Scale Down

Scale down refers to the practice of simulating large-scale in smaller laboratory-scale systems, recreating the conditions and challenges that cells might experience in large bioreactors – e.g. heterogeneity conditions that result in gradients. This process involves the complex task of configuring a laboratory-scale model to emulate the physical, chemical, and chemical conditions of larger bioreactors. Critical variables to test include nutrient availability, oxygen levels, pH, temperature, shear stress, and mixing patterns.

Why are scaling down processes important?

Scale-down process plays a crucial role in the bioprocess industry for a number of reasons:

Process Development and Optimization: scale down processes because their capabilities to recreate large processes at a smaller scale result in cost-effective and rapid ways to test conditions, such as growth media, temperature, pH, or oxygen scale.

Understanding Process Challenges: large-scale reactors face typical problems, such as mixing or aeration, which can be overseen in the process development. These issues can significantly impact industrial production, but scale-down processes can help identify these potential challenges.

Regulatory requirements: regulatory authorities require a demonstration of process understanding and control in manufacturing. Scale-down models contribute to proving this understanding as part of the quality by design.

Two approaches arise in recreating the physical, chemical, and biological parameters that cells encounter at a large scale in small reactors.

Statistical scale-down

This approach aims to identify the critical factors influencing the process outcome and simulate their effects using statistical analysis. In this type of scale-down, lab-scale reactors are used to perform a large number of experiments to generate a matrix of variables corresponding to conditions that affect the performance of the bioprocess. This method is less time-consuming and costly, but on the other hand, it can lack the fidelity of a physical scale-down.

Generally, statistical scale-down involves designing experiments that will capture the variability of large-scale processes, followed by data collection of both the variable conditions and response variables. Based on the data, a statistical model can be constructed describing the process performance, which will finally be validated based on the accuracy of its predictions.

Physical scale-down

This approach aims to simulate large-scale conditions, mimicking heterogeneous conditions encountered in small-scale reactors. To achieve this, laboratory-scale bioreactors are engineered. These smaller-scale reactors reproduce environmental conditions and simulated physicochemical parameters, such as gradients, which will introduce heterogeneity using approaches like intermittent feed or pulsing air flow. Detailed monitoring of cell behaviors will show how they react to the different conditions in the reactor and how they affect growth, product formation, and nutrient consumption, among other parameters. The results obtained from physical scale-down will then go through an iterative optimization process, helping to improve the efficiency of the process.


BioXplorers can play a fundamental role in scale-down process analysis. Using H.E.L’s WinISO, BioXplorer 100 and 400 can be finely controlled to replicate the conditions found at a larger scale accurately. Multiple reactors can be used to increase the throughput of the process, positioning them as a potent tool for statistical scale-down processes. On the other hand, larger reactors, such as BioXplorer 5000, can be used to replicate complex conditions, such as physicochemical gradients, and therefore can be instrumental in physical scale-down processes. The application of scale-down processes using BioXplorer models results in significant time and cost reductions, underscoring their importance in efficient and economical bioprocess management.

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