Microfluidic Manufacturing of SN-38-Loaded Polymer Nanoparticles with Shear Processing Control of Drug Delivery Properties
Bibliographic record
Abstract
Two-phase gas–liquid microfluidic reactors provide shear processing control of SN-38-loaded polymer nanoparticles (SN-38-PNPs). We prepare SN-38-PNPs from the block copolymer poly(methyl caprolactone-co-caprolactone)-block-poly(ethylene oxides) (P(MCL-co-CL)-b-PEO) using bulk and microfluidic methods and at different drug-to-polymer loading ratios and on-chip flow rates. We show that, as the microfluidic flow rate (Q) increases, encapsulation efficiency and drug loading increase and release half times increase. Slower SN-38 release is obtained at the highest Q value (Q = 400 μL/min) than is achieved using a conventional bulk preparation method. For all SN-38-PNP formulations, we find a dominant population (by number) of nanosized particles (<50 nm) along with a small number of larger aggregates (>100 nm). As Q increases, the size of aggregates decreases through a minimum and then increases, attributed to a flow-variable competition of shear-induced particle breakup and shear-induced particle coalescence. IC25 and IC50 values of the various SN-38-PNPs against MCF-7 cells show strong flow rate dependencies that mirror trends in particle size. SN-38-PNPs manufactured on-chip at intermediate flow rates show both minimum particle sizes and maximum potencies with a significantly lower IC25 value than the bulk-prepared sample. Compared to conventional bulk methods, microfluidic shear processing in two-phase reactors provides controlled manufacturing routes for optimizing and improving the properties of SN-38 nanomedicines.
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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 | 0.000 | 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 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".