Stroke Biomarker Modeling for Infarct and Edema Prediction
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Solution Overview
Problem
Current treatments for ischemic stroke, such as tPA and mechanical thrombectomy, have limited effectiveness due to the heterogeneity of human patients and the lack of understanding of inflammatory pathways, leading to many patients missing the therapeutic window and receiving only supportive care, while existing animal models fail to accurately predict human responses.
Innovation Solution
A novel preclinical assay using aged rats and mechanical thrombectomy models to mimic human conditions, analyzing gene and protein expression in blood samples proximal and distal to the clot, combined with machine learning to identify predictive genes and proteins for infarct and edema volume, and determining intervention needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current treatments (tPA and mechanical thrombectomy) are used for ischemic stroke, then recanalization of occluded cerebral artery is achieved, but many patients miss the therapeutic window and receive only supportive care
Solution Approach 1:
The patent applies preliminary action by identifying inflammatory biomarkers before the therapeutic window closes. The machine learning model predicts stroke outcomes and identifies inflammatory pathways early, enabling proactive intervention strategies that extend the effective treatment window beyond the traditional 4.5-hour tPA limit to 24 hours for mechanical thrombectomy, and potentially longer for anti-inflammatory therapies.
Solution Approach 2:
The patent implements feedback through continuous monitoring of inflammatory biomarkers in blood samples. The machine learning algorithm processes real-time biomarker data to predict treatment response and adjust therapy timing dynamically, creating a closed-loop system that optimizes intervention timing based on individual patient inflammatory profiles rather than fixed time windows.
2Productivity
If mechanical thrombectomy is used to restore blood flow, then recanalization is achieved, but patient outcomes remain heterogeneous due to lack of understanding of inflammatory pathways
Solution Approach 1:
The patent uses inflammatory biomarkers as intermediaries to bridge the gap between stroke treatment and outcome prediction. These biomarkers serve as measurable proxies for underlying inflammatory pathways, enabling the machine learning model to translate complex biological processes into predictable clinical outcomes and personalize treatment strategies.
Solution Approach 2:
The patent replaces mechanical recanalization focus with a biological/molecular approach. Instead of solely relying on physical thrombus removal, the system substitutes this with targeted anti-inflammatory therapy based on molecular biomarker profiles, enabling precision medicine that addresses the root inflammatory causes of stroke damage.
3Ease of manufacture
If animal models are used to study ischemic stroke, then preclinical testing is enabled, but they fail to accurately predict human responses
Solution Approach 1:
The patent applies parameter changes by using aged rats (18 months old) instead of young animals, fundamentally changing the biological parameter of the model subjects to match human stroke pathology. This age-related parameter change enables the animal model to replicate human inflammatory responses and stroke outcomes, improving predictive accuracy for human treatment responses.
Solution Approach 2:
The patent creates a composite approach combining aged rat models with human blood sample analysis. The system integrates data from both animal models and human biomarkers into a unified predictive framework, leveraging the ease of animal experimentation while compensating for species differences through human-specific biomarker validation and machine learning algorithms.
Data Source
AI summary
The presently-disclosed subject matter generally to methods for identifying and analyzing biomarkers to determine group effects. The presently-disclosed subject matter also relates to methods for identifying genes and proteins that increase or decrease in response to ischemic stroke damage. The disclosed subject matter further describes methods of predicting edema and infarct volume in a patient. Also described herein are methods of determining the necessity of intervention for smokers. Further disclosed herein, are methods for testing therapies for ischemic stroke.