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A framework for the quantitative weight-of-evidence (QWoE) analysis of 'omics data for regulatory purposes is presented. The QWoE framework encompasses seven steps to evaluate 'omics data (also together with non-'omics data): (1) Hypothesis formulation, identification and weighting of lines of evidence (LoEs). LoEs conjoin different (types of) studies that are used to critically test the hypothesis. As an essential component of the QWoE framework, step 1 includes the development of templates for scoring sheets that predefine scoring criteria with scores of 0-4 to enable a quantitative determination of study quality and data relevance; (2) literature searches and categorisation of studies into the pre-defined LoEs; (3) and (4) quantitative assessment of study quality and data relevance using the respective pre-defined scoring sheets for each study; (5) evaluation of LoE-specific strength of evidence based upon the study quality and study relevance scores of the studies conjoined in the respective LoE; (6) integration of the strength of evidence from the individual LoEs to determine the overall strength of evidence; (7) characterisation of uncertainties and conclusion on the QWoE. To put the QWoE framework in practice, case studies are recommended to confirm the relevance of its different steps, or to adapt them as necessary.

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in ‘omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other ‘omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during ‘omics data processing and analysis, the TRF will increase confidence processing of ‘omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory ‘omics studies, and it can be executed using different commonly available software tools.

Skin absorption testing in vitro is a regulatory accepted alternative method (OECD Guideline 428). Different tests can be applied to evaluate the integrity of the skin samples. Here, we compared the pre- or post-run integrity tests (transepidermal electrical resistance, TEER; transepidermal water loss, TEWL; absorption of the reference compounds water, TWF, or methylene blue, BLUE) and additionally focused on co-absorption of a (3)H-labeled internal reference standard (ISTD) as integrity parameter. The results were correlated to absorption profiles of various test compounds. Limit values of 2kΩ, 10 gm(-2)h(-1) and 4.5∗10(-3)cmh(-1) for the standard methods TEER, TEWL and TWF, respectively, allowed distinguishing between impaired and intact human skin samples in general. Single skin samples did, however, not, poorly and even inversely correlate with the test-compound absorption. In contrast, results with ISTD (e.g. (3)H-testosterone) were highly correlated to the absorption of (14)C-labeled test compounds. Importantly, ISTD did not influence analytics or absorption of test compounds. Therefore, ISTD, especially when adjusted to the physico-chemical properties of test compounds, is a promising concept to assess the integrity of skin samples during the whole course of absorption experiments. However, a historical control dataset is yet necessary for a potential routine application.