Computational Model for Schizophrenia Treatment Prediction
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Solution Overview
Problem
Current treatments for schizophrenia and other neuropsychiatric disorders are often ineffective and carry significant side effects due to a lack of understanding of underlying causes, with existing animal models being inadequate for accurately predicting human responses to medications.
Innovation Solution
A computational model that simulates schizophrenia by incorporating NMDA deficit, decreased connectivity, and hyperdopaminergia, allowing for the evaluation of therapeutic options and predicting their effectiveness by applying virtual medications to a biophysically realistic hippocampal model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If animal models are used to test medications, then the testing process is more manageable and query-able, but the results cannot accurately predict human responses due to brain dissimilarities
Solution Approach 1:
The patent creates a computational copy of the human brain's neural networks and biomarkers, rather than using animal models. This virtual human brain model replicates human neural architecture and physiology, allowing medication testing to be performed in silico with high predictive accuracy for human responses while avoiding the limitations of animal-to-human translation.
2Measurement precision
If computational models are used to screen candidate medications, then the ability to evaluate efficacy and side effects is enhanced, but the ability to develop truly novel agents is limited
Solution Approach 1:
The computational model dynamically simulates the interaction between candidate medications and human neural networks, allowing evaluation of both known and novel mechanisms of action. The model can adapt to test various drug classes and mechanisms by adjusting parameters representing different biological pathways, enabling both precise evaluation and development of truly novel therapeutic agents.
3Adaptability or versatility
If current pharmacologic treatments are used, then treatment options are available, but efficacy is incomplete and side effect burdens are significant
Solution Approach 1:
The computational model performs preliminary screening and evaluation of medication candidates before human testing. By simulating drug effects on virtual human neural networks, the model identifies promising candidates with optimized efficacy and minimized side effects in advance, allowing only the most promising treatments to proceed to clinical trials.
Data Source
AI summary
A system and method for evaluating an effectiveness of a therapy for a psychological condition includes selecting a therapy to be analyzed relative to psychological pathology. The selected therapy is applied to a model of the psychological condition that includes hyperdopaminergia as a function. A response is determined using an output of the model of the psychological condition. The response is compared to a control to determine a wellness metric and a report is generated indicating an effectiveness of the therapy based on the wellness metric.