Transcriptome Classifier for Disease Agent Viability Scoring
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Solution Overview
Problem
Current methods for developing treatment regimens for disease agents, such as Mycobacterium tuberculosis, are inefficient due to the large number of testable drug combinations and the challenge of addressing phenotypically diverse subpopulations, which leads to ineffective drug treatments in vivo.
Innovation Solution
A computer-implemented method that uses a classifier to generate a disease agent viability score from transcriptome data, defining a universal transcriptome signature for viability across different host-relevant contexts, and determines a treatment recommendation based on the viability state, facilitating the evaluation of single-drug and multi-drug treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drug combination testing methods are used, then comprehensive drug evaluation can be performed, but the complexity and time required for testing increases exponentially
Solution Approach 1:
The patent replaces mechanical/drug-based testing systems with a computational classification system. A classifier trained on transcriptome data from drug-treated and untreated disease agents predicts drug combination effectiveness in silico, substituting physical drug testing with computational modeling. This reduces testing complexity while maintaining evaluation comprehensiveness through virtual screening of drug combinations.
Solution Approach 2:
The patent creates computational copies of drug treatment effects through transcriptome profiling. By measuring gene expression changes that mimic the biological response to drugs, the system generates virtual representations of drug activity. These transcriptome copies allow prediction of drug combination effects without physically testing each combination, reducing experimental burden while preserving predictive accuracy.
2Reliability
If extensive drug combination testing is performed to ensure treatment effectiveness, then reliable treatment outcomes can be achieved, but the time required for drug development increases
Solution Approach 1:
The patent performs preliminary classification and prediction using the trained classifier before committing to extensive experimental testing. The system pre-screens drug combinations in silico using transcriptome-based predictions, identifying promising candidates that warrant further experimental validation. This preliminary computational assessment filters out ineffective combinations early, reducing overall development time while maintaining reliable identification of effective treatments.
3Reliability
If phenotypically diverse subpopulations of disease agents are addressed, then treatment robustness improves, but the difficulty of finding effective drug combinations increases
Solution Approach 1:
The patent develops a universal classifier that functions across diverse disease agent subpopulations and drug treatment contexts. The classification system is trained on transcriptome data from multiple conditions and drug treatments, creating a multi-functional model that can predict effectiveness for various disease agent types and drug combinations. This universal approach simplifies the assessment process while maintaining robustness across phenotypically diverse subpopulations.
Data Source
AI summary
Predicting viability and treatment of disease agents is described herein. In an example, a system accesses a disease agent transcriptome data of a disease agent. The system generates a disease agent viability score by applying a classifier to the disease agent transcriptome. The classifier defines a universal transcriptome signature for a viability of the disease agent in different host-relevant contexts. The system generates a viability state of the disease agent by determining a deviation of the disease agent viability score from a viability threshold of the universal transcriptome signature for viability and determines a treatment recommendation based on the viability state of the disease agent. The system outputs the treatment recommendation.