Speaking to Jack is an informative experience, due to his multidisciplinary history. He has, in his own words, been afforded the luck and privilege of being able to jump between industries over the years, following what interested him. After receiving his PhD in neuroscience from Oxford and a brief lecturership at Newcastle, he entered the commercial world, working with the Boston Consulting Group, then into healthcare investment at Sanford Bernstein, and then back and forth between biotech drug discovery, investment, and R&D consulting. This journey has given him a very broad view of the industry; all the way from the academic lab to the shelves of your local pharmacy. 

It was this expertise that led him to write the seminal paper, “Diagnosing the Decline in Pharmaceutical R&D Efficiency” in 2012. The paper was the result of Jack’s puzzling over a pervasive contrast occurring in the biopharma industry. At the time, much of the messaging surrounding drug discovery was exceptionally positive and hopeful. The advent of technologies such as genomics, computational chemistry, genetically modified animals, and high-throughput screening were said to have delivered an injection of efficiency into drug discovery. The scientific rhetoric was that things were good. However, Jack saw the opposite productivity trend in terms of the things that really mattered; fewer and fewer new drugs per unit of scientists’ time, or dollar of public or private sector R&D investment. So why was the scientific rhetoric so different from the economic reality? 

It was in the act of examining why huge falls in R&D output efficiency happened in the face of huge gains in R&D input efficiency, that the term ‘Eroom’s Law’ was born. Eroom’s Law is Moore’s Law (the exponential increase in computer power over time) spelled backward; a play on the exponential decrease in pharmaceutical R&D output efficiency in the face of exponential gains in input efficiency. Inflation-adjusted R&D expenses per new drug launched roughly doubled every nine years from 1950 to 2010 while, for example, DNA sequencing had become ten billion times cheaper, and X-ray crystallography has become ten thousand times cheaper.

Jack's Hypotheses and Future Outlook

It was clear that something was definitely wrong, but what? Certain things like management and organizational structure had not changed drastically enough over the past 60 years to create such a situation, so there had to be some other explanation for the dramatic change.

 Jack had a number of hypotheses, which he lumps into two main groups: 

(1)   Running out of critical resources

 Roughly 90% of all prescription drugs prescribed in the US are generics. Think about the last time someone you know got antibiotics at the pharmacy, or medication for stomach ulcers, hypertension, or depression. Did they get a generic, or did they get a branded drug? Chances are it was a generic; a drug that was discovered decades ago and which is now available at low cost. When there are cheap generic alternatives that work just fine, it is very hard for expensive new drugs to compete. This has progressively pushed drug R&D into the areas where, over the last 100 years, the drug industry has had the least success and, one way or another, such areas are likely to be difficult.

Another kind of resource depletion is regulatory. Drug R&D in the 1950s and 1960s was, compared with today, like the wild west. Regulation and convention has, effectively, built a bunch of fences that stop the drug industry doing things that would, quite rightly, be regarded as risky or unethical, but which made innovation cheaper and easier. 

(2)   Doing the wrong things with superficially higher efficiency

What if the technologies and discoveries that were thought to be a drastic improvement on the scientific method were in fact, not that helpful? Had there been an overestimation of the impact of improving basic science? And what if brute-force screening methods weren’t actually increasing the chances of finding more small molecules that would make it all the way through clinical trials?

The 2012 paper was an excellent exercise in fleshing out the issues plaguing drug development. As with most things, there was no single factor sabotaging the industry, but rather a coalition of many small and large issues. The paper raised other contributing factors as well – for example, clinical trials are getting bigger and longer too, escalating expenses. 

The article concluded with a musing about the future; the authors speculated that Eroom’s Law would not hold over the coming years, and it hasn’t. In 2020, Nature Reviews published a short follow-up to the 2012 article, detailing an uptick in drug productivity. The news of Eroom’s Law being broken was wrapped in caution as well as optimism, warning against complacency. More diseases are being genetically segmented, cancer types for example, meaning that while more drugs may be being approved, they are applicable to fewer and fewer people. Rare diseases have also become attractive to pharmaceutical companies as the route to drug approval can be much shorter and/or easier. 

Ultimately, Jack’s goal was to trigger a bigger public debate around the issue. With over 1600 citations, Eroom’s Law is certainly being disseminated through the scientific community.

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So, how do BioDAOs and Molecule help?

By creating a decentralized open-source ecosystem, BioDAOs are circumventing many pitfalls plaguing traditional drug development. Independent reviewers, optimized collaboration, large talent pools, and well-vetted capital allocation all pose effective counters to Eroom’s Law. By utilizing an open-source philosophy and technology that allows for shared governance, productivity is improved.

BioDAOs also improve the efficiency of idea generation. By removing the geographic layer, experts from around the world are clustered into these communities, providing exceptional resources and support. In doing so, the DAOs can also source a much wider range of ideas, compared to the bubbles that often exist in biotech hubs such as the Bay Area, Boston, or Basel.

They also arm researchers with the crucial skill that so many lack; the ability to translate their research into commercial success. Researchers are often not equipped to pitch their ideas to biotech firms, and they shouldn’t need to be. Researchers should be researching. In streamlining this process, drug development should, in theory, improve.

What advice does Jack have for researchers looking to get funded via Molecule?

Two things. First, consider the provenance or pedigree of your data. Things tend to fail because screening and disease models point us towards the wrong targets or the wrong compounds. More important than your p-values or effect sizes is an honest appraisal of why the p-values and effect sizes in your model systems have any relevance to human therapy. Jack has written another paper here, emphasizing the importance of predictive validity, highlighting accurate disease recapitulation. 

Second, accept that elements of your pet project may be changed in order to make it more likely to succeed. Ultimately, the shared goal is to translate your scientific advancements into practical applications for the benefit of patients. Many of the projects that are pitched at Molecule of the DAOs have the core of a great idea but are untranslatable in their initial form. Be prepared for remodelling when you work with Molecule or the DAOs, particularly if you don’t have much experience of taking drugs from the laboratory and into the clinic.

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What’s next for Jack?

Currently, Jack is in the process of building a new company with Sir Marc Feldmann and Professor Laura Dugan. The company, Etheros, is set to produce an entirely new class of neuroprotectants, based on research performed by Prof. Dugan. Previously, Sir Marc Feldmann established anti-TNFs, which have subsequently become the best-selling drug class worldwide.    

A special thanks to Dr Jack Scannell for his time and input while writing this piece.