GAN-Based Generative Medicines for Personalized Dementia Treatment
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Solution Overview
Problem
Current approaches to developing dementia treatments are ineffective due to overlooked factors, genetic variability, and biased clinical trials, leading to high costs and limited success in personalized medicine, with machine learning tools underutilized in dementia research.
Innovation Solution
A computer-implemented system using generative adversarial neural networks (GANs) processes patient data, bio-samples, and dementia severity to create personalized medicines, reducing the need for pre-discovery and preclinical processes, and enabling decentralized trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clinical trials with rodent models are used for dementia drug discovery, then pre-discovery and discovery processes can be completed, but the efficacy data does not translate well to human subjects due to genetic mutations and telomere length differences
Solution Approach 1:
The patent creates virtual human patient copies with realistic genetic profiles, telomere lengths, and disease characteristics through generative AI models. These synthetic patients replicate human biology without requiring physical rodent models, allowing efficacy data to translate directly to human subjects while eliminating the need for complex pre-discovery animal studies
Solution Approach 2:
The patent replaces the mechanical biological system of rodent models with an information-based system of generative AI models. Instead of using physical animal subjects with inherent biological differences, the system uses computational models that can be precisely configured to match human genetic and physiological characteristics, substituting wet-lab biology with dry-lab computation
2Measurement precision
If personalized medicine approaches are implemented for dementia treatment, then treatment accuracy for individual patients improves, but costs increase significantly
Solution Approach 1:
The patent enables patients to generate their own personalized treatment plans through accessible AI tools that analyze their genetic data and disease characteristics. The system empowers patients to self-configure their virtual twins and explore treatment options without requiring expensive centralized laboratory analysis or specialized medical infrastructure, making personalized medicine economically viable
Solution Approach 2:
The patent creates a universal generative AI platform that can serve multiple functions: creating virtual patients, simulating disease progression, testing treatment efficacy, and generating personalized treatment plans. This multi-functional system replaces multiple separate expensive processes with a single cost-effective platform that delivers personalized medicine at scale
3Adaptability or versatility
If machine learning tools are underutilized in dementia research, then existing research methods can be maintained, but the ability to explore complex interplay between lifestyle, genetics, and biological reactions is limited
Solution Approach 1:
The patent performs preliminary actions by pre-training generative AI models on comprehensive datasets including genetic information, lifestyle factors, and biological reactions before actual research begins. This pre-computation creates a ready-to-use virtual population that can immediately explore complex interactions without requiring real-time machine learning processing during experiments, accelerating research capabilities
Data Source
AI summary
Computer-implemented systems and methods for creating generative medicines for dementia. The computer-implemented system includes a processor, a memory, and a server. The processor is configured to register a user over a communication application through a registration module; receive patient data of a user through a patient data module; receive bio-sample data of the user through a bio-sample module; receive dementia type and dementia severity data of the user through a dementia categorization module; and transmit a final dataset through a data transmission module. The server is configured to process the final dataset received from the data transmission module by applying a machine learning module; select the generative medicines; and transmit the generative medicines to the computing devices.