The combination of polyphenols and phospholipids as an efficient platform for delivery of natural products
Bibliographic record
Abstract
Abstract Although nature is a rich source of potential drugs and drug leads, the widespread application of natural products (NPs) is limited due to their poor absorption when administered orally. A strategy of using phytosome has emerged as a promising technique to increase the bioavailability of NPs. Here, a comprehensive computational investigation is performed to explore the nature of interactions in the formation of phytosomes between phosphatidylcholine (PC) and a series of polyphenols (PP), including epigallocatechin-3-gallate (Eg), luteolin (Lu), quercetin (Qu), and resveratrol (Re). Our quantum mechanical calculation revealed that the intermolecular hydrogen bonds (HBs) of phosphate and glycerol parts of PC with the polyphenol compounds are the main driving force in the formation of phytosomes. The strongest HB (with energy HB = − 108.718 kJ/mol) is formed between the Eg molecule and PC. This hydrogen bond results from the flexible structure of the drug which along with several van der Waals (vdW) interactions, makes Eg-PC the most stable complex (adsorption energy = − 164.93 kJ/mol). Energy decomposition analysis confirms that the electrostatic interactions (hydrogen bond and dipole-diploe interactions) have a major contribution to the stabilization of the studied complexes. The obtained results from the molecular dynamics simulation revealed that the formation of phytosomes varies depending on the type of polyphenol. It is found that the intermolecular hydrogen bonds between PP and PC are a key factor in the behavior of the PP-PC complex in the self-aggregation of phytosome. In Eg-PC, Lu-PC, and Qu-PC systems, the formation of strong hydrogen bonds (H BCP < 0 and ∇ 2 ρ BCP > 0) between PP and PC protects the PP-PC complexes from degradation. The steered molecular dynamics simulation results have a good agreement with experimental data and confirm that the phytosome platform facilitates the penetration of PP compounds into the membrane cells.
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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 | 0.000 |
| 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 | 0.000 |
| 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".