In June of 1836, Nathan Rothschild left London for Frankfurt to attend the wedding of his son Lionel. Nathan was probably the richest man in the world and, needless to say, could afford whatever he pleased.

At his 59 years old, Nathan was in great health. But immediately after leaving London he started suffering from a painful inflammation on his lower back.

Nathan Rothschild continued business as usual, attending the wedding and working with the help of his wife while leading doctors were called from London and German cities to treat him.

Nothing worked, the infection spread and a few weeks later Nathan Rothschild died. The cause of death: staphylococcus or streptococcus septicemia from the inflammation or the surgeons' knives.

This was before the germ theory existed, hence before any notion of the importance of cleanliness, much less antibiotics.

David S. Landes wrote in the opening page of The Wealth and Poverty of Nations:

"And so the man who could buy anything died of a routine infection easily cured today for anyone who could find his way to a doctor or a hospital or even a pharmacy."

Since the times of Nathan Rothschild, medical research has taken gigantic strides. We can now perform complex transplants, diagnose and manage almost every disease and extend life expectancy.

But, as Tyler Cowen and Ben Southwood concluded, in the past few decades the rate of scientific progress has slowed down.

"To sum up the basic conclusions of this paper, there is good and also wide-ranging evidence that the rate of scientific progress has indeed slowed down, In the disparate and partially independent areas of productivity growth, total factor productivity, GDP growth, patent measures, researcher productivity, crop yields, life expectancy, and Moore’s Law we have found support for this claim."

There are many reasons for this stagnation, and people much smarter than me are trying to figure out why (and how to revert it).

My understanding is that root problems can be grouped into “human problems” and “technological problems”.

Since this is a Europe tech newsletter, today I’m going to try to understand the technological side and review how an Amsterdam-based company called Castor is trying to fix it and accelerate scientific progress.

To write this piece I reached out to Castor’s CEO, Derk Arts (who definitely fits under the “much smarter than me” bucket). He helped me untangle the problem. I have zero financial interest in this, Castor didn’t pay me a penny. I just thought it was cool.

And with that said, let’s dive right in.

(Sidenote: If you are curious to understand more about the ‘human problem’ I recommend you start with the incentives behind scientific publishing in Can Twitter save science?. I’m surprised by how many times the answer to something is Alex Danco)

The paradox of Eroom’s Law

You probably think of the clinical research industry as this forward-thinking, innovative field where humble scientists work together using cutting-edge tools to advance science in the name of humanity.

Not quite.

Like all systems with human intervention, medical research is struck by inefficiencies and guided by incentives, making it a slow moving, conservative industry in terms of how it adapts to things.

Most people claim the “human problem” is bigger but what caught my attention about the “technological problem” is that in spite of the fact that our technological progress has been substantial, the cost of developing a new drug roughly doubles every nine years.


This phenomenon has been named Eroom’s Law (yes. that’s “Moore’s Law” spelled backwards):

Eroom’s Law indicates that powerful forces have outweighed scientific, technical and managerial improvements over the past 60 years, and/or that some of the improvements have been less ‘improving’ than commonly thought. The more positive anyone is about the past several decades of progress, the more negative they should be about the strength of countervailing forces. If someone is optimistic about the prospects for R&D today, they presumably believe the countervailing forces — whatever they are — are starting to abate, or that there has been a sudden and unprecedented acceleration in scientific, technological or managerial progress that will soon become visible in new drug approvals.

So let’s break up some of these technological problems into two.

First, you have a data problem.

Big pharmaceutical companies run each study as a separate silo. Their focus is on the outcome of each individual project, ignoring the big-picture.

This means that each study has its own separate database that can’t talk to other databases (instead of having one large, comprehensive dataset of all studies).

This reminds me of the meme I used a few months ago when trying to explain the European Commission’s data strategy:

This is a big problem because these multi-billion dollar companies can’t even answer simple questions like how many female diabetes patients were ever enrolled in your trials, let alone merge complex data from multiple sources and run a meta-analysis to produce stronger or novel outcomes.

Here's Derk Arts from Castor:

"No one is asking themselves: how do we create exponential value out of the combination of all these projects instead of just focusing on the outcome or the individual projects? It's very hard to do any sort of drug development or discovery on your data if you don't even start investing in it today. Every day we are starting new trials, we should also start investing in, standardizing your data, making machines so in a few years from now we can turn this into a huge competitive advantage."

Second, you have a tools problem. If you think about the typical clinical study it looks like roughly like this:

  1. Recruit patients
  2. Gather consent from patients
  3. Monitor patients and collect data overtime (sometimes from different sources)
  4. Analyze data

Recruiting patients and gathering their consent is an arduous step. But the most complex challenge comes later, when researchers have to monitor and capture data from a variety of sources and monitor patients over time.

Do you want to hit your head against the keyboard? Here's Derk again:

"All the aforementioned inefficiencies are quite surprising to lots of people. You still find that researchers are conducting medical research and putting down data on paper, and then importing that into Excel files and into really basic systems."

Elon Musk is trying to send people to space but researchers are conducting medical research and putting down data on freakin’ paper and manually entering it into Excel. Now Eroom’s Law makes a lot more sense.

Enter Castor

Castor is an Amsterdam-based electronic data-capture platform that allows medical researchers to enroll patients, gather consent and then capture and manage medical research data in a smarter way, making it 10x easier for non-technical researchers to conduct clinical studies.

Here’s why this is important.

“One of the biggest bottlenecks holding more people from doing more medical research is patient group recruitment, just finding enough patients ultimately. We need to start democratizing trials by making it easy for patients to decide on their own if they want to participate in a trial and to enroll through digital means. So Castor helps the world by making it very easy to enroll, online and e-consent online and get enough information over video consultation with the researcher to talk about the trial, understand if you're eligible and if you are, decide if you want to participate in. Really removing those barriers, like the need for a face to face contact  to be recruited into a trial is the key.”

After raising €5.3 million a couple years ago and growing to 60 employees, they managed to build a platform that enables researchers to capture data from multiple sources (surveys, wearable devices, etc.), collaborate with colleagues, invite patients through questionnaires, import, export and analyze their data in a secure environment.

You know what’s the output of that model? Faster scientific progress.

But it doesn’t stop there.

Castor’s mission is to solve the larger problem – they believe that by helping standardize their data using machine learning, researchers will be able to create the most extensive, most diverse, distributed medical research dataset the world has ever seen. This would allow the medical community to discover potential cures for diseases even faster.

Here’s Derk Arts again when their funding round was announced:

“Medical data has become the world’s most valuable asset, yet it wastes away in modern-day research. At Castor, we have built a cutting-edge, user-friendly Electronic Data Capture [EDC] platform that we are putting in the hands of every medical researcher worldwide. With the help of our system, we allow researchers to streamline the clinical trial process and use their data to its full potential.”

40,000 researchers from 90+ countries are using their platform, collecting 140 million data points from 1,5 million patients. A long way to go, but they are getting there.