Synthesis, Biological Evaluation, and QPLD Studies of Piperazine Derivatives as Potential DPP-IV Inhibitors.

BACKGROUND: Diabetes mellitus is a serious global health issue, currently affecting 425 million people and is set to affect over 690 million people by 2045. It is a chronic disease characterized by hyperglycemia due to relative or absolute insulin hormone deficiency. Dipeptidyl peptidase- IV (DPP-IV) inhibitors are hypoglycemic agents augmenting the action of the incretin hormones that stimulate insulin secretion from the pancreatic beta cells. OBJECTIVE: In this study, synthesis and biological evaluation of seven piperazine derivatives 3a-g was carried out. METHODS: The synthesized molecules were characterized using proton-nuclear magnetic resonance, carbon-nuclear magnetic resonance, infrared spectroscopy and mass spectrometry. RESULTS: In vitro biological evaluation study showed comparable DPP-IV inhibitory activity for the targeted compounds ranging from 19%-30% at 100 µM concentration. Furthermore, the in vivo hypoglycemic activity of 3d was evaluated using streptozotocin-induced diabetic mice. It was found that compound 3d significantly decreased the blood glucose level when the diabetic group treated with 3d was compared to the control diabetic group. Quantum-Polarized Ligand Docking (QPLD) studies demonstrate that 3a-g fit the binding site of DPP-IV enzyme and form H-bonding with the backbones of R125, E205, E206, K554, W629, Y631, Y662, R669, and Y752. CONCLUSION: Piperazine derivatives were successfully found to be new scaffolds as potential DPP-IV inhibitors.

Classification of Patients with Coronary Microvascular Dysfunction.

While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 National Research Consensus Conference on Gender-Specific Research in Emergency Care. In this study, we utilized methods from machine learning that leverage structured and unstructured narratives in clinical notes to detect patients with CMD. We have shown that structured data are not sufficient to detect CMD and integrating unstructured data in the computational model boosts the performance significantly.