ABSTRACT
Disruption of microtubule stability in mammalian cells may lead to genotoxicity and carcinogenesis. The ability to screen for microtubule destabilization or stabilization is therefore a useful and efficient approach to aid in the design of molecules that are safe for human health. In this study, we developed a high-throughput 384-well assay combining immunocytochemistry with high-content imaging to assess microtubule disruption in the metabolically competent human liver cell line: HepaRG. To enhance analysis throughput, we implemented a supervised machine learning approach using a curated training library of 180 compounds. A majority voting ensemble of eight machine learning classifiers was employed for predicting microtubule disruptions. Our prediction model achieved over 99.0% accuracy and a 98.4% F1 score, which reflects the balance between precision and recall for in-sample validation and 93.5% accuracy and a 94.3% F1 score for out-of-sample validation. This automated image-based testing can provide a simple, high-throughput screening method for early stage discovery compounds to reduce the potential risk of genotoxicity for crop protection product development.