GEN Are there bottlenecks, and if so, where are they?
Lam: There are three bottlenecks: image acquisition, software to analyze the image data, and information management. These are the areas we need to improve upon, rather than just the hardware.
von Leoprechting: We are collaborating with the open-source networks to help break some of the bottlenecks in image analysis and storage. The point is not just managing and analyzing the data, but also making it more accessible to users. This is also where we see the difference between some of the pharma labs, especially in assay development, and academic labs.
In academia you often have many highly experienced experts who can run complex data-mining and data-analysis programs. But in a drug discovery therapeutic group, such expertise is often not available for assay development, as this knowledge mainly resides in the screening labs. So you would need to develop two-tiered software packages—one for the experts and one to allow new users to set up assays easy and fast.
Evans: I definitely believe that information management has been a key aspect of being able to scale high-content screening. One of the problems that we’ve been facing for the last few years is how to actually take all that information and build knowledge, because that’s what you ultimately want. You want to be able to make informed decisions on which drugs to develop or which targets to follow up on.
A major part of the pipeline that is missing is a modeling environment where new information can be piped in as it is being generated and models refined.