Abstract
The ability of non-animal methods to correctly predict the outcome of in vivo testing in repeated applications is referred to as precision. Due to dichotomising continuous read-outs into discrete "positive/negative" hazard data, non-animal methods can reveal discordant classifications if results are sufficiently close to a defined classification threshold. This paper explores the impact of precision uncertainty on the predictive accuracy of non-animal methods. Using selected non-animal methods for assessing skin sensitisation hazard as case study examples, we explore the impact of precision uncertainty separately and in combination with uncertainty due to varying composition and size of experimental samples. Our results underline that discrete numbers of non-animal methods' sensitivity, specificity and concordance are of limited information for evaluations of non-animal testing methods' predictivity. Instead, information on the variability, and the upper and lower limits of accuracy metrics, should be provided to ensure a transparent assessment of testing methods' predictivity, and to allow for a meaningful comparison of the predictivity of non-animal methods with that of animal tests.
Highlights
Animal studies do not accurately predict human toxicity (Russell and Burch, 1959), they are still regarded as the “gold standard” and results of so-called “alternative methods” are compared to results of respective animal studies
LuSens and Direct Peptide Reactivity Assay (DPRA) results that are not in the borderline range” (BR) may be sufficient to conclude a “2 out of 3” integrated testing strategy (ITS) result to be outside the BR even if the result obtained in the human cell line activation test (h-CLAT), as a third assay, is within the BR
4.1 Impact of uncertainty due to limited precision Acknowledging that borderline substances cannot be classified as “positive” or “negative”, we expected that predictive accuracy metrics derived from the full samples would differ from those of adapted samples (i.e., Tab. 5: Minimum and maximum number of substances (k) in randomized samples resulting from bootstrap resampling after borderline substances were excluded
Summary
Animal studies do not accurately predict human toxicity (Russell and Burch, 1959), they are still regarded as the “gold standard” and results of so-called “alternative methods” ( termed “non-animal methods” or “new approach methods”) are compared to results of respective animal studies. Sufficient data from humans are available to serve as a reference for comparison. The degree of agreement between results obtained by in vivo and non-animal methods is quantified as accuracy metrics. The analysis of inter-laboratory variability, i.e., the reproducibility of test results across different, independent laboratories, has received wide attention in the scientific literature (Sakaguchi et al, 2010; Sirota et al, 2014). Though the relevance of intra-laboratory variability, i.e., the ability of in vivo tests or non-animal methods to reproduce outcomes in repeated experimental applications, has been recognized (Bruner et al, 1996), systematic analyses of intra-laboratory variability have not been taken up for a long time
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