The most widely used animal models to develop sleep-disorder drugs are rodents, particularly rats and mice. However, unlike humans, these rodents are nocturnal. Thus, diurnal non-human primates represent a valuable and more translational animal model to study sleep. Although sleep-disorder drugs have been screened in non-human primates, the use of a telemetry system is not a desirable method for a rapid drug efficacy assessment system because of the need for expensive equipment, complicated surgery, and the long time before results can be obtained from analysis by inspection. Locomotor activity has traditionally been used as an indicator of the effects of drugs, genes, and disease models. The Nano-Tag, a new device for analyzing activity after an easy implantation surgery, measures locomotor activity without expensive equipment and the need for inspection for data processing, and the overall cost is much lower than that of a telemetry system. In this study, we compared the data obtained from polysomnography and on locomotor activity in telemetry transmitter-embedded cynomolgus monkeys by implanting the Nano-Tag subcutaneously in the forehead and administering sleep-disorder drugs to confirm if sleep–wake states could be measured using the Nano-Tag. When we compared the changes in awake time per unit time measured using polysomnography and locomotor activity counts per unit time measured using the Nano-Tag, cynomolgus monkeys exhibited a diurnal preference, and the correlation coefficients were positive during the 24-h period. Additionally, the correlation coefficients during the 12-h dark period were positive when the hypersomnia treatment drug modafinil was administered. The correlation coefficients during the 12-h light period were also positive when the insomnia treatment drug triazolam was administered. These results suggest that measuring locomotor activity is an effective tool for identifying sleep–wake states and screening sleep-disorder drugs at low cost and with less burden to animal subjects.
All Science Journal Classification (ASJC) codes