In silico analysis of Senna alexandrina compounds as potential cyclooxygenase-2 inhibitors for targeted anti-inflammatory properties

Abstract

Senna alexandrina, a medicinal plant from the Fabaceae family, is known and traditionally recognised for its laxative properties, but also contains bioactive compounds with potential anti-inflammatory effects. Inflammation, driven by cyclooxygenase-2 (COX-2) activity, is associated with numerous pathological conditions, making COX-2 a validated pharmacological target. Currently, non-steroidal anti-inflammatory drugs (NSAIDs) and selective COX-2 inhibitors (COXIBs) are effective but associated with gastrointestinal and cardiovascular risks, highlighting the need for safer alternatives. The study employed an integrated in silico approach to evaluate the phytochemicals from S. alexandrina as potential COX-2 inhibitors. Methanolic leaf extracts were profiled using LC-MS, revealing the diverse classes of compounds, including anthraquinones and flavonoids. Molecular networking further confirmed the chemical diversity, while SwissADME and pkCSM predicted favourable pharmacokinetic properties for most compounds, which followed Lipinski’s Rule of Five and Viber’s criteria. Molecular docking revealed superior binding affinities for anthraquinones (-10 to -8.3 kcal/mol) and flavonoids (-9.7 to -8.7 kcal/mol). In comparison, NSAIDs (-8.1 to -6.2 kcal/mol) are supported by hydrogen bonding, π-π stacking, and hydrophobic interactions within the COX-2 active site. Network pharmacology further complements the multi-target engagement, including estrogen receptors, angiogenesis regulators, and oxidative stress pathways, suggesting therapeutic potential beyond COX-2 inhibition. The compound-target-disease network highlighted polypharmacology as a potential strategy for simultaneously addressing inflammation, hormonal imbalance, and angiogenesis. The findings suggest that S. alexandrina phytochemicals are a promising candidate for safer anti-inflammatory therapies and may have potential in the management of endometriosis and dysmenorrhea. Future work should focus on experimental validation, formulation optimisation, and clinical evaluation to integrate the computational insights into therapeutic applications.