Useful Fictions or Distorting Frames? How Models Shape Drug R&D

We rely on models — deliberate simplifications — to navigate, make sense of, and engage productively with an ever-more complicated world.

Patients understand their illnesses through what Dr. Arthur Kleinman called “explanatory models.” Physicians use models such as the pathophysiologic model or the biopsychosocial model to integrate their patients’ symptoms and fashion treatments. Biopharma R&D would be impossible without models as well. Every program rests on a series of them, at every stage of the R&D process.

The catch, as Alfred Korzybski observed nearly a century ago, is that the map is not the territory. Unless we recognize the assumptions built into the models we use, we are liable to be led astray — especially because we tend to reach for models that are cognitively accessible, experimentally tractable, or organizationally useful, whether or not they are especially predictive.

Enter Jack Scannell

In a recent interview with Pablo Lubroth of Decibio, Jack Scannell offers an eloquent and deeply resonant account of the role of models across R&D, highlighting not only their limitations but, more interestingly, why we continue to rely on them. He conveys a deep familiarity with the lived experience of R&D — the critical tacit knowledge that other biopharma veterans will immediately recognize.

Scannell, a longtime analyst of drug R&D productivity who now runs the early-stage biotech Etheros, is perhaps best known for coining “Eroom’s Law”: the depressing observation that, from 1950 to roughly 2010, the number of new drugs approved per inflation-adjusted billion dollars of R&D spending fell by about half every nine years. The name is “Moore” spelled backward — a pointed contrast with Moore’s Law, the emblem of exponential progress in computing.

Within R&D, Scannell highlights problems with both the financial and biological models on which the industry relies. I’ll focus mostly on the biological side and simply note that his assertion that “standard drug and biotech valuation models are blind to decision quality,” making companies less sensitive to the value of better decisions, especially early in development.

Reductive Models in Biology

The key biological question Scannell — and really everyone in drug R&D — wrestles with is how to think about something as complex as human biology in a way that allows you to develop a medicine, introduce it into the midst of all this complexity, and have reasonable confidence that it will improve a particular condition without doing harm elsewhere.

It takes a prodigious amount of chutzpah to believe we can do this. The many remarkable medicines that have been developed deserve to be celebrated.

In the words of Dr. Roger Perlmutter, former head of R&D at Merck, “we have no idea what we’re doing.” He continues, “It’s a bloody miracle if you ever make a drug that works.”

To manifest such miracles more reliably, biopharma — following the lead of medical science — has leaned heavily into reductionism: the idea that we can best understand life by breaking complex processes down and studying their individual components.

This has worked especially well for monogenic diseases, conditions that result from a single catastrophic glitch that can be targeted and, ideally, remediated.

It has also encouraged a focus on individual targets, often receptors, and on developing the most potent possible binders. These assays are especially attractive because they are amenable to scale: you can evaluate a huge number of candidates in a high-throughput fashion.

This approach can be effective. But it can also generate premature confidence, since identifying a great ligand is not the same as finding a promising drug candidate. Tight binding to an individual target may be useful to select for, while still failing to predict physiological promise in the organism as a whole.

When Clean Stories Meet Messy Biology

Highly reductionist approaches tend to struggle with complex diseases and psychiatric conditions, Scannell explains, noting “drugs don’t know they’re meant to bind a target,” and often turn out to bind multiple targets at once. Some of the most effective drugs we have, he says, may be more like “magic shotguns” than “magic bullets.”

In psychopharmacology, for instance, clozapine — the only drug approved specifically for treatment-resistant schizophrenia — binds dopamine, serotonin, histamine, muscarinic, and adrenergic receptors. After more than fifty years of research, its mechanism of action remains unclear and debated. Its clinical efficacy may depend on this promiscuity across receptor systems rather than any single interaction. 

Clozapine is also a reminder of what highly reductionist approaches can miss. A target-first assay tends to reward exceptional activity against a specified interaction. A phenotypic readout may capture a pattern of modest effects across many interactions — a profile that might look unimpressive receptor by receptor, yet prove meaningful in the organism.

The broader point is not confined to clozapine and psychopharmacology. Some TKIs developed against specific kinase targets turned out to inhibit panels of kinases, and their clinical activity may depend on this broader activity. “In large parts of psychopharmacology,” Scannell explains, “there’s no easy relationship between pattern of binding across receptors and phenotypic impact on people.” He adds that we can confuse “neat stories about targets, creation myths, with how drugs actually work.”

He also points out that a number of drugs approved before the target-centric era — medicines like metformin, the first antipsychotics, and the first antidepressants — have messy or contested mechanisms. Metformin has been attributed to AMPK activation, mitochondrial complex I inhibition, and effects on gut microbiota, with little consensus around a single unifying mechanism. Chlorpromazine and the first antidepressants were discovered empirically, with mechanistic stories built around them later.

Even targeted drugs with seemingly clear mechanisms can become more complicated over time. Anti-VEGF therapy, for example, emerged from Judah Folkman’s elegant insight that tumors depend on angiogenesis. But subsequent work, especially by Rakesh Jain, suggested an additional mechanism: anti-VEGF agents may transiently normalize chaotic tumor vasculature, improving delivery of chemotherapy. The original story was not so much wrong as incomplete — a reminder that even elegant mechanistic stories tend to get more complicated as we learn more.

A Little More Predictive Validity Goes a Long Way

Scannell has particularly emphasized the importance of improving disease models — increasing the “predictive validity” of the systems in which drug candidates are studied. He notes that while everyone recognizes that good models are better than bad models, the less obvious point is that “marginally better models are much better than marginally worse models.” A model that improves predictive ability even slightly can be more valuable than running ever more compounds through a less predictive system.

By contrast, he warns that “feeding AI with data from poor biological models simply increases the number of wrong answers you can generate per second.” He worries that some AI drug-development companies may industrialize a poor biological model because it is easiest to fit “into an AI-led design-make-test loop” — leading at best, he says, to virtual ligands optimized against a good in silico representation of an irrelevant biological system. Other companies, he encouragingly notes, appear more focused on using AI to develop better biological models.

Why Flawed Models Endure

Given the obvious value of improved models, why are they so hard to come by? Asked differently, what makes existing models so sticky?

The answer, Scannell suggests, comes down largely to incentives.

Academic researchers, he explains, are not rewarded for the slow work of model validation. You “do not get Nature papers by doing long, tedious, gritty work to evaluate whether a particular mouse model of disease X recapitulates the human pathology and then responds the same way to drugs that have been in people,” he says.

Industry researchers, for their part, desperately want better models but are not always incentivized to invest in developing them. A validated model may quickly become useful to everyone, eroding any competitive advantage. Moreover, Scannell explains, while industry scientists may privately acknowledge the limitations of their models, admitting this too directly could threaten the programs built on them.

At the portfolio level, simpler explanations often prevail over more nuanced accounts. Scannell shares a thoroughly relatable story about a colleague preparing to present a project to a portfolio management committee at a mid-sized pharma. The colleague had slides with “really complicated maps and models of biological pathways.” Before the meeting, he was told to come back with something much simpler: “three or four boxes and a couple of arrows,” otherwise “the board would never approve it.”

Context Matters

Perhaps Scannell’s most important observation involves a distinction he borrows from innovation scholar Fred Steward: the difference between “technoscientific” problems and “sociotechnical” problems.

A technoscientific problem, Scannell says, is “like the classic Apollo moonshot” — a problem that can be solved with enough engineers and enough funding.

Sociotechnical problems, by contrast, involve “a complicated mix of economic, regulatory, and process changes happening simultaneously over time.” He cites the building of modern healthcare systems as an example and believes AI in R&D likely falls into this category.

On the process side, he explicitly cites the classic work from Stanford scholar Paul David that I’ve discussed before, on how the arrival of electricity itself did not immediately improve factory productivity. The benefits came later, when electricity allowed factory work to be reorganized. The new technology mattered, but the impact was felt only after the structure of work changed around it.

“I suspect that no one really knows what the reconfigured factory is going to look like when it comes to AI in drug R&D,” Scannell wisely observes — a statement that could apply just as well to the application of contemporary AI across healthcare.

It also speaks to the remarkable opportunity now before us, from biopharma R&D to disease prevention: to use emerging technologies such as AI to reconfigure the work itself, improve the models on which we rely, and turn better models into better decisions — decisions that help us stay healthy longer and, when we become patients, receive treatments more likely to work.