Technology-enabled drug discovery promises to increase the efficiency of drug development. By combining artificial intelligence, robotic labs, and sometimes supercomputers, the emerging field thinks it can find better drug candidates faster. If those assets progress through clinical trials more easily, then it can shave years and tens or even hundreds of millions of dollars off traditional drug development cycles.
Recursion Pharmaceuticals (RXRX) is one of the leading technology-enabled drug developers. The company's pitch to investors includes many buzzy words and phrases. For example, the precommercial business aims to "industrialize drug discovery" with robotic labs. A unique "operating system" powered by "mapping biology" could yield tremendous efficiency gains, according to investor presentations.
Investors might be wondering, "What the heck does all that mean, exactly?" Perhaps the best way to answer that question is by asking another: Can Recursion Pharmaceuticals live up to the hype?
Industrializing Biology for Improved Standardization, Throughput
Nerds in lab coats still do most of the work in the lab, but biotech experiments are increasingly completed with robots. Conveyer belts transport samples from one instrument to the next, while robotic arms load them into instruments. Samples are bardcoded, both with actual barcodes and chemical tags, to track their journey through the lab. The overall approach allows companies to standardize science, which can reduce error rates and increase reproducibility. It also increases the volume of experiments that can be conducted.
In other words, drug discovery and synthetic biology are increasingly being industrialized – that part isn't hype. Remember that the next time you see the same old stock photos of scientists in lab coats, wearing the wrong kind of eye protection, moving food-colored water in pipettes and test tubes that are much too large. That's not actually how lab work is completed.
Recursion Pharmaceuticals has the advantage of being a tech-native drug developer. That means it built robotic labs as a core part of its lab infrastructure from the beginning. In fact, the company can conduct over 1.5 million experiments each week.
Of course, the ability to conduct so many experiments can lead to bottlenecks in data processing. That's why tech-native companies hire almost as many software engineers as scientists and often create their own custom software tools. In a move that's increasingly more common, many drug developers are also building or renting time on supercomputers to plow through the insane amounts of data they produce. Recursion Pharmaceuticals owns the BioHive 1 supercomputer, which was one of the top 100 most powerful machines as of November 2021.
Touting robotic labs and petabytes of data always turns heads in an investor presentation. Can it drive business success through more efficient drug discovery?
Mapping Biology for Novel Insights
The complexity of biology is a major limitation to drug development. Traditional drug discovery leans on simplified genetic pathways, or sequences of genetic interactions. For example, if the activity of gene A is reduced, then it could impact the activity of disease-driving gene B. Companies then go and design drug candidates that can reduce the activity of gene A. It doesn't always work, but it may take years and hundreds of millions of dollars to realize that. Mistakes are expensive in this industry.
Recursion Pharmaceuticals relies heavily on its ability to map the complexity of biology. That means understanding how any single gene impacts or is impacted by the activity of hundreds or thousands of other genes. The company claims it can discover novel interactions, which can be leveraged to design new drug candidates.
Will it work? Investors should acknowledge the potential improvement compared to traditional drug discovery. More data and more detailed biological maps could lead to more insights. However, whether they lead to clearer insights is uncertain.
Designing a drug candidate aimed at a newly discovered genetic pathway doesn't mean success is guaranteed. That's true for the exact same reason traditional drug discovery has such as low success rate: Biology is very complex.
Recursion Pharmaceuticals has discovered this the hard way. Management decided that it needed to conduct additional animal studies for one pipeline asset, REC-3599, to better understand how different doses were processed in the body. The decision is expected to delay the start of a phase 2 clinical trial by 24 months. That's not exactly increasing the efficiency of drug development.
To be fair, delays happen. The company has done a solid job overall advancing drug candidates into and through clinical trials. It even touts having "one of the largest, broadest, and deepest pipelines" in the emerging field.
However, investors should consider the financial risks of such a hefty pipeline. A business model requiring significant cash for many years may not be so advantageous in a world with tightening financial conditions. The current cash position of $591 million at the end of March 2022 can fund a couple of years of development, but much more will be needed in the next five years. That promises to result in painful amounts of dilution.
Investors Should Keep an Eye on This Biotech Stock
Recursion Pharmaceuticals is an intriguing technology-enabled drug developer. The tech-native approach really could lead to more efficient drug discovery and development, which could earn the business a premium compared to traditional drug developers. However, it will take time to understand if efficiency gains are meaningful -- or are delivered at all.
One promising sign was the formation of a massive collaboration with Roche (RHHBY) and its subsidiary Genentech. The partnership will leverage Recursion Pharmaceuticals' technology platform for up to 40 programs in neuroscience indications, which could result in billions of dollars in milestone payments over the next decade. It certainly validates the young company and shows the titans of industry are interested in the new approach. Whether it yields successful drug candidates remains to be seen.
