Today’s guest on The Long Run is Otello Stampacchia.
He’s the founder and managing director of Omega Funds.
Otello started Omega in 2004, and it’s now on Fund VI. Based in Boston, Omega has $1 billion under management, and invests in a wide variety of biotech companies – early stage, later stage, American, European, oncology, immunology, rare disease. There’s a lot going on here, in terms of different fields of science, and different kinds of business challenges at different stages in a company life cycle.
Otello, as you’ll pick up immediately from his accent, was born and raised in Italy. He got his PhD in molecular biology, then figured out what he really wanted to do – apply his scientific curiosity in the world of investments.
Readers of Timmerman Report will have seen Otello’s series of articles on the COVID-19 pandemic. He’s been consistently ahead of the curve. His writing has been a pleasure to edit and publish.
In this conversation, we talk about Otello’s early life influences, the beginning of Omega Funds, and the trends that make this the best time ever to do what he does. At the end, he and I ruminate a bit on luck – how important it is, how much is truly blind luck, and what kinds of fortuitous happenings are more the result of consciously preparing oneself to be in position to receive a lucky break.
Now, before we get started…a word from the sponsor — BIO-Europe.
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The biotech community makes connections at partnering conferences. Now, I don’t travel to many international events in an ordinary year, and I’m definitely not traveling internationally this year. But I’m excited to join BIO-Europe as it’s delivered digitally.
On October 26, I’m moderating a panel on “Innovating the Partnering Future” with Marianne De Backer from Bayer, Paul Stoffels from J&J, and Melanie Saville from CEPI.
Join us by registering at BIOEUROPE.com — use VIP code “LongRun” for $100 Off.
Now, please join me and Otello Stampacchia on The Long Run.
In today’s Wall Street Journal, I review The Innovation Delusion, a new book arguing that innovation is overrated and maintenance is underrated; moreover, the authors assert, we have magical thinking around innovation, and often view it as fairy dust that can be imported from Silicon Valley then sprinkled on ossifying organizations to revive and rejuvenate them.
(Steve Blank discussed the all-too-familiar phenomenon of “innovation theater” in last October’s Harvard Business Review).
The Innovation Delusion offers a thought-provoking read, as I discussed for the WSJ’s broad business audience. But it may also be of particular relevance to biopharma readers, inspiring me to expand on three thoughts of particular interest to my hardcore life science colleagues who read Timmerman Report:
Amid all the handwringing that reliably accompanies technology development, and has probably been with us since Og first rubbed two sticks together, there are compelling data that the world is actually getting (much) better, not worse.
While naturally controversial (especially at a time of such social and political angst), a compelling case for technology leading toward meaningful economic and health progress has been made by Steven Pinker (Enlightenment Now), Hans Rosling (Factfulness), and Andrew McAfee (More From Less – my WSJ review here), among others. I’d also recommend the “Our World In Data” website.
Asserting the world is getting better does not negate the existing serious problems, including well-documented inequities. Rather, it highlights the many ways we’ve made human existence so much better over the years, and emphasizes our potential to drive still more positive change.
The authors of The Innovation Delusion are correct to point out our intrinsic bias for novelty – what Nassim Taleb has called “neomania.”
There is a tendency to imbue new tools and technologies with an imaginary ability to fix what ails us, and a contrasting disinclination to attend to the often-tedious responsibilities of keeping what we already have going. Few of us like to clean air conditioner filters or repair roof tiles, though objectively we recognize the value of doing so.
At the same time, we should recognize that the importance of stability is hardly lost on successful tech companies – even Facebook refined its mantra from “move fast and break things” to “move fast with stable infrastructure.” Twitter became a more serious player once it consigned the “Fail Whale” to the dustbin of history.
As I write in the review:
“Leading technology companies may talk innovation, but they already compete (as the writers themselves acknowledge) on reliability and uptime—striving for standards like the ‘five nines,’ meaning that service is available 99.999% of the time and down no more than five minutes a year.”
The most important and most underappreciated hurdle in leveraging powerful emerging technologies is figuring out how to use them.
New technologies by themselves do not equate to progress, and won’t result in progress unless and until they are gainfully applied to solving a relevant problem. It’s also why there’s such a long gap between when a technology is first discovered, and when it is adapted into something commercially relevant. (This also ties into the much-discussed delay between the arrival of new technologies and the ability to discern significant productivity gains.)
In life sciences, we have, and largely continue to live this disconnect. We hear extravagant promises from enthusiastic technology developers. We then try to relate it to our own lived experience in the trenches of biopharma, pursuing our daily mission of discovering, developing, and delivering impactful new medicines to improve human life.
The view from many on front lines is that a lot of technology, despite the hype, despite the pleadings of biopharma executives intrigued by notion of “digital transformation,” continues to feel peripheral to the core mission, at best perhaps helpful around the operational edges, but hardly moving the needle on the core business of curing disease, and as likely to be a distraction as a contribution. This is the largely unspoken reality I continue to see on the ground.
This pattern is not, and should not be, surprising.
We shouldn’t despair.
This is characteristic of the way emerging technologies evolve, and also why there tends to be such a gap between the original discovery of the new technology, and its effective incorporation at scale.
This gap reflects — and defines — the implementation challenge, the need to actually figure out how to leverage a powerful, emerging technology to solve a relevant problem in a given domain. Moreover, the key advances tend to come from “lead users,” curious pragmatic front-line workers who are looking to solve a discrete problem and believe a particular technology could be useful if appropriately applied.
In short, what this means is that whether you’re an aspiring healthtech startup looking to disrupt/transform/reinvent/reimagine some aspect of biopharma, or a large technology company seeking to add a lucrative biopharma vertical, your ultimate success requires deep domain expertise.
For startups, we need leaders — and investors — who are not only attuned to the technology to be deployed but, critically, who also inhabit and are fluent in the relevant domain, and who have acquired with time and lived experience a nuanced understanding of the real-world problems to be solved.
These leaders and their companies would have what maize geneticist Barbara McClintock classically described as a “feel for the organism.”
For larger companies, we need a strategy more substantive than hiring once-prominent healthcare and biopharma figures to facilitate customer outreach; we need products designed and developed in deep partnership with lead users, relationships focused on solving pressing customer problems, not jamming in largely-established solutions.
I recently reviewed the (always) fantastic year-end summary from 2019 (available here) presented by Bruce Booth of Atlas Ventures, a prominent early-stage life science venture firm. The discussion affords an invaluable 20-year historical perspective of the life science industry. Despite some profound advances, the persistence of several core problems in the industry was striking.
In the last two decades, the timelines for discovery and development “haven’t budged,” Booth demonstrates, while “costs continue their steady climb,” and the rate of mid- and late-development failures — exceptionally costly — remains staggeringly high.
“Failure,” Booth ruefully notes, remains “an inescapable reality of drug R&D.”
The impact on burden of disease has been significant for some patients with some conditions), but in aggregate, perhaps not exactly transformative. For example, one graph required a conspicuously attenuated X-axis going from 64% to 70% to reveal the improvement in relative survival rate for all invasive cancers from 1999 to 2016. The rate bumped up from 66.0% to 69.3% which is meaningful, especially if you’re in the new 3.3%, but likely not the transformative progress towards which we all aspire. Some cancers, like pancreatic cancer and brain cancer, remain a difficult struggle. And cancer is the therapeutic area attracting by far the greatest amount of industry investment and attention.
In short, despite the proliferation of promising biological modalities, the process of prosecuting new therapeutics remains astonishingly difficult and expensive, with success still the rare exception. Despite our best scientific efforts and extraordinary resource commitments we remain at the core a miracle-driven business.
Here’s the point: the job of life science companies remains as difficult as ever. Coming up with safe and effective new medicines is really, really hard, mostly because biology is complex and messy, and it’s almost unimaginably hard to come up with something you can safely introduce into the human body that will effectively attack only the disease. It’s a preposterously difficult problem — and it’s also what the R&D folks in biopharma courageously tackle every single day.
To the extent that emerging digital and data technologies can help — can authentically and meaningfully help — life scientists tackle these outsized problems more effectively: that would be fantastic.
But it will also require a far deeper focus on implementation and lead user engagement than most of what we’ve seen from tech so far. It’s also why the impact of tech on life science has been so outrageously minimal and so elusive.
Factories weren’t meaningfully helped by the replacement of steam power with electric generators; productivity improvements were driven when factories themselves were reimagined and reconfigured to optimize the flow of material. The big change came when these dense three-dimension structures designed around a single source of power were replaced by the long linear layout more familiar today.
As I wrote last year in Clinical Pharmacology and Therapeutics,
“In areas ranging from the power loom where efficiency improved by a factor of twenty, to petroleum refinement, to the generation of energy from coal, remarkable improvements occurred during the often lengthy process of implementation, as motivated users figured out how to do things better, ‘learning by doing’ as Bessen describes it in his book of the same name.
Many of these improvements are driven by what Massachusetts Institute of Technology professor Eric von Hippel calls ‘field discovery,’ involving frontline innovators motivated by a specific, practical problem they’re trying to solve. Such innovative users—the sort of people who Judah Folkman had labeled ‘inquisitive physicians’—play a critical role in discovering and refining new products, including in medicine; a 2006 study led by von Hippel of new (off‐label) applications for approved new molecular entities revealed that nearly 60% were originally discovered by practicing clinicians.”
I conclude my recent Wall Street Journal book review by describing the opportunity before us:
“New technologies really can make the world better, though how they will deliver their benefits can be hard for the originator to perceive. (Thomas Edison expected that his phonograph player would be used for recording wills.) The hustle and hype around emerging technologies, so easy to ridicule, reflect the chaotic effort to identify something of real value. More often than not, it is pragmatic, curious frontline workers—‘lead users,’ in innovation-speak—who make the incremental advances that fulfill the promise and realize the potential.”
For biopharma, specifically this means that we should avoid our initial impulse to either ridicule emerging technologies or endow these technologies with extravagant expectations (and sometimes both).
Rather, we should seek opportunities to leverage our deep expertise as lead users, and actively shape the implementation of emerging technologies to address the many challenges before us.
Success will require not only thoughtful engagement from life scientists, but an evolution in the mindset of the technology developers — including, critically, their early champions and investors, who tend to grok technology but are often far more superficial in their knowledge of drug development. A deep understanding of drug development, and a deeper, more authentically collaborative relationship with creative, front-line drug developers will be essential if the technology’s promise is to find expression in the delivery of important new medicines that improve the lives of patients.
I could not resist using the above, peerless comic strip from Stephan Pastis. Indeed, the two “plagues” are not just co-existing, but they are mutually reinforcing each other and making things worse.
We should take the “great wise ass on the hill” seriously and invest in science and (investigative, high quality) journalism.
About two months ago, I wrote here about “How We Are Losing the Fight Against the Virus.” At that time, the US had 2.5 million confirmed cases of COVID-19 and reported 126,000 deaths. As of this writing, we now have tallied up more than 6 million confirmed cases, and 183,000 deaths.
Sadly, those numbers will continue to grow this fall, based on my latest look at the relevant work in epidemiology, virology and immunology (and the social-political vibes you get from watching too much cable news).
I am dividing today’s contribution in four parts:
If you are, like most people reading Timmerman Report, up to date with the zeitgeist, I suggest perhaps you skip parts 1) and 2) (though a refresher never hurts) and jump to part 3).
The above picture is from the COVID Tracking Project (@COVID19Tracking): after a worrying summer that saw 7-days rolling averages of confirmed cases exceed 60,000/day for several weeks, and rolling averages of deaths exceed 1,000/ day, the situation appears to be stabilizing / mildly improving, though on a much higher plateau than in May. Rolling averages for hospitalizations and percentage of positive tests are also showing improvements, though, again, at levels that are far from reassuring (roughly around 40,000 positive cases / day and ~900 deaths / day).
Now that summer is winding down and schools, universities and workplaces are moving back into the rhythms of fall, people want to know what to expect.
It’s going to be painful.
I’ve been following an open-source AI-driven model from Youyang Gu which has been relatively accurate to date and is followed by the well-known statistical modeler Nate Silver at FiveThirtyEight.
Sadly, the model forecasts roughly 220,000 deaths by November 1st, from a current (already very grim) body count of over 180,000 confirmed cases. Please note that the current confirmed toll is already DOUBLE the number of US servicemen who died in Vietnam, Afghanistan and Iraq combined, and with no signs of stopping. (The US death toll for World War II was about 400,000).
Add to this the (very roughly) 50,000 excess deaths that are very likely already attributable to COVID-19, and the toll is very heavy indeed. This is not “normal”, it is not even remotely close to (non-pandemic) seasonal flu, and we must resist the (all too human) tendency to become numb and fatalistic about this sorry state of affairs.
According to the same model, the current best estimate of cumulative American COVID-19 infections is (roughly) 14% (close to 47 million out of 330 million people). We are (obviously, but it needs to be stated clearly) nowhere near the >70% of the population needed to achieve “herd immunity”. Please remember also that the death toll does not take into account the (unclear, but significant) number of #LongCovid sufferers (patients who have recovered from the infection but suffer long term, serious health consequences). Even ignoring all that resulting chronic disease with COVID “long haulers,” and using some very, very rough math, “letting it rip” (letting the pandemic propagate without any attempt at mitigating its spread) can lead to ~800,000-900,000 total confirmed US fatalities (more if some hospital systems become overwhelmed; possibly *many* more if we try to input “excess deaths” in the calculation).
To put this into a broader context: in the 5 days prior to Sunday, Aug. 30, the largest European countries (with a population total of 340M, roughly equivalent to the US’ 330M), reported a combined 250 deaths. During that same period, the US reported 5,500+ deaths. When you look at the death toll, the pandemic at this point is about 20x worse in the US.
This is staggering, and will have implications for the upcoming autumn.
I will attempt to summarize some of the knowledge / facts accumulated on the virus and its properties / means of propagation (and implications thereof) since the beginning of the pandemic.
a) Deaths lag hospitalizations; hospitalizations lag confirmed cases increases: the steep increase in number of cases in the US observed in the early summer led, predictably, after several weeks, to an increase in hospitalizations and then in subsequent deaths. There is an obvious lag between these events, caused by several factors: i) as the infection spreads first in the younger, less vulnerable (but not *invulnerable*) strata of the population; ii) as (unconscionable) delays in test results do not give us an accurate and timely picture of the infections spreading, and, finally (and even more unconscionably) iii) as it might take several *weeks* to report COVID-related deaths (a particular dishonorable mention to the Sunshine State, Florida, for the egregious delays, if not outright obstructionism, in reporting deaths). Remember that we are all actually living in the future versus what we normally perceive to be the present (meaning, what we could perceive the current status of the pandemic spread to be: human beings are particularly prone to recency biases). It is of particular concern when, in areas where tests are performed at scale and some speed, positive rates increase quickly. In such situations, measures such as localized / temporary lock-downs / further enforcing of social distancing / obligatory mask wearing in public are likely warranted (the latter should be enforced nation-wide ASAP).
b) This is a very infective virus: It is not as bad as measles, but it is very transmissible, especially in indoor, crowded, poorly ventilated environments. You have heard (am sure) about the dreaded R0 (R “naught”) number. Crucially, R0 is not a fixed number, but it is influenced by many factors, including human behavior (masks…) and context in which a potential transmission could occur (shouting / singing in an small / contained indoor space). The virus is spread by droplets / aerosol particles generated by breathing / talking loudly from an infected person. Worryingly enough, there is now emerging evidence that such droplets can also propagate the infection by connecting with unprotected ocular surfaces (your eyes).
By now, you should also be familiar with the importance of “super-spreading events” in transmitting / propagating the virus. As little as 10% of infected individuals could be the source of ~80% of overall infections, again especially in specific environments / context (indoor, poorly ventilated, talking loudly / singing, no masks): for example, a wedding in rural Maine, an area as yet untouched by the pandemic, led to a large outbreak in the state.
Another extremely well-researched example, very close to home for me and for many readers of Timmerman Report, was the meeting of Biogen’s executive management in Boston towards the end of February (the meeting ended on Feb 27th): the event was attended by ~175 people, from different regions in the US as well as from other countries (including Italy). Scientists have estimated that over 20,000 individual infections were originated from that single conference and then spread all across the globe.
Which leads us to:
c) Non-Pharmaceutical Interventions (NPIs) are very effective in preventing viral spread: social distancing and, especially, wearing masks / face shields in indoor areas have been proven (by a whole body of scientific literature, on this pandemic and others before it) as being very effective in preventing transmission. The graph below is a great summary of the transmission risk in various circumstances and could act as a very rough guideline (always wear a mask anyway). Please note that everything is relative and highly context-dependent: if you are in an area of high prevalence of infections (Miami-Dade County in Florida, for example, or NYC during the peak of the pandemic there in March), even low-risk endeavors, if repeated many times and by a highly susceptible individual can lead to infection. Remember the probabilities here are additive. I am not going to go into which type of masks are most effective but they are an essential component. In addition, face masks also help in reducing the amount of virus that you are infected by (viral inoculum, sometimes confused with viral load), which leads to this one essential point below:
d) Viral inoculum size is very important (and may be the factor making the difference between you ending up in the hospital or just walking it off): forgive me for perhaps stating the obvious here, but I feel this topic, of paramount importance, has not been discussed much (if at all) in the general discourse surrounding the pandemic. People seem to only assume binary outcomes from encountering the virus: you either get infected or not, and then, if you get infected, you either die or survive. This is *OBVIOUSLY* not the case (not sure if you can picture me rolling my eyes right now, but, I assure you, it is happening). How much virus you are exposed to at the beginning of your infection (viral “inoculum”) has a strong correlation with the subsequent severity of your symptoms. We have known this for a while, and yet, we are still having these inane / insane and sometime unhinged discussions / fights (including at various stores across the country) about how the obligations to wear masks infringe on people’s civic liberties. Masks do, at the very least, SUBSTANTIALLY reduce the viral inoculum you are exposed to in an infective situation, and therefore *will* either i) prevent the (non-infectious) wearer from catching a potentially lethal dose of the virus or ii) prevent the (infectious) wearer from spreading a potentially lethal dose of the virus in his/her surroundings. I honestly cannot believe we are still debating this stuff 6 months into a pandemic, but there you have it.
e) The virus is not becoming less lethal over time / the virus has not mutated into a (measurably) milder version: the (very much welcome) reduction in the mortality rates that you are seeing across countries (most of them developed: emerging countries are still seeing very high case fatality rates: see Mexico, Peru, India) is because of a variety of cumulative factors: hospitals / caregivers are no longer overwhelmed; a larger percentage of the population now being infected is younger / with less co-morbidities and risk factors; practitioners have learned / shared more information on how to deal with severely ill patients (for example, by not putting them on ventilators immediately; for those of you with an interest, I suggest joining the slack channels used to exchange know-how and tips by healthcare practitioners); more people are wearing masks / being more careful in their behaviors and therefore are being exposed to lower viral inocula when infected; finally, a few pharmaceutical interventions have since become available and are making an impact (especially dexamethasone), and with more to come: People have been focusing a lot (for good reason) on vaccines, but there is a whole bunch of other stuff that might show its effectiveness before they become widely available. As any rational person would (should?) tell you, in a(ny) pandemic you do not want to get sick early. If you’re going to get sick, it’s better that it happen later, when healthcare professionals know more about the virus and its effects.
f) There is no pre-existing, foolproof, 100% protective “immunity” to this virus. I have seen over the last few weeks the spreading of this fascinating, completely unproven “theory” that there is a large amount of existing immunity to COVID-19 in the population due to the fact that, supposedly, up to 50% of people have T cells with cross-reactivity (in Petri dishes, in a lab) to this virus because of previous exposure to other (seasonal) coronaviruses. This reminds me of the late ‘90s, when curing cancer in mice was enough to make stock prices for certain (mildly overpromoting) biotech companies skyrocket… (yes, I know this statement ages me quite a bit, thanks very much). Even given this (completely unproven) “theory” (I think “opinion” might be closer to the mark) the benefit of the doubt, again we come back to the *fact* that a smaller viral inoculum (and subsequent viral load in your bloodstream) is probably more important in determining your progression towards the worst symptoms. Even some mild degree of cross-reactivity and therefore (possibly, not surely) protection will not help you if you are inhaling infectious particles by the millions without masks at an (indoor, poorly ventilated) bar. In the words of Ricky Gervais (if you do not watch his comedy stand-ups, you should): “You can have your own opinions, but you cannot have your own facts”.
g) Children (especially very young children) are not, by and large, severely affected by the virus: what I mean by saying “by and large”, is that they are not as affected as adults, probability-wise. There is, also in children, a continuum in the risk probability, again increasing with age, and with other pre-conditions predisposing to severe symptoms (obesity etc.). That said, the risk is not exactly zero even for very young children. To provide some quantification of the probabilities here, Dr. Stephanie Graff (@DrSGraff), described a recent State of Florida pediatric report (using data since March)… OK, HOLD ON. I know what you are thinking. So let me address the elephant in the room here first: far be it from me to rely upon, let alone celebrate, “Florida statistics” on the pandemic, which is as close to an oxymoron as I can think of, but this is all I got so far. At any rate, according to said (possibly very flawed) Florida statistics, 48,928 children statewide have tested positive; of these, 600 have been hospitalized (~1.2%), with most of them recovering, and 8 have tragically died (~0.01%). 50 children <18 have been diagnosed with MIS-C, which is a severe inflammatory disorder caused by the virus (~0.1%, or 1/1000 for those of you who suck at math). I have not been blessed with children, so will leave to parents their own risk-reward calculations analysis. At any rate, it is also abundantly clear that children are infected by the virus and can transmit it, sometimes very efficiently: young, college-age adults effectively seem to transmit as well as older adults. This has severe implications for community transmission as soon as schools reopen (see below), and particularly for inter-generational households with grandparents or people with other pre-existing conditions.
Most of the unknowns that still plague us are related to this incredibly complex organ of the human body, the immune system. This is a new virus for the human species, and, notwithstanding the incredible progress made to date, there are still lots of things we do not know, some of which are (unfortunately) essential before being able to plan a return to some semblance of normal life.
a) How long does immunity last? In early July, a British study pre-print showed declines in neutralizing antibody titers (antibody amounts / volume of blood) observed during a ~3 months follow up period post-infection. Cue (predictably) apocalyptic commentaries. However, there are reasons (I know, this is unlike me, but bear with it for a bit) to be a bit less pessimistic: as eloquently described by Derek Thompson in The Atlantic in this very thoughtful piece as well as by his (very witty) colleague Ed Yong in many great articles (this one in particular is worth a read) the immune system is extremely complex and has many components. First, even a lower antibody titer later on might still be sufficient to forestall a severe re-infection (see below) DEPENDING ON THE VIRAL INOCULUM (see? Keep wearing a mask please). Also, the authors of the British study only looked at B cell antibody responses, and did not measure T cell responses (to be fair, that is a much harder measurement to make): there are reasons to believe that T cell responses might provide the strongest / longest-lasting immunity to COVID-19 (read here if you want to geek out). Perhaps some very rough calculations might also provide some guidance: we know that existing, seasonal coronaviruses (responsible for ~25% of seasonal common colds) are capable of re-infecting people every year or so which means immunity might last for a few months; on the other hand, SARS and MERS (the other recent coronavirus epidemics) *seem* to provide some degree of immunity for ~24 months. That said, both SARS and MERS only infected (luckily: their fatality rate was very high) a few individuals, so we do not have a deep data set to use here. If I were a betting man, I would work under the operating assumption that exposure to infection (and subsequent recovery) might confer some protection for anywhere between 12 and 24 months. The uncertainty is due to myriad factors (how much virus were you exposed to; the infected individual’s sex and the overall state of his / her immune system; the amount of virus exposed to in a subsequent re-infection; etc.). Which brings us to the latest developments:
b) People can get re-infected: note, this should technically be in section 2), but it is a very new development and there is still a lot of uncertainty / debate on the possible consequences. As described by more and more reports in the last few days, there have been (some more, other less “properly” confirmed) cases of COVID-19 re-infection (in Hong Kong, Belgium, Netherlands, and now Nevada): in these cases, the virus genetic sequence in the second infection was sufficiently different from the one from the earlier infection to rule out artifacts of detection and other explanations. Before I go on, I would like to highlight the sheer “amazingness” of this: we are testing and sequencing viruses of many infected individuals, globally. This is leading us to important learnings on the virus (the above is a not-so-snide retort to people / organizations who would like to reduce testing volumes or only test symptomatic people; apparently one such organization included, until recently, the US CDC). I’d start with the (important) premise that such cases, at the population level, are in my opinion quite rare and likely to remain so and be restricted to individuals who have decided to have the questionable bad luck of i) living in areas of very high pandemic prevalence, ii) being particularly susceptible to infection, and / or iii) engage in very risky behaviors. That said, the Nevada case is of particularly concern, since the individual had much more serious / severe disease symptoms the second time around than during the first infection: normally, even if the immune response generated after the first infection is unable to prevent a second one, it should, however, conceivably, at least forestall more severe symptoms. However, as brilliantly put by Dr. Sarah Cobey (epidemiologist / evolutionary biologist from U. Chicago, quoted in the article above), “Infection is not some binary event”, and with a reinfection, “… the question is how much is the immune system getting engaged?” Which, again, takes us back to viral inoculum size and its interaction with any individual’s immune system. As a corollary of this (extremely important) topic and the consequences on testing, I would strongly suggest you follow Prof. Michael Mina on Twitter (@michaelmina_lab): in particular, this thread (on the consequences of using PCR testing alone in guiding public health decisions) is very important.
c) Implications for vaccines: the above questions have huge implications for vaccines’ development / testing / distribution logistics: if immunity generated by the virus is indeed short-lived, then we might have to become accustomed to annual booster shots, for example. Add to this huge uncertainty some other important complexities, such as i) the mRNA vaccines currently under development are likely to require -70 Celsius or -20 Celsius cold chains; ii) that as much as 30% of the US population does not believe in vaccines and, finally, and perhaps very importantly, iii) that the recent public communication blunders by the FDA and the CDC are very likely to further increase the suspicion that any vaccines approved in a hurry just ahead of a close, contested election might be more motivated by politics than science, and you have all the ingredients for a very messy situation in which not enough people get vaccinated. [Clarification: 9:34 am ET, Sept. 1. The cold chain conditions can vary depending on the mRNA vaccine candidate. A previous version of this article grouped them together.]
First of all, thanks for sticking with me so far. Obviously what follows is speculation. That said, I am quite concerned about the upcoming autumn / winter season. As mentioned above, the virus spreads readily and easily indoor, virus inoculum sizes become probably much bigger, and as we transition to indoor classes / living in general, in a much more generally dry environment, this is very dangerous, especially as the baseline of people already infected in the US is extremely high, driving a steady rate of community transmission (compared to most other countries). As a data point that further corroborates this, we should look at Australia: as well discussed by Silvia Merler (@SMerler), and as shown below, Australia is now truly undergoing a “second wave”, with much higher mortality rates than the first. For those of you who have not been to Australia (and who are not familiar with the fact that our planet has opposite seasons in the two hemispheres: yes, am talking to you, flat-earthers…), Australia underwent its first wave around the end of their summer and is now undergoing their second wave in their winter. With the usual lag, the second wave appears to be having a disproportionately high fatality rate vs the first. At the same time, we cannot say Australia is governed by a “negationist” government or was not warned about the virus ahead of its second wave.
To conclude, some suggestions:
a) I am extremely glad that a number of jurisdictions, including some universities, seem to be starting to administer the current season’s flu vaccine early, to prevent a flooding / overrun of their healthcare systems. I suggest this should become mandatory across the country, as soon as possible.
b) I understand the economic imperative for universities and schools to reopen (also, which parent would want their teenage children brooding at home for another year???). However, we need to be extremely mindful for the potential to further increase spread due to disorderly behavior usually associated with young adults (who are not usually showing the most compliant and obedient behavior at the best of times). It is truly amazing to observe the wave of innovative solutions being tested by various universities / colleges. Some examples of such innovation are shown here: I believe more colleges should follow the University of Arizona and University of North Carolina approach and implement wastewater virus testing from college dorms to have an early warning signal which allows them to implement contact tracing and isolation of infected / asymptomatic individuals very early in the process. Amongst many reasons to use this testing is that i) there is evidence that the virus shows in stools before or at least concomitantly with the early infectiousness period and ii) the method does not require asking teenagers to be compliant with various testing / masking / social distancing guidelines.
c) The recent launch of a fast, cheap and easy to perform antigen test format (from Abbott Laboratories) could also finally introduce a much-needed game changer on the playing field: again as discussed by Michael Mina this test format could really be useful to break transmission chains, therefore limiting / containing local outbreaks early on in their spread (which is not the case, still, with the current test types available in the US, due to their lack of reliability and the incredible, mind-boggling delays in obtaining results). This test, and hopefully soon others like it, should be deployed at scale across the country ASAP. What do I mean by “at scale”? Looking at the scope of the pandemic in the US, we should test tens of millions of people A WEEK to have a chance of contain local outbreaks / superspreading events.
d) Wear a mask.
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.
Please help me understand the value of OUR Centers for Disease Control and Prevention in 2020.
It’s baseball season — sort of. We have been waiting and waiting for the CDC to step up to home plate. The bases have been loaded for 7 months, and their fans have been waiting. The CDC is supposed to be our national cleanup hitter.
Yet, instead of trying to hit the ball out of the park for the grand slam, they have either struck out each time or tried for the occasional bunt single. This week, they decided to not even leave the on-deck circle.
Here’s the situation. Millions of kids and parents are seeking solid, science-based recommendations on what to do about school this fall during the pandemic. The CDC responded to this challenge by issuing revised guidance on Aug. 26 that said asymptomatic people who have been exposed to an infected person don’t necessarily need to get tested.
That revised guidance — to do less, not more testing — reportedly came from the White House and top levels of the Department of Health and Human Services. Maybe it did; maybe it didn’t. What matters is that the CDC issued the information.
Scientists were baffled, as the new guidance makes no sense. Within hours, CDC director Robert Redfield backpedaled, telling reporters that “testing may be considered” for anyone who has been exposed to the virus.
In a statement, Redfield said:
“Everyone who needs a COVID-19 test, can get a test. Everyone who wants a test does not necessarily need a test; the key is to engage the needed public health community in the decision with the appropriate follow-up action.”
The public needs firm guidance. This statement is so wishy-washy, it is meaningless.
Is there any real value left for this aging, high-priced player?
Stats in baseball don’t lie. Stats in healthcare generally don’t lie either. Roughly 40% of the spread of COVID-19 virus is from asymptomatic people. In a country with 5.9 million confirmed cases of COVID-19, and many more unconfirmed cases, it’s safe to say we have several million people capable of spreading this disease without even knowing they are sick. That’s undebatable at this point (though I know even the most basic facts are seemingly up for debate these days.)
Let’s recap some of the most visible and critical missteps by the CDC since the start of the pandemic, and let’s consider the consequences.
Now we have to deal with this latest blunder — actually discouraging testing for asymptomatic individuals who have been exposed to a known infected person. Clearly, these people are at high risk of infection to themselves, and they pose a risk of infecting others — potentially many, many others if they show up unmasked, at a large event, especially indoors.
Why not test these people to reduce the chance of them becoming spreaders, or even superspreaders? Instead of making this common-sense recommendation, the CDC just walked off the field and did not even demonstrate an effort to hit the ball.
Our nation is at a loss. On what scientific justification did they make this decision?
We know “The key to containing the virus is how effectively a country builds up systems for testing, tracing and isolating potential virus carriers, while establishing clear safety rules as the economy reopens.” (WSJ)
Let’s face it; the CDC has clearly demonstrated it either doesn’t have its head in the game, or it is a cleanup hitter that has been forced onto the bench.
In the last few days I have been wondering how Michael Joyner and Arturo Casadevall have been feeling.
Joyner and Casadevall are the first and senior authors, respectively, of the report, “Effect of Convalescent Plasma on Mortality among Hospitalized Patients with COVID-19: Initial Three Month Experience,” posted on Medrxiv on August 12. The preprint server allows researchers to make their work public before peer review, and has become a popular source for producers and consumers of evolving research on the SARS-CoV-2 pandemic.
The report describes the experience of an Expanded Access Program, providing convalescent plasma from recovered COVID-19 patients to roughly 35,000 hospitalized patients over 3 months. All received plasma transfusions, so patients receiving the product could not be compared with those who did not get it.
Despite the lack of any comparison between patients who did and did not receive plasma transfusions, this report has formed the basis for the FDA’s Emergency Use Authorization for convalescent plasma to treat COVID-19 patients. It’s the report that was embarrassingly misinterpreted by the commissioner of the FDA, Stephen Hahn, and by administration officials. And it is the report that may well have spelled the death knell for controlled trials that were hoping to give us a convincing answer as to whether and how well the treatment actually works.
It is hard to imagine that the report’s authors actually intended for all of this to happen. Yet, by doing what medical researchers do all the time, they played into the hands of an administration hungry for anything that would show they were making progress against an intractable virus that has killed at least 200,000 Americans.
To understand how this unfolded and why it bodes so badly for the future, we need to examine the story that the report lays out. The report itself is mostly reasonably transparent, at least about the patients involved (roughly 35,000 over three months from April to July) and how sick they were (52% ICU and 27% on ventilators). The authors obviously recognized that they were reporting an experience rather than a randomized study, and that there was no comparison group since all included patients received at least one unit of plasma. But they still concluded that their findings provided “signatures of efficacy for convalescent plasma.”
What were these “signatures of efficacy”? According to the report and the FDA announcement, there were two: that patients given the product earlier in the course of their illness and those with high antibody levels in their donated plasma were less likely to die than those given plasma later or whose transfusions had low antibody levels.
More specifically, 8.7% of those given plasma within the first three days following their COVID-19 diagnosis died within a week, whereas 11.9% of those given plasma four days or later died. And, of the few patients (roughly one out of ten) for whom information on antibody titer could be retrieved, 8.9% of those in the top 20% and 13.7% of those in the bottom 20% died within a week. Notwithstanding the modest absolute numbers, the relative differences are clinically significant.
At first glance, these results might seem somewhat convincing.
I mean, if you wait to get plasma you do worse, and if your plasma is antibody rich you do better. Surely this must mean plasma works!
Not so fast.
There are major issues with these results that go beyond the (utterly justified) criticism that the data doesn’t come close to the established standard for evidence about novel treatments, the randomized trial. Indeed, the report’s authors lacked not only a randomized comparison between a treated and an untreated group, they were missing the untreated group entirely. So, they presented two alternative comparisons.
Each is problematic for its own reason.
The question of whether it is better to treat a patient earlier rather than later is universally challenging. In prostate cancer, a major controversy has raged for years about whether high-risk prostate cancer cases should be offered additional (adjuvant) radiation treatment right after primary surgery, or wait until their cancer relapses. A similar question occurs in breast cancer. But we can’t just compare patients given radiation in the adjuvant setting with patients given radiation for recurrent disease. They are simply not the same; indeed, some of the patients treated early would never have progressed to relapse even in the absence of radiation therapy.
In general, patients treated early in their disease course are a mix of those who would and would not be eligible for treatment later. In the case of COVID-19, patients at day 2 include those who would make it to day 5 or 10 without treatment and also those who would not! So, the analysis in the report that compares the patients treated within three days against those treated at four days or later is ill conceived. In the end, the results are basically useless.
Alright, you say, but what about the plasma titer comparison? The authors go to great pains to note that the titer was not known at the time of treatment and even use the term “pseudo-randomized” to describe this comparison.
I am going to leave aside the fact that the antibody titer analysis only included about 10% of the sample, and that it is therefore unclear how representative this group might have been, because there is a bigger issue here. This is the matter of how the high- (and low-) titer groups were defined.
There is no established threshold to define high- versus low-antibody titer for SARS-CoV-2. The authors chose the top 20% to be the high-titer, and the bottom 20% to be the low-titer group. This seems reasonable until you realize that they could have chosen the highest and lowest 25%. Or the highest and lowest 33%! Maybe they even tried these thresholds but presented just the results provided. We don’t know. Regardless however, the results show only that survival was improved in the high-titer group compared with the low-titer group. We don’t know if those who received low-titer samples did any better than if they had not been treated at all. In fact, there are real concerns about whether low-titer plasma might induce a harmful effect, referred to as antibody-dependent enhancement (ADE) of coronavirus infection.
At best, we can only conclude from the titer comparison that high-titer plasma, amounting to approximately one-fifth of the plasma samples, might possibly constitute an effective treatment. There is absolutely no signal of efficacy for low-titer samples. But the FDA’s Emergency Use Authorization actually emphasizes that it covers all plasma samples, reflecting the report’s conclusions which also do not differentiate.
I don’t have a crystal ball and I can’t read the minds of the report’s authors in terms of what other comparisons they may have considered, or what they might be working on now to deliver more reliable answers. But their choices and their conclusions thus far are entirely consistent with an academic culture that rewards the finding of significant, positive effects associated with new treatments. It is a culture that encourages positive spin about novel medical interventions and overstatement of marginal clinical results.
In normal times, we might take the admittedly cautious but still overstated conclusion of benefit in this report with a grain of salt and modulate our expectations of effectiveness in practice accordingly. Doctors wouldn’t rush off and change prescribing habits instantly on this thin body of evidence – not until they could see a clear and convincing difference between patients who got convalescent plasma, and patients who didn’t.
Clearly, these are not normal times. Scientists need to do whatever they can to make sure that their science is not misinterpreted, exaggerated, or even hijacked to make a political point. I sure hope that Drs Joyner and Casadevall are alarmed that their research featured so prominently in what can only be considered a dress rehearsal for the vaccine study results that are expected later this fall.
As the summer draws to a close, I thought TR readers might enjoy a final August distraction. I’ve always been an avid reader, and lately, I’ve found myself increasingly drawn to the history and science of American politics.
On the history front, and inspired by Stanford professor Jeffrey Pfeffer (an expert on power and leadership), I’ve started Robert Caro’s famously comprehensive multi-part biography of Lyndon Johnson. I’ve completed the first volume: The Path To Power (1982), which describes how the Johnson family first arrived in Texas, and takes us from Johnson’s rough childhood in the unforgiving hill country, through his election as a Congressperson in 1937, and concludes with his razor-thin loss in the special Senate election of 1948.
Johnson emerges as a striking if deeply flawed individual, with indomitable ambition, a relentless work ethic, a need to be in charge, and a drive to win at all costs. A student of human nature, he cajoles those whose support he craves and dominates those beneath him, tirelessly manipulating all who he encounters.
Johnson steals a series of elections, starting in college. Ironically, his defeat in the Senate 1948 race apparently reflected not the propriety of the election, but rather the ability of his opposition to cheat more effectively.
It becomes immediately apparent that ambition, narcissism, and the privileging of victory over ethics is not the sole provenance of a particular person, party, place, or time. I’m just beginning the next volume (Means of Ascent ), and the author cautions in the preface that (somehow) it promises to be even darker than the first.
For those interested in a deeper understanding of the contemporary political state, the obvious must-read here is Tim Alberta’s American Carnage (2019), describing the transformation of the Republican party into the party of Trump. Alberta is now the Chief Political Correspondent at Politico, and previously wrote for the conservative National Review.
Alberta brings unusually deep insight into his subject, and the sense that he’s reporting about the party’s current state more in sorrow than in triumph. You can get a feel for his style from his deeply perceptive recent essay about the state of the GOP, lamenting that no one really knows what the party believes in anymore.
Technology and the sophisticated use of data and social media is often said to have played a central role in Trump’s 2016 victory. We’re afforded an insider view of this in Targeted, Brittany Kaiser’s account of her involvement with the infamous data firm Cambridge Analytica (CA), and its close relationship with the Trump campaign. Aspects of this are also covered in the Netflix documentary, “The Great Hack.”
While Kaiser suggests Cambridge Analytica played a key role in Trump’s victory, many others are more doubtful. Writing in The Atlantic, Ian Borogos and Alexis Madrigal dismiss the contribution from Cambridge Analytica, and emphasize the role of the Facebook algorithm itself.
Even here the impact isn’t clear; Hugo Mercier, a cognitive scientist immersed in this literature, tells me that “the effects of advertising [including online ads] for political campaigns in general elections are small at best.”
He adds “the effects of online ads are small and noisy, indeed, so small and noisy (as a rule) that even researchers at Google admit that it’s impossible to know whether online ads bring positive ROI [return on investment].” I recently downloaded his new book, Not Born Yesterday, and look forward to his discussion of why persuasion is so difficult.
The challenge of voter persuasion in particular was highlighted by a high-profile 2017 study from two academic researchers: David Broockman, now at UC Berkeley and Joshua Kalla, at Yale. They examined data from 49 field trials, and concluded that political campaigns have essentially no detectable impact on candidate choice in general elections. Most people ultimately vote along familiar party lines, and it’s apparently much more effective to mobilize your own partisans compared to trying to get partisans on the other side to switch.
It also turns out that the fraction of persuadable voters is probably also a lot less than you might think. A key research finding is that while ever-more people self-identify as “independent, if you push them, most of these acknowledge they lean left or lean right. Data suggest these leaners are at least as partisan in their ultimate voting as the voters who affiliate from the outset as either Democrat or Republican. Thus the number of true independents is comparatively small.”
There’s also the added challenge of how to think about this middle group, often referred to as “undecideds.” This can refer to likely voters who are actively weighing who to vote for, but could also encompass voters who feel alienated from politics (and the increasingly partisan nature of politics), and opt not to vote at all; such “low frequency” voters are notoriously challenging to draw to the polls.
The difficulty of changing votes meanwhile doesn’t mean persuasion is entirely useless. Work from Todd Rogers applying psychology to election strategy through an iterative series of field trials has resulted in a series of ways to “nudge the vote,” as Sasha Issenberg of the New York Times put it in 2010. (Issenberg subsequently wrote a book on the topic, The Victory Lab (2012), examining the science behind Obama’s 2008 win).
One successful method: closing the intention/action gap by asking voters questions that force them to think through how they’ll actually go about voting; this approach has been shown to increase turnout in a statistically significant fashion.
Importantly, while it can be difficult to convince voters to change their minds, research suggests that some efforts to persuade people to change their opinion – particularly outside the partisan signaling associated with a general election – can be successful. For example, Broockman and Kalla published a study in 2016 that found a small but significant number of respondents (about 10%) could be persuaded to embrace a more sympathetic view of transgender rights, a change that endured for at least three months.
The key, it turned out, was “deep canvassing,” meaning the field volunteers engaged in an extended, empathetic conversation with the respondents, listening intently and asking thoughtful follow-up questions.
In these fractious times, this is a hopeful message from which we all can learn. When everyone is so busy trying to talk, there can be remarkable power in attentive listening.
With today’s Aug. 24 deadline looming, it’s important to explain how President Trump’s “most favored nations” executive order to purportedly lower drug prices would actually backfire, and hurt patients both at home and abroad.
The order, which ties prices for certain drugs paid for by Medicare to the lowest prices paid in other countries, including Canada and much of Europe, also conspicuously ignores the real problems faced by the American healthcare system.
Trump says that “Americans are funding the enormous cost of drug research and development for the entire planet,” which is why he doesn’t like that other countries are getting lower prices.
The trouble is that the president is wrong. Right now, other countries do pitch in – and his new order isn’t going to make other countries pay their fair share for pharmaceutical R&D.
Instead, it’s going to result in them paying no share at all.
Even a bad roommate that chips in a bit of money here and there helps defray the cost of a mortgage. For drug companies, some profit from Europe and elsewhere is better than nothing. In the meantime, Americans continue to benefit as Europeans, Canadians, and many others defray at least some cost of paying off the mortgage on pharmaceutical innovations that American patients need.
But instead of ensuring that Americans pay less for drugs, Trump’s order – while intending to “import” prices from abroad – would actually force companies to “export” U.S. prices for drugs to other countries in an attempt to protect their most important market. And when European and other countries refuse to pay, patients suffer the world over.
Turning Europe and Canada into entirely untapped markets would mean that instead of benefiting from other countries pitching in for, as President Trump has said, “the enormous cost of drug research and development,” Trump’s order will leave America standing by itself to shoulder those costs.
The downstream impact is obvious: because he doesn’t seem to understand basic economics, the president’s “favored nations” order would literally cost Americans more.
The dynamic is pretty simple. To make up for the loss of revenue in Europe, Canada and elsewhere, pharmaceutical companies would respond by raising US drug prices higher than they already are. And given our dysfunctional healthcare system, this would translate directly into higher out-of-pocket drug costs for the people that Trump claims to be fighting for.
Other nations have demonstrated that they’re willing to deny life-saving medicines to their citizens on the basis of cost. They have demonstrated that they’re willing to wait longer for access to new, lifesaving drugs – even when they do eventually decide they’re worth paying for.
Those kinds of price controls are simply un-American and mistakes we do not want to import or normalize.
Nevertheless, since President Trump’s orders do nothing to lower out-of-pocket costs, Americans would still be saddled with high deductibles and copays.
Some might be inclined to just fall back on blaming the drug industry, mistaking high drug prices for large profits. But the drug industry’s collective profits are only 10-12% of revenues when the successful companies are combined with all the unprofitable biotechs still investing tremendous amounts of money, at risk, on developing new drugs, including treatments and vaccines for COVID-19.
Because drugs eventually go generic, in order to stay in business, drug companies have to invent new drugs that Americans need. If they don’t invent, they die.
Meanwhile, insurance companies invent nothing. Insurance companies generated greater profits when COVID-19 struck because they were able to continue collecting monthly premium payments while patients stopped going to doctors and getting treatments. Even in the pandemic when so many people had to sacrifice so much, insurers still had the gall to reject coverage for COVID-19 testing and care when people got sick.
The diagnosis for what ails America is clear – our health insurance plans.
What the president could do – but hasn’t done – is push for all Americans to have insurance with low out-of-pocket costs. He might also consider eliminating out-of-pocket costs for drugs when there is no clinically equivalent, inexpensive, high-quality, generic alternative.
This, however, would require an acknowledgement of a simple but elusive truth: what makes any aspect of healthcare affordable for patients is a function of the insurance they have. If an insurance plan sticks patients with massive out-of-pocket costs, then treatment is expensive and inaccessible. If an insurance plan covers treatment fully, treatment is accessible.
The bottom line: America needs insurance reform. We need our next president and Congress to reform health insurance so that Americans’ insurance plans actually offer what they advertise – affordable access to healthcare.
Peter Kolchinsky, a biotechnology investor and scientist, is Managing Partner of RA Capital Management, L.P., and author of The Great American Drug Deal.
All of us need some time and space for spiritual renewal. Especially in a year like 2020.
Mountain climbing does it for me.
Last weekend, I was fortunate to get out to Mt. Shuksan in the remote North Cascades of Washington. It was a gorgeous climb, led by my friends and partners at Alpine Ascents International.
The climb was a charity fundraiser for Facing Homelessness. It’s a Seattle-based group that uses the power of photography to expand our sense of empathy for our fellow humans. It’s an important first step.
I hope you enjoy these mountain photos, and that you may find some inspiration in the good work of Facing Homelessness.
Watch for the Frontpoints column next week.
Thanks for reading, and stay well.–Luke
Today’s guest on The Long Run is Rosana Kapeller.
She’s the president and CEO of Cambridge, Mass.-based ROME Therapeutics.
ROME aims to discover and develop drugs based on emerging science in what is sometimes called the “repeatome.”
These are long repeat stretches of DNA that scientists until recently knew very little about, and still have a lot to learn about with regard to its function. ROME made its official debut in April with a $50 million Series A financing from GV, Arch Venture Partners and Partners Innovation Fund. The stated plan is to interrogate this territory to look for new targets to treat cancer and autoimmune disease.
Rosana was born and raised in Rio de Janeiro, and went to medical school there before coming to the Boston area to do her PhD in molecular and cell biology. She got in early at Millennium Pharmaceuticals, was there during its rise to prominence in the first genomics boom, and then took on increasing roles of responsibility at a couple of startups – Aileron Therapeutics and Nimbus Therapeutics.
She has serious science and technology chops, and is now learning to adjust to the new role of being a CEO.
Rosana is a warm person and has a wonderful laugh, which you’ll hear from the start. This is an enjoyable conversation with a consummate scientific entrepreneur.
Now, please join me and Rosana Kapeller on The Long Run.
Are you a fan of The Long Run podcast? Trying to raise awareness of your company, your organization, or your services with a high-powered crowd of entrepreneurs and venture investors who listen to The Long Run? My business representative, Stephanie Barnes, can tell you about sponsorship opportunities of this show. But first, tell me about your company in a brief email – email@example.com.
There are very few things many of us can say with 100 percent certainty. But after three decades of experience in molecular diagnostics and the life science industry, I can say this: never before has the healthcare profession, and even the American public, been so laser-focused on diagnostics.
For all the tragedy it has caused, the COVID-19 pandemic has shone a much-needed spotlight on the value of medical diagnostics. It has forced important discussions about how we think about and deploy these tools.
Challenges in running enough tests to quickly and accurately diagnose everyone infected with COVID-19 in the United States – in order to properly treat those in need and gather the information necessary to slow the spread of infection – have forced our country to take a crash course in diagnostic tests.
Everyone from the United States’ leading infectious disease experts and our country’s most influential healthcare reporters to a broad swath of the general public are intensely engaged in COVID-19 tests, weighing accuracy against turnaround time, sensitivity against specificity, or the appropriate time to use a molecular test to spot an active infection versus an antibody test to determine whether an individual was previously exposed to SARS-CoV-2.
Beyond the education factor, COVID-19 has revealed the broader utility of these tests. Diagnostics have become the gatekeeper to getting the economy back on track, sending children back to school and allowing American citizens to travel into other countries.
COVID-19 has also – finally – forced our hand in terms of making testing more accessible. Diagnostic testing is no longer limited to clinical centers. Instead, people are getting creative, expanding test access in order to meet a previously unfathomable demand for tens of millions of tests. Around the country, we’ve seen the dramatic expansion of mobile testing centers, pop-up testing locations, and point-of-care testing. Employers are testing their workforce — a move that’s been largely lauded by both groups as a means of getting people back to work safely and responsibly.
In some of these settings, a doctor’s prescription isn’t always necessary to get these tests. The surge in demand forces us to ask ourselves basic questions, such as when a prescribing healthcare professional is necessary to maintain safe and effective healthcare decision-making and when having to see one creates an unnecessary bottleneck in the system. In this case, the RT-PCR tests marketed by reputable companies and performed by well-equipped academic and private industry labs are so simple and reliable that it makes more sense in the long term to open the floodgates for consumers to directly order up tests themselves, have sample collection kits shipped straight to their homes, and then ship their samples back to a lab for analysis.
As diagnostics have become a central character in the pandemic story, public and private funding has poured in for the development of new tests.
Valuations for companies designing and manufacturing diagnostic tests have soared. New tests are cheered by reporters on the national news. The success we’ve seen has come from a massive, concerted effort by diagnostic developers, healthcare providers, regulatory agencies, and insurance companies.
As a veteran of the diagnostics industry, I am pleased to see that these tests are finally getting their due.
But it also makes me wonder why it took a global pandemic to get us here. And when COVID-19 is finally conquered, will the interest in diagnostics fade along with it?
Funds started flowing freely to diagnostic development when it became clear these tests would be necessary to get the economy back up and running at full capacity. Unfortunately, we have never seen this response when just as many lives were at stake, but the economy was functioning as usual.
Consider lung cancer, an area that I have focused on for more than a decade. The American Cancer Society estimates that more than 228,000 people will be diagnosed with lung cancer and nearly 136,000 people will die from it in the U.S. this year. The vast majority of funding dedicated to lung cancer goes toward research efforts focused on developing new therapeutics. But diagnostic tools that could detect lung cancer at much earlier stages and significantly improve patients’ chances of survival might offer more benefit and take a lot less funding to bring to market.
The most common objection I hear about introducing new diagnostic tests is that they will add to the already unsustainable costs of healthcare. This is a fundamental misunderstanding of diagnostics; good tests, developed carefully and targeted at specific health needs, will in fact reduce costs.
Today, just 2.3% of healthcare spending in the U.S. comes from in vitro diagnostics. Think about that for a second – it’s a statement about our traditional view of the value of diagnostics. This is the number one reason why we see so many therapeutics startups receive large sums of venture capital, while relatively little is directed toward diagnostics.
We pay a lot for drugs, not so much for diagnostics.
Looking a little further into the healthcare spending data, we can presume that advanced genomic tests from specialty laboratories – one of the most technologically promising aspects of the diagnostics industry — account for an even smaller slice of overall spending. If we doubled or even tripled that spending in order to detect health problems earlier — to start treatment of diseases when they are far less expensive to manage than they are in later stages — we would still be coming out ahead on overall expenditures. Testing is cheap compared to long-term illness, treatment, and death.
Placing a higher priority on the development and availability of advanced genomic tests for early disease detection would represent a major step in shifting our healthcare system’s focus from treatment to wellness. Diagnostics can help us catch diseases early, prevent the spread of illness, keep people healthy at work, and reduce all kinds of costs associated with undiagnosed disease and therapies. While those of us in the diagnostics community have known this all along, the COVID-19 pandemic has raised awareness of it for a much larger audience.
We need payers to buy into the value of early diagnosis through more sensitive and less invasive testing. This model can save lives and lower costs. Yet in the past, government agencies and insurance companies have often balked at covering such tests.
Consider the example of idiopathic pulmonary fibrosis (IPF), a serious and progressive lung disease. It takes the typical patient years to get diagnosed, often through multiple invasive, costly and potentially dangerous procedures. During that time, more than 20% of these patients will be given treatments that can actually harm them. My company has developed a clinically validated genomic test that can help end the diagnostic odyssey and get patients on the right treatment faster. This would improve health and reduce healthcare costs, and yet private payers have been very slow to cover it.
If we can conceive of pandemic-related diagnostic strategies to determine which people are safe to go to school, get on a plane, or return to work, then surely we can see the value in using diagnostic tests to tackle other leading causes of death.
We need to start waking up and realizing the benefits of early diagnosis of disease — the idea that we can test to stay well, instead of counting on therapies to make us well again.
Perhaps the new models of testing established for the pandemic will jump-start a different approach to diagnosis.
Could the new model of workplace testing be an avenue that opens up diagnostics beyond just COVID-19 testing? For example, does it open the door for broader screening and earlier detection of lung cancer? There are obvious issues with how much access employers have to their employees’ health information, and those will have to be managed carefully. But if employees come to trust their employers to deliver high-quality diagnostic tests — perhaps better tests than those they could access through typical healthcare channels — this could transform the valuation of and payment for important tests designed to keep people healthier and keep insurance premiums in check.
Self-insured employers could be the first to adopt this approach, and if it proves successful, other large employers might quickly follow suit. This is not enough, but it could be an interesting place to start.
COVID-19 has changed the way we are delivering healthcare and has — at least for the moment — dramatically increased the value we place on diagnostics and early detection. I truly believe if we can hold onto the lessons we are learning from this pandemic, there could be a brighter future for improving health and reducing healthcare costs through the use of diagnostic tests across a wide range of diseases and health conditions.
Bonnie H. Anderson is Chairman and Chief Executive Officer of Veracyte, a global genomic diagnostics company that improves patient care by providing answers to clinical questions that inform diagnosis and treatment decisions.