Following Fierce Biotech’s story revealing $10 million has been raised to bring a $4,000 lab robot into the mainstream, we delve into the driving factors that are required to make process and system automation commonplace in the pharma and biotech R&D sector.
Imagine a world with no need for manual intervention and increased throughput which runs 24/7…
Although process automation and systems refinement are already part of the mainstream for the manufacturing plants of pharma and biotechs, the higher complexities within the R&D element of drug development has made the adoption of automated tasks far trickier, but we’re getting closer every day.
Robots have been around for many years in the laboratory, and are used heavily in the high throughput (HTS) screening areas. However, many of the analytical processes involve important process steps before and after the ‘analysis’ stage, including sample preparation, layout and data reduction. All this “data” that is produced is also needed in further parts of the process: running the process in an automated fashion is not enough – the data flow also needs to be automated, and this is the reason we’ve seen a slower rate of adoption so far.
Nevertheless, while these moving parts and complexities have been hurdles to automation in R&D, there has been real change in the way laboratories have been organised, and with new technologies developed close to hitting the market, robots, such as Opentrons’, are likely to become a regular fixture soon enough. With the global market for lab automation already estimated at $4 billion, reports are also forecasting a growth of 7.4% annually between now and 2023.
So, what benefit will this bring to the R&D lab?
While automation comes with a data management and reporting requirement alongside it (adding even more complexity), process automation in R&D has, and will, allow staff to perform additional functions over and above additional activity – and even run concurrent tasks alongside the automated function. This will give Lab Managers the ability to re-assess where their scientists and researchers can provide additional value.
The hardest quantifiable task, however, will be to show its impact on reducing time to market and the cost of new drugs – after all, what else would be the point of a robot?