Cellular Response Measurement System for Predictive Therapeutic Selection
Solution Overview
Problem
Current prognostic tools are inadequate for predicting the effectiveness of therapeutics in individual patients, particularly for diseases like cancer, due to the heterogeneity of genetic makeup and protein expression levels, leading to high failure rates in clinical trials and poor efficacy in actual patient populations.
Innovation Solution
A method involving a cellular response measurement system (CReMS) where label-free live whole cells from diseased tissue are exposed to therapeutic agents, monitoring changes in physiological parameters over time to select agents that show therapeutic efficacy, and a kit comprising containers for cell samples, a transport medium, and a biosensor to convert data into outputs indicating cellular responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current genetic biomarker detection methods are used to predict therapeutic response, then genetic information can be obtained, but predictive accuracy is poor due to disease heterogeneity and limited correlation with actual therapeutic outcome
Solution Approach 1:
The patent creates a functional copy of the patient's diseased cells through cell culture, which then serves as a surrogate system for testing therapeutic agents. This cell model copy preserves the patient-specific genetic and phenotypic characteristics while allowing controlled experimental assessment of drug response, thereby improving predictive accuracy over direct genetic biomarker analysis alone
Solution Approach 2:
The patent introduces a cellular assay system as an intermediary between genetic biomarker detection and therapeutic outcome prediction. This intermediate step measures actual cellular response to therapeutic agents, bridging the gap between genetic information and clinical efficacy by providing direct functional readout of drug-cell interactions
2Productivity
If engineered cell lines are used for drug screening, then optimized screening environments can be provided, but the results are misleading regarding actual patient efficacy due to disease heterogeneity
Solution Approach 1:
The patent transitions from using uniform engineered cell lines to using patient-specific cell models that preserve individual genetic and phenotypic characteristics. This local customization of cell models to match individual patient disease profiles allows the screening system to account for disease heterogeneity while maintaining screening efficiency through standardized assay protocols
3Loss of information
If genetic data analysis is performed to identify disease causes, then genetic biomarkers can be detected, but the information does not effectively predict disease course or therapeutic response
Solution Approach 1:
The patent changes the measurement parameters from static genetic biomarker detection to dynamic functional response measurement. By assessing actual cellular responses to therapeutic agents in patient-specific cell models, the system captures functional phenotypic parameters that directly reflect therapeutic efficacy, thereby improving prediction accuracy over genetic information alone
Data Source
AI summary
The disclosure provides methods for analysis of disease cell response to a therapeutic agent. In embodiments, a method comprises administering the therapeutic agent to a disease cell sample from the subject in a device that measures at least one physiological parameter of a cell; determining whether a change occurs in the physiologic parameter of the disease cell sample in response to the therapeutic agent as compared to a baseline measurement or the physiological parameter before administration of the therapeutic agent, and selecting the therapeutic agent that results in the change in the at least one physiologic parameter. In embodiments, the disease cells are whole, viable, and/or label free.