Physiological modeling and extrapolation of pharmacokinetic interactions from binary to more complex chemical mixtures.
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
The available data on binary interactions are yet to be considered within the context of mixture risk assessment because of our inability to predict the effect of a third or a fourth chemical in the mixture on the interacting binary pairs. Physiologically based pharmacokinetic (PBPK) models represent a potentially useful framework for predicting the consequences of interactions in mixtures of increasing complexity. This article highlights the conceptual basis and validity of PBPK models for extrapolating the occurrence and magnitude of interactions from binary to more complex chemical mixtures. The methodology involves the development of PBPK models for all mixture components and interconnecting them at the level of the tissue where the interaction is occurring. Once all component models are interconnected at the binary level, the PBPK framework simulates the kinetics of all mixture components, accounting for the interactions occurring at various levels in more complex mixtures. This aspect was validated by comparing the simulations of a binary interaction-based PBPK model with experimental data on the inhalation kinetics of m-xylene, toluene, ethyl benzene, dichloromethane, and benzene in mixtures of varying composition and complexity. The ability to predict the kinetics of chemicals in complex mixtures by accounting for binary interactions alone within a PBPK model is a significant step toward the development of interaction-based risk assessment for chemical mixtures.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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