Madeline M. Farley, Scientific Director at the Society for Laboratory Automation and Screening (SLAS), discusses the changing role of analytical science in drug discovery, the trade-offs involved in adopting more physiologically relevant models, and how automation can help researchers manage variability and focus on the insights their experiments produce.
How is the role of analytical science changing in drug discovery today?
Analytical science has always played an important role in drug discovery, but one of the biggest changes today is the scale of data that can be generated and used to inform decisions earlier in the process. Researchers have access to richer datasets across a broader range of biological systems, creating opportunities to make more informed decisions throughout discovery. At the same time, the increasing use of AI and automated discovery workflows is making it possible to evaluate more conditions and generate even larger datasets. Advances in analytical technologies and methodologies are also improving the consistency and reliability of the data being generated. The opportunity now is not only to generate more data, but to effectively use that information to guide discovery decisions.
What are the main analytical challenges in working with more physiologically relevant models?
As models become increasingly complex and physiologically relevant, they also introduce greater variability and can be more difficult to scale. One of the biggest challenges is ensuring that data remain consistent and reproducible despite that added complexity. These models can also require more sophisticated handling and maintenance, which can further contribute to variability. While the technology continues to advance, generating robust and reproducible data from complex biological systems remains an important challenge.
How do we balance biological complexity with the need for assays that are scalable, reproducible, and practical for screening?
I think biological complexity is a worthwhile goal when it helps answer the scientific question being asked, but not every question requires the most complex model available. Simpler assays can still provide valuable information and are often easier to scale and reproduce, particularly early in the discovery process. As programs advance and researchers need greater confidence in how a therapy may perform, more physiologically relevant models can provide additional insight. Ultimately, the right approach depends on the question being asked and where a program is in the discovery process.
What role does automation play in making complex models more useful for drug discovery?
Automation plays an important role in making more complex models practical for drug discovery. Automating the generation, maintenance, and handling of these models helps improve consistency and reduce variability. Automation can also reduce variability within the assays themselves by minimizing differences introduced through manual processes. While the complexity of the model will always introduce some degree of variability, automation helps control the variability that can be controlled. In addition, automation allows scientists to spend less time managing workflows and more time focusing on the data and insights generated from the work.
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