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The economic tale of the pandemic, as we’ve often written in the past year and half, has been one where the acceleration of pre-existing trends has been remarkable — especially when it comes to technology.

The after-effects of pandemic-era policies will be with us for some time — with many supply chains and labor markets still experiencing significant disruption. Even the worst of this “scarring” will eventually heal. But the pandemic acceleration of tech adoption — especially all the manifestations of business digitization — will, in many cases, be with us permanently.

In our view, this reality raises the profile of tech as a critical component of investment analysis. It almost makes tech a necessary and universal theme across the board — something which investors should evaluate in any publicly traded company in which they are interested. 

Many themes of varying durability will come and go on investors’ radar screens, but many of the best will have a tech element as a critical component. Biotech is a good example of a theme with enduring significance within which tech-enabled companies are of particular long-term interest. 

Biopharma discovery and development — the arduous process through which new drugs are developed and brought to market — is itself in the early stages of digital transformation.

The Biopharma Discovery Process

Everyone likes to complain about drug prices, but here is the unpleasant truth: discovering new drugs is expensive and risky. Profiteering and gouging does occur. But the response of biopharma companies to public and political pressure — that they need to charge high prices to subsidize the huge amount of R&D spending that goes towards eventual failures — is largely true.

There is a reason why the lion’s share of biopharma innovation occurs in the United States, as opposed to other developed countries where pharma price regulation has constricted the ability of most companies to engage in high-risk, high-reward research.

Technology that can improve the biopharma R&D process by making it faster, cheaper, and more productive will be watched carefully by every biopharma leadership team, and the innovators who can successfully deploy it have the potential to be major stock-market winners.

Stages and Phases

To describe how difficult, expensive, and time-consuming this process is — and how disruptive tech could change it — we’ll briefly sketch the major stages. In discovery, companies do basic research — deepening their understanding of disease mechanisms on the level of genetics, proteins, and cellular mechanisms. 

They then identify a therapeutic target, and validate therapeutic agents that might successfully act on that target — narrowing the field from tens or hundreds of thousands of candidates, often by laborious in vitro experimentation. (Note to younger readers contemplating the life sciences: most scientific work is arduous and boring.)

All of this occurs before any molecule is injected into an animal, let alone a human. This process can take three to five years, and can cost close to a billion dollars. In early development, in vitro work continues, and eventually, in vivo experiments occur in animal models, allowing a more detailed preliminary study of safety, tolerability, and efficacy.

Finally, the company can file an IND (investigational new drug) application with the FDA and begin clinical trials in human subjects in Phase 1 trials. This early development work can take another two to three years, and cost another $400 to $600 million. 

Finally, in late development, assuming all those hurdles are successfully passed, the drug advances to larger trials in humans, evaluating safety and efficacy and monitoring side effects in Phase 2, before advancing to pivotal trials in Phase 3, often with thousands of patients. Phase 3 trials are typically the most expensive, as they are the largest; together, Phase 2 and 3 can take three to five years and cost another billion dollars. 

At the end of that process, the company can file an NDA (new drug application, for small-molecule) or a BLA (biologics license application, for large-molecule drugs) and hope that after ten to fifteen years of work and two or three billion dollars spent, the drug will be approved.

Often, it is not. The overall success rate of clinical drug candidates — from Phase 1 to approved drug — is about 5–10%, and has been declining. Here is where computational biology, high-throughput genomics, advanced proteomics, big data analytics, and artificial intelligence could make a big difference.

Digitization In Biopharma Discovery

Digitization has the potential to make both discovery and development efforts more systematic and more efficient. Genomic analysis has permitted a transformation in the identification of disease mechanisms as sequencing has become exponentially cheaper (dropping from $100 million to sequence a complete genome in 2001, to less than $1000 currently).

Illumina (ILMN) pioneered next-generation sequencing and has continued to advance the technology since. This analysis has given researchers greater insight into the genetics of disease mechanisms, and to begin to apply that knowledge to the identification of therapeutics.

Less well-known than genomics to the public is proteomics. The genome indicates the totality of an organism’s genetic information; the proteome is the complete set of all the proteins that exist in a given biological individual. It is a vastly diverse set of chemicals, and obviously deeply relevant to the identification of drug therapies.

No revolution corresponding to contemporary genomics has occurred in proteomics, but there are many companies working on new proteomics technologies, including Seer (SEER), QuantumSI (QSI), and Nautilus (NAUT), among others.

The development of high-throughput proteomics analysis will be a critical weapon in the drug discovery process, enabling biopharma companies to narrow the list of drug candidates before advancing to further and more expensive stages in the development process. 

A further dimension of proteomics is precise high-resolution protein visualization and imaging, which permits a more precise evaluation of the ways in which the proteome will interact with potential drug candidates. Novel technologies such as cryogenic electron microscopy are playing a role here.

Working With the Data: Computational Biology

Beyond the understanding of the underlying genetics and proteomics, so-called “dry” research presents the prospect of replacing early stage lab work with high-intensity computer modelling. Big data in general offers the possibility of using superhuman intelligence to identify patterns that would elude human observers. 

Schrodinger (SDGR), for example, uses proprietary software to crunch genomic and proteomic data in order to come up with new molecules of interest; Certara (CERT) and Simulations Plus (SLP) offer similar tools for discovery. Other companies, such as Recursion Pharmaceuticals (RXRX), which uses one of the world’s most powerful supercomputers for this purpose, combine “dry” and “wet” research under the same roof.

Why We’re in the Early Innings

With all the progress made in genomic and proteomic analysis and all the growth in computing power over the past two decades, why are we just at the beginning of tech-enabled transformation of drug discovery and testing?

For one thing, promises of this technological transformation have been around almost as long as microcomputers — maybe 50 years — and they have thus far not borne fruit. The technology was promising, but simply had not advanced to the level where it could offer substantial improvement over the work and intuition of human investigators. 

Unfortunately that poor track record has contributed to the general conservatism of the biopharma industry when it comes to research innovations. Most biopharma companies are loath to move away from an imperfect status quo towards a new one that might take a decade to prove itself. 

This, of course, is the larger problem — drug discovery and development is a complicated and lengthy process, which makes a dollars-and-cents analysis of the value added by new digitization tools all that more difficult to justify. 

Investment Implications

New platform technologies will allow tech-enabled biopharma innovators to offer substantial efficiency and cost improvements to the drug discovery and development process. In some cases, these technologies will employed in-house by biopharma companies developing their own pipelines; in other cases, they will be SaaS (Software as a Service) offerings made by stand-alone companies. 

Although the promise is great, and we believe that critical technological thresholds have been crossed that will allow it to succeed where it has failed in the past, it may be another decade before the inertia of the majority opinion in biopharma boardrooms is overcome. In the meantime, there will be individual success stories. 

We believe that pure-play, “dry” providers of technology — rather than biopharma with in-house platforms — could perform better in an environment of higher regulation and government influence on healthcare provision; as the pressure on drug company R&D spending rises, they may become more willing to look for potential money-savers and productivity-boosters.