ReviewReproductive toxicology (Elmsford, N.Y.)2026
Hypothesis-driven approach to developmental toxicity assessment: Using mechanistic information to inform testing.
Review in Reproductive toxicology (Elmsford, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.