Personalized Variability Pattern Detection for Drug Resistance
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Solution Overview
Problem
Current systems and therapies fail to account for individual variability in human responses to treatments and devices, leading to drug resistance, inefficacy, and suboptimal performance in various applications, including medicine, autonomous vehicles, and cybersecurity, due to their non-personalized nature.
Innovation Solution
The development of methods and algorithms that identify and quantify intra and inter-subject variability patterns from cellular to whole-body levels, combining them with other personalized patterns to improve the function of systems and devices used by or for humans, using machine learning and closed-loop deep learning to implement subject-tailored variability signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If therapies are developed for the average patient, then the treatment can be applied broadly to population, but it fails to fit individual subjects leading to drug resistance and loss of effect
Solution Approach 1:
The system performs preliminary identification and quantification of individual variability patterns before treatment is administered. Machine learning algorithms analyze baseline biological variability characteristics of each patient, enabling personalized treatment protocols to be designed in advance that account for individual response patterns, thereby preventing drug resistance before it develops
Solution Approach 2:
The treatment system dynamically adapts to individual patient variability patterns rather than using fixed protocols. The system continuously monitors biological variability during treatment and adjusts therapy parameters in real-time based on detected patterns, transforming static treatment regimens into dynamic, personalized interventions that maintain efficacy over time
2Reliability
If dosages are increased to overcome resistance, then the immediate effect may be improved, but resistance further increases leading to clinical deterioration
Solution Approach 1:
The system implements continuous feedback monitoring of biological variability patterns during treatment. By detecting changes in variability that indicate emerging resistance, the system provides real-time feedback that enables clinicians to adjust therapy before resistance becomes problematic, avoiding the need to increase dosages and the associated harmful effects
Solution Approach 2:
Instead of changing only dosage parameters, the system modifies multiple treatment parameters based on individual variability patterns, including dosing intervals, administration timing, and combination therapies. This multi-parameter optimization overcomes resistance through personalized parameter optimization rather than simple dose escalation
3Ease of operation
If regular drug dosing regimens are used, then the administration is simple and consistent, but it is not compatible with inherent intra and inter patient variability and increases drug resistance
Solution Approach 1:
The system performs preliminary analysis of individual variability patterns to pre-calculate optimized dosing schedules. By preparing personalized regimens in advance based on baseline characteristics, the system maintains ease of administration while incorporating individualized parameters that prevent resistance development
Solution Approach 2:
The system enables self-adjusting dosing regimens where patients can autonomously modify their treatment based on monitored variability patterns. Through patient-friendly interfaces that display personalized guidance, the system maintains simplicity while adapting to individual needs, eliminating the need for complex clinical management
Data Source
AI summary
The disclosure provides methods for improving the function of systems regimens, devices by identifying, quantifying, and implementing at least one inherent variability pattern which is based on patterns learned from a specific subject or from other subjects including subject's variability patterns, such as, DNA, genes, nucleic acids, RNA, proteins, cells, organs, biological pathway(s), or whole body variability. There are provided herein devices, systems, and methods for real time or delayed altering of the parameters of system's regimens, for improving biological systems functions. Any system used by humans, or affect human function, wherein the parameters are updated using inherent variabilities signatures with and without other individualized patterns from a subject or from other subjects, can increase the accuracy and efficacy of the system for achieving the desired goal. Output parameters are continuously, semi continuously, or conditionally being updated based on measurements and inputs provided to a compute circuitry configured to facilitate closed loop machine learning capabilities.