Bench-Scale Microfluidic Manufacturing of Cross-Linked Polyester Microparticles
Bibliographic record
Abstract
Polymeric microparticles used as long-acting drug delivery systems provide advantages relative to conventional oral dosage forms including improved efficacy and safety. However, development of these formulations, including generics, is constrained by current manufacturing techniques. Conventional approaches have limited control over process parameters and are difficult to scale. Droplet microfluidic techniques produce individual particles sequentially enabling unparalleled consistency on key material properties including particle size and dispersity. While microfluidics approaches have much promise, including affording continuous rather than batch production; designing, constructing, and operating these systems is challenging reducing adoption by formulation scientists. Herein, we describe the operation of a modular microfluidic system built with commercially available components to prepare photo-cross-linked microparticles by droplet generation, inline dilution, and inline irradiation with UV. We synthesized monodisperse cross-linked polyester microparticles with a median size of 37.6 ± 0.4 μm at 20, 60, and 120 mg batch sizes with average yields of 92 ± 5%. Additionally, as a means to tailor material properties, particles were produced at varying degrees of cross-linking. The particle’s properties were further characterized, loaded with celecoxib at a low and a high level, then the in vitro drug release evaluated. Overall, the degree of cross-linking and drug loading modulated key formulation properties such as in vitro release rate. With this work, we showcase the potential of microfluidic systems and aim to foster further adoption of microfluidic techniques to manufacture comparable materials.
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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 | 0.000 |
| 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".