Facile fabrication of microparticles with pH-responsive macropores for small intestine targeted drug formulation
Bibliographic record
Abstract
Oral drugs present the most convenient, economical, and painless route for self-administration. Despite commercialization of multiple technologies relying on micro- and nanocrystalline drugs, research on microparticles (MPs) based oral biopharmaceuticals delivery systems has still not culminated well enough in commercial products. This is largely due to the drugs being exposed to the destabilizing environment during MP synthesis process, and partly because of complicated process conditions. Hence, we developed a solvent swelling-evaporation method of producing pH-responsive MPs with micron-sized macropores using poly(methacrylic acid-co-ethyl acrylate) in 1:1 ratio (commercial name: Eudragit® L100-55 polymer). We investigated the effects of temperature and evaporation time on pore formation, freeze-drying induced pore closure, and the release profile of model drugs (fluorescent beads, lactase, and pravastatin sodium) encapsulated MPs in simulated gastrointestinal tract conditions. Encapsulated lactase/pravastatin maintained >60% of their activity due to the preservation of pore closure, which proved the potential of this proof-of-concept microencapsulation system. Importantly, the presence of macropores on MPs can be beneficial for easy drug loading, and solve the problem of bioactivity loss during the conventional MP fabrication-drug encapsulation steps. Therefore, pH-sensing MPs with macropores can contribute to the development of oral drug formulations for a wide variety of drugs and bio-macromolecules, having a various size ranging from genes to micron-sized ingredients with high therapeutic efficacy.
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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 | 0.001 |
| 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".