Models for H₃ receptor antagonist activity of sulfonylurea derivatives

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Synthesis of Novel 1-(4-Substituted pyridine-3-sulfonyl)-3-phenylureas with Potential Anticancer Activity

A series of novel 4-substituted-N-(phenylcarbamoyl)-3-pyridinesulfonamides 11–27 have been synthesized by the reaction of 4-substituted pyridine-3-sulfonamides 2–10 with the appropriate aryl isocyanates in presence of potassium carbonate. The in vitro anticancer activity of compounds 11, 12, 14–21 and 24–26 was evaluated at the U.S. National Cancer Institute and in light of the results, some structure-activity relationships were discussed. The most prominent compound, N-[(4-chlorophenyl)carbamoyl]-4-[4-(3,4-dichlorophenyl)piperazin-1-yl]pyridine-3-sulfonamide (21) has exhibited a good activity profile and selectivity toward the subpanels of leukemia, colon cancer and melanoma, with average GI₅₀ values ranging from 13.6 to 14.9 µM.