Development of lipid nanoparticles and liposomes reference materials (II): cytotoxic profiles
Bibliographic record
Abstract
Lipid based nanocarriers are one of the most effective drug delivery systems that is evident from the recent COVID-19 mRNA vaccines. The main objective of this study was to evaluate toxicity of six lipid based formulations with three surface charges-anionic, neutral or cationic, to establish certified reference materials (CRMs) for liposomes and siRNA loaded lipid nanoparticles (LNP-siRNA). Cytotoxicity was assessed by a proliferation assay in adherent and non-adherent cell lines. High concentration of three LNP-siRNAs did not affect viability of suspension cells and LNP-siRNAs were non-toxic to adherent cells at conventionally used concentration. Systematic evaluation using multiple vials and repeated test runs of three liposomes and three LNP-siRNA formulations showed no toxicity in HL60 and A549 cells up to 128 and 16 µg/mL, respectively. Extended treatment and low concentration of LNPs did not affect the viability of suspension cells and adherent cells at 96 h. Interestingly, 80% of A549 and HL60 cells in 3D conditions were viable when treated with cationic LNP-siRNA for 48 h. Taken together, anionic, cationic and neutral lipid formulations were non-toxic to cells and may be explored further in order to develop them as drug carriers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".