Operational scale-up is a hurdle that scientists inevitably face during the successful development of a biological product as they move from discovery to manufacturing. Scaling up can be complicated, potentially requiring new technologies or infrastructure (i.e., pivoting from small-batch adherent cell culture to large-batch suspension cell culture). Scaling up also cannot compromise product quality or consistency. Incorporating automation can be vital to overcoming scale-up obstacles, helping scientists improve both workflow efficiency and throughput as well as product consistency and quality.
Making Bigger Better
Fundamentally, automation uses mechanical and programming means to operationalize manual processes to make them more efficient and consistent. The effects of automation can be felt even for smaller scale operations—a handheld electronic pipette improves volume dispensing consistency and reduces operator fatigue and a programmable PCR machine reads multiple plates sequentially without a user’s direct presence. As such, scientists in the discovery stage will want to leverage automation at the individual instrument level to gather more data, improve experimental consistency, and reduce operator workload. They should emphasize flexibility, programmability, and data collection in their platforms.
In manufacturing-scale workflows, automation is more prevalent—automation-capable bioreactors and incubators use sensor-detected information to precisely control cell culture environments for extended periods of time. Automation is also often used to move samples between different pieces of equipment, such as transferring cells from a bioreactor culture to a plate reader for analysis. This greatly bolsters overall workflow efficiency and throughput and helps maintain environmental consistency and product quality across multiple batches.
Automation can also extend to data collection, management, and analysis. Software programs direct workflow conditions and experimental steps based on user-determined parameters. They can also collect data from multiple instruments, collating them for storage, visualization, monitoring, and analysis at a single workstation. Because of this, automated electronic data collection not only increases productivity, but is also an important part of maintaining regulatory compliance.
Scientists involved in manufacturing likely already use workflows that incorporate automation to some extent. Their goals would therefore be to identify areas where efficiency can be improved, either by automating currently manual aspects of the workflow or identifying automation gaps. These gaps can be instruments or processes that are not linked with the overall workflow, data that cannot be automatically moved to a centralized location, or differences in production scale from one process to another.
How to Find the Right Solution for the Situation
The overall purpose of implementing automation is improved efficiency in production, management, finance, and quality. While there is no onesize-fits-all solution when it comes to automating a biomanufacturing workflow, Cytiva offers modular hardware and consumable options that integrate into existing processes, technologies designed for efficient scaling up or down, and technical expertise for identifying how automation can be best implemented in a given scenario. Finally, more encompassing products like Cytiva’s ChronicleTM Automation Software aim for flexibility and broad applicability. Chronicle is compatible with equipment from Cytiva and many third parties, and serves as an interface for designing, implementing, and monitoring electronic standard operating procedures (eSOPs) across entire workflows, including experimentation, data collection, logistics, and longterm storage. This flexibility means that researchers can use Chronicle for process development, commercial cGMP-compliant manufacturing, and everything in between.