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Antilipase and antioxidant activities of topiramate-phenolic acid conjugates: Computational modelling, synthesis, and in-vitro investigations.
Biochem Biophys Res Commun
December 2024
School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India.
A series of ten topiramate-phenolic acid conjugates (T1-T10) were synthesized, and evaluated for their pancreatic lipase inhibitory and antioxidant potentials. The design of the compounds reflected the structural attributes extracted from robust QSAR models developed for predicting the pancreatic lipase inhibition potency. Conjugate T4 competitively inhibited pancreatic lipase with IC value of 8.
View Article and Find Full Text PDFEvaluation of Fifteen 5,6-Dihydrotetrazolo[1,5-]quinazolines Against : Integrating In Vitro Studies, Molecular Docking, QSAR, and In Silico Toxicity Assessments.
J Fungi (Basel)
November 2024
Department of Biosciences and Biotechnologies, Graduate School of Bioresources and Bioenvironment Sciences, Kyushu University, 744 W5-674, Motooka Nishi-ku, Fukuoka 819-0395, Japan.
(), the second most prevalent Candida pathogen globally, has emerged as a major clinical threat due to its ability to develop high-level azole resistance. In this study, two new 5,6-dihydrotetrazolo[1,5-]quinazoline derivatives ( and ) were synthesized and characterized using IR, LC-MS, H, and C NMR spectra. Along with 13 previously reported analogues, these compounds underwent in vitro antifungal testing against clinical isolates using a serial dilution method (0.
View Article and Find Full Text PDFComprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals.
J Cheminform
December 2024
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Ensuring the safety of chemicals for environmental and human health involves assessing physicochemical (PC) and toxicokinetic (TK) properties, which are crucial for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Computational methods play a vital role in predicting these properties, given the current trends in reducing experimental approaches, especially those that involve animal experimentation. In the present manuscript, twelve software tools implementing Quantitative Structure-Activity Relationship (QSAR) models were selected for the prediction of 17 relevant PC and TK properties.
View Article and Find Full Text PDFIntelligent consensus-based predictions of early life stage toxicity in fish tested in compliance with OECD Test Guideline 210.
Aquat Toxicol
December 2024
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India. Electronic address:
Early life stage (ELS) toxicity testing in fish is a crucial test procedure used to evaluate the long-term effects of a wide range of chemicals, including pesticides, industrial chemicals, pharmaceuticals, and food additives. This test is particularly important for screening and prioritizing thousands of chemicals under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. In silico methods can be used to estimate the toxicity of a chemical when no experimental data is available and to reduce the cost, time, and resources involved in the experimentation process.
View Article and Find Full Text PDFEvaluation of Machine Learning Based QSAR Models for the Classification of Lung Surfactant Inhibitors.
Environ Health (Wash)
December 2024
Department of Environmental Science, Baylor University, Waco, Texas 76798-7266, United States.
Inhaled chemicals can cause dysfunction in the lung surfactant, a protein-lipid complex with critical biophysical and biochemical functions. This inhibition has many structure-related and dose-dependent mechanisms, making hazard identification challenging. We developed quantitative structure-activity relationships for predicting lung surfactant inhibition using machine learning.
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