<i>In Vitro</i> and <i>in Silico</i> Competitive Binding of Brominated Polyphenyl Ether Contaminants with Human and Gull Thyroid Hormone Transport Proteins
Bibliographic record
Abstract
Tetradecabromo-1,4-diphenoxybenzene (TeDB-DiPhOBz) is a highly brominated additive flame retardant (FR). Debrominated photodegradates of TeDB-DiPhOBz are hydroxylated in vitro in liver microsomal assays based on herring gulls (Larus argentatus), including one metabolite identified as 4″-OH-2,2',2″,4-tetrabromo-DiPhOBz. Chemically related methoxylated tetra- to hexabromo-DiPhOBzs are known contaminants in herring gulls. Collectively, nothing is currently known about biological effects of these polybrominated (PB) DiPhOBz-based compounds. The present study investigated the potential thyroidogenicity of 2,2',2″,4-tetrabromo-(TB)-DiPhOBz along with its para-methoxy (MeO)- and hydroxy-(OH)-analogues, using an in vitro competitive protein binding assay with the human thyroid hormone (TH) transport proteins transthyretin (hTTR) and albumin (hALB). This model para-OH-TB-DiPhOBz was found to be capable of competing with thyroxine (T4) for the binding site on hTTR and hALB. In silico analyses were also conducted using a 3D homology model for gull TTR, to predict whether these TB-DiPhOBz-based compounds may also act as ligands for an avian TH transport protein despite evolutionary differences with hTTR. This analysis found all three TB-DiPhOBz analogues to be potential ligands for gull TTR and have similar binding efficacies to THs. Results indicate structure-related differences in binding affinities of these ligands and suggest there is potential for these contaminants to interact with both mammalian and avian thyroid function.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".