Natural AI-based drug designing by modification of ascorbic acid and curcumin to combat buprofezin toxicity by using...

Buprofezin, a widely employed insecticide in agricultural practices, has elicited significant apprehension due to its prospective deleterious effects on non-target organisms and ecological systems. Its enduring presence in terrestrial and aquatic environments presents potential hazards to human health and biodiversity, thereby necessitating the investigation of safer alternatives or strategies for mitigation. The research focuses on five principal receptors: CAT (Catalase), IL-1B (Interleukin-1 Beta), IL-6 (Interleukin-6), TNF-alpha (Tumor Necrosis Factor-alpha), and SOD (Superoxide Dismutase). These receptors are integral to the processes of inflammation, oxidative stress, and immune responses, rendering them critical for comprehending the biochemical pathways affected by toxic substances and the potential for protective interventions. The investigation employed WADDAICA (Webserver-Aided Drug Design by Artificial Intelligence) to formulate AI-driven pharmaceuticals, complemented by ADME (Absorption, Distribution, Metabolism, Excretion) evaluations, Molecular Dynamics (MD) simulations, as well as MMGBSA and MMPBSA methodologies to examine the stability and interactions of the compounds with the designated receptors. Docking experiments disclosed that the interaction of CAT with the ascorbic acid AI-derived drug demonstrated a binding energy of -7.1 kcal/mol, signifying a robust interaction, while the complex of IL-1B with the curcumin AI-derived drug exhibited a binding energy of -7.3 kcal/mol. The ADME analysis revealed favorable gastrointestinal absorption and aqueous solubility for both compounds. Furthermore, the drug-likeness metrics were deemed satisfactory, with no breaches of Lipinski's rule of five, suggesting promising potential for subsequent advancement as therapeutic agents.