Dapoxetine Hydrochloride

Selective serotonin reuptake inhibitors (SSRIs) are known to have anticancer activity against different types of cancer. In this study, an integrative informatics approach was applied to identify compound and genetic perturbations that produce similar effects to SSRIs to formulate systems biology hypotheses and identify biological pathways involved in the putative anticancer effects of SSRIs in prostate cancer. An 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay assessed the antiproliferative effects of SSRIs and drug combinations. Cell death mechanisms were studied using annexin V-FITC/PI staining, and the cell cycle analysis was carried out by counterstaining with propidium iodide. Relative gene expression was assessed using a real-time polymerase chain reaction (PCR). Computational results hypothesized that SSRIs could potentially exert anticancer effects in prostate cancer cell lines by modulating apoptotic and tumorigenesis pathways and significantly inhibiting the growth of prostate cancer cells in a time and concentration-dependent manner. The combination of SSRIs with cisplatin, 5-fluorouracil, and raloxifene resulted in either synergistic or additive effects. SSRIs resulted in a significant increase in the early and late apoptotic activity in PC3 cells. Dapoxetine, paroxetine, and sertraline resulted in cell cycle arrest at the G0/G1 phase. Treatment with either dapoxetine or paroxetine decreases the expression of Bcl-2, CASP8, DR5, and VEGF. At the same time, sertraline decreases the expression of Bcl-2 and VEGF and increases the expression of CASP8 and DR5. Results revealed that SSRIs can potentially act as antiproliferative agents against prostate cancer cells, and their activity is mediated through different signaling pathways.

Phosphodiesterase type 5 inhibitors (PDE5is), primarily used for the treatment of erectile dysfunction, have been severely misused in recent years, posing a threat to public health and safety. This study developed a method that combines Surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to rapidly identify different PDE5is types. A total of 948 SERS spectra from 79 PDE5is were collected using gold nanoparticles (AuNPs) as the enhancement agent, and dimensionality reduction was performed through principal component analysis (PCA). Subsequently, six traditional machine learning models, partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), and multilayer perceptron (MLP) were applied for data classification and identification. Results showed that the MLP model achieved the highest classification accuracy of 99.65 %, with only 1.82 % of the samples misclassified from thiosildenafil to sildenafil analogues, significantly outperforming the other models. This method offers a rapid, cost-effective, and accurate alternative for the detection of PDE5is in health foods, with implications for improving regulatory oversight and public health safety.