The rise of automation in drug discovery

Automation is not just for high-throughput screening anymore. New devices and greater flexibility are transforming what’s possible throughout drug discovery and development. This article was written by Thomas Albanetti, AstraZeneca; Ryan Bernhardt, Biosero; Andrew Smith, AstraZeneca and Kevin Stewart, AstraZeneca for a 28-page DDW eBook, sponsored by Bio-Rad. Download the full eBook here.

Automation has been a part of the drug discovery industry for decades. The earliest iterations of these systems were used in large pharmaceutical companies for high-throughput screening (HTS) experiments. HTS enabled the testing of libraries of small molecule compounds by a single or a series of multiple experimental conditions to identify the potential of those compounds as a treatment for a target disease. HTS has evolved to enable screening libraries of millions of compounds, but the high cost of equipment has largely resulted in automation occurring primarily in large pharmaceutical companies. Today, though, new types of robots paired with sophisticated software tools have helped to democratise access to automation, making it possible for pharma and biotechnology companies of almost any size to deploy these solutions in their labs.

Adaptive and flexible

Originally, automated solutions were only implemented for projects that involved a lot of repetitive tasks, which is typical of high-throughput experiments and assays. The equipment used in early automation efforts was expensive, specialised, and physically integrated together, effectively making the equipment unavailable for any non-automated use. Now, both the approaches and equipment are far more adaptive and flexible. The latest automation software is also much simpler to program, making it easier to swap in different instruments and robots as needed. For example, labs can run a particular HTS assay for a few weeks and then quickly pivot to run a new assay. Labs can also create and run bespoke standard operating procedures, assays, and experiments for drug targets they are interested in pursuing. 

Another major challenge has been scientists’ willingness to adopt automation in drug discovery. With high start-up costs and intensive training required to maintain processes, many teams were not eager to give automation a try. But as costs have come down and flexibility has increased, these systems have become more attractive. 

In this article, we review various uses of automation in drug research and development and how it can offer the most value. We also look at innovative new approaches to automation — including how it has made a difference in the Covid-19 pandemic — and how companies can overcome common cultural challenges associated with the implementation of automated solutions.

Where to find automation in drug discovery

While the earliest adopters of automation were scientists working on HTS applications, lab automation is now applicable in various stages of the drug discovery pipeline. It can be used in early-stage research during the experimental design process, in drug profiling experiments to identify and classify different kinds of compounds, and in assay development and screening applications. Automation is also being used in medicinal chemistry for applications including parallel and combinatorial synthesis, virtual screening, molecular design for medicinal chemistry discovery cycles, and compound repositories. It also has the potential to be deployed for drug formulation studies focused on creating stable formulations for everything from small molecules to protein to RNA-based to cell therapies.

Automated liquid handlers are among the most common types of automated infrastructure used in the drug discovery industry. These range from machines that simply dispense small volumes of liquid to more complicated systems that enable variable-driven volumetric transfers, error handling to address experimental variability, and even integrating additional lab instruments and devices. These systems are a boon for scientists because manual liquid handling that requires accuracy and precision can be time-consuming and increase the risk of repetitive stress injuries. Automating the process reduces the risk and improves both the quality and integrity of the data that is collected from experiments. Data integrity can be assured due to documention of every aspect of the experiment in real-time in a log file. These logs can be leveraged to simplify replication of conditions and establish standard operating protocols.  

Across all labs, there is a growing need for flexible automation that allows scientists to work with different kinds of assays and a range of throughputs. Drug discovery teams might need to run different assays with fewer samples per experiment for a few weeks and then switch to running a single assay with a large number of samples. This blending of high-mix, low-throughput and low-mix, high-throughput tasks means that flexible solutions will pay dividends, while single-use solutions will rapidly become obsolete.

As automation is adopted more broadly across these different areas of drug discovery, there has been a greater need for management software capable of coordinating the many instruments and systems in a lab. This type of automation software is constantly evolving to enable more functions and robust data capture of a range of factors, from experimental processes to environmental conditions. Many of these software improvements are geared toward allowing scientists to get the most out of their infrastructure by contextualising the data collected and using the information to optimise all the available resources — instruments, consumables, and even team member time. Ideally, automation and integration solutions should increase the value and flexibility of how the team focuses their time by moving them away from task-based work to more goal-oriented work.

Benefits of automation

One of the most common misconceptions about lab automation is that the primary benefit is speed. It’s true that automation can help scientists get to the results they need faster and expedite project timelines; but these systems are important for several reasons that have nothing to do with speed. 

For example, lab automation makes it possible to perform experiments using nanolitre volumes of liquid — volumes that are much too small for humans to dispense manually. By shrinking volumes to this vanishingly small scale, robots decrease the amount of material needed to run experiments. This saves a significant amount of money typically needed for consumables and reagents and extends the life of samples because so much less of them is needed for each experiment. Reducing the overall scale of experiments also cuts down on waste products such as pipette tips that are harmful for the environment.  

Another key benefit of automation is safety. Scientists routinely work with hazardous materials, including potential carcinogens, mutagens, and infectious agents. Robots can be tasked with handling and moving materials that would put scientists at risk. 

Automation can also deliver healthier workplaces. Spending time on repetitive tasks in the lab can cause wear and tear to scientists’ bodies. Using robots to perform these tasks instead not only reduces the risk of repetitive strain injuries for scientists, but also frees them up to focus on the higher-value tasks of planning experiments and analysing data. In addition, robots can continue operations around the clock, enabling a truly 24/7 laboratory without requiring scientists to work longer hours or stagger shifts. That means increased productivity for the lab and better mental health for employees.

Another benefit of automation is the improvement in data quality and integrity. With automation, experiments can be performed in precisely the same way, over and over again, while software management systems capture all experimental data in real-time. Automation solutions document all aspects of experiments in detail, including things that scientists might not think to capture in a lab notebook, such as the room’s ambient temperature during the experiment or the time at which samples were moved from the liquid chromatography instrument to the incubator. These systems can also record when experiments fail and the conditions under which the failure happened, allowing scientists to assess what went wrong and make adjustments. This kind of auditability contributes to a clearer understanding of experimental outcomes.

Finally, automation helps boost the reproducibility of drug discovery research. Replicating experiments is next to impossible without access to complete information about the conditions of the initial assay. Protocols for a particular screening assay, for instance, can vary from one lab to the next, as well as from scientist to scientist. Lab automation gives scientists the ability to capture details about standard operating procedures to ensure that experiments are performed in the same way no matter where they take place. 

Real-world case study: mobile robots

New innovations in lab automation have led to even broader applicability of robotic systems throughout pharmaceutical and biotech organisations. At AstraZeneca, scientists have incorporated autonomous mobile robot technology to reshape how teams think about their experiments and work protocols across multiple departments and buildings.

Historically, automated systems have been strategically decentralised. If scientists in two different buildings on a pharmaceutical campus are running similar workflows on the same instrument, lab managers are likely to set up two separate workstations — one in each building — to avoid the inevitable productivity reduction that would ensue if one group had to visit the other group’s building each time the workflow had to be run. While this makes sense for how these teams operate, it’s a costly concession because the company has to pay for and maintain multiple instruments that may sit idle for much of the day.

At AstraZeneca, scientists hypothesised that autonomous mobile robots could allow them to shift to a more centralised model, putting key workstations in a core facility and using a robot to bring samples to it from labs across the campus. Now, the team there has adopted an innovative ‘beehive’ model — one where a robot ‘worker bee’ goes out to collect samples as needed, bringing them back to the central beehive for processing. Scientists get to keep working in their labs without any loss of productivity, and they can analyse data generated and recorded in the core laboratory. 

A key value proposition of autonomous robots is that they allow scientists to connect a variety of instruments, including bioreactors, liquid handlers, and more, in one continuous workflow. They also allow scientists to prepare experiments whenever they want, rather than trying to time their work to when their own lab’s workstation is available. Autonomous robots can discard used labware as well as return samples to storage, ensuring that valuable samples are never left out or exposed to inhospitable conditions.

Automation and Covid-19

A commitment to automation set some drug discovery laboratories apart during the Covid-19 pandemic. When Covid-19 emerged as a global threat in early 2020, labs in many pharmaceutical companies pivoted from business as usual to developing therapies and vaccines for the disease. Researchers needed to gather and share information, collect vital samples, run assays, analyse results, and much more — all with fewer people working in the lab under stringent safety protocols and social distancing guidelines. 

 Lab automation truly was the differentiator in scientists’ ability to do good research that led to important discoveries around Covid-19. At AstraZeneca, automation was widely adopted, enabling scientists to shift quickly to focus their research efforts on combating the disease. Automation was used in the design of experimental assays, testing of samples, and optimisation of experimental methods as needed. Automation was leveraged to generate and handle reagents essential to the R&D efforts by aliquotting, capping, and printing human readable labels on tens of thousands of tubes containing reagents. Thanks to automation, most of the experiments and uses mentioned above could be performed by robots in the lab without requiring direct oversight from a scientist, enabling scientists to observe social distancing in compliance with various lockdown requirements. 

The culture challenge

Although automation is becoming more common in drug discovery, it is still not ubiquitous. The problem is often cultural. Scientists are not automation engineers; getting them on board with a new system may be difficult, especially if they are not familiar with how it works. Many researchers do not understand how to run automated systems, and they may be resistant because they fear being replaced or having experiments run incorrectly. Even in labs where scientists are familiar with automation, they may not be taking full advantage of what these systems have to offer.  

One way to address resistance to automation is to reframe the conversation, focusing not on the system but on a team’s research goals and then reverse-engineering a solution that addresses those goals.

Another approach that was successfully adopted at AstraZeneca is to ensure that scientists play a key role in designing automated workflows and systems. Drug discovery scientists receive extensive engineering training, breaking down the common silo between automation teams and research teams. These hybrid scientist-engineers can take a more hands-on approach to designing their own automation workflows, giving them confidence that the resulting system will address their research needs. This approach has changed the way AstraZeneca has rolled out automation in its laboratories. When scientists are handed automation solutions and told to trust them, uptake is usually poor. However, by making them an integral part of the engineering process, they are more excited about the solution and eager to implement it in their day-to-day research.

The future of automated drug discovery

It’s possible that scientists will routinely see mobile robots wheeling around the lab, picking up samples from the freezer, dispensing them into trays, and placing them in instruments. Those same labs might have a standalone instrument that’s semi-automated, multiple devices with operations controlled by robotic arms, or even hundreds of instruments connected via autonomous mobile robots to operate as a single choreographed ecosystem. 

But even if a fully automated laboratory setting is not the path pursued by your company, automation will be in every lab to some degree or another. Automation will help scientists discover new pharmaceuticals faster and more efficiently than ever before, saving time and cost in the process. Automation will be the default starting point to handle routine, repetitive tasks that scientists are no longer willing to do. 

Newer robots and software management tools are finally simple enough for scientists to use without having to master complex coding and flexible enough to adapt to changing needs of the laboratory scientist. These systems will be widely adopted, empowering scientists to do more while improving lab safety, efficiency, and performance.

About the authors

Kevin Stewart is Senior Automation Engineer at AstraZeneca, based in Maryland. He previously worked at GlaxoSmithKline, and began as a laboratory scientist in the Protein Purification Sciences Department at AZ before transitioning into automation with a focus on innovation and novel devices. He holds a bachelor’s degree in biology from Howard University and a master’s in biotechnology from Johns Hopkins University.

Thomas Albanetti is an Associate Principal Automation Engineer at AstraZeneca, based in Maryland. He began his career as a laboratory scientist before transitioning into automation, specialising in Hamilton robots and collaborative automation. He holds a bachelor’s degree in biochemistry and molecular biology from Gettysburg College and a master’s in biochemistry from the University of Connecticut.

Ryan Bernhardt is Chief Commercial Officer at Biosero, a lab automation software provider. Previously, he worked at Eli Lilly and Company as part of the Discovery Automation Research and Technologies Group, where he led a team of automation engineers and scientists. He holds a bachelor’s degree in chemistry from Marian University in Indianapolis.

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