Lab automation in pharma and medtech: More than a question of capacity
Laboratory automation is often associated with one thing: doing more in less time.
Capacity is certainly part of the discussion. But in pharmaceutical and medtech laboratories, the reasons for automating are often more complex.
Qualified laboratory specialists are a valuable resource. Repetitive processes need to be performed consistently. Samples and data move between different steps, instruments, and systems. At the same time, requirements for quality, documentation, and traceability leave little room for uncertainty.
This makes laboratory automation relevant for reasons that go beyond speed.
Automation can support capacity and make better use of specialist resources. It can reduce manual handling in repetitive processes. And when applied to the right tasks, it can contribute to greater consistency in the way laboratory processes are performed and data is captured.
The interesting question is therefore not how much of the laboratory can be automated.
It is where automation can make a meaningful difference.
Making better use of laboratory expertise
Across many industries, access to qualified employees is becoming an increasingly important consideration. Laboratories are not isolated from this challenge. Specialist skills are valuable, and when recruitment is difficult or workloads increase, it becomes even more important to consider how those skills are used.
Laboratory work contains very different types of tasks. Some require scientific judgment, interpretation, troubleshooting, or decisions based on specialist knowledge. Others are repetitive and highly structured: moving samples, loading and unloading equipment, labeling, recording information, or performing the same handling sequence repeatedly.
Automation cannot replace the specialist expertise a laboratory depends on. But it can change how that expertise is used.
By automating suitable repetitive tasks, laboratories may be able to free up specialist time for work that requires professional judgment and experience. In this context, the value of automation is not simply about reducing labor. It can also be about making better use of the expertise already available.
Consistency matters as much as speed
A process does not become better simply because it becomes faster.
In pharma and medtech laboratories, repeated activities often need to produce consistent and reliable results. Every additional manual handling step or transfer of information creates another point at which variation can potentially be introduced.
This is one reason reproducibility is important when considering automation.
A well-defined automated process can perform the same programmed operation repeatedly without the natural variation associated with manual handling. This can be relevant for tasks such as sample movement, dosing, labelling, loading and unloading, or other structured handling processes.
That does not mean that automation automatically creates a reproducible laboratory process. The method, equipment, software, data flow, and surrounding procedures still need to be appropriate for the application.
Automation is one part of that system.
Data integrity changes the automation discussion
For pharmaceutical laboratories in particular, there is another dimension that cannot be separated from the physical process: data.
Laboratory data is not simply documentation produced after an activity has taken place. It forms part of the basis for quality decisions.
Current EMA GMP guidance describes data integrity as a fundamental requirement of the pharmaceutical quality system. The guidance also identifies the degree of automation and human interface as factors to consider when assessing data integrity risk, including when data is transferred between systems.
That makes the handovers in a laboratory particularly interesting.
A sample may be handled by one piece of equipment and analyzed by another. Results may then move into another software environment or become part of subsequent quality documentation. If information must be manually entered, copied, or reconciled along the way, the data flow deserves just as much attention as the physical movement of the sample.
This is where automation and integration begin to overlap.
Connecting processes can potentially reduce manual interfaces, but the objective is not simply to remove a person from the data flow. Any automated or digital interface must preserve the integrity and traceability required for the process.
Automation can start with a defined process
Laboratory automation does not have to mean automating the entire laboratory. In some cases, the relevant opportunity may be a clearly defined sequence of repetitive tasks, while activities requiring scientific judgment remain with laboratory specialists.
A specialist may still need to prepare a process, assess a result, handle a deviation, or make a decision. Between those activities, however, there may be repetitive steps that can be performed automatically and consistently.
The level of automation does not have to be fixed. A laboratory may start with one process or workflow and expand later as methods, volumes, products, or requirements develop.
New possibilities for sample movement
Automating individual laboratory processes also raises another question: how do samples move between them?
In a conventional automated flow, transport is often based on a predefined route. New transport technologies create other possibilities. Beckhoff XPlanar, for example, uses magnetically levitating movers that can be controlled independently, allowing individual movers to follow different routes and interact with other technologies such as vision systems or robots.
PPS has built a small XPlanar test setup to gain hands-on experience with the technology and its programming. From a laboratory perspective, the interesting potential lies in the ability to handle samples individually rather than requiring every sample to follow exactly the same route.
Testing the concept before scaling
This is particularly relevant when the automation concept itself needs to be proven.
A proof of concept can make it possible to test selected process steps, integration, and practical operation on a smaller scale before deciding whether the concept should be expanded.
PPS has used this approach in a QC Lab project for a pharmaceutical customer. The solution was developed as a modular proof of concept for testing an automated laboratory workflow before potential integration into a larger production environment. The compact setup was specifically developed with laboratory environments and limited space in mind.
[Read our QC Lab proof-of-concept case.]
The purpose of a proof of concept is not simply to demonstrate that a process can be automated. It can also reveal where it does not add enough value, where human involvement is still required, and what needs to change before a concept is scaled.
What should laboratory automation achieve?
There is no single measure of successful laboratory automation. Higher throughput may be important in one laboratory, while another may focus on reducing repetitive manual handling, improving consistency and traceability, or making better use of specialist expertise.
For pharma and medtech laboratories, the potential therefore goes beyond making an existing manual task faster. Automation can change how people, samples, equipment, and data interact throughout a laboratory process.
The most valuable result is not necessarily a fully automated laboratory. It may be one where specialists spend more of their time on work that requires their expertise, while repetitive and structured processes are handled with the consistency they require.
Have a question?
If you are exploring laboratory automation, equipment, or how different technologies could support your laboratory processes, get in touch with PPS for an informal discussion.