Table 2.
Qualitative evaluation categories and criteria.
| Evaluation category | Evaluation criteria |
|---|---|
| Characteristics | Principle |
| Prediction (i.e. hazard vs. potency [categories or continuous]) | |
| Publication | |
| Information sources | |
| Input data | Test method (in vitro and in chemico) |
| - read-out used | |
| - validation status | |
| - reproducibility | |
| - issues (e.g. IP, availability) | |
| In silico/expert system data/physicochemical properties | |
| - read-out used | |
| - availability | |
| - reliability | |
| - issues (e.g. IP, availability) | |
| Expert knowledge | |
| - input used | |
| - availability | |
| Principle | |
| Prediction (i.e. hazard vs. potency [categories or continuous]) | |
| Publication | |
| Information sources | |
| Prediction algorithm | Type |
| Availability | |
| Transparency | |
| Requirements for implementation (specific software) | |
| Self-learning | |
| Complexity | |
| Sequential information generation | |
| All inputs required? | |
| Predictivity: Sample size (total and for categories) | |
| Predictivity: Parameters (sensitivity, specificity, concordance) | |
| Mechanistic relevance | OECD AOP key events covered |
| Sequence of OECD AOP events considered | |
| Justification/discussion of the mechanistic relevance | |
| Applicability domain | Chemical spectrum tested |
| Limitations (solubility, surfactants,...) | |
| Potential limitations for cosmetic ingredients (e.g. natural extracts cannot be processed by in silico approaches) | |
| Practical aspects | Costs |
| Can be conducted by CRO? | |
| Time required (per substance) |