High throughput microfluidics-based synthesis of PEGylated liposomes for precise size control and efficient drug encapsulation
Bibliographic record
Abstract
The low scalability and reproducibility of existing synthesis methods have hindered the translation of liposome nanoparticles as carriers for targeted drug delivery from conventional laboratory techniques to mass production. To this end, in this study, we present a high-throughput microfluidics-based approach for the synthesis of PEGylated liposomes with a primary focus on achieving precise size control and efficient encapsulation of hydrophobic drug molecules. In this platform, liposomes were self-assembled through a controllable mixing of lipids (EYPC, cholesterol, and DSPE-PEG 2000) dissolved in ethanol and an aqueous solution. The key parameters, including the chip design, total flow rate, flow rate ratio, lipid concentrations, as well as variations in buffer (HEPES and NaCl) and solvent composition (commercial and reagent-grade ethanol) were explored in detail. Through comprehensive parametric studies, we gained valuable insights into the influence of these variables on the size distribution of liposomes and succeeded in producing highly reproducible liposomes ranging from approximately 60 nm (corresponding to small unilamellar vesicles) to 150 nm (representing large unilamellar vesicles), all while maintaining a polydispersity index (PDI) of less than 0.2. To assess the encapsulation efficiency of hydrophobic drug molecules, Nile red (NR) was employed as a surrogate. We meticulously examined the impact of NR concentration on the drug encapsulation process, resulting in up to 74% drug encapsulation efficiency within the PEGylated liposomes. This research offers crucial advances in liposome synthesis and drug delivery, providing a high-throughput, controllable method for PEGylated liposomes with potential in pharmaceutical and biomedical fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".