Issues in the Design and Interpretation of Chronic Toxicity and Carcinogenicity Studies in Rodents: Approaches to Dose Selection
Bibliographic record
Abstract
For more than three decades chronic studies in rodents have been the benchmark for assessing the potential long-term toxicity, and particularly the carcinogenicity, of chemicals. With doses typically administered for about 2 years (18 months to lifetime), the rodent bioassay has been an integral component of testing protocols for food additives, pesticides, pharmaceuticals, industrial chemicals, and all manner of byproducts and environmental contaminants. Over time, the data from these studies have been used to address an increasing diversity of questions related to the assessment of human health risks, adding complexity to study design and interpretation. An earlier ILSI RSI working group developed a set of principles for the selection of doses for chronic rodent studies (ILSI, 1997). The present report builds on that work, examining some of the issues that arise and offering new perspectives and approaches for putting the principles into practice. Dose selection is considered both from the prospective viewpoint of the choosing of dose levels for a study and from the retrospective interpretation of study results in light of the doses used. A main theme of this report is that the purposes and objectives of chronic rodent studies vary and should be clearly defined in advance. Dose placement, then, should be optimized to achieve study objectives. For practical reasons, most chronic studies today must be designed to address multiple objectives, often requiring trade-offs and innovative approaches in study design. A systematic approach to dose selection should begin with recognition that the design of chronic studies occurs in the context of a careful assessment of the accumulated scientific information on the test substance, the relevant risk management questions, priorities and mandates, and the practical limitations and constraints on available resources. A stepwise process is described. The aim is to increase insofar as possible the utility of an expensive and time-consuming experiment. The kinds of data that are most commonly needed for dose selection and for understanding the dose-related results of chronic rodent studies, particularly carcinogenicity studies, are discussed as "design/interpretation factors." They comprise both the inherent characteristics of the test substance and indicators of biological damage, perturbation or stress among the experimental animals. They may be primary toxicity endpoints, predictors or indicators of appropriate dose selection, or indicators of conditions to be avoided in dose selection. The application and interpretation of design/interpretation factors is conditioned by the study objectives-what is considered desirable will depend on the strategy for choice of doses that is being followed. The challenge is to select doses that accommodate all of the issues raised by the relevant design/interpretation factors. Three case studies are presented here that illustrate the interplay between study objectives and the design and selection of doses for chronic rodent studies. These examples also highlight issues associated with multiple plausible modes of action, multiple pathways for biotransformation of the chemical, extraneous high-dose effects, the use of modeling in dose selection, and the implications of human exposure levels. Finally, looking to the future, the report explores seven potential paradigm shifts for risk assessment that will significantly impact the design and interpretation of toxicity and carcinogenicity studies.
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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 | 0.546 | 0.416 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".