Blood Processing Controller Optimizing Cell Yield and Purity
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Solution Overview
Problem
Current blood processing systems lack the ability to optimize the collection and treatment of blood components such as mononuclear cells (MNCs) and stem cells, as they fail to accurately target specific cell quantities and minimize cellular contamination, which is crucial for therapeutic applications like extracorporeal photopheresis and drug development.
Innovation Solution
A blood processing system equipped with a controller that uses historical data, pre-determined values, and real-time input to calculate and display optimized procedure settings for collecting and treating blood components, including a user interface for entering data and adjusting parameters like flow rates and contamination limits to ensure efficient and contaminant-minimized collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated blood processing systems are used to separate blood components, then collection efficiency is improved, but the ability to accurately target specific cell quantities and minimize contamination is insufficient
Solution Approach 1:
The system performs preliminary actions by obtaining pre-counts of blood components before the separation procedure and storing historical data from previous procedures. This preliminary data collection enables the controller to calculate optimized procedure settings in advance, ensuring accurate targeting of specific cell quantities while maintaining high collection efficiency.
Solution Approach 2:
The system implements feedback mechanisms by using historical data from previous blood processing procedures to continuously refine and optimize collection parameters. The controller analyzes past performance data to adjust procedure settings, improving both the accuracy of targeting cell quantities and minimizing contamination in subsequent procedures.
2Ease of operation
If standardized collection procedures are used, then operational simplicity is maintained, but the ability to optimize for specific therapeutic applications is limited
Solution Approach 1:
The system dynamically adapts collection procedures by allowing the controller to calculate optimized settings based on specific therapeutic requirements, patient-specific blood component pre-counts, and desired yields. This dynamic optimization capability enables customization for different therapeutic applications while maintaining ease of operation through automated calculations and user-friendly interfaces.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting procedure settings such as collection targets, contamination limits, and processing parameters based on the specific therapeutic application. The controller modifies these parameters automatically based on input data, providing versatility for different therapies without complicating the user interface.
3Adaptability or versatility
If manual optimization of collection procedures is attempted, then customization for specific therapies is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically calculating optimized procedure settings using the controller, which processes blood component pre-counts, desired yields, and historical data to determine optimal collection parameters. This automated self-optimization eliminates the need for manual procedure setup, providing customization for specific therapies without increasing operational complexity or time consumption.
Solution Approach 2:
The system replaces manual optimization processes with an automated electronic controller that uses algorithms to calculate optimized procedure settings. This substitution of mechanical/manual operations with automated computational processes enables rapid customization for different therapies while minimizing procedure setup time and operational complexity.
4Productivity
If collection procedures prioritize high yield, then productivity is improved, but cellular contamination increases
Solution Approach 1:
The system optimizes the balance between yield and contamination by dynamically adjusting collection parameters such as collection targets, contamination limits, and processing settings. The controller modifies these parameters based on blood component pre-counts, desired yields, and historical data to achieve high productivity while maintaining acceptable contamination levels specific to each therapeutic application.
Solution Approach 2:
The system performs preliminary analysis of blood component pre-counts and historical contamination data before the collection procedure to calculate optimized settings that balance yield and contamination. This preliminary optimization ensures that the collection procedure is configured to achieve high productivity while preventing excessive cellular contamination from the outset.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables precise optimization of blood component collection and treatment procedures, ensuring targeted yields with minimal contamination, thereby enhancing the effectiveness of therapies like ECP and improving the quality of collected cells for further processing.
Implementation Method 1
Mononuclear cells may be collected by introducing whole blood into a centrifuge chamber wherein the whole blood is separated into its constituent components based on the size and densities of the different components
Data Source
AI summary
Systems and methods for the optimization of blood collection and therapies using an automated blood cell separator are disclosed. The systems and methods calculate or determine recommended settings based on, among other things, one or more of historical collection data, set defaults, contamination limits, blood, and blood component characteristics.