Mechanistic Pharmacokinetic Modeling of the Bioamplification of Persistent Lipophilic Organic Pollutants in Humans during Weight Loss
Bibliographic record
Abstract
Bioamplification means the liberation of persistent lipophilic organic pollutants (PLOPs) into blood from their storage in inert adipose tissue during rapid weight loss. Here, using a modified mechanistic pharmacokinetic model, we investigated how chemical properties and anthropometric parameters interact to influence the bioamplification of various PLOPs in humans. The model succeeds in reproducing literature documented weight loss-induced increments in human blood PLOP concentrations. We simulated the degree of bioamplification, as characterized by the bioamplification factor (BAmF), of hypothetical PLOPs with different combinations of partitioning and biotransformation properties at various rates of lipid loss. We also investigated how BAmF evolves with the duration of weight loss. Results show that bioamplification is expected to occur for any chemical with even moderate lipophilicity (log K OW > 2 and log K OA > 6) as long as the half-life for metabolic elimination is long relative to the time scale of relative lipid loss (e.g., exceeding 10 4 h in the case of lipid loss of 3 kg month –1 with an initial lipid mass of 40 kg). While BAmF of a chemical is time-variant, whether bioamplification occurs for a chemical or not is independent of the duration of weight loss. The successful application of such a simple model demonstrates that it is the lipid dynamics that predominantly govern the dynamics of PLOPs rather than vice versa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".