Although many neurotoxicity prediction studies of food additives have been developed, they are applicable in a qualitative way. We aimed to develop a novel prediction score that is described quantitatively and precisely. We examined cell viability, reactive oxygen species activity, intracellular calcium and RNA transcription level of potential prediction related genes to develop a high-throughput neurotoxicity test method in vitro to screen the neurotoxicity of hazardous factors in food using AI-based machine learning. We trained artificial intelligence models (random forest and neural network) to predict neurotoxicity precisely, establishing a universal classification assessment score (CA-Score) that relies on the expression status of only 13 of prediction related genes. The CA-Score system is almost universally applicable to food risk factors (p<0.05) in a manner independent of platform (microarray or RNA sequencing) by being compared with cut-off value 23.487 to judge whether it's neurotoxic or not. We finally validated our prediction with the external validation of CA-Score on neural precursor cells derived from embryonic stem cells. Therefore, we draw a conclusion that the AI-based machine learning including neural network and random forest is likely to provide a useful tool for large-scale screening of neurotoxicity in food risk factors.
Keywords: Artificial neural network; High-throughput screening; Neurotoxicity testing; Random forest.
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