High-Throughput Image-Based Assay for Identifying In Vitro Hepatocyte Microtubule Disruption

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.