Applying New Approach Methods for Toxicokinetics for Chemical Risk Assessment
Bibliographic record
Abstract
Toxicokinetic (TK) modeling provides critical information linking chemical exposures to tissue concentrations, predicting persistence in the body and determining the route(s) of elimination. Unfortunately, TK data are not available for most chemicals in commerce and the environment. To better understand and address these important information gaps, researchers and regulatory scientists from the international consortium of Accelerating the Pace of Chemical Risk Assessment herein present a flexible framework for characterizing the suitability of TK new approach methods (NAMs) to address chemical risk questions. High throughput toxicokinetics (HTTK) combines chemical-specific in vitro measures of TK with reproducible transparent and open-source TK models. HTTK supports the interpretation of data from in vitro bioactivity NAMs in a public health risk context and enhances the interpretation of biomonitoring data. A tiered framework has been developed focusing on two key aspects: (1) the regulatory decision context and (2) chemical properties and data. Differing levels of certainty are needed for relative risk prioritization, prospective risk assessment, and for protecting susceptible populations. Here HTTK is described with respect to measurement and modeling applications, relevant decision contexts, applicable chemistry, value of information, and certainty of predictions. In some cases, quantitative structure-property relationship (QSPR) models exist as alternatives to measurement and are discussed when they are appropriate. A series of examples applying the decision trees in specific public health scenarios are provided to illustrate that writing short responses, prompted by the decision trees and supported by the discussion and references collected here, may provide defensible written justification for or against the use of HTTK. The framework is intended to serve as a guide to chemical regulators and risk assessors who are interested to know when and where HTTK might be used for public health safety or risk decision making and when further expert guidance is needed.
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.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".