Steen Krogsgaard's presentation on the implementation of laboratory automation at Novo Nordisk provided a detailed overview of the challenges and successes in this field. He began by acknowledging that the issues faced by Novo Nordisk were similar to those encountered by other large corporations, such as AstraZeneca. Krogsgaard emphasised the need for centralised automation expertise within the R&D group to ensure the success of automation initiatives.
He highlighted the significant benefits of laboratory automation, including a 5 to 10 times increase in output, 40% savings in direct costs, and 50% savings in time. These figures demonstrated the value of investing in automation in real statistical terms. However, Krogsgaard also noted the varying levels of automation across the R&D organisation, with some labs still using handheld multi-channel pipettes while others had end-to-end automation systems.
To address the lack of coordination and standardisation, Novo Nordisk created the Distributed Automation Network (DAN), a team over a dozen people with laboratory backgrounds. This team handles everything from scoping and writing user requirement specifications to commercial negotiations and project execution. The DAN team also focus on custom automation, IT integration, and procurement, ensuring alignment and standardisation across the organisation.
Krogsgaard provided examples of automated systems with varying complexity, including an LCMS-based plasma binding assay, a system for automated sample preparation in cell therapy, and a conceptual system for reconstituting lyophilised compounds. Each example illustrated the process of scoping, designing, and implementing automation systems, as well as the challenges encountered, such as the need for custom devices and the integration of systems with corporate IT frameworks.
The presentation concluded by summarising the value created by automation, including increased sample throughput, reduced time per sample, and improved consistency and quality of results. Krogsgaard also acknowledged the challenges, such as resistance from scientists to change their processes and the need for realistic expectations regarding the return on investment in automation.
