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 IC50 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".