Screening 800 putative endocrine disrupting chemicals in a representative mammal, bird, and fish using a neurochemical cell-free testing platform
Bibliographic record
Abstract
There is global demand for novel ecotoxicity testing tools that are based on alternative to animal models, have high throughput potential, and may be applicable to a wide diversity of taxa. Here we scaled up a microplate-based cell-free neurochemical testing platform to screen 800 putative endocrine disrupting chemicals from the U.S. Environmental Protection Agency's ToxCast e1k library against the glutamate (NMDA), muscarinic acetylcholine (mACh), and dopamine (D2) receptors. Each assay was tested in cellular membranes isolated from brain tissues from a representative bird (zebra finch = Taeniopygia castanotis), mammal (mink = Neogale vison), and fish (rainbow trout = Oncorhynchus mykiss). The primary objective of this short communication was to make the results database accessible, while also summarising key attributes of assay performance and presenting some initial observations. In total, 7200 species-chemical-assay combinations were tested, of which 453 combinations were classified as a hit (radioligand binding changed by at least 3 standard deviations). There were some differences across species, and most hits were found for the D2 and NMDA receptors. The most active chemical was C.I. Solvent Yellow 14 followed by Diphenhydramine hydrochloride, Gentian Violet, SR271425, and Zamifenacin. Nine chemicals were tested across multiple plates with a mean relative standard deviation of the specific radioligand binding data being 24.6%. The results demonstrate that cell-free assays may serve as screening tools for large chemical libraries especially for ecological species not easily studied using traditional methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".