Microfluidics and Cell Modeling
Drug discovery often relies heavily on biological knowledge gleaned from working with cells and tissues in functional assays. Miniaturizing cell culture models using microfluidic systems is ramping up data collection and allowing more in-depth biochemical analyses.
Ivar Meyvantsson, Ph.D., engineering manager at Bellbrook Labs, provides some insights into the field. “Microfluidics opens the portal to a new way to culture cells in vessels that expand our ability to control the local cellular microenvironment and, just as importantly, to create three-dimensional models that provide more complex and detailed information. Also, interfacing microfluidics with standard automation makes the models much more accessible to drug discovery scientists than in the past.
“For example, a plate that has 96 structures allows one to set up a stable gradient to perform chemotaxis experiments. The cells can be observed with a microscope, which provides more information content compared to existing solutions as to the effects of a drug candidate on living cells.
“One can determine what population of cells moves and how far. You can employ automated image processing to detect morphological features. In other words, once you’ve established that a compound inhibits chemotaxis you can dig deeper and ask what type of effect it has on the cells.”
According to Dr. Meyvantsson, such automation often can be easily employed in labs to allow generation of large datasets.
“Because most labs that do this type of work have automated liquid handlers and high-content analysis systems already in place, they can get up and running quickly without any new equipment purchases.”
The new technology still has some challenges to overcome. “We are still just scratching the surface of this emerging technology,” Dr. Meyvantsson notes. “Some challenges that remain are finding the best way to gather and analyze information and improving manufacturing methods. We’ve made a lot of progress, but there’s still a lot of work needing to be done before we realize the full potential of cell modeling in microfluidic devices.”
Handling Data Deluge
The path from hit to therapeutic involves a complex maze of interacting multidisciplinary drug discovery teams. Handling not only the data, but the communication among all teams can be a monumental challenge that can spell the difference between success and failure and significantly alter the time taken to achieve the overall objective.
“The last five years have seen the industry begin to make changes in how they address the problems of siloed data,” explains Andy Vines, Ph.D., product manager for Activity Base™ at IDBS. “The issues here are primarily that when each department chooses its own solutions it becomes invisible to the rest of the organization. Communication between groups is often poor as a result, with e-mail or other static file types such as pdfs often used to send out their data to other functional disciplines. So, it’s a question of timing and efficiency. Lack of close coordination can be very costly.”
According to Dr. Vines, improving communication between in-house therapeutic program teams can help facilitate the planning and resourcing of screening activities in lead optimization. “Better orchestration and managing all the processes involved in drug discovery among the various disciplines provides a number of important benefits.
“IDBS’ Assay Cascade Solution helps reduce the overall cycle time for biological screening processes. By providing business intelligence dashboards of compound status, scientific processes can be carefully monitored and optimized, providing a single portal for therapeutic program teams to progress or usher molecules through the process. Another benefit is creation of an audit trail and transparency of the decision-making steps.”
IDBS offers a number of software solutions such as the Activity Base Suite that provides drug discovery data management for biological, chemical, screening, and structure-activity reporting. Additionally, its E-WorkBook next-generation ELN provides for capturing data from disparate sources, particularly preclinical.