Mussel-inspired mucoadhesive hydrogels for drug delivery
Bibliographic record
Abstract
A controlled drug delivery system can deliver a drug with the right dose, control its release rate and time, and target the site of action in the body.Chitosan (CS) and its derivatives have been extensively used in drug delivery.Additionally, CS is mildly mucoadhesive, i.e. it can stick to mucus.Many drug delivery routes such as oral, buccal, and rectal delivery exploit the presence of a mucosa to deliver the drug.Drug delivery systems made from mucoadhesive materials can stick to the mucosa, thus prolonging the retention of drugs on site and allowing a sustained release of the loaded drugs.CS-based mucoadhesive drug delivery systems have shown improved therapeutic effects in many applications.Recently, the strong adhesion of marine mussels under the sea inspired the development of several water-resistant adhesives.The catechol groups present in large amount in the mussel adhesive proteins contribute to the outstanding adhesion of mussels on many different surfaces.Catechols also interact with biological surfaces, including mucus.These findings inspired us to use catechol-CS mucoadhesive systems to facilitate drug delivery.This was the first time that catechol groups were used to improve the mucoadhesion and thus the efficacy of a drug delivery system.In this work, we developed three types of catechol-containing CS hydrogels as mucoadhesive drug delivery systems for oral, buccal and rectal drug delivery.We introduced catechols into CS hydrogels by simple physical mixing or covalent conjugation.We assessed the efficacy of catechol-containing hydrogels as drug delivery systems and their mucoadhesion both in vitro and in vivo.Our first study showed that simple physical mixing of hydrocaffeic acid in CS-based hydrogels enhanced the mucoadhesion of the gels.The electrostatic interaction between hydrocaffeic acid and CS caused a slow release of this molecule from the CS hydrogel, and enhanced the adhesion of CS on mucus.The mucoadhesion of this hydrogel can be further increased in the presence of oxidizing agents during the contact with mucin.However, if the gel is oxidized before contacting mucin, there is no mucoadhesion enhancement.In our second study we developed a chemically conjugated catechol-CS (Cat-CS) hydrogel crosslinked by genipin.In this case, the immobilized catechols were not released from the gel.This system showed good mechanical properties and great mucoadhesion both in vitro and in vivo.Using this mucoadhesive hydrogel, we succeeded in delivering lidocaine to rabbits through the buccal mucosa.Differently from gels made with unmodified CS, this system established an intimate contact with the rabbit buccal mucosa, achieved a sustained release of the drug, and maintained the drug concentration in the blood at a relatively high level during the 2 h experiment.In our third study we developed an injectable sulfasalazine (SSZ) loaded Cat-CS hydrogel formulation for rectal treatment of ulcerative colitis (UC).In UC mice, rectal SSZ/Cat-CS formulation showed better therapeutic effects with only 50% of the normal oral dose.Most importantly, this formulation reduced the plasma levels of the SSZ metabolite sulfapyridine, which is associated with many side effects of SSZ and toxicity.Thus, these results proved the great potential of mucoadhesive SSZ/Cat-CS rectal formulation in UC treatment.Overall, we developed mussel-inspired catechol-containing CS hydrogels that improved the mucoadhesion and thus the drug efficacy and therapeutic effects in drug delivery.iii
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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) | 0.001 | 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".