Harte, D. S. G. | Search bioRxiv

bioRxiv2026

Mutagenic and carcinogenic potency determinations for NDMA support the cumulative dose assumption underpinning the less-than-lifetime Threshold of Toxicological Concern

The management of N-nitrosamine impurities challenges pharmaceutical development and regulation worldwide. Because most medicinal exposures are shorter than lifetime and absolute impurity exclusion is impossible, reliable approaches to define duration-specific intake limits are essential. On the premise that carcinogenic risk is proportional to cumulative dose, the Less-Than-Lifetime (LTL) Threshold of Toxicological Concern (TTC) framework defines progressively lower intake limits for mutagenic impurities over longer exposures. However, N-nitrosamines are currently treated as a cohort of concern, necessitating compound-specific evaluation placing reliance on in vivo mutagenicity assays for impurity qualifications. To better understand durational potency relationships and the application domain of the LTL-TTC, we apply benchmark dose (BMD) modelling to cumulative-dose-scaled transgenic rodent (TGR), error-corrected sequencing and rodent carcinogenicity datasets for N-nitrosodimethylamine (NDMA) obtained from the published literature. For TGR, cumulative-dose scaling better resolved liver as the most sensitive organ and reduced interstudy variability: liver BMDs spanned [~]80-fold in daily-dose units but only [~]20-fold when scaled to cumulative dose. Among closely-matched mouse liver gavage studies, cumulative-dose BMDs only varied by [~]2.5-fold across 1 to 28-day treatment regimens. Error-corrected sequencing also demonstrated parity, with acute-dose regimens producing mutation burdens near-identical (< 1.2-fold) to those cumulated from 28-day repeat-dose regimens. Comparable results were obtained from carcinogenicity datasets confirming proportionality-of-effect to cumulative dose. These findings empirically support the validity of the LTL-TTC concept. More broadly, they demonstrate that short-term in vivo mutagenicity assays can serve as reliable surrogates for lifetime carcinogenicity studies, strengthening the scientific and regulatory basis for duration-adjusted acceptable intakes for N-nitrosamine impurities.

pharmacology and toxicology↗

bioRxiv2021

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 {micro}g/mL) and/or carbendazim (0.8 - 1.6 {micro}g/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 dose 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.

pharmacology and toxicology↗