Live-Cell Imaging Framework for Drug Concentration Trajectory Optimization

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Solution Overview

Problem

Current live-cell imaging systems for evaluating chemotherapeutic drug administration are costly, require significant space, and lack automation, leading to inefficiencies in data acquisition and modeling, particularly in optimizing drug concentration trajectories for cancer treatment.

Innovation Solution

A closed-loop live-cell imaging framework using machine learning and artificial intelligence to optimize chemotherapeutic drug concentration trajectories by modeling cellular signal transduction pathways, allowing for automated data acquisition and personalized chemotherapy protocols.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If existing live-cell imaging systems are used for evaluating chemotherapeutic drug administration, then data acquisition can be performed, but the systems are costly, require large amounts of space, and lack automation

Engineering Contradiction:

Improveautomation of data acquisitionVSAvoidsystem complexity and cost

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical imaging systems with a computational approach using machine learning models that process simplified optical measurements. The system substitutes sophisticated hardware-based automated imaging with software-based analysis of basic optical data, achieving automation without requiring complex mechanical systems

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a virtual model (copy) of cellular signal transduction pathways through machine learning that replicates the behavior of actual cells. This computational copy allows for automated evaluation of drug effects without requiring complex physical imaging systems, as the model can be processed through standard computational devices

Inventive Principle:

Principle #26Copying

2Measurement precision

If biological models are used for data acquisition, then parameter estimation can be performed, but parameter estimation can introduce sources of error that negatively affect efficiency and accuracy

Engineering Contradiction:

Improveaccuracy of parameter estimationVSAvoiderror introduction in evaluation

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent implements a feedback mechanism where the machine learning model continuously refines its parameter estimates by comparing model predictions with actual optical measurement data. This iterative feedback process reduces estimation errors by adjusting model parameters to better match observed cellular responses, thereby improving measurement precision while minimizing information loss

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary training of the machine learning model using known biological data before actual drug evaluation. This preliminary action establishes accurate baseline parameters and reduces potential errors in subsequent measurements, improving the overall accuracy of parameter estimation before the actual experimental data is collected

Inventive Principle:

Principle #10Preliminary action

3Productivity

If conventional chemotherapy regimens are used, then drug administration can be performed, but the cytostatic or cytotoxic effects on cancer cells are not maximized

Engineering Contradiction:

Improveefficacy of chemotherapeutic effectVSAvoidoptimization of concentration trajectory

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent transitions from static, fixed-dose chemotherapy regimens to dynamic, adaptive concentration trajectories. The machine learning model continuously adjusts drug concentration recommendations based on real-time or near-real-time cellular response data, creating a dynamic treatment protocol that adapts to the specific behavior of the cancer cells, thereby maximizing therapeutic efficacy

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the key parameter of drug concentration from fixed values to optimized time-varying trajectories. By using machine learning to determine optimal concentration profiles based on cellular signal transduction models, the system adjusts drug dosage parameters dynamically to maximize cytotoxic effects on cancer cells while accounting for cellular adaptation and resistance mechanisms

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20220406434A1Method and system for evaluating optimized concentration trajectories for drug administration

Publication Date: 2022.12.22 DEUTES KREBSFORSCHUNGSZENT STIFTUNG DES OFFENTLICHEN RECHTS

AI summary

The present invention is in the field of experimental data acquisition. In particular, the present invention relates to a live-cell imaging method and a corresponding system for acquiring experimental data of one or more biological probes. More specifically, the present invention relates to methods and systems for evaluating an optimized concentration trajectory for administration of a drug, in particular a chemotherapeutic drug.