Abstract
Recent advances in molecular therapeutics underscore the central role of dysregulated endogenous enzymes in the onset and progression of non-infectious diseases. Urease and thymidine phosphorylase are two clinically relevant enzymes implicated in microbial virulence, inflammation, and cancer biology, yet potent dual modulators remain limited. Here, we report the design and synthesis of a novel library of bis-thiazole linked oxazine Schiff base hybrids (1–10) as candidate dual-enzyme inhibitors. Structural elucidation using FT-IR, 1HNMR, 13C NMR spectroscopy, and HRMS confirmed the structural integrity of the synthesized scaffolds. Biological evaluation revealed potent inhibitory activity across the series, with compounds 1, 2, 5 and 8 demonstrating superior efficacy compared with the standard inhibitors thiourea and 7-deazaxanthine. Notably, compound-1 acted as the most effective dual inhibitor, achieving IC50 values of 4.30 ± 0.73 µM (thymidine phosphorylase) and 3.90 ± 0.84 µM (urease). Molecular docking analyses further identify stable binding conformations and extensive interaction networks within the catalytic sites of both targets, providing a structural rationale for the observed potencies. These findings position the bis-thiazole linked oxazine Schiff base scaffold as a compelling chemotype for next-generation enzyme-directed therapeutics. The strong inhibitory profiles and well-defined molecular interactions provide a clear rationale for further structure-guided optimization. Together, these results lay the groundwork for advancing this scaffold toward translational development.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Y. Khan; Project Supervisor, Methodology, data curation, Conceptualization, Experimental, MU. Naeem; Visualization, Conceptualization, original draft Write-up, SF. Naqvi; Experimental, formal analysis, investigation, validation, S.Aslam; Formal Analysis, Biological activity, R. Adnan; Software, analyze data, formal analysis, M. Naseem- Formal analysis, Review, S.B. Fatima- Investigation, Azmatullah-project administration, supervision, Enzyme inhibition; U. Gulshan: Formal Analysis, Writing-Original Draft S. Aminullah; writing-review and editing.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors declare no competing interests.
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Research funding: Not applicable.
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Data availability: All data obtained or analyzed during this study are included in this article.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/pac-2025-0649).
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