Bottom-Up Design and Synthesis of Limit Size Lipid Nanoparticle Systems with Aqueous and Triglyceride Cores Using Millisecond Microfluidic Mixing
Bibliographic record
Abstract
Limit size systems are defined as the smallest achievable aggregates compatible with the packing of the molecular constituents in a defined and energetically stable structure. Here we report the use of rapid microfluidic mixing for the controlled synthesis of two types of limit size lipid nanoparticle (LNP) systems, having either polar or nonpolar cores. Specifically, limit size LNP consisting of 1-palmitoyl, 2-oleoyl phosphatidylcholine (POPC), cholesterol and the triglyceride triolein were synthesized by mixing a stream of ethanol containing dissolved lipid with an aqueous stream, employing a staggered herringbone micromixer. Millisecond mixing of aqueous and ethanol streams at high flow rate ratios (FRR) was used to rapidly increase the polarity of the medium, driving bottom-up synthesis of limit size LNP systems by spontaneous assembly. For POPC/triolein systems the limit size structures consisted of a hydrophobic core of triolein surrounded by a monolayer of POPC where the diameter could be rationally engineered over the range 20-80 nm by varying the POPC/triolein ratio. In the case of POPC and POPC/cholesterol (55/45; mol/mol) the limit size systems achieved were bilayer vesicles of approximately 20 and 40 nm diameter, respectively. We further show that doxorubicin, a representative weak base drug, can be efficiently loaded and retained in limit size POPC LNP, establishing potential utility as drug delivery systems. To our knowledge this is the first report of stable triglyceride emulsions in the 20-50 nm size range, and the first time vesicular systems in the 20-50 nm size range have been generated by a scalable manufacturing method. These results establish microfluidic mixing as a powerful and general approach to access novel LNP systems, with both polar or nonpolar core structures, in the sub-100 nm size range.
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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) | 0.000 | 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 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".