Accounting for data variability, a key factor in in vivo/in vitro relationships: application to the skin sensitization potency (in vivo LLNA versus in vitro DPRA) example

Abstract

When searching for alternative methods to animal testing, confidently rescaling an in vitro result to the corresponding in vivo classification is still a challenging problem. Although one of the most important factors affecting good correlation is sample characteristics, they are very rarely integrated into correlation studies. Usually, in these studies, it is implicitly assumed that both compared values are error-free numbers, which they are not. In this work, we propose a general methodology to analyze and integrate data variability and thus confidence estimation when rescaling from one test to another. The methodology is demonstrated through the case study of rescaling the in vitro Direct Peptide Reactivity Assay (DPRA) reactivity to the in vivo Local Lymph Node Assay (LLNA) skin sensitization potency classifications. In a first step, a comprehensive statistical analysis evaluating the reliability and variability of LLNA and DPRA as such was done. These results allowed us to link the concept of gray zones and confidence probability, which in turn represents a new perspective for a more precise knowledge of the classification of chemicals within their in vivo OR in vitro test. Next, the novelty and practical value of our methodology introducing variability into the threshold optimization between the in vitro AND in vivo test resides in the fact that it attributes a confidence probability to the predicted classification. The methodology, classification and screening approach presented in this study are not restricted to skin sensitization only. They could be helpful also for fate, toxicity and health hazard assessment where plenty of in vitro and in chemico assays and/or QSARs models are available. Copyright © 2016 John Wiley & Sons, Ltd.

The borderline range of toxicological methods: Quantification and implications for evaluating precision

“…Kolle et al (2013) defined a range around the classification threshold of the LLNA, within which discordant outcomes can be expected, by determining coefficients of variation based on individual animal data. This range is called the "borderline range" (BR) (Kolle et al, 2013) or "grey zone" (Dimitrov et al, 2016). The percentage of substances that fall into the BR of a test method's prediction model reflects how limited a test method's precision is.…”

An economic approach to non-animal toxicity testing for skin sensitisation

“…As shown for the case of skin sensitisation potential assessment, technical and biological variability can lead to misclassifications (Kolle et al, 2013;Hoffmann, 2015;Dimitrov et al, 2016;Dumont et al, 2016). The impact of biological and technical variability of the LLNA on the misclassification of substances, is associated with the fact that the classification of substances as "sensitisers/non-sensitisers" is based on clear-cut thresholds (Hoffmann, 2015).…”

“…The uncertainty of information derived from toxicity testing has been frequently discussed in the toxicology literature (Paparella et al, 2013;Heringa et al, 2015). For example, the impact of technical and biological variability on test results derived from testing methods (Kolle et al, 2013;Hoffmann, 2015;Dimitrov et al, 2016;Dumont et al, 2016;Leontaridou et al, 2017a), and the effect of overfitting experimental data from testing (Kopp-Schneider et al, 2013) have been discussed. However, the estimation of non-animal testing methods' borderline range and the identification of substances yielding borderline test results provide a novel approach to classify substances, to identify discordant test results and to decide if additional information is needed.…”

“…Specifically, we analyse limitations with regard to the reproducibility of results when continuous dose-response data are transformed into "toxic/ non-toxic" outcomes. Kolle et al (2013), Hoffmann (2015), Dumont et al, (2016), and Dimitrov et al (2016) analysed the intra-assay variability of the LLNA. Kolle et al (2013), showed that outcomes of repeated testing of a substance are not always concordant.…”

“…This may lead to erroneous hazard classifications and to over-or underestimation errors of a testing method's predictive capacity. Again, using skin sensitisation as an illustrative case we showed that ambiguity of hazard classification is a general problem that applies to both animal tests (Kolle et al, 2013;Hoffmann, 2015;Dimitrov et al, 2016) and non-animal testing methods. Defining a quantifiable measure for this ambiguity, called "borderline range", Chapter 4 allows assessing its impact on the predictive accuracy of (non-animal) testing methods, i.e.…”