Dual extrusion-based 3D-printed core–shell tablets for colorectal delivery of Mebeverine hydrochloride
Bibliographic record
Abstract
OBJECTIVE: This study aimed to fabricate a delayed-release tablet of Mebeverine hydrochloride using a novel dual extrusion-based 3D printing approach to improve the management of irritable bowel syndrome (IBS). SIGNIFICANCE: This study highlights a dual extrusion-based 3D printing approach that integrates a hydrogel core of Mebeverine hydrochloride with a melt-extruded polymeric shell in a single step, protecting from acidic and thermal stress while achieving controlled release. The 3D-printed tablet meets USP quality standards, demonstrating a promising strategy for IBS management and personalized therapy. METHODS: VA 64, PEG 4000, HPMCP, Eudragit RL100, triethyl citrate, and talc, the hydrogel core methylcellulose, sodium alginate, sodium chloride, and the drug, deposited into the internal cavity by pressure-assisted micro-syringe (PAM) extrusion. RESULTS: The resultant tablet limited drug release in acidic circumstances while achieving 98.6% drug release over 12 h in phosphate buffer. Weight variation, friability, hardness, assay, and content uniformity met USP specifications. SEM imaging indicated smooth and consistent surface morphology. Moreover, FTIR spectrums showed no unwanted chemical interactions, TGA and DSC analysis verified the thermal stability the drug and excipients at printing temperatures. CONCLUSIONS: The dual extrusion based-3D printing of drug-loaded hydrogel and thermoplastic polymers provides a promising delayed-release single dosage to deliver of thermo- and acid-labile drugs for the management of IBS and associated gastrointestinal disorders.
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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".