Fragrance Skin Sensitization Evaluation and Human Testing: 30-Year Experience
Bibliographic record
Abstract
BACKGROUND: The human repeated insult patch test (HRIPT) has a history of use in the fragrance industry as a component of safety evaluation, exclusively to confirm the absence of skin sensitization at a defined dose. OBJECTIVE: The aim of the study was to document the accumulated experience from more than 30 years of conducting HRIPTs. METHODS: A retrospective collation of HRIPT studies carried out to a consistent protocol was undertaken, with each study comprising a minimum of 100 volunteers. CONCLUSIONS: The HRIPT outcomes from 154 studies on 134 substances using 16,512 volunteers were obtained. Most studies confirmed that at the selected induction/challenge dose, sensitization was not induced. In 0.12% of subjects (n = 20), there was induction of allergy. However, in the last 11 years, only 3 (0.03%) of 9854 subjects became sensitized, perhaps because of improved definition of a safe HRIPT dose from the local lymph node assay and other skin sensitization methodologies, as well as more rigorous application of the standard protocol after publication in 2008. This experience with HRIPTs demonstrates that de novo sensitization induction is rare and becoming rarer, but it plays an important role as an indicator that toxicological predictions from nonhuman test methods (in vivo and in vitro methods) can be imperfect.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".