Disease‐Adaptive Drug Delivery to the Inflamed Intestinal Mucosa Using Poly(Lactic‐<i>Co</i>‐Glycolic Acid)‐cyclodextrin Hybrid Nanocarriers
Bibliographic record
Abstract
Abstract Fluctuating severity of symptoms is a common hallmark of many inflammatory disorders, including inflammatory bowel disease (IBD). Addressing the pH changes during active and resting phases in IBD‐affected tissue, a disease‐adaptive nanocarrier system is designed for oral administration, enabling pH‐dependent local drug release. The hybrid carrier combines poly(lactic‐co‐glycolic acid) and an amphiphilic cyclodextrin derivative, with physicochemical properties and drug release kinetics controlled by adjusting polymer ratios. The systems exhibited baseline drug release at pH 5 with increased rates at pH 2, which is characteristic of actively inflamed IBD tissue. Assessing the impact of biomolecule adhesion, biocorona formation was studied using ex vivo human intestinal fluids. Corona composition highly depended on the patient's prandial state and the nanocarrier matrix, with proteins predominating in the fasted state and lipids in the fed state. Notably, differences in the attachment of proteins and free fatty acids are detected in the latter. Transport studies using human in vitro models of the inflamed intestine revealed mucosal accumulation, facilitating localized drug delivery and effectively reducing cytokine levels to basal concentrations. This hybrid system highlights the potential of disease‐adaptive drug release for inflammatory disease treatment and underscores the impact of biocorona formation on therapeutic performance in the gastrointestinal tract.
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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".