Publications · 26. January 2023

Full title: Comparison of strategies for iterative model-based upstream bioprocess development with single and parallel reactor set-ups Abstract: The bioprocess industry shows an increased interest to use model-based approaches for upstream bioprocess development. Iteratively, one or multiple experiments need to be designed with the objective to iteratively learn the process behavior and drive it towards a desired state. Due to the inherent dynamic nature of upstream bioprocesses, dynamic...

Bioprocess Blog · 18. January 2023

As innovation floods the Biopharma industry, innovation leads have more choices than ever. This fortunate problem creates a new challenge - how to select the best approach?

Bioprocess Blog · 10. January 2023

Digitalization is the buzzword in Pharma as the industry embraces new technology to create more effective organizations. Before rushing into new projects, pause and take a step back to determine what your goals are.

Publications · 31. December 2022

Full title: Hybrid modeling supported development of an industrial small-molecule flow chemistry process Abstract: Continuous manufacturing of small molecules finds increasing application in developing novel processes due to its significant advantages. The development of these processes is increasingly being supported by models, in a quest to reduce the number of experiments and increase insight. With the Quality by Design paradigm in mind, answers to questions anent the impact of changes in...

Publications · 15. February 2022

Full title: Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions Abstract: Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledgeor black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged...

Publications · 01. December 2021

Full title: Hybrid modeling — a key enabler towards realizing digital twins in biopharma? Abstract: Digital twins (DTs) represent a vividly emerging technology in the manufacturing industry strongly motivated by the goals of industry 4.0. It strives for smart factories with completely interconnected devices and data archives as well as their automated and adaptive operation based on autonomous decisions of smart algorithms. In this article, we want to provide our vision on the broad...

Publications · 14. September 2021

Full title: Design of Biopharmaceutical Formulations Accelerated by Machine Learning Abstract: In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian...

Publications · 01. September 2021

Full title: Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning Abstract: Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture...

Publications · 01. September 2021

Full title: A roadmap to AI-driven in silico process development: bioprocessing 4.0 in practice Abstract: In silico process development constitutes a viable option for accelerating CMC development timelines and can be achieved through either a hybrid-modeling-driven or an Artificial Intelligence (AI)-driven avenue. Each pathway has its own pros and cons but the biggest difference is that the former can be developed in-house whereas the latter requires inter-corporation collaboration. Motivated...

Publications · 03. August 2021

Full title: Knowledge transfer across cell lines using hybrid Gaussian process models with entity embedding vectors Abstract: To date, a large number of experiments are performed to develop a biochemical process. The generated data is used only once, to take decisions for development. Could we exploit data of already developed processes to make predictions for a novel process, we could significantly reduce the number of experiments needed. Processes for different products exhibit differences in...