This technology is a screening tool for identifying individuals with increased risk of developing major depressive disorder (MDD).
Unmet Need: Early detection of highest risk population for MDD
Major depressive disorder (MDD) is the leading cause of disability worldwide, affecting over 300 million people. Development of an early detection test, and subsequent implementation of preventive interventions could help reduce the worldwide prevalence of MDD and associated economic burden.
The Technology: Method for identifying individuals with high-risk for MDD using PET imaging
This technology is a screening tool for detecting individuals who are most susceptible to developing MDD, and allows for targeted preventive interventions by identifying the children of parents who carry the endophenotype for depression. Using positron emission tomography (PET), this technology quantifies serotonin 1A receptor binding potential and compares results to a predetermined threshold to distinguish between individuals with and without MDD. Machine learning-based, multivoxel pattern analyses are then applied to accurately classify high- and low-risk individuals. This technology may reduce the prevalence of MDD by enabling early preventive intervention and can also be used to track the efficacy of treatment.
This technology has been validated in a pilot study of human subjects with MDD, high-risk of MDD, and healthy volunteers.
Applications:
- Identifying individuals at high-risk for MDD
- Method for diagnosing depressive disorder
- Identification of patients in need of preventative intervention for MDD
- Monitoring treatment efficacy
Advantages:
- Early-detection
- Screening and stratification of individuals based on risk of developing MDD
- Minimally invasive
- Can facilitate early preventative intervention for MDD
- May potentially reduce economic burden of MDD
- Minimizes burden on healthcare professionals by emphasizing patients that need immediate intervention
- More individualized approach to treatment
- Can detect patients with MDD who discontinued treatment
Lead Inventor:
J. John Mann, M.D.
Patent Information:
Patent Status
Related Publications:
Metts AV, Rubin‐Falcone H, Ogden RT, Lin X, Wilner DE, Burke AK, Sublette ME, Oquendo MA, Miller JM, Mann JJ. “Antidepressant medication exposure and 5‐HT1A autoreceptor binding in major depressive disorder” Synapse. 2019 Jun; 73(6): e22089.
Zanderigo F, Pantazatos S, Rubin-Falcone H, Ogden RT, Chhetry BT, Sullivan G, Oquendo M, Miller JM, Mann JJ. “In vivo relationship between serotonin 1A receptor binding and gray matter volume in the healthy brain and in major depressive disorder” Brain Struct and Funct. 2018 Jul 1; 223(6): 2609-25.
Milak MS, Pantazatos S, Rashid R, Zanderigo F, DeLorenzo C, Hesselgrave N, Ogden RT, Oquendo MA, Mulhern ST, Miller JM, Burke AK. ”Higher 5-HT1A autoreceptor binding as an endophenotype for major depressive disorder identified in high risk offspring–A pilot study” Psychiatry Res Neuroimaging. 2018 Jun 30; 276: 15-23.
Kaufman J, Sullivan GM, Yang J, Ogden RT, Miller JM, Oquendo MA, Mann JJ, Parsey RV, DeLorenzo C. “Quantification of the serotonin 1A receptor using PET: identification of a potential biomarker of major depression in males” Neuropsychopharmacology. 2015 Jun; 40(7): 1692-9.
Miller JM, Hesselgrave N, Ogden RT, Zanderigo F, Oquendo MA, Mann JJ, Parsey RV. “Brain serotonin 1A receptor binding as a predictor of treatment outcome in major depressive disorder” Biol Psychiatry. 2013 Nov 15; 74(10): 760-7.
Gray NA, Milak MS, DeLorenzo C, Ogden RT, Huang YY, Mann JJ, Parsey RV. “Antidepressant treatment reduces serotonin-1A autoreceptor binding in major depressive disorder” Biol Psychiatry. 2013 Jul 1; 74(1): 26-31.
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