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As the excitement around generative AI sweeps across the globe, biopharma R&D groups (like most everyone else) are actively trying to figure out how to leverage this powerful but nascent technology effectively, and in a responsible fashion.
In separate conversations, two prominent pharma R&D executives recently sat down with savvy healthtech VCs to discuss how generative AI specifically, and emerging digital technologies more generally, are poised to transform the ways new medicines are discovered, developed, and delivered.
The word “poised” is doing quite a lot of work in the sentence above. Both conversations seamlessly and rather expertly blend what’s actually been accomplished (a little bit) with the vision of what might be achieved (everything and then some).
The first conversation, from the a16z “Bio Eats World” podcast, features Greg Meyers, EVP and Chief Digital and Technology Officer of Bristol Myers Squibb (BMS), and a16z Bio+Health General Partner Dr. Jorge Conde. The second discussion, from the BIOS community, features Dr. Frank Nestle, Global Head of Research and CSO, Sanofi, and Flagship Pioneering General Partner and Founder and CEO of Valo Health, Dr. David Berry. (Readers may recall our discussion of a previous BIOS-hosted interview with Dr. Nestle, here.)
Rather than review each conversation individually, I thought it would be more useful to discuss common themes emerging from the pair of discussions.
AI has started to contribute meaningfully to the design of small molecules in the early stages of drug development. “A few years ago,” Meyers says, BMS started “to incorporate machine learning to try to predict whether or not a certain chemical profile would have the bioreactivity you’re hoping.” He says this worked so well (producing a “huge spike” in hit rate) that they’ve been trying to scale this up.
Meyers also says BMS researchers “are currently using AI pretty heavily in our protein degrader program,” noting “it’s been very helpful” in enabling the team to sort through different types of designs.
Nestle also highlights the role of AI in developing novel small compounds. “AI-empowered models” are contributing to the design of modules, he says, and are starting to “shift the cycle times” for the industry.
AI is also now contributing to the development of both digital and molecular biomarkers. For example, Meyers described the use of AI to analyze a routine 12-lead ECG to identify patients who might have undiagnosed hypertrophic cardiomyopathy. (Readers may recall a very similar approach used by Janssen to diagnose pulmonary artery hypertension, see here.)
Nestle offered an example from digital pathology. He described a collaboration with the healthtech company Owkin, whose AI technology, he says, can help analyze the microscope slides with classically stained tissue samples.
Depending on your perspective, these use cases are either pretty slim pickings or an incredibly promising start.
I’ve not included what seemed to me as still exploratory efforts involving two long-standing industry aspirations:
We’ll return to these important but elusive ambitions later, in our discussion of “the magic vat.”
I’ve also not included examples of generative AI, because I didn’t hear much in the way of specifics here, probably because it’s still such early days. There was clearly excitement around the concept that, as Meyers put it, “proteins are a lot like the human language,” and hence, large language models might be gainfully applied to this domain.
The aspiration for AI in in biopharma R&D were as expansive as the established proof points were sparse. The lofty idea seems to be that with enough data points and computation, it will eventually be possible to create viable new medicines entirely in silico. VC David Berry described an “aspiration to truly make drug discovery and development programmable from end to end.” Nestle wondered about developing an effective antibody drug “virtually,” suggesting it may be possible in the future. Also possible, he suggests: “the ability to approve a safe and effective drug in a certain indication, without running a single clinical trial.”
Both Nestle and Meyers cited the same estimate – 10^60 – as the size of “chemical space,” the number of different drug-like molecular structures that are theoretically possible. It’s a staggering number, more than the stars in the universe, and likely far beyond our ability to meaningfully comprehend. The point both executives were making is that if we want to explore this space productively, we’re going to get a lot further using sophisticated computation than relying on the traditional approaches of intuition, trial and error.
The underlying aspiration here strikes a familiar chord for those of us who remember some of the more extravagant expectations driving the Human Genome Project. For instance, South African biologist Sydney Brenner reportedly claimed that if he had “a complete sequence of DNA of an organism and a large enough computer” then he “could compute the organism.” While the sequencing of the genome contributed enormously to biomedical science, our understanding of the human organism remains woefully incomplete, and largely uncomputed. It’s easy to imagine that our hubris – and our overconfidence in our ability to domesticate scientific research, as Taleb and I argued in 2008 – may be again deceiving us.
For years, healthcare organizations have strived towards the goal of establishing a “learning health system (LHS),” where knowledge from each patient is routinely captured and systematically leveraged to improve the care of future patients. As I have discussed in detail (see here), the LHS is an entity that appears to exists only as ideal with the pages of academic journals, rather than embodied in the physical world.
Many pharma organizations (as I’ve discussed previously) aspire towards a similar vision, and seek to make better use of all the data they generate. As Meyers puts it, you “want to make sure that you never run the same experiment twice,” and you want to capture and make effective use of the digital “exhaust” from experiments, in part by ensuring it’s able to be interpreted by computers.
Berry emphasized that a goal of the Flagship company Valo (where he now also serves as CEO) is to “use data and computation to unify how… data is used across all of the steps [of drug development], how data is shared across the steps.” Such integration, Berry argues, “will increase probably of success, will help us reduce time, will help reduce cost.”
The problem – as I’ve discussed, and as Berry points out, is that “drug discovery and development has historically been a highly siloed industry. And the challenge is it’s created data silos and operational silos.”
The question, more generally is how to unlock the purported value associated with, as Nestle puts it, the “incredible treasure chest of data” that “large pharmaceutical companies…sit on.”
Historically, pharma data has been collected with a single, high-value use in mind. The data are generally not organized, identified, and architected for re-use. Moreover, as Nestle emphasizes, the incentives within pharma companies (the so-called key performance indicators or “KPIs”) are “not necessarily in the foundational space, and that not where typically the resourcing goes.” In other words, what companies value and track are performance measures like speed of trial recruitment; no one is really evaluating data fluidity, and unless you can directly tie data fluidity to a traditional performance measure, it will struggle to be prioritized.
In contrast, companies like Valo; other Flagship companies like Moderna; and some but not all emerging biopharma companies are constructed (or reconstructed — eg Valo includes components of both Numerate and Forma Therapeutics, as well as TARA biosystems) with the explicit intention of avoiding data silos. This concept, foundational to Amazon in the context of the often-cited 2002 “Bezos Memo,” was discussed here.
In contrast, pharmas have entrenched silos; historically, data were collected to meet the specific needs of a particular functional group, responsible for a specific step in the drug development process. Access to these data (as I recently discussed) tends to be tightly controlled.
Data-focused biotech startups tend to look at big pharma’s traditional approach to data and see profound opportunities for disruption. Meanwhile, pharmas tend to look at these data-oriented startups and say, “Sure, that sounds great. Now what have you got to show for all your investment in this?”
The result is a standoff of sorts, where pharmas try to retrofit their approach to data yet are typically hampered by the organizational and cultural silos that have very little interest in facilitating data access. Meanwhile, data biotech startups are working towards a far more fluid approach to data, yet have produced little tangible and compelling evidence to date that they are more effective, or are likely to be more effective, at delivering high impact medicines to patients.
Both BMS and Sanofi are exploring emerging technologies through investments and partnerships with a number of healthtech startups, even as both emphasize that they are also building internal capabilities.
“We have over 200 partnerships,” Meyers notes, “including several equity positions with other companies that really come from the in silico, pure-play sort of business. And we’ve learned a ton from them.”
Similarly, Nestle (again – see here) emphasized key partnerships, including the Owkin relationship and digital biomarker work with MIT Professor Dina Katabi.
Meanwhile, Pfizer recently announced an open innovation competition to source generative AI solutions to a particular company need: creating clinical study reports.
In addition to these examples, I’ve become increasingly aware of a number of other AI-related projects attributed to pharma companies that upon closer inspection, turn out to represent discrete engagements with external partners or vendors who reportedly are leveraging AI.
One of the most important lessons from both discussions was the challenge for aspiring innovators and startups.
Berry, for example, explained why it’s so difficult for AI approaches to gain traction. “If I want to prove, statistically, that AI or an AI component is doing a better job, how many Phase Two clinical readouts does one actually need to believe it on a statistical basis? If you’re a small company and you want to do it one by one, it’s going to take a few generations. That’s not going to work.”
On the other hand, he suggested “there are portions of the drug discovery and development cascade where we’re starting to see insights that are actionable, that are tangible, and the timelines of them and the cost points of them are so quickly becoming transformative that it opens up the potential for AI to have a real impact.”
Meyers, for his part, offered exceptionally relevant advice for AI startups pitching to pharma (in fact, the final section of the episode should be required listening for all biotech AI founders).
Among the problems Meyers highlights – familiar to readers of this column – are the need “for companies that are focused on solving a real-world problem,” rather than solutions in search of a problem. He also emphasized that “this is an industry that will not adopt something unless it is, really 10x better than the way things are historically done.”
This presents a real barrier to the sort of incremental change that may hard be appreciate in the near term but can deliver appreciable value over time. Even “slight improvements” in translational predictive models, as we recently learned from Jack Scannell, can deliver outsized impact, significantly elevating the probability of success while reducing the many burdens of failure.
Meyers also reminded listeners of the challenges of finding product-market fit because healthcare “is the only industry where the consumer, the customer, and the payor are all different people and they don’t always have incentives that are aligned.” (See here.)
On a more optimistic note, Berry noted that one of the most important competitive advantages a founder has is recognizing that “a problem is solvable, because that turns out to be one of the most powerful pieces of information.” For Berry, the emergence of AI means that “we can start seeing at much larger scales problems that are solvable that we didn’t previously know to be solvable.” Moreover, he argues, once we realize a problem is solvable, we’re more likely to apply ourselves to this challenge.
In thinking about how to most effectively leverage AI, and digital and data more generally, in R&D, I’m left with two thoughts which are somewhat in tension.
The first borrows (or bastardizes) a phrase from the brilliant Stephen Wolfram: look for pockets of reducibility. In other words – focus your technology not on fixing all of drug development, but on addressing a specific, important problem that you can meaningfully impact.
For instance, I was speaking earlier this week with one of the world’s experts on data standards. I asked him how generative AI as “universal translator” (to use Peter Lee’s term) might obviate the need for standards. While the expert agreed conceptually, his immediate focus was on figuring out how to pragmatically apply generative AI tools like GPT-4 to standard generation so that it could be done more efficiently, potentially with people validating the output rather than generating it.
On the one hand, you might argue this is disappointingly incremental. On the other hand, it’s implementable immediately, and seems likely to have a tangible impact.
(In my own work, I am spending much of my time focused on identifying and enabling such tangible opportunities within R&D.)
There’s another part of me, of course, that both admires and deeply resonates with the integrated approach that companies like Valo are taking: the idea and aspiration that if, from the outset, you deliberately collect and organize your data in a thoughtful way, you can generate novel insights that cross functional silos (just as Berry says). These insights, in principle, have the potential to accelerate discovery, translation (a critical need that this column has frequently discussed, and that Conde appropriately emphasized), and clinical development.
Integrating diverse data to drive insights has captivated me for decades; it’s a topic I’ve discussed in a 2009 Nature Reviews Drug Discovery paper I wrote with Eric Schadt and Stephen Friend. The value of integrating phenotypic data with genetic data was also a key tenet I brought to my DNAnexus Chief Medical Officer role, and a lens through which I evaluated companies when I subsequently served as corporate VC.
Consequently, I am passionately rooting for Berry at Valo – and for Daphne Koller’s insitro and Chris Gibson’s Recursion. I’m rooting for Pathos, a company founded by Tempus that’s focused on “integrating data into every step of the process and thereby creating a self-learning and self-correcting therapeutics engine,” and that has recruited Schadt to be the Chief Science Officer. I’m also rooting for Aviv Regev at Genentech, and I am excited by her integrative approach to early R&D.
But throughout my career, I’ve also seen just how challenging it can be to move from attractive integrative ambition to meaningful drugs. I’ve seen so many variations of the “magic vat,” where all available scientific data are poured in, a dusting of charmed analysis powder is added (network theory, the latest AI, etc), the mixture is stirred, and then – presto! – insights appear.
Or, more typically, not. But (we’re invariably told) these insights would arrive (are poised to arrive) if only there was more funding/more samples/just one more category of ‘omics data, etc. — all real examples by the way.
It’s possible that this time will be the charm – we’ve been told, after all, that generative AI “changes everything” — but you can also understand the skepticism.
My sense is that legacy pharmas are likely to remain resistant to changing their siloed approach to data until they see compelling evidence that data integration approaches are, if not 10x better, then at least offer meaningful and measurable improvement. In my own work, I’m intensively seeking to identify and catalyze transformative opportunities for cross-silo integration of scientific data across at least some domains, since effective translation absolutely requires it.
For now, big pharmas are likely to remain largely empires of silos – and will continue to do the step-by-step siloed work comprising drug development at a global scale better than anyone. Technology, including AI, may help to improve the efficiency of specific steps (eg protocol drafting, an example Meyers cites). Technology may also improve the efficiency of sequential data handoffs, critical for drug development, and help track operational performance, providing invaluable information to managers, as discussed here.
But foundationally integrating scientific knowledge across organizational silos? Unless a data management organization already deeply embedded within many pharmas – perhaps a company like Veeva or Medidata – enables it, routine integration of scientific knowledge across long-established silos, in the near to medium term, seems unlikely. It may take a visionary, persistent and determined startup (Valo? Pathos?) to capture persuasively the value that must be here.
Biopharma companies are keenly interested in leveraging generative AI, and digital and data technologies more generally, in R&D. To date, meaningful implementations of AI in large pharmas seem relatively limited, and largely focused on small molecule design, and biomarker analysis (such as identifying potential patients through routine ECGs). Nevertheless, the ambitions for AI in R&D seem enormous, perhaps even fanciful, envisioning virtual drug development and perhaps even in silico regulatory approvals. More immediately, pharmas aspire to make more complete use of the data they collect but are likely to continue to struggle with long-established functional silos. External partnerships provide access to emerging technologies, but it can be difficult for healthtech startups to find a permanent foothold with large pharmas. Technology focused on alleviating important, specific problems – “pockets of reducibility” – seems most likely to find traction in the near term. Ambitious founders continue to pursue the vision of more complete data integration.
Today’s guest on The Long Run is Deb Palestrant.
Deb is a partner with 5AM Ventures and the executive chair of the 4:59 Initiative. 5AM invests in early-stage startups working on a variety of novel biological targets and some of the emerging new treatment modalities – gene therapy, gene editing, oligonucleotides. As the name suggests, it’s not afraid to get involved in companies in very early days, when they are high-risk/high-reward propositions.
Deb comes to this venture work with a deep scientific background, and significant hands-on operating experience. She got her PhD in structural biology at Columbia University, and made the move to industry at the Novartis Institutes of Biomedical Research in the mid-2000s. She found her way into the Boston biotech startup world in the 2010s, and was a part of building a series of ambitious companies – Blueprint Medicines, Editas Medicine, and Relay Therapeutics included.
We talk in this episode about Deb’s career journey, about how she and her partners think about creating companies, and what areas of opportunity she sees in science and medicine.
And now for a word from the sponsor of The Long Run.
Occam Global is an international professional services firm focusing on executive recruitment, organizational development and board construction. The firm’s clientele emphasize intensely purposeful and broadly accomplished entrepreneurs and visionary investors in the Life Sciences. Occam Global augments such extraordinary and committed individuals in building high performing executive teams and assembling appropriate governance structures. Occam serves such opportune sectors as gene/cell therapy, neuroscience, gene editing, the intersection of AI and Machine Learning and drug discovery and development.
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Now, please join me and Deb Palestrant on The Long Run.
Today, we’ll begin with a consideration of the promise for AI some experts see in healthcare and biopharma.
Next, we’ll look at some of the obstacles – some technical, some organizational – and re-visit the eternal “data parasite” debate.
Finally, we’ll conclude with a few suggestions for summer reading.
Earlier this month, I moderated a conversation about AI and healthcare (video here, transcript here) at Harvard’s historic Countway Library of Medicine, in a room just down the hall from a display of Phineas Gage’s skull and the tamping iron that lanced it on September 13, 1848, famously altering his behavior but sparing his life. The episode soon became part of neurology history and lore.
With less overt drama, but addressing a topic of perhaps even greater biomedical importance, the panelists – Harvard’s Dr. Zak Kohane, Microsoft’s Peter Lee, and journalist Carey Goldberg (all co-authors of the recently published The AI Revolution in Medicine: GPT-4 and Beyond, discussed here), addressed their subject.
A key opportunity for AI in health that Kohane emphasized was the chance to elevate care across the board by improving consistency. He told the story of a friend whose spouse was dealing with a series of difficult health issues.
Kohane said his friend described “how delightful it was to have a doctor who really understood what was going on, who understood the plan. The light was on.”
However, Kohane continued, the friend would then “go talk to another doctor and another doctor, and the light was not on. And there was huge unevenness.”
The story, Kohane reflected, “reminds me of my own intuition just from experiencing medical training and medical care, which is there are huge variations. There are some brilliant doctors. But there are some also non-brilliant doctors and some doctors who might have been brilliant but then are harried, squished by the forces that propel modern medicine.”
Kohane says he saw Chat-GPT as a potential response to physician inconsistency. For Kohane, generative AI represented a disruptive force that “was going to happen, whether or not medicine and the medical establishment were going to pick up the torch.” Why? Because “patients were going to use it.”
Goldberg, too, recognized the opportunities for patients, and spoke to the urgent need she felt to access the technology:
“Okay, we get it. It has inaccuracies, it hallucinates. Just give it to me. Like, I just want it. I just want to be able to use it for my own queries, my own medically related queries. And I think that what I came away from working on this book with was an understanding of just the incredible usefulness that this can have for patients.”
Goldberg also shared a story of a nurse who suffered from hand pain and was evaluated by a series of specialists who were unable to identify the cause. Desperate, the nurse typed her symptoms into Chat-GPT, and learned that one of her medications could be causing the pain. When this was changed, the pain resolved.
Kohane sees the ready availability of a savvy second-opinion a tremendous resource for physicians. When he was training, he said, the physicians used to convene after clinic and review all the patients. “Invariably,” he notes, “we changed the management” of a handful “because of what someone else said. That went away. There’s no time for it.”
The lack of review represents a real loss, Kohane points out, because “even the best doctors will not remember everything all the time.” Kohane says he is convinced that generative AI will restore this capability and enable it serve a co-pilot function, providing real-time assistance to busy providers.
Another opportunity to make physicians’ lives better, the panelists suggested, was in the area of paperwork and documentation, such as the dreaded pre-authorization letters, often required to beseech payors for reimbursement.
Since Lee contributed an entire chapter about the impact on paperwork reduction in healthcare, I asked him whether we’re just going to see AI’s battling with each other: provider AI’s writing pre-authorization letters, and payor AI’s writing justifications for rejection.
Lee responded that this was very similar to a scenario Bill Gates has mentioned, where an email starts as three bullet points you want to share, GPT-4 translates this into a well-composed email, then GPT-4 at other end reduces it back to three bullet points for the reader.
I told Lee this reminded me of Peter Thiel’s famous quote: “We wanted flying cars, instead we got 140 characters.” Surely, I asked, generative AI must offer healthcare something more profound than more efficient paperwork?
In response, Lee highlighted the opportunities associated with the ability to better connect and learn from data – perhaps getting us closer to at long last fulfilling the elusive promise of a “learning healthcare system” (see here). In particular, Lee highlighted the potential of AI serving as a “universal translator of healthcare information,” allowing for the near-effortless extraction and exchange of information.
For more perspectives on how AI could benefit healthcare and the life sciences, I’d recommend:
The inconvenient truth is that while generative AI and other emerging technologies have captivated us with their promise, we’re still figuring out how to use them.
Even user-friendly applications like ChatGPT and GPT-4-enabled-Bing are not always plug-and-play. For example, in preparation for an upcoming workshop I’m leading for a particular corporate function highlighting the capabilities of GPT-4, I tried out some of the team’s most elementary use cases with Bing-GPT. The results were disappointing and included a number of basic mistakes. Often, Bing-GPT seemed to perform worse than Bing or Google search alone. The results generated seemed unlikely to inspire corporate colleagues to urgently adopt the technology.
These challenges are hardly limited to GPT-4 or Bing. From the perspective of a drug development organization, technology issues seem to surface in every area of digital and data. Far more often than not, the hype and promise touted by eager startups seem at odds with the capabilities these nascent companies demonstrably can deliver. In fairness, the difficulty many legacy biopharma companies have figuring out how to work in new ways with these healthtech startups probably also contributes to the challenge.
To understand the issues better, let’s consider one example, outside of biopharma, recently discussed by University of North Carolina Vice Chair and Professor of Medicine Spencer Dorn. His focus: the adoption of AI in radiology.
Dorn notes that while AI expert Geoffrey Hinton predicted in 2016 that AI would obviate the need for radiologists within five years, this hasn’t happened. In fact, Dorn says, only a third of radiologists use AI at all, “usually for just a tiny fraction of their work.”
Dorn cites several reasons for AI’s limited adoption in clinical radiology:
Dorn warns that generative AI “in healthcare will need to overcome these same hurdles. Plus, several more.”
Similar issues apply to the adoption, for high-stakes use-cases, of a range of emerging technologies, including digital pathology, decentralized trials, and “the nightmare” of digital biomarkers – challenges this column has frequently discussed.
But remarkably, technology problems are probably not the most difficult issue for healthtech innovators to solve. Technology tends to improve dramatically over time (think about the camera on your smartphone). No, the most difficult sticking point may well be organizational behavior. Essentially, the return of the eternal, dreaded “Data Parasite” debate (as I discussed in 2016 in a three-part series in Forbes, starting here.)
In most large organizations, both academic and corporate (I am unaware of many exceptions), there is a constant battle between those who effectively own the data and those who want to analyze the data. In theory, of course, and depending upon the situation, data belong to: patients / the organization / taxpayers, or some combination of the three. Researchers, meanwhile, are just “stewards” or “trustees” of the data. Yet in practice, someone always seems to control and zealously guard the access to any given data set within an organization.
Typically, those who “own” the data (whether an academic clinical investigator or a pharma clinical development team) are using the data to pursue a defined, high-value objective. Others who want access to these data tend to have more exploratory tasks in mind. Theoretically, there’s a huge amount of value that can be obtained by enabling data exploration. Once again, in practice, the theoretical value is often difficult to demonstrate, and is often viewed as offering little upside – and a fair amount of perceived downside risk, as well as gratuitous aggravation – to the data “owners.” Much of this perceived risk relates to the concern about sloppy or ill-informed analyses that generates, essentially, “false positive” concerns, as I allude to here.
I’ve seen very few examples where the data “analyzers” have sufficient leverage to win here. In general, the data “owners” tend to hire data scientists of their own and say “let us know what you want to know, and we’ll have our people run the analysis for you.” This has the effect of slowing down integrative exploratory analyses to a trickle, particularly given the degree of pre-specification the data “owners” tend to require.
If you are a data owner, you probably view this as an encouraging result, since analyses are only done by people who ostensibly have a feel for how the data were generated and understand the context and the limitations. As discussed in a previous column, “data empathy” is vitally important.
But if you are a data analyzer not working directly with a data “owner,” you are constantly frustrated by the near-impossibility of obtaining access to data you’d like to explore. Perhaps most strikingly, many researchers who fiercely defend their own data from external analyses are often fiercely critical of others for not sharing data the same researchers hope to explore. As Miles famously observed, “where you stand depends on where you sit.”
Of course, it’s possible that technology could help ease sharing. Even so, it’s really difficult to envision the tight hold on data changing, so long as so much power in organizations clearly rests with those in control of the data. Perhaps, as Lakhani and others suggest, this can be addressed in new companies who have a fundamentally different view of data (Amazon – driven by the “Bezos Mandate” — is the canonical example), and can readily monetize data fluidity. Alternatively, the demonstrated utility of exploratory integrated analyses across multiple data silos and “owners” in legacy organizations could potentially facilitate more consistent access.
For now, in both academia and biopharma, virtuous stated preferences to the contrary, this revealed tension remains very much alive.
A must-read for all biotechies, For Blood and Money, by Marketwatch’s Nathan Vardi, tells the captivating story of two cancer medicines targeting the BTK kinase: ibrutinib and acalabrutinib. A decade ago, for Forbes, I wrote about the beginning of the ibrutinib story.
It was thrilling to read Vardi’s account of the medicine’s complete journey – and the journey of its competitor, acalabrutinib (which, fun fact, was originally discovered by the same company in the Netherlands that discovered the product that became the blockbuster Keytruda, see here). As Jerome Groopman’s thoughtful review in the New York Review of Books suggests, Vardi’s book also raises difficult questions about the role of luck vs skill in drug development, as well as the role of capital vs labor, since the investors appeared to make out far better than the scientists who did the lion’s share of the work. This pithy review by Adrian Woolfson, in Science, also provides a good summary.
Less essential but fascinating for readers who recall the rise of companies like Gawker and Buzzfeed, is Traffic, by Ben Smith. He describes how emerging media companies – and the young men and women who contributed the content – desperately chased reader traffic, with important consequences both for them and society. See here for an excellent review of the book by the Bulwark’s Sonny Bunch.
Also intriguing, if a bit uneven: Beyond Measure, a book about the history of measurement, written by James Vincent, Senior Reporter at The Verge. See here for a thoughtful review of Vincent’s book by Jennifer Szalai in The New York Times.
Finally, a few recommended posts. On the concerning side, this piece about the devolution of clinical medicine captures what I seem to be hearing from nearly every single physician I know. Even doctors who were once so excited about taking care of patients now seem abjectly miserable, trapped in a system that has reduced them to widgets. (See also here, here, here.)
On the innovation front, several comments about the wildly popular GLP-1 medicines tirzepatide and semaglutide caught my eye (see also my last piece, here). On the one hand, it’s clear the development of these powerful and promising medicines was, as Dr. Michael Albert of Accomplish Health suggests, clearly the result of deliberate, meticulous effort, both by companies like Lilly and Novo Nordisk, and pioneering academics like physician-scientist Daniel Drucker (who also maintains this authoritative website on the evolving science). On the other hand, it’s interesting that (as Sarah Zhang writes in The Atlantic), these medicines may have entirely unanticipated application in the management of addictions and compulsions.
Generative AI offers the possibility of elevating the quality of healthcare patients receive. However, the implementation of AI and other digital technologies may be impaired both by the growing pains of nascent technology and, more significantly, by the territoriality of those who control access to data silos within large organizations — although this may also ensure that the data are more likely to be analyzed by those who have a greater feel for the context in which they were generated). Finally, For Blood and Money, by Nathan Vardi, Traffic, by Ben Smith, and Beyond Measure, by James Vincent are all good additions to your summer reading list.
Anthony Mancini is the chief operating officer of Denmark-based Genmab, one of the leading innovators of antibody therapies for patients living with cancer and other serious diseases.
After many years working as a behind-the-scenes innovator, Genmab is now becoming a significant commercial entity. In 2021, the company began marketing its first commercial product, Tidvak® (tisotumab vedotin) for the treatment of advanced cervical cancer, in partnership with Seagen (acquisition by Pfizer pending).
In 2022, Genmab and AbbVie submitted an application for regulatory approval to start marketing epcoritamab, a CD3 and CD20-directed bispecific antibody T-cell engager for non-Hodgkin’s lymphoma. It’s the lead program in AbbVie and Genmab’s multi-faceted cancer collaboration that was announced in June 2020. It is a massive $3.9 billion collaboration that sees the two companies working together in a true 50:50 collaboration to research, develop, and commercialize new therapies around the world.
Anthony was instrumental in executing the deal and now in managing the collaboration. He leads several functions at Genmab, including commercialization, IT & Digital. Anthony’s mission is to help lead Genmab’s evolution to become a best-in-class fully integrated biotech company.
Before joining Genmab three years ago, Anthony had strategic and operational leadership roles over a 24-year career at Bristol Myers Squibb (BMS) including the leadership of BMS’ US Innovative Medicines Unit. While at BMS, he was an integral member of the multiyear Cardiovascular Alliances with Pfizer, as well as partnerships with Sanofi, AstraZeneca and Otsuka.
Anthony has stellar insights around how companies can work with partners to develop and launch amazing medicines, while building their own capabilities at the same time. He sat down with me a few months ago to share his excitement about Genmab’s future, as well as his advice on how to build successful commercial partnerships.
Q: What was the situation at Genmab when you joined? What opportunities did you see for the company, the pipeline, and patients?
AM: [I joined Genmab in 2020] at a pivotal moment. During our first 20 years , which I’ll call phase one of Genmab, we outlicensed our programs to others for them to further develop and commercialize. By 2020, Genmab had helped invent more than 20 therapeutic candidates that were approved or in active clinical development. We were known throughout the industry for creating novel and differentiated antibodies and had started to build a foundation of capabilities in drug development.
2020 was a pivot point in our strategy. Our objective during this phase two of Genmab was to transition from a purely R&D focused organization into a fully integrated biotech company. I was excited to come in at such an important inflection.
Developing a medicine and then throwing it over the fence to commercialize often just leads to wasted investment. You really need to integrate deep insights around patient journeys, regulatory frameworks, healthcare systems, and reimbursement [environments] across different countries. Getting that input early in program development requires commercialization and R&D teams to work together. And so, in this phase of our strategy, the 50-50 phase, we chose to work with partners like Seagen, BioNTech, and now AbbVie.
And we believe our investigational compound epcoritamab may have potential in blood cancers. Epcoritamab is a bispecific antibody designed to target both CD3 on T cells and CD20 on B cells, has shown highly effective killing of CD20+ positive tumors in nonclinical studies, and shown encouraging clinical responses in early studies in large B-Cell lymphoma (LBCL). If cleared by health authorities, we believe it has the potential to transform the treatment of B cell malignancies.
We sought a partner with scale to leverage that potential and help us appropriately develop and commercialize the drug. And we were excited to land a great partner like AbbVie. Our AbbVie collaboration is a broad oncology collaboration where we jointly make strategic decisions – it is a true 50:50 partnership. In November, the FDA accepted for Priority Review the Biologics License Application (BLA) for epcoritamab (DuoBody®-CD3xCD20) for the treatment of relapsed/refractory LBCL.
Q: Why did Genmab want to pursue a co-development and co-commercialization partnership?
AM: Our priorities were very clear. We believe, together with the right partners, we can leverage each other’s strengths with the goal to bring our medicines to patients even faster. Our CEO, Jan van de Winkel, publicly discussed working with a partner for epcoritamab. [The treatment of] B cell malignancies can involve complex regimens and it can be difficult to recruit patients. We have built a stellar research and development organization and could have done it on our own, but with AbbVie as a partner, we add speed and scale to our expertise. In a competitive marketplace, you really need to move quickly. AbbVie also has deep hematologic cancer experience.
We wanted to leverage the talent and experience we were already bringing [as a company] as well as expand capabilities in development and build them in commercialization. In the first instance, Genmab is focused on the US and Japan markets. AbbVie adds to our strength in these countries, and they also add the scale of their global footprint.
[And ultimately,] both Genmab and AbbVie wanted something more collaborative. So, in addition to jointly developing and commercializing epcoritamab, we also entered into a discovery research collaboration to create additional antibody therapeutics for cancer. We believe we have set up a win-win partnership with AbbVie.
Q: How do you negotiate and structure such a broad collaboration?
AM: It’s not easy [laughs]. It’s a marriage for the long haul and it takes a huge effort across many different functions. Deals of this breadth require close collaboration across BD, R&D, Legal, Finance and Commercialization.
While you should think through as many scenarios as possible upfront, you can’t sort everything out in the agreement. You should also have clarity on each other’s negotiation ‘must haves’ and clarity on how each party can achieve those goals. It is also important to build a governance process as to how the two parties will sort unforeseen things out.
How will the committees make fast decisions? How will the partners work together to make decisions as well or better than they could on their own? Do the governance committees have the right people on them? Are those people appropriately empowered to make decisions? How will we adequately allocate resources to each program, and regularly revisit those FTE and resource allocations? How will the commercialization teams apportion roles & responsibilities?
There are many aspects to consider depending on the collaboration construct, the number of programs, as well as their stages of development.
There are also many activities to map out in a co-development / co-commercialization structure, including marketing, manufacturing, regulatory, market access and pricing, publications, medical affairs, distribution, etc. All these functions are critical to launch success and best practices vary country to country. Which partner is responsible for which activity in each geography? What overall product strategy will resonate across different countries? How should we optimally sequence launches?
Q: How do you manage a successful commercial partnership?
AM: We have three 50:50 partnerships at Genmab — AbbVie, Seagen, and BioNTech. We will hopefully have products commercialized with all three partners.
I’ve spent several years working inside and leading co-development and co-commercialization partnerships and it was great to bring that experience to bear [in our discussions with AbbVie, Seagen and BioNTech.] It’s important to get the teams aligned on the ambition and the strategy and work on being crystal clear on the decision-making processes.
It’s also OK to rethink strategy. I was a small part of a large partnership between Bristol Myers Squibb and AstraZeneca. It was a successful alliance that included the launch of many products; the portfolio generated multibillion-dollar revenues. After several years of a well-functioning alliance there was a strategic shift that led to a transaction. BMS sold the franchise to AZ in order to simplify its operating model as a specialty biopharma company. For AZ, it was able to focus even more on one of its key growth platforms. Ultimately, it was a win-win evolution of the strategy.
Q: What comes next for Genmab?
AM: We will continue to focus on delivering our vision to transform the treatment of cancer through our knock your socks off (KYSO) antibodies. We will continue to build on our capabilities across R&D, Commercialization and enabling functions to become a best-in-class end to end biotech company.
Once our commercialization execution in the US and Japan is up and running smoothly, it creates a platform to move to the third phase of Genmab, the >50% phase. We will discover, develop and launch more medicines on our own. Partnerships will, of course, still be part of that. Over the past 3 years we have signed many earlier stage deals which are more often structured in such a way that gives Genmab worldwide commercialization rights.
We recently announced that we are entering the therapeutic area of immunology and inflammation. While oncology will remain as our primary focus, ultimately our aspiration is that our KYSO innovations can help make a meaningful difference to as many people as possible.
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.
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.)
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).
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.
Today’s guest on The Long Run is John Maraganore.
John is best known as the former CEO of Alnylam Pharmaceuticals, the RNA interference drug developer. He spent 19 years there as CEO, before stepping down at the end of 2021. Alnylam figured out how to make a new therapeutic modality — gene-silencing with double-stranded oligonucleotide therapies.
Alnylam’s technology has now been translated into five marketed medicines. The company has more than 2,000 employees, and a market value that exceeds $26 billion.
Since leaving Alnylam, John has taken on a sort of senior statesman role in biotech – wired in with investors such as Arch Venture Partners, Atlas Venture, RTW Investments and Blackstone. He serves on a variety of public company boards, such as Agios Pharmaceuticals, Beam Therapeutics, Kymera Therapeutics and Takeda Pharmaceuticals. He advises a number of young scientific entrepreneurs. He seems to be everywhere there’s some cool translational science work to be done. I joke with him that he’s the Dos Equis Man of biotech – the beer commercial that features the supposedly most interesting man in the world.
This conversation was recorded live in Seattle on Apr. 25 in front of an audience at the Life Science Innovation Northwest conference. We talk about John’s early life, key early career experiences, a few major events at Alnylam, and a bit of his views on science and policy.
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Now, please join me and John Maraganore on The Long Run.
[Editor’s Note: This is a preface by Jeremy Levin supporting the following essay by Steve Potts.]
Biotech is an industrial tapestry woven together by remarkable people with deep intellect, determination, passion, and bravery all driven to create the next and best medicine.
Underpinning it is an immense and complex infrastructure including capital formation, regulatory processes, patient advocacy, policy making, health insurance, media and many other stakeholders. Together they make up the core of what has not just delivered hundreds of lifesaving medicines but positioned America as the preeminent producer of medicines in the world.
Biotech is a national strategic asset.
But to continue to be successful on this mission we need to take account of the evolving and increasingly complex social, political and economic environment we operate within.
It is no longer sufficient, appropriate or adequate to operate as if we still in the 1970’s of Milton Friedman. If we do, we will help undermine our industry. It is incumbent on participants in our industry to engage with all stakeholders, express our values publicly and encourage and seek the support of those we serve, the patients.
If we don’t, we risk allowing the fabric we have created, being torn asunder by policy makers and those that do not understand the contribution biotech makes to those tackling disease and disorders, their families, the economy and the nation. They will hear only a narrative which focuses on price and downplays the value of innovation and medicines. They won’t hear the voices articulating our social contact.
This approach and the commitment it takes from all of us, is not woke. It’s good business.
In his essay below, Steve Potts, a serial entrepreneur and CEO, makes a powerful statement on the role of stakeholder engagement and the importance of the social contract. He points out those that have stepped forward and how they have stepped forward. The list includes names of long recognized leaders and encouragingly, a new and more diverse generation of female and male biotech executives, including most recently, the biotech sisterhood. Steve’s voice should be listened to, his data are sobering, and his “ask” is both admirable and achievable. To those reading this, I echo his appeal: step forward.
—Dr. Jeremy Levin, chairman and CEO, Ovid Therapeutics
Dr. Steve Potts – Anticipate Biosciences
Mountain biking is my weekend escape from the stress of drug development. The trails of Arizona provide a place to get some exercise, and to enjoy beautiful natural scenery.
Previous generations worked hard to build those trails that all of us can enjoy today. It’s our job to maintain them.
One recent Saturday, a few friends and I joined a work party to clear debris and fix some erosion damage, especially on tricky curves. Trail upkeep was needed after a long stretch of monsoon rains. Someone needed to do it, and my friends and I figured we should do our part. A few hours on a Saturday weren’t much to ask.
My friends, fellow biotech executives and investors, we have similar upkeep work to do. We’re fortunate to work in an industry that benefits from decades of investment and is now brimming with possibility to improve human health. But if we don’t do our part to maintain a healthy ecosystem, we’ll find ourselves on increasingly rocky, slippery, dead-end trails.
Creating a new therapeutic is like a long mountain climbing expedition. Maybe one in 50 ideas reach the base camp of a first-in-human milestone. From there, one in 10 (at best) reach the actual summit of FDA approval to become revenue-generating products. At least those are the odds in my area of oncology. Cancer is a tough mountain.
The Inflation Reduction Act of 2022, passed by Congress and signed into law by President Biden, contains provisions intended to control drug prices, and curb the amount the federal government spends on them. There are reasons why the public has been outraged over drug prices for many years, and why elected officials felt compelled to respond to the outrage. But in crafting this policy, the government has created a toxic side effect for small molecule drug development. It is so severe that it could all but wash out a section of the oncology mountain. The IRA subjects all NDA-path drug candidates including small molecules, oligonucleotides, and peptides to price setting by Medicare merely nine years after they come to market.
Today, companies and their investors typically count on having 14 years on the market before generic competitors erode the price and profits.
Nine years is far shorter than 14, especially when you consider that between the challenges of securing reimbursement and proving that the drug works in different stages of the disease, revenues take a while to ramp up. Truncating a drug’s marketability by five years can cut its profitability by half. By comparison, biologic drugs get 13 years before Medicare negotiation knocks their price down, which is close enough to 14 not to alter how we think about the reward for antibodies or other biologics.
Science wouldn’t necessarily lead us to a policy that prioritizes large molecules over small molecules. In both neurodegenerative diseases (Alzheimer’s, Parkinson’s, etc.) and cancer brain metastases, small molecules are the weapon of choice over biologics because of their ability to penetrate the blood-brain barrier. If a biologic kills off all cancer cells in the body but cannot cross the blood-brain barrier, the cancer could come back lethally as brain metastases. We need both small molecule and biologic weapons in our toolkit.
The fix is straightforward: we need 13 years for small molecules, as we have for biologics, for investors to continue supporting this area.
It’s not like small molecules cost less to develop or are less risky to develop than biologics. Their development has simply been rendered much less rewarding to investors.
I have been fundraising for a Series A to support our clinical trials and have noticed a distinct change in investor attitudes toward small molecule drug development after the IRA was signed into law.
I’m a data-driven guy, so I conducted a survey online of nearly 100 biotech investors and CEOs. The results are deeply disturbing for the future of oncology drug development. Since the IRA’s passage, six of every seven investors have moved away from funding small-molecule drug development programs for the elderly because the nine-year negotiation clock for NDA-path medicines makes them unattractive investments.
I’ve been working in oncology for three decades. Conducting this survey was the first time I went to work to improve the hiking and biking trails of drug development. It struck me as necessary in the aftermath of the IRA monsoon, much like trail upkeep seemed necessary after the heavy rains hit Arizona.
As I spent a little volunteer time in trail management, I became more aware and appreciative of others who had spent years ahead of me on these trails. I also noticed how many biotech executives like me have taken the stability and the incentives of our industry ecosystem for granted. Many haven’t yet gotten involved but it’s starting to change. It must change.
The nonprofit organization No Patient Left Behind has pulled our industry together in a type of trail maintenance. Many identify as Biotech Builders on the NPLB website in support of the biotech social contract.
The statement is simple and powerful: “People must be able to afford (through proper insurance) all appropriate treatments and all medicines must go generic without undue delay.”
That idea guides us to support reforms that lower out-of-pocket costs for patients, opposes patent gaming by companies, and opposes government price controls on new drugs.
The IRA caps individual out-of-pocket drug costs at $2,000 a year. That’s a positive change. The IRA is aligned with the biotech social contract in several important ways, except in its treatment of small molecule drugs.
I believe that no congressional leader seeks to do harm deliberately. Like all of us, they have aging family members who hope for better drugs for deadly diseases like Alzheimer’s and cancer. They need our feedback on how to ensure that new drug development expeditions are financed and that all patented medications either become generic or are priced as generics after patents expire.
Engaging in dialogue with policymakers is one form of trail maintenance. In the last two years, hundreds of executives and investors have written and signed letters to Congress trying to explain what it takes to keep biomedical innovation going, how it generates value for our society, and the advantages of insurance reform over price controls in achieving affordability without sacrificing progress.
Many people in our industry are rolling up their sleeves. From 2021 to 2023 five Letters to Washington posted on the No Patient Left Behind website have attracted over 3,000 signatures from leading investors, biotech executives and employees, big biopharma companies, bankers, service providers, as well as researchers, patient advocates, and economists.
In looking at who signed all these letters, it’s clear how active some investors have been in standing up for biomedical innovation.
A total of 189 total venture funds are represented by the signatories across the five letters, shown in Table 1 ranked by the number of letters they signed and how many of their people participated.
Many VCs firms are quite small, so a few signatures may represent a notable fraction of a firm, or maybe everyone feels represented by the signature of a leader.
But it’s also notable how some firms inspired widespread participation among their employees, suggesting a clear commitment by their leaders to mentoring others on the importance of actively preserving the innovation ecosystem and tending to the trails. RA Capital, RTW Investments, Atlas, Boxer, 5AM, Deep Track, Omega, and others have done this, setting an inspiring leadership example for all of us.
And yet, I noticed that some firms were missing among the signers.
To get a sense of who was missing, I looked at which investment funds we normally think of as active in biotech (in this case based on how much each deployed into private financings in 2022, based on data from Endpoints) and cross-referenced them with the letter signers.
Table 2 shows that, disappointingly, 70 out of the top 100 did not sign a single letter despite how much they would seem to lose from biotech trails eroding.
Signing letters is not the only measure of how much someone cares to stand up for biomedical innovation (e.g. it was great to see Eli Lilly CEO Dave Ricks participate in an NPLB webinar on the harms of the IRA).
But, given the stakes and how easy it is to show support for a message by signing a letter, it’s telling when institutions repeatedly remain on the sidelines.
It’s not as though they don’t use the biotech trails. They are running the trails and supporting companies that run the trails. They just aren’t preserving them. That’s disheartening, because scaling the oncology mountain and developing treatments for other diseases is not just about profits for any of us.
Our ability to innovate could save the lives of the people who are closest to each of us. We all have a personal stake in keeping the biotech trails viable.
Reviewing the lists of signers, I noticed other key contributors in our ecosystem took a stand. Commendable examples include the IR firm LifeSci Advisors (with an impressive 68 different employees represented among the signers), the investment banks Cowen and Bloom Burton, the CRO ICON. Also, Derek Lowe and Bruce Booth, two of our best biotech bards.
It would be great to see more participation from banks, service firms, and contract research organization partners, if not because of the personal impact of biomedical innovation for all their employees and their families then because their own businesses depend on the success of biotech R&D.
Big biopharmas were not much represented in the early letters, but the signatories of the latest letter (on the primacy of the FDA as the proper arbiter of which medicines should be on the market) attracted CEOs of giants like Pfizer and Biogen and executives from many other large companies. Their continued involvement is deeply appreciated by all of us small company executives.
The biotech social contract is under attack as never before. We can no longer take for granted that the trails for current and future drug development treks will remain fundable unless we all get involved.
Rod Wong of RTW said it well: “We should never take for granted the biotech ecosystem we have in the United States. No other place has this kind of biotech ecosystem. If we do take this for granted, one… the world will be worse off, because so much of biotech innovation happens here, or two… the US will lose its leadership position.”
If you are considering getting involved, don’t wait to be asked. Sign up with NPLB as a First Responder so that you hear about these initiatives and follow people like Peter Kolchinsky, Jeremy Levin, Laura Shawver, Paul Hastings, John Maraganore, and no doubt many others who tweet about these initiatives so there is no chance that you miss another grassroots action. Talk to your Representatives and Senators in your home state. Protecting healthcare innovation is a truly bipartisan challenge.
The IRA’s treatment of NDA-path drugs is causing more than a monsoon’s worth of damage to our trails; it could leave entire swaths of the oncology mountain untraversable.
As we make plans and gather teams for new biotech expeditions, we will deliberately seek to partner with investors who share our love for the biotech social contract and who spend the time to teach others in their firms to contribute to societal good in this way. It’s essential to maintain the trails established by previous generations.
Let us all be part of this work.
 How do we get to 14? Patents last 20 years, but a lot of that patent life is eaten up by the drug development process. Up to five years of patent life can be restored via the Hatch-Waxman Act, a decades-old law designed to both incentivize drug development and create a thriving generics market.
 There are four letters written to the public and Congress and one meant to align industry around the importance of fixing the IRA. Four letters are about the IRA and one recent one was about the importance of preserving the FDA’s standing as the scientific arbiter of which medicines can be on the market.
Today’s guest on The Long Run is Emile Nuwaysir.
Emile is the CEO of Boston-based Ensoma.
Ensoma is developing gene-editing therapies that can be delivered in a single shot, in vivo, inside the body. The name is derived from the Greek word for ‘in the body.’
The basic idea is to make these gene editing medicines so they can be given off-the-shelf, to any patient, and that they can deliver a fairly long and complex set of genetic instructions in a single shot.
The hope is to deliver these treatments — which you could call a genetic form of surgery — without having to go through the steps common to some of the first-generation gene editing procedures, which are often performed outside the body, or ex vivo.
Some of the well-known variations on CRISPR gene editing require a patient to undergo a blood draw, have certain cells isolated — like hematoepoeitic or blood-forming stem cells, for example. Then the patient undergoes another procedure, such as chemotherapy preconditioning, before the engineered cells can be re-infused back.
That takes time and money. It’s a process that has to be carefully choreographed. It’s not likely to ever reach global scale, like in poor countries that might someday want access to the curative power of gene editing therapies. Ensoma hopes to eliminate the need for those blood withdrawals, and preconditioning therapies, and re-infusions. It wants to deliver the gene editing therapy once, and in a single shot.
Emile is a scientist by training, with a PhD in molecular toxicology with a focus on oncology from the University of Wisconsin-Madison. He has been through a series of startups that have given him a wide variety of experiences in genomics, diagnostics, cell therapy, and antibodies. He’s been a part of three startups that were ultimately acquired by big players – Roche, Fujifilm, and Bayer. From 2021-2022 he was chairman of the board for the Alliance for Regenerative Medicine.
None of this was preordained or predictable when he first got interested in science. Like so many scientific entrepreneurs, he discovered his technology interests and inclination to work in startups along the way.
And now for a word from the sponsor of The Long Run
Occam Global is an international professional services firm focusing on executive recruitment, organizational development and board construction. The firm’s clientele emphasize intensely purposeful and broadly accomplished entrepreneurs and visionary investors in the Life Sciences. Occam Global augments such extraordinary and committed individuals in building high performing executive teams and assembling appropriate governance structures. Occam serves such opportune sectors as gene/cell therapy, neuroscience, gene editing, the intersection of AI and Machine Learning and drug discovery and development.
Connect with them at:
And do you want to be a part of the world’s largest biotechnology industry event?
Come to Boston from June 5 to June 8 for the 2023 BIO International Convention. Join over 14,000 biotech leaders from dozens of countries, for one week of partnering, networking, and robust educational sessions.
With unparalleled networking events like the receptions at Boston’s premier music hall “Big Night Live” or the party at the “MGM Fenway Music Hall”, This is the place you’ll want to be this June.
Register today at:
Now, please join me and Emile Nuwaysir on The Long Run.