ACBP Platform for Genomic Patient Stratification
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Solution Overview
Problem
The existing process for FDA drug approval is complex, and identifying the right patients and biomarkers for clinical trials is challenging, leading to increased costs and reduced likelihood of drug approval due to incorrect patient selection.
Innovation Solution
An accelerated clinical biomarker prediction (ACBP) platform that utilizes genomic profiles and electronic medical record data to identify responsive patient subgroups, applying topological data analysis, non-matrix factorization, and neural network techniques to select patients for clinical trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clinical trial patient selection methods are used, then the drug approval process follows established procedures, but the complexity of the process increases and the likelihood of correct patient selection decreases
Solution Approach 1:
The system performs preliminary patient stratification and biomarker identification using machine learning models before clinical trials begin. By pre-processing and analyzing electronic health records, genomic data, and clinical data to identify responsive patient subgroups in advance, the system reduces the complexity of patient selection during the actual trial process and improves selection accuracy.
Solution Approach 2:
The patent introduces an intermediary AI-based patient selection platform that mediates between raw medical data and clinical trial enrollment decisions. This intermediary system processes and integrates multiple data sources (EHR, genomic, clinical trial data) and provides structured patient recommendations, simplifying the overall clinical trial process while improving patient selection reliability.
2Measurement precision
If comprehensive multi-omics and genomic data analysis is performed to identify responsive patient subgroups, then patient selection accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex data analysis process into distinct analytical components: topological data analysis for patient similarity, non-matrix factorization for data decomposition, and neural network analysis for pattern recognition. Each segment processes specific aspects of the multi-omics and genomic data, making the overall complex analysis manageable and interpretable while maintaining high precision in biomarker identification.
Solution Approach 2:
The system employs a universal machine learning framework that can handle multiple types of data (electronic health records, genomic profiles, multi-omics data, clinical trial data) through a single integrated platform. This multi-functional approach consolidates various analysis methods into one system, reducing overall complexity while maintaining high measurement precision across different data types.
3Productivity
If traditional clinical trial processes are used without accelerated biomarker prediction, then the drug approval process follows standard procedures, but the time required for new drug application submissions increases and resources are consumed inefficiently
Solution Approach 1:
The system performs accelerated biomarker prediction and patient identification before clinical trials commence. By using machine learning models to predict responsive patient subgroups and identify relevant biomarkers in advance, the system reduces the time required for patient recruitment and trial setup, thereby accelerating the overall drug approval process without compromising scientific rigor.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system continuously learns from clinical trial outcomes and refines its patient selection predictions. This feedback loop allows the system to improve its accuracy over time and accelerate future drug approval processes by making more precise patient recommendations, reducing wasted time on non-responsive patients.
Data Source
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AI summary
A device may receive medical data associated with potential patients for a clinical trial of a drug, where the medical data includes one or more of multi-omics data associated with the potential patients, genomic profiles of the potential patients, dosage and time associated with the drug, electronic medical records of the potential patients, or clinical trial data associated with the drug. The device may perform, in parallel, a topological data analysis, a non-matrix factorization analysis, and a neural network analysis of the medical data. The device may identify a group of patients, of the potential patients, for the clinical trial of the drug based on performing the topological data analysis, the non-matrix factorization analysis, and the neural network analysis of the medical data, and may provide information identifying the group of patients.