Rethinking Data Acquisition to Data Analytics in Bioprocessing

Sophia Bongard (), Nicole Kees (), Pedro Ivo Guimarães () and Tobias Großkopf ()
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Sophia Bongard: Therapeutic Modalities, Roche Innovation Center Munich, Bioprocess Research, Roche Pharma Research and Early Development
Nicole Kees: Therapeutic Modalities, Roche Innovation Center Munich, Bioprocess Research, Roche Pharma Research and Early Development
Pedro Ivo Guimarães: Data & Analytics, Roche Innovation Center New York
Tobias Großkopf: Therapeutic Modalities, Roche Innovation Center Munich, Bioprocess Research, Roche Pharma Research and Early Development

A chapter in Innovation in Life Sciences, 2024, pp 77-93 from Springer

Abstract: Abstract High-throughput experimentation systems advanced (online) sensor technologies, high-resolution product analytics, and advanced and automated data analytics are the basis for next-generation bioprocess research and development in the Pharmaceutical industry. The need for an end-to-end data infrastructure is often neglected but fundamental in order to unleash the full potential of the generated data. This case study showcases the digital evolution of our laboratories in Bioprocess Research at Roche Pharma Research and Early Development (pRED), starting with an ELN (Electronic Laboratory Notebook) based approach in the center of the IT landscape. In the last decade laboratory, automation and high-throughput experimentation progressed dramatically, to the point where data collection and efficient storage were becoming the bottleneck in the process. We introduced some major changes to the IT architecture to fit the new data types and to enable the collection of high amounts of well-contextualized data to meet data analytics needs. Our new IT landscape is a microservice-based architecture, which allows us to reuse and build upon key functional systems. At the core sits an in-house developed experiment management tool (Experiment Manager) as a new user-centric platform for bioprocess data management, with the capability of executing workflow orchestration routines for laboratory automation. By eliminating major technical debts from legacy systems and setting up the basis for a new infrastructure we enabled the easy implementation of laboratory and data automation routines as well as providing analytical dashboards and programmatic data access for cross-functional and cross-process scale data analytics, modeling, and data science.

Keywords: Lab of the future; Bioprocessing 4.0; Digital twin; FAIR data principles; Digital maturity; Bioprocessing; Workflow automation; Digital laboratory workflows; Biopharmaceutical R&D; Data engineering; Automation framework; Data science (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-47768-3_6

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