In Silico Systems Biology Models for Cardiovascular Therapy Prediction
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Solution Overview
Problem
Current methods for treating cardiovascular disease, such as atherosclerosis, rely on group-level treatment efficacy, failing to provide personalized therapeutic recommendations, leading to inadequate patient-specific care and inefficient use of costly advanced therapies.
Innovation Solution
Development of in silico systems biology models that analyze non-invasive imaging data to simulate the effects of various therapies, allowing for personalized pharmacotherapies and procedural interventions tailored to individual patients based on their specific plaque characteristics and molecular profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If group-level treatment efficacy is used to guide therapy, then treatment coverage is broad and simple to implement, but personalized therapeutic recommendations are lacking and treatment outcomes are suboptimal
Solution Approach 1:
The patent applies preliminary action by pre-building comprehensive systems biology models that integrate multiple data types (imaging, genomics, proteomics, metabolomics) before patient treatment decisions are made. These models are prepared in advance to enable rapid personalized therapy simulation and prediction, eliminating the need for complex real-time analysis during clinical decision-making.
Solution Approach 2:
The patent uses copying by creating virtual in silico replicas of patient-specific biological systems based on their molecular profiles and imaging data. These digital twins allow therapists to simulate and evaluate multiple treatment scenarios without exposing the actual patient to risks, enabling precise personalized recommendations through virtual experimentation.
2Reliability
If advanced personalized therapies are implemented, then treatment effectiveness is improved, but treatment costs increase significantly
Solution Approach 1:
The patent applies partial action by selectively applying personalized therapy only to patients who demonstrate specific molecular profiles and imaging characteristics indicating they will benefit from advanced treatments. The systems biology models identify subsets of patients for whom personalized therapy provides marginal benefit over standard care, avoiding unnecessary expenditure on expensive therapies for patients who would not respond.
Solution Approach 2:
The patent implements feedback by using systems biology models to continuously evaluate patient responses to therapy and adjust treatment recommendations accordingly. The models integrate real-time patient data with pre-established biological pathways to provide dynamic feedback on treatment effectiveness, enabling optimization of therapy selection and dosage to maximize outcomes while minimizing resource consumption.
3Measurement precision
If non-invasive imaging data is analyzed alone, then patient comfort is maintained and procedural risk is reduced, but diagnostic accuracy and molecular profile characterization are insufficient
Solution Approach 1:
The patent applies merging by integrating multiple data types including non-invasive imaging data, genomic information, proteomic data, and metabolomic profiles into unified systems biology models. These diverse data sources are combined and analyzed together to create a comprehensive view of patient-specific biological pathways, enabling accurate molecular profile characterization that leverages the complementary strengths of each data type.
Solution Approach 2:
The patent uses intermediary by employing systems biology models as mediators that translate and integrate data from different measurement modalities. The models serve as intermediaries that process imaging data, omics data, and clinical information through established biological pathway knowledge, converting heterogeneous data into coherent molecular profile characterizations and therapeutic predictions.
Data Source
AI summary
Provided herein are methods and systems for making patient-specific therapy recommendations of a combination of any two or more therapies selected from a lipid-lowering therapy, an anti-inflammatory therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.