Laboratories are constantly evolving. Systems, processes, machines, techniques, and layouts are always changing and adapting to the science and innovation required to design, test, and bring to market new products—and automation has a huge role to play.
Automation and other developments like machine learning and robotics are already making waves in the industry by relieving researchers of mundane tasks and increasing accuracy and efficiency. Just look at high-throughput labs, where automation and robotics have been commonplace for decades. Thanks to modern technologies and the bidirectional nature of communication, the door is slowly being opened to more opportunities and innovations that could reduce time to market further while addressing other important issues such as regulatory compliance and transparency.
So, what might the lab of the future look like, and where will automation fit in? Here are my predictions.
The next five years
Despite the speed of technological advancements, introducing new systems and processes in a laboratory can take time. Over the next five years, it might not look as though labs are changing significantly—but if you look beneath the surface, some major developments are likely to be taking place.
The cloud becomes the norm even in regulated environments
The rate at which organizations are adopting cloud providers and technologies has increased significantly as companies recognize their legacy systems are costing them both money and their competitive edge. Informatics providers are simply better placed to maintain, upgrade, and provide systems, meaning companies can avoid the costs and resources needed to maintain different systems. The security, service level agreements, consistent upgrades, and access to new features mean companies will move away from running systems themselves and instead move to vendors that can provide SaaS offerings.
Mind-sets change from best-in-breed to easily integrated technologies
Labs are already looking at technologies that can be easily integrated with other systems. By using integrated systems with more open standards—like OPC—systems and instruments can actively communicate, so data can be combined and information can carry more meaning. Connections between software, services, and physical devices will be critical to providing a backbone for nearly every other future technology.
As the world becomes more and more connected—our televisions, phones, tablets, and watches can all connect to the internet already—it is only logical that laboratories will also invest in these technologies.
Collaboration and externalization become the norm
The rise of CROs/CMOs shows that companies are increasingly outsourcing what was once thought sacrosanct. The labs of the CROs/CMOs often need to be as transparent as if they were down the hallway in your building. Data has to be easily transferred, and tasks need to be managed— Excel, PowerPoint, or e-mail simply won’t be up to the task. In the future, the systems in your lab will need to connect and communicate dynamically with the lab of your CRO.
Think about a scenario where you have created a product and want to send it to a CRO for stability testing. You could do all this from your ELN; you select the type of test you need to run and when you need the results back, and you submit. The system could automatically look for an appropriate vendor and send the associated data along with samples and any other specific requirements. Once that is finished, the results—having been checked for accuracy already—are passed back to your ELN. This means that you don’t need to package information, find a supplier, build the case, look at costs, chase down the results, and then check them—this all can be done for you on time and on budget.
Data lakes and the availability of data across the business
Data, and access to it, will need to be improved for any organization looking to take advantage of future technologies. We have already seen the rise of the data scientists—individuals in organizations who are employed to build data strategies, clean the data, and draw insights from it. But in the future, these roles will change substantially, and they are likely to evolve into those concerned with breaking down barriers and getting as much data as possible available across the business.
Semantic search, artificial intelligence (AI), and machine learning all will require access to data for training purposes. With access to the right data, organizations could benefit from simple, timesaving services such as those used in online shopping, where suggestions are made based on buying patterns—helping scientists avoid rabbit holes and dead ends by giving them the right information at the right time. This can be expanded to include the preparation of materials, the calibration of instruments, automated requests for the servicing of instruments, and even the ordering of assays from external partners.
The death of the keyboard
Voice recognition technology is improving rapidly, and it’s only a matter of time before it starts to understand scientific terms and vocabularies. Therefore, it’s likely the keyboard will soon become redundant technology.
The advances in 3-D printing lend themselves to prototyping and product development in the lab. Rapid product ideation and creation for testing purposes are a necessity, and as the technology matures, 3-D printing will work its way further into manufacturing. In the coming years, prototyping and manufacturing could even work from the same platform—improving product development and outcomes earlier in the process.
The next 10 years
This is where we start to break from certainty and start to look at changes that we expect to see based on the current evolution of technologies.
RFID tags become cheap enough to be ubiquitous
At face value, this doesn’t seem like a big deal, but radio-frequency identification (RFID) tags allow the automatic reading of data. This means that test tubes won’t need to be scanned or have their information inputted, because devices such as fridges or storage rooms automatically know what is in them. This could reduce transcription errors and revolutionize logistics through the automation of mundane bookkeeping tasks. Fridges and storage rooms could even reorder items that are running low.
In the future, this has the potential to transform safety practices, with instruments able to check whether an individual has the correct training records before allowing an experiment to be conducted and whether procedures are followed during an experiment.
AI and machine learning becoming more prevalent
The only factor limiting the ability of AI is the amount of data available. With access to world health data, systems would be able to see trends and suggest routes to solving problems before they become critical issues. Likewise, with access to medical data, systems could draw conclusions about lifestyle and genetic conditions, offering greater insight into preventive action.
With AI, devices and experiments could be prepared based on assumptions of what might be required next. Take a preclinical test. The system would look at your results and suggest that you consider running safety or toxicology studies based on the similarity to other therapeutics that passed this test but failed much later in toxicology studies. Such a technique could save millions of dollars in personnel and experimental costs and result in a higher probability of success.
Businesses could decide to unlock their legacy data for the greater good. By allowing other organizations to leverage their legacy information, organizations have the potential to rapidly accelerate both scientific and machine-learning developments.
IoT is everywhere
The internet of things (IoT) will create a steep change in efficiency by allowing for bidirectional communication between instruments, robots, and the systems and services used. Although IoT is not a new concept, it will evolve further to create a more seamless experience in the lab. Checking calibration records, managing servicing, turning equipment on and off automatically based on usage patterns, and automating the transfer of data are all scenarios that the IoT and connected devices could enable.
It will be interesting to see how augmented reality develops over the next decade—but it’s something we could see slowly integrated into the laboratory environment. A hands-free screen that can direct and show users how to do complete tasks could improve safety and speed up training.
The next 10 to 20 years
This is where we step a little more into the unknown.
Live data and insight
Labs could have access to live updates and data from field testing or even direct from the patient, meaning dosages could be adjusted quickly, depending on how a patient is responding to treatment. Similar embedded technologies and connections to consumer devices could provide real-time insight to better address outcomes. AI kicks in
By analyzing consumer behaviors, competitive information, and even economic information, AI could inform the best direction and approach for an organization to take and even recommend products to design and make. In the drug-development world, AI could even start to predict diseases based on upto- date genetic information or world health information.
Given the rate of technological advancement and the speed at which technology is maturing and developing in the consumer space, it’s certain that new technologies will also find their way into the lab—but it’s hard to predict exactly what will happen next.
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