Novel technologies and an overall strategy to allow hazard assessment and risk prediction of chemicals, cosmetics, and drugs with animal-free methods
Bibliographic record
Abstract
Several alternative methods to replace animal experiments have been accepted by legal bodies. An even larger number of tests are under development or already in use for non-regulatory applications or for the generation of information stored in proprietary knowledge bases. The next step for the use of the different in vitro methods is their combination into integrated testing strategies (ITS) to get closer to the overall goal of predictive "in vitro-based risk evaluation processes." We introduce here a conceptual framework as the basis for future ITS and their use for risk evaluation without animal experiments. The framework allows incorporation of both individual tests and already integrated approaches. Illustrative examples for elements to be incorporated are drawn from the session "Innovative technologies" at the 8th World Congress on Alternatives and Animal Use in the Life Sciences, held in Montreal, 2011. For instance, LUHMES cells (conditionally immortalized human neurons) were presented as an example for a 2D cell system. The novel 3D platform developed by InSphero was chosen as an example for the design and use of scaffold-free, organotypic microtissues. The identification of critical pathways of toxicity (PoT) may be facilitated by approaches exemplified by the MatTek 3D model for human epithelial tissues with engineered toxicological reporter functions. The important role of in silico methods and of modeling based on various pre-existing data is demonstrated by Altamira's comprehensive approach to predicting a molecule's potential for skin irritancy. A final example demonstrates how natural variation in human genetics may be overcome using data analytic (pattern recognition) techniques borrowed from computer science and statistics. The overall hazard and risk assessment strategy integrating these different examples has been compiled in a graphical work flow.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".