Physiologically based pharmacokinetic modeling of metal nanoparticles for risk assessment of inhalation exposures: a state-of-the-science expert panel review
Bibliographic record
Abstract
A critical review of the current state-of-the-science for the physiologically based pharmacokinetic (PBPK) modeling of metal nanoparticles and their application to human health risk assessment for inhalation exposures was conducted. A systematic literature search was used to identify four model groups (defined as a primary publication along with multiple supplementary publications) subject to review. Using a recent guideline document from the Organization for Economic Cooperation and Development (OECD) for PBPK model evaluation, these model groups were critically peer-reviewed by an independent panel of experts to identify those to be considered for modeling and simulation application. Based upon the expert panel input, model confidence scores for the four model groups ranged from 30 to 41 (out of a maximum score of 50). The three highest-scoring model groups were then applied to compare predictions to a different metal nanoparticle (i.e. not specifically used to parameterize the original models) using a recently published data set for tissue burdens in rats, as well as predicting human tissue burdens expected for corresponding occupational exposures. Overall, the rat models performed reasonably well in predicting the lung but tended to overestimate systemic tissue burdens. Data needs for improving the state-of-the-science, including quantitative particle characterization in tissues, nanoparticle-corona data, long-term exposure data, interspecies extrapolation methods, and human biomonitoring/toxicokinetic data are discussed.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".