Quantitative approaches for assessing dose–response relationships in genetic toxicology studies

Abstract

Genetic toxicology studies are required for the safety assessment of chemicals. Data from these studies have historically been interpreted in a qualitative, dichotomous ''yes'' or ''no'' manner without analysis of doseresponse relationships. This article is based upon the work of an international multi-sector group that examined how quantitative dose-response relationships for in vitro and in vivo genetic toxicology data might be used to improve human risk assessment. The group examined three quantitative approaches for analyzing dose-response curves and deriving point-of-departure (POD) metrics (i.e., the no-observed-genotoxic-effectlevel (NOGEL), the threshold effect level (Td), and the benchmark dose (BMD)), using data for the induction of micronuclei and gene mutations by methyl methanesulfonate or ethyl methanesulfonate in vitro and in vivo. These results suggest that the POD descriptors obtained using the different approaches are within the same order of magnitude, with more variability observed for the in vivo assays. The different approaches were found to be complementary as each has advantages and limitations. The results further indicate that the lower confidence limit of a benchmark response rate of 10% (BMDL 10 ) could be considered a satisfactory POD when analyzing genotoxicity data using the BMD approach. The models described permit the identification of POD values that could be combined with mode of action analysis to determine whether exposure(s) below a particular level constitutes a significant human risk. Subsequent analyses will expand the number of substances and endpoints investigated, and continue to evaluate the utility of quantitative approaches for analysis of genetic toxicity dose-response data. Environ. Mol. Mutagen. 54:8-18, 2013. V V C 2012 Wiley Periodicals, Inc.

The Applicable Use of the HPRT Gene Mutation Assay as a Practical Tool in Mutagenesis and DNA Repair Studies

“…The most well-regarded approach for defining a PoD using HPRT data is the benchmark dose (BMD) approach [30,31]. BMD analysis can be conducted using PROAST, the dose-response modeling software developed at the National Institute for Public Health and the Environment (RIVM) in the Netherlands (www.…”

Approaches for identifying germ cell mutagens: Report of the 2013 IWGT workshop on germ cell assays☆

“…The POD is the point on the dose-response curve that marks the starting point for low-dose extrapolation to exposure levels of concern for human risk [160]. Recent evaluations of appropriate models to generate PODs recommend that the benchmark approach (see EPA, 2012 for guidance [159]) is the most appropriate for modeling genetic toxicity data [161][162][163][164].…”

Assessment of mechanisms driving non-linear dose–response relationships in genotoxicity testing

“…This classification system is based on the 32 argument that even a single molecule, interacting with DNA, could 33 result in DNA damage, mutation and subsequent tumor initiation 34 (one-hit model). However, it was recognized that this linear 35 assessment might not be appropriate for all chemical classes 36 [2,3]. Evidence against the linear hypothesis was first generated for 37 aneugens [4].…”

“…The outcome of respective analyses thereby strongly depends 81 on sensitivity of the detection method used and endpoint, the 82 variability of the effect, and also the number and range of doses and 83 the spacing between doses, hence the robustness of the assays and 84 experimental design used. A thorough assessment of the different 85 statistical methods used to evaluate low-dose responses was 86 performed elsewhere [3,15] and will thus not be the focus of this 87 manuscript. The term (practical) threshold in general is only applied 88 when bilinear analysis calculates a threshold dose (TD) with lower 89 95% confidence intervals above 0.…”

“…More complex mathematical 77 models for data evaluation have been developed, comprising the 78 ''bilinear hockey stick model'' [12,14], ''smoothing regression 79 spline'', and the so-called ''benchmark dose analysis (BMD)'' 80 [3,15]. The outcome of respective analyses thereby strongly depends 81 on sensitivity of the detection method used and endpoint, the 82 variability of the effect, and also the number and range of doses and 83 the spacing between doses, hence the robustness of the assays and 84 experimental design used.…”