Exploration of a Novel Terpolymer Nanoparticle System for the Prevention of Alcohol-Induced Dose Dumping
Bibliographic record
Abstract
Alcohol-induced dose dumping (AIDD) remains a serious challenge in the controlled delivery of high potency drugs, such as opioids, which requires extensive investigation and innovative solutions. Current technologies rely on ethanol-insoluble excipients, such as guar gum and sodium alginate, to counteract the increased solubility of hydrophobic polymeric excipients in ethanol. However, these excipients pose several shortcomings, such as high viscosity of coating dispersion, high solution temperature, rapid gelation, and heterogeneity of resulted film. In this work, we explored the application of a cross-linked terpolymer nanoparticle (TPN) as an alcohol-resistant excipient in a water-insoluble controlled release film of ethylcellulose (EC) for the prevention of AIDD. Herein, we optimized the composition of TPN using a central composite design (CCD) to minimize swelling and weight loss of TPN-EC film in the presence of 20% ethanol. The optimized TPN showed a negligible effect on the viscosity of the coating dispersion, while guar gum increased the viscosity by 76-fold. Permeability studies in a pH 1.2 media containing 0% or 40% v/v ethanol revealed that cationic drugs (propranolol HCl, diltiazem HCl, and naloxone HCl (an opioid receptor-binding model drug)) exhibited significantly lower permeability ratios ( P 40% / P 0% ) than un-ionized drugs (theophylline and salicylic acid). FTIR analysis indicated an increase in ionic hydrogen bonding between TPN and the cationic drug in the presence of ethanol. These results suggest that drug–polymer–solvent interactions play an important role in alcohol-independent drug permeability through the TPN-EC film. By leveraging the drug permeability altering capability of the TPN-EC system, the release of cationic drugs in hydroethanolic media appeared to be suppressed, suggesting a promising new mechanism of alcohol resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".