Loading
Life sciences · Journal article
Pharmacognosy Magazine · October 12, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Aim/Background Obesity is a multifactorial metabolic disorder associated with substantial morbidity and mortality. Human pancreatic lipase is a validated therapeutic target because its inhibition can reduce the digestion and intestinal absorption of dietary triglycerides. This study aimed to computationally evaluate cucurbitacins as candidate pancreatic lipase inhibitors. Materials and Methods A library of 71 cucurbitacins was screened against human pancreatic lipase using AutoDock Vina. Orlistat, a clinically approved pancreatic lipase inhibitor, was included as the positive-control compound. The three highest-ranked cucurbitacins and orlistat were subjected to 100-ns molecular dynamics simulations using GROMACS 2022. Physicochemical, pharmacokinetic, and toxicity properties of the selected cucurbitacins were predicted using the absorption, distribution, metabolism, excretion, and toxicity–artificial intelligence (ADMET-AI) platform. Results Cucurbitacins F, H, and K produced docking scores of −8.8, −8.7, and −8.4 kcal/mol, respectively, compared with −6.7 kcal/mol for orlistat. The selected compounds occupied the pancreatic lipase catalytic pocket and maintained persistent protein-ligand interactions during the 100-ns simulations. Cucurbitacin K showed the lowest mean protein-backbone root mean square deviation (RMSD), whereas cucurbitacin F showed the lowest ligand RMSD and the highest average number of hydrogen bonds. Numerical ADMET predictions supported further prioritization of the compounds while identifying physicochemical and metabolic properties requiring experimental evaluation. Conclusion Cucurbitacins F, H, and K were computationally prioritized as candidate binders of human pancreatic lipase. These findings provide a basis for subsequent binding free-energy calculations, pancreatic lipase inhibition assays, and in vivo validation, but do not establish confirmed inhibitory or anti-obesity efficacy.