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David Shaywitz on the Longfellow Bridge
Every other week, I stroll across the Longfellow Bridge from Cambridge to Boston. It can be a magnificent walk in the fall and spring, when the weather is temperate and the skies clear. You can see the deep blue of the Charles River, the Esplanade on your left, with the Boston skyline behind it, and the Citgo sign in the distance.
On the other side, you see the biotechnology cluster of Kendall Square and a large swath of MIT, expanding out from the river’s northern shore. Boston University’s campus lies a bit further upstream, on the south, while Harvard’s campus is a few bridges further down, to the north.
For me, the regular walk to Boston provides me with critical grounding – partly because I’ve spent most of my adult life within the vista before me, but mostly because of the hour-long teaching conference at Massachusetts General Hospital (MGH) I attend, learning about the complicated challenges faced by a patient receiving care on internal medicine service. The discussion is often inspirational and is invariably humbling.
The Charles River, as seen from the Longfellow Bridge
The conference, and the activities that support and enable it, were originally conceived by two MGH medicine residents, Dr. Lauren Zeitels and Dr. Victor Fedorov. In 2016, they founded the Pathways program, “dedicated time during residency for house staff to connect with scientists and delve into the fascinating biological questions that arise at the bedside.” Particularly in a busy hospital like MGH, with many acutely ill patients requiring focused attention, such time to reflect can be fleeting, yet remains vital, as Dr. Denny Ausiello and I discussed here.
Tragically, Drs. Zeitels and Fedorov died in 2017, in an avalanche while snowshoeing in Canada. The pathway program endures as a legacy to the power and urgency of their vision.
Pathways conferences are led by talented medicine residents who spend two weeks focused intently on the biological questions underlying a patient’s illness, with the hope of identifying key scientific questions that, if answered, could point the way to improved diagnosis and treatment. Like most clinical cases discussions, the conference presentation is oriented around the patient, starting with a review of how the patient came to be in the hospital, and generally ending with a discussion of the patient’s hospital course and anticipated trajectory. In between these essential bookends, there’s invariably an intriguing discussion around the often-mysterious biology underlying a patient’s illness.
By design, the team focuses on complex or confusing cases – at times, it feels like it might be called the “idiopathic conference” since many of the diagnoses featured tend to fall into this category (“idiopathic” is the fancy medical term for “we don’t understand what’s going on”).
Unfailingly, these deep dives into biology expose a central truth that has long impressed me about medicine: how little we still understand about how the body works, about what causes many diseases, and how limited are our arsenal of tools to diagnose and effectively, precisely treat many ailments. I felt this profoundly when I was a medical student seeing patients on the wards at MGH, and again when I was an internal medicine resident and later endocrinology fellow at MGH. I continue to experience these exact emotions today.
When you look at the examples of progress in medicine and biology, the list is impressive. The modalities available to diagnose and treat disease continues to expand, while the terrible diseases that we’ve learned to prevent (like polio) or more effectively manage (like cystic fibrosis) speak uncontestably to the promise of science.
Yet when you take your measure by your ability (or lack of ability) to diagnosis and treat the patient in front of you – much less prevent the disease from occurring in the first place – you are quickly overwhelmed by profound humility.
But you also feel something else. Not only do you leave these conferences with a deep appreciation for the limitations of our existing knowledge, you invariably emerge with a palpable sense of urgency, a determination that we must do better, and drive harder to ensure that nascent technologies, both biological and digital, are brought to bear, relentlessly as well as thoughtfully, in service of patients.
If the MGH Pathway conferences regularly remind me of how urgently improved diagnostics and therapeutics are required, recent events have reminded us how difficult the journey will be.
Several recent examples stand out:
Consider this question from Andreessen-Horowitz general partner and Bio+Health lead Vijay Pande, during his podcast interview of Olga Troyanskaya, a computer scientist and geneticist at Princeton.
With remarkable candor, Pande asks,
“There was so much excitement about precision medicine with genomics, right? I think the idea was that, okay, we could sequence a patient and maybe sequence a tumor and from that we would be able to do so much. We’d be able to figure out which cancer drug to give or which drug in general to give. And that hasn’t quite come to fruition yet, it feels like — or maybe it has happened so gradually that we’ve lost track. What’s your take on precision medicine?”
The idea that precision medicine hasn’t quite lived up to the admittedly extravagant expectations is a familiar perspective on the wards and in lab, but to hear it from within the reliably exultant investment community feels like real progress.
Olga Troyanskya, professor, Princeton University
Troyanskaya’s response offers similar candor. While pointing out that “we have seen a lot of successes,” she also recognizes that “a lot of it was hype,” adding “None of us, I think, quite appreciated quite how complex it is. We sort of thought we’ll find these few genes, they’re the drivers, we’ll develop targeted therapies, we’re done.”
To be sure, she points out, there are “true revolutionary successes of precision medicine.” She cites our increasingly sophisticated approach to breast cancer and some subtypes of lung cancer as two examples.
But, Troyanskaya adds, “What I think we underestimated is that it’s not enough to actually just know the genome, that it’s not, you know, it’s not just a few causal genes.” We also need to understand the non-coding 98% of the genome, she says, and to capture other types of data, including proteomics and metabolomics.
Coming up with impactful new medicines is remarkably difficult, and the idea that AI can improve the odds offers obvious appeal.
But at least some of the bloom is off the rose, as it’s now clear that many of the so-called “AI discovered” drugs have not made it through development.
“The first AI-designed drugs have ended with disappointment,” writes Andrew Dunn in Endpoints.
He continues,
“Over the last year-plus, the first handful of molecules created by artificial intelligence have failed trials or been deprioritized. The AI companies behind these drugs brought them into the clinic full of fanfare about a new age of drug discovery — and have quietly shelved them after learning old lessons about how hard pharmaceutical R&D can be.”
He also points out that while the last few years have been notoriously challenging for small biotechnology companies (the XBI biotechnology stock index has dropped by 50% over the last two years), it’s been even worse for companies focused on AI drug discovery, dropping by 75-90% over the same time.
[Chart courtesy of A. Dunn, Endpoints News]
(It’s also been a challenging time for digital health, see this report from Rock Health, and for healthtech – see this report from Bessemer Venture Partners. Interestingly, investor focus in these areas seems to be shifting from the aspiration of radically disrupting healthcare and biopharma to the appreciably more modest goal of making existing processes, especially non-clinical or back-office, somewhat more efficient.)
As many commentators have pointed out, the obsession with “AI drugs” is a little silly, in that AI is a tool that may inform a complicated, multi-step process. Moreover, a range of computational techniques are often involved in the development of medicines. Thus, the idea that one product is an “AI drug” and another isn’t feels more than a little arbitrary.
There’s also the issue of expectations – even if AI is able to contribute to the efficiency of early activities in drug discovery, at best this might only improve the overall process slightly given the multiple downstream hurdles that remain (especially since it’s not clear, as we’ve discussed, that AI can offer particularly useful predictions around late phase clinical studies, where most of the time and dollars are spent).
VC Patrick Malone (cited by Dunn) may be exactly right when he says,
“If you take the hype and PR at face value over the last 10 years, you would think [the probability of creating a successful drug] goes from 5% to 90%. But if you know how these models work, it goes from 5% to maybe 6% or 7%.”
In a separate post on LinkedIn, Malone (citing Leonard Wossnig, Chief Technology Officer at LabGenius) riffs on a key underappreciated challenge in drug discovery, particularly drug discovery that involves algorithms: figuring out what to optimize for – the so-called “objective function.”
For instance, you might try to identify a compound that binds incredibly tightly to a specific receptor, say. Yet it turns out that in many cases, such an approach isn’t quite right – empirically, you may discover you need a molecule that binds with moderate affinity to multiple receptors. (I’ve discussed this nuance in the context of phenotypic screening and the discovery of olanzapine, a medicine for schizophrenia, for example; see also this captivating recent commentary about phenotypic drug discovery, penned by researchers at Pfizer).
Generative AI captured our collective attention with the November 2022 release of ChatGPT, as this column has frequently discussed.
Despite the insistence of consultants that if your company is not already leveraging generative AI, it’s behind, I’ve not seen many examples of generative AI in actual use at large companies, particularly biopharmas.
It turns out, this perception is well-founded. According to Andreessen-Horowitz co-founder Ben Horowitz – a great champion of AI – “We haven’t seen anybody [involved in generative AI] with any traction in the enterprise.” (“Enterprise” in this context refers to the information technology systems and processes used by large companies, like pharmas.)
According to the technologist Horowitz was interviewing — Ali Ghodsi, co-founder and CEO of the big data analytics company, Databricks – there are at least four reasons generative AI hasn’t yet entered the enterprise:
From what I’ve seen across our industry to date, I can only say that Ghodsi seems to deeply understand his enterprise customer base.
Ali Ghodsi, co-founder and CEO, DataBricks
I was also struck by how Ghodsi – whose business embraces and enables generative AI – readily acknowledged the limitations of this technology. “It’s stupid and it makes mistakes,” Ghodsi says, adding “it quickly becomes clear that you need a human in the loop. You need to augment it with human. Look, there’s no way you can just let this thing loose right now.” (The need for a human in the loop was also a key assertion Harvard’s Zak Kohane made in his recently-published book about ChatGPT and medicine – see here.)
Ghodsi was also skeptical about performance benchmarks cited for generative AI – including around their supposedly high scores on medical examinations. He suggested that the models might have picked up the test questions during the course of their training (training that’s shrouded in mystery, as Harvard’s Kohane has pointed out), and thus, their high scores may be deceptive — like an exam where you got the answers the night before.
To be sure, Ghodsi remains excited about the potential of generative AI; he’s just realistic about its present limitations.
The talk of tech these days is the latest manifesto penned by Andreessen-Horowitz co-founder, Marc Andreessen, this time on “Techno-Optimism.” In case you missed it, his simplified thesis is that technology=good, and anyone who would constrain it=bad.
The piece has triggered an energetic and largely critical response from writers like Ezra Klein in the New York Times (“buzzy, bizarre”), Steven Levy in Wired (“an over-the-top declaration of humanity’s destiny as a tech-empowered super species—Ayn Rand resurrected as a Substack author”), science fiction author Ted Chiang (“it’s mostly nonsense”), and Jemima Kelly of the Financial Times (“is the billionaire bitcoin-backing venture capitalist OK?”).
Levy’s analysis was perhaps the most resonant:
“[Andreessen] posits that technology is the key driver of human wealth and happiness. I have no problem with that. In fact, I too am a techno-optimist—or at least I was before I read this essay, which attaches toxic baggage to the term. It’s pretty darn obvious that things like air-conditioning, the internet, rocket ships, and electric light are safely in the ‘win’ column. As we enter the age of AI, I’m on the side that thinks that the benefits are well worth pursuing, even if it requires vigilance to ensure that the consequences won’t be disastrous.”
But if I’m being honest, I thought there was something visceral in Andreessen’s screed – or perhaps more accurately, in the impulse behind it — that really touched a chord for me, emphasizing both the value emerging technology can bring and the incredible challenge of nurturing new technologies through their growing pains (what Carlota Perez might describe as the Installation phase), to the point where the demonstrated value is unassailable (the Deployment phase).
Not only is it intrinsically difficult to figure out how to implement new technology effectively, necessitating all sorts of incremental innovations (see here), but there’s exceptional resistance all along the way (at least outside of public relations, where new technology is championed relentlessly).
Incumbents – particularly in healthcare — resist technology for years before eventually (perhaps) absorbing it. In part this reflects inertia – we’re most comfortable with what we already know.
Another factor, as I’ve written, and as Andreessen observes (with his usual bluster), involves the precautionary principle. Stakeholders (in my view) are appropriately concerned about doing harm with novel technologies, but routinely overlook the harm that’s done by inhibiting the adoption of promising new approaches.
Moreover, new technologies often bring significant uncertainties, as Dr. Paul Offit has nicely described in You Bet Your Life (my WSJ review here). This is true not only for new medical treatments, as Offit nicely discusses, but also in how high-stakes research is conducted.
Consider a typical example from the world of clinical trials, the lifeblood of biopharma and academic clinical researchers. Imagine that you hear about a new tech-enabled approach for a key aspect of this process — patient recruitment, say, or decentralized data collection, an approach which is claimed to work better than traditional methods.
If you are leading a clinical development team in a pharma company, where everyone in the business is already breathing down your neck with advice and suggestions and telling you that you’re already behind and reminding you how important it is to be successful, the last thing on earth you want is to take on more risk.
If you use established procedures, at least you know what you’re getting into. But if you try a new approach (even an approach perhaps piloted under far more forgiving conditions), you could get yourself in deep trouble. This actually happens. Not surprisingly, faced with these sorts of decisions, many rational actors within pharma prefer to be “fast followers” rather than adventurous pioneers, a mindset Safi Bahcall has described in Loonshots (see my WSJ review here, and this additional, biotech-focused discussion in Forbes, here).
Concern about assuming new risk (beyond intrinsic risk of new molecules) is also why Gregg Meyers, the Chief Digital and Technology Officer at Bristol-Myers Squibb, says “this is an industry that will not adopt something unless it is really 10x better than the way things are historically done.” See also this generally accurate perspective on the challenges of selling innovative tech into pharma, written from perspective of a startup that’s developing the technology.
Ultimately, for me, it comes back to the patients we discuss every two weeks at MGH. We need far better diagnostics, far better therapeutics, and we need to understand health and disease at a far more sophisticated level.
Historically, improved tools – the microscope, ultrasound, MRI, DNA sequencing – have radically redefined the way we understand and approach disease. Today, emerging modalities and tools — like antibody-drug conjugates (which are enjoying a banner year), cell and gene therapy (enduring real growing pains but with demonstrated successes and tantalizing possibilities), spatial biology techniques (quite exciting), and of course artificial intelligence (which is getting more capable by the second) offer exceptional promise, even if in many cases we’re just beginning to contemplate how to leverage these new technologies effectively. Safety and ethics – appropriately — remain paramount consideration as well; see my recent discussion of Ziad Obermeyer’s important research revealing hidden bias in medical algorithms, and my WSJ review of Brian Christian’s essential The Alignment Problem, here.
While I disagree with Andreessen’s denunciation of skeptics and naysayers, I feel his frustration, and share his belief that technology (guided, I would add, by inquisitive researchers, practitioners, and other “lead users” who are passionate, thoughtful, and deeply empathetic) offers the most promising path forward in our drive to improve the human condition in the area of health and medicine.
Sometimes, it seems, we get so caught up in our clever criticism and overwrought angst that we get in our own way, stall our own momentum, and impede the progress upon which the health of our patients, and of our future patients, will so critically depend.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Today’s guest on The Long Run is Jeff Huber.
Jeff Huber, co-founder and general partner, Triatomic Capital
He’s the co-founder and general partner at Triatomic Capital. It’s a relatively new venture fund that seeks to back “century-defining” businesses in Engineered Biology, New Materials, Next Gen Compute, New Energy, & Automated Economy.
Jeff and his fellow partners are looking at startups that collect and analyze data to address big challenges of the 21st century, where deep learning / AI could be useful.
Jeff was the founding CEO of Grail, the company that sequences DNA from blood samples to detect early signs for 50 different types of cancer. Before that, he had a long and storied career in Silicon Valley, including 15 years at Google. Jeff has his fingerprints all over some of the most common apps that Google developed in those rapid growth years, and which are now used by people around the world every day.
Jeff has certainly come a long way. He grew up on a family farm in northwest Illinois, about 20 miles away from where I grew up. I had no idea we had this much in common until we were hiking together on the trails of the Himalayas in 2022, on a fundraising expedition for the Fred Hutchinson Cancer Center.
In this conversation, Jeff expanded on some of those early life experiences and how they affected his life and career trajectory.
Part of Jeff’s story involves personal tragedy. His first wife, Laura, died of cancer in 2015. This experience was part of what motivated him to work on Grail, to help more people detect cancer earlier. Although we didn’t discuss it during this conversation, I should note that Jeff is now happily re-married and has a blended family. I asked some about his oldest daughter Grace because I got to know her on that expedition to Nepal.
This conversation ran longer than usual, but I think it will be absorbing for people thinking about the intersection of tech and biotech, where so much potential for innovation resides.
Now, please join me and Jeff Huber on The Long Run.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Dr. Jingyi Liu, clinical fellow in medicine, Brigham & Women’s Hospital
Two gene therapies for sickle cell disease, CRISPR/Vertex’s exa-cel and Bluebird Bio’s lovo-cel, are being reviewed by the FDA and are likely to win US approval for sale by the end of the year.
While it will take more time to fully establish long-term safety and efficacy of these therapies, they offer a glimmer of hope for a cure.
This is the culmination of a decades-long scientific journey. In 1957, molecular biologist Vernon Ingram’s discovery revealed the genetic basis of sickle cell disease — a single amino acid substitution in the hemoglobin beta chain. Since then, investments in sickle cell disease research trailed behind other genetic disorders, resulting in limited progress and treatment options. Current treatments like hydroxyurea, Endari, Adakveo, and Voxelotor manage but don’t cure sickle cell disease, leaving patients with an average life expectancy of just 50 years. Bone marrow transplants offer a cure, but they come with life-threatening risks.
Exa-cel and lovo-cel offer the potential for transformative change. These therapies target the genetic root of the disease, potentially functioning as cures that would alleviate fatigue, excruciating pain crises and cardiovascular complications.
Recent data releases suggest that these therapies are breakthroughs from standard of care. 31 of the 32 evaluable patients who had received lovo-cel had resolution of severe vaso-occlusive crises through 24 months of follow-up. Similarly, 16 of 17 evaluable patients who had received exa-cel were free of severe vaso-occlusive crises for at least 12 consecutive months. For context, the median number of severe vaso-occlusive events among these patients in the 2 years preceding enrollment was 3 to 4 events per year.
However, approval is only the initial step towards realizing the promise of sickle cell gene therapy.
Physicians and patients are about to confront some thorny issues around cost and access. Prices are sure to be in the sticker-shock range of $1 million and up.
Sick Cells, a patient advocacy group, asks a pressing question: “Will eligible patients have full access to coverage to utilize these therapies?”
Historical barriers to care for sickle cell disease are complex, encompassing socioeconomic, geographic, informational, and racial factors. About 100,000 people in the US are thought to have sickle cell disease, but only one in four patients has access to standard oral medications.
The cost of sickle cell gene therapy will be substantially higher than with oral pills. Gene therapy will also require substantially more monitoring and support. Patients must commit to a multi-month process that involves hospitalization, chemotherapy, and an extensive recovery process. While there is the potential for a long-term cure with gene therapy, this isn’t exactly as simple as a one-and-done procedure.
Through interviews with the SCD community, including patient groups, physicians and payors, I compiled a few ideas on how to improve the accessibility of sickle cell gene therapy post FDA approval:
As we celebrate the imminent approval of sickle cell gene therapy, it’s vital to remember that the true relevance of this milestone will depend on what comes next – how many patients receive the treatment and its benefits. If some of these roadblocks aren’t smoothed out, it could create more healthcare disparities, said Dr. Maureen Achebe, a hematologist at the Brigham and Women’s Hospital and the Dana Farber Cancer Institute.
Science has made great advances against sickle cell disease. But realizing the potential of gene therapy for sickle cell disease will require a fundamental overhaul of our healthcare system’s approach to financing and distributing these groundbreaking treatments. We have more work to do to fulfill the promise of sickle cell gene therapy for eligible patients.
Disclosures
Today’s guest on The Long Run is Lori Lyons-Williams.
Lori Lyons-Williams, CEO, Abdera Therapeutics
She’s the CEO of Menlo Park, Calif. and Vancouver, BC-based Abdera Therapeutics.
Abdera is a startup developing targeted antibody drugs loaded with potent microdoses of radiation to give them extra tumor-killing punch. This type of treatment modality has been around for decades. Targeted radiation hasn’t lived up to the full potential scientists have long imagined, but Abdera is part of an emerging crop of new companies that are working through some the classic technical challenges. Abdera announced a $142 million combined Series A and B financing, which I wrote about at the time for subscribers of Timmerman Report.
In this conversation, we talk about Lori’s early career experiences in sales and marketing, a couple key turning points for her growth, the opportunity she sees for targeted radiopharmaceuticals as an emerging class of therapy – especially for tough-to-treat solid tumors. Toward the end, she has some advice for women in biotech.
Now, before we get started, a word from the sponsor.
In the 10 years it takes for a new biopharmaceutical or device to be developed, more than 304 million people die waiting for treatment. Don’t settle for slow, ineffective patient engagement — not when people are counting on you! Elligo Health Research gives you immediate access to known, diverse patients so you can quickly get your product to the people who need it. Go to ElligoHealthResearch.com and get the patients you need, right now. Because they’ve waited long enough.
Now, please join me and Lori Lyons-Williams on The Long Run.
This episode is unusual.
Rob Perez, operating partner, General Atlantic; founder, Life Science Cares
Many listeners know of Rob Perez. He’s an operating partner at General Atlantic, the former CEO of Cubist Pharmaceuticals, and the founder of Life Science Cares.
Rob and I have gotten to know each other better the past couple years through our shared passion for mobilizing the biotech community to fight poverty. The Timmerman Traverse for Life Science Cares campaigns have raised $2.9 million over the past three years.
Luke Timmerman, founder & editor, Timmerman Report
Longtime listeners may recall Rob was a guest on this show five years ago, and he spoke about Life Science Cares then. This time around, Rob wanted to turn the tables. He asked the questions, and I was the guest.
We discuss how I grew up on a small family farm in southwestern Wisconsin, some early career influences in newspapers, and how I adapted to the market forces that upended journalism in the 21st century. Those experiences all combined to lay the foundation for this new chapter as both journalist and social entrepreneur.
Now, before we get started, a word from the sponsors.
With Elligo Health Research, a proud sponsor of The Long Run, clinical trial patient engagement takes just two simple steps. First, Elligo searches through their network with access to millions of diverse patients — who are pre-vetted for protocol inclusion through HIPAA-compliant identified healthcare data — to determine the best intersection for optimized engagement. Second, Elligo collaborates with you to assess feasibility, ensuring your protocol works in a real-world healthcare environment so you easily get the patients you need when you need them.
Look forward to face-to-face interactions this October at the BIO Investor Forum—or BIF—in San Francisco.
Accessible and intimate, this conference is designed to accelerate growth within the biopharma industry. BIO’s One-on-one partnering system seamlessly brings together emerging biotech companies with industry partners, investors, and bankers.
Don’t miss your chance to network, learn from experts about what’s on the horizon for biotech, and pitch your unique story to potential partners. Held October 17th and 18th at the Hilton San Francisco Union Square.
Now, please join me and Rob Perez on The Long Run.
David Shaywitz
Two recent Wall Street Journal deep-dives nicely bookend a critical, and unresolved, tension faced by large pharmaceutical companies: how can their R&D organizations discover, develop, and deliver the new medicines patients await, and the growth and return on investment that shareholders demand?
Early this year, I discussed an April 2023 profile of Lilly by journalist Peter Loftus, who described how the company, led by a physician-scientist named Daniel Skovronsky, revitalized Lilly’s R&D, after a decade of documented struggles.
Skovronsky’s pivotal insight, as I wrote, was “recognizing that 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.”
Poor decision making driven by non-scientific considerations – particularly commercial desiderata—is a frequently-encountered challenge in drug development, as VC David Grainger, in particular, has highlighted (see here).
But other large pharma companies (I suspect many if not most large pharma companies) are concerned that they’ve provided excessive latitude to their R&D organizations, rather than insisting on a greater commercial focus.
For instance, last week, Journal reporter Jared Hopkins described how Novartis’s CEO had decided the company’s early efforts were excessively driven by science, and needed a greater emphasis on commercial prospects. Consequently, future sales will now be forecast for products before they enter clinical trials, Hopkins wrote.
Associated with this change in emphasis, the early research arm of Novartis, the Novartis Institutes for BioMedical Research (NIBR), “will soon simply be known as Novartis Biomedical Research,” according to STAT.
The new Novartis mindset contrasts, directly, with the approach instituted by former NIBR head Mark Fishman, who deliberately excluded such forecasts from early-stage decision-making (as I discussed a decade ago in Forbes). A decade later, Novartis brass clearly became concerned that NIBR had become “too academic” – pursuing interesting questions without sufficient regard for commercial applications.
While appealing in theory, Novartis’s proposed remedy – essentially, use early commercial forecasting to “pick winners” – makes sense only if you have the actual ability to do this. Otherwise, you have just an exercise in false, if comforting, precision.
A classic study by BCG found that there was essentially no correlation between predicted peak sales for a drug at the time it was approved (i.e. with complete phase 3 data) and actual peak sales. Several years later, as I discussed in Forbes, McKinsey confirmed the fragility of sales forecasts. Consequently, the notion that one can predict sales before human studies in any meaningful way is purely wishful thinking – the sort of “sterile information” (to use Nassim Taleb’s term) that corporate planners typically love, but which is functionally worthless.
As Taleb testified before the Financial Services subcommittee of the U.S. House of Representatives in 2014,
“Some may use the argument about predicting risks equal or better than nothing; using arguments like ‘we are aware of the limits.’ Risk measurement and prediction —any prediction — has side effects of increasing risk-taking, even by those who know that they are not reliable. We have ample evidence of so called ‘anchoring’ in the calibration of decisions. Information, even when it is known to be sterile, increases overconfidence.”
How to pick projects, then?
One view – as analyst Jack Scannell has argued, as I discussed here — is to prioritize areas with good translational models, as slightly better models contribute more impactfully to the odds of success than increasing by several orders of magnitude the number of molecules screened (which is the canonical large pharma approach). In fact, Scannell describes this excessive reliance on scale vs smarts as the “mis-industrialization” of science, and this description seems spot on. (Taleb and I made a similar point in a 2008 commentary in the Financial Times, here.)
Focusing on areas with good translational models is explicitly the approach Vertex is taking, and arguably Regeneron as well. Both Vertex and Regeneron are compelling (and remarkably rare) examples of established drug discovery companies that are still dominated by their powerful R&D cultures; more commonly, the business of running a drug development company is considered too complex to be left to the scientists (in the same way that running a hospital is increasingly considered too complex to be left in the hands of doctors, rather than corporate managers). It’s also true that Vertex and Regeneron are both comparatively small; Regeneron, the larger of the two, employs about only about a tenth the number of people that Novartis does, for example.
Without question, there are many areas of biopharma that arguably benefit from the rigor of traditional corporate industrialization – in particular, the focus on consistent, repeatable processes that operate on a global scale. Manufacturing, engagement with regulators, sales and marketing, even late phase clinical trials largely fall into this category, and tend to be areas that big pharmas do particularly well, and with which corporate executives are fairly comfortable.
But coming up with new products? Not so much.
While pharma companies aspire to develop new products with a range of defined characteristics – the so-called “target product profile” – you generally can’t discover on command.
Moreover, many commercially successful products were developed without any initial interest or support from commercial teams. Merck (see here) was days away from out-licensing pembrolizumab (Keytruda), a drug that had been kept alive by researchers despite the best efforts of successive management to stop the program. Similarly, when Millenium acquired Leukosite, they had no idea that the transaction included a molecule, bortezomib, that would become the most important commercial product in their portfolio.
Big pharmas are built around establishing repeatable processes…but there simply isn’t a playbook for serial creative success.”
Big pharma brings considerable resources to discovery, as well as an extremely high degree of rigor, and often an exceptional amount of implicit knowledge, particular in areas like medicinal chemistry. But large pharma (as Safi Bahcall has nicely described – see here and here) also tend to be exceptionally bureaucratic and process-driven, and I’m not optimistic you can process and administrate your way to creativity and insight. I’d argue the process-driven mindset that serve other aspects of drug development and delivery so effectively (enabling the distribution of safe and effective medicines to patients around the world) have, on balance, an adverse impact on novel discovery. Moreover, for all the efforts of big pharma to enable research groups that are more “biotech-like,” the incentives and cultures of most large corporations just doesn’t seem to allow for this, in practice.
This isn’t an argument for aimless discovery research. Rather, it seems inevitable that an ever-greater number of future first-in-class medicines will come from the focused and high-performing teams in the start-up world. Big pharmas are built around establishing repeatable processes, and driving incremental refinements. But original discovery – to the dismay of most large pharma – often isn’t about turning the crank faster.
Rather, originality in drug discovery, as in other areas, requires, in addition to an intended destination, a high degree of agility, freedom, and imagination; the capacity to embrace and live with uncertainty, rather than obscure it with sterile forecasts; and the humility and ambition to imagine a future different from and beyond what we might anticipate, and extrapolate to, today. Perhaps most difficult for large, process-driven corporations, it requires a recognition that there simply isn’t a playbook for serial creative success. These are principles that exceptional leaders like Ed Catmull, the CEO of Pixar in its heyday, have intuited, and that most large corporate cultures struggle to sustain.
Recent Astounding HealthTech columns on Pharma R&D
Today there are two guests on The Long Run: Zach Weinberg and Alexis Borisy.
Zach Weinberg, co-founder, CEO, Curie Bio
They are co-founders of Curie.Bio.
Curie is a venture capital fund with $520 million, mostly for seed investments and Series A rounds in biotech startups. It also is also building up in-house R&D expertise which it uses to help the entrepreneurs it backs.
Curie pitches itself as different from other venture firms partly because it allows the CEO/founders it backs to hold onto a greater percentage of ownership than traditional VCs have been willing to hand over. Curie also says it wants to allow entrepreneurs to retain more control over decision-making. This boils down to a battle cry of ‘Free the Founders.’
Alexis Borisy, co-founder, operating chairman, Curie Bio
They are tapping into the zeitgeist. Many biotech entrepreneurs feel they sweat bullets for years, shouldering too much of the hard work and risk, without reaping enough of the rewards.
It’s still early days for Curie Bio, but this is conversation worth having about the terms of engagement in biotech startups.
Now, before we get started, a word from the sponsors.
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Now, please join me and Zach Weinberg and Alexis Borisy on The Long Run.
Welcome to the next big biotech expedition — Timmerman Traverse for Damon Runyon Cancer Research Foundation.
A team of executives, investors, and scientists are coming together on Kilimanjaro, the highest peak in Africa, in February 2024.
We’re on a mission to raise $1 million for young scientists across the US.
Damon Runyon gives promising young researchers the freedom to pursue brave and bold ideas. Its grant recipients have opened up entire new fields with targeted therapies, CRISPR gene editing, and cancer immunotherapy.
The biotech community knows the value of this work and is stepping up to support it.
I’m happy to announce that PPD Biotech Solutions, part of Thermo Fisher Scientific, is our lead sponsor.
Corporate sponsorship opportunities are available for this special expedition for cancer research. See Elyse Hoffmann, senior director of partnership strategy at Damon Runyon, to brainstorm ideas. elyse.hoffmann@damonrunyon.org.
Please see our team fundraising page to learn more and donate.
We’re pushing ourselves to do something difficult. We’ll enjoy Mother Nature. We’ll form meaningful relationships on the trails.
We’re going to make a positive impact for young scientists.
Thank you for your support!
Timmerman Traverse
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Today’s guest is Fred Appelbaum.
Fred is a physician, scientist, and administrator. He’s an executive vice president at the Fred Hutchinson Cancer Center in Seattle.
He’s also the author of a new book, “Living Medicine: Don Thomas, Marrow Transplantation and the Cell Therapy Revolution” published by Mayo Clinic Press. It’s excellent.
Fred knows a lot of this story from firsthand experience.
He has spent his career conducting research and treating patients with leukemias, lymphomas, and other cancers of the blood. He’s a pioneer in the field of bone marrow transplantation and was the lead author of a 1978 paper in the journal Blood that heralded the first successful engraftment of autologous bone marrow in patients with malignant lymphoma.
Fred Appelbaum
One of Fred’s key influences was E. Donnall Thomas. Don Thomas won the Nobel Prize in 1990 for the discoveries that paved the way for bone marrow transplantation to become a common, and lifesaving procedure, for people with blood malignancies and more. Thomas died in 2012.
There’s a story to tell here about Don Thomas.
In this conversation, Fred discusses the book, the researching and writing, and a few things he learned.
Now, please join me and Fred Appelbaum on The Long Run.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $199 per year, you reward quality independent biotech reporting, and encourage more.
