AI Bioinformatics Database for AMD Therapeutic Target Identification
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Solution Overview
Problem
There is a lack of effective animal models for studying age-related macular degeneration (AMD), which complicates research due to the complex nature of the disease and its multifactorial causes.
Innovation Solution
The development of a data processing system that utilizes artificial intelligence (AI) and bioinformatics to analyze and correlate live patient data with donor eye data, creating a bioinformatics database that links imaging, clinical, and tissue data to identify therapeutic targets for AMD.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional animal models are used to study AMD, then research can be conducted with living subjects, but the models fail to accurately represent human AMD due to anatomical differences (rodents lack macula, primates don't live long enough)
Solution Approach 1:
The patent creates a digital copy of human AMD pathology through computational modeling. Instead of using physical animal models, the invention generates a virtual human retina model that replicates human AMD characteristics, including drusen formation and RPE cell dysfunction, by integrating human-specific genomic, proteomic, and imaging data into a computable framework.
Solution Approach 2:
The patent introduces an intermediary computational layer between animal models and human AMD study. The in silico model acts as a mediator that translates human clinical data into a virtual testing environment, allowing researchers to study human-specific AMD mechanisms without relying on imperfect animal proxies.
2Device complexity
If AMD research focuses on single gene, protein, or pathway analysis, then the research scope is manageable, but the complex multifactorial nature of AMD cannot be adequately captured
Solution Approach 1:
The patent merges multiple data types (genomic, proteomic, metabolomic, imaging, and clinical data) into a unified computational model. This integration allows the system to capture the multifactorial nature of AMD by combining information from different biological layers and sources, providing a holistic view of disease mechanisms that single-factor studies cannot achieve.
Solution Approach 2:
The patent creates a composite computational model that integrates diverse biological data types. The in silico AMD model combines genomic variations, protein expressions, metabolic profiles, and imaging characteristics into a unified virtual representation of AMD pathology, enabling comprehensive disease mechanism analysis.
3Loss of information
If extensive human tissue sampling is performed to study AMD, then comprehensive disease data can be obtained, but ethical concerns and patient safety issues arise
Solution Approach 1:
The patent creates virtual replicas of human retinal tissue through computational modeling. The in silico model replicates human RPE cell behavior, drusen formation, and AMD progression without requiring physical tissue sampling from living patients, thereby eliminating the harmful effects of invasive procedures while preserving data completeness.
Solution Approach 2:
The computational model allows human tissue data to be used post-mortem from donated eyes, allowing the tissue to 'serve itself' for research purposes without requiring additional sampling from living patients. This approach maximizes data utility from available donor tissues while avoiding harm to living subjects.
Data Source
AI summary
Methods and systems of data analysis in connection with age related macular degeneration (AMD) are described. Among other examples, techniques are described for generating a bioinformatics database of AMD data, based on correlation of live patient data with eye bank (donor) patient data. Such techniques may include performing data analysis to correlate characteristics of live patient image data with characteristics of eye bank image data, with the correlated characteristics including common image data characteristics of each set of image data based on a disease progression of AMD. Further data analysis may correlate tissue data characteristics from eye bank donor eyes with the common image data characteristics based on the disease progression of AMD. Still further data analysis may correlate clinical data characteristics from live patient observation data with eye bank observation data and with the common image data characteristics based on the disease progression of AMD.