How Lab Simulators are Changing the Game to Accelerate Biotherapeutic Drug Development

The concept of the digital twin is anchored in Industry 4.0 – the trend toward automation and data exchange in the so-called fourth industrial revolution. This revolution enables a more holistic and better-connected environment by pairing physical manufacturing with smart digital technology, such as computational models, that can be used to predict process function.

Scientists at Boehringer Ingelheim are leveraging these advances to revolutionize biotherapeutic drug development. They’re creating digital copies of experiments that would previously have been performed in the laboratory to model production outcomes, aiming to understand and predict results under conditions that cannot normally be tested in the laboratory environment. Until now, the wet lab experiments have been the ‘gold standard’ for modeling production variables, but there are some limitations, particularly in relation to manufacturing at large scale, where the digital twin is at least as good, and in some cases better in its ability to predict results.

The essential ingredient for a successful digital twin is the mechanistic model behind it. The more accurate the model, the better the digital twin.

Joey Studts, Director of Late-Stage Downstream Process Development at Boehringer Ingelheim believes this approach is fundamental to future success. “My team took an interest in modeling because of two key aspects. Firstly, we have a lot of data from our internal biotherapeutics pipeline as well as many years of experience as a leading contract manufacturer that can help us to better understand the accuracy of our models. Secondly, we believe this technology is the biggest game-changer for the industry in over 20 years because we can generate much more understanding with significantly less effort and cost.”

The goal was to use mathematical algorithms to develop a model that simulated real, or ‘wet lab’, experiments on a scale large enough and robust enough to predict results that would be approved by regulatory authorities, and to verify these models with real-world data sets from our production facilities.

The model focuses on predicting differences that emerge when production is scaled up to enable a better understanding of bioprocesses and consequently improvements in antibody manufacturing. The ultimate goal is to increase the speed of drug development.

Studts added, “We practice executing the processes in a simulated environment. So instead of doing the 10s of experiments that we would need to do in the wet labs that would take weeks or months and cost hundreds of thousands of dollars, we can now do experiments on a scale of 1,000s a day. And all at the click of a button and with a really smart person running the computer! The computer simulations allow us to explore the processes much more rapidly. Therefore, we understand them better, enabling us to predict and control what’s going to happen at the manufacturing scale for years to come.”

The model is a combination of tried and trusted mathematical equations, novel algorithms and statistical models to improve prediction, together with extensive scientific knowledge. “Some of the equations behind the models have been around for 30 years or more. What’s new is how we apply these models and how we validate the models. We use the mathematical equations to precisely understand how our molecules behave at the manufacturing scale and combine this with a deep knowledge of the process resulting in a powerful tool,” said Studts.  With this type of knowledge, only a small amount of data is needed to gain an in-depth understanding of the process and to predict how a specific product and process will perform.

The team’s work has resulted in a model that requires around 20 simple wet lab experiments, which are easy and quick to perform compared to the 100s of labor-intensive ones that used to be conducted. With the model, based on the 20 simple experiments, the scientist can carry out 1,000s of in silico experiments to greatly enhance our process understanding. They have demonstrated that the in silico process they have described in published works^1,2,3 will represent what happens when a drug is manufactured at full scale. And this industry-leading approach has now been given the green light by the US FDA’s Emerging Technology Team that has agreed to the use of the model in regulatory submissions if supported by model validation data for the specific product and process step.⁴

Crucially, this technology will deliver even greater consistency in manufacturing, particularly relevant for a complex biotherapeutic medicine. This means doctors and patients will get the reliability and consistency they expect from a small molecule therapeutic with a large molecule medicine. For Joey Studts, these patient benefits are what drives him and his team. “We’re greatly improving our ability to achieve our first4patients ambition by getting medicines to patients more safely, and with the effects on their disease they expect, with the hope that this early experience will lead to faster and more cost-effective drug development.”

References

1. D. Saleh et al., Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications. Biotechnol Progress (2020)
2. D. Saleh et al., Cross-scale quality assessment of a mechanistic cation exchange chromatography model. Biotechnol Progress (2021)
3. D. Saleh et al., In silico process characterization for biopharmaceutical development following the Quality by Design concept. Biotechnol Progress (2021)
4. https://www.linkedin.com/posts/jan-visser-824b6018_bioprocessing-digitalinnovation-proteinpurification-activity-6809866162835394560-a02D