Design and Computational Study of Sulfonamide-Modified Cannabinoids as Selective COX-2 Inhibitors Using Semiempirical Quantum Mechanical Methods: Drug-like Properties and Binding Affinity Insights

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 Δ⁹-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.

Figure 1

Distribution plots of the mean signed deviation (MSD) for computed interaction energies in the benchmark data sets: (a) S66, (b) X40, (c) HB375, (d) HB300SPX, and (e) PLA15.

Figure 2

Lowest-energy GFN2-xTB (ALPB) optimized pose (pink sticks) and best-docked pose (yellow sticks) for (a) aspirin, (b) diclofenac with key COX-1 residues, and (c) flurbiprofen with key COX-2 residues. Hydrogen bonds (green dashed lines) include distances in angstroms.

Figure 3

Pearson correlation coefficient for (a) uncorrected binding free energy (ΔG_bind,solv^′) and (b) corrected binding free energy in implicit aqueous solvation (ΔG_bind,solv) of 30 NSAIDs in the truncated COX-2 pocket, calculated using the GFN2-xTB (ALPB) method.

Figure 4

Selectivity index (SI) derived from ΔG_bind,solv^′ for (a) NSAIDs, (b) parent C-5 cannabinoids, and (c) carboxylic derivatives of cannabinoids with COX and CB receptors. Error bars represent the standard error from the top three bound poses (n = 3).

Figure 5

Lowest-energy optimized poses of (a) CBCA, (b) CBNA, (c) CBEA, (d) CBTA, and (e) CBLA using the GFN2-xTB (ALPB) method overlaid with celecoxib (gray stick) in the fully relaxed COX-2 binding site.

Figure 6

Binding interactions of sulfonamide-modified CBTA-C2-SO₂NH₂ (11) with key amino acids in the fully relaxed COX-2 complex calculated using the GFN2-xTB (ALPB) method. Hydrogen bonds are shown as green dashed lines.

Figure 7

2D interaction diagrams of sulfonamide-modified analogs: (a) CBCA-C3-SO₂NH₂ (3), (b) CBNA-C3-SO₂NH₂ (6), (c) CBEA-C2-SO₂NH₂ (8), (d) CBTA-C2-SO₂NH₂ (11), (e) CBLA-C1-SO₂NH₂ (13), and (f) celecoxib with key amino acid residues in the fully relaxed COX-2 binding pocket, optimized using the GFN2-xTB (ALPB) method.

Figure 8

Thermodynamic cycle illustrating the relative binding free energy (ΔΔG_bind,solv) between two ligands (“ligand 1” and “ligand 2”). Green arrows indicate ligand association with the COX-2 pocket. Black circles highlight the sulfonamide substitution sites.

Figure 9

Radar map of oral bioavailability for (a) celecoxib, (b) CBCA-C3-SO₂NH₂, (c) CBNA-C3-SO₂NH₂, (d) CBEA-C2-SO₂NH₂, (e) CBTA-C2-SO₂NH₂, and (f) CBLA-C1-SO₂NH₂.

Figure 10

Chemical structures of sulfonamide-modified cannabinoid analogs.