Hexabromocyclododecane (HBCD): A case study applying tiered testing for human health risk assessment
Bibliographic record
Abstract
Current global efforts are aiming to increase use of mechanistic information in regulatory testing. In tiered testing paradigms, in vitro, in silico, and in vivo studies are employed progressively to identify and classify health hazards, which are then compared against human equivalent doses. We used data from three companion papers on the brominated flame retardant hexabromocyclododecane (HBCD) to conduct a case study on tiered testing. We included ToxCast™ and in vitro-in vivo extrapolation (Tier 1), rat liver transcriptomic (Tier 2), and conventional rat (Tier 3) data. Bioactivity-exposure ratios (BERs) were derived by comparing human administered dose equivalents of the measured effects to Canadian exposure levels. Biological perturbations were highly aligned between Tiers 1/2, and consistent with apical effects in Tier 3. Tier 1 had the smallest BERs, and Tiers 2/3 were similar. The study demonstrates the promise of using physiologically-based pharmacokinetic modeling and mechanistic analyses in a tiered framework to identify pathways through which chemicals exert toxicological effects; however, they also point to some shortcomings associated with in vitro and in silico approaches. Additional case studies of chemicals from multiple classes are required to define optimal tiered screening procedures to reduce future in vivo requirements in health hazard assessments.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".