Real-Time Bioprocess Development Using Parallel Experiment Feedback
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Solution Overview
Problem
Bioprocess development faces challenges due to limited material supply, inefficiencies in early-stage testing, and the need for high protein concentrations, leading to high costs and resource demands, particularly in assessing therapeutic protein candidates for manufacturing processes, where small-scale assessments do not accurately represent manufacturing conditions and result in significant material waste and delayed results.
Innovation Solution
The method involves automating process development by dynamically modifying experimental parameters in real-time based on data from parallel experiments, allowing for more efficient use of valuable materials and reducing the number of experimentation cycles, thereby shortening development time and increasing data yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If small-scale assessments are used to test therapeutic protein candidates, then material consumption is reduced, but the assessments do not accurately represent manufacturing conditions and require multiple iterations
Solution Approach 1:
The system dynamically adjusts experiment parameters in real-time based on data from parallel experiments, allowing the experimental conditions to evolve and improve representativeness without requiring multiple static iterations. This dynamic adaptation enables the system to capture manufacturing-like conditions progressively.
Solution Approach 2:
The invention creates a scaled-down model system that replicates manufacturing conditions through carefully designed parallel experiments. By copying the essential features of manufacturing processes at smaller scale, the system achieves representativeness while reducing material consumption.
2Ease of operation
If traditional static experimental designs are used, then experimental protocols are simple to execute, but development cycle time is extended and material waste increases
Solution Approach 1:
The system implements real-time feedback mechanisms where data from parallel experiments continuously informs parameter adjustments. This feedback loop enables dynamic optimization during execution, reducing development time while maintaining operational simplicity through automated control.
Solution Approach 2:
The system performs preliminary experiments in parallel to gather data that informs subsequent parameter adjustments. This preliminary action enables faster convergence to optimal conditions without extending the overall development cycle.
3Quantity of substance
If high protein concentrations are required for injectable formulations, then therapeutic efficacy is improved, but the amount of material needed for testing increases significantly
Solution Approach 1:
The invention segments the testing process into multiple parallel experiments, each using smaller volumes of high-concentration material. By dividing the total material requirement across several simultaneous tests, the system achieves necessary protein concentrations while reducing overall material consumption.
Solution Approach 2:
The parallel experiment system serves multiple functions simultaneously: testing different parameters, validating manufacturing conditions, and optimizing formulations all in one integrated platform. This multi-functionality reduces the total material needed compared to sequential testing approaches.
4Measurement precision
If multiple candidate molecules are assessed through comprehensive testing, then selection accuracy is improved, but the resource demand and cost increase significantly
Solution Approach 1:
The system merges multiple candidate assessments into a unified parallel experiment platform. By combining testing of different molecules, parameters, and conditions in a single integrated system, the invention achieves comprehensive evaluation accuracy while reducing overall resource demand through shared infrastructure.
Data Source
AI summary
A method for automating process development in a bioprocessing environment is provided. The method comprising: executing a first experiment run according to a set of parameters; retrieving a first real-time set of data of the experiment run while the experiment run is being executed; retrieving a second real-time set of data of an experiment run being executed in parallel, analysing the retrieved first real-time set of data and the second real-time set of data to determine an adjusted set of parameters; and, modifying, based on the analysis, the parameters upon which the experiment run is being executed during execution of the run such that the run continues to be executed according to the modified set of parameters. A system, computer program and computer readable medium are also provided.