Neuronal Model Simulation for Personalized Drug Selection
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Solution Overview
Problem
Current methods fail to effectively select personalized drug treatments by accurately predicting drug effects on neurophysiological traits based on patient genotype information, often relying on trial and error or machine learning without efficiently identifying optimal ion channel parameter combinations.
Innovation Solution
A computer-implemented method using evolutionary algorithms, soft thresholding, and partial least squares regression to generate neuronal models that identify ion channel parameter combinations capable of restoring healthy neurophysiological traits, facilitating the selection of targeted drugs for personalized treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trial and error or machine learning methods are used for drug selection, then the process can be completed without complex simulations, but the accuracy and personalization of drug treatment selection deteriorates
Solution Approach 1:
The patent creates virtual copies of neuronal systems through computational models that replicate the electrical and biochemical properties of real neurons. These digital twins allow researchers to simulate drug effects on neurophysiological traits without requiring actual patient trials, thereby improving prediction accuracy while avoiding the complexity of extensive physical experimentation.
Solution Approach 2:
The methodology performs preliminary simulations using patient-specific genetic data to predict which drugs will most effectively restore healthy neurophysiological trait ranges. By conducting these virtual experiments before actual drug administration, the system identifies optimal treatment candidates in advance, improving personalization accuracy while reducing the need for complex trial-and-error clinical testing.
2Reliability
If comprehensive neuronal model simulations are performed to identify optimal ion channel parameters, then personalized treatment accuracy improves, but computational time and resources increase
Solution Approach 1:
The system varies ion channel parameters within physiologically realistic ranges to generate multiple neuronal model configurations. By systematically exploring parameter space and identifying combinations that restore healthy neurophysiological trait ranges, the methodology achieves reliable personalized treatment predictions while maintaining computational efficiency through targeted parameter exploration rather than exhaustive simulation.
Solution Approach 2:
The patent replaces time-consuming wet-lab experimentation and clinical trials with computational simulations. By substituting physical experimentation with in-silico models that incorporate patient genetic data, the system achieves reliable treatment predictions significantly faster than traditional methods, reducing computational time while maintaining or improving reliability.
3Productivity
If multiple ion channel parameters are adjusted to restore healthy neurophysiological traits, then treatment effectiveness improves, but the complexity of identifying optimal parameter combinations increases
Solution Approach 1:
The patent divides the complex task of optimizing multiple ion channel parameters into manageable segments by focusing on specific neurophysiological traits that can be independently measured and targeted. By breaking down the overall treatment goal into trait-specific optimization sub-problems, the system identifies optimal parameter combinations more efficiently while maintaining treatment effectiveness.
Solution Approach 2:
The computational model serves as an intermediary between patient genetic data and drug selection decisions. This intermediate layer processes complex genetic information and translates it into actionable treatment recommendations by simulating how different drugs affect virtual neuronal models, thereby simplifying the identification of optimal parameter combinations without compromising treatment effectiveness.
Data Source
AI summary
One or more embodiments of the present invention include a computer-implemented method for generating neuronal models for personalized drug treatment selection for a patient. The method includes receiving allelic information for at least one neurophysiological coding region of a genome of the patient, and a physiological model of a disease associated with the genome. The method further includes determining a set of ion channels correlated with the allelic information, and receiving a set of phenotypic measurement ranges associated with the ion channels from the determined set. The method further includes performing a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding phenotypic measurement ranges, and analyzing the generated neuronal models to identify components that affect the physiological model. The method further includes selecting a drug for the patient based at least in part on the identified components.