapies specific for bladder cancer patients in different risk scores were identified.

Conclusion

s. In conclusion, we investigated the lipid metabolism-related genes in bladder cancer through comprehensive bioinformatic analysis. A novel 6-gene signature associated with lipid metabolism for predicting the outcomes of patients with bladder cancer was conducted and validated. Furthermore, the risk score model could be utilized to indicate the choice of therapy in bladder cancer.

Quantitative characterization of toxicity interaction within antibiotic-heavy metal mixtures on Chlorella pyrenoidosa by a novel area-concentration ratio method

Toxicity interaction, synergism and antagonism, may occur when multiple pollutants are exposed to the environment simultaneously, which limits the utility of some standard models to assess toxicity hazards and risks. The development and application of models which can provide an insight into the combined toxicity of pollutants becomes necessary. Therefore, a novel model, area-concentration ratio (ACR) method, was developed to characterize the toxicity interaction within mixtures of three aminoglycoside antibiotics (AGs), kanamycin sulfate (KAN), paromomycin sulfate (PAR), tobramycin (TOB) and one heavy metal copper (Cu) in this study. The inhibition toxicity of single contaminants and mixtures designed by direct equilibration ray method and uniform design ray method to Chlorella pyrenoidosa (C. pyrenoidosa) was determined by the microplate toxicity analysis (MTA). The results showed that the novel method ACR could be used for quantitative characterization of combined toxicity. According to the ACR, all the binary AG antibiotic mixture systems display obvious synergism and weak antagonism. The addition of the heavy metal Cu into binary AG antibiotic mixtures can obviously change toxicity interaction, but toxicity interaction changing trend varies greatly in different ternary mixture systems. Toxicity interaction in the six mixture systems has component concentration-ratio dependence. ACR can be suggested as an effective novel method to quantitatively characterize toxicity interaction when assessing the hazards and risks of the combined pollution.

Overview of Dual-Acting Drug Methotrexate in Different Neurological Diseases, Autoimmune Pathologies and Cancers

Methotrexate, a structural analogue of folic acid, is one of the most effective and extensively used drugs for treating many kinds of cancer or severe and resistant forms of autoimmune diseases. In this paper, we take an overview of the present state of knowledge with regards to complex mechanisms of methotrexate action and its applications as immunosuppressive drug or chemotherapeutic agent in oncological combination therapy. In addition, the issue of the potential benefits of methotrexate in the development of neurological disorders in Alzheimer’s disease or myasthenia gravis will be discussed.