Development and Characterization of Liposomal Disodium Ascorbyl Phytostanyl Phosphates (FM‐VP4)
Bibliographic record
Abstract
The specific objectives of this project were (1) to develop liposomal disodium ascorbyl phytostanyl phosphate (FM-VP4) formulations, (2) to develop a liquid chromatography/mass spectrometry/mass spectrometry (LC/MS/MS) assay for quantification of FM-VP4 in liposomal formulations and plasma sample, and (3) to characterize liposomal FM-VP4 formulations by finding optimal drug-to-lipid ratios and determining the degradation of FM-VP4 in liposomes. Section 2 describes an LC/MS/MS assay developed for the identification and quantification of FM-VP4 in liposomal formulations to provide estimates of drug concentrations and encapsulation efficiency. The extra step of removing plasma proteins prior to LC/MS/MS assay yields an analysis of FM-VP4 in plasma samples. Section 3 describes experiments designed to find the optimal drug-to-lipid ratio for liposomal FM-VP4 formulations by comparing encapsulation efficiencies and varying the lipid compositions. Additionally, this section details our degradation studies to determine if liposomes have any protective effects on FM-VP4; these studies tested various lipid compositions at 37 degrees C in rabbit plasma. The mechanism of how FM-VP4 lowers low-density lipoprotein (LDL) cholesterol and total cholesterol levels in various animal models is presently unknown. However, before the mechanism of action could be studied, FM-VP4 first had to be delivered efficiently into plasma or cultured cell. The low systemic bioavailability and cellular uptake of FM-VP4 further suggested the importance of finding an efficient delivery vehicle for this drug. This project proposed a framework for such delivery and paves the way for further investigation into how FM-VP4 works in vivo and in vitro.
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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".