Trending Technologies that Accelerate Process Development for CDMOs
Adopting a strategy that facilitates rapid and reliable development and scale-up of bioprocesses is an urgently growing priority for contract development and manufacturing organizations (CDMOs). Effective process development is, in fact, essential for achieving cost-effective CDMO operations. The most successful CDMOs, therefore, have identified strategies for completing projects efficiently and effectively while incorporating QbD approaches that provide increased process understanding and lead to optimal bioprocesses.
This article is posted on our Science Snippets Blog.
CDMOs can use data analytics and integrated technologies to create a strong and future-proof differentiated offering around QbD and continuous bioprocess approaches.
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The global demand for biologics is surging like never before – and so is the need for state-of-the art process development. This trend is evident if we look at the market statistics regarding the inter-scale distributions between lab, clinical and commercial operations at CDMOs. You can see in the chart below that lab and clinical scale efforts make up the majority of CDMO operations across many different modalities, including vaccines, cell and gene therapy, antibodies, biosimilars, and proteins and peptides.
Biopharmaceutical CDMOs: Distribution of Biologics by Operation
The question, then, becomes what is the most efficient and reliable way to accelerate and digitalize the development process – from cell line to scale-up?
Trends in Bioprocessing Development
Some of the top trends in the bioprocessing development space are related to digitalization and process efficiency. These include:
- In-Silico Experimentation. The hype around this disruptive technology in the pharma and biotech industries is real. In-silico-based tools are part of drug targeting, screening and discovery, clinical studies, and predictive analytics related to any risk involved.
- High-throughput Process Development. This trend involves the miniaturization, automation, and parallelization of process development activities in order to create a systematic approach for a time- and resource-efficient workflow. The idea of creating digital twins is a big part of this concept right now.
- Continuous Bioprocessing. The trends toward high-throughput process development and QbD reflect an overall push toward being able to operate in a continuous fashion. The intensification of both upstream and downstream operations will require higher levels of control during PD and presents new scale-up challenges.
Let’s Not Forget about QbD
Regulatory agencies are pushing for a quality by design approach because it meets the fundamental expectations of how bioprocess development should be done – in a way that supports effective documentation and process reproducibility. Following the QbD approach can help you develop a workflow that takes into account your objectives by defining your quality target product profile (QTPP), performing the proper risk assessment, including determining realistic goals for your certificate of a pharmaceutical product (CPP) and critical quality attributes (CQAs), and optimizing your process.
With QbD, you use a mathematics-based approach to modeling (typically Design of Experiments, DOE) which allows you to more fully understand the factors and responses that will affect your desired outcomes. This also helps you determine a more accurate design space characterization and link that to your control strategy. Each of these steps ultimately contributes to the underlying method of continuous process improvement, which along with online monitoring, can be applied during manufacturing.
An increased push toward QbD means that there is a growing need for process development expertise, which presents a real opportunity for CDMOs to step in and fill a gap that pharma partners may have. Meeting QbD requirements obviously increases the level of work that must be completed during the different process development phases, but the increased process understanding enables greater cost control in the long run. It results in a more robust process that is able to achieve higher yields and productivity while being able to minimize impurities and variations. In addition, it provides a method for trouble-shooting and achieving problem resolution more rapidly.
Why Should CDMOs Have Strong QbD Packages?
For CDMOs, having a strong QbD package and making this known to partners provides a competitive advantage in a number of ways. We covered this in more detail in a previous article and webinar.
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Some of the key reasons to develop a strong QbD package for your CDMO:
Demonstrate Expertise
Being able to demonstrate expertise, not only in the science behind biopharma development but also in creating a robust process that will enable scale-up and create reproducible results, is a key differentiator that will attract pharma and biopharma partners. QbD plays an essential role in ensuring your processes can withstand regulatory scrutiny.
Overcome Resource Crunches
CDMOs are under timeline pressure to meet delivery dates and stay within budgets. Overcoming resource pressure becomes more realistic when you have a process that is developed to minimize the risk of failure.
Ensure Well-Developed, Scalable Processes
QbD helps ensure that quality is built into the development process and that the required regulatory documentation is in place for any process adaptations. And at the same time, QbD actually helps minimize such changes because your process is optimized at the beginning without needing frequent adjustments later.
Reduce Risks and Delays
With QbD, risk assessment is built into the process by tying your QTPP and critical quality attributes to your process. The control strategy becomes an integral part of the process from the start. This sort of quality risk management is what is behind the ICHQ8/Q9/Q10 guidelines.
Attract Potential Partners
For CDMOs, offering QbD expertise is a way to differentiate yourself from the competition. The key thing to keep in mind is that in order to support QbD, you also have to develop the internal organization expertise needed in both data analytics and process development.
Your goal should be to develop clear strategies and procedures for managing QbD projects. Having a dedicated program being committed to the idea of innovation and continuous improvement, especially in the difficult CDMO market, creates a very real advantage.
“The most successful CDMOs, therefore have identified strategies for completing process development projects officially and efficiently while incorporating the DOE and QbD approaches that provide increased process understanding and lead to optimal processes.”
Craig Flyte, GSK, CMO Alliance and program management partner
Sartorius Instruments Support QbD with Integrated Data Analytics
Several product lines in the Sartorius portfolio of biopharma instruments have been developed specifically with QbD in mind. This means that they offer features that focus on automation, control and, of course, scalability.
One example of this is the Sartorius Ambr® family. Ambr® systems simplify the adoption of QbD principals through their ability to easily integrate PAT, DOE, and MVDA techniques across experiments. Both MODDE®-Q and SIMCA®-Q can easily pull and push data from Ambr® 15 and 250 systems to speed up experimental design and data analysis.
When using MODDE®-Q, users can automatically import set points and extract experiment responses from Ambr® 15 and 250 experiments. But it’s not only the endpoints – you can also look at calculations for minimums, maximums and averages. MODDE®-Q can also automatically account for the different system types that you might have, whether it's a 12-vessel configuration, 24-vessel configuration, limitations of the culture station, or varying experimental conditions across locations.
You can quickly and easily generate tables and charts and see the time course of what happened during your experiments. You can investigate experiments via the audit trail to see exactly what happened and when, and you can actually combine and visualize experiments from multiple experiments at the same time within the results of your application.
When to Use MVDA vs DOE
Both Design of Experiments (DOE) and Multivariate Data Analysis (MVDA) tools from Umetrics can connect to Sartorius Ambr® systems. But do you when know to use MVDA vs. DOE?
DOE is a systematic approach to building your experimental designs that enables you to understand cause and effect relationships, as well as to understand the design space. DOE helps you select the right variables to adjust in order to test how they affect the outcomes (responses) and to do this in the most efficient way possible, with the fewest experiment variations.
With DOE, you’re looking at two things: factors and responses. Factors are the variables in the study that you believe may influence the responses, or results, in the bioreactor. They may be the process parameters like pH, feed timing, temperature or oxygen levels.
Responses are the effects that you’re concerned about in your cell culture process. This is often the level of productivity of the system, the viability, titer, VCD, doubling time, etc…
So, while DOE is about generating data, MVDA is more about interpreting the results of that data. And specifically, for high throughput systems where many experiments are done and lots of experiments are generating data, MVDA can be really useful in helping you interpret the data and understand which factors contributed to the responses you’re seeing.
So how do you know when to use SIMCA® (MVDA) and when to use MODDE® (DOE)?
Consider this list of areas in which MODDE® DOE is useful:
- Media components screening
- Cell line and clone screening
- Bioreactor condition optimization
- Feed strategy optimization
- Bioreactor characterization
- Scale-up/down and model development
SIMCA® MVDA is useful for:
- Bioreactor condition optimization
- Feed strategy optimization
- Spectroscopy calibration modeling
- Scale-up/down model verification
Case Examples for DOE
Take a look at some examples of how DOE was used with Ambr® to create efficient experiments that provided useful insights.
Media Component Screening. In this example, a new approach to media component screening was achieved using DOE principles to systematically screen for productivity effects, make the interactions apparent, and find them early in the process.
Challenge:
- Not all commercially available media supports every cell line, therefore in some cases additional supplements must be added to improve cell performance
- The classical approach to media component screening is time consuming and labor-intensive
Data-driven solution:
- Use DOE principles to systematically screen components for productivity effects
- Recognize interaction and define them early in the process, setting optimum levels accordingly
Bioreactor Optimization and Characterization. In this example, DOE was used to identify the process parameters that directly impact product quality and yield in bioreactors. The example covers a transient transection for bioreactor optimization and characterization.
Challenge:
- A significant challenge for developing viral vector gene therapies is ensuring that it is well characterized and can be scaled up
Data-driven solution:
- Use DOE principles to identify which process parameters impact product quality and yield
- Justify and adjust manufacturing operating ranges (control strategy) and acceptance criteria
Ambr® MVDA Examples
Spectroscopy Calibration Modeling. This example from GSK shows a Raman application, which is one of many different kinds of spectroscopy data that can be analyzed with MVDA. Spectroscopy and PAT devices produce huge amounts of complex data that MVDA can help analyze, including providing accurate prediction models for analyte concentrations that can be used to optimize monitoring and control in the future.
Challenge:
- PAT tools (such as spectral devices, advanced sensors and analyzers) can generate large amounts of complex data presenting a challenge when it comes to interpreting accurately
Data-driven solution:
- MVDA is a fast and flexible spectral calibration tool that can handle multiple types of spectral data (NIR, IR, Raman, fluorescence, mass-spec)
- MVDA provides accurate prediction models for analyte concentrations and can be used to optimize monitoring and control of culture
Feed-Strategy Optimization. In this case, MVDA was used to rank the potential inhibitors or promoters of cell culture productivity in order to optimize the feed strategy, which can be a significant challenge in cell culture.
Challenge:
- Predict the nutritional requirements of the culture so that an appropriate feeding strategy can be implemented
Data-driven solution:
- Use MVDA to identify and rank any potential inhibitors or promotors of cell-culture productivity in order to optimize feed strategy
Combining Both DOE & MVDA
In some cases, utilizing both DOE and MVDA can create the best results. An example of this is in developing and validating a scale-down model for Ambr® systems. When using MVDA to develop scale-down models, the goal is to decrease the distance between the scale-down runs and the target scale. To do this, we can use DOE to evaluate the scale difference and give suggestions as to how to change operating parameters to reduce it.
The yellow dot in the multivariate chart below represents the 5-L reference scale and the grey dots are the Ambr® runs. Using DOE, we want to identify which operating parameters to adjust in order to decrease the distance between the scale-down runs (grey) and the target scale (yellow). What the DOE results tells us in the figure (right) below is that we can reduce scale differences by lowering the headspace and stirrer speed in the 5-L scale.
Once these changes suggested by DOE were implemented, the experiment was repeated, shown in the multivariate chart below. These results show that using the combined MVDA-DOE approach, we were able to decrease the distance between the 5-liter reference scale (yellow) and the Ambr® (grey) scale.
In order to validate this approach, we compared the MVDA-based scaling to single parameter only results (parameters such as set point scaling, tip speed and P/V). And what we see (below) is that the MVDA scaling really delivered superior process comparability.
Creating Advanced Offerings
These case studies and examples for uses of DOE and MVDA highlight how CDMOs can use data analytics and integrated technologies to create a strong and future-proof differentiated offering around QbD and continuous bioprocess approaches.
Want to Know More?
Watch a recorded webinar “Putting the D in CDMO” which goes into more details about the technologies and strategies that CDMOs can use to accelerate process development and make their companies more profitable.
Find resources and tools on our CDMO page.