Computational Tumor Models Predict Drug Response
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Solution Overview
Problem
Current methods for predicting individual patient drug response, particularly in cancer treatment, are limited by the differences between in vitro and in vivo experimental systems and the inability to accurately account for inter-individual variability, leading to ineffective treatment plans and severe side effects.
Innovation Solution
A method combining microarray chip analysis of patient tissues with discriminant analysis to generate a model that correlates biological profiles with treatment outcomes, allowing for personalized treatment predictions without the need for pathway information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in vitro chemosensitivity assays are used to predict patient response to drug therapy, then drug response prediction is attempted, but the differences between in vitro and in vivo environments reduce the validity and accuracy of predictions
Solution Approach 1:
The patent creates virtual copies of in vivo human tumor environments through computational models that replicate physiological conditions, allowing accurate prediction of drug response without physical in vitro experiments. The virtual tumor models incorporate human-specific biological characteristics to maintain validity while enabling precise predictions.
Solution Approach 2:
The patent replaces physical in vitro experimental systems with computational algorithms and in silico modeling approaches. This substitution eliminates the fundamental disconnect between in vitro and in vivo environments by directly simulating human physiological responses to drugs through mathematical models rather than physical cell cultures.
2Measurement precision
If in vitro assays are used for drug efficacy prediction, then experimental data is obtained, but the long incubation time of 14 to 28 days makes the assay impractical for clinical practice
Solution Approach 1:
The patent performs preliminary computational analysis using pre-established virtual tumor models and algorithms that can rapidly process drug response predictions. By preparing computational frameworks in advance and using in silico methods that do not require lengthy incubation periods, the system delivers predictions in clinically relevant timeframes while maintaining measurement precision.
Solution Approach 2:
The patent replaces time-consuming in vitro incubation processes with immediate computational simulations. The in silico models calculate drug responses through mathematical algorithms that provide results in hours or minutes rather than requiring 14-28 days of physical cell culture incubation, eliminating the time loss while preserving measurement accuracy.
3Measurement precision
If in vitro chemosensitivity assays are used, then tumor cell response to drugs is measured, but the inability to distinguish between growth of malignant and nonmalignant cells reduces prediction accuracy
Solution Approach 1:
The patent applies local quality by creating specialized computational models that are tailored to specifically simulate malignant tumor cell behaviors and responses. The virtual models incorporate cancer-specific biological characteristics, genetic mutations, and metabolic pathways that distinguish malignant cells from nonmalignant cells, enabling precise measurement of tumor response without contamination from normal cell growth signals.
4Quantity of substance
If current clinical trial design is used to determine drug efficacy, then statistical probability of effectiveness for a group is obtained, but there is no specific information about drug efficacy for an individual patient
Solution Approach 1:
The patent segments the population-level data into individual patient-specific predictions by using virtual tumor models that are customized for each patient's unique genetic profile, tumor characteristics, and clinical parameters. This segmentation transforms aggregate statistical probability into personalized efficacy predictions, maintaining the benefits of population data while recovering individual-specific information.
Solution Approach 2:
The patent changes the parameters from population-averaged statistical measures to individual-specific biological and clinical parameters. By incorporating patient-specific genomic data, tumor characteristics, and physiological parameters into the computational models, the system transforms general population-level efficacy statistics into precise individual patient predictions.
Data Source
AI summary
Methods, systems, and computer program products that predict an individual's treatment outcome from a sampling of a group of patients' biological profiles. Biological profile information is received from patients who had a medical condition and who received a treatment. Treatment outcome information regarding the patients who had the medical condition and who received the treatment is also received. A discriminant analysis-based pattern recognition process is then performed on the biological profile information and the treatment outcome information, thereby generating a model that correlates between the biological profile information and the treatment outcome information. The model can be used for, among other things, predicting treatment outcome for the new patient for the treatment.