Synthesis and Characterization of Hybrid Lipid Nanoparticles Containing Gold Nanoparticles and a Weak Base Drug
Bibliographic record
Abstract
Hybrid lipid nanoparticles containing gold nanoparticles (LNP-GNPs) and drugs have potential for imaging applications as well as triggered release of LNP contents in response to pulsed laser or X-ray radiation mediated by the GNPs. However, methods to synthesize LNP-GNP systems that efficiently entrap GNPs (the potential triggered release and imaging agent) and then load and retain the drug cargo in a manner that may have clinical applications have proven elusive. Here, we develop a straightforward "bottom-up" approach to manufacture drug-loaded LNP-GNP systems. We show that negatively charged GNPs of 5 nm diameter can be stably loaded into LNPs containing 10 mol % ionizable cationic lipid using an ethanol dilution, rapid mixing approach and that these systems also exhibit aqueous compartments. Further, we show that such systems can also entrap ammonium sulfate, enabling pH-dependent loading of the weak base anti-cancer drug doxorubicin into the aqueous compartments. Cryo-transmission electron microscopy (Cryo-TEM) imaging clearly demonstrates the presence of GNPs in the interior of the resulting hybrid nanostructures as well as the formation of electron-dense drug precipitates in the aqueous core of the LNP-GNPs. The approach described here is a robust and straightforward method to generate hybrid LNP-GNP-drug and other LNP-metal nanoparticle-drug systems with potential applications for a variety of triggered release protocols.
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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 | 0.001 | 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".