Human Abuse Liability Assessment of Tobacco and Nicotine Products: Approaches for Meeting Current Regulatory Recommendations
Bibliographic record
Abstract
Many regulatory bodies now recommend that tobacco product manufacturers provide information regarding new tobacco products' abuse liability to inform regulatory authorization of currently marketed tobacco products or new product applications (including premarket tobacco product applications in the United States). In addition, the US Food and Drug Administration (FDA) recommends including this information as part of modified risk tobacco product applications. Regulators, including FDA, and many public health officials and researchers consider abuse liability assessment a model which predicts the likelihood that the use of the tobacco product would result in addiction and be used repeatedly or even sporadically resulting in undesirable effects. Abuse liability of a new, potentially reduced harm product can also inform its ability to substitute completely for more harmful tobacco products. While many methods exist, no standard tobacco product abuse liability assessment has been established. The purpose of this review is to provide background information and practical recommendations for human abuse liability testing methods to meet tobacco regulatory needs. A combination of nicotine test product pharmacokinetic, subjective effect and/or behavioral response, and physiological response data relative to comparator products with known abuse liability satisfies some regulatory requirements. Implications: This review provides a practical inspection of the current, international regulatory recommendations for abuse liability assessment of tobacco and regulatory review of such information within the United States and also recommends study designs and methods for abuse liability testing of tobacco products based on scientific and regulatory knowledge. Given that tobacco product abuse liability testing is of increasing interest to regulatory bodies globally, especially with the emergence of novel tobacco products, this timely work provides background and functional recommendations for tobacco product abuse liability testing.
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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 | 0.116 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".