Branched-chain and dendritic lipids for nanoparticles
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) for drug-delivery applications are largely derived from natural lipids. Synthetic lipids, particularly those incorporating branched hydrocarbons and hyper-branched hydrocarbon architectures, may afford enhanced lipophilicity with enhanced fluidity and thereby lead to LNP stabilization. Hydrocarbon anchors based on serinol diesters were prepared from linear Cn (n = 14, 16, 18) and branched (n = 16) acids with Boc-protected serinol. These diesters were further dimerized on an iminodiacetamide backbone to provide eight branched-chain and dendritic lipid anchors. Derivatization of these core structures provided eight PEG-lipids and seven thiopurine linked lipid–drug conjugates. LNPs were prepared by microfluidic mixing from mixed lipids in ethanol diluted into aqueous media. The lipid–drug conjugates incorporated 5 mol% of a phosphocholine and 5 mol% of a commercial PEG-lipid to form LNPs with a thiopurine drug loading of 15 wt%. The PEG–lipids prepared were formulated at 1.5 mol% as a surface stabilizer to LNPs containing dsDNA lipoplexes. The stability of the LNPs was assessed under different storage conditions through monitoring of particle size. For both LNPs from lipid–thiopurine conjugates and the PEG-lipid systems, there is strong preliminary evidence that hydrocarbon branching results in LNP stabilization. Four of the lipid–drug conjugate formulations were stable to cell culture conditions (10% serum, 37 °C) and the toxicity of these LNPs was assessed in two cell lines relative to the free thiopurines in the medium. The observed toxicity is consistent with cellular uptake of the LNPs and reductive release of the cargo thiopurine within the cell.
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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) | 0.004 | 0.003 |
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".