Current topics in medicinal chemistry
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A research on mood disorder pathophysiology has hypothesized abnormalities in glutamatergic neurotransmission, by suggesting further investigation on glutamatergic N-methyl-Daspartate (NMDA) receptor modulators in treating Major Depressive Disorder (MDD). Esketamine (ESK), an NMDA receptor antagonist able to modulate glutamatergic neurotransmission has been recently developed as an intranasal formulation for treatment-resistant depression (TRD) and for rapid reduction of depressive symptomatology, including suicidal ideation in MDD patients at imminent risk for suicide. ⋯ The U.S. Food and Drug Administration (FDA) granted fast track and Breakthrough Therapy Designation to Janssen Pharmaceuticals®, Inc. for intranasal ESK in 2013 for treatment-resistant depression (TRD) and in 2016 for the treatment of MDD with an imminent risk of suicide. However, further studies should be implemented to investigate the long-term efficacy and safety of intranasal ESK.
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Type 2 diabetes mellitus is one of the most common forms of the disease worldwide. Hyperglycemia and insulin resistance play key roles in type 2 diabetes mellitus. Renal glucose reabsorption is an essential feature in glycaemic control. ⋯ Through glucosuria, SGLT2 inhibitors reduce body weight and body fat, and shift substrate utilisation from carbohydrates to lipids and, possibly, ketone bodies. These pleiotropic effects of SGLT2 inhibitors are likely to have contributed to the results of the EMPA-REG OUTCOME trial in which the SGLT2 inhibitor, empagliflozin, slowed down the progression of chronic kidney disease and reduced major adverse cardiovascular events in high-risk individuals with type 2 diabetes. This review discusses the role of SGLT2 in the physiology and pathophysiology of renal glucose reabsorption and outlines the unexpected logic of inhibiting SGLT2 in the diabetic kidney.
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The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an indispensable tool for drug designers to mine chemical information from large compound databases for developing drugs at a much faster rate as never before. The applications of AI have gone beyond bioactivity predictions and have shown promise in addressing diverse problems in drug discovery like de novo molecular design, synthesis prediction and biological image analysis. In this article, we provide an overview of all the algorithms under the umbrella of AI, enlist the tools/frameworks required for implementing these algorithms as well as present a compendium of web servers, databases and open-source platforms implicated in drug discovery, Quantitative Structure-Activity Relationship (QSAR), data mining, solvation free energy and molecular graph mining.