Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning.

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from -8.0 to -9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07-0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25-0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

2,4-Dichlorophenoxyacetic acid induced hepatic and renal toxicological perturbations in rat model: Attenuation by selenium supplementation.

2,4-Dichlorophenoxyacetic acid (2,4-D) is a commercially used herbicide to manage broadleaf weeds that have various toxicological and ecological effects. In view of ever-escalating use of 2,4-D, risk assessment becomes mandatory to ensure the safety of both human health and the ecosystem. Oxidative injury has been expected as a possible mechanism implicated in 2,4-D toxicity. The present study was planned and conducted to explore the antioxidant potential of selenium (Se) supplementation to moderate the 2,4-D hepatic and renal toxicity in a rat model. The rats were randomly assigned to four equal groups and treated via oral gavage for a period of 4 weeks. Group I: received deionized water as a vehicle, group II: received 2,4-D (150 mg-1 kg-1 day-1), group III: received Se supplement (1 mg-1 kg-1 day-1), and group IV: received 2,4-D (150 mg-1 kg-1 day-1) and Se supplement (1 mg-1 kg-1 day-1) simultaneously. After 4 weeks of administration, 2,4-D induced toxicity was observed, as manifested by disrupted levels of plasma urea, creatinine, alkaline phosphatase (ALP), aspartate aminotransferase (AST), and alanine aminotransferase (ALT). Further, 2,4-D caused a considerable increase in tissue malondialdehyde (MDA) levels and decreased activity of antioxidant enzymes, including superoxide dismutase, catalase, and glutathione reductase. Se supplementation exhibited its antioxidant properties by significantly improving urea, creatinine, ALP, AST, and ALT, and MDA levels and antioxidant enzyme activities. In conclusion, the results suggest that 2,4-D induced hepatic and renal toxicities were attenuated by Se supplementation probably owing to its antioxidant properties.