Editorial: Bioprocess Development in the era of digitalization

Abstract

Peter Neubauer Florian Glauche M. Nicolas Cruz-Bournazou In the last few decades, a vast number of research findings in biotechnological processes were reported with high commercial relevance. Still only a small fraction of these molecules was successfully brought to industrial production. The development of production processes in biotechnology still requires a lot of time and money compared to other industries. Due to the complexity of biological systems, the commercialization of cell-derived products needs a combination of deep scientific knowledge and a thorough understanding of process engineering. In addition, regulatory requirements must be met, which may increase the overall risk of success. Process development in biotechnology is still mainly driven by experienced personnel creating and evaluating experimental data. However, in recent times, automation, miniaturization and data science are setting new paths for drastic changes in bioprocess development. Today, hundreds of automated experiments can be performed in parallel miniaturized cultivation and purification systems per day. Additionally, statistical experimental planning and evaluation is applied to utilize the experimental capacity of these facilities efficiently. Still, to exploit the full potential of automated laboratories, innovative software concepts and workflows are needed. Moreover, the conditions in which small-scale experiments are performed need to resemble the production scale as closely as possible 1. Today, there are numerous possibilities to create, collect, store, and share data. This has a significant potential in biotechnology where: i) the complexity of living organisms can be studied deeper and analyzed by computer aided tools, ii) complex biochemical pathways including hundreds of thousands of reactions can be reconstructed, and iii) proteins can be designed using in silico tools and gene synthesis. Nevertheless, while today´s development of processes and products in other areas of engineering can be performed strongly on the basis of computer based models with a minimum of practical experiments, the future bioprocess development is still coupled to extensive wet-lab experimentation due to the nature and the complexity of bioprocesses. The most significant challenge is that it is difficult to create a reliable mathematical model of even the simplest prokaryotic cell. Changes in protein expression, metabolic activity, mutations, etc. can neither be foreseen, described, nor deeply understood. In other words, model based methods in biotechnology will always be coupled with experimental activities to validate and re-adjust models to the reality of living systems. From advanced plant-wide control and optimization strategies for bioprocesses to custom made therapeutics, fully automated experimental facilities are required to run thousands of experiments in parallel to produce large amounts of data needed to constantly validate the mathematical models. These robotic laboratories must be intelligent when designing experiments and learning from the data. Today's laboratory automation systems aim rather at an increased experimental throughput than at performing complex intelligent operations. However, for making bioprocess development more efficient, these systems must be equipped with adaptive experimental design methods, to be able to plan and perform complex experiments and e.g. decide when to take representative and informative samples and to initiate the analysis. Failures should be detected automatically and promptly, the experimental strategies should be re-adjusted as data is being generated, and the experiment should deliver the maximal amount of information possible. In the near future, the user should be able to enter some specifications in the computer (e.g. operating conditions, clinical status, available substrates, desired product) which will be used to: i) find candidate organisms, ii) design screening experiments, iii) carry out the