The manufacturing industry has already embraced a modern approach to automation – with a focus on holistic, flexible automated processes such as formulation, blending, packaging, and cleaning taking place simultaneously. From this approach, the pharma industry can take a number of lessons, and implement them into the laboratory space to increase efficiencies, ensuring that wide-scale, effective automation is in place for the long term.

While automation isn’t necessarily new for pharma – the industry has utilised the technology for tasks like liquid handing and library preparation for years – change is challenging and the industry remains quite rigid in the way it applies automated technologies into laboratory spaces.

To relieve scientists of repetitive and time-consuming admin tasks, a more modern approach to automation is needed. Advancements in technology mean more complex processes, and even whole workflows in labs, can now be automated. This can elevate the scientists’ role by freeing up their valuable time to focus on more skilled tasks, like plate reading and interpretation, enabling them to discover new drugs and bring them to market faster and more effectively.

In manufacturing, it is common for one tool to be optimised to perform multiple processes at the same time. This is also known as a capability-first approach. However, the pharma industry often requires one instrument to carry out many tasks – without being truly optimised for the specific process.

By moving towards a capability-first model, it will be possible for single automation tools in the lab to perform multiple processes – such as liquid handing or thermocycling – efficiently. In turn, this can make instruments in the lab far more flexible. The same piece of automation technology can be optimised to carry out new tasks when needed, enabling labs to meet the rising demand for new drugs and medicines. As batch sizes become smaller, and product lifecycles and time-to-market become shorter, increasing efficiencies and optimising any automation tools will be critical. For example, one biotech organisation working with Automata saw that, while an automated liquid handler alone worked in an accurate and repeatable way, it couldn’t support the lab to scale and increase throughput.

This was partly because the physical lab space limited how many liquid handlers could be installed in the space, and because they were using a workflow design that means processes have to happen sequentially, rather than in parallel.

This means that an expensive piece of equipment, like a liquid handler, is often the bottleneck in a process. If, for example, the liquid handler is waiting on a plate reader or thermocycler to be able to carry out its role, looking at optimising when and how the handler is used can be key to improving efficiency.

The manufacturing industry has also long made use of robotics and automation to connect and power different parts of the production line, rather than relying on humans to move the parts between various automated systems. In labs, automated systems are often effective in standalone form – an approach of ‘partial automation’ – but rarely connected to automate an entire workflow.

While partial automation removes some repetitive tasks involved in one particular process, the machine must still be operated by a lab worker, and many in-between steps – like barcode scanning – have to be done manually. Ultimately, the benefits of automating just one small part of the assay are limited, and it can result in reproducibility and analysis being difficult.

Automating end-to-end laboratory workflows, on the other hand, integrates multiple processes and workflows, across both hardware and software.

To deliver the next generation of lab automation, collaboration between science and engineering will be incredibly important. Only by embracing new ideas and taking a different view of how to approach automation, can scientists feel empowered to improve the quality of their output and deliver better outcomes.

Change is challenging to implement, especially with ‘new’ technology like robotics, as this can require great trust in the tools. Part of this comes from scientists being able to see what is going on – rather than taking a ‘black box’ approach. This means scientists and the teams that are engineering and designing automation tools must work closer together. For example, designing tools with a human-first approach and truly understanding how scientists work and the type of support they need can ensure products are effective, and drive adoption. It’s also about finding the right scientists that are open to new ways of working and who look at lab processes in a holistic, end-to-end way.

Taking insights from the manufacturing industry around optimising automation tools to increase the flexibility and connectivity in labs, pharma will be empowered to increase productivity, free up scientists to work on more complex and skilled tasks, and bring innovative drugs and treatments to market more quickly.

The pharma industry is already embracing automation and robotics, but with the capability-first mindset used in manufacturing, labs will be set to take automation to the next level.