Synthesis and activity of a novel diether phosphonoglycerol in phospholipase-resistant synthetic lipid:peptide lung surfactants
Bibliographic record
Abstract
This paper reports the chemical synthesis and purification of a novel phospholipase-resistant C16:0, C16:1 diether phosphonoglycerol with structural analogy to ester-linked anionic phosphatidylglycerol (PG) in endogenous pulmonary surfactant. This diether phosphonoglycerol (PG 1) is studied for phospholipase A(2) (PLA(2)) resistance and for surface activity in synthetic exogenous surfactants combined with Super Mini-B (S-MB) peptide and DEPN-8, a previously-reported diether phosphonolipid analog of dipalmitoyl phosphatidylcholine (DPPC, the major zwitterionic phospholipid in native lung surfactant). Activity experiments measured both adsorption and dynamic surface tension lowering due to the known importance of these surface behaviors in lung surfactant function in vivo. Synthetic surfactants containing 9 : 1 DEPN-8:PG 1 + 3% S-MB were resistant to degradation by PLA(2) in chromatographic studies, while calf lung surfactant extract (CLSE, the substance of the bovine clinical surfactant Infasurf®) was significantly degraded by PLA(2). The 9 : 1 DEPN-8:PG 1 + 3% S-MB mixture also had small but consistent increases in both adsorption and dynamic surface tension lowering ability compared to DEPN-8 + 3% S-MB. Consistent with these surface activity increases, molecular dynamics simulations using Protein Modeller, GROMACS force-field, and PyMOL showed that bilayers containing DPPC and palmitoyl-oleoyl-PC (POPC) as surrogates of DEPN-8 and PG 1 were penetrated to a greater extent by S-MB peptide than bilayers of DPPC alone. These results suggest that PG 1 or related anionic phosphono-PG analogs may have functional utility in phospholipase-resistant synthetic surfactants targeting forms of acute pulmonary injury where endogenous surfactant becomes dysfunctional due to phospholipase activity in the innate inflammatory response.
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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) | 0.000 | 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".