Challenges in the Development of Prescription Opioid Abuse-deterrent Formulations
Bibliographic record
Abstract
Opioid analgesics remain the cornerstone of effective management for moderate-to-severe pain. In the face of persistent lack of access to opioids by patients with legitimate pain problems, the rate of prescription opioid abuse in the United States has escalated over the past 15 years. Abuse-deterrent opioid products can play a central role in optimizing the risk-benefit ratio of opioid analgesics--if these products can be developed cost-effectively without compromising efficacy or creating new safety issues for the target treatment population. The development of scientific methods for assessing prescription opioid abuse potential remains a critical and challenging step in determining whether a claim of abuse deterrence for a new opioid product is indeed valid and will thus be accepted by the medical, regulatory, and reimbursement communities. To explore this and other potential impediments to the development of prescription opioid abuse-deterrent formulations, a panel of experts on opioid abuse and diversion from academia, industry, and governmental agencies participated in a Tufts Health Care Institute-supported symposium held on October 27 and 28, 2005, in Boston, MA. This manuscript captures the main consensus opinions of those experts, and also information gleaned from a review of the relevant published literature, to identify major impediments to the development of opioid abuse-deterrent formulations and offer strategies that may accelerate their commercialization.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".