Simplifying the Scaling Process Between Bioreactors

Whether you want to mimic your full-scale production process or scale up your process from a small bioreactor, using the right equipment, technologies and tools can make all the difference. Getting it right means a bioprocess that meets your critical quality attributes versus one that flags regulatory scrutiny (or worse) or fails to deliver the quality and product volume you need.

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Why is scaling critical to biopharmaceutical manufacture?

For one thing, regulators require manufacturers to demonstrate control of product quality across changing scales. For another, a qualified down-scale model is required prior to commencing process characterization work.

Consider the different steps in the upstream bioprocess lifecycle. The process development and process characterization stages typically are performed at smaller scales (5 liters or less). Whereas pilots and production scale could be anywhere from 50 liters up to 25,000 or more liters. That’s a huge difference. It’s not surprising, then, that the process parameters between them are going to change. The question is how to maintain acceptable process performance and product quality while scaling.

Key Considerations for Scaling Bioreactors

What are some of the considerations to think about when changing bioreactor scales?

You may have to change scales multiple times
Scaling can involve very large changes in scale
Single-use bioreactors are more and more commonly used within the industry (at large scale or small scale)
The bioreactors you’re using may have different characteristics or geometries
Very large or very small bioreactors are likely to behave differently

What Can You Achieve with Bioreactor Scaling?

Typically, scaling a process up means using larger volumes of product for clinical or commercial manufacture. When scaling down, you’re developing a representative downscale model that can be used for troubleshooting and process characterization.

Fundamentally, with any change in scale, the aim is to avoid significant impacts or changes to the key performance indicators (KPIs) and the critical quality attributes (CQAs), which affect product safety and efficacy.

To do this, it’s necessary to mimic the environment the cells are exposed to as closely as possible between bioreactor scales. And from a cost point of view, you want to minimize the number of runs to be carried out at a large scale.

The complex nature of the bioreactor environment means you have a large number of factors to consider that could have an influence on the quality and process. The way in which you control the bioreactor will influence these factors as well as the important scale-related factors, Such as kLa and PPV.

Some of the factors that have to be considered when scaling up or down include:

kLa is the measure of how well the bioreactor can transfer oxygen from the gas to the liquid phase. This will be influenced by agitation, gassing rates and type; media properties, and vessel configuration; as well as other factors. This determines the oxygen limitation of the bioreactor system.
Power per (unit) volume is how much energy is transferred from the stirrer to the liquid and is influenced by the impeller and bioreactor design, as well as the media properties and the stirring rate. This influences shear, mixing, and oxygen transfer in the bioreactor.
Mixing time is the amount of time it takes to achieve homogeneity in the liquid, which will be influenced by the impeller and bioreactor configuration as well as the stirring and gassing rate. This parameter ensures that you achieve even nutrient, cell, and gas distribution.
Tip speed is a measure of how fast the impeller tip is moving through the liquid and is influenced by the rotational speed of the impeller and the impeller size. It is an important factor to consider for mechanical shear.
Gas transfer defines how gas is delivered to the bioreactor and is influenced by the gassing rate, sparger type, and pressure. It plays an important role in gas velocity, bubble shear, CO2 stripping, foam, and kLa.

Bioreactor Environment Creates Design Space for Scaling

The design space is defined by the bioreactor parameters and their impact on the physical and biological factors, which are unique for each bioreactor scale. The boundaries are made up of factors such as the shear limit, heterogeneity, and foam bubble formation, as well as oxygen limitation, and CO2 accumulation.

You’ll obtain the best results for your cell culture by staying within this design space. Determining what the limits of the space are and knowing how they change as the scale of your bioreactor changes is critical. Typically, the design space becomes smaller as your production scale increases, which is important to remember when you’re scaling from one bioreactor to another.

Challenges for Upscaling and Downscaling

Upscaling. Consider how you can translate the process up the scales with the lowest risk. Because the design space is typically smaller and the cost is higher when running at a large scale, you want to decrease the risk at the larger scale. In order to stay within the correct design space, you need to understand:

What are the key factors and drivers for the processes?
How might these change with scaling up?
Is this the final scale or is there an intermediate scale?
What are the limitations of the larger-scale bioreactor?

Downscaling. Here you want to develop a small-scale process that closely matches the larger scale. You may have to adjust the lab model to match the large scale, but you may not realize the full potential of the process in a small scale.

It’s often easier to scale down than to scale up, due to the larger design space in smaller scales.
You need to understand the limitations of the small-scale bioreactor in terms of volume changes or vaporization rates.
Some of the adjustments challenges you might face when using very small-scale bioreactors would include increased agitation speeds, increased evaporation loss, larger proportional sampling volumes, increased foaming, and differences in CO2 accumulation.

Some other considerations to make with scaling miniature bioreactors are:

Can you achieve the same kLa across the scale changes? It might be higher or lower in the smaller bioreactors compared to the larger ones.
For a similar tip speed, power per volume is typically higher in miniature bioreactors. How does that affect your scaling?
Mixing time will always be far higher in a larger scale compared to the smaller scale.
Miniature reactors tend to require more antifoam Does this impact kLa gassing and mixing?

Limitations of Scaling Using Single Parameters

Often, the approach taken to bioreactor scaling is a single parameter matching approach, i.e. you match the tip speed or PPV of one reactor to another. This approach can achieve acceptable scaling but often can be limiting and may not produce effective scaling Some of the limitations of this approach are:

The bioreactor parameters, such as volume, can vary significantly during the process, which will impact scaling factors such as PPV
Fixing one factor can neglect the impact of other factors
Multiple scaling factors are intrinsically linked
The sensitivities are different between scales

Scaling can be highly complex since you are looking at multiple factors across multiple scales with a wide range of settings. It often requires a fine balance between factors selected, particularly with large changes in scale. Without having a way to manage multiple variables, you may have to compromise between the factors to achieve efficient scaling, and you may not have a way to consider the entire design space.

Another approach is to use data analytics, along with equipment and technologies designed to support scale-up will improve your success.

Elements to Improve the Scale-Up Process

Bioreactor geometric similarity is the foundation to simplifying bioreactor scaling. This ensures that you can achieve constant mixing and oxygen transfer rates while reducing the risk of cell damage during scaling. Sartorius bioreactors have a classic stirred tank design with a top stirred center shaft, a constant vessel height to vessel diameter ratio, and constant impeller diameter to vessel diameter ratios.

BioPAT^® Process Insights facilitates simple scale conversion between Sartorius bioreactors, provides a deeper understanding of the performance of all bioreactors, and can help define the design space for scaling for each reactor. It provides a translation of settings and processes between bioreactor scales. The software can answer complex scaling problems between multiple scales, with the ability to set numerous scaling criteria and limitations. This saves time and effort, reduces the need for complex spreadsheets, and can shorten the time required for in-house scaling experiments.

Data Analytics tools allow you to define and analyze experiments to explore the design space and demonstrate robust, successful scaling.

With DOE (Design of Experiments) tools you can explore the design space, understand the influence of the scaling factors on the responses, and provide a systematic refinement to the scaling approach.
With Multivariate Data Analysis (MVDA), you can compress the complete process information into a single point, help understand the differences between the bioreactor runs, and demonstrate and validate successful scaling based on the entire data set.

After scaling with your bioreactor process using BioPAT^® Process Insights, the next step is to use MVDA and DOE to assess and refine scaling.

Data Analytics Helps Scale Up

Small bioreactors can sometimes be challenging to scale optimally, but by using tools such as bioreactors designed for geometric scale-up, process insights software, and tools for data analytics, you can save time and de-risk the transfer between scales. With the right data analytics system, you can create a robust downscale model and provide a full understanding of the design space for all bioreactors being used.

Find out more about how to use the data generated in the small scale for control charts in larger scales and how to use statistical process control to monitor complex processes at larger scales.

Learn more about Using Small-Scale Models as a Starting Point for Large-Scale Multivariate Control.

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