Hypothesis-driven approach to developmental toxicity assessment: Using mechanistic information to inform testing
Bibliographic record
Abstract
Developmental toxicity assessment relies on standardized guideline protocols in which animals (usually rats and/or rabbits) are exposed to the test(s) agent(s) and pregnancy outcomes are assessed at an organismal level. Increasing information about mechanisms of toxicity now allows improved selection of in vivo and in vitro models for assessing developmental toxicity and prediction of developmental outcome by investigating the mode of action (MoA) of the test agent, allowing for a more flexible resource-efficient approach. Read-across, already widely used for chemical assessment, relies on a combination of cheminformatics to select suitable analogs and any of a variety of methods to prove biological similarity and/or a common metabolic pathway. Some of these methods include high-throughput test batteries (e.g., ToxCast) and transcriptomics linked to large databases of gene expression profiles. These can be used to both generate and test hypotheses about MoA of novel compounds. Increasing availability of induced pluripotent stem cells provides greater range of biological models that closely mimic the human biology relevant for addressing a specific hypothesis. Examples are given of how (1) understanding mode of action can be used to identify activity cliffs in a series of analogous chemicals, (2) the use of metabolism data in an example demonstrating that closely related analogs do not all have to be tested in developmental toxicity protocols, and (3) how analysis of gene expression can be used to identify divergent pharmacology in similar chemicals. It is possible using the approaches described to design more flexible, hypothesis-driven approaches to assess developmental toxicity.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".