Approaches for Applications of Physiologically Based Pharmacokinetic Models in Risk Assessment
Bibliographic record
Abstract
Physiologically based pharmacokinetic (PBPK) models are particularly useful for simulating exposures to environmental toxicants for which, unlike pharmaceuticals, there is often little or no human data available to estimate the internal dose of a putative toxic moiety in a target tissue or an appropriate surrogate. This article reviews the current state of knowledge and approaches for application of PBPK models in the process of deriving reference dose, reference concentration, and cancer risk estimates. Examples drawn from previous U.S. Environmental Protection Agency (EPA) risk assessments and human health risk assessments in peer-reviewed literature illustrate the ways and means of using PBPK models to quantify the pharmacokinetic component of the interspecies and intraspecies uncertainty factors as well as to conduct route to route, high dose to low dose and duration extrapolations. The choice of the appropriate dose metric is key to the use of the PBPK models for the various applications in risk assessment. Issues related to whether uncertainty factors are most appropriately applied before or after derivation of human equivalent dose (or concentration) continue to be explored. Scientific progress in the understanding of life stage and genetic differences in dosimetry and their impacts on variability in susceptibility, as well as ongoing development of analytical methods to characterize uncertainty in PBPK models, will make their use in risk assessment increasingly likely. As such, it is anticipated that when PBPK models are used to express adverse tissue responses in terms of the internal target tissue dose of the toxic moiety rather than the external concentration, the scientific basis of, and confidence in, risk assessments will be enhanced.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".