Biopharma relies on innovation to stay in business. Success depends on our collective ability to discover, develop, and deliver new products that cure or meaningfully mitigate disease over and over again.
Patents allow for innovators to be rewarded, for a while. When patents expire, allowing us to purchase powerful generic medications like atorvastatin for pennies, manufacturers must come up with something new to support the enterprise.
The pressure to discover and develop the big new thing is intense.
We have seen remarkable advances in many areas, including cystic fibrosis and of course the rapid development of COVID vaccines. We routinely contemplate a range of modalities that a decade ago would have been considered fanciful (see here). We also acknowledge that, tragically, many dreadful conditions like glioblastoma multiforme, pancreatic cancer, and amyotrophic lateral sclerosis remain largely resistant to our efforts – so far.
While we recognize the value of innovation, we also appreciate that often, there seems to be a lot more heat than light, at lot more self-congratulatory social media posts than real evidence of progress.
The Harm of Innovation Theater
Writing this month in Forbes, Dr. Sachin Jain, a physician-executive with experience across all of healthcare, from academic medicine to pharma to payors, plaintively expressed his frustration with the excessive celebration of innovation. He called out the dichotomy between the triumphant characterization of innovation by many healthcare and biopharma organizations, and the often far less impressive reality.
“I was struck by the difference between what I read and [what] I was seeing on the ground in practice,” he writes, noting, for example, that many highly-touted advances were only small pilot programs, and never actually scaled (or planned to scale).
He’s previously described the difference between what he calls the “change layer” – “the cloud in which visionary ideas about transforming healthcare resides” – and the “reality layer,” the place “where most care is delivered.” While both layers are necessary, he writes, he’s observed “little mixing between them.”
Moreover, he suggests, the change layer perversely may insulate organizations from real change by providing a conspicuous, dynamic narrative around innovation and disruption, even though these innovations and disruptions rarely meaningfully permeate into the day-to-day business of the company. He cites several examples of prominent healthcare demonstration projects that persist (if at all) only as isolated examples.
Jain is hardly the first to note the distinction between speaking and acting. Aesop, born more than two and half millennia ago, reportedly observed, “when all is said and done, more is said than done.” More recently, University of Chicago economist John List has examined, in The Voltage Effect, some of the reasons why promising pilots often fail to scale.
But Jain is making an important, somewhat more provocative point: that our relentless celebration of innovation sustains a false illusion of progress, enabling incumbents to highlight their commitment to change while continuing to practice business as usual.
While his current focus is on healthcare, he’s also discussed some of the challenges he observed when he worked in pharma, writing:
“I watched with curiosity as the industry launched countless initiatives to move ‘beyond the pill’ to build services and solutions business to enhance patient outcomes, only to undercapitalize them and quietly shut down without notice. The industry was unable to sustainably think about a future outside of high margin molecules – just as many hospitals are unable to think of a future without fee-for-service.”
Here, of course, we immediately think of the famous observation by Upton Sinclair in 1934, “It is difficult to get a man to understand something, when his salary depends upon his not understanding it.”
Jain, to be sure, acknowledges that disruptive innovation is, by definition, difficult. But what worries him is that the gap between the innovation we trumpet and the innovation we implement seems to be growing, cultivating an abiding sense of deep cynicism – call it disruptive innovation fatigue — in the trenches, which makes true change even more challenging and less likely.
I can think of several examples from digital and data: we constantly hear about triumph of distributed clinical trials, which bring clinical trials to the patient. This is truly a worthy and important goal. Yet the success of these endeavors has been far more limited, the logistics far more difficult, and the impact far less profound, than the constant publicity would suggest. It is perhaps not surprising, for instance, to hear that CVS is shutting down its nascent clinical trial business.
Similarly, we are constantly hearing about the great success of AI drug discovery. I am extremely optimistic about the ability of AI to dramatically improve aspects of the process. Nevertheless, the realized impact to date has been far less than publicity would suggest. I recently read in a prominent publication about a supposed triumph of AI-based drug discovery by a VC-backed startup, leading to an attractive licensing deal around a promising molecule.
Perplexed by this “retconning” – a term from cinema and politics that refers to “retroactive continuity,” revising an established narrative to align with a new storyline — I pinged one of the founding venture capitalists. The VC was also amused by what the investor termed “revisionist history.” Instead – and far more credibly — the VC attributed the success to the team of “smart people” doing structure-based drug discovery.
Nevertheless, the investor shrugged, AI is “the buzzword of the day.”
At the far extreme of cynicism, I think about an assertion I once heard from a senior management consultant, who argued that at its core, big pharma is about clinical trial orchestration and product commercialization, rather than about innovative early research. The consultant argued that the work in pharma labs essentially serves as a public relations distraction while corporations seek new products to license from biotechs. As this consultant saw it, pharmas excel at orchestration at scale, rather than organic scientific innovation. The key competencies of pharma, in this view, are successfully managing the incredibly complex processes required for global clinical development, international regulatory approvals, and worldwide commercialization.
(I’ve also heard some suggest that the most significant contribution of discovery research teams in big pharma is understanding a field in enough detail to enable rigorous evaluation of in-licensing candidates.)
Efficiency Matters, Even For Innovators
Before we consider a more sanguine view of pharma innovation, it’s important to recognize that for all large organizations, even the most innovative, it’s critical not only to develop new products, but to ensure this is done with ruthless efficiency.
As World War II General Omar Bradley reportedly said, “Amateurs talk strategy; professionals talk logistics.” (Or, if you prefer Frederick the Great: “An army, like a serpent, goes upon its belly.”) True, the vision for Apple’s success was developed by Steve Jobs – but the ability to make it happen required the supply chain management led by Tim Cook, who later became CEO.
The Wall Street Journal recently profiled Zach Kirkhorn, the CFO of Tesla, who the Journal says performs a behind-the-scenes role similar to the one Cook played for years at Apple. “While Mr. Musk revolutionized the auto industry by taking often risky bets that upended the status quo,” the Journal writes, “Mr. Kirkhorn earned a reputation for fine-tuning operations.”
The Journal quotes Tesla’s former Chief Technology Officer, JB Straubel, who says, “It’s probably the hundreds and thousands of hours of slaving away to make things incrementally better where he left the biggest mark and is leaving the biggest mark.”
Adds former Tesla board member Steve Westley, “Predictability is everything with a CFO. What you can’t do is surprise people, and he has not surprised people.”
Thus, while it’s exciting to imagine AI helping us come up with important new drugs, it’s not surprising that many of the earliest uses have been focused on improving process efficiencies (see here).
Pharma Innovation: Making The Elephant Dance
Efficiency may be necessary, but it’s hardly sufficient. A tight supply chain may be critical for the commercial success of Apple and Tesla, but only if these companies are producing innovative products that customers want to buy.
For big pharma, innovation often means in-licensing the right products or acquiring the right biotechs, typically in oncology. Such transactions were critical to the recent success of Gilead (Kite, Immunomedics) and AstraZeneca (Acerta Pharma).
Encouragingly, several of big pharma’s most promising medicines of the moment were developed entirely in house. For example, Lilly discovered and developed both donanemab (for Alzheimer’s disease – see here) and tirzepatide (Mounjaro, FDA-approved for type 2 diabetes and likely soon, weight loss). (Notably, Novo Nordisk’s semaglutide [Wegovy/Ozempic], already FDA-approved for both diabetes and weight loss, was also developed internally.)
A recent, in-depth Wall Street Journal article by Peter Loftus examined Lilly’s R&D, and described a culture that underwent a profound change after a key acquisition – in this case, in the person of physician-scientist Daniel Skovronsky, the CEO of Avid Pharmaceuticals, a neuro-biomarker company acquired by Lilly in 2010.
As he experienced the big pharma’s culture, Loftus writes, Skovronsky “was frustrated with Lilly’s slow pace. ‘Let me understand this,’ he recalled saying at a committee meeting setting timetables for getting experimental drugs to market. ‘Our goal is to be slower than average, and we’re failing at that goal? This can’t be the way to do things.’”
Consequently, in 2015, according to the Loftus, Lilly’s board asked Skovronsky (then senior vice president of clinical and product development), to “help analyze Lilly’s research flops over the prior 10 years and figure out how to do R&D better.”
Skovronsky’s big conclusion: key decisions were being driven by commercial needs, rather than the best science. Marginal products were advanced (only to later fail) because they targeted a specific commercial need.
According to the Loftus, Skovronsky recommended that “Lilly pursue drug projects where it best understood the science and lean less on commercial sales estimates. Lilly was not very good at predicting a drug’s sales over time anyway, he concluded, but could better predict the scientific probability of a drug’s success.” (I’ve discussed the challenge of predicting drug sales here, and also, in collaboration with Nassim Taleb, here.)
Skovronksy was soon promoted to Chief Science Officer and Chief Medical Officer, where he pushed to address another challenge he observed, endemic to large organizations (and described in excruciating detail by Safi Bahcall in Loonshots – see here, also here).
As Loftus writes:
“One internal committee after another second-guessed every recommendation to advance a promising drug candidate. ‘The decisions got revisited every step of the way,’ recalled J. Anthony Ware, who led product development at Lilly before retiring in 2017. The committees were intended to ensure thorough vetting, but in practice became a limiting process that squeezed out bold ideas, according to Dr. Skovronsky.”
To address this, Skovronsky “reorganized to move more quickly.”
“To stop the second-guessing of decisions, Lilly established independent internal units operating like biotech companies—with less bureaucracy and faster decision-making—to manage each of its high-priority drug projects,” including the one that would lead to Mounjaro. Each unit “had its own board of directors, made up of senior researchers and executives from Lilly’s diabetes business unit. They were given a budget, and charged with making quick decisions on their own.”
For example, according to Loftus, “after a Lilly researcher proposed a last-minute change to the design of the second phase of human testing” for a study of tirzepatide, the review board “met within 24 hours and approved the change so the study could start on time.”
Lilly’s agility may be familiar to colleagues at smaller biotechs and also to those familiar with Pfizer’s CEO-led development of the COVID-19 vaccine (see here) but is otherwise not representative of how most big pharmas go about their business, as Bahcall trenchantly observes.
Loftus’s narrative about Lilly is also shared by several colleagues either at Lilly or who have deep familiarity with the company.
According one to colleague, the innovation expert Bernard Munos who spent 30 years at the company, Lilly’s CEO Dave Ricks (who took the job in 2017) played a critical role:
“He understood that Eurekas cannot be scheduled, and that innovation is a byproduct of culture, not the outcome of a process – even if some amount of process is clearly necessary. He realigned his leadership team with like-minded executives and let Lilly’s talented scientists (and there were many), free from bureaucracy, return to what they loved doing: cutting-edge science and translation.”
“In short, there was no magic recipe. Lilly’s scientists had innovation in their DNA but could not express it under the culture that swamped the company for a couple of decades. Lilly was not alone in its predicament. The whole industry got caught in the same warp. This was the heyday of Six Sigma and its black belts. In the 1990s, the scientists had lost the leadership of the industry to non-scientists, and the idea that you could de-risk drug R&D by codifying work into processes, optimized by efficiency experts, that would deliver innovation on demand, that idea really resonated with non-scientist leaders — as harebrained as it was to most scientists. Today, the pendulum has swung back.”
I suspect it may be reasonable to offer two cheers for Lilly here, for the success of their innovative mindset and agile approach. My reservation is that every success quickly finds a narrative. I don’t know of any biopharmas that have not conspicuously adopted a “biotech” approach and mindset and might well attribute any success to this structure.
In other words, maybe Lilly’s recent success is attributable to their adoption of a more nimble approach, or maybe their products happened to work, and then the organizational characteristics – which may not be all that unique – are suddenly elevated.
(In the same way culture is said to eat strategy for breakfast, you could argue, especially in biopharma, that good luck, aggressively pursued [e.g. Merck’s Keytruda – see here] eats both.)
Consider Lilly’s decision to de-emphasize commercial influence. On the one hand, the observation resonates, at every level of R&D. For example, an industry colleague recently shared an example where translational oncology researchers evaluating early-stage compounds felt pressure to interpret coarse biomarker data in a fashion that would support the advancement of a compound into one of the company’s priority indications.
On the other hand, the deliberate and successful expansion into areas of high commercial value (e.g. oncology) are critical to the elevated stock prices now enjoyed by companies like Gilead.
History, according to the old saw, is written by the victors. Unfortunately, this often leads to the most-repeated, least-actionable strategic advice in our industry: pick winners, our equivalent to “buy low, sell high.” Moreover, since the selection of winners often feels like a crapshoot, it’s not surprising that management tends focus on seemingly more tractable parameters, like improving operational efficiencies.
In the same way culture is said to eat strategy for breakfast, you could argue, especially in biopharma, that good luck, aggressively pursued [e.g. Merck’s Keytruda] – eats both
The biopharmaceutical industry relies upon innovation to develop new products to replace the medicines whose patent protection has expired. As Sachin Jain observes, relentlessly hyping innovation, particularly early pilot projects that never scale, generates harmful cynicism. We also recognize that even the most innovative companies, like Apple and Tesla, still need to pay attention to the unsexy details of supply chain optimization – and big pharmas must focus on improving process efficiencies as well. Even so, efficiencies won’t generate the new products pharma needs (though it might help them develop promising products faster). Pharmas might learn from Lilly’s recent re-organization that seems to have liberated the innate creativity of company scientists.