NeuroPace said today that its epilepsy neuromodulation technology will be studied for its efficacy in treating binge-eating disorder in certain patients.
The Mountain View, Calif.-based company’s RNS system is a closed-loop technology approved to treat adults with medically refractory focal onset epilepsy. The clinical trial will examine whether neuromodulation could help patients with a body mass index of 45-60 kg/m for whom medication and gastric bypass surgery have been ineffective, and for whom loss-of-control eating is a contributing factor.
“Loss-of-control eating” refers to a feeling that one cannot control what or how much one is eating. This typically takes one of two forms, either eating objectively large amounts of food or eating continuously, and can compromise even the most aggressive of obesity treatments, such as bariatric surgery. There is evidence to suggest that long-term alterations in brain function, particularly in the reward and impulse control circuitry, occur with eating disorders, including loss-of-control eating, according to the company. In addition, there is growing evidence to suggest that there are discrete changes in brain activity, or biomarkers, which immediately precede loss-of-control eating events, NeuroPace said.
A recent study published in the Proceedings of the National Academy of Sciences demonstrated that applying stimulation to the nucleus accumbens when a specific pattern of brain activity was detected could suppress binge-eating behavior in mice. The study observed similar changes in activity in the homologous brain region in people when they were anticipating a reward, opening up the possibility of using a closed-loop therapeutic approach in humans.
“We have long known that the neural recording and personalized interventional capability of the RNS system provides an unprecedented window to the brain, resulting in an effective treatment that works by identifying the precursors of seizures and treating them immediately and automatically,” said Dr. Martha Morrell, NeuroPace’s chief medical officer, in a news release. “We’re pleased to support this new research, which will be among the first to explore additional applications of the RNS system beyond epilepsy. This may help lead to the treatment of other conditions where there may be signals or biomarkers that can be identified and used to time therapy delivery, and where there is also a real clinical need.”
The five-year study will be conducted at Stanford University School of Medicine and funded by a grant from the National Institutes of Health’s (NIH) Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, a public-private effort supporting research to revolutionize the understanding of the human brain.
“Obesity is a disease with many contributing factors. Unfortunately, obesity is often associated with multiple comorbidities that can lead to reduced quality of life and even premature death,” said study researcher and NeuroPace principal scientist Tara Skarpaas. “This study will evaluate whether neurostimulation can automatically respond to the signals that occur immediately before a loss-of-control eating episode to help patients regain control, a potentially groundbreaking intervention for the most severely affected patients. The RNS system’s ability to continuously monitor brain activity and respond with therapy when these biomarkers are detected makes the system an ideal fit for this research.”
Most commercially available neuromodulation devices deliver stimulation continuously or on a fixed schedule, independent of the underlying brain activity. NeuroPace’s RNS system treats focal onset seizures by continuously monitoring brainwaves, recognizing each patient’s unique “seizure fingerprint,” and automatically responding with imperceptible electrical pulses to stop seizures before they occur.
As part of the study, the RNS system will provide the first long-term ambulatory recordings of human brain activity from this key area of the reward circuit, the nucleus accumbens. Data will be analyzed using artificial intelligence and machine learning to provide initial insights into the function of the human reward circuit as well as to initiate the development of algorithms that can individualize treatment in the future.