Biological Modeling With AI for Personalized Cancer Care Pathways
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Solution Overview
Problem
Existing methods for personalized medicine, particularly in oncology, fail to account for the diverse and personalized biology of cancer patients, leading to variable treatment responses despite the use of genomic and transcriptomic markers.
Innovation Solution
A system that combines electronic health records with tissue modeling data using artificial intelligence to recreate aspects of human biology, such as cell lines and organoids, to predict personalized care pathways and test treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genomic and transcriptomic markers are used for personalized therapy, then treatment predictions are improved, but variability in treatment responses persists due to unaccounted biological differences
Solution Approach 1:
The patent creates virtual copies of patient-specific tissues through in silico models that replicate the unique biological characteristics of each patient's tissue architecture, cellular composition, and molecular pathways. These digital twins allow researchers to simulate treatment responses in a virtual environment before clinical application, capturing individual biological variability that genomic markers alone cannot predict.
Solution Approach 2:
The system dynamically adjusts multiple biological parameters including tissue architecture, cellular density, molecular expression profiles, and physiological conditions to accurately represent each patient's unique biology. By varying these parameters across different virtual tissue models, the system can predict how specific biological differences influence treatment responses and identify optimal therapies for each individual.
2Measurement precision
If patient-derived cell cultures such as tumor organoids are used to model cancer growth, then personalized treatment estimation is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent replaces complex physical tissue culture systems with computational in silico models that simulate tissue behavior through mathematical algorithms. Instead of maintaining actual patient-derived organoids in laboratory conditions, the system uses virtual representations that capture essential biological dynamics, eliminating the need for complex bioreactors, media formulations, and contamination control infrastructure while preserving predictive accuracy.
Solution Approach 2:
The system extracts only the essential biological characteristics needed for treatment prediction from complex patient tissues, creating simplified virtual models that retain predictive power without requiring the full complexity of original tissue structures. This extraction process identifies and models key parameters such as cellular composition, metabolic pathways, and drug response mechanisms while omitting non-essential biological details.
3Measurement precision
If multiple data sources including EHR and tissue modeling data are combined, then care pathway prediction accuracy is improved, but data integration complexity increases
Solution Approach 1:
The patent integrates multiple data sources including electronic health records, genomic data, imaging data, and in silico tissue modeling results into a unified predictive framework. This merging process combines structured clinical data with unstructured medical notes, molecular profiles, and virtual tissue simulations to create a comprehensive patient model that captures the full spectrum of biological and clinical information for accurate care pathway prediction.
Solution Approach 2:
The system employs an intermediary layer of in silico tissue models that translate and harmonize data from different sources into a common biological framework. These virtual models serve as mediators that convert diverse data formats and measurement scales into unified biological parameters, enabling seamless integration of EHR data, genomic information, and treatment response predictions without requiring complex direct data mapping between heterogeneous systems.
Data Source
AI summary
Methods, systems, and software are provided for predicting care pathway options for a medical condition in a test subject. In one implementation, a method includes retrieving a set of characteristics of the test subject from an electronic medical record for the test subject, retrieving data from a system modeling human tissue, and providing information comprising the set of characteristics from the electronic medical record and the data from the system modeling human tissue to an artificial intelligence (AI) component to receive as output from the AI component one or more care pathways for the medical condition.