Patient-Specific Dosing Using PK/PD Feedback Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current clinical practices rely on generic dosing regimens based on population data, leading to inefficiencies and increased risks due to underdosing or overdosing, particularly for medications with slow response times, necessitating a trial-and-error approach to optimize dosing.
Innovation Solution
A computerized system using medication-specific mathematical models and patient-specific responses to predict, propose, and evaluate personalized dosing regimens by integrating pharmacokinetic and pharmacodynamic models, refining them with Bayesian analysis to account for individual variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic dosing regimens based on population data are used, then ease of prescription is improved, but dosing precision deteriorates
Solution Approach 1:
The system performs preliminary calculations and predictions of optimal dosing regimens before the physician prescribes medication. By pre-computing personalized dosing recommendations based on patient-specific factors and pharmacokinetic/pharmacodynamic models, the system prepares accurate dosing information in advance, eliminating the need for trial-and-error adjustments while maintaining ease of prescription.
Solution Approach 2:
The system creates a virtual copy of the patient's physiological system through pharmacokinetic and pharmacodynamic modeling. This computational model replicates the patient's drug metabolism and response characteristics, allowing physicians to test and evaluate different dosing regimens in silico before actual administration, thereby achieving precise dosing without complex manual calculations.
2Measurement precision
If trial-and-error approach is used to optimize dosing, then dosing precision may be improved, but time consumption deteriorates
Solution Approach 1:
The system performs preliminary action by calculating the optimal dosing regimen in advance using pharmacokinetic/pharmacodynamic models and patient-specific data. This pre-computation eliminates the need for time-consuming trial-and-error adjustments during clinical practice, providing precise dosing recommendations immediately when needed.
Solution Approach 2:
The system incorporates feedback mechanisms where actual patient response data is continuously monitored and fed back into the pharmacokinetic/pharmacodynamic models. This feedback loop allows the system to refine and update dosing predictions in real-time, achieving high dosing precision without requiring multiple trial-and-error cycles, thereby reducing time consumption.
3Loss of energy
If generic dosing regimens are used, then resource efficiency is improved, but treatment effectiveness deteriorates
Solution Approach 1:
The system applies local quality by tailoring dosing regimens to each patient's specific characteristics rather than applying a uniform generic regimen. By considering individual patient factors such as age, weight, renal function, and genetic profile in conjunction with pharmacokinetic/pharmacodynamic modeling, the system optimizes dosing for each patient's unique needs, maximizing treatment effectiveness while avoiding resource waste from ineffective dosing.
Solution Approach 2:
The system dynamically adjusts dosing parameters based on patient-specific factors and observed treatment responses. By changing dosing parameters (dose amount, frequency, timing) according to individual patient characteristics modeled through pharmacokinetic/pharmacodynamic equations, the system achieves optimal treatment effectiveness for each patient while minimizing resource consumption compared to both generic dosing and trial-and-error approaches.
Data Source
AI summary
This disclosure relates to determining a personalized dose of a pharmaceutical for an individual. First data representative of one or more characteristics of the individual prior to administration of the pharmaceutical is received, and second data representative of a measurement of a physiological parameter of the individual after administration of the pharmaceutical is received. A computational model having pharmacokinetic and pharmacodynamic components is used to generate a first target concentration and one or more first doses determined to likely achieve the first target concentration for the pharmaceutical. The computational model is updated to reflect the measurement of the physiological parameter. A second target concentration and one or more second doses determined to likely achieve the second target concentration are generated, wherein the update to the pharmacodynamic component of the computational model is used to predict that the second target concentration will have a therapeutic effect on the individual.