Scaling R&D Drug Discovery: The Future Of Lab Automation
Contributed Commentary by Jonathan Brennan-Badal, Opentrons Labworks
September 8, 2023 | In the coming decades, many of the grand challenges that society faces, from global food shortages to disease control and climate change, has the opportunity to be solved by biology. To achieve this, we must enable researchers within the biotech and healthcare ecosystems to be more productive than ever by improving scale, reproducibility, and accessibility. By doing so, we can reduce the costs and timelines of drug discovery and create safer, more effective personalized medicines.
A prime example of an industry boosting scale and automation through leveraging existing frameworks is the software industry. Through their software apps, small teams of software engineers can significantly impact millions of users, solving significant problems worldwide.
By taking a page from the software industry, biomedical researchers can upend the traditional drug discovery and R&D paradigm and better address the challenges of reproducibility and scale. Furthermore, to emulate the success of the software industry, we need lab automation and standardized platforms to boost productivity by scientists, extracting their time from experimental execution to focus on more impactful, value-added activities.
Extracting Scientists From Experimental Execution: Scaling Productivity
The software industry has gone through a productivity transformation over the past few decades with the continual removal of engineers from problems. Today, most engineers rarely need to think about coding or building applications from scratch. Rather, they can tackle other big-picture challenges by focusing on higher-level issues.
To scale productivity, the software industry is maximizing efforts through usage of existing and reproducible frameworks, templates, and pre-existing code. Furthermore, they have found ways to quickly and affordably push information directly to the cloud, not needing to establish servers themselves. Scalability, reproducibility, and ultimately, widespread adoption mean that software engineers rarely need to reinvent the wheel.
The net result is that a small team can have massive leverage in their work and the problems they solve. For example, WhatsApp scaled to 1 billion users with the support of only 50 engineers. Another example is the company that developed ChatGPT, OpenAI, which was founded in 2015 and currently houses just over 350 full-time employees. With just a handful of engineers, Open AI created a tool with 100 million users.
A useful software app can propagate across the entire community in days or months. This type of scale and adoption of life science tools becoming mainstream is unheard of in drug discovery, where it takes 10-15 years and $1 billion to get a single drug to market.
Today, a drug discovery scientist can spend hours each day at the lab bench reading over methods and materials, purchasing reagent kits, and finally executing manual, time-consuming experiments. We need to find ways to extract scientists from experimental execution, so they can be exponentially more impactful for scientific discovery and getting life-saving medicines into patients’ hands.
Lack of Reproducibility on Biomedical Research
Engineers have been highly productive because software, by its nature, is unambiguously reproducible. Developers can tailor a validated, established code to their needs and experiences. However, reproducibility has become a bottleneck in scientific discovery because researchers can rarely or accurately reproduce procedures and, ultimately, results based on information taken from a postdoc’s lab notebook or the methods section of a published paper.
Even when researchers purchase a well-established reagent kit, it is unknown if it will perform reliably with their specific equipment. These manual lab activities are rife with artisanal techniques that make it difficult for someone across the same lab, let alone across the world, to reproduce the same results, leading to wasted time and resources.
Solving Scale and Reproducibility Through Digitization
To solve the scale and reproducibility problem in biomedical research, we need to take a page from the software industry and attempt to translate these manual processes into digital code. First, we need ubiquitous automation and platforms, especially where there is a time sink. At the benchside, this automation can be in the form of robotics or machine learning algorithms. Second, with a growing number of standards, we need instruments, protocols, workflows, and reagents validated against these ubiquitous platforms such that the resulting output is not questioned. Lastly, we must improve open access in the life sciences community, enabling researchers to freely contribute and use these digital technologies on those ubiquitous systems.
The biotech and healthcare ecosystem must incorporate the scalability and reproducibility that the software industry has utilized to great success, impacting millions of users worldwide. By taking this approach, drug discovery scientists can use existing workflows that other researchers have already validated. If we can ensure reproducibility in experiments and offload work to a core or cloud lab, we can quickly and instantaneously scale-up experiments and operations by parallelizing activity. By developing standardized, validated, and automated workflows, industry-wide adoption and productivity will have a higher success rate. Identifying opportunities for automation and abstracting execution can dramatically reduce the time, effort, and costs associated with bringing drugs into the clinic and more rapidly put targeted, more personalized medicines into the hands of patients.
Jonathan Brennan-Badal is the CEO of Opentrons Labworks Inc., a company focused on providing advanced laboratory automation solutions. As the leader of Opentrons, Jonathan has established a presence for the company in the industry, developing innovative technology and supporting the Lab 4.0 movement. He can be reached at email@example.com.