Reducing Microsleeps: Oveporexton's Impact on Patient Outcomes

CONFERENCE REPORTER

Takeda shared results from 2 pivotal studies at the SLEEP 2026 Annual Meeting which showed that oveporexton (TAK-861) improved daily functioning, cognition, and sleep-related symptoms associated with narcolepsy type 1 (NT1).¹ Psychiatric Times interviewed Elena Koundourakis, PhD, the head of the Orexin Franchise Development & Neuroscience Programs at Takeda, to learn more.

Microsleeps are defined as sleep episodes that last for 15 seconds or less. In the electroencephalogram, microsleeps are mainly characterized by a slowing in frequency. The identification of early signs of sleepiness and sleep is of considerable clinical and practical relevance. Under laboratory conditions, the maintenance of wakefulness test is often used for assessing vigilance.²

Microsleeps have a huge impact on patients. One study found that patients were often deeply embarrassed when microsleeps occurred publicly, and are influenced by factors such as the weather, seasons, or menstruation. While the manifestation of microsleep was typically uncontrollable, some patients reported premonitory sensations, including discomfort in the eyes or incoherent speech.³

Oveporexton is an oral orexin receptor 2-selective agonist that is designed to address the underlying orexin deficiency that causes NT1 by restoring orexin signaling. These data, along with previously shared phase 3 results, demonstrated improvement across the broad disease spectrum, including in microsleeps.

Dr Koundourakis is head of the Orexin Franchise Development and Portfolio Strategy at Takeda.

References

1. New pivotal study data show Takeda’s oveporexton improved daily function, cognition and nighttime sleep for people with narcolepsy type 1. News release. Monday June 15, 2026. Accessed June 17, 2026. https://www.takeda.com/newsroom/newsreleases/2026/oveporexton-phase-3-narcolepsy-type-1/

2. Skorucak J, Hertig-Godeschalk A, Schreier DR, et al. Automatic detection of microsleep episodes with feature-based machine learning. Sleep. 2020;43(1):zsz225.

3. Hlodak J, Geckova AM, Veselska ZD, Feketeova E. The burden of narcolepsy symptoms from patients’ perspective: a narrative qualitative study. Sleep Breath. 2025;29(4):231.