Microfluidics for Development of Lipid Nanoparticles: Paving the Way for Nucleic Acids to the Clinic
Bibliographic record
Abstract
Nucleic acid therapeutics hold an unprecedented promise toward treating many challenging diseases; however, their use is hampered by delivery issues. Microfluidics, dealing with fluids in the microscale dimensions, have provided a robust means to screening raw materials for development of nano delivery vectors, in addition to controlling their size and minimizing their polydispersity. In this mini-review, we are briefly highlighting the different types of nucleic acid therapies with emphasis on the delivery requirement for each type. We provide a thorough review of available methods for the development of nanoparticles, especially lipid nanoparticles (LNPs) that resulted in FDA approval of the first ever nucleic acid nanomedicine. We then focus on recent research attempts for how microfluidic synthesis of lipid nanoparticles and discuss the various parameters required for successful formulation of LPNs including chip design, flow regimes, and lipid composition. We then identify key areas of research in microfluidics and related fields that require attention for future success in clinical translation of nucleic acid nanomedicines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".