Incorporation of metabolic activation potentiates cyclophosphamide-induced DNA damage response in isogenic DT40 mutant cells

Elucidating the DNA repair pathways that are activated in the presence of genotoxic agents is critical to understand their modes of action. Although the DT40 cell-based DNA damage response (DDR) assay provides rapid and sensitive results, the assay cannot be used on genotoxic compounds that require metabolic activation to be reactive. Here, we applied the metabolic activation system to a DDR and micronucleus (MN) assays in DT40 cells. Cyclophosphamide (CP), a well-known cross-linking agent requiring metabolic activation, was preincubated with liver S9 fractions. When DT40 cells and mutant cells were exposed to the preactivated CP, CP caused increased cytotoxicity in FANC-, RAD9-, REV3- and RAD18-mutant cells compared to isogenic wild-type cells. We then performed a MN assay on DT40 cells treated with preactivated CP. An increase in the MN was observed in REV3- and FANC-mutant cells at lower concentrations of activated CP than in the parental DT40 cells. These results demonstrated that the incorporation of metabolic preactivation system using S9 fractions significantly potentiates DDR caused by CP in DT40 cells and their mutants. In addition, our data suggest that the metabolic preactivation system for DDR and MN assays has a potential to increase the relevance of this assay to screening various compounds for potential genotoxicity

The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability

© 2019 Royal Pharmaceutical Society. This is the peer reviewed version of the following article: The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability, which has been published in final form at https://doi.org/10.1111/jphp.13203 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Objectives The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp) and choice of diffusion cell on model quality and performance. Methods Data were collated from the literature. Static and flow‐through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C. Key findings Using data from flow‐through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow‐through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures. Conclusions This study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz‐type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.Peer reviewe