Susceptibility Detection Model for Rapid Pathogen Drug Resistance Analysis
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Solution Overview
Problem
The slow process of identifying drug resistance in pathogens, such as M. tuberculosis, delays the selection of effective drug combinations, leading to ineffective treatments and potential spread of resistant strains, particularly in patients with compromised immune systems.
Innovation Solution
Employing gene sequencing and machine learning to train a susceptibility detection model that rapidly and accurately determines drug resistance by correlating genetic variations in pathogen nucleotide sequences with drug responses, enabling timely and appropriate medication prescriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional culture medium methods are used to identify drug resistance in M. tuberculosis, then measurement precision is achieved, but loss of time increases significantly (4-6 weeks)
Solution Approach 1:
The patent replaces the mechanical/biological culture medium system with a computational machine learning system. Instead of using physical culture media that require weeks of incubation, the invention uses trained machine learning models that analyze genetic data to predict drug resistance, reducing the process from weeks to hours while maintaining accuracy.
Solution Approach 2:
The patent creates a virtual model (machine learning susceptibility detection model) that copies and simulates the drug resistance detection process. The model is trained on existing data and then used to predict resistance patterns without requiring actual cultural exposure of the pathogen, thereby eliminating the time-consuming physical cultivation step.
2Loss of time
If rapid detection methods are implemented, then loss of time is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent performs preliminary action by training the machine learning model in advance using extensive datasets. The model learns from pre-collected data the relationships between genetic markers and drug resistance patterns. When actual detection is needed, this pre-trained knowledge enables rapid and accurate predictions without compromising precision, as the model has already internalized the complex patterns during the training phase.
Data Source
AI summary
Examples of determining drug resistance of a target strain of a pathogen against a target drug are described. In an example, a nucleotide sequence data of the target strain of the pathogen is obtained from a test sample. The nucleotide sequence data may then be analyzed to locate a genetic variation in a nucleotide sequence of the target strain. Based on a susceptibility-detection model, the genetic variation may be analyzed to identify association of the genetic variation with drug resistance of the target strain with respect to a specific target drug. The susceptibility-detection model is trained based on a plurality of association mappings, wherein an association mappings associates a genetic variation in a training base strain of the pathogen with drug resistance to one or more drugs.