Targeting Breast Cancer with N-Acetyl-D-Glucosamine: Integrating Machine Learning and Cellular Assays for Promising Results.

BACKGROUND: Breast cancer is a common cancer with high mortality rates. Early diagnosis is crucial for reducing the prognosis and mortality rates. Therefore, the development of alternative treatment options is necessary. OBJECTIVE: This study aimed to investigate the inhibitory effect of N-acetyl-D-glucosamine (D-GlcNAc) on breast cancer using a machine learning method. The findings were further confirmed through assays on breast cancer cell lines. METHODS: MCF-7 and 4T1 cell lines (ATCC) were cultured in the presence and absence of varying concentrations of D-GlcNAc (0.5 mM, 1 mM, 2 mM, and 4 mM) for 72 hours. A xenograft mouse model for breast cancer was established by injecting 4T1 cells into mammary glands. D-GlcNAc (2 mM) was administered intraperitoneally to mice daily for 28 days, and histopathological effects were evaluated at pre-tumoral and post-tumoral stages. RESULTS: Treatment with 2 mM and 4 mM D-GlcNAc significantly decreased cell proliferation rates in MCF-7 and 4T1 cell lines and increased Fas expression. The number of apoptotic cells was significantly higher than untreated cell cultures (p < 0.01 - p < 0.0001). D-GlcNAc administration also considerably reduced tumour size, mitosis, and angiogenesis in the post-treatment group compared to the control breast cancer group (p < 0.01 - p < 0.0001). Additionally, molecular docking/dynamic analysis revealed a high binding affinity of D-GlcNAc to the marker protein HER2, which is involved in tumour progression and cell signalling. CONCLUSION: Our study demonstrated the positive effect of D-GlcNAc administration on breast cancer cells, leading to increased apoptosis and Fas expression in the malignant phenotype. The binding affinity of D-GlcNAc to HER2 suggests a potential mechanism of action. These findings contribute to understanding D-GlcNAc as a potential anti-tumour agent for breast cancer treatment.

Targeting MDM2-p53 Axis through Drug Repurposing for Cancer Therapy: A Multidisciplinary Approach.

Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 µM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 µM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.

Data mining and molecular dynamics analysis to detect HIV-1 reverse transcriptase RNase H activity inhibitor.

HIV-1 is a deadly virus that affects millions of people worldwide. In this study, we aimed to inhibit viral replication by targeting one of the HIV-1 proteins and identifying a new drug candidate. We used data mining and molecular dynamics methods on HIV-1 genomes. Based on MAUVE analysis, we selected the RNase H activity of the reverse transcriptase (R.T) enzyme as a potential target due to its low mutation rate and high conservation level. We screened about 94,000 small molecule inhibitors by virtual screening. We validated the hit compounds' stability and binding free energy through molecular dynamics simulations and MM/PBSA. Phomoarcherin B, known for its anticancer properties, emerged as the best candidate and showed potential as an HIV-1 reverse transcriptase RNase H activity inhibitor. This study presents a new target and drug candidate for HIV-1 treatment. However, in vitro and in vivo tests are required. Also, the effect of RNase H activity on viral replication and the interaction of Phomoarcherin B with other HIV-1 proteins should be investigated.