Beneficial effect of 5-HT1b/1d agonist on Parkinson's disease by modulating glutamate and reducing deposition of α-synuclein.

The given investigation examined the neuroprotection role of 5-HT1b/1d agonist in reserpine induced Parkinson's disease (PD) in male Wistar rats. PD was induced in rats by reserpine at 5 mg/kg ip for 3 days and thereafter the rats were provided with the following treatments for 4 days, zolmitriptan (ZLM) group (30 mg/kg ip); STD group (levodopa + carbidopa, 200 + 5 mg/kg ip); ZLM + GA group (zolmitriptan, 30 mg/kg ip and glutamic acid, 1.5 mg/kg); ZLM + DX group (zolmitriptan, 30 mg/kg ip and dextromethorphan, 20 mg/kg ip). All the groups were then assessed for cognitive and motor functions at the end of the protocol. Moreover, oxidative stress parameters and histopathological changes were observed in rats of all treatment groups. Deposition of α-synuclein in the brain tissue was observed by silver staining. Data of this investigation revealed that motor and cognitive functions were improved in the ZLM-treated group compared with the negative control group, which was observed to be reversed in ZLM + GA group. Treatment with ZLM ameliorated oxidative stress and histopathological changes in the brain tissue of PD rats. Further, ZLM reduced the deposition of α-synuclein in PD rats, which reversed in ZLM + GA-treated group. This study concludes by stating that 5-HT1b/1d agonist can prevent neurodegeneration and reduce oxidative stress in PD rats. The probable underlying mechanism of such an effect of 5-HT1b/1d agonist could be by regulating the deposition of α-synuclein and reducing the expression of NMDA receptor.

Unravelling Phyto-Compound Therapeutics against SRC Protein via FGF Pathway Targeting-A Comprehensive Approach Integrating Omics Data Analysis, Network Pharmacology, Virtual Screening, and Molecular Dynamics.

INTRODUCTION: Colorectal cancer is a complex condition influenced by genetic mutations and environmental factors. Due to its intricate nature, the diagnosis and treatment of this condition require a comprehensive approach that considers individual circumstances. The study aimed to identify genes linked with colorectal cancer and their therapeutic agents from natural bioactive compounds. METHODS: The significantly prognostic differentially expressed genes (DEGs) were screened out from NCBI Gene Expression Omnibus (GEO) datasets. A protein-protein interaction network was constructed using STRING Database, and key genes were identified using Network Analyzer and CytoNCA plugins within Cytoscape. Further analysis involved functional annotations, and biological pathways analysis, SRC mechanism to uncover the role of SRC in CRC. Additionally, we performed virtual screening and molecular docking, Physiochemical property analysis along with MD simulation study to propose suitable natural compounds for promising therapeutic targets. RESULTS: The study conducted differential gene expression analysis, identifying 3621 statistically significant genes, with 1467 upregulated and 2154 downregulated. The top ten genes with the highest degree, betweenness centrality, and closeness centrality in the PPI network were selected as key genes. The SRC gene was found to have the highest degree and closeness centrality. Functional annotation and pathway analysis of key genes with a specific focus on the SRC mechanism revealed that the SRC's role in activating the RAS-RAF-MEK-ERK and Wnt/ß-catenin pathways in CRC cells, promoting proliferation and invasion. Molecular modelling of SRC led to the screening of phyto-compounds from tropical fruits, with Rutinexhibiting a higher docking score compared to FDA-approved anticancer drugs. MD simulations over 100 ns and the post-MD analysis i.e. RMSD, SASA, RMSF, FEL, RG, Hydrogen bond, PCA, and MMPBSA, comprehended the stable and robust interactions of a protein-ligand complex. These findings suggest Rutin's potential as a potent natural molecule for treating CRC. The study concludes that SRC plays a pivotal role in CRC, influencing cellular processes critical to cancer development and Rutin has been found to be a promising SRC inhibitor, suggesting a potential alternative therapeutic strategy for CRC. The consistent molecular interactions of Rutin necessitate further validation through wet lab experiments, offering hope for individuals affected by CRC.

Does sildenafil citrate affect the pharmacokinetics of metformin in rats? Screening of mechanism through analytical and molecular docking approach.

OBJECTIVE: In the present study, the effect of sildenafil on the pharmacokinetics of metformin was studied in experimental rats, and we also postulated the molecular mechanism by performing molecular docking studies. MATERIALS AND METHODS: Analysis of metformin and sildenafil (SIL) from rat plasma was done by high performance liquid chromatography. Optimum chromatographic separation and quantification of MET, SIL and Cetirizine was achieved on Phenomenex EVO C18 column with triethyl amine (0.3%): Methanol: Acetonitrile (70:05:25 v/v) as mobile phase maintaining flow rate of 1 ml/min, the detector was tuned at 224 nm. The extraction of MET and sildenafil from rat plasma was achieved by solid-phase extraction using Strata-X cartridges. The method was validated as per the ICH guidelines. For docking studies, the crystal structure of organic cation transporter 1 (OCT1) protein and multidrug and toxin extrusion (MATE) protein (5XJJ) were downloaded from the PubChem database. The docking study was performed by PyRx virtual screening software, and the results were analyzed by BIOVIA Discovery Studio. RESULTS: The validation of HPLC method was done, intraday and interday precision study of HPLC method demonstrated %RSD values less than 5%, the extraction recovery for MET and SIL were near to 80 % for low, medium and high QC samples. The plasma stability of MET and SIL showed % RSD values <10% for low, medium, and high QC samples. A sensitivity study for MET and SIL in rat plasma suggested a lower limit of quantification values of 8 and 10 ng/mL, respectively. The pharmacokinetic parameters were recorded, Cmax of experimental and control rats was 611.2 and 913.2 ng/mL; t1/2 1.66 and 1.98, AUC (0-t) 1637.5 and 2727.24, AUC (0-∞) 1832.38 and 2995.24 for MET. The results suggested that the Cmax of MET in experimental rats (MET + SIL) was 33.07% lower than the control (MET only) and also the t1/2 was 0.32 h shorter. Docking analysis suggested a higher binding affinity of sildenafil with MATE protein (5XJJ) compared to OCT1, suggesting possible involvement of MATE family proteins for pharmacokinetic alterations of MET. CONCLUSIONS: The HPLC and solid-phase extraction method were developed and applied successfully for the pharmacokinetics of MET and SIL. Intake of SIL altered the pharmacokinetics of MET in rats. Molecular docking studies suggested the involvement of MATE family proteins for alterations of MET pharmacokinetics.

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.