Abstract
Several anticancer drugs used in cancer therapy induce chemotherapy-induced peripheral neuropathy (CIPN), leading to dose reduction or therapy cessation. Consequently, there is a demand for an in vitro assessment method to predict CIPN and mechanisms of action (MoA) in drug candidate compounds. In this study, a method assessing the toxic effects of anticancer drugs on soma and axons using deep learning image analysis is developed, culturing primary rat dorsal root ganglion neurons with a microphysiological system (MPS) that separates soma from neural processes and training two artificial intelligence (AI) models on soma and axonal area images. Exposing the control compound DMSO, negative compound sucrose, and known CIPN-causing drugs (paclitaxel, vincristine, oxaliplatin, suramin, bortezomib) for 24 h, results show the somatic area-learning AI detected significant cytotoxicity for paclitaxel (* p < 0.05) and oxaliplatin (* p < 0.05). In addition, axonal area-learning AI detected significant axonopathy with paclitaxel (* p < 0.05) and vincristine (* p < 0.05). Combining these models, we detected significant toxicity in all CIPN-causing drugs (** p < 0.01) and could classify anticancer drugs based on their different MoA on neurons, suggesting that the combination of MPS-based culture segregating soma and axonal areas and AI image analysis of each area provides an effective evaluation method to predict CIPN from low concentrations and infer the MoA.