Remarkable utilization of quinazoline-based homosulfonamide for cytotoxic effects with triple kinase inhibition activities: cell cycle analysis and molecular docking profile.

We tested newly synthesized compounds 1-13 on 59 cancer cell lines and found that acylhydrazones 5, 6, 7, 9, and 12 showed the best cytotoxic activity. They stopped the mean growth percentage (MG%) by an average of 23.5, 55.

Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models.

This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nanoparticle properties, with a focus on their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney tissues. In order to address the complex and non-linear nature of the data, three machine learning models were utilized: Bayesian Ridge Regression (BRR), Kernel Ridge Regression (KRR), and K-Nearest Neighbors (KNN).

A novel role for nonactin: interfering with G-quadruplex in RET-driven medullary thyroid cancer.

Background: Medullary Thyroid Carcinoma (MTC) is closely associated with mutations in the RET proto-oncogene, placing the activated RET protein at the center of MTC pathogenesis. Existing therapeutic solutions, primarily tyrosine kinase inhibitors such as selpercatinib, vandetanib, and cabozantinib, have shown moderate efficacy but are accompanied by increased risks of side effects and resistance. This study unveils a promising avenue using nonactin, a compound historically recognized for its antibacterial properties, targeting the G-quadruplex interactions within the RET proto-oncogene.

Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation.

This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models-Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)-are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis.

Quantitative analysis of loxoprofen sodium loaded microspheres comprising pectin and its thiolated conjugates: evaluation of their sustained release behavior.

Continuous use of oral NSAIDs can damage mucosal membrane, which results in decreased bioavailability and non-compliance with the therapy. But the use of sustained release drug delivery systems might offer a solution. Objective was to synthesize mucoadhesive SR microspheres by using different combinations of pectin (PEC) and its thiolated derivative (T-PEC3100) for improved loxoprofen (LS) permeation.