Ames test study designs for nitrosamine mutagenicity testing: qualitative and quantitative analysis of key assay parameters.

The robust control of genotoxic N-nitrosamine (NA) impurities is an important safety consideration for the pharmaceutical industry, especially considering recent drug product withdrawals. NAs belong to the 'cohort of concern' list of genotoxic impurities (ICH M7) because of the mutagenic and carcinogenic potency of this chemical class. In addition, regulatory concerns exist regarding the capacity of the Ames test to predict the carcinogenic potential of NAs because of historically discordant results. The reasons postulated to explain these discordant data generally point to aspects of Ames test study design. These include vehicle solvent choice, liver S9 species, bacterial strain, compound concentration, and use of pre-incubation versus plate incorporation methods. Many of these concerns have their roots in historical data generated prior to the harmonization of Ames test guidelines. Therefore, we investigated various Ames test assay parameters and used qualitative analysis and quantitative benchmark dose modelling to identify which combinations provided the most sensitive conditions in terms of mutagenic potency. Two alkyl-nitrosamines, N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) were studied. NDMA and NDEA mutagenicity was readily detected in the Ames test and key assay parameters were identified that contributed to assay sensitivity rankings. The pre-incubation method (30-min incubation), appropriate vehicle (water or methanol), and hamster-induced liver S9, alongside Salmonella typhimurium strains TA100 and TA1535 and Escherichia coli strain WP2uvrA(pKM101) provide the most sensitive combination of assay parameters in terms of NDMA and NDEA mutagenic potency in the Ames test. Using these parameters and further quantitative benchmark dose modelling, we show that N-nitrosomethylethylamine (NMEA) is positive in Ames test and therefore should no longer be considered a historically discordant NA. The results presented herein define a sensitive Ames test design that can be deployed for the assessment of NAs to support robust impurity qualifications.

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 µg/mL) and/or carbendazim (0.8-1.6 µg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.