Physiologically-Based Pharmacokinetic and Toxicokinetic Models in Cancer Risk Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Physiologically-based pharmacokinetic (PBPK) and toxicokinetic models are increasingly being used for the conduct of high dose to low dose and interspecies extrapolations required in cancer risk assessment. These models, by simulating tissue dose of toxic chemicals, help address the uncertainty associated with the default approaches for interspecies and high dose to low dose extrapolations. The applicability of PBPK models in cancer risk assessment has been demonstrated with a number of chemicals (e.g., acrylonitrile, 2-butoxyethanol, chloroform, 1,4-dioxane, methyl chloroform, methylene chloride, styrene, trichloroethylene, tetrachloroethylene, vinyl chloride, vinyl acetate). Recent advances in PBPK modeling facilitate the consideration of population distribution of parameter values, age-dependent changes in physiology and metabolism, multi-route exposures as well as multichemical interactions for application in cancer risk assessment. Whereas the average values for various input parameters have been used to evaluate the age-dependency of tissue dose, the Markov Chain Monte Carlo technique can be applied to address variability and uncertainty in parameter estimates, thus facilitating a more accurate estimation of cancer risk in the population. The PBPK models also uniquely facilitate the simulation of tissue dose, and thereby cancer risks, associated with multi-route and multichemical exposure situations. Overall, the recent advances reviewed in this article point to the continued enhancement of the scientific basis and applicability of PBPK models in cancer risk assessment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it