Bayesian Drug Dosing Regimens for Personalized Toxicity Control
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Solution Overview
Problem
Existing methods for determining drug dosing regimens are cumbersome, prone to errors, and limited in scope, often leading to suboptimal treatment outcomes due to reliance on time-constrained measurements and manual rule-of-thumb adjustments, which can result in resistance or toxicity issues.
Innovation Solution
A method using Bayesian models to estimate drug concentration time-courses, incorporating patient-specific data and pharmacokinetic parameters, and comparing these estimates to efficiency and toxicity thresholds to determine an optimal dosing regimen, adaptable to individual patient characteristics and pathogenic agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rule-of-thumb adjustments are used for dosing, then ease of operation is improved, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent replaces manual mechanical calculation methods with an automated computer-based Bayesian dosing system. The system automatically processes patient data, applies pharmacokinetic models, and calculates optimal dosing regimens without requiring manual intervention, thereby eliminating the inaccuracies of rule-of-thumb adjustments while maintaining ease of use through automated computation.
Solution Approach 2:
The dosing system performs self-service by automatically adjusting dosing regimens based on real-time patient data and pharmacokinetic modeling. The system independently calculates optimal doses without requiring manual calculation or intervention, using integrated algorithms that process patient characteristics, drug properties, and measured concentrations to generate personalized dosing recommendations.
2Loss of time
If time-constrained measurements are used, then loss of time is reduced, but reliability and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic dosing adjustments that adapt to real-time patient responses and pharmacokinetic changes. The system continuously updates dosing recommendations based on measured drug concentrations, patient characteristics, and individual pharmacokinetic parameters, allowing flexible adaptation to changing clinical conditions without requiring fixed time-constrained measurements.
Solution Approach 2:
The system incorporates feedback mechanisms by using measured drug concentrations to continuously refine and adjust dosing regimens. Real-time feedback from pharmacokinetic monitoring enables the system to optimize dosing parameters dynamically, improving reliability through iterative refinement while maintaining time efficiency through automated real-time processing.
3Device complexity
If fixed dosing regimens are applied, then device complexity is reduced, but adaptability and productivity deteriorate
Solution Approach 1:
The patent utilizes parameter changes by adjusting dosing parameters (dose amount, frequency, duration) based on individual patient characteristics, drug properties, and pharmacokinetic data. The system dynamically modifies these parameters to optimize treatment effectiveness for each patient while maintaining manageable complexity through automated parameter optimization algorithms.
Solution Approach 2:
The system applies local quality by tailoring dosing regimens to individual patient-specific characteristics such as weight, age, renal function, and drug metabolism. Each patient receives a customized dosing strategy optimized for their unique pharmacokinetic profile, enhancing adaptability while the automated nature of the system keeps overall complexity manageable.
4Device complexity
If manual dosing interpretation is performed, then device complexity is reduced, but productivity and reliability deteriorate
Solution Approach 1:
The patent replaces manual dosing interpretation with automated computer-based calculations that process patient data and pharmacokinetic models to generate dosing recommendations. This substitution of manual mechanical calculation with automated computational systems significantly improves productivity through rapid processing while maintaining or enhancing reliability through systematic algorithmic decision-making.
Data Source
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AI summary
The invention concerns a method determining an optimum drug dosing regimen to treat a patient efficiently against drug-sensitive pathogenic agents. From relevant patient-related data and the drug concentration measured at a random time in the patient's body, the method estimates the drug concentration time-course based on a Bayesian model. The residual concentration (Cr) and an efficiency pharmacokinetic parameter (PPE) adapted to said drug and patient are computed from the estimated concentration. A dosing regimen is then determined by comparing the PPE to an efficiency target (CE) for efficiency purposes. The method further may further take into account toxicity constraints by comparing the residual drug concentration (Cr) to at least a toxic concentration (Ctox). A concentration of said drug is determined (E13) based on the result the above comparisons to provide a proposed dose for said optimum dosing regimen.