A Novel Partial PPARα/γ Dual Agonist SN159 Improves Insulin Sensitivity

Application of machine learning methods for the development of antidiabetic drugs

Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development, which opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Design and Evaluation of Licochalcone A Derivatives as Anticancer Agents

New derivatives of licochalcone A were synthesized and evaluated for their potential anticancer activities. Compounds 6 (( E)-N-(4-(3-(5-bromo-4-hydroxy-2-methoxy phenyl) acryloyl) phenyl)-4-isopropylbenzamide) and 8 (1-(3-dimethylamino-phenyl)-3-(2-trifluoromethyl-phenyl)-propenone) showed potent activity against the screened cancer cell lines with that of compound 6 ranging from 6.9 ± 0.2 μM to 22.9 ± 3.1 μM, and that of compound 8 from 4.2 ± 0.5 μM to 11.8 ± 0.7 μM. Both compounds showed stronger cytotoxicity than that of licochalcone A. These two candidates have very different substituents and could be considered as promising lead compounds for further development of potent anticancer agents.