As technology advances, so too does the ways scientists can leverage these innovations in their work. For example, in biopharma research this year, Gautam Prabhu, Chief Technology Officer at Opentrons, predicts expanded adoption of an “automation-first” approach.
Labcompare recently spoke with Prabhu, who leads Opentrons' data science and engineering teams dedicated to accelerating product development for automated lab robotics, about what other trends and robotics influence he expects to see in life science and drug discovery research in 2024.
Q: What's a theme you're closely watching as we head into 2024?
A: ChatGPT has made a tremendous impact on the world of AI and the impact of utilizing large language models (LLMs). In just over a year, every industry, including biopharma, is now able to realize the transformational capabilities of LLMs. As we come into the new year, I’m eagerly awaiting more scientists in academia and drug discovery R&D announcing that they are using LLMs and generative AI to design experiments. For example, our liquid-handling robotic systems are tapping into LLM technology where researchers can use simple, plain-language prompts to control the robots. Our hope is to use AI to further break down the barriers to entry in automation allowing scientists to focus more on scientific breakthroughs rather than manual, hands-on experimental processes.
Q: What's a development in lab automation that others should know? How do you expect the way we work to change?
A: Embracing an "automation-first" approach in the biopharma industry is crucial for accelerating drug discovery processes. This mindset, from academia to reagent manufacturers, will enhance efficiency, reduce costs, and shorten timelines in drug development.
In the conventional experimental process, researchers will develop manual processes first, and then transition those workflows into automated procedures. Scientists must adopt an "automation-first" mindset, planning for automation before fully vetting workflows. This proactive approach enables them to anticipate the most effective ways to automate hands-on experiments.
Automation not only improves reproducibility and scalability but also minimizes human errors, ensuring accurate results and optimal resource utilization. To meet the growing demands of larger sample sizes, adopting automation as a fundamental part of experimental workflows is essential for advancing therapeutics and delivering life-saving medicines to patients more rapidly. In 2024, we will see a significant amount of work offloaded to robots, so that researchers can focus more on discovery rather than experimental processes.
Q: What are you hearing from clients in terms of robotics needs, use and expansion in their labs in 2024?
A: There’s a perception in the life sciences industry that automation is expensive and unattainable for your typical academic scientific lab or biotech startup. Today, robots the size of coffee makers are more affordable than ever, and the industry is starting to realize that automation is extremely accessible.
Many also believe that lab automation is highly complex and requires dedicated automation engineers on staff. While this has been true in the past, the industry it’s becoming less so as the industry adopts SAAS-level standards of ease-of-use. Additionally, with the use of AI, we can control robots using simple LLM-based protocols, for many different molecular biology workflows including genomics, proteomics and everything in between.
Q: What is one or few more things labs wish robots could do that they currently can’t do or are limited in doing?
A: Labs want robotics that are truly “all-in-one” solutions, meaning that one robot has multiple functions with an all-encompassing hardware and software package. Lab space, and more importantly, lab personnel are at a premium nowadays, necessitating novel, more condensed solutions. The lab of the future will stray away from the “one-robot, one-task” approach (with a random assortment of robots that are “glued together” to perform multiple tasks), and lean towards a singular technology where all experiments can be done on one system. This approach will help streamline the laborious nature of scientific discovery and lead to more cost-effective solutions in the industry.
Q: When non-scientists think about robots, some imagine Boston Dynamics’ Spot robots. Can you provide insight into the different types of robots that are currently available to labs and/or will soon be?
A: The robots found inside a typical lab will be very different from the canine robots made famous by Boston Dynamics or the humanoid robots seen in countless movies. Life sciences labs are typically a constrained, controlled environment. Therefore, the robots you’ll find in them are purpose-built and controlled—think of highly complicated functions happening inside a box rather than robots moving around in uncontrolled space. There will be some limited functions like robotic arms that are moving labware precisely from one machine to another, but even these operate under much tighter constraints than the Cyberdyne T-1000 from the Terminator.
Q: What things should companies/lab managers take into consideration when deciding whether to incorporate robotics into their labs and research work flows?
A: In order to make an educated decision about introducing robotics into their workflows, lab managers should first assess a few aspects of their current operations. Are there components that are repetitive or ones that are prone to human error? Are there higher-value tasks that don’t get sufficient attention? These are all questions that are good indicators that a robotic solution could be an improvement to the current state.
If that turns out to be the case, initial cost, ongoing costs and ease of use are all critical to take into account. Lab managers should be looking for robots that can meet their needs of today, while also being flexible to meet their needs tomorrow, and ideally for a price that’s appropriate for the value being delivered.
About the interviewee
Gautam Prabhu is the Chief Technology Officer at Opentrons, where leads the Opentrons' data science and engineering teams to accelerate product development for automated lab robotics, and help usher in a new era of laboratory solutions to empower researchers to achieve groundbreaking discoveries.