Clinic-in-the-Loop

For the last several years, I have been trying to understand why biomedical progress, especially in therapeutics, has become less productive despite staggering advances in basic science.

I am not the only person vexed by this. In 2012, biotechnologist Jack Scannell formally described the dwindling returns on therapeutic investments, coining the term Eroom’s Law (Moore’s Law in reverse). Eroom’s law states that the inflation-adjusted cost to bring a new drug to market roughly doubles every nine years: a trend that has held since the 1950s. With the goal of upending Eroom’s law, I have spent the last year studying the structural bottlenecks that shape how new medicines are tested and the FDA’s role in such decisions.

Much of my time has been focused on clinical trials, which, despite their central role in the creation of pharmaceuticals, receive remarkably little systematic attention. This led me to launch the Clinical Trial Abundance Project, a framework aimed at increasing not only the number of clinical trials, but also their speed and how much we learn from them. Recently, I co-authored an essay with Scannell, arguing that making trials more efficient and informative is essential to breaking Eroom’s Law.

Critics of our essay, however, argued that making clinical trials more efficient risks treating biotechnology like a casino. In their view, making it easier to run clinical trials would risk allowing more potentially harmful drugs to be tested in patients and, instead, biotechnologists should focus on making better drugs that are more likely to gain approval. These critics see Clinical Trial Abundance as accepting the status quo of drug development rather than challenging it.

But this is a misunderstanding.

In fact, Clinical Trial Abundance and better hypotheses for drugs are not merely compatible, but self-reinforcing. Faster testing in the clinic creates a feedback loop: ideas become trials, trials generate rich data (including both successes and failures), these data improve models, and better models inform the next generation of ideas. In this view, the clinic is not an endpoint of discovery but a central component of it.

To understand why clinical abundance is important, we must step outside the prevailing view of clinical testing as a mere “validation step” for scientific ideas. The familiar funnel metaphor of drug discovery, depicting a linear progression from basic science to regulatory approval, reinforces the flawed notion of clinical testing as a passive filter designed to screen pre-existing ideas.