This technology is a set of metabolite profiles for identifying disruptions in the brain glutamate metabolic cycle that can enable more targeted treatment of schizophrenia and related psychotic disorders.
In early-stage schizophrenia, excess glutamate levels in the hippocampus drive hyperactivity and atrophy. The enzyme responsible for the glutamate elevation can be identified through changes in the metabolites of the glutamate metabolic cycle: glutamine, glutamate, and GABA. Identifying which enzyme has altered activity in early-stage schizophrenia will allow for more targeted treatments of schizophrenia and improve the effectiveness of clinical trials. However, current methods for non-invasive measurements of metabolite concentrations in the brain rely on 1H-magnetic resonance spectroscopy (1H-MRS), which struggles to distinguish glutamine from glutamate at low magnetic field (3 tesla) and faces a lack of magnetic field uniformity at higher magnetic field (7 tesla) due to hippocampus’s proximity to tissue, air, and bone.
This technology utilizes metabolite profiles consisting of glutamate, glutamine, and GABA to pinpoint glutamate metabolic cycle disruptions in the brain. These metabolite profiles are obtained from deep learning deconvolution of 3 tesla 1H-MRS measurements. One profile that consists of increases in glutamate and GABA but not glutamine concentration is observed in the hippocampus of patients with early-stage schizophrenia or related psychotic disorders. This indicates hyperactivity of glutaminase (GLS1), the enzyme that converts glutamine to glutamate, as the driver of glutamate increase. As glutamate accounts for most of the convoluted glutamate-glutamine 1H-MRS measurement, the combination of this peak and GABA elevation is sufficient to act as a biomarker for GLS1 hyperactivity and to distinguish between patients and control.
This technology has been validated with 3 tesla 1H-MRS measurements of the hippocampus of patients with early-stage schizophrenia or related psychotic disorders and controls.
IR CU21216
Licensing Contact: Jerry Kokoshka