Office of Research and Development Publications

A High-throughput Analytical Framework for Efficient Analysis of In Vitro Micronucleus (MN) Dose-response Data

Citation:

Kuo, B., J. Wills, P. White, F. Marchetti, A. Nong, A. Williams, K. Houck, R. Tice, AND C. Yauk. A High-throughput Analytical Framework for Efficient Analysis of In Vitro Micronucleus (MN) Dose-response Data. Health Canada Science Forum, OttawaC, March 08 - 09, 2018. https://doi.org/10.23645/epacomptox.6947897

Impact/Purpose:

A high-throughput analytical approach was developed including a robust decision tree and the use of benchmark dose modelling. While further evaluation and refinement of the approach is necessary, preliminary results suggest that a high-throughput version of the traditional MN assay, augmented by our proposed assessment approach, can improve the efficiency of genetic toxicity testing. This high-throughput analytical approach to analyze large in vitro MN assay data sets can provide a rapid screen for genotoxicity, particularly data-poor chemicals being evaluated under the Chemicals Management Plan.

Description:

Health Canada assesses the health risks posed by chemicals in commerce in Canada. An important endpoint in chemical assessment is genetic toxicity (e.g., ability to damage DNA) which is associated with cancer and inherited genetic diseases. The in vitro micronucleus (MN) assay is a standard test used for genetic toxicology assessment. It measures genetic damage by detecting DNA fragments or extra chromosomes that are present outside the cell nucleus after cell division (i.e., micronuclei). In vitro MN frequency data for 292 chemicals was analyzed in collaboration with the United States Environmental Protection Agency (US EPA). Due to the number of the chemicals requiring assessment, a novel high-throughput analytical approach was developed and applied. A high-throughput analytical approach was developed including a robust decision tree and the use of benchmark dose modelling. The approach was then applied to classify test chemicals. Key elements of the decision tree include comparison of the observed DNA damage response to control groups, as well as scrutiny of relative cellular survival, and the nature of the dose-response. Using the developed paradigm, 157 (54%) of the chemicals were classified as positive for genetic toxicity, 30 (10%) as negative, and 105 (36%) as inconclusive. While further evaluation and refinement of the approach is necessary, preliminary results suggest that a high-throughput version of the traditional MN assay, augmented by our proposed assessment approach, can improve the efficiency of genetic toxicity testing. This high-throughput analytical approach to analyze large in vitro MN assay data sets can provide a rapid screen for genotoxicity, particularly data-poor chemicals being evaluated under the Chemicals Management Plan.