Positron emission tomography (PET) is an effective tool for noninvasive examination of the body and provides a range of functional information. PET imaging with [(18)F]fluoro-2-deoxy-d-glucose ([(18)F]FDG) has been used to image alterations in glucose metabolism in brain or cancer tissue in the field of clinical diagnosis but not in the field of toxicology. A single dose of N-methyl-d-aspartate (NMDA) receptor antagonist induces neuronal cell degeneration/death in the rat retrosplenial/posterior cingulate (RS/PC) cortex region. These antagonists also increase local cerebral glucose utilization. Here, we examined the potential of [(18)F]FDG-PET as an imaging biomarker of neurotoxicity induced by an NMDA receptor antagonist, MK-801. Using [(18)F]FDG-PET, we determined that increased glucose utilization involved the neurotoxicity induced by MK-801. The accumulation of [(18)F]FDG was increased in the rat RS/PC cortex region showing neuronal cell degeneration/death and detected before the onset of neuronal cell death. This effect increased at a dose level at which neuronal cell degeneration recovered 24h after MK-801 administration. Scopolamine prevented the neurotoxicity and [(18)F]FDG accumulation induced by MK-801. Furthermore, in cynomolgus monkeys that showed no neuronal cell degeneration/death when treated with MK-801, we noted no differences in [(18)F]FDG accumulation between test and control subjects in any region of the brain. These findings suggest that [(18)F]FDG-PET, which is available for clinical trials, may be useful in generating a predictive imaging biomarker for detecting neurotoxicity against NMDA receptor antagonists with the same pharmacological activity as MK-801.
In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.