Resource requirements for ecotoxicity testing: A comparison of traditional and new approach methods
Bibliographic record
Abstract
ABSTRACT Toxicity testing is under transformation as it aims to harness the potential of New Approach Methods (NAMs) as alternative test methods that may be less resource intensive (i.e., fewer animals, cheaper costs, quicker assays) than traditional approaches while also providing more data and information. While many stakeholders are of the opinion that this unfolding transformation holds significant promise as a more efficient and ethical way forward, few studies have compared the resources required for NAMs versus those needed for traditional animal-based toxicity tests, particularly in the field of ecotoxicology. The objective was to compare resources needed for traditional animal-based ecotoxicity tests versus alternative tests using emergent NAMs. From a bibliometric review, we estimate that traditional tests for a single chemical cost $118,000 USD, require 135 animals, and take 8 weeks. In comparison, alternative tests cost $2,600, require 20 animals (or none), and take up to 4 weeks to test 16 (to potentially hundreds of) chemicals. Based on our analysis we conclude that NAMs in ecotoxicology can be more advantageous than traditional methods in terms of resources required (i.e., monetary costs, number of animals needed, and testing times). We note, however, that the evidence underpinning these conclusions is relatively sparse. Moving forward, groups developing and applying NAMs should provide more detailed accounts of the resources required. In addition, there is also a need for carefully designed case studies that demonstrate the domain of applicability of NAMs (and make comparisons to traditional tests) to ultimately build confidence among the user community.
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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 | 0.055 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".