Pharmacology Model Optimization via Distributed Data Acquisition
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Solution Overview
Problem
Current conventional systems are limited in their ability to provide highly customized medication dosing regimens for patients, as they do not effectively account for individual patient characteristics such as genetics, co-administered medications, age, sex, race, and biomarkers, and lack the capability to continuously relearn and optimize dosing models for therapeutic benefit.
Innovation Solution
A system and method for pharmacology model optimization using distributed data acquisition, which generates an optimized pharmacology model based on patient data, including pharmacokinetic and pharmacodynamic information, to provide customized dosing regimens through non-linear mixed effects models and continuous machine learning, allowing for continuous validation and refinement of the model with new patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simplified dosing guidelines and static pharmacology models are used, then the system is easy to operate and implement, but the dosing precision and individualization for each patient is insufficient
Solution Approach 1:
The patent transforms static dosing guidelines into dynamic, adaptive models that continuously learn from patient response data. The pharmacology models are updated in real-time based on distributed data acquisition from multiple sources, allowing the system to adapt dosing recommendations to individual patient characteristics and responses, thereby improving dosing precision without requiring complete system redesign
Solution Approach 2:
The system implements continuous feedback loops where patient response data is collected from distributed devices, fed back into the pharmacology models, and used to optimize future dosing recommendations. This feedback mechanism enables the system to learn from actual patient outcomes and continuously improve dosing precision while maintaining operational simplicity through automated model updates
2Adaptability or versatility
If conventional dosing calculator systems are used, then the system structure is simple, but the capability to continuously relearn and optimize models for therapeutic benefit is lacking
Solution Approach 1:
The patent enables the pharmacology models to self-optimize through automated machine learning algorithms that continuously process distributed patient data and update model parameters without manual intervention. The system performs self-validation and self-improvement by comparing predicted versus actual patient responses, automatically refining dosing recommendations to maximize therapeutic benefit while minimizing complexity for end users
Solution Approach 2:
The system creates a universal platform that handles multiple pharmacology models, data sources, and patient characteristics through a unified architecture. This multi-functional system can accommodate different medication types, patient populations, and data formats while maintaining consistent optimization capabilities, thereby improving adaptability without proportionally increasing operational complexity
3Measurement precision
If distributed data acquisition from multiple sources is implemented, then the dosing recommendation precision is improved, but the data aggregation and model optimization complexity increases
Solution Approach 1:
The patent introduces intermediary components including standardized data interfaces, aggregation layers, and validation mechanisms that mediate between diverse distributed data sources and the pharmacology models. These intermediaries translate and harmonize data from different formats and sources into a unified structure suitable for model optimization, thereby improving dosing precision while containing data processing complexity through modular architecture
Solution Approach 2:
The system dynamically adjusts data processing parameters and model complexity based on data quality, availability, and patient-specific factors. By changing parameters such as data sampling frequency, model granularity, and optimization intensity, the system achieves high dosing recommendation precision when needed while reducing processing complexity when data is limited or less critical, balancing precision and complexity adaptively
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for pharmacology model optimization based on distributed data acquisition. A computer system stores a first pharmacology model associated with a drug dosage model for a particular medication. The computer system receives patient data including pharmacological data and data values for a drug identifier and a pharmacology model identifier. The pharmacological data being associated to drug dosing for the particular medication. The computer system aggregates the received data into a first data set based on the drug identifier value. The computer system optimizes the first pharmacology model using the first data set, thereby generating a second pharmacology model. The computer system provides access to the optimized second pharmacology model.