- Industry as check and balance for the quality and relevance of university science? That's how it goes from study to reality
- There’s an urgent need for medical research to focus on human physiology and human measurement
- Many university studies which take place in contrived models are never actually pressure-tested in a way that matters
The central dogma of drug discovery is that academic basic research -> industry applied research -> new medical product. The disappointing number of impactful new medical products have been a source of endless soul searching, and could in theory be attributable to any (and all) of the steps in this model – and might also, as some have suggested, reflect the need for an entirely new conceptual framework.
The latest issue of Nature features a spot-on commentary (subscription only) by Glenn Begley and Lee Ellis (nicely summarized in this terrific Reuters article) that focuses in on the first arrow, the translation of academic oncology basic research into application by industry, and highlights the uncomfortable and inconvenient truth that by now isn’t a very well-kept secret: basic science is unbelievably fragile, and a lot of it doesn’t stand up to serious scrutiny.
This observation is absolutely consistent with my own experience and observations (see here, here, here, and here), as well as with those of Bruce Booth (this is a terrific discussion), and of course the pioneering research of Stanford Professor John Ioannidis, whose work I discussed six years ago (here), and who has been profiled extensively since (e.g. this piece from The Atlantic).
"While there’s a frequently advanced popular view that academic science is pristine and pure, the reality is vastly different. Of course it is."
We could spend a lot of time discussing why science is fragile; Begley and Ellis, for example, emphasize the need for a cultural change in the way preclinical research is conducted, particularly in the field of cancer.
At the end of the day, I suspect that the problem involves some combination of the law of small numbers, the appeal of narrative, the structural advantages of reinforcing dogma, and the difficulties of publishing negative results that might challenge it, especially if the dogma was advanced by senior leaders in the field who tend to play critical roles in reviewing papers for high-profile journals and in selecting which new research gets funded. While the process may ultimately be self-correcting (and I certainly believe that science “works”), the cycle time for this can be a lifetime (literally – in some cases I’ve heard it said you need to wait for someone to pass away before contrary ideas can truly gain traction).
It is important, and revealing, to recognize that these very real challenges in academic science reflect the many human attributes that drive science – ambition, power, curiosity, determination, eagerness, pettiness, competition, etc. While there’s a frequently advanced popular view that academic science is pristine and pure, the reality is vastly different. Of course it is.
Perhaps even more surprising (at least to some) — it’s actually industry (and beyond that, the market) that plays a vital role in checking the quality and relevance of university science. While academic investigators can, and in some cases do, make entire careers of publishing research that is ostensibly medical (i.e. funded by the NIH, aspires to ultimately impact patients), many of these studies — in highly reduced systems, or contrived and often woefully unvalidated disease models – are never actually pressure-tested in a way that matters. Publication, and success in academia, doesn’t require this – in a sense, the academic science can literally be a world unto itself.
To this point: there’s often a circular quality to academic research, where a particular model system, or particular enzyme, or particular brain region, or particular analytical approach becomes very trendy, and then it takes on a life of its own. In many cases, the work may be precise (clear and consistent results in a particular animal model of disease, say) but not remotely accurate (i.e. have little bearing on patients who actually suffer from that disease). In other cases, the work simply reflects limited data expertly spun into a sexy and highly publishable narrative. If you are looking for the best story tellers in a university, it’s not clear that the humanities department should be your first stop.
Fortunately, here’s where industry comes in. The goal of medical product companies is … to make, and sell, medical products. This means robustly demonstrating safety and efficacy in afflicted patients. Consequently, a promising research result is only a potential starting point – and is valuable only if it’s actually (a) robust (meaning the original result is repeatable – something that’s often not the case), and (b) relevant to patients (researchers might be exactly right that inhibiting a particular kinase works wonders for a mouse model of cancer, but it could unfortunately fail miserably in people). Because they need to drive results to application, companies are uniquely situated to evaluate and challenge existing dogma.
There’s a catch here, of course – which the Reuters article points out as well: if a particular medical result isn’t reproducible, the company will generally just drop the project, an outcome that makes sense in the context of the business (little upside in spending resources to publically refute the work of influential academic leaders), but it doesn’t seem to do much immediate good for science. Even so, by advancing ideas that are robust and which appear generalizable, companies in effect offer highly visible validation to the concepts that pass muster, and recognition for the academic researchers responsible.
Ultimately, I suspect that the real solutions here will come not just from higher standard for preclinical research, as Begley and Ellis propose, but also from academic researchers redefining the problems they are trying to solve. I hope more will decide that their goal isn’t a pyrrhic victory in a highly reduced system, but rather a translatable advance that measurably impacts human health. In other words, the emerging scientific challenge isn’t pulling Humpty Dumpty apart, but rather figuring out how all the pieces fit back together.
As I’ve previously discussed (e.g. here, here, here), there’s an urgent need for medical research to focus on human physiology and human measurement; I also believe, and have argued, that disease-focused foundations will play an increasingly important role in ensuring that promising science is aggressively driven into application (stand-out examples include the Myelin Repair Foundation, the Cystic Fibrosis Foundation (which played a central role in the development of Vertex’s new CF drug – see here), and the Michael J. Fox Foundation, among others).
By keeping the focus (as I’ve continued to emphasize) on better health for real people, perhaps we’ll develop both the humility to recognize how little we still understand as well as the drive to ensure — and emphatically demand — that our advances ultimately wind up not only in papers, but also in patients.
David Shaywitz is an AEI adjunct scholar.