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Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
Most biopharma companies can talk at some length about embracing data science, and sometimes it’s hard to get beneath the surface of what they’re doing, at least publicly.
But it was my privilege recently to frame and moderate a very insightful recent conversation – recorded for on-demand viewing at this year’s virtual BIO 2020 conference.
This provided an unusual opportunity to get beyond the aspirations and slogans companies tend to use when discussing how to integrate tech and pharma.
For this conversation, we got deep into the sort of issues I tend to encounter in my advisory work, and talked substantively about both the pain points that key players encounter and the specific solutions they are testing as they try to figure out how to make the most of data science in biopharma.
My discussion partners were Ray Deshaies of Amgen, Shahram Ebadollahi of Novartis, Amy Abernethy of the FDA, Chad Robins of Adaptive Biotechnologies, and Peter Lee of Microsoft.
We covered a lot of ground over nearly 90 minutes; rather than summarize, I wanted to share a couple of thematic takeaways that particularly resonated.
It takes a long time for a technology revolution to truly be absorbed by the pharmaceutical industry. Ray Deshaies, SVP of Global Research at Amgen (and before that, a distinguished biologist at Caltech and a HHMI Investigator) described the lengthy process associated with the adoption of biologics (eg antibodies) by an industry that had been built around the discovery, evaluation and development of small molecules.
The revolution clearly took. By 2018, eight of the top ten best-selling drugs in the world were biologics.There’s no doubt these products transformed the industry.
The first monoclonal antibody therapy, Deshaies reminded us, was actually a mouse anti-CD3 antibody, OKT3 (Orthoclone), approved by the FDA in 1986. From there, it took eleven years before the approval of the anti-CD20 antibody rituximab (Rituxan), which would go on to be the first blockbuster drug of this class.
Deshaies reviewed the ongoing evolution of antibody technology, from mouse antibodies to chimeric (part-mouse DNA/part-human protein sequence) antibodies (like rituximab [Rituxan]) to “humanized” (almost all human) antibodies like pembrolizumab (Keytruda) to “fully human” antibodies like secukinumab (Consentyx).
This progression, Deshaies pointed out, reflected a considerable amount of deliberate effort, including extensive investment in successive technology platforms, along with the work required to understand each of them, embellish them, and fully understand the issues with each. In the manufacture of biologics, he noted, there are all sorts of less-appreciated complexities that crop up over time, including issues like the impact of cell line and media, which can each significantly impact the performance of the final protein product, despite a constant DNA sequence encoding it.
The rise of biologics gave rise to profound new needs on the talent side, Deshaies added. It created a need to train a new generation of people; for example, the way you think about pharmacokinetics for biologics is very different than how you think about it for small molecules. Additional learning was required, and it took time for all the newly trained people to get in place.
Deshaies also described a second revolution that also required (ongoing) adaptation: human genetics. This became especially important at Amgen once they resolved to pursue targets validated by human genetics, and acquired Icelandic genetics company deCode in 2012. Most lab researchers, Deshaies explained, were trained in mouse genetics, and need to be retrained in human genetics and human physiology. He reports that while Amgen been able to recruit increasing numbers of scientists trained in statistical genetics and human genetics, there’s still a lot of education required for the organization. “It takes repetition,” he said.
These efforts – at Amgen and across the industry – have persuaded most drug hunters of the value of human genetics; such validation significantly increases a compound’s probability of success. (Of course, a drug may still not work as expected, a tough lesson Amgen itself learned when a genetics-inspired bone drug turned out to cause cardiovascular problems.)
Pursuing human genetics at Amgen, Deshaies added, required more than executive leadership and top-down messaging. While it’s helpful for top brass to say “we want to do human genetics,” he said, it takes so much more for that message and thinking to permeate the organization, and become part of its fabric; considerable time and persistence seem to be required.
Pharma’s experience with these emerging biological technologies seems like useful context as the industry now contemplates the adoption of digital technologies, highlighting the effort, capital, and patience required to effect durable change in the industry. While it’s encouraging that pharma constantly seeks out new technology, it’s sobering to appreciate how long it takes for large legacy organizations to fully internalize new ways of thinking. It’s just not realistic to expect tech adoption to occur overnight in an enterprise this complicated, with this many moving parts.
Novartis is one of those traditional pharma companies that is trying. With deliberate intentionality, and led by CEO Vas Narasimhan since 2018, Novartis is conspicuously trying to take itself through the revolution it sees happening in digital technology.
There has been no shortage of top-down messaging, starting with Narasimhan himself, who essentially introduced himself to the world by offering a vision of Novartis as a data-driven medicines company. Not unexpectedly, as Narasimhan has subsequently reported, progress has been uneven.
Thus it was especially interesting to listen to Novartis’s Global Head of Data Science and AI, Shahram Ebadollahi, discuss his ambitions and approach.
What really struck me was that while Ebadollahi offered an example of “AI exploration”- some sexy longshot project in discovery chemistry – he spent a lot more time discussing “AI empowerment.” He described this effort as an attempt to introduce AI and data science tools (many available through a high-profile collaboration with Microsoft) at every step of the work flow; he wants to distill machine learning into “bite size engines,” so employees don’t even realize they’re using ML – it’s just running in the background. His perspective seemed very aligned with Jim Manzi’s view, using AI to solve unsexy discrete problems.
In addition to the new technology, Ebadollahi described a very deliberate effort to “shift the mindset of people” via constant “multichannel communication.” Basically, if you work at Novartis, it sounds as though you hear about data science relentlessly. There’s an effort to educate current talent through a “data science academy” and other initiatives, and an AI residency program that seeks to embed a recently graduated AI expert within R&D teams.
Successful cultural change in pharma will require an elevation of the practice of data science within the industry, Ebadollahi argued. Today, he said, pharma is driven by biologists and physicians, but if a pharmaceutical organization is really to become a data science company, data scientists have to become truly equal partners.
I imagine most pharma physicians and biologists would reply that this elevation will happen when data scientists demonstrate they can deliver substance and value, not just grandiose promises – which I imagine is why Ebadollahi also highlighted the importance of actually delivering value to programs – of really proving out an approach, not just living in the world of innovation, pilots, and proof-of-concept work. Success, he argued, required putting people with complementary skills in a room and setting them to work.
Drawing from her many experiences over her career trying to bring such groups together, Dr. Amy Abernethy, now Principal Deputy Commissioner of the FDA, emphasized not only the importance of integrating health and tech talent, but also the difficulty of pulling it off. The required collaboration is critical but hard, she explained, adding that we need to acknowledge and confront just how hard it is.
Creating a culture that makes all the collaborators equal is a non-trivial challenge, Abernethy said, but also worth it. She argued that the interesting opportunities happen when we pull together streams of information and talent that generally don’t exist in the same place. Like Ebadollahi, she also emphasized the need to move from idea of collaboration to actually getting stuff done.
Abernethy also highlighted the importance of thinking about the application of technology as a “full stack opportunity” – in other words, it’s not only about how tech can help discover a new drug. We also need to think about how it can help accelerate clinical development, and contribute to clinical trials and through the approval and post-marketing phase as well.
Abernethy spoke to the particular importance of not neglecting the “last mile” work – to be sure that the relevant information is delivered to the treating physician in a useful fashion. Doctors operate in an increasingly complex environment, she reminded us, with rapidly evolving knowledge, and limited time with patients who are often (and understandably) distraught. As a physician, you need clarity to offer relevant, useful, data driven advice that technology ought to be able to facilitate.
Abernethy also emphasized that while randomized controlled trials remain the gold standard, the COVID-19 pandemic has highlighted the importance of trying to bring to bear all the data available, and leverage this information to the fullest extent possible, from electronic health record data to claims data to biosensor data. The problem, she explained, is how to bring it all together; everyone thinks there’s a “magic button,” she said, but there’s not, and it’s actually very hard. She hopes that the collaborations driven by the “COVID-19 Evidence Accelerator,” which focuses the community on identifying and then answering a critical set of questions, can help. I hope so too, though my mind kept returning to comments Walmart’s Marcus Osborne made at a recent healthcare conference: the U.S., he said, has no public health infrastructure. That said, if anyone can kluge together something useful from the scattered pieces we have, that person would be Amy Abernethy.
The opportunity for emerging tools to potentially add value to established techniques was highlighted by the work of Adaptive Biotechnologies, and their partnership with Microsoft. Adaptive’s focus is sequencing immune receptors – which is probably why their co-founder and CEO, Chad Robins, highlighted the “huge opportunity” in clinical sequencing.
Like me, your first reaction was probably, “Clinical sequencing? How quaint!”
But if we’ve learned anything about revolutions in healthcare, it’s that they take awhile, and often require the confluence of just the right technologies. Adaptive’s thesis is essentially that because the immune systems responds to everything adverse we encounter, it harbors in essence a version of our medical record, documenting the many enemies, both foreign (like a virus) and domestic (like a cancer) we may have encountered and mustered a response against. If this record could be understood, if our history could be “read,” the information could be used to diagnose diseases (like Lyme Disease, celiac disease, cancers, and now infectious diseases like COVID-19), as well as to suggest potentially effective antibodies to be tested as a biologic therapy. For example, they are working with Amgen to prioritize potential therapeutic antibodies against SARS-CoV-2 that Amgen could then develop.
Because of the volume and complexity of the sequencing data they intended to study far exceeded the capabilities of a traditional spreadsheet application, Robins said, they needed to develop their own software tools that could allow researchers to interpret, analyze and visualize the data. This required, from the outset, a culture that necessarily integrated bioinformaticians and biologists.
The Adaptive team also realized that even with their own software, they needed access to ever-more computational power, especially as they set their sights on a particularly ambitious goal – matching specific antigens with specific receptor sequences. This turns out, according to Peter Lee, Microsoft’s Corporate Vice President (and former head of the computer science department at Carnegie Mellon University) a difficult but not impossible machine learning problem, on par with language translation, and a perfect opportunity for a partnership, which is what the two companies announced in 2018, and expanded recently to incorporate work on COVID-19.
In the foundational agreement, Lee explained, Adaptive gained access to the computational power it required to extract remarkable value (if successful) from the immuno-sequencing data the company generates; Adaptive also received embedded Microsoft talent, and an equity investment from Microsoft as well. In exchange, they were compelled to move all their compute to the Microsoft Cloud (and away from competing clouds like Amazon’s AWS and Google Cloud, for example).
Microsoft’s goal, said Lee, is to develop substantial partnerships that drive value through use of cloud-enabled digital technologies. Microsoft sales teams are compensated based not on whether a client buys Microsoft technology (such as access to their Azure cloud), but rather based on the extent to which the client actually uses it. As Microsoft sees it, this places them in strong alignment with customers; the more valuable the cloud is to the business, the more it will be used, and the more revenue Microsoft will receive. (This assumes, of course, that customer use of the cloud services actually correlates with the business value the service generates; there’s a parallel here to fee-for-service medicine.)
Microsoft, in any case, is highly motivated to find ways for the cloud to be useful for customers, as we saw manifest at Novartis as well. (Lee notes the relationship with Novartis is obviously structured very differently than their relationship with Adaptive, which he describes as a model for their relationships with other especially promising startups; however, he notes that even in his relationship with Ebadollahi, Microsoft’s success is “completely indexed” on Novartis’s success.)
Pharma is highly attuned to technological innovation in a range of domains; the most impactful medicines of today look very different, and were discovered in a very different way, than the medicines of yesterday. Tomorrow’s medicines will look very different than today’s, and are likely to be created, evaluated, and developed in a different fashion as well.
The promise of digital technologies has appropriately captured pharma’s attention, but figuring out how to effectively engage with and implement these emerging approaches remains a work in progress. Powerful computational tools, combined with rich data, offer potentially transformational opportunities in both diagnostics and therapeutics – often in combination with established technologies such as next-generation sequencing and advanced imaging. More immediately, the application of machine learning to the thousands of intricate processes that underlie pharmaceutical operations are likely to provide the earliest indications of impact.
Success of the digital revolution in pharma will require a culture that authentically recognizes and equally values the contributions of data scientists, as well as evidence that the promise of data science is translating into tangible pipeline results. We’ll know we’ve arrived when the impact of data science extends from tools used by patients to stay healthy, prevent disease, and discern early signs of trouble, to the discovery, development, and post-market assessment of new medicines to treat patients when they’re sick, through the provision of timely, point-of-care decision support so providers can most effectively guide patients back to health.
It’s an ambitious vision, at once deeply worthy and highly uncertain. Who’s in?
Luke Timmerman, founder & editor, Timmerman Report
The emotions this week were especially intense. Anger. Fear. Despair.
But there was also hope. That gets me up in the morning.
In this slice of America, the biotech industry, many people are mission-driven.
People in this industry work every day to improve the human condition by making better medicines.
Not everyone feels the full weight, but many in this part of the scientific problem-solving enterprise didn’t need a news flash that African Americans suffer terrible disparities in things like heart disease, cancer, diabetes, asthma, and now COVID-19.
What I saw this week were glints of new awareness — a growing recognition, let’s say — that the injustice inflicted on African Americans for 400 years is inextricably linked to those health disparities. The mission of biopharma is about improving health. But health is connected to all of those underlying things like housing, education, and criminal justice that are terribly unjust.
If people don’t dig into the root causes to make a better world for everyone, how can we begin to improve health?
The good news is, these protests are starting to move people.
This is a start.
Events move so fast now, it’s easy to lose track of actual progress being made. The police officer that killed George Floyd in Minneapolis had his charge upgraded to second-degree murder. The other three officers — who stood there and didn’t do anything to save him while he cried “I can’t breathe” and begged for his mother — they were charged with aiding and abetting. After days of protestors demanding they be charged.
George Floyd’s family asked for Minnesota Attorney General Keith Ellison to prosecute the case. The attorney general of Minnesota, no less, answered that call.
New investigations into troubling cases of African Americans dying in police custody are being opened up all around the country.
Mayors, Governors, police chiefs, and even some police union leaders all over the country, are speaking out like never before against the horrific acts of the Minneapolis police caught on video. The Republican governor of Maryland called Minneapolis police officer Derek Chauvin a “murderer in a blue uniform.”
Thoughtful executives, like Ken Frazier of Merck, could do the easy thing and say nothing. Or issue a statement with some generic platitudes. Instead Frazier said something real, both about George Floyd’s case and about how his life fortunately went down a different path.
He said it on national TV, on a channel that the powers-that-be actually watch.
This is a start.
If you’re ever going to change laws, change police union contracts, change structures around citizen review boards, and begin to hold cops accountable – you have to start with recognizing there’s a problem and applying pressure.
This is a start.
These things are happening because of the public pressure being brought by protestors.
Young people are demanding action at the local and state level. People in power in Minnesota, and elsewhere, seem to be listening and acting accordingly. They might listen some more, and continue to act accordingly, if the pressure remains relentless.
We need to watch out for the violence and senseless rioting. That’s destructive and wrong. It’s damaging to people’s livelihoods, including livelihoods of African American business owners. It needs to be stopped. Throwing bricks through windows and stealing merchandise? It doesn’t do any good, and we don’t really know who’s doing it and why. Then we watch cops do more abusive things in the heat of the moment, whether it’s because of all-too-human nerves, or because of foolish orders from above.
These past few days have gotten better on these counts. I believe the protestors, as long as they stay peaceful, might just have staying power during this summer of so much pain.
Tremendous acts of community kindness – offerings of water, sunscreen, food and more – are sustaining protestors. See this young woman describe the remarkable generosity she has witnessed in Minneapolis.
Many of us in biopharma are wondering what we can do. It’s easy to surrender to helplessness against a problem this big, this deeply engrained. Where to start? What difference can anyone make? Why bother?
That’s nihilism talking. That’s no way to live.
There are things we can do. Small things, maybe, but things that matter.
Many of you read Rob Perez’s guest article on things biopharma can do to dig deep and fight hard, against injustice. I think people are listening more than in the past.
This is a start.
We can use this moment to teach our children about right and wrong, in terms of racial justice. We can use this surge of energy to vote out dangerous leaders in Washington DC, and vote in a new generation of people willing and able to do the hard work in our communities of dismantling systemic racism – in criminal justice, in housing, in education, in health.
All these things are connected. We need to get our hands dirty, down there at the roots, if we really want to make progress across the board.
Nobody can do this alone. But if everybody digs in – and, crucially, is willing to stick with this long past the end of the news cycle – then we will have accomplished something meaningful.
Politics
The President said he’s pulling the United States out of the World Health Organization in the middle of a pandemic. It will harm people around the world, especially in developing countries. If he follows through with this threat, it will diminish US influence on the world stage. It will create an opening for China and others to seize, gaining more influence over how the pandemic response is run. To say the WHO is perfect would be wrong. It has made serious errors, like not declaring COVID-19 a global pandemic in January. But pulling out of the WHO completely, at this moment, is impetuous and short-sighted and dangerous.
The Pandemic Toll
The pandemic was pushed down in people’s news feeds. But let’s not forget. We have 108,000 deaths in the US, and almost 1.9 million cases. The New York Times reports 17 states where cases were increasing over the past week, and 15 states where cases were decreasing.
Science
Science Features
Treatments
Gilead Sciences released some Phase III data from a remdesivir study of patients with moderate forms of COVID-19. These results, as many expected, were more encouraging than the earlier results from a study of more severely ill patients. The 5-day course of treatment was also the way to go, which is good news for stretching out manufacturing supplies around the world.
If you’re looking for important, under-covered stories on the pandemic, look at Eli Lilly’s neutralizing antibody candidate for COVID-19. In collaboration with AbCellera and researchers at the National Institute of Allergy and Infectious Disease, the team picked an antibody from the blood of a recovered patient, made recombinant copies as biopharma companies do so well, and now have it in patients in a clinical trial in the span of about three months. Along with Regeneron and VIR Biotechnology, these therapeutic antibodies have a chance to mitigate the pandemic in the interim period before we get a vaccine.
Hydroxychloroquine failed to prevent healthy people from getting COVID-19 in a clinical trial. The study was conducted at the University of Minnesota, and enrolled healthcare workers. See the Washington Post coverage.
Vaccines
Public Health
A Big Retraction
The Lancet retracted a widely-read May 22 paper on hydroxychloroquine and chloroquine as treatments for COVID-19. The original article said there was no benefit seen, based on evaluating a registry with records from 96,000 patients. The authors wrote in their retraction, “we can no longer vouch for the veracity of the primary data sources.” Retractions are never good, but especially in a pandemic when we need trustworthy sources of scientific information and guidance. What’s the big takeaway from this? See Ashish Jha below.
Another paper that depended on Surgisphere for raw data collection appeared suspicious enough to generate an “Expression of Concern” in the New England Journal of Medicine. That was for a paper titled “Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19.” Strangely, the authors of that paper “all the authors were not granted access to the raw data and the raw data could not be made available to a third-party auditor.” Maybe this is just naïvete, but aren’t authors of a scientific paper supposed to have access to raw data so they can properly analyze and summarize? This doesn’t smell right.
Deals
Cambridge, Mass.-based Intellia Therapeutics pocketed $100 million upfront ($70 million cash and $30 million equity investment at $32.42 a share) as part of a collaboration with Regeneron Pharmaceuticals to develop CRISPR-based treatments for hemophilia A and B. The companies have experience working together already, and this is an expansion of the prior collaboration.
Berkeley, Calif.-based Aduro Biotech merged with privately-held Vancouver BC and Seattle-based Chinook Therapeutics. The new company, called Chinook Therapeutics, will be led by Chinook CEO Eric Dobmeier. Chinook now will get a Nasdaq listing, will have $200 million of cash in the bank, and the runway to pursue its precision medicine approach to kidney disease.
Lexington, Mass.-based Accent Therapeutics, the developer of cancer drugs that target RNA-modifying enzymes, secured a $55 million upfront payment as part of a collaboration with AstraZeneca.
Data That Mattered
Waltham, Mass.-based Minerva Neuroscience said its drug candidate for schizophrenia failed in a placebo-controlled Phase III trial of 515 patients. The stock collapsed, down 70 percent.
Gilead Sciences released updated safety data from more than 4,500 patient years of experience with filgotinib, the oral JAK1 inhibitor for rheumatoid arthritis, at the European League Against Rheumatism conference. The drug is part of the collaboration with Galapagos NV of Belgium.
Regulatory Action
AbbVie won FDA clearance to market elagolix estradiol, and norethindrone acetate (Oriahnn) capsules as a treatment for heavy menstrual bleeding due to uterine fibroids. It’s the first oral treatment of its kind.
Eli Lilly won FDA approval for its IL-17 alpha inhibitor, ixekizumab (Taltz) for non-radiographic Axial Spondyloarthritis, including ankylosing spondylitis.
Personnel File
Mark Genovese was hired as senior vice president of inflammation at Gilead Sciences. He was on the faculty at Stanford University for 21 years.
Admiral Brett Giroir, who has been leading coronavirus testing efforts for the federal task force, said he’s being “demobilized” from that effort, and going back to his regular role at the Department of Health and Human Services, according to NPR.
Sanofi Ventures hired Jim Trenkle as head of investments. He was previously with Pivotal Bioventures. Cris de Luca also joined as global head of digital investments. He was previously with Johnson & Johnson Innovation and JLABS.
Financings
On Grace
Tony Dungy, the Hall of Fame football coach, is a man of deep religious faith. I have respected him for years. He offered some wisdom — that rarest and most valuable of things — on a sports radio call-in show.
Dungy spoke his calm, empathetic words in the middle of the outraged, frenetic aftermath of what New Orleans Saints quarterback Drew Brees said about protests. Brees, a man with some great African American teammates, said something that was clueless and insensitive and self-centered. He later apologized.
We’ve seen this kind of foot-in-mouth moment a million times. In 400 years, we’ve never had an adult conversation about race in the US in which everyone sits and truly listens. If we’re going to take a deep breath and have that necessary conversation, the kind that’s incredibly painful but builds bridges of awareness needed to make things right, then our best bet is to look to community leaders like Dungy.
He’s giving people some space. Allowing them some room to say dumb things. Things that are awkward or tone deaf. Notice, Dungy didn’t excuse what Brees said and didn’t apologize for him. But he’s creating some room where people can listen to each other, and begin to understand why what the great quarterback said was so hurtful to so many of our fellow citizens.
People need to be given the room to make mistakes, and to see the error of their own ways. To begin to realize that perhaps they have blind spots. If people don’t feel they can be open and honest, and let their ignorance and clumsiness be forgiven, then they’ll crawl back into a shell, clam up, or stay in denial.
The worst among us will lash out in performative defensiveness and cash in off their perceived “victimhood.”
If that happens, we’ll stay right where we are, stuck in a rut, with terrible injustice happening all around us every day. With the majority of the country blind to the horror.
I want this terrible racism to end with our generation, and for our kids to grow up in a more humane and empathetic world.
Those of us — like me — who are privileged and don’t walk down the street afraid of police, need to stand shoulder-to-shoulder with those who do. We need to insist on this change, and be relentless about that change, right there with our black brothers and sisters.
It’s our job to have the awkward, painful, tearful conversations with our family members and friends who aren’t yet all the way there with us on this journey.
As Martin Luther King said: “Injustice anywhere is a threat to justice everywhere.”
As Frederick Douglass said: “Power concedes nothing without a demand.”
As John Legend wrote: “Justice for All Ain’t Specific Enough.”
The injustice is so pervasive, and so deep. We need to demand it end. Then we roll up our own sleeves. It’s not someone else’s job. For us to rip out the roots of this enduring injustice, we need to get comfortable with being uncomfortable.
Do everything Rob Perez wrote about this week. When you’ve done all that, think about doing more.
Do it with empathy, with patience. The conversations we need to have to change people’s hearts will happen in unusual places, unusual settings. Not just from a podium or a pulpit.
If you listen to Tony Dungy talking with a sports talk radio host — a young white guy — then you’ll get a feel for the words, the tone, the setting. This is what it’s about. Watch the empathy and respect he has for his conversation partner.
This is change in action.
David Shaywitz
An abiding challenge at the intersection of technology and pharma R&D is the need to bring together two historically disparate cultures.
Tech companies tend to be led by engineers. Pharma R&D is generally run by chemists and biologists in the early stages, and by physicians further on in development.
Each of these domains has its own distinct language, culture and norms that don’t all neatly overlap on a Venn diagram.
It’s clear by this point that effective integration across these disciplines is both required and difficult.
Data science is likely to make an impact on R&D when it’s perceived not as an exotic, novel capability, but rather when it becomes woven into the way medical researchers go about their daily work.
This is how science progresses. In the 1980s, molecular biology was exotic, and clinician-scientists famously abandoned physiology to embrace it, as Brown and Goldstein classically described. Today, molecular biology is a given, a standard part of the researcher’s toolbox, and the leaders of pharma R&D are all fluent with the standard tools and techniques.
I suspect data science today is where molecular biology was 40 years ago. It is now widely recognized as an incredibly powerful emerging tool, and many young physicians and medical scientists are eager to embrace it.
Talent at intersection of medical research and data science is rare, and in high demand. I think of people like Dr. Alvin Rajkomar, formerly of UCSF and now at Google; Dr. Krishna Yeshwant, at GV; Dr. Taha Kass-Hout, formerly of the FDA and now at Amazon; Dr. Andrew Trister, formerly at Apple, now leading the Innovative Technology Solutions team at the Gates Foundation; and Imran Haque, trained in computer science at Stanford and now at Recursion; among others.
In what I suspect may be an early indication of where the puck is heading, Genentech recently announced what I believe is the first example of a “digitally native” researcher to lead a major pharma R&D group. The distinguished biotech drug discovery company recruited highly regarded computational biologist and systems biologist, Aviv Regev from the Broad Institute to run gRED (Genentech Research and Early Development). Her predecessor, Michael Varney, trained as an organic chemist. Varney’s predecessor in that high-powered, priority-setting role, Richard Scheller, was a molecular neurobiologist.
To be sure, there are plenty of strong data scientists in pharma, but generally within data science-specific functions; at Genentech, Regev is responsible for all of early R&D. She isn’t being charged with running a science project, an innovation initiative, or some novel effort to link up disparate databases and see what happens. Her department must deliver actual drug candidates that move forward in development, and eventually become products that help patients.
Regev, known for her work in single-cell sequencing at high volumes, generating data that can be placed in a larger biological context, is the rare kind of individual who is equally comfortable at a computer science and molecular biology symposium. She inhabits several domains that seem critical for pharma’s future.
Digital natives like Regev remain extremely rare, though I suspect they will increasingly come to populate top pharma and medical research leadership roles – if data science is perceived to deliver on its promise (not a given).
But “digital nativist” isn’t the only path to success. A second route is the “skilled orchestrator” – leaders who are knowledgeable, curious, and have a particular knack for effectively aligning health and tech talent against an objective. There’s a high degree of emotional intelligence that seems to be required, as well as an intuitive feel for both domains. A few role models spring to mind: Dr. Amy Abernethy (currently Deputy Commissioner of the FDA) and her leadership at Flatiron is one; Chris Gibson, the founder and CEO of Recursion seems like another.
Successful orchestration requires a common sense of mission; this is a much higher bar than most appreciate, requiring more than simply putting an engineer and a biologist in a room, say, or appending a data scientist onto a pharma product team. But a skilled orchestrator who can pull this off has the opportunity to leverage comparatively large pools of specialized tech and pharma talent, and doesn’t require all the competencies to reside in a single, remarkable person.
Whether through digital natives or skill orchestrators, it’s exciting to see pharma embrace the challenge of bringing data science to bear. The proof, however, will be in the pipeline.
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Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
If you were responsible for the health of a large population, and could access just one of the following types of data, what would you choose (assuming all were acquired legally and responsibly)?
(a) Genetics and other –omics data;
(b) EHR data;
(c) Consumer/adtech data.
For at least the last 20 years, we’ve been obsessively focused on the first two categories, as proponents of precision medicine were enthralled with the possibilities of what we could learn from genomics and other types of -omics data. Advocates for a learning healthcare system, meanwhile, tended to emphasize the value of medical record data. These efforts have yielded important progress.
At the same time, if we’re being honest, we must acknowledge that these approaches haven’t (yet) delivered anything close to the envisioned healthcare transformation originally promised. Perhaps, we just need to be more patient.
While some academic groups have begun looking at consumer/adtech data, this information has remained largely off-limits for serious healthcare companies and researchers — mostly because it’s just too fraught, enmeshed as it is with ethical and legal landmines. The very existence of the copious amounts of data on consumer buying patterns and expressed affinities is viewed by some as intrinsically problematic. Many worry about the ecosystem in which these data were generated, concerned about the increasing pervasiveness of what sometimes called “surveillance capitalism.”
According to some critics, these data shouldn’t even exist. Given the reality of their enduring presence, many believe we should do all we can to minimize their impact.
Similarly, I suspect some medical researchers viscerally view adtech data the same way they (and I) view the results of historically unethical experiments, like (to pick a deliberately extreme example, and risk invoking Godwin’s Law) Nazi doctor Josef Mengele’s notorious twin studies. The repugnant method in which Mengele’s data were obtained are generally felt to outweigh any potential utility; the results are not typically considered part of the scientific canon.
While Mengele analogy may be over the top, the discomfort towards this information is real, and consequently, most everyone in healthcare steers clear – well clear – of adtech data. Medical systems, to the extent they are even aware such data exist, are unlikely to go anywhere near them; it would risk reducing patient trust, with no apparent upside. (I’m sure the marketing department of medical systems are well-aware of these data, but have no interest in connecting this information with traditional health data.)
Tech companies like Google and Amazon are keenly aware of consumer and adtech data; their business models and staggering valuations are built on these data. Yet, most large tech companies seem to have decided that they want to become trusted partners to healthcare. The business opportunity they see in this space doesn’t involve their traditional model of harvesting consumer data; rather, they hope to sell cloud computing and related services (like AI) to hospitals, drugmakers, health insurers and others.
In their interactions with healthcare systems, Google, Amazon, and Microsoft each seem to go out of the way to emphasize the robust firewall between patient data and consumer data. At a recent healthcare conference, senior physician-executives at Microsoft and Google, for example, repeatedly emphasized the importance of trust and equity in all discussions of technology. These tech giants want healthcare data from clinical care and pharmaceutical research to be managed and analyzed on their respective cloud services.
The tech companies evince little interest in “crossing the streams,” and somehow connecting adtech data and healthcare data. The goal is to be trusted stewards of the health industry’s sensitive data, and to help healthcare customers use analytical tools to extract greater value from this information.
Apple, for its part, has never been particularly interested in monetizing individual data, but rather seems focused on providing a secure data ecosystem, so it can sell the devices that collect (Apple Watch) and integrate (iPhone) this information.
Facebook, meanwhile, was exploring the possibility of integrating user and health data back in 2018. This work was happening largely out of public view for more than a year. As described by Sidney Fussell in The Atlantic in January 2020, Facebook had sent cardiologist Freddy Abnousi (current head of health technology at Facebook) on a mission:
“He was to get the Stanford University School of Medicine and the American College of Cardiology on board with a new project that would combine Facebook user information with hospital-patient data in order to influence patient outcomes. Facebook hoped it could leverage the cache of data users already give it – about their education, relationships, habits, spoken languages, employment status, and more, all of which have an enormous impact on health outcomes – to create a sort of subclinical health-care system, warning providers if, for example, a user recovering from surgery had a small support group.
Then came the Cambridge Analytica debacle, and with it a cascade of highly public inquires and privacy revelations that resulted in the cessation [of these efforts].”
The Cambridge Analytica scandal, in which Facebook user data was harvested without consent and ultimately used to inform political campaigns brought new scrutiny on many aspects of the social network’s business. In response, Fussell reports, Facebook opted to roll out a dramatically scaled-down version, “Facebook Preventive Health.” The capability is offered as an (opt-in) feature in the Facebook mobile app, and uses only two user data points: age and gender.
Just a few months after The Atlantic report, Facebook still seems to be pursuing an ambitious health vision. Facebook’s Abnousi is currently hiring a Head of Health Tech Research, seeking “a world leader in outcomes research to develop and execute a digital health tech research agenda that is aimed at improving morbidity, mortality, cost, and inequity in health outcomes” (you can apply here). This suggests that although Facebook told The Atlantic that the Preventive Health effort is unrelated to the original data sharing proposal, the underlying goal outlined in talks with Stanford and the American College of Cardiology has endured.
Without question, there’s a creepy, icky aspect to adtech data, information that speaks to our habits and predilections, and are collected every time we Like something on Facebook, purchase something on Amazon, or search for something on Google.
Responsible health researchers typically seek to understand patients based on parameters like genetic sequence or physician notes or claim records – each of which could be exceptionally useful. But meanwhile, reams of data describing our actual, real-world behaviors are now the exclusive provenance of advertisers seeking to hawk soda and politicians. Health scientists and hospital administrators, for the most part, seem to have a knee-jerk reaction against touching these data; concerned about the risks and anxious about the many uncertainties. But the inconvenient truth remains: our behavior and choices play an outsized role in determining our health – and adtech data may offer the most relevant window into this critical parameter.
From the perspective of a startup, the ickiness of the data, and its existence in a grey area, may represent an opportunity, since established healthcare companies and tech giants are likely to give these data wide berth, creating an opportunity for a startup in the model of Uber or PayPal willing to embrace and intelligently navigate the risk, as VC Josh Kopelman has described.
A startup that could leverage adtech data and generate relevant health insights for traditional stakeholders could be an intriguing proposition. Think of all the research that seeks to elucidate the genetic basis for extreme phenotypes, including exceptional responders; imagine if there was a way to identify characteristics of patients most likely to maintain weight loss, or patients most likely to respond to treatment A vs treatment B.
Intriguing as these research questions are, figuring out the right business model for a startup in this space remains a critically important challenge.
As a leading data guru told me:
“The main issue is what do you do with this information … that’s what I don’t think anyone has figured out. Let’s say you can figure out that [some factor] is correlated with a particular health outcomes, etc. — how does that actually change the course of care, or what pharma companies/payers/providers do?”
This feels to me like a solvable problem – and a worthy challenge. The risks can be responsibly managed. To the extent that we remain awash in ultra-granular, legally-obtained behavioral data, it seems a shame not to gainfully leverage this information for the improvement of human health.
Luke Timmerman, founder & editor, Timmerman Report
The US death toll from COVID-19 exceeded 100,000 this week.
Another 40 million people are unemployed.
We’ve been on this terrible trajectory for what seems like forever, with 20,000 new cases a day and 1,500 or so deaths every day. The New York Times lists 18 states where new cases are increasing and 19 states where cases are decreasing.
Memorial Day weekend came and went. People are stressed and fatigued and antsy and lonely and depressed. We’re all seeking social contact. Some are gathering outside in large groups without masks. Images of crowds were everywhere from the Georgia coast to the lakes of Missouri to the Montlake cut in Seattle.
Knowing what we know about how the virus transmits, and everything we’ve seen this spring, we can expect an increase of cases in 2-3 weeks. (See Otello Stampacchia’s May 26 analysis).
Will there be just a “blip” in new cases, in the optimistic scenario outlined by Tony Fauci, or will we see a Second Wave surge?
We don’t know yet. But we do know that a pharmaceutical intervention will not be riding to the rescue this summer. If we want to reduce the toll of suffering and death, we will have to get by with non-pharmaceutical interventions that have caused so much pain since March.
Another thing we do know is that our country is at war with itself. Many people can’t even speak to members of their own families. Many can’t speak to old friends across the partisan divide. Many, inhaling toxic fumes from our 24/7 online networks and extremists on cable TV, suspect bad faith and fraud and conspiracy everywhere. It strikes some people as hard to believe that anyone might mean well, make a mistake, and then try to fix it.
All of the bitterness and resentment and suspicion of other people’s motives makes it incredibly hard to mount the kind of consistent, disciplined, collective action response we need.
The consequences of the infodemic are here, staring us in the face.
This is a good time to re-think what kind of information commons we want to have in the 2020s and beyond. One place to start is by re-reading the late Neil Postman’s “Amusing Ourselves to Death: Public Discourse in the Age of Show Business.” It was written in 1985, when TV was king, and social media didn’t exist. It’s a brilliant, biting work of media criticism. But it doesn’t let us off the hook as citizens. It asks an uncomfortable question: are we willing to do the work of being citizens, or are we just empty consumers of entertainment and advertisements?
Even though Postman didn’t predict the nightmare of the entertainer-in-chief — that was too preposterous for the time — he correctly saw the conditions that made that rise possible.
I’d like to think we can reclaim what it means to be citizens. What are the responsibilities that come with that? What sort of work does this demand of us? What do we owe our fellow citizens? It means more than just showing up to vote once every four years.
Right now, a lot of people are wrestling with deep questions like this. In my more optimistic moments, I imagine that people will come out of this terrible time dedicated to others and to issues larger than themselves.
Communication in the 21st Century
Headline: “Zuckerberg Says Twitter is Wrong to Fact-Check Trump.” Newsweek. May 27. (Daniel Villarreal).
Early in the pandemic, a reporter asked the President of the United States about the diagnostic testing failures and delays.
He replied: “I don’t take any responsibility at all.”
He’s not the only powerful man shirking his responsibility. So is the man in control of the most powerful information distribution network in human history.
Mark Zuckerberg, the Facebook founder and CEO, built an empire that siphoned away most of the revenue from advertising that used to support local news publishers all over the United States.
While Zuckerberg was demolishing the local news gathering engines that hold local officials accountable and build community tensile strength, he did nothing to fill the civic void. Zuckerberg, for years, has insisted Facebook is just a platform, and doesn’t really have to shoulder any responsibility of being a publisher. Grudgingly, the company has instituted a few bare-minimum policies to take down the worst kind of posts that incite imminent harm. These are expensive and tedious and difficult things that come with a publisher’s responsibility. It’s a big responsibility. Like making sure the things you publish are rooted in fact. Like adhering to basic standards of fairness. Like correcting your mistakes. Like giving a damn about serving your community with the kind of quality journalism that people need to make informed decisions in a democracy.
Instead of owning up to his role in hollowing out local media and doing something meaningful about it, Zuckerberg has spent years in a defensive crouch. This week, he threw some shade at Twitter. Twitter, like Facebook, is under pressure to behave like a responsible publisher. So Twitter started pointing out the baselessness of certain tweets by the President. That provoked Mr. Zuckerberg. Maybe he’s afraid that if citizens demand accountability from Twitter, they might also demand it of Facebook?
Zuckerberg went on offense. He told FOX News that “Facebook shouldn’t be the arbiter of truth of everything that people say online.”
I agree. It shouldn’t be the arbiter. No Internet company should be that big, and that unaccountable to the public. While we are free to speak out and consume on Mr. Zuckerberg’s propaganda-drenched social network, we’re also free to ignore it — even though persuasive design and algorithm enhancements make that psychologically very difficult. We’re also free to demand that Zuckerberg take responsibility as a publisher, or force him to do so through legislation.
It will take tenacity and discipline, but we can do that.
That won’t happen anytime in 2020, as lawmakers have other things to do. But if you want to do something positive right now to reverse the catastrophic degradation to our information system, I suggest permanently deleting your Facebook account and purchasing a subscription to your local newspaper.
Headline: “Sorry, No Mask Allowed. Some Businesses Pledge to Keep Out Customers Who Cover Their Faces.” Washington Post. May 28. (Teo Armus).
Some people don’t like being told to wear masks. But as things get more extreme, we now have businesses banning people from setting foot in their establishment if they choose to wear masks.
This can only happen in a society that cares little about others. Wearing a mask isn’t much to ask. We have got to take a deep breath as a people, and find a way to talk to each other, listen to each other, and help each other adopt the collective behaviors necessary to reduce the risks of the pandemic.
Headline: “Why Scientists Are Changing Their Minds and Disagreeing in the Pandemic.” CNBC. May 23. (Chrissy Farr).
With distrust on the rise, a fair bit is directed at scientists. Scientists need to be aware, and communicate with care.
Glancing at the headline above, you might say “duh, that’s how science works.” Of course, good scientists change their minds when presented with new evidence, or evidence that contradicts guidelines based on prior studies that have been overturned. But not everyone knows that. Scientists, and journalists, should take great care to communicate with humility and caution about what’s known and what’s not, and what’s the best course of action for the time being. Readers and viewers should know things could change if new evidence emerges, and it probably will with a virus that’s brand new to science.
Headline: “He Experienced a Severe Reaction to Moderna’s Covid-19 Vaccine Candidate. He’s Still a Believer.” STAT. May 26. (Matthew Herper)
Say what you want about reporting on an anecdotal adverse event in the midst of an ongoing clinical trial of global significance. I would rather wait to see the text and data tables from a mature analysis in the New England Journal of Medicine. When anecdotes dribble out in the middle of a trial, it injects bias for both investigators and participants. Then it can spread like cynicism wildfire.
See this young man’s succinct summary of what happened after this article ran about his experience on the Moderna vaccine.
Merck on the Case
The big drugmaker company hasn’t been the loudest voice in the pharmaceutical world this spring, but it announced a series of aggressive moves after Memorial Day. The company:
Scientific Articles of Note
Science Features
Thoughts on Evidence in a Pandemic (Debate in Boston Review)
Humanity
Testing
Wuhan, China, the original epicenter of the SARS-CoV-2 outbreak, reportedly ran 9 diagnostic tests in 10 days as it sought to gain clarity on where the virus is, so it can be isolated and give people the confidence necessary to resume activities of normal living. (WSJ coverage).
Treatments
Roche / Genentech said it has initiated a Phase III trial of its IL-6 inhibitor tocilizumab (Actemra) in combination with Gilead Sciences’ remdesivir as combo therapy for hospitalized patients with severe COVID-19 pneumonia. This would combine the Gilead drug’s antiviral effect with the antibody that suppresses cytokine storms which are known to cause major complications. Enrollment is expected to start in June, with 450 patients globally. Separately, Genentech said it’s close to completing enrollment in the Phase III Covacta study, and expects results this summer, to see how the IL-6 inhibitor alone performs for hospitalized COVID-19 patients.
Vaccines
GSK said it plans to scale up production to make 1 billion doses of vaccine adjuvants in 2021. These are the compounds that can boost the potency of vaccines to elicit robust immune responses, and which could be used to reduce the actual dose of vaccine protein per dose, thereby stretching out potentially limited supplies to a larger swath of Earth’s population. GSK said it believes more than one vaccine will be necessary, and that it’s working with partners to make adjuvants that will complement more than one vaccine candidate.
Gaithersburg, Maryland-based Novavax said it began enrolling 130 healthy adult volunteers in a randomized Phase 1/2 clinical trial of its SARS-CoV-2 vaccine candidate. The protein-based vaccine comes in a standard version, and with another version that includes a proprietary adjuvant designed to boost higher levels of neutralizing antibodies. A low and high dose are being evaluated, with and without the adjuvant. The study is being run at two sites in Australia. The program is funded by the Coalition for Epidemic Preparedness Innovations (CEPI).
Reuters had a detailed piece on the US plan to enroll 100,000 volunteers in COVID-19 vaccine studies in the hopes of hitting the extraordinary deadline of delivering a vaccine by the end of 2020. Dr. Larry Corey, principal investigator of the HIV Vaccine Trials Network based at Fred Hutch, was quoted in the story as one of experts designing trials. (Listen to Dr. Corey on this recent episode of The Long Run podcast).
US Healthcare Dysfunction
Read this piece by Dr. Sachin Jain and nod along about all the entrenched interests and how they stymie positive change in healthcare. To summarize, quoting Upton Sinclair: “It’s hard to get a man to understand something if the man’s salary depends on not understanding it.”
Financings
Data That Mattered
Netherlands-based Argenx passed a Phase III clinical trial with its drug candidate efgartigimod for myasthenia gravis, a rare autoimmune disease that causes debilitating muscle weakness. About two-thirds of patients on the drug met the primary endpoint of improvement in activities of daily living, compared with about one-third of those on placebo. The company said it plans to submit an application for FDA approval by the end of 2020. Two days later, the company seized on the momentum in its share price to haul in $750 million through a stock offering.
South San Francisco-based Atara Biotherapeutics reported on Phase I results for an experimental T-cell therapy against the progressive form of multiple sclerosis, which is notoriously hard to treat. Results were from a Phase I trial of ATA188, an off-the-shelf T cell therapy candidate, designed to target B cells that carry the Epstein-Barr Virus, and which become a troublesome cell type attacking neuronal sheaths, causing the pain and misery of multiple sclerosis. It was a small study of 25 patients in four dose cohorts, but the company reported that the treatment was safe and patients saw improvements on disability out to 12 months of follow-up. Atara immediately sought to cash in on the data, raising $175.5 million in a stock offering a day later.
Roche/Genentech said it passed a Phase III clinical trial with a next-generation permanent refillable implant to deliver the VEGF inhibitor ranibizumab to the eyes of patients with the vascularized form of age-related macular degeneration. The new implant, about the size of a grain of rice, can be refilled with drug after a period of months. In this study, it was refilled after six months, and delivered similar results as the standard form that requires frequent injections of ranibizumab (Lucentis).
Merck said it passed a Phase III trial, in which its single-agent PD-1 inhibitor pembrolizumab (Keytruda) was found superior to standard chemotherapy in patients with microsatellite-instability high or mismatch repair deficient forms of metastatic colorectal cancer. The drug showed a 40 percent improvement on Progression Free Survival.
Deals
Hayward, Calif.-based Arcus Biosciences collected $175 million upfront from Gilead Sciences as part of a 10-year collaboration to work on Arcus’ treatments based on how tumors evade the immune system. Arcus, like so many other companies in this bull market biotech spring, followed up that news by announcing plans to raise more money from Wall Street.
Cambridge, Mass. and Montreal-based Repare Therapeutics collected a $65 million upfront payment from Bristol-Myers Squibb as part of a collaboration to work on the small company’s CRISPR-enabled synthetic lethal cancer drug discovery platform.
Chi-Med and Beigene announced a partnership to test combinations of a couple VEGF receptor inhibitors with Beigene’s PD-1 inhibitor for solid tumor cancers.
Roche acquired Stratos Genomics to advance its development of a nanopore-based DNA sequencer. Terms weren’t disclosed.
Moderna struck a deal with Luxembourg-based CordenPharma to provide lipid excipients for delivery of its SARS-CoV-2 vaccine candidate. Corden, a supplier to Moderna since 2016, said it will provide “large scale volumes.”
Regulatory Action
Regeneron Pharmaceuticals won clearance from the FDA to expand the label for its IL-4 and IL-13 inhibitor dupilumab (Dupixent) as a treatment for kids ages 6-11 with moderate to severe atopic dermatitis.
The FDA cleared Takeda’s brigatinib (Alunbrig) as a treatment for ALK-positive forms of metastatic non-small cell lung cancer.
Worth a Listen
Epidemiologist Marc Lipsitch on Whether We’re Winning or Losing Against COVID-19. 80,000 Hours Podcast
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Today’s guest on The Long Run is Nina Kjellson.
Nina Kjellson, general partner, Canaan Partners
Nina is a general partner with Canaan Partners on the West Coast.
Her investing style leans toward high science, which you can see in portfolio companies like PACT Pharma, a company developing neoantigen targeted T cell therapies for cancer, Tizona Therapeutics, a targeted antibody developer for cancer, and Vineti, a software platform to help cell and gene therapy companies manage their supply chains and scale up with these intricate medicines.
Nina is also a powerful advocate for helping women advance in biotech. She has developed her own platform with a podcast called Women who Venture, or WoVen, to amplify voices of strong women leaders. She organizes meetings for help women executives extend their networks.
As a successful VC who sits on a lot of company boards, she’s in a position to open doors and uplift people. She takes that role seriously.
I saw Nina do this at close range, when she joined me last summer on a trip to climb the highest peak in Africa, Mt. Kilimanjaro, as part of a group fundraiser for the Fred Hutchinson Cancer Research Center.
It was very important to me as the team captain to assemble a diverse, gender balanced group. Nina played a key role in helping make that happen.
Now before we start, this interview was recorded in early March. I was already sheltered in place, and a bit anxious, but we didn’t talk about the pandemic. I’ve held onto this conversation for a couple months to squeeze in more timely news-oriented episodes of The Long Run. This episode is more in the typical format of this show, which runs longer and discusses the guest’s personal journey.
I know everyone’s busy, but I think we need to have these kinds of deeply humanizing conversations to understand each other better as people, not just one-dimensional professionals who do X or Y.
I hope you enjoy the conversation.
Now, please join me and Nina Kjellson on The Long Run.
Otello Stampacchia, founder, Omega Funds (illustration by Praveen Tipirneni)
On April 17 — seemingly a long time ago — I wrote about what steps would have to be taken in order to (cautiously) “reopen” US states from their various lock-downs and stay-at-home advisories.
The piece tried to focus on the “how”, versus the “when”.
By the early days of May, most states had begun re-opening, and now all 50 states are well along in the process. So how are we doing in terms of following through on the actions that we know can mitigate the spread? What can we expect in the next few months? How should we behave?
This follow-up piece will be divided in the following sections (I have been told I try to cram too much into everything I write, with excessively long sentences, such as this one: I am hoping making this a bit more structured might make easier to follow my writing. Fingers crossed.):
Medical Definition of “Second wave”: A phenomenon of infections that can develop during a pandemic: the disease infects one group of people first. Infections appear to decrease. And then, infections increase in a different part of the population, resulting in a second wave of infections.
Public health officials and policymakers are concerned about a “second wave” of the pandemic hitting, potentially with even greater force than the first. Most are focused on a timeline towards the fall / end of the year. Some of the (justified) concerns are centered about a renewed surge of COVID infections coinciding with a flu season of unknown severity, taxing healthcare systems even further. That would be bad news for sure.
Yogi Berra: “It’s tough to make predictions, especially about the future.”
Here is the really bad news: in my opinion (and being mindful of the quote above), we are going to see a continuous, substantial increase of infections and fatalities in the US starting (very, very roughly) early / late July, if not sooner. Quite likely, by August / September we might revisit the peaks of daily confirmed infections and fatalities we saw in April.
What am I basing such a pessimistic forecast on?
This bears repeating: this is a very infectious, new virus that spreads at an exponential rate. Please re-read my previous Timmerman Report articles discussing R0 and the rapid spread of the virus. I am still absorbing the visual shock of the historical New York Times front page for Sunday May 24, which listed names of roughly 1% of the dead across the US, in a stunning display covering several pages. I spent hours on its interactive version on the website. Scrolling through it, I was reminded that the total US death toll from COVID-19 was about 100 on Mar. 17. We are now on track to exceed 100,000 deaths by early next week, just as we are “re-opening”. It is easy to forget that.
Let that sink in for a second: 100,000 fatalities in two months.
Let’s also keep in mind that, as of right now, only / roughly a few percentage points, if that, of the US population has been exposed to the virus. Exposure rates appear to vary greatly from state to state, of course (with NY likely being the most exposed): that said, I would be personally shocked if it turns out that more than 3-5% of the US has been exposed as of right now. The problem is we will not know that for sure for a while yet.
For those of you who think this virus will stop circulating altogether over the summer: I would like to point you towards the rapidly escalating pandemic in South America, particularly in Brazil. Rio de Janeiro has had temperatures in the 60s-70s degrees Fahrenheit for a while and cases have spread very fast there for weeks now. Yes, we should expect some R0 reduction (driven by higher humidity, sunlight, etc.), but again it will not magically disappear.
Another important statistic: back in March, two-thirds of US newly-confirmed cases were concentrated in just five states: NY, NJ, MA, CA, IL. The rest was mostly in MI and WA, which were also hit early (and very hard in the case of MI). Those states reacted early and with the strongest distancing guidelines and measures. At great economic cost, for sure. But they saved lives.
Last week, two-thirds of newly confirmed cases came from the other 45 US states: the virus has continued to spread widely across the country during those two months.
It’s not “just a New York thing” because the city is dense and has subways. It is accurate that highly dense urban areas were hit first and (so far) hardest, and that high population density “weaponizes” the virus. But other, less dense and even rural areas of the country are not “immune”. There is no existing immunity. The virus will keep spreading.
If we do not beat this virus everywhere, we won’t beat it anywhere.
Also an important contributor to my pessimism: since the virus started spreading in Jan / Feb in the US, we did not ramp up PCR (nucleic acid) or serological (antibody) testing to a sufficient scale to identify early on asymptomatic / symptomatic infection spreaders and thus limit the reach of the virus. There is an understandable supply scarcity of reagents needed to perform the tests at a scale that few previously imagined. In addition, a number of antibody tests have been faulty (to the point of being useless).
That said, a country with the technological prowess of the US should have figured this out months ago. We did not.
A final component leading me to my pessimistic predictions: there is no way we can beat this pandemic without clear, transparent, coherent communication by authorities, followed by the citizenship’s broad compliance with behavior modifications to reduce transmission. Assistance from drugs and (hopefully, one day) safe and effective vaccines will come, but we need to limit the damage and survive before the cavalry arrives.
Needless to say, almost all of those components are missing or confusing at the very least. The sacrifices of so many healthcare and other essential workers and the many people sheltering at home away from their families are potentially being squandered when a minority of the population refuses to comply. When enough come together in close proximity indoors, the math clearly shows that it increases the risk of “super spreader” events like the choir practice group in Skagit County, Washington.
As I am sure you have seen on the news from Memorial Day weekend, multitudes of people have celebrated the “end” of the lock-down and the long weekend by frolicking in large assemblies in swimming pools, beach town sidewalks, across the US. No masks to be seen. Social distancing nonexistent. This is unconscionable.
I want to express just how important those non-pharmacological interventions are by discussing briefly the Japanese experience and making a “compare & contrast” exercise between theirs and other countries’ response to the pandemic.
Japan has surprisingly managed to avoid a catastrophic outbreak, contrarily to most pundits’ predictions. It has the oldest population in the world, with very densely populated cities, and large public transportation networks used by most citizens. It also has very extensive travel links to China, so it is impossible to think that it was not seeded early in the pandemic.
Japan did not perform mass testing of its citizenship, like South Korea, nor did it impose restrictive lockdowns (hairdressers and restaurants have been kept open: for those who know me, I do care way more about the latter than the former, but not everybody is as follicularly challenged as I am). According to Worldometer’s dashboard, Japan has had only ~800 fatalities to date due to the virus. This is a miracle that cannot be overstated and that everybody is quite puzzled by.
Very early on (in late January), Japan activated its public health centers, which are heavily staffed with nurses trained in contact tracing. So, no fancy contact tracing app like Korea or Singapore, but old-fashioned shoe-leather epidemiology performed by an army of well-trained individuals. People with suspected symptoms, or in contact with people with suspected viral infections, were first screened for other respiratory viruses (influenza, RSV, etc.) and only if tested negative, were then tested for COVID, maximizing the use of scarce COVID test resources. Quarantines of infectious clusters were also sensibly put in place, like in Korea.
A huge emphasis was also put on behavior modification of the entire population to minimize infection spread: preventing one cluster of patients from creating another cluster.
To that extent, government and health authorities spread early, broadly and effectively a very simple message: “avoid the three Cs!”: 1) Closed spaces (with poor ventilation), 2) Crowded places (with many people nearby) and 3) Close-contact settings (such as close-range conversations).
Those recommendations are so sensible — and easy for people to remember — that there is no need, hopefully, to elaborate further. That said, they were given very prominently, very early, and very clearly, by the entire government apparatus as a single voice.
Finally, and essentially, the population broadly complied. Everybody wears masks at all times when walking outside. Nobody speaks loudly (or at all, actually) on their phones on subways / other means of public transportation / while walking down the street (yes, that was already the case in Japan even before the pandemic: I am hoping to sneak in some permanent behavioral modifications also in the US for after the pandemic).
There are probably other factors helping protect Japan from the worst. The population has arguably the lowest rate of obesity and diabetes in the developed world, but again that should be counter-balanced by the demographic skew towards elderly citizens. Who knows. But I am sure there are lessons to be learned here. And that you, non-mask-wearing runners in downtown residential Boston, should learn (that is where I live).
To summarize:
First, a preamble: this gets into some cultural aspects that might be controversial. You might want to skip this next section if you find it disagreeable and go to PSA (Part II) with some more concrete suggestions. Those should be objectively useful, I hope.
According to the CDC, 45% of the US population has pre-existing conditions (diabetes, hypertension, obesity) that appear to increase severity and fatality outcomes when infected with the virus. This will certainly worsen the already dismal count of blood and treasure (or lives and livelihoods) that the country will have to disburse to get on the other side of this pandemic.
What I would like to discuss, and I am on very thin ice here, as I am a guest in this amazing country, is the role of pre-existing convictions in increasing infection spreading. A number of people in the US reflexively dismissed, early on, warnings coming from science journalists and healthcare / government about the virus. Some of that, certainly early on, might be based on partisan positioning, some of that might be due to a narrow / misguided interpretation of the “American exceptionalism” that seemed to think “it can’t happen here, because nothing like this has in anyone’s living memory.” (I want to state for the record that I do truly believe that this is indeed an incredible country). It might be also hard for people to truly understand the implications of a tragedy that has not yet affected them / their region. However, the virus does not care. It follows a biological imperative to reproduce and spread itself.
Equally, we need to face this pandemic as a challenge for us as a species, not as something to face as a jigsaw of disparate strata of society (or regions) with different socio-economic status, education, access to healthcare, etc. Again, the virus does not care. You should. Your behavior affects others. They might be more or less privileged than you, might be younger or older, it does not matter.
Let me briefly introduce Ayn Rand, celebrated author of “The Fountainhead” and “Atlas Shrugged.” Rand was the founder of a philosophical system named Objectivism (which I am not going to get into, but let’s say succinctly she was not the biggest champion of socialized medicine or collectivism, and she’s become a kind of hero to modern libertarians in the US).
Even Rand, skeptical of the role of government and a champion of individual rights as she was, discussed pandemics as situations where society (as a whole) needs to respond together for the benefit of the herd versus the narrow benefit of the individual. According to Rand, knowingly or negligently subjecting another person to infection with a deadly pathogen falls under the category of the initiation of physical force (like dumping toxic waste on another person’s property).
She makes the illustrative example of “Typhoid Mary,” an asymptomatic carrier of a typhoid fever. She was released from quarantine in 1910, but repeatedly broke her promise to stop working as a cook and ended up spreading the disease further. She eventually returned to quarantine (and spent decades there on an isolated island). It appears to me this is a case that pretty clearly falls under the governmental function of protecting individual rights (not to be infected by others).
This pandemic needs to be tackled as a societal response instead of an individualistic response. With a science-driven, probabilistic approach and using statistics instead of basing ourselves on pre-existing positions and convictions based on whatever politics we might belong to.
You will find below some suggestions for behaviors that might help identify different susceptibility risks and factors this summer as things re-open. These guidelines are not perfect, and, to quote Harvard Business School, “it depends” on your circumstances which of them are more relevant to you. The corollary to that, is that the trick is to know what it depends on. The important part is to understand that this is not a set of binary recommendations. Risk is on a sliding scale and different factors have different importance depending on your personal situation.
You are probably familiar with the known individual risk factors: age and pre-existing conditions are still the single largest contributors, as far as we know. I personally believe a medical history of inflammatory disorders (asthma, etc) also increases risk, but it is hard to quantify that or to find hard data on that. It stands to reason, though, since the single largest driver of fatalities appear to be driven by a hyperactive response of the immune system to the virus.
It is also very, very important to consider the sheer volume of virus (size of the viral “inoculum”) you are potentially exposing yourself to with your behavior: time spent in enclosed spaces, with a lot of people for long-term interactions, are very, very risky, obviously, especially if containing younger individuals who might have had promiscuous contacts with a large number of people beforehand, and might well be asymptomatic.
However, even open spaces are risky if in close physical contacts (handshakes, hugs, etc) with multiple parties. For example, we are learning from parts of developing world, like India and Brazil, that even belonging to a younger demographic does not protect individuals from severe prognosis and possibly fatality: so population density / inoculum size could outweigh young age / lack of pre-existing conditions.
Even the amount of physical distancing is relative. Say the person running / biking across from you happens to be a super-spreader, breathing heavily, with a tailwind at his back and close to you? It is impossible to calculate the risk. It is probably not 100%, but it is not 0% either. If you are young, vigorous, with an impeccable immune system (Are you? Really? How do you know?), and avoid touching your face and wash your hands when you are back at home after the run, your immune system is likely to beat this since you are probably not going to be exposed to too many copies of the virus. But you still could become an asymptomatic spreader and nobody would be able to check that.
So, it’s great to walk outside and exercise, but unless you live in a desert with nobody around for miles, always carry a mask (and wear it in case you come across other individuals / groups), stay as far apart from other people as possible, avoid physical interactions unless you have isolated with these individuals for weeks, and don’t talk loudly on the phone without a mask while walking (or, ideally, ever).
This pains me personally as a Southern Italian, but again there are some lessons here from the Japanese: no touching, no hugging / kissing on the cheeks, no handshakes, no buffets, we should think of individually-wrapping snacks and cookies. All those behaviors limit infectious disease spreading. Civilizations in the Middle East and elsewhere have developed religious precepts prohibiting the consumptions of certain foods, mainly to avoid diseases.
The whole world is going through this tragedy like us: we should all learn from other experiences. The only thing that might move faster than this virus is information. And we all have a lot of things to learn.
Follow Otello Stampacchia on Twitter: @OtelloVC
This article expresses the personal views and perspectives of the author. The views and perspectives expressed here do not necessarily represent the views or perspectives of Omega Fund Management, LLC or any officer, director, partner, member, manager or employee of Omega Fund Management, LLC or any of its affiliated entities.
Luke Timmerman, founder and editor, Timmerman Report
We all woke up Monday morning and saw an encouraging headline.
Then things started to go downhill.
To recap, Moderna, the Cambridge, Mass.-based messenger RNA therapeutics and vaccines company, provided a snapshot of preliminary data from its Phase I trial of a SARS-CoV-2 vaccine candidate, being tested in collaboration with Tony Fauci’s crew at the National Institute of Allergy and Infectious Disease.
From the first 45 volunteers who got this vaccine candidate, data from eight patients at the low 25 microgram dose and others at the mid-level 100 microgram dose were evaluated with 43 days of follow-up from first shot. These eight patients have developed neutralizing antibodies that have “exceeded the levels seen in convalescent sera,” according to the company.
Not a bad way to start the day, I thought, over a morning cup of coffee.
Neutralizing antibodies are what we want to see. But eight patients out of 45? And what level of neutralizing antibodies compared with what level in convalescent sera? And these patients were followed up for how long?
Did we need to hear this interim report – a half-baked dataset prematurely press-released, let’s get real – or would we be better off waiting for a few weeks for the data to mature in a way that might stand up to scientific scrutiny in a public forum?
From the company statement, we don’t know how many neutralizing antibodies were counted, or what the baseline figure was that Moderna used for comparison from convalescent plasma in recovered patients. We don’t know, and can’t know at this point, how long-lasting the neutralizing antibodies are. Not enough time has passed. We don’t know if neutralizing antibodies are produced at consistent levels across age groups, or whether they are reaching sufficient concentrations for the elderly who need it most. We have no idea if these antibodies are enough to protect people from exposure to the virus.
We certainly don’t know the optimal dose – but we better pray it’s as low as possible in order to stretch out the limited supply to vaccinate as many people as possible if the thing works at all.
These questions can, and will, be answered. In time.
By jumping the gun, this press release raised more questions than it answered.
So what actually did happen after the Monday morning release?
Moderna stock boomed. For a normal product in a normal time, nothing would happen when a company released a half-baked Phase I interim snapshot like this. A few specialist biotech investors would roll their eyes and move on. But on May 18, with this all-important vaccine candidate, Moderna’s valuation rocketed to the stratosphere of $30 billion. It’s an unheard-of valuation for a biotech company with no products that have won FDA approval, no products that generate revenue.
The euphoria didn’t stop with Moderna. The entire US stock market was lifted – I repeat, the entire US stock market was boosted by a press release that couldn’t pass Medical Evidence 101 and that few people understood, but which offered a faint early whiff of success.
Moderna struck while the iron was hot, raising $1.25 billion in a public offering.
Yes, we are in bizarro-land. Within 24 hours, people started asking questions about what just happened.
The questions go on and on — and there are plenty that aren’t even being asked yet by enough people.
For starters, we don’t know the right dose. If it works, can we make enough lipid nanoparticles to deliver the mRNA constructs for the 4-5 billion people worldwide in need? How about the medical-grade sterile glass vials needed for the fill & finish steps? Who’s managing the whole supply chain? How will all of that supply-chain calculus shift if the 50 microgram dose is sufficient, or whether we have to go as high as 100 micrograms or 250 micrograms to get the necessary immunity? In a supply constrained situation, who will get the limited supplies of vaccine first? Who will decide that, and how will they do it?
To give Moderna the benefit of the doubt, they know all this. Maybe they know that every penny of the $1.25 billion they just raised is legitimately needed to manufacture this vaccine at global scale. Maybe they need the money RIGHT NOW to build up capacity, before we even know if it’s a winner. I’m willing to accept that. I want to see the company succeed.
The problem is we are not operating in a normal environment. Biotech plays a dangerous game when raising hopes too early.
Look at what else happened this week. The Moderna “success” inspired imitators. Other companies looked at the rising indices and said it was time to cash in. Some were inspired to pump up their own programs, issue truly ridiculous press releases, then turn to gullible reporters and cynical provocateurs, pumping up what in all likelihood is vaporware.
This isn’t the time for the usual biotech BS. We have about 94,000 dead Americans as of this writing, and another 1,000-2,000 deaths adding up every day. We’re looking at the very real prospect of a big second wave as we re-open the country. About 38 million people have lost their jobs in the past nine weeks. Millions more are going hungry, uncertain about their income, and desperate to get their lives back on track.
We are all fatigued. We are desperate as a people for some good news. We are counting on the scientific enterprise – academia, government and industry together – to rise to the occasion.
Industry has a chance to show the world what it is all about.
At a time when average citizens are drowning in propaganda and don’t know what to believe, and when trust in biopharma is at an all-time low, biopharma must level with people and tell the world it has delivered the goods only when it truly has the goods.
Now, on to other news in biotech this week.
Science
Science Features
Testing
Roche said on May 19 that its serology test for antibodies against SARS-CoV-2 was installed and live at 20 commercial and hospital labs in the US and on pace to scale up another full order of magnitude in the next several weeks – creating capacity for millions of tests a week. This test has 99.8 percent specificity and 100 percent sensitivity for SARS-CoV-2, with no cross-reactivity to other coronaviruses.
Investigations
Mental Health
Annals of Misinformation
California biopharmaceutical company claims coronavirus antibody breakthrough. FOX News. May 21. This story stirred false hope. It’s full of outrageous hype, mixed in with the occasional copy-and-pasted comment from a credible person to give it the appearance of credibility. Yet the report fails to say the study in was done only in a cell line in the lab, not even an in vivo preclinical model. The evidence is a million miles from a controlled clinical trial. The story also failed to point to the corporate stock promotion angle, and the absurd $1 billion market valuation. This is the kind of bad behavior that is too often rewarded, and which gives biopharma a bad name.
Doctors such as Bob Wachter of UCSF are doing their level best on social media, in defense of rational inquiry, and to try to make facts “go viral.” See WSJ coverage.
Data That Mattered
ViiV Healthcare, the HIV company majority owned by GSK, said researchers stopped a 4,600-patient global HIV prevention study early because of positive results. Subjects who got ViiV’s injectable cabotegravir once every two months appeared to have a lower chance of getting HIV than patients who got the current standard of care for HIV prevention — daily oral emtricitabine/tenofovir disoproxil fumarate. The trial technically hit its primary endpoint, powered to show non-inferiority, while approaching superiority, pending final analysis, the company said.
Gilead Sciences and its partner, Galapagos NV, said they passed a Phase 2b/3 study of the JAK1 inhibitor filgotinib in a randomized study of 1,348 patients with moderate to severe ulcerative colitis. The high dose of 200 milligrams met the primary endpoint of clinical remission at Week 10, and the effect held up for a full year. The lower dose, 100 milligrams, didn’t reach the goal of clinical remission at 10 weeks.
Business Model Disruption
Hospitals Knew How to Make Money. Then Coronavirus Happened. NYT. May 20. (Sarah Kliff)
Big Science
The CDC said it’s planning to run a set of big serology studies in as many as 325,000 people in 25 metro areas to see how many have developed antibodies to SARS-CoV-2. The agency’s failures to this point, and the invisibility of its leadership in a time of crisis, have shaken my confidence in this great public health agency to the core. But it’s never too late to turn things around and start the long, hard work of rebuilding trust. Let’s let the scientists gather the data, analyze it to the best of their abilities, and communicate it to the public without fear or favor.
Financings
Regulatory Action
Waltham, Mass.-based Deciphera Pharmaceuticals won FDA clearance to market ripretinib (Qinlock), a kinase inhibitor for patients getting fourth-line therapy for gastrointestinal stromal tumors (GIST).
Bristol-Myers Squibb won FDA approval to market the combination of its anti-CTLA-4 and anti-PD-1 antibodies for patients whose tumors express at least a little PD-L1, who don’t have EGFR and ALK mutations, and who are getting their first round of treatment for non-small cell lung cancer that has spread.
Boulder, Colo.-based Clovis Oncology won FDA clearance to start selling rucaparib (Rubraca), a PARP inhibitor, as a treatment for men with BRCA1/2 gene mutations and have prostate cancer that has spread and no longer responds to hormone deprivation treatment, and androgen-receptor therapy, and taxane chemotherapy.
The FDA warned the Seattle Coronavirus Assessment Network, an academic collaborative group formerly known as the Seattle Flu Study, to stop its surveillance programs, which include at-home tests. (See NYT report).
Roche / Genentech won FDA clearance to market the PD-L1 inhibitor atezolizumab (Tecentriq) as a first-line treatment for patients metastatic non-small cell lung cancer, if they express PD-L1 and don’t have EGFR or ALK mutations.
AstraZeneca won FDA clearance to market its PARP inhibitor olaparib (Lynparza) for patients with metastatic prostate cancer who have homologous recombination repair mutations. An estimated 20-30 percent of men with prostate cancer that resists chemical castration therapy have this form. On the same timeline, Foundation Medicine won approval for a companion diagnostic test to identify those patients.
Chiesi Global Rare Diseases, a Boston-based unit of the Italian company, won FDA clearance to sell deferiprone (Ferriprox) to treat iron overload disorders in thalassemia patients.
Deals
San Diego-based Vividion Therapeutics collected a $135 million upfront payment from Roche, as part of a partnership that allows the big company access to its proteomics screening platform and library of small molecules aimed at E3 ligases, as well as a range of oncology and immunology targets.
New York-based Aetion formed a research collaboration with the FDA to analyze real-world data for the coronavirus pandemic.
Cambridge, Mass.-based Surface Oncology formed a partnership with Merck to test its CD39 directed drug candidate in combo with Merck’s pembrolizumab (Keytruda) for solid tumors. Surface also raised $30 million via an at-the-market financing.
Cambridge, Mass.-based Synlogic said its partnership with AbbVie has ended.
San Francisco-based Atomwise, the company developing AI for small molecule drug discovery, said it’s participating in 15 research collaborations against COVID19.
Personnel File
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David Shaywitz
Come for the tech, stay for the culture.
That seems to be the hope of most digital champions inside large pharma companies. These executives hope to instill in their organizations not only important new capabilities, but also a “Silicon Valley” mindset, an innovative spirit characteristically associated with tech entrepreneurs.
The reality, of course, is more complicated; pharma executives – and to some degree, all of us, perhaps – tend to imagine ourselves as more audacious, more creative, more risk-embracing than in the end we actually are.
It’s not just a big pharma phenomenon; when I was in venture capital, I saw the same tendency emerge whenever a startup on whose board I served conducted a search for a new executive. Typically, the headhunter would be asked to think expansively, and identify a broad range of talent, including less traditional candidates. Yet in the end, the consensus generally landed on someone who felt comfortable and familiar, a leader who was perhaps less interesting than some candidates, but also viewed as less risky, and more of a known quantity. Flirt with risk, marry stability.
The challenge facing innovators who want to bring change to pharma was captured nicely by Craig Lipset, a digital health pioneer at Pfizer. On a recent Tech Tonics episode, Lipset told Lisa Suennen and me that the three unofficial rules for anyone pitching digital health inside the company was that it needed to be safe for patients (of course); not threaten anyone’s job; and not threaten to land the company in court.
Venture capitalist Josh Kopelman of First Round Capital (early investors in Uber, Square, Flatiron Health) captured this tendency perfectly on an April episode of Patrick O’Shaugnessy’s always excellent podcast, noting that one of the best opportunities for startups is to leverage (arbitrage) the intrinsic risk aversion of large corporations.
Kopelman described an experience he had early in his career, after he sold a company he started to eBay, and he got to know the big online marketplace. eBay, he said, noticed that payments on its platform were being consummated by the startup, PayPal, and eBay wanted to handle those transactions itself, so that PayPal wouldn’t be able to take a cut.
So eBay set up a joint venture with Wells Fargo called Billpoint, in theory a formidable partnership between a top bank and the top online market. But the reality, Kopelman said, was that at the initial product meetings there were nearly as many lawyers in the room as there were product managers. Digital payments were a fairly new space. There was a lot of grey area. Wells Fargo in particular couldn’t afford to jeopardize its business by skating too close to the regulatory line.
PayPal, on the other hand, embraced the risk; when PayPal filed its S-1 form as it prepared to go public, Kopelman says, the company disclosed it was under investigation by a slew of state attorneys general. PayPal’s willingness to take on this risk – and do so successfully – were key to its ultimate success (including its post-IPO acquisition by eBay for $1.5 billion).
Kopelman also cites the example of Uber, specifically calling out controversial founder Travis Kalanick for his fearless approach to regulatory grey areas.
It’s amusing to contemplate how innovators like Kalanick and the PayPal leaders would fare within big pharma, an industry that is highly regulated to begin with, and then has arguably exacerbated these challenges through a culture that reflexively resists innovation through a deep, shared belief that “regulators will object.”
Moreover, these concerns are not unfounded; as much as FDA leaders such as former Commissioner Scott Gottlieb, current Principal Deputy Commissioner Amy Abernethy, and the head of the Oncology Center of Excellence Richard Pazdur have passionately championed innovation and flexibility, this attitude is still not universally embraced (to say the least) by the reviewers responsible for most of the heavy lifting inside the agency.
Other qualities of startup founders described by Kopelman might also struggle in the context of a large pharma. For example, Kopelman highlights the value of betting on people rather than a specific idea – he views a founder’s initial product proposal as more of lens to understand how the founder makes decisions and processes signals than it is a detailed plan describing what’s actually going to happen. Similarly, Kopelman says his firm tends not to be “thematic” investors, contending that by the time something bubbles up as a discrete theme, it is effectively too obvious, and something everyone is doing. (I entirely agree; however, most VCs, and virtually all corporate VCs, are explicitly thematic.)
Again, anyone who’s tried to get buy-in to anything in a large pharma appreciates immediately the contrast; extensive and excruciatingly deliberative processes generate themes and areas of focus (leading invariably to oncology, immunology, and rare disease these days), and even individual early-stage projects that hope to be resourced must be described and mapped out in exaggerated, if not comical, precision. (Then again, I’ve heard the same said from academic researchers applying for NIH research grants.)
Several attributes of successful founders cited by Kopelman can and — I’ve often insisted — should be adopted by pharmas. For example, Kopelman emphasizes that you can’t build a great platform without first having a killer app; while he was making an even deeper point, one simplistic way to think about this is that pharmas contemplating data science would do well to consider what very specific and concrete problems they hope to solve, and then focus on developing a pragmatic solution that clearly works.
Instead, there’s an unfortunate tendency to spend huge amounts of time and resources investing in and developing a massive data and analytics platform in hopes that it will someday prove useful – despite considerable experience to the contrary. Startups are often critiqued for creating an elegant solution in search of a problem. The related mistake large companies tend to make is architecting a vague, expansive solution they hope will solve every problem, presumably reflecting the promises required to secure funding in complex, matrixed organizations.
Kopelman also emphasizes the importance of getting product feedback early and often – an idea long-advocated by “Lean Startup” guru Steve Blank. If instead, you decide you know from the outset exactly what’s needed and work head-down until you’ve perfected your product, the product you emerge with may be elegant but totally off base.
Bottom line: A large, legacy pharma is never going to have the agility of a startup, and compelling new ideas will always struggle for traction given the intrinsic risk-aversion of massive multinational corporations. Many (I assume most) new health technology concepts will have to be developed and proven out in a less constrained environment first. The innate skepticism towards tech solutions by most people in the trenches at big pharmas remains exceptionally high, and the meticulously-structured, rigidly-articulated way most work is done in pharmas (and other large companies) tends to be poorly aligned with the agility and improvisation new tech product development often requires. The most exciting and most innovative opportunities at the interface of health and tech almost certainly remain in the startup space.
Yet pharmas desperately need better technology, and promising technologies can benefit from the scale, experiences, and established relationships of a large pharma.
To succeed, pharmas need to scope projects in a fashion that’s relentlessly focused on tangible outcomes that matter, results that are palpably experienced by users. Uber was adopted because you press a button and a ride shows up; Google was adopted because you type a query and the answer shows up; Amazon was adopted because you place an order and your purchase shows up. Simple. And effective.
A successful technology in pharma doesn’t need to be all things to all people. Paradoxically, it’s this very need to gain the approval of multiple stakeholders that often results in poorly-scoped, ill-fated projects. Instead, at least initially, the tech solution should brilliantly meet the substantive needs of an individual stakeholder, and accelerate an important process. In actually delivering on this promise – in delivering tangible delight and utility for somebody in the organization — a technology champion in pharma can do more to change a company’s culture and attitude than all the Successories posters, inspirational corporate messaging, and aspirationally hip (tucka-tucka) corporate hackathons combined.
Ruth Etzioni, Full Member, Division of Public Health Sciences, Fred Hutch Cancer Center
When I was in college, everyone wanted to major in psychology. I signed up, but switched out after only a few weeks.
Why? Well, the more I read, the less I seemed to know. Psychology, after all, is an inexact science.
I sought refuge in the exact worlds of computer science and mathematics. Those courses led me to build a career in statistics, the science of uncertainty.
For the past 20 years, I have partnered with colleagues in the Surveillance Program at the National Cancer Institute to explain why cancer rates go up and down from one year to the next. We can do this work because we know how many cancers cases – and deaths – there are every year. Our analysis is grounded in hard data that is reliably and consistently collected.
For years, an information infrastructure has existed to funnel data from pathology laboratories to local cancer registry offices, who interface with hospitals and doctors’ offices to catalog each cancer case.
I always appreciated the NCI’s Surveillance Program for its tireless work to bring us the numbers and double-check to make sure they are right. Reliable data is the bedrock of our analysis.
I appreciate the fundamental, unglamorous data-checking and cleaning work even more as I look at the state of COVID-19 surveillance. I wonder about the data we are relying on to track cases and deaths, to prepare for the future, and to make critical policy decisions.
It seems to be a house of cards.
We know that the reported daily tally of cases is hopelessly wrong. Cases can only be confirmed if they are tested by one of the reliable RT-PCR diagnostic tests that uses samples from nasal swabs. From the beginning, the US has struggled with a shortage of these tests. So the number of cases reported on any given day is determined not only by the spread of the virus itself, but also by the availability of tests. Many people don’t know that they have the virus, and of those that exhibit symptoms, many who want a test can’t get one. Access to reliable diagnostic testing varies widely over time and across locations. So the meaning of “number of confirmed cases” differs depending on the date and where you are.
The number of cases simply can’t be interpreted without understanding the state of testing. But, even if we knew how many tests were being done each day, this would not be enough. Just figuring out how many symptomatic cases there are would require how many of these people actually present themselves to a healthcare provider, asking for a test. We don’t have the data to understand this, but it surely varies depending on the date and where you are. And this doesn’t even get at how to account for asymptomatic cases.
New antibody studies can provide us valuable snapshots in time, if conducted with a reliable blood test, and with a rigorous random sample of the population – not just people who volunteer because they think they might have COVID-19. These studies may help us to reconstruct how many surviving individuals had the virus, whether they know it or not, and this could potentially help correct our daily tally of cases, retroactively. But, results of these studies are subject to debate and cannot be taken at face value.
As an example, an antibody study conducted in Santa Clara county was roundly criticized, because the underlying test produced false positive results that could have accounted for many of those told they had antibodies. Still, as these studies accumulate, we will likely gain a better idea of the foothold the virus has established in the population.
Given that we are in a pickle when it comes to counting cases, we might turn to harder data, like the numbers of hospitalizations or deaths. Hospitalizations are limited by system factors that have nothing to do with disease burden. But deaths? Surely deaths are more reliable. You are either alive or not, there’s no squishy subjective judgment at work, and no faulty test to wonder about. Right?
It’s not quite that simple. Until recently, it seemed that most people trusted the counts of COVID-19 deaths reported around the world. Even as China kept changing its definition of what constituted a coronavirus case, its death tally was not questioned. On Mar. 19, breathless headlines declared a “grim milestone” – Italy’s COVID-19 death toll of 3,405 had exceeded China’s reported death toll of 3,249. Reported deaths from China and Europe became the basis for models that predicted the scale of the epidemic in the US and informed social distancing policies across the country. I have written in these pages about the models and warned about how they are being miscommunicated, but I never mentioned my concerns about the death data which drives them.
I can’t remember when I first became skeptical about the deaths. But it was definitely before Mar. 19 because I recall reading the grim milestone headlines on that day and wondering why everybody believed the official number from China. Maybe it was after I personally asked a Chinese statistician colleague now living the US who said that in her opinion the numbers from China were made up so as to imply a fatality rate that was between one and two percent.
Still, I told myself, our surveillance systems in the US would never allow this to happen here.
Recently, important data has emerged that strongly suggests that we are undercounting COVID-19 deaths in this county – by tens of thousands. The basis for this is in the numbers; the official count of deaths due to the virus is far below the excess in the overall number of deaths compared to what would be expected at this time of the years based on data from the last few years. On May 13, Nicholas Kristof of The New York Times reviewed the numbers and concluded the COVID-19 death toll had already exceeded 100,000.
The counting of excess deaths to shed light on cause-specific mortality is not new. We do it to assess our progress against cancer all the time. Sometimes it is hard to know whether a person with a history of cancer died of, or with, their disease.
Suppose a breast cancer patient dies of a heart attack. Was that death due to cardiovascular disease or to the chemotherapy drug she was given and that is known to weaken the heart? Death certificates can only do so much to tell us, and generally focus on what we call the proximal cause of death, which in this case would be cardiac arrest and not cancer. To avoid undercounting deaths among patients with a specific cancer, we can tally the excess deaths among individuals with a history of that cancer and stack the result against the deaths among comparable individuals (e.g. same age and race) in the population. This technique, developed in the 1950s, is called relative survival. It has been shown to work really quite well for many cancers.
In the case of COVID-19, which can cause death via a diverse array of awful disease symptoms, this makes a lot of sense. Death certificates list first the proximal cause of death – what actually caused you to die.
We know COVID-19 can kill you by suffocating you, giving you a stroke, causing your heart to stop beating or your kidneys to fail. This is what gets listed first. If COVID-19 is known or suspected, this is recorded as a second, third or even fourth cause.
My cousin, an all-knowing MD and research scientist, who directs a critical care unit in Denver, tells me that even when patients die of respiratory failure, the best known COVID-19 cause of death, the proximal cause is listed as Acute Hypoxemic Respiratory Failure (AHRF) and the next cause is the condition, Acute Respiratory Distress Syndrome (ARDS). COVID-19 infection only appears third – so long as the patient is known to have the virus.
Patients not tested, who don’t make it to the hospital, or whose deaths masquerade as being from strokes or heart attacks may not even have COVID-19 listed as an underlying cause in the death certificate.
So it makes sense to look beyond the cause-specific tally to the overall mortality data.
I think we can trust the overall number of deaths. It is possible that heart attack or stroke deaths may have gone up a bit this year even in the absence of COVID-19. And we certainly don’t want to count deaths that were truly due to these causes as COVID-19 deaths just because a person had a confirmed diagnosis and then had a fatal heart attack or stroke.
But, on balance, if we look at overall all-cause mortality rather than cause-specific deaths, we are left with the inescapable conclusion that the official tally of COVID-19 deaths in the US is way low. When you look at all-cause mortality, and make a direct comparison of March 2020 to March 2019, or April 2020 to April 2019, you will see an unmistakable increase in death rates. This is despite various conspiracy theories to the contrary to which I won’t devote any airtime. Let their proponents debate our National Center for Health Statistics.
At this point I hope we are all on the same page about the need to look beyond the official COVID-19 death toll to the cases who don’t get to have the virus listed on their death certificate. But what is happening in some states now is worse; they are changing their definition of a COVID-19 death to only count those for whom the virus is coded as a proximal cause.
I know that this is happening in Colorado because my cousin shared his frustrations about his patients’ causes of death being wrongly recorded by the state. Colorado coroners are objecting, according to a piece run by CBS4 in Denver. A new Scientific American article out this morning cites the Colorado issue.
In Florida, the state forced medical examiners to stop releasing their counts of COVID-19 deaths which were at odds with official state figures, and fired the creator of the state’s COVID-19 data portal, who claims she was terminated for refusing to manipulate the numbers. And in states like Georgia, where COVID-19 can only be listed in as a cause of death in confirmed cases, reducing the number of tests performed will automatically trigger a decline in deaths. Of note: Georgia is no longer releasing data on testing numbers.
We tend to think of numbers as facts and data as absolute; it feels safer that way. Unfortunately, when it comes to COVID-19, it seems that there is no safety in the numbers. When we wake up each morning and check the dashboards of cases and deaths in our local papers or cable TV news, we need to bring a healthy dose of curiosity – and perhaps skepticism – to the table.
If we don’t, we may jeopardize our understanding of the true burden of COVID-19 and compromise our ability to navigate our way out of it.
David Shaywitz
I was recently speaking with a friend of mine, a pulmonologist at a large academic medical center in the Midwest, about his COVID-19 experience. I was especially interested, in the context of iterative experimentation, to learn how his hospital was working on improving the care of COVID-19 patients, especially those in the ICU, which he oversees.
It’s real problem, he said. On the one hand, there are specific initiatives he’s trying to evaluate, in a classic, controlled fashion, so he can figure out if the intervention is effective and should become part of the standard of care.
That’s the goal.
In reality, however, here’s what he says is actually happening: most of the front-line doctors are hearing about the very latest approaches, generally from social media (such as Twitter or medical podcasts), and are trying to immediately apply these methods to their patients. As a result, the care patients receive depends (to some degree) on the specific physician involved, as well as the extent to which that physician has been influenced by other opinionated doctors.
At a recent Boston innovation conference (discussed here), Dr. Paul Biddinger, an emergency medicine physician who leads emergency preparedness at the Massachusetts General Hospital, made a remarkably similar observation. He praised the “unprecedented information sharing” associated with the COVID crisis. But he also expressed concern about the “practicing by anecdote,” and more generally the “temptation to fall off what have been the time-proven methodologies of science.”
The tension here captures an especially distinctive aspect of American medicine, a characteristic beloved by some and lamented by others.
Intrinsically, doctors value their independence, and the opportunity and obligation to be masters of their own domain. This is how U.S. medicine generally works: once doctors are trained and licensed, they more or less can treat patients as they see fit, within a fairly wide band of professional expectations. Care generally isn’t cookie-cutter, and doctors are typically free to offer a range of potential approaches, constrained mostly by what the patient’s insurance is likely to accommodate or reject (and by the hassle of navigating that process).
To be sure, physicians are supposed to stay up to date, and not commit blatant malpractice like treat a strep infection with a blood thinner, say, but there’s a lot of room for individual interpretation and improvisation, which many doctors have embraced, believing it acknowledges the individualized nature of each patient/doctor encounter. Call this the “libertarian” view of medicine.
Contrast this approach with what might be called the “systemic” or “operations” view of healthcare, described eloquently nearly a decade ago by Dr. Atul Gawande in his classic “Cheesecake Factory” New Yorker essay. In this view, while doctors are the ones actually laying on hands, the expectation as well as the mindset is that these providers should be more focused on consistency than individuality; care should adhere clearly to “best practice” guidelines, and while modest customization is permitted, significant deviations should be evaluated deliberately and programmatically, not ad-hoc.
In this view, much of what’s wrong with American medicine is precisely the individual physician’s insistence on acting, well, individually, leading to dramatic variances in care, and often, it is said, the suboptimal treatment of patients. Medicine practiced in this way tends to view physicians as providing the “customer service,” but adhering to master pathways and algorithms. Not surprisingly, many (but not all) doctors in systems like this tend to view themselves as interchangeable cogs and data entry clerks, rather than empowered inquisitive physicians in the Judah Folkman tradition.
The uncomfortable question raised by advocates of a systems approach is whether it promises better care of patients but lacks traction because it challenges the self-esteem of doctors.
The COVID-19 crisis highlights the dilemma. In the unlucky event you were hospitalized with COVID-19, would you want your care driven by a standardized algorithm, rigorously followed, or would you want your doctor to improvise, and potentially apply the latest and greatest – although it might not be so great, and perhaps may reflect little data and instead just the strongly-held opinion of a persuasive, social-media-savvy clinician? Or, it might incorporate a valuable emerging insight, the sort of adjustment that could take a relatively long time to incorporate into a standardized protocol.
On the one hand, most physicians wouldn’t want to practice, and don’t like practicing, in a climate of rigid pathways and defined approaches – that’s not why most doctors went into medicine. Moreover, the physicians who self-select for environments with great autonomy may be more likely to have the imagination or creative insight that the clinicians in more rigid systems lack.
But it’s also possible – especially as more care becomes templated and algorithmic – that the consistency and transparency provided by these systems offers not only a higher average level of care, but also enables a degree of continuous, iterative improvement in care that is far more difficult to achieve in less structured environments.
One worry is that in such a command-and-control framework, you’re potentially relying on centralized planning – on detached, “enlightened” supervisors to suggest modifications, rather than benefiting from the experience and insights of front-line providers, doctors whose imagination and creativity could well be crushed in a system that valorizes provider conformity, and expects providers to perform their job consistently and empathetically, but looks elsewhere for insight and originality.
Historically, the physicians so many of us have looked up to are the brilliant, iconoclastic individuals who figured out on their own a new way to do something. It will be interesting to see if there are future heroes who distinguish themselves by their ability to apply deliberate, iterative experimentation to raise the standards of a system.
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