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.

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.

“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.”