Propofol emulsification in Intralipid and SMOFlipid: A promising alternative in response to future shortages
Bibliographic record
Abstract
INTRODUCTION: The Covid-19 pandemic has intensified shortages in various pharmaceutical products, notably injectable propofol in lipid emulsion form. Its demand surged sharply due to its critical role in intubating patients with respiratory distress during the pandemic, exposing vulnerabilities in the supply chain for this essential product. OBJECTIVES: This project aims to develop an alternative formulation to commercially available propofol products and to evaluate its stability through a detailed study. METHODS: Two lipid emulsions commonly used for intravenous nutrition, Intralipid 20% and SMOFlipid 20%, were selected as diluents for pure propofol due to their composition's similarity to DIPRIVAN, the standard propofol product. We developed and validated an HPLC method for quantifying propofol and employed an optimized laser diffraction technique to measure particle size. Additionally, we assessed the pH of the formulations. RESULTS: The preparation method demonstrated repeatability and homogeneity. Stability studies revealed that the propofol concentrations remained close to the target of 10 mg/mL (1%). Although particle sizes were larger compared to DIPRIVAN, they were consistent with those of the lipid emulsions before propofol addition. The pH of the formulations remained stable throughout the study period. CONCLUSIONS: The developed propofol emulsion formulations met USP standards for all tested parameters over a period of at least 7 days, indicating that these alternatives are a viable and stable substitute for commercial propofol products.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".