Many laboratory procedures generate data on properties of chemicals, but they cannot be equated with toxicological "test methods". This apparent discrepancy is not limited to in vitro testing, using animal-free new approach methods (NAM), but also applies to animal-based testing approaches. Here, we give a brief overview of the differences between data generation and the setup or use of a complete test method. While there is excellent literature available on this topic for specialists (GIVIMP guidance; ToxTemp overview), a brief overview and easily-accessible entry point may be useful for a broader community. We provide a single figure to summarize all test method elements and processes required in the development (setup and adaptation) of a test method. The exposure scheme, the endpoint, and the test system are briefly outlined as fundamental elements of any test method. A rationale is provided, why they are not sufficient. We then explain the importance and role of purpose definition (including some information on what is modelled) and the prediction model, aka data interpretation procedure, which depends on the purpose definition, as further essential elements. This connection exemplifies that all fundamental elements are interdependent, and none can be omitted. Finally, discussion is provided on validation as a measure to provide confidence in the reliability, performance, and relevance of a test method. In this sense, validation may be considered a sixth fundamental element for practical use of test methods.

Skin sensitizer potency assessment based on new approach methodologies is key to deriving a point of departure (PoD) for risk assessment. Regression models to predict a PoD based on OECD-validated in vitro tests and trained on local lymph node assay (LLNA) data were previously presented, and results from human tests were recently compiled. To integrate both data sources, the Reference Chemical Potency List (RCPL), which provides potency values (PV) for 33 chemicals integrating LLNA and human data in a structured weight-of-evidence approach, was developed. When calculating regression models vs PV or LLNA data, different weights for the input parameters were noted. As the RCPL is based on too few chemicals to train robust statistical models, the list of human data was extended to a larger set of PV (n = 139) with associated in vitro data. This database was used to retrain the regression models and to compare regression models trained vs (i) LLNA, (ii) PV or (iii) human DSA04 values. Using the PV as a target, predictive models of similar predictivity to the LLNA-based models were obtained, which mainly differ in a lesser weight of cytotoxicity and a higher weight of cell activation and reactivity parameters. Analysis of the human DSA04 dataset indicates a similar pattern but also shows that the human dataset is too small and biased to be a key dataset for potency prediction. Hence, an enlarged set of PV values appears to be a complementary tool to train predictive models next to an LLNA-only database.

The assessment of skin sensitizing properties of chemicals has moved away from animal methods to new approach methodologies (NAM), guided by qualitative mechanistic understanding operationalized in an adverse outcome pathway (AOP). As with any AOP, the molecular initiating event (MIE) of covalent binding of a chemical to skin proteins is particularly important. This MIE has been modelled by several test methods by measuring the reaction of a test chemical with model peptides in chemico. To better understand the similarities and differences, a data repository with publicly available data for the direct peptide reactivity assay (DPRA), amino acid derivative reactivity assay (ADRA) and kinetic DPRA (kDPRA), as well as the peroxidase peptide reactivity assay (PPRA) was assembled. The repository comprises 260 chemicals with animal and human reference data, data on four relevant physicochemical properties, and between 161 to 242 test chemical results per test method. First, an overview of the experimental conditions of the four test methods was compiled allowing to readily compare them. Second, data analyses demonstrated that the test methods’ predictivity was consistently reduced for poorly watersoluble chemicals and that the DPRA and ADRA can be used interchangeably. It also revealed new categorization thresholds for the DPRA and ADRA that are potentially relevant for strategic uses. In summary, a detailed assessment of reactivity test methods is provided, highlighting their potential and limitations. The results presented are intended to stimulate scientific discussion around test methods modelling the MIE of the skin sensitization AOP.

In chemical safety assessment, benchmark concentrations (BMC) and their associated uncertainty are needed for the toxicological evaluation of in vitro data sets. A BMC estimation is derived from concentration-response modelling and results from various statistical decisions, which depend on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software, often without being aware of the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision-making can influence the outcomes of data analysis and interpretation, we have developed an automated platform that includes statistical methods for BMC estimation, a novel endpoint-specific hazard classification system, and routines that flag data sets that are outside the applicability domain for an automatic data evaluation. We used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation as well as on final hazard classification. We identified five crucial statistical decisions the experimenter must make during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, and choice of benchmark response levels. The insights gained are intended to raise more awareness among experimenters on the importance of statistical decisions and methods but also to demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for objective hazard classification.