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Updated: May 17, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
Synthesis, structural studies, and inhibitory potential of selected sulfonamide analogues: insights from in silico
Tahira Noor1,2,3, Daniel C Schultz2, Gustavo Seabra2
1Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Islamabad 45550, Pakistan.
Abstract:
Antimicrobial resistance is a growing public health threat worldwide, and the current drug development pipeline has thus far been inadequate in addressing this impending crisis. Further research into antibiotic agents, both existing and novel, is therefore paramount for identifying suitable candidates to combat antibiotic-resistant pathogens. Sulfonamides, the first class of synthetic antibiotics, target dihydropteroate synthase (DHPS), a key bacterial enzyme. While this class of antibiotics has historically demonstrated great utility, their use has diminished due to resistance and undesired side effects. In the present study, we synthesized a selection of four sulfonamide analogues (FQ5, FQ6, FQ7 and FQ12), validated their structures through NMR spectroscopy, and evaluated their inhibitory potential through computational docking and MIC assays against four bacterial strains: Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 35401 and Bacillus subtilis ATCC 6633. Each compound exhibited antibacterial activity; FQ5 demonstrated the most potent activity, with an MIC of 32, 16, 16, and 16 µg/mL against aforementioned strains, respectively. FQ6, FQ7 and FQ12, on the other hand, exhibited moderate activity against P. aeruginosa and E. coli (MIC = 128 µg/mL each) and low activity against S. aureus and B. subtilis (MIC = 256 µg/mL each). Molecular docking studies indicated that FQ5 captures multiple hydrogen bonding, ionic, and π-π interactions with key binding pocket residues of DHPS, and FQ5 also demonstrated superior predicted drug-likeness in in silico ADMET studies compared to other compounds. FQ5 is therefore a favorable starting point for further optimization.
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