Prior to the 1980s the vast majority of drug discovery was performed on tissues, cells or living organisms, where the tests looked for how compounds effected the phenotype. Since then the industry has adopted in-vitro target based approaches, leveraging advancements in high-throughput lab platforms to screen vast compound libraries against a target protein. But what impact does this significant increase in scale have on today’s researchers?
Adopting these highly scalable target based assays has led to a huge increase in throughput of compounds tested, and supported the increase in size of compound libraries within the industry. However leads discovered via this approach have suffered from high attrition rates and some now question if adopting a purely target based approach has resulted in fewer drugs reaching the market.
Historically phenotypic assays were tricky to scale up. It was often slow or extremely hard to interpret and refine results from these assays to drive drug development, due to a lack of understanding of the target or targets interacted with.
Today phenotypic assays can increasingly be scaled up and the cost and time needed to run these assays is falling. To interpret how the effect occurred, and on what target, data from these assays is being brought together with data from the omics space.
One example of this increasingly high throughput phenotypic analysis is high-content screening (HCS). This is an imaging assay which measures the phenotypic effect on cells when introduced to a pharmacological substance.
Analyzing these assays using imaging software is one part of the solution, but these assays generate highly complex data that needs to be analyzed and stored. It also needs to be linked back to the original image of the cells.
Managing this data is one of the most important aspects of phenotypic assays. These assays need a capable informatics system to capture, interpret and present normalised data, as well as the images, simply to an end user. In addition, to get true value, the outcomes of these assays need to be interpreted with data from other analysis or by other informatics teams. This requires a solution that can store results in a structured and easily accessible format, enabling organizations to find, interpret and refine these results further.
Look out for my second post next week which looks at key areas to consider around HCS data management technology in the lab.