Digital Animal Free Testing System for Neurotoxicity Prediction
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Solution Overview
Problem
Current animal testing methods for assessing neurovirulence and neurotoxicity in vaccines are unreliable and ethically concerning due to interspecies differences, leading to inaccurate predictions of human responses and potential adverse effects, necessitating the development of alternative, cruelty-free testing strategies.
Innovation Solution
The implementation of Digital Animal Free Testing (DAFT) utilizing human Microphysiological Systems (hMPS) combined with AI/ML tools and robotic process automation, which configures a modular assay system like NeuroSAFE for predicting human neurovirulence or neurotoxicity without the need for animal models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If animal testing methods are used to assess neurovirulence and neurotoxicity, then testing can be performed using established protocols, but the results are unreliable due to interspecies differences and ethical concerns arise
Solution Approach 1:
The patent creates a digital copy of the animal's nervous system response through machine learning models trained on animal test data. The NeuroSAFE system generates in-silico predictions that replicate animal test outcomes without requiring actual animal testing, thereby eliminating cruelty while maintaining predictive accuracy for human responses
Solution Approach 2:
The patent replaces the mechanical/biological system of animal testing with a computational system. Machine learning algorithms and digital twins substitute for physical animal experiments, transforming the testing mechanism from in-vivo biological processes to in-silico computational simulations that predict neurovirulence and neurotoxicity
2Ease of manufacture
If animal models are used for testing, then existing testing infrastructure can be utilized, but the translational reliability to human outcomes is low due to physiological and genetic differences
Solution Approach 1:
The patent introduces a machine learning model as an intermediary layer between animal test data and human outcome predictions. The NeuroSAFE system processes animal test results through trained algorithms that learn species-specific differences and translate findings into human-relevant predictions, bridging the translational gap while utilizing existing animal testing infrastructure
Solution Approach 2:
The patent transforms the testing parameters from direct animal observations to computationally adjusted predictions. The machine learning models modify the raw animal test data by applying learned corrections for interspecies physiological and genetic differences, converting animal responses into accurate human outcome predictions
3Reliability
If traditional animal testing protocols are followed, then regulatory compliance can be maintained, but the number of animals used remains high and resources are misdirected
Solution Approach 1:
The patent creates virtual animal tests through digital twins and machine learning models that replicate the functionality of physical animal testing. The NeuroSAFE system generates digital test results that can substitute for actual animal experiments, reducing animal usage while maintaining regulatory compliance through validated computational methods
Solution Approach 2:
The patent replaces physical animal testing infrastructure with computational testing systems. The machine learning-based NeuroSAFE platform substitutes for laboratory animal facilities, eliminating the need to house, feed, and care for test animals while providing equivalent or superior testing capabilities through in-silico simulations
Data Source
AI summary
The present invention discloses a workstation solution for test predicting human safety concerns and efficacy measurements in a test agent. The workstation solution comprises, a real-time platform or human MicroPhysiological Systems (hMPS) unit, and a digital platform. The digital platform with embedded artificial intelligence (AI) is configured to predict safety (pharmacology) risks from phenotypes, genotypes and proteotype data sets acquired from test agents treated hMPS platform in a modular assay system. The digital platform is trained with phenotypes, genotypes, proteotypes, biochemical data sets as benchmark patterns and signals configured as positive or negative controls to provide a bandwidth to AI for detecting anomalies in a real-time assaying and for measuring the analyzed insights in real-time assaying. The workstation solution is a Digital Animal Free Testing (DAFT), which is a foundational scheme while the modular assay system is one of a derived application.