A physiologically-based algorithm for predicting internal dose of inhaled toluene: applications for high dose to low dose and rodent to human extrapolations
Bibliographic record
Abstract
The objective of this study was to develop a physiologically-based algorithm for predicting the steady-state internal dose of inhaled volatile organic chemicals (VOCs) in rats and humans at various exposure concentrations, using toluene as the model chemical. This was accomplished by the systematic development of the solution to the set of equations constituting pulmonary uptake and metabolic clearance, including the consideration of dose-dependent change in the free concentration of chemical at the metabolizing site (liver). The resulting algorithm, based on critical determinants of the internal dose during chronic exposure to VOCs (i.e., alveolar ventilation rate, blood flow rate to liver, blood:air partition coefficient, maximal velocity of metabolism, Michaelis affinity constant and free concentration of chemical at the metabolizing site) provides predictions of dose metrics (i.e., arterial blood concentration and rate of amount metabolized) identical to those of the full-fledged PBPK models. The algorithm was then applied to conduct high dose to low dose and rodent to human extrapolations of internal dose of inhaled toluene. The physiologically-based algorithm, developed in this study, for the first time facilitates the direct computation of steady-state internal dose for a variety of exposure concentrations of toluene, by consistently accounting for the non-linear processes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".