Microfluidic Generation of Therapeutically Relevant Polycaprolactone (PCL) Microparticles: Computational and Experimental Approaches
Bibliographic record
Abstract
Drug releasing microparticles play an important role in drug delivery as they can be used for site specific delivery as well as control over the release time of therapeutics. The use of microfluidic technologies for the fabrication of these particles is of increasing interest since they provide enhanced control over microparticle size and size distribution compared to bulk production methods. However, the use of microfluidic platforms in the production of drug releasing microparticles with therapeutically relevant cargo still requires optimization depending on their application, and the effect of the addition of cargo on the production process is still unexplored. Here we show the formation of therapeutically relevant (in terms of size and dose of cargo) polycaprolactone (PCL) microparticles using a microfluidic platform and analyze the effect of the addition of cargo in the microparticle size and size distribution. This microfluidic platform was designed with the aid of computational fluid dynamic simulations, allowing us to construct a polydimethylsiloxane (PDMS) microfluidic device capable of making microparticles in the range of 15 to 35 μm with low coefficient of variation (CV) both with and without cargo by varying the flow rate ratios of the phases used during droplet generation. Our data show the effect of the addition of cargo on the droplet and microparticle sizes and monodispersity. Our fabrication method allows the formation of spherical microparticles, optimal for biomedical applications. In addition, our microfluidic platform can maintain the generation of monodisperse droplets (with an average size of 52.5 μm) over extended periods of time, suggesting it has the capacity to be used for scaled-up production of PCL microparticles. This microfluidic device is a robust and reliable method for the fabrication of PCL microparticles with cargo, which can potentially be loaded with other relevant therapeutic molecules for biomedical applications.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".