New immediate release formulation for deterring abuse of methadone
Bibliographic record
Abstract
CONTEXT: Drug abusers are known to take a dosage form containing an opioid analgesic and crush, shear, grind, chew, or dissolve it in water or in alcohol, in order to extract the opioid component. OBJECTIVE: Develop an anti abuse immediate release formulation using methadone as model drug. MATERIALS AND METHODS: Tablets combining methadone and alkalizing agents were manufactured. A methadone assay was used to determine extraction efficiency from tablets in aqueous and alcohol solvents. In vitro dissolution testing was used to determine drug release in different media. RESULTS AND DISCUSSIONS: Meglumine-based formulations prevented extraction of 70 to 100% of methadone from tablets. Addition of this alkalizing agent caused methadone to precipitate out of a solution along with other ingredients and be retained on standard filters. Meglumine-containing and control tablets showed similar dissolution profiles in acidic media, suggesting adequate solubilisation of the drug early in the gastrointestinal tract. Finally, stability upon storage of the formulations for 6 months at 25°C/60%RH and 40°C/75%RH was confirmed. CONCLUSION: Incorporation of an alkalizing agent into methadone tablets significantly reduced the preparation of a methadone solution for intravenous administration and abuse, while allowing the formulation to release methadone in gastric media and provide desired pharmacological effect.
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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) | 0.002 | 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".