Design and Computational Study of Sulfonamide-ModifiedCannabinoids as Selective COX‑2 Inhibitors Using SemiempiricalQuantum Mechanical Methods: Drug-like Properties and Binding AffinityInsights

Cyclooxygenase (COX) is one of the concerned targets in the development of anti-inflammatory therapies. Using semiempirical quantum mechanical (SQM) methods with implicit solvation, we investigated the binding free energies and selectivity of natural cannabinoids and their sulfonamide-modified derivatives with the COX and cannabinoid (CB) receptors. Validation against benchmark data sets demonstrated the accuracy of these methods in predicting binding affinities while minimizing false positives and false negatives often associated with conventional docking tools. Our findings indicate that Δ<sup>9</sup>-THC and its carboxylic acid derivative exhibit strong binding affinities for COX-2 and CB2, suggesting their potential as anti-inflammatory agents, though their significant CB1 affinity suggests psychoactive risks. In contrast, carboxylic acid derivatives such as CBCA, CBNA, CBEA, CBTA, and CBLA demonstrated selective binding to COX-2 and CB2, with low CB1 affinity, supporting their potential as promising anti-inflammatory leads with reduced psychoactive side effects. Sulfonamide-modified analogs further enhanced COX-2 binding affinities and selectivity, displaying favorable drug-like properties, including compliance with Lipinski’s rules, noninhibition of cytochromes P450, and oral bioavailability. These results highlight the utility of GFN2-xTB in identifying and optimizing cannabinoid-based therapeutic candidates for anti-inflammatory applications.

Publication Details

Journal
Figshare
Published
2025-03-27
DOI
https://doi.org/10.1021/acsomega.5c00562.s001
Primary Topic
Plant pathogens and resistance mechanisms
Type
article
Field-Weighted Citation Impact
0.00
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Design and Computational Study of Sulfonamide-ModifiedCannabinoids as Selective COX‑2 Inhibitors Using SemiempiricalQuantum Mechanical Methods: Drug-like Properties and Binding AffinityInsights

Figshare
Plant pathogens and resistance mechanisms
article

Design and Computational Study of Sulfonamide-ModifiedCannabinoids as Selective COX‑2 Inhibitors Using SemiempiricalQuantum Mechanical Methods: Drug-like Properties and Binding AffinityInsights

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Abstract

Cyclooxygenase (COX) is one of the concerned targets in the development of anti-inflammatory therapies. Using semiempirical quantum mechanical (SQM) methods with implicit solvation, we investigated the binding free energies and selectivity of natural cannabinoids and their sulfonamide-modified derivatives with the COX and cannabinoid (CB) receptors. Validation against benchmark data sets demonstrated the accuracy of these methods in predicting binding affinities while minimizing false positives and false negatives often associated with conventional docking tools. Our findings indicate that Δ<sup>9</sup>-THC and its carboxylic acid derivative exhibit strong binding affinities for COX-2 and CB2, suggesting their potential as anti-inflammatory agents, though their significant CB1 affinity suggests psychoactive risks. In contrast, carboxylic acid derivatives such as CBCA, CBNA, CBEA, CBTA, and CBLA demonstrated selective binding to COX-2 and CB2, with low CB1 affinity, supporting their potential as promising anti-inflammatory leads with reduced psychoactive side effects. Sulfonamide-modified analogs further enhanced COX-2 binding affinities and selectivity, displaying favorable drug-like properties, including compliance with Lipinski’s rules, noninhibition of cytochromes P450, and oral bioavailability. These results highlight the utility of GFN2-xTB in identifying and optimizing cannabinoid-based therapeutic candidates for anti-inflammatory applications.

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Openalex Percentile: Top 41%
Plant pathogens and resistance mechanisms
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Design and Computational Study of Sulfonamide-ModifiedCannabinoids as Selective COX‑2 Inhibitors Using SemiempiricalQuantum Mechanical Methods: Drug-like Properties and Binding AffinityInsights · Figshare (2025) | TGRS Research Map | TGRS