Personalized Cancer Treatment via Cell Population Evolution Modeling
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Solution Overview
Problem
Conventional cancer treatment regimens are non-specific and often ineffective due to tumor heterogeneity and dynamic resistance mechanisms, leading to significant side effects and limited patient outcomes, as they primarily focus on average tumor properties and static molecular profiles without considering future tumor states or minor subclones.
Innovation Solution
A mathematical model incorporating genetic evolutionary dynamics and single-cell heterogeneity is used to predict the time evolution of sub-populations of cells, allowing for the systematic evaluation of non-standard personalized medicine strategies that target specific cell states and resistance mechanisms, optimizing therapeutic agent combinations and schedules based on predicted growth and transition rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional cytotoxic chemotherapy drugs are used to kill rapidly dividing cells, then the overall population of cancerous cells is reduced, but non-cancerous cells are also damaged and significant side effects occur
Solution Approach 1:
The patent applies local quality by shifting from uniform treatment of all rapidly dividing cells to targeted treatment based on specific molecular characteristics. Treatment regimens are customized to match the unique molecular profile of each patient's tumor, delivering therapeutic agents that specifically bind to cancer cells with particular genetic or protein markers while sparing normal cells that lack these targets.
Solution Approach 2:
The patent employs parameter changes by utilizing advances in molecular biology to detect and measure specific biochemical parameters (gene expressions, protein levels, genetic mutations) that distinguish cancer cells from normal cells. These measured parameters guide the selection and dosing of therapeutic agents, transforming treatment from a blanket approach to a precision approach based on quantifiable molecular differences.
2Reliability
If personalized cancer treatment regimens are designed based on molecular characterization, then treatment effectiveness is improved, but the complexity of treatment planning increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of treatment selection into discrete, manageable steps: (1) molecular characterization of the tumor, (2) identification of specific molecular markers, (3) matching markers to corresponding therapeutic agents, and (4) formulation of a customized regimen. This segmented approach makes the complexity tractable by processing information in sequential stages rather than requiring simultaneous consideration of all factors.
Solution Approach 2:
The patent introduces molecular markers as intermediaries that bridge the gap between tumor biology and treatment selection. These markers serve as measurable proxies that simplify the decision-making process by providing clear, objective criteria for matching patients to specific therapies, reducing the need for complex clinical judgment about heterogeneous tumor behaviors.
3Ease of operation
If treatment focuses on average tumor properties, then treatment planning is simplified, but heterogeneity within tumors leads to treatment resistance
Solution Approach 1:
The patent applies preliminary action by performing comprehensive molecular characterization of the tumor before treatment begins. This upfront analysis identifies the full spectrum of molecular markers present, including those that may indicate potential resistance mechanisms. By knowing the tumor's molecular profile in advance, clinicians can select therapies that address not only current tumor characteristics but also anticipate and prevent the emergence of resistance.
Data Source
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AI summary
An approach to determining a specific treatment plan for a subject uses of a prediction of time evolution of sub-populations of cells with different types of resistance to a set of therapeutic agents based at least in part on a measurement (e.g., tissue sample, bodily fluid sample, or molecular imaging) of a subjects tumor and determines a therapeutic schedule for administration of selected ones of the agents according to a criterion that is based at least in part on a factor that depend on evolution of one or more sub-populations.