Bibliographic record
Abstract
Identifying the abuse potential of drug products in the premarketing and postmarketing environment has been a critical component in the implementation of drug abuse control laws worldwide. In the US, the Controlled Substances Act of 1970 (CSA) is a comprehensive federal law enacted to prevent the abuse or diversion of substances with abuse liability or addiction potential (for present purposes, these terms are used interchangeably). Under the jurisdiction of the Drug Enforcement Administration, the law applies to the manufacture and distribution of narcotics and other drug substances with potential of abuse. The CSA classifies substances with abuse potential into schedules I-V based on the substance's risk of diversion or abuse, and thus provides a legal framework for the assessment of abuse liability of New Molecular Entities. When the Food and Drug Administration reviews the safety and efficacy of a New Drug Application it also determines whether the drug has potential for abuse, and if so, will begin the process to schedule the drug under the CSA. As the assessment of abuse potential is a critical component of a marketing application, pharmaceutical companies (sponsors) bear the responsibility of generating a comprehensive preclinical and clinical data package for regulators to review and make decisions on labeling and the corresponding postmarketing surveillance. Recent regulatory guidelines adopted in the European Union (EU) (2006), Canada (2007), and USA (2010) provide recommendations to sponsors on preclinical and clinical methodologies for the assessment of abuse potential. This paper reviews the legal framework of the assessment of abuse liability and scheduling of controlled substances in the USA and describes the current global regulatory environment and the challenges that sponsors and regulators face when assessing abuse liability of New Molecular Entities, from the early stages of development through the late stages, review, and approval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".