Smart Microparticles with a pH-responsive Macropore for Targeted Oral Drug Delivery
Bibliographic record
Abstract
The development of a smart microencapsulation system programmed to actively respond to environmental pH change has long been recognized a key technology in pharmaceutical and food sciences. To this end, we developed hollow microparticles (MPs) with self-controlled macropores that respond to environmental pH change, using an Oil-in-Water emulsion technique, for oral drug delivery. We observed that freeze-drying of MPs induced closure of macropores. The closing/opening behavior of macropores was confirmed by exposing MPs encapsulating different ingredients (sulforhodamine b, fluorescent nanoparticles, and lactase) to simulated gastrointestinal (GI) fluids. MPs maintained their intact, closed pore structure in gastric pH, and subsequent exposure to intestinal pH resulted in pore opening and ingredients release. Further, MPs displayed higher protection (>15 times) than commercial lactase formulation, indicating the protective ability of the system against harsh GI conditions. This study showed development of a hybrid MP system combining the advantages of solid particles and hollow capsules, exhibiting easy solvent-free loading mechanism and smart protection/release of encapsulates through controllable macropores. Ultimately, our MPs system strives to usher a new research area in smart drug delivery systems and advance the current oral drug delivery technology by solving major challenges in targeted delivery of pH-sensitive therapeutics.
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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 | 0.000 |
| 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 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".