Discovery of Bioactive Properties in the Seagrass Ruppia maritima Extract: A Triplatform Assessment of Antioxidant, Antidiabetic, Antibacterial, Cytotoxic, and Analgesic Activities

Abstract

Ruppia maritima, a seagrass, was investigated for its potential antioxidant, antidiabetic, antibacterial, cytotoxic, and analgesic activities. The acetone extract of its leaves (AERM) was studied using in vitro, in vivo, and in silico methods. To determine its secondary metabolites, the total phenolic and flavonoid content was measured. Antioxidant capacity was evaluated using DPPH and ABTS radical scavenging assays, while antidiabetic potential was assessed through the alpha-amylase inhibition assay. The disc diffusion method was used to examine antibacterial effects, and cytotoxicity was determined using both the brine shrimp lethality assay and the MTT assay on HeLa cells. Analgesic activity was tested via the acetic acid–induced writhing test and formalin-induced paw licking test. Quantitative analysis revealed that AERM contained 19.04 ± 1.91 mg/g of phenolics and 14.71 ± 1.09 mg/g of flavonoids. The extract demonstrated antioxidant activity with IC₅₀ values of 87.92 μg/mL (DPPH) and 209.75 μg/mL (ABTS). Antidiabetic testing indicated an IC₅₀ of 132.05 μg/mL, likely due to α-amylase inhibition. Strong antibacterial effects were observed, with efficacy comparable to the standard drug pefloxacin. In cytotoxicity assays, AERM showed an LC₅₀ of 31.41 μg/mL in brine shrimp and a dose-dependent reduction in HeLa cell viability. At doses of 200 and 400 mg/kg, AERM produced significant analgesic effects (p < 0.001) in both the acetic acid and formalin-induced pain models. Molecular docking and ADME/T analyses suggested high binding affinities, good pharmacokinetic properties, and a nontoxic profile. Overall, the results indicate that AERM has promising pharmacological potential as a natural therapeutic agent. However, further studies with larger sample sizes, repeated trials, and broader dose-response evaluations in various animal models are essential to confirm these findings.