Computational Drug Combination Optimization for Acute Heart Failure
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Solution Overview
Problem
Current methods for determining optimal drug combinations for treating acute heart failure are inadequate due to the complexity of pharmacologic effects and unclear interactions between multiple drugs, leading to inaccurate clinical decision-making and high mortality rates.
Innovation Solution
A computer-based system that receives current cardiovascular performance metrics and candidate drugs to determine optimal dosages, optimizing drug combinations to achieve desired cardiovascular parameters and performance metrics through a mapping process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple drugs are used to treat acute heart failure, then treatment effectiveness is improved, but drug interaction complexity increases
Solution Approach 1:
The patent segments the complex drug interaction problem into individual drug component analyses. Each drug's effect on cardiovascular parameters is modeled separately, and these individual effects are then combined to determine the optimal drug combination. This segmentation allows the system to handle multiple drugs without being overwhelmed by the complexity of their interactions.
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between drug dosages and cardiovascular outcomes. This intermediary system uses mathematical models to predict how different drug combinations affect cardiovascular parameters, eliminating the need for complex direct analysis of drug-drug interactions while still achieving accurate treatment optimization.
2Ease of operation
If conventional trial and error methods are used for drug combination selection, then clinical decision-making simplicity is maintained, but accuracy of treatment determination deteriorates
Solution Approach 1:
The patent replaces the mechanical trial-and-error clinical decision-making process with a computational optimization system. The system uses mathematical models and algorithms to automatically determine optimal drug combinations, substituting the simplistic but inaccurate manual approach with a sophisticated computational approach that maintains ease of use while dramatically improving accuracy.
Solution Approach 2:
The patent creates a virtual copy of the patient's cardiovascular system through mathematical modeling. This computational model replicates the complex physiological responses to drug treatments, allowing clinicians to test and optimize drug combinations in silico before implementation, thereby achieving high accuracy without complicating the actual clinical decision-making process.
3Adaptability or versatility
If dosage optimization considers multiple cardiovascular parameters, then treatment comprehensiveness is improved, but computational complexity increases
Solution Approach 1:
The patent transforms the complex multi-parameter optimization problem into a more manageable form by changing the mathematical parameters and relationships in the model. The system uses parameter transformations and normalization techniques that allow comprehensive consideration of multiple cardiovascular parameters while reducing the computational burden through efficient mathematical formulations.
Data Source
AI summary
Embodiments disclosed herein may include operations of receiving a plurality of current cardiovascular performance metrics of a patient and a plurality of candidate drugs to be used to reach a plurality of desired cardiovascular performance metrics and determining optimal dosages of the plurality of candidate drugs to reach the plurality of desired cardiovascular performance metrics. The determining may include optimizing a dosage combination of the plurality of candidate drugs to reach a plurality of desired cardiovascular parameters, corresponding to the plurality of desired cardiovascular performance metrics, from a plurality of current cardiovascular parameters corresponding to the plurality of current cardiovascular performance metrics and mapping the plurality of desired cardiovascular performance metrics from the plurality of desired cardiovascular parameters and the plurality' of current cardiovascular performance metrics from the plurality of current cardiovascular parameters. The operations may further include outputting