Created with Sketch.

About the Interviewee:

Dr Jochen Gerlach is a chemist who has worked at Boehringer Ingelheim since 2012 and is Head of Manufacturing Science at the Vienna biopharmaceuticals facility. The new large-scale cell culture (LSCC) production facility there is scheduled to commence operations in 2021. The investment is around 700 million euros, including infrastructure, and 500 new jobs will be created. Boehringer Ingelheim has been one of the industry’s pioneers in biological molecule production since the 1980s.

Created with Sketch.

“As complex as a jumbo jet”

Biomolecules from an AI-driven factory, constantly enhanced by self-learning algorithms: this is the vision that biopharmaceutical specialists from Vienna are working on with data experts at Boehringer Ingelheim’s BI X digital incubator. So far, they have only been dealing with one step in the process – fermentation. If the approach works for the whole process chain, it will open up completely new possibilities. Dr Jochen Gerlach, project owner of Smart Process Design, explains what the initiative has achieved to date – and where the journey is going.

Mr Gerlach, do computers make better researchers?

Jochen Gerlach (JG) (laughs) No. But they help people to interpret data. The best approach is for people and computers to research together.

Is that the idea behind your Smart Process Design project? To create an intelligent assistant for conducting research into production processes?

JG You could say that. The development of biopharmaceutical production processes in the laboratory is very complex, expensive and demands a high degree of knowledge and experience. We have a software solution in mind that contains a model of the complete production process. It could then analyse this model and make predictions. Our goal is to discover the optimal production process.

Is the production of biopharmaceuticals more complex than the conventional, purely chemical manufacture of medicines?

JG In production we use living organisms that react extremely sensitively to their environment. In addition, biopharmaceutical active ingredients are as a rule highly complex biomolecules. If a standard chemical active ingredient has as complex a structure as a bicycle, then an antibody is equivalent to a jumbo jet. Controlling these two aspects is the challenge for our process developers.

How did you come up with the idea of looking for a technical solution to this issue?

JG At the end of 2017, within the framework of the innovation strategy of biopharmaceuticals in Vienna, we considered how new, digital possibilities could help us to extend our technological and market leadership. The topic of developing and steering production processes crystallised from this. We have ever-increasing data at our disposal, and we knew that major advances have since been made in the field of data science. Even large volumes of data can be analysed relatively easily to identify correlations.

So you turned to BI X.

JG Exactly. After all, BI X is our internal centre of excellence for digitalisation. So we asked our colleagues at BI X whether they could use their data expertise to help us. We refined the idea at an ideation workshop. The plan we came up with was to build and test a prototype for a fermentation step in order to test our hypothesis. That is to say, we hoped to find better production processes if we employed novel data analysis in our biopharmaceutical process development.

What approach have your colleagues taken so far when developing new production processes?

JG In many experiments, they have systematically varied parameters, like temperature, pH value or duration. Many years of experience are of special significance here. It involves collecting as much information as possible in as few experiments as possible. For this, we already use software that subsequently analyses the data. In this way, we arrive at a process variant in which the active ingredient volume is the highest, that is, optimal. We repeat this until we have achieved the predetermined target.

How did you come up with the idea of further enhancing all of this?

JG We have repeatedly observed at production scale that there is still potential for improvement in our production processes. Evidently, the tool we at present use for analysing development data is not capable of discovering the best process variant. The current software cannot learn. It does not improve when I do more experiments, but rather it only ever analyses the one predefined dataset. Furthermore, it only ever uses a single method of data analysis. These are the two things that we have addressed through Smart Process Design.

Learning means gathering experience: should Smart Process Design also be able to do that now?

JG Precisely. Our approach uses the so-called process models that are capable of learning with new data. Every time we generate new data, the model improves and learns new aspects of the production process. We are completely free here as to which machine learning technologies we deploy. Currently, for example, we use neural nets. They are particularly well suited to detecting patterns in large volumes of data. The volume of data is of no significance here. That is particularly important as ever larger amounts of data accumulate in development.

“Major advances have been made in the field of data science. Even large volumes of data can be analysed relatively easily to identify correlations.”

Biopharmaceuticals are cultivated in bioreactors with a capacity of up to 15,000 litres.

Was it a culture shock, as chemists, biotechnicians and plant technicians, to suddenly find yourself working alongside the digital experts from BI X?

JG (laughs) It’s true that there were cultural differences. We had to ensure first that we spoke the same language and understood each other. Our colleagues at BI X had to familiarise themselves with biotechnology, and the scientists here in Vienna had to learn a certain amount about software development.

What will come after the prototype? What is the next step?

JG Shortly before the end of the first project phase, we succeeded in proving our hypothesis in an experiment. Smart Process Design has in fact identified a process variant for our fermentation with significantly higher efficiency. In the next stage, we want to demonstrate that it is also possible to create models for several successive process stages. Then we could at some time model complete production processes and take into account all interdependencies between the process stages.

You would have a model that describes the entire process chain up to the purified end product.

JG Correct. Then we will have come a big step closer to our vision of employing process models in our production plants. We would be in a position to analyse what influence disruptions have on the subsequent process steps – in the computer, while the production process is still running. Model-based instructions are the logical next step. We could change settings in further production in order to offset disruption. When the regulatory issues are clarified, we can steer our production plants predictively.

At some point in time, the self-learning system will perhaps no longer just suggest what you should change, but rather will decide itself.

JG Yes, and you would then have production that is managed by artificial intelligence. That is a very long-term vision. You could say that, with our Smart Process Design, we have taken a first small step on the path towards an AI factory.

“We have developed a program that is capable of selflearning. Every time we generate new data, the process model improves.”