Every stage of biopharma drug development depends on what happens in the laboratory. Consequently, any one of these stages—which include target identification, lead optimization, candidate screening, process development, quality control, and regulatory compliance—can become a bottleneck. Worse, multiple bottlenecks can occur.
To keep drug candidates flowing through biopharma pipelines, drug developers are turning to laboratory automation technologies. Fortunately, laboratory automation technologies exist for virtually all of the stages in drug development. Besides eliminating time-consuming and labor-intensive tasks, these technologies facilitate contamination control, reproducibility, standardization, and data security efforts.
Going forward, laboratory automation will do more than reduce costs and improve productivity. It will also open opportunities for competitive differentiation, especially when artificial intelligence (AI) technology augments laboratory automation platforms to set up and sustain highly complex workflows.
Gauging expectations
The management consulting firm McKinsey & Company recently issued a study titled, “From bench to bedside: Transforming R&D laboratories through automation.” It emphasized that laboratory automation has “significant potential to contribute to improvements across all primary laboratory [key performance indicators].” Specific benefits cited by the study included shorter cycle times, higher throughput, better reproducibility, and better predictability.
A similar view is held by Sergey Vlasenko, PhD, associate vice president, pharma and biopharma end markets, Agilent Technologies. He says that laboratory automation has the potential to revolutionize the entire spectrum of drug development activities—from discovery to production.
“Implementing automated systems allows for high-throughput screening, where thousands of compounds can be evaluated rapidly for potential therapeutic effects,” he explains. “This efficiency accelerates the research phase and enhances the reproducibility and accuracy of experimental results. The implementation of laboratory automation can drastically cut the time and expenses involved in launching a new drug in the market, ultimately leading to benefits from faster access to new treatment methods.”
Assessing demand
“The demand for automation within the biopharma industry is robust and growing, driven by an increasing need for efficiency, precision, and scalability in drug discovery and process development laboratories,” Vlasenko observes. “The advent of personalized medicine, complex biologics, and advanced therapies has heightened the need for technologies that can support intricate workflows and data-intensive processes.”
According to Vlasenko, an increasing number of biopharma companies are turning to automation to overcome discovery bottlenecks, such as identifying drug targets and optimizing lead compounds. Likewise, in process development, automation is crucial for scale-up processes, ensuring a smooth and scalable transition from laboratory scale to production scale.
“As companies face mounting pressure to reduce costs and time to market for new drugs, automation technologies become essential tools to meet these challenges,” Vlasenko argues. He notes that companies are trying to meet these challenges by investing more in robotics, liquid handling systems, and informatics solutions that integrate seamlessly with existing workflows.
“The agility provided by these systems allows companies to rapidly respond to emerging health crises, such as pandemics, where the ability to expedite vaccine or therapeutic development can have global implications,” Vlasenko remarks. “The integration of these systems is becoming a competitive differentiator, with early adopters demonstrating the ability to innovate faster and more reliably than their less automated counterparts.”
Improving data management
Modern laboratory software systems automate the handling of experimental data and facilitate complex analyses that would be impractical manually. According to Vlasenko, These capabilities are “crucial in an era where personalized medicine and genomics play pivotal roles, requiring the handling of large-scale biological data.”
The benefits of automated data management are also recognized by David Solbach, global marketing manager at Eppendorf. “Automated systems,” he says, “often come with integrated data collection and analysis tools, ensuring data integrity and facilitating easier data mining and insights generation. This leads to accelerated research and development timelines, which is invaluable in this fast-paced industry.”
The approach is also being embraced in the process development laboratory. “Automation is not a luxury but a necessity for process development laboratories if they are to remain competitive and efficient,” Solbach stresses. “Many companies are actively seeking ways to integrate more automated processes into their workflows, indicating a healthy and expanding market.”
Dealing with complexity
And for cell and gene therapy developers, the need for automation in the laboratory is even greater than in the protein drug space due to the sensitivity of the materials involved. “The automation needs of protein drug developers and cell and gene therapy developers diverge significantly due to the intrinsic differences in their production processes and regulatory environments,” Vlasenko elaborates. “Protein drug developers often utilize automation for process optimization, high-throughput screening, and analytical testing to ensure product purity and functionality. These processes benefit from open-system automation that allows for flexibility in handling various proteins and reagents.
“On the other hand, cell and gene therapy developers face unique challenges that make closed-system automation more appealing. Manipulating live cells and viral vectors requires stringent contamination control, which closed systems can provide by minimizing human interaction and maintaining aseptic conditions.”
Automation is also helping the cell and gene therapy firms link laboratory operations to manufacturing and supply activities in compliance with regulatory demands. According to Vlasenko, the regulatory landscape for cell and gene therapies “often requires detailed and comprehensive data tracking for each product batch, where automation technologies can provide traceability and compliance.”
Advancing standardization
The biopharma industry needs consistent, reproducible experimental results. One way to obtain these results is through automation in the laboratory. “The benefits of automation are standardization and reproducibility,” declares Vipin Bhambhani, vice president/general manager, clinical solutions, BD. She adds that automation can streamline workflows and reduce opportunities for human error—capabilities that are especially welcome in understaffed laboratories.
Standardization and reproducibility can reassure the biopharma industry that its investments in sample collection and analysis are worthwhile. Also, as Bhambhani notes, “[Not having] to worry about human error helps decision makers make pipeline investment decisions with confidence.”
“Given the sheer amount of data that is produced in flow cytometry, biopharma is always looking for ways to minimize variability whether it is in sample procurement, sample analysis, or data interpretation,” Bhambhani says. “We are committed to refining our software capabilities and to working with biopharma to address its pain points.”
Consistency of laboratory results is also relevant to safety testing and quality control, particularly in the production of sensitive and complex products. “There is a lot of overlap in terms of protein or small-molecule drug development and cell and gene therapy drug development,” Bhambhani remarks, “but a key difference is that with cell and gene therapy, a cell population is the regulated drug product, so manufacturing and quality control processes have to be developed to characterize a far more complex product.”
Eppendorf has also recognized the differing automation needs of traditional biopharma and the cell and gene therapy sector. “Protein drug developers often focus on technologies that aid in high-throughput screening and protein expression systems,” Solbach says. “On the other hand, cell and gene therapy developers require more sophisticated systems.”
Solbach notes that the cell and gene therapy industry is gravitating toward single-use stirred-tank bioreactors. “In this industry, where the cell is the product, there are not many downstream processes following the bioreactor,” he explains. “Therefore, working in a closed, automated system ensures that the process can be precisely characterized and is reproducible. The nature of cell and gene therapies demands closed, aseptic environments to prevent contamination and maintain product integrity.”
Embracing AI
At present, laboratory automation is valued for its ability to make discovery, process development, and quality control as efficient as possible while safeguarding the transfer of data. In the future, laboratory automation is likely to be seen as more of a complement to advanced technologies such as AI.
“Laboratory automation and AI technologies can have a profound impact,” Vlasenko declares. “Besides improving efficiency, they can usher in a new era of innovation. AI algorithms are integral to interpreting complex datasets generated by automated processes, providing predictive analytics that can foresee outcomes and optimize conditions without human intervention.
“In drug discovery, AI enhances the screening of compounds by predicting their potential as successful drugs, thereby reducing the number of compounds that need to be physically tested. Beyond drug discovery, AI and automation will have a role in formulation development.” One possibility is that machine learning models will be used to predict the stability and efficacy of protein drugs.
“AI is instrumental in the design and execution of synthetic biology experiments, where it can predict the outcomes of genetic modifications, leading to faster and more reliable production of biologics,” Vlasenko says. “In cell and gene therapy development, AI-driven automation is critical for personalized medicine, where it can be used to tailor treatments to the genetic profiles of individuals. AI also plays a pivotal role in regulatory compliance, where it can ensure that data recording and process control meet stringent standards.”
According to Vlasenko, increasingly sophisticated combinations of AI and laboratory automation will allow for “unprecedented levels of precision, customization, and scalability in the biopharma industry.” Such combinations, Bhambhani points out, are already shaping the development of products in BD’s development laboratory. “Our digital capability is an area of intense focus for us,” she remarks. “AI is not only something we apply to software, but it is also something we use to develop products. For example, we used AI-guided algorithms to determine the fluorochrome spectral positions of our newest family of reagents.”
