Compilation of Historical Local Lymph Node Data for Evaluation of Skin Sensitization Alternative Methods
Bibliographic record
Abstract
Background: Within the toxicology community, considerable effort is directed toward the development of alternative methods for skin sensitization testing. The availability of high-quality, relevant, and reliable in vivo data regarding skin sensitization is essential for the effective evaluation of alternative methodologies. Ideally, data derived from humans would be the most appropriate source because the test methods are attempting to predict a toxicologic effect in humans. Unfortunately, insufficient human data of the necessary quality are available, so it is necessary to rely on the best available animal data. In recent years, the local lymph node assay (LLNA) has emerged as a practical option for assessing the skin sensitization potential of chemicals. In addition to accurately identifying skin sensitizers, the LLNA can also provide a reliable measure of relative sensitization potency, information that is pivotal to the successful management of human health risks. Objective: To provide a database of robust in vivo data to calibrate, evaluate, and eventually validate new approaches for skin sensitization testing. Methods: LLNA data derived from previously conducted studies were compiled from the published literature and unpublished sources. Results: We provide a database that comprises LLNA data on 211 individual chemicals. This extensive chemical data set encompasses both the chemical and biologic diversity of known chemical allergens. To cover the range of relative allergenic potencies, the data set includes data on 13 extreme, 21 strong, 69 moderate, and 66 weak contact allergens, classified according to each allergen's mathematically estimated concentration of chemical required to induce a threefold stimulation index. In addition, there are also 42 chemicals that are considered to be nonsensitizers. In terms of chemical diversity, the database contains data pertaining to the chemical classes represented by aldehydes, ketones, aromatic amines, quinones, and acrylates, as well as compounds that have different reactivity mechanisms. In addition to two-dimensional chemical structures, the physicochemical parameters included are log Kp, log KO/W, and molecular weight. Conclusions: The list of chemicals contained in the data set represents both the chemical and biologic diversity that is known to exist for chemical allergens and non-allergens. It is anticipated that this database will help accelerate the development, evaluation, and eventual validation of new approaches to skin sensitization assessment.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".