Scalable Production of Lipid Nanoparticles Containing Amphotericin B
Bibliographic record
Abstract
Lipid-based formulations have been developed to improve stability profiles, tolerability, and toxicity profiles of small molecule drugs. However, manufacture of such formulations involving lipophilic compounds can be labor-intensive and difficult to scale because of solubility and solvent compatibility issues. We have developed a rapid and scalable approach using rapid-mixing techniques to generate homogeneous lipid nanoparticle (LNP) formulations of siRNA, triglycerides, and hydrophilic weak-base drugs. Here, we used this approach to entrap a hydrophobic small molecule, Amphotericin B (AmpB), a hydrophobic drug not soluble in ethanol. The three prototypes presented in this study were derived from LNP-siRNA systems, triglyceride nanoparticles, and liposomal systems. Cryogenic transmission electron microscopy (cryo-TEM) revealed that all three LNP-AmpB formulations retain structural characteristics of the parent (AmpB-free) LNPs, with particles remaining stable for at least 1 month. All formulations showed similar in vitro toxicity profiles in comparison to AmBisome. Importantly, the formulations have a 2.5-fold improved IC 50 for fungal growth inhibition as compared to AmBisome in in vitro efficacy studies. These results demonstrate that the rapid-mixing technology combined with dimethyl sulfoxide (DMSO) for drugs insoluble in other organic solvents can be a powerful manufacturing method for the generation of stable LNP drug formulations.
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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 | 0.001 |
| 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".