When using experimental data and/or In Silico predictions, it is important to assess the quality of the information as part of an overall assessment of confidence. When assessing the reliability of experimental data, it is usual to consider the reproducibility of the test performed by comparing the study design and execution against published and accepted test guidelines and the extent to which this information is documented. The Klimisch score is a simple and widely used method to quantify the reliability of experimental data based on a score of 1-4, as summarized below1:
| Score | Description | Summary |
| 1 | Reliable without restriction | • Well documented and accepted study or data from the literature • Performed according to valid and/or accepted test guidelines (e.g., OECD) • Preferably performed according to good laboratory practices (GLP) |
| 2 | Reliable with restriction | • Well documented and sufficient • Primarily not performed according to GLP • Partially complies with test guideline |
| 3 | Not reliable | • Interferences between the measuring system and test substance • Test system not relevant to exposure • Method not acceptable for the endpoint • Not sufficiently documented for an expert review |
| 4 | Not assignable | • Lack of experimental details • Referenced from short abstract or secondary literature |
In Silico results are being increasing used for hazard and risk assessment alongside experimental data and hence it is important to assess the reliability of both types of results within a harmonized grading scheme. Such a score, called the Reliability Score, was generated as part of the in silico toxicology protocols project.2 This score was created by a team of over 80 experts in the field and reflects the current state of the art. It considers that there is an increase in reliability when multiple and complementary models are used that predict the same results. In addition, a further increase in reliability can be achieved by performing an expert review of the totality of information, including any output from the in silico models (e.g., results for chemicals analogs, important QSAR descriptors, proposed mechanism(s) of action). Where such a review increases the reliability, the Reliability Score is adjusted accordingly. A read-across assessment, which includes a similar type of expert review, is considered in this same category. The Reliability Score is a five-point grading that extends the Klimisch score to include In Silico model results as well the aforementioned factors that increase the reliability. The following table summarizes the Reliability Score alongside the Klimisch score:
| Reliability Score | Klimish Score | Description |
| RS1 | 1 | Data reliable without restriction |
| RS2 | 2 | Data with restriction |
| RS3 | – | Expert review of in silico result (s) and/or Klimisch 3 or 4 data |
| RS4 | – | Multiple concurring prediction results |
| RS5 | – | Single acceptable in silico result |
| RS5 | 3 | Data not reliable |
| RS5 | 4 | Data not assignable |
Reliability is an important component of any confidence assessment for a toxicological hazard outcome, alongside an evaluation of the relevance and completeness of the information. A framework for assessing such a confidence measure is described in a number of recent publications.2,3
Please do not hesitate to get in touch if you wish to discuss such approaches with a scientist at Instem (info@instem.com).
References
- Klimisch, H.-J., Andreae, M., Tillmann, U., 1997. A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul. Toxicol. Pharmacol. 25, 1–5. http://dx.doi.org/10.1006/rtph.1996.1076
- Myatt et al., In silico toxicology protocols, Regul. Toxicol. Pharmacol. 96 (2018) 1–17. https://doi.org/10.1016/j.yrtph.2018.04.014
- Johnson et al., Evaluating confidence in toxicity assessments based on experimental data and in silico predictions, Comput. Toxicol. 21 (2022) 100204. https://doi.org/10.1016/j.comtox.2021.100204