Microscopy Image Analysis for Cellular Disease Model Prediction
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Solution Overview
Problem
Current patient treatments are inefficient and costly due to the difficulty in predicting disease onset and varying efficacy across different subjects, leading to slow and serendipitous therapeutic development, with clinical trials often showing inconsistent safety and efficacy profiles.
Innovation Solution
Development of machine-learning enabled cellular disease models that utilize trained machine learning models to analyze phenotypic assay data from genetically engineered cells, enabling in vitro prediction of in vivo disease phenotypes and clinical outcomes, allowing for faster and more targeted therapeutic screening and development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional patient treatments are used, then therapeutic development can proceed through traditional clinical trials, but the process is slow and costly with inconsistent safety and efficacy profiles across different subjects
Solution Approach 1:
The patent creates in vitro cellular models that replicate human disease phenotypes and patient-specific genetic backgrounds. These cellular copies serve as surrogates for human patients, allowing therapeutic screening to be performed on cell cultures rather than requiring extensive clinical trials on human subjects. The cellular models capture disease-relevant phenotypes and genetic variations, enabling reliable prediction of therapeutic responses before human testing.
Solution Approach 2:
The patent performs preliminary therapeutic screening and validation in vitro using cellular disease models before advancing to clinical trials. By conducting initial safety and efficacy assessments on cellular surrogates that replicate patient-specific genetics and disease phenotypes, the system identifies promising candidates early, filtering out ineffective or harmful therapies before human exposure. This preliminary action reduces later trial failures and accelerates development timelines.
2Adaptability or versatility
If comprehensive therapeutic screening is performed on diverse patient populations, then effective treatments can be identified for different subjects, but the resources required become difficult and expensive
Solution Approach 1:
The patent generates patient-specific cellular models that replicate individual genetic backgrounds and disease phenotypes. These cellular copies allow comprehensive screening across multiple patient virtual populations without requiring actual patient recruitment for each screen. By creating in vitro surrogates for diverse patient genotypes and disease states, the system can evaluate therapeutic responses across many virtual patients simultaneously, reducing the resource burden of real-world clinical trials.
Solution Approach 2:
The patent develops a universal cellular disease modeling platform that can be applied across multiple diseases and patient populations. The same in vitro infrastructure and machine learning framework can screen therapies for different genetic backgrounds, disease types, and patient cohorts. This multi-functional platform eliminates the need to build separate testing systems for each patient population, significantly reducing overall resource requirements while maintaining broad adaptability.
3Loss of information
If genetic basis analysis is performed to understand disease mechanisms, then insights into disease etiology are gained, but prediction of disease onset timing and triggering factors remains insufficient
Solution Approach 1:
The patent creates cellular models that not only replicate genetic backgrounds but also phenotypic manifestations of disease. By copying both genotype and phenotype in vitro, the system can observe actual disease progression and response to interventions rather than merely analyzing static genetic data. This phenotypic copying enables prediction of disease onset timing and triggering factors by observing when and how diseases manifest in the cellular models under various conditions.
Solution Approach 2:
The patent employs machine learning models that analyze phenotypic data from cellular models and feed predictions back into the disease modeling framework. The system learns from observed phenotypic patterns, treatment responses, and progression timelines in the cellular surrogates, then uses this learned knowledge to predict disease onset and triggering factors in new cases. This feedback loop transforms static genetic analysis into dynamic predictive modeling.
Data Source
AI summary
Embodiments of the disclosure include systems and non-transitory computer readable media for analyzing microscopy images for developing machine learning models for disease modeling. Microscopy images are captured from cells of one or more exposure response phenotypes (ERPs) and further used to train machine learning models. Thus, trained machine learning models can distinguish between microscopy images captured from healthy and diseased samples.