Bioassay-guided isolation and <i>in silico</i> study of antihyperlipidemic compounds from <i>Onosma hispidum</i> Wall
Bibliographic record
Abstract
Isolation of bioactive compounds from plants and their therapeutic evaluation is crucial in the pursuit of novel phytochemicals and contributes an indispensable role in drug discovery and design. The literature has documented the hypolipidemic effect of numerous Onosma species. Taking that into consideration, the current study was designed to isolate, purify and evaluate the antihyperlipidemic potential of leaves of Onosma hispidum Wall. For the first time, the bioassay-guided isolation led to the separation of 3 compounds that were identified by spectroscopic techniques as o-phthalic acid bis-(2-ethyl decyl)-ester (1), bis (2-ethyloctyl) phthalate (2), and 1,2 benzenedicarboxylic acid bis(2-methyl heptyl) ester (3). Lipase inhibition assay was utilized to scrutinize the antihyperlipidemic potential of methanolic extract fractions and subsequently isolated compounds. Further, the isolated compounds were employed for in silico studies via molecular docking, molecular mechanics with generalized born and surface area solvation (MM-GBSA), and MD simulations with Pancreatic Lipase Colipase (PDB ID: 1LPB). Molecular docking and MM-GBSA of isolated compounds were employed to explain the mode of binding between the protein–ligand complex and binding free energy calculation, respectively. Since compound (3) displayed the best docking score of −6.689 kcal/mol as compared to orlistat −5.529 kcal/mol with PDB: 1LPB. So, it was chosen for MD simulations to evaluate ligand stability and flexibility of the complex which was validated by the fluctuation of α-carbon chain, root mean square deviation (RMSD), root mean square fluctuation (RMSF), and type of interactions involved which authenticated the in vitro lipase inhibitory potential. Overall, in silico and in vitro results validated that compound (3) could be exploited as a promising pancreatic lipase inhibitor. Communicated by Ramaswamy H. Sarma
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".