Accounting for Precision Uncertainty of Toxicity Testing: Methods to Define Borderline Ranges and Implications for Hazard Assessment of Chemicals. Issue 2 (9th December 2020)

• Record Type: Journal Article
• Authors: Gabbert, Silke; Mathea, Miriam; Kolle, Susanne N.; Landsiedel, Robert
• Is Part Of: Risk analysis. Volume 42:Issue 2(2022)
• Page Start: 224
• Page End: 238
• Publication Date: 2020-12-09
• Subjects: Borderline range -- classification threshold -- decision‐making -- OECD test guideline -- precision uncertainty -- prediction -- toxicity testing; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13648
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: For hazard classifications of chemicals, continuous data from animal‐ or nonanimal testing methods are often dichotomized into binary positive/negative outcomes by defining classification thresholds (CT). Experimental data are, however, subject to biological and technical variability. Each test method's precision is limited resulting in uncertainty of the positive/negative outcome if the experimental result is close to the CT. Borderline ranges (BR) around the CT were suggested, which represent ranges in which the study result is ambiguous, that is, positive or negative results are equally likely. The BR reflects a method's precision uncertainty. This article explores and compares different approaches to quantify the BR. Besides using the pooled standard deviation, we determine the BR by means of the median absolute deviation (MAD), with a sequential combination of both methods, and by using nonparametric bootstrapping. Furthermore, we quantify the BR for different hazardous effects, including nonanimal tests for skin corrosion, eye irritation, skin irritation, and skin sensitization as well as for an animal test on skin sensitization (the local lymph node assay, LLNA). Additionally, for one method (direct peptide reactivity assay) the BR was determined experimentally and compared to calculated BRs. Our results demonstrate that (i) the precision of the methods is determining the size of their BRs, (ii) there is no "perfect" method to derive a BR, alas, (iii) a consensus on BR is needed to account for the limited precision of testing methods.