A manifesto for reviving biopharma productivity

When it comes to Eroom's Law, some of the most important problems are the boring ones

I first encountered Eroom’s Law, the observation that biopharma productivity, measured as new drugs approved per billion dollars of R&D spending, has been steadily declining since the 1960s, halfway through my PhD. The fact that I completed a supposedly world-class undergraduate biology degree without ever hearing about this concept, and then spent another two years in a PhD program before discovering it, now feels damning. If anything deserves a place in Biology 101, it’s the recognition that our ability to turn biological insight into effective medicines has been deteriorating for decades.

Over time, I became somewhat obsessed with this problem. I also discovered that most biologists aren’t. That lack of engagement, I think, fuels a kind of magical thinking: the belief that simply doing “more and better science” will reverse the productivity decline. But we have had more and “better” science for decades. Clearly, we need to start thinking about this differently.

On one hand, I think people should be engaging much more with the predictive validity of our preclinical models. On the other, I believe we should appreciate the important of in-human testing much more, realize that nothing is going to replace it and focus on improving clinical development itself.

Earlier this year, I learned that Jack Scannell, who coined Eroom’s Law, is similarly frustrated with the endless list of fashionable “solutions” that hand-wave past these fundamental problems. Since he has written at length about how to think about predictive validity, we ended up focusing on the importance of improving clinical trials for a new article in IFP’s new metascience newsletter, Macroscience.

To Get More Effective Drugs, We Need More Human Trials

We think that public debate around improving biopharma productivity often focuses only on the most visible parts of the drug discovery funnel:

Public debates about how to revive productivity in the biopharmaceutical industry tend to be dominated by two camps. Technological optimists usually argue that declining industry outputs relative to investment reflect gaps in biological knowledge, and that advances in basic science will eventually unlock a wave of new therapies. The second camp, which traces its intellectual lineage to libertarian economists, focuses on easing the burden of regulation. In their view, excessive FDA caution has slowed innovation. They propose solutions that largely target regulatory approval: either loosening evidentiary standards or narrowing the FDA’s mandate to focus solely on safety rather than on efficacy.

Both perspectives contain some truth. Yet by focusing on the two visible ends of the drug discovery pipeline, early discovery and final approval, both camps miss the crucial middle: clinical development, where scientific ideas are actually tested in people through clinical trials. This stage is extraordinarily expensive, operationally intricate, and crucially, generates the field’s most consequential evidence. We believe that systematic optimization of this middle stage offers significant untapped leverage and deserves far greater focus.

This piece is also my way of signaling that I’ll be spending much more of my time on this issue—and that a longer list of concrete proposals is coming soon. In the meantime, if you have ideas, especially around regulatory changes or anything that could meaningfully improve clinical trials, I’d love to hear from you.

I think this topic has been relatively ignored compared to its importance, as I argue in the piece:

Clinical Trial Abundance, a framework for scaling and accelerating human trials, stresses the importance of optimizing clinical development. We already have a menu of promising solutions. Increasing regulatory transparency, strengthening clinical trial infrastructure through targeted investment, applying the Australian Phase I model in the US, relaxing excessive Good Manufacturing Practices requirements for early-stage development, and enabling remote and decentralized trials are just a few examples. But many of these ideas remain underdeveloped: the specific policy mechanisms, implementation pathways, and operational models are underspecified and insufficiently advocated for.

For the US, the ideal strategy lies in combining world-class science with highly agile clinical development. Yet clinical development has long been overshadowed by basic research, largely because it is operational, less glamorous, and thus, poorly suited for study within academic frameworks. This persistent asymmetry in attention must be addressed.