Neuroplasticity Treatment Selection Using EEG and Receptor Modulators
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Solution Overview
Problem
Current antidepressant treatments for depression, including major depressive disorder, bipolar disorder, post-traumatic stress disorder, and schizophrenia, are often selected through trial-and-error and lack personalized effectiveness, particularly for patients with cognitive impairments or specific EEG patterns, leading to high rates of treatment resistance and disability.
Innovation Solution
Administering therapeutic agents such as 5-HT2A agonists, AMPA positive allosteric modulators, and NMDA receptor positive allosteric modulators, such as stinel compounds, to patients with objectively determined cognitive impairments and specific EEG patterns to promote neuroplasticity and release BDNF, thereby treating depressive symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional antidepressants (SSRIs, SNRIs, NDRIs) are selected through trial-and-error, then treatment coverage is broad, but treatment effectiveness is low and time-consuming
Solution Approach 1:
The patent applies preliminary action by using EEG biomarkers to pre-identify patient subgroups before treatment initiation. Specifically, the patent identifies patients with depressive episodes who exhibit specific EEG patterns (such as increased theta power, decreased alpha power, or specific EEG spectral ratios) and pre-assigns them to particular antidepressant treatments based on their EEG profile, eliminating the need for trial-and-error and reducing time to effective treatment while maintaining broad treatment coverage across different patient types
Solution Approach 2:
The patent applies parameter changes by using EEG spectral parameters (theta power, alpha power, theta/alpha ratio, other EEG frequency ratios) as objective criteria to select treatments. Instead of using subjective symptom profiles, the patent changes the selection parameter from clinical observation to quantifiable EEG measurements, enabling more precise and faster treatment matching while maintaining versatility across different depression types
2Ease of operation
If antidepressant treatment is initiated without objective biomarkers, then treatment initiation is simple, but treatment precision is low
Solution Approach 1:
The patent applies mechanics substitution by replacing the mechanical/subjective system of clinical symptom assessment with an objective electrophysiological measurement system (EEG). The patent uses automated EEG spectral analysis to objectively identify patient subgroups based on brain wave patterns, substituting clinician judgment and patient-reported symptoms with quantifiable electrical brain activity measurements, thereby improving treatment selection accuracy while maintaining ease of operation through automated analysis
Solution Approach 2:
The patent introduces EEG biomarkers as an intermediary between patient presentation and treatment selection. Instead of directly matching symptoms to treatments, the patent uses EEG spectral patterns as an intermediate objective measure that bridges clinical observation and treatment decision-making, enabling more precise treatment matching while keeping the overall process relatively simple through automated EEG interpretation
3Adaptability or versatility
If trial-and-error medication selection is used, then all patient types can be covered, but treatment resistance develops
Solution Approach 1:
The patent applies segmentation by dividing the homogeneous group of 'depressed patients' into distinct EEG-based subgroups (such as patients with increased theta power, patients with decreased alpha power, patients with specific theta/alpha ratios). Each subgroup is then matched to specific antidepressant treatments based on their EEG profile, maintaining broad patient type coverage through systematic categorization while improving treatment response consistency by assigning treatments tailored to each subgroup's neurophysiological characteristics rather than using trial-and-error
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
These agents effectively treat depressive symptoms and cognitive impairments by inducing neuroplasticity and BDNF release, offering targeted treatment options beyond traditional antidepressants.
Implementation Method 1
a 5-HT2A agonist (including 5-HT2A agonists that bind to a BDNF receptor, e.g., a 5-HT2A agonist that binds to a TrkB receptor or p75NTR)
Implementation Method 2
an alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) positive allosteric modulator
Implementation Method 3
an NMDA receptor positive allosteric modulator (such as a stinel compound)
Data Source
AI summary
This invention relates to the use of (i) a 5-HT2A agonist (including 5-HT2A agonists that bind to a BDNF receptor, e.g., a 5-HT2A agonist that binds to the TrkB receptor or p75NTR), (ii) a therapeutic agent that promotes neuroplasticity by stimulation of the 5-HT2A receptor, (iii) a therapeutic agent that releases BDNF by stimulation of the 5-HT2A receptor (including those therapeutic agents that stimulate the 5-HT2A receptor and bind to a TrkB receptor or p75NTR), (iv) an AMPA positive allosteric modulator, (v) a therapeutic agent that promotes neuroplasticity by stimulation of the AMPA receptor, (vi) a therapeutic agent that releases BDNF by stimulation of the AMPA receptor, or (vii) an NMDA receptor positive allosteric modulator (such as a stinel compound) in the treatment of a psychiatric condition in which depressive symptoms are prominent, including major depressive disorder (MDD), bipolar disorder, post-traumatic stress disorder, substance use disorder, and depression-related aspects of schizophrenia (e.g. negative symptoms) in select patients who, for instance, have objectively determined cognitive impairment or poor cognition (such as objectively determined impaired learning and/or memory) and/or certain EEG characteristics.