Department of Pharmacy
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Item Investigation of antibacterial potential of Natsiatum herpeticum Buch.-Ham. ex Arn. using in silico-in vitro approach(Elsevier, 2024-01) Jadhav, Hemant R.Since ages, natural products have laid the foundation for the development of promising antimicrobials. With the advent of antimicrobial resistance, the search for effective antimicrobials continues as its shortfall will menace the healthcare system. Natsiatum herpeticum remained the least explored plant despite its ethnopharmacological claims. DNA barcoding was performed to identify and ensure quality control of the plant materials used in the experiment. QToF-MS analysis followed by network pharmacology revealed TNF and IRAK4 to be the two gene targets that can be modulated by the compounds present in the extract. Analysis of potential drug-like compounds using molecular docking (against 1KZN, 2VF5, 2W9S, and 4CJN) and MD simulation suggested compound CPD2 to be the most potent molecule against the bacterial targets. Bacteriostatic activity against E. coli was exhibited by the extract (MIC=50 μg/ml) in the microtiter-plate dilution method. Our results suggest that N. herpeticum not only exhibits potential bacteriostatic activity against E. coli but can also modulate host-immune responses via TNF and IRAK4-associated pathways.Item Bioactivity predictions and virtual screening using machine learning predictive model(Taylor & Francis, 2024-01) Jadhav, Hemant R.Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.