Application of a Multiplexed Flow Cytometric Assay and Machine Learning to Provide Genotoxic Mode of Action Information

Abstract

In an effort to more easily and efficiently generate genotoxic mode of action data, several biomarkers associated with cellular responses to DNA damage or overt cytotoxicity were multiplexed into a homogenous flow cytometric assay. Reagents included a detergent to liberate nuclei, a nucleic acid dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent particles to serve as counting beads. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 continuous hrs in 96-well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4- and 24-hrs aliquots were removed and added to microtiter plates containing the reagent mix, and robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. A particularly high performing multinomial logistic regression model was comprised of four factors: 4 hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 91% concordance with our a priori classifications. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals’ genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.