Buy Biotech Stocks? Cathie Wood’s Ark Thinks So (And, Here’s Why)
Cathie Wood’s Ark Investing has a relatively simple investment strategy. The company describes its philosophy in one simple sentence.
“We Invest Solely In Disruptive Innovation.”
That’s a broad statement, but one that also clearly defines what the company does. In addition, it’s sort of a warning. Disruptive innovation does not always work, nor does it always follow a straight line to success.
As we’ve seen during the pandemic, technology innovation can be a bumpy ride. Changing demand can open doors for innovators — think what happened with Zoom (ZM) – Get Zoom Video Communications, Inc. Class A Report or Peloton (PTON) – Get Peloton Interactive, Inc. Class A Report during the early days of the pandemic. Both saw a heavy acceleration of their disruptive business models but only Zoom has shown that it has a long-term sustainable model.
That does not mean that either Zoom or Peloton has succeeded or failed, it simply illustrates that investing in disruptive technology can be a bumpy ride. Now, however, Ark Analyst Alexandra Urman has written a blog post for the company which explains why it thinks technology can enhance the value of certain biotech assets.
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Are Clinical Assets Undervalued?
“Investment analysts using antiquated throughput rates to determine future ones could be undervaluing clinical assets at each stage of development. Next-generation sequencing (NGS), artificial intelligence (AI), CRISPR, and other gene therapies should boost clinical trial efficiency, shorten development timelines, and reduce failure rates. As a result, return on investment could improve dramatically,” Urman wrote.
Ark sees a number of recent advances that could enhance clinical trials while also making them cheaper to conduct. That’s a recipe that leads to more successful drug development.
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“Recent advances in neural network protein modeling could reduce the costs of clinical pipelines and increase returns on drug R&D beyond what we initially modeled,” Urman added. “Based on our ongoing research in this space, we now believe pre-clinical development could become even less expensive and yield results more quickly: a marketable therapy cost only $50 million in pre-clinical investment — halving the number we estimated previously.”
Biotech Development Could Get Cheaper
Being able to spend less money on clinical trials, means being able to conduct more trials. Essentially, a company will get more shots at finding drugs that work, and a failure won’t be nearly as catastrophic as it once was for early-phase companies.
Urman wrote that the ley breakthrough comes from Alphabet’s (GOOGL) – Get Alphabet Inc. Class A Report Deep Mind, which has created, “AlphaFold, a neural-network-based algorithm that can predict protein folding, as well as an open-sourced database of protein structures powered by AlphaFold2.”
That should lower costs for drug developers.
“Knowing the shape of a protein structure at the start of research could reduce by 50% the cost and time from discovery to Investigational New Drug (IND) approval,” Urman wrote.
Previously, Ark estimated pre-clinical costs per commercialized drug at $243 million.
“If technologies such as NGS and AI were to improve throughput rates, however, pre-clinical costs per commercialized drug could be cut by more than half to $103 million…and new predictive neural-network-based algorithms like AlphaFold could halve those costs to $52 million,” she added.
This removes some uncertainty for investors but it does not make investing in early-phase biotech dramatically less risky. These are investments that remain speculative for years — you won’t know if Ark made a smart choice until a drug passes clinical trials and gets brought to market successfully.
Ark does believe, however, that new technological advances will shorten those timelines.
“Historically, the duration of pre-clinical testing from discovery to phase 1 has averaged four years,” Urman wrote. “Based on our model assumptions, that number drops to three years and, with neural-network-based algorithms, two years…For the entire pipeline — from discovery to the registration of a therapeutic drug — technology could improve efficiency, reduce costs, and eliminate 3.5 years of research and development time.”
