Digital twins have become popular in traditional manufacturing as a way to make assembly lines more efficient and consistent. So it’s natural to ask if this same approach can work for bioprocessing and GMP production. In conventional manufacturing, digital twins usually represent real-time digital models of the operational and mechanical properties of machines, products, or systems. But by using a slightly broader definition of a digital twin, and integrating concepts from statistical quality control, digital twins can serve as powerful tools to drive consistency, predict quality outcomes, and support compliance.
What is a Digital Twin?
The narrowest definition of a digital twin is a digital model of a physical object or system that uses its mechanical properties and other relevant physical properties to predict how changes to certain measurable or controllable inputs will affect outputs that are harder to observe directly.
For example, when putting multiple parts together in a complex linkage, making one part slightly larger or smaller may shift the rest of the assembly so that two completely different parts interfere with each other. It’s impossible to directly measure this interference until you’ve put most of the assembly together. A digital twin, on the other hand, can identify that this issue will arise as soon as that first part is created.
By understanding the relationship between the size of the first part and the interference of the other parts, operators can preemptively address the issue by either not using the incorrectly sized part or making other changes that will ensure its size is appropriate.
Digital Twins for Biopharmaceutical Manufacturing
In bioprocessing, biopharmaceutical manufacturing, and other biological contexts, you are not modeling rigid mechanical systems, but the core idea of a digital twin can still be applied.
The goal of a digital twin is to predict the properties that matter at the end of the manufacturing process based on properties that are measurable, or even controllable, at the beginning. All that’s necessary for this is an understanding of the relationship between what you care about and what you can actually measure.
In traditional manufacturing, models are often based on the physical design of the object and directly model causality. In bioprocessing, you don’t need to fully model every biological component to gain similar benefits. As long as a model is reasonably accurate—whether mechanistic or statistical—you can get valuable predictive insights.
And as modern bioprocessing software allows labs to collect larger volumes of real-time data, building these models is becoming increasingly practical.
Learning From Statistical Process Control
Statistical process control (SPC), is the application of statistical models to improve manufacturing processes. While it traces its origins to the 1920s, it really took off around World War II, with the introduction of tools such as control charts to more deliberately model and manage variability in production.
The main premise of SPC, and control charts in particular, is that you can improve quality and efficiency by using statistical models to monitor process behavior, without needing to directly model the mechanical properties of the output.
Digital twins based on physical or mechanistic models are often more detailed than classical SPC models. But they’re not the only way to improve process outcomes—statistical models still play a vital role, especially when paired with rich process data.
Accurate Models of Biological Processes
The models available for a biological digital twin can mostly be split into two categories: models that use biological knowledge to narrow down the set of possible correlations, and models that don’t.
The models that leverage biological knowledge include things like signaling pathway models that make assumptions about how levels of each gene or protein will impact the others. These models may use observed data to tune how they calculate these correlations, but they are inherently constrained by the structure of known biology.
The second class of models can range from simple regression models to complex neural networks and beyond. These approaches look for correlations in the data, with minimal domain-specific assumptions, which allows them to discover unexpected patterns but also increases the risk of overfitting.
The problem with biology-aware models is that they are by necessity built on a highly simplified understanding of very complex biology. So they are limited in the kinds of phenomena they can model.
The problem with biology-unaware (data-driven) models is that they can’t easily distinguish real patterns from noise, especially when the data is sparse.
Thus, biology-aware models tend to perform better when less data is available, while data-driven models excel when large, high-quality datasets exist. In many cases, it’s possible, and often preferable, to combine the two into a hybrid model that balances the strengths of both approaches.
Identifying Key Factors for Quality Control
The first step in building a digital twin is to identify the practically measurable factors at the beginning of a production process that correlate with the quality measures you care about at the end.
Some of these factors can be anticipated based on your understanding of the underlying process. But others may be less obvious. Manufacturing facilities have found unexpected influences, such as the brand of air filter used or the specific location where materials were stored, can significantly affect the final result. So it’s best to cast a wide net during the discovery phase.
Next, you’ll need to capture as much data as possible on these factors from every available production run. This is where the right bioprocessing software can really make a difference—automating data collection and standardizing inputs to improve model accuracy.
Once you have that data, you’re ready to begin building a model.
Applying Digital Twins for Quality Control
Once you’re able to understand correlations between initial factors and downstream quality measures, there are several ways you can use this to improve quality, though some may require additional investigation.
The most straightforward approach is to proactively halt a production process when the digital twin predicts the result is likely to fail quality controls. This can save significant time and expense by cutting your losses early.
A second approach is to adjust controllable factors before the process begins, using the digital twin to simulate scenarios and identify settings that are likely to produce a successful outcome. If this works, it’s a much better resolution.
However, this is where the difference between correlation and causation becomes critical. If your digital twin is based on correlations alone, there’s no guarantee that adjusting those inputs will actually improve the outcome.
That’s where a third application comes in: using the digital twin to simulate a wide range of hypothetical conditions and uncover potential contributors to process variability. You can then use these insights to guide further investigations to determine if the relationships are truly causal.
For example, if your model reveals that the process fails whenever reagents are stored on the left side of the storage room, further investigation might uncover that this side is exposed to direct sunlight. Then you can test this exposure as a potential causal factor.
Once you validate these causal relationships, you can adjust upstream processing to ensure more consistent downstream performance i.
Conclusion
Digital twins have proven highly effective for quality control in traditional manufacturing. While bioprocessing and biopharmaceutical production lack those rigid physical structures, the core idea of using measurable inputs to predict and optimize outcomes still holds. By uncovering key process drivers and enabling proactive interventions, digital twins can help achieve more predictable, consistent product quality in biomanufacturing.
