17
Apr
2020

The Real Question is How to Restart

Otello Stampacchia, founder, Omega Funds (illustration by Praveen Tipirneni)

When??

When can we finally go back to something resembling a “normal” life, go back to work and restart our economies, embrace again long-lost family, friends, co-workers? And, in too many cases, when can we get together to properly mourn those we have lost?

I cannot tell you “when.” Everyone wants the answer to that question. I do, too. But that is not the first question to ask.

The first question is “how”?

So, let’s discuss “how”.

First, just as much as “when” depends on “how,” “how” depends on “where”.

We are still in the “first wave” of the pandemic. There are substantial differences in rates of localized spreading as well as containment measures: differences between countries and also within different regions/states of the same country.

For example, Lombardia (Milan region in Italy), London (UK), and New York City are all at a very different stage in their pandemic than the vast majority of the rest of the countries they are in. There are all sorts of very messy complications in assuming what a “second wave” could look like and when it could hit. A LOT of these complications arise from having different regions not maintaining contemporaneous strict lockdowns (because the virus spreads there later, or because of much lower population density, or because of different local health policies). I will not go into that as my head hurts just by thinking about it (I am maintaining a strict regimen of responsible alcohol consumption during the lockdown, so that is NOT the reason for the headache, thanks very much for asking).

I will add, however, one comment from Rhode Island Governor Gina Raimondo (another fellow Italian, and a former medtech VC) on Mon. Apr 13. Her remarks came during the launch of a regional coordination initiative between governors of NY, NJ, CT, RI, DE, PA, MA: “The reality is this virus doesn’t care about state borders, and our response shouldn’t either.”

I will focus below mostly on the US, since this is where I now live. I will also try to translate some lessons hopefully learned by other countries who entered the peak of their regional clusters before the US. For some background, please refer to the previous articles in the Timmerman Report.

Before I start, a (not so quick, it turns out) reminder. Lesson No. 1 with exponential infection spreading: you might think you are running ahead of the viral spread, but it is probably the virus that is way ahead of you.

There is now substantial evidence, as if things were not bad enough, that this virus has an R-naught (R0) probably well in excess of 2, which makes it very infectious indeed. If you want a primer on this, and do not want to read my older Timmerman Report posts, you should watch a movie. 

“Contagion”, the eerily prescient film from 2011 on a fictitious pandemic originating from Hong Kong, in my opinion, should be shown in every house all around the world for the next 6 months. It should become practically required school viewing, and get a “posthumous” Oscar. Watch, in particular, the TV interview Jude Law’s character gives roughly mid-film. Though a great actor (one of my wife’s very favorites), he is not a good guy in the movie (and I am still confused by how he manages an Australian accent at times). BUT he is spot-on about exponential spreading. The scientific consultant to the movie, Larry Brilliant, helped eradicate smallpox. Apart from having the most apt last name in the universe (can you tell I am jealous? No? Did you forget my first name? Go check your Shakespeare), Larry is a GREAT epidemiologist: read more about him here.

Apart from a few, required dramatic Hollywood flourishes (a vaccine created, manufactured and distributed in only 133 days against a totally new virus is practically science fiction; viruses do not “mutate” dramatically that quickly, etc.), the movie covers incredibly well virus provenance (also from bats originally), infectivity from touch (do not touch your face, wash hands), and contact tracing, as well as many other important aspects (who gets vaccine first etc.). I watch it almost once a week now. It does also have a happy ending, I think. This tragedy we are living through will, too.

Now, back to “when”.

I am assuming by now that you are all more or less familiar with the three gating “phases” to re-open US states’ economies in the coming months: after all, the audience of this report is quite knowledgeable about Phase I, II and III trials. And this is very much a trial we are all going through.

For those who are not as avid a consumer of news (or not US-based), the US administration has given state governors guidance and criteria on how to reopen state economies. Note that states (from the very little I know of the US Constitution) very much have discretion on how to execute these guidelines. The full outline from the administration is here.

There are a number of gating criteria which need to be satisfied before entering Phase I:

  • Downward trajectory of influenza-like illnesses (ILI) AND COVID-like syndromic cases reported within a 14-day period;
  • Downward trajectory of documented positive cases within a 14-day period AND downward trajectory of positive tests as a % of total tests within a 14-day period (with flat or increasing volume of tests)
  • Hospitals need to be able to treat all patients without resort to crisis care (i.e., I guess, without having to ration critical care like ICU beds or ventilators); AND they need to have a robust / at scale testing program in place for healthcare workers (including serological testing, not yet available).

Subsequently to satisfying these criteria, each of the phases should last, at a minimum, 14 days. Phase I includes many of the current lockdown measures (avoid non-essential travel; do not gather in groups). BUT it says venues such as restaurants, churches and sport arenas “can operate under strict physical distancing protocols”. In Phase II, non-essential travel can resume, schools can reopen and bars can operate “with diminished standing-room occupancy”. Phase III is almost back to our old definition of “normal”, in which states that are seeing a continued downward trend of symptoms and confirmed cases could allow “public interactions” with physical distancing and unrestricted staffing at worksites. Visits to care homes and hospitals can resume and bars can increase their standing room capacity. The at-risk demographic should still avoid public, crowded areas.

So, back to “how”. What do we need to achieve to get even to Phase I?

  • Testing: the guidelines rightly focus on tests as a key gating criterium for starting to think about re-opening. We need two types, and probably many different formats of test, since there is a tradeoff between speed vs volume to think about: a) RNA tests, to check if you have virus genetic material in you (which, presumably, means you are / have been infected); b) serological tests, unfortunately not yet really available at scale in the US, to check if you have developed antibodies against the virus (which, presumably, means you have been infected and have developed some sort / level of immunity, at least for some time). What we need is BOTH types of testing, in massive scale, deployed as soon as possible BEFORE we can think of re-opening. Ideally you should be able to test ~1% of the US population per week with RNA tests, and very, very roughly half of that in serological tests. We are, unfortunately, very far from achieving that level of scale in the US. Just to give some statistics, as I write this morning Eastern Time April 17, the US (population ~330M) has performed ~a total of 3.4M RNA tests (~1% of the US population, or ~10k tests/1M people), of which ~678k came back positive (~20%). In comparison, Germany and Italy (population ~80M & 60M respectively), have performed ~1.7M and ~1.2M tests, or roughly 2x as many as the US per 1M population. So, it is absolutely essential to increase the volume of RNA tests by a factor of at least 5-10x in the US in the next 4-5 weeks. The speed of obtaining a test result also needs to dramatically increase: developing rapid testing formats that can provide a response within 15-30 minutes is essential to allow contact tracing and to isolate infected people immediately (more on contact tracing below). Serological testing is also absolutely necessary. There have been, and continue to be, a series of issues on these tests but a number of diagnostics companies (from Abbott to Roche) are announcing rapid plans to scale up their platforms to address these. That said, there is unlikely to be any serious availability of serological testing until June at the earliest. Without serological testing, it is very hard to identify immune people and therefore to “release” them back into the community, with the obvious positive implications for the economy, healthcare infrastructure, etc. Also, and very important: as I write, NY State + NJ represent ~44% of all confirmed cases in the US, and have performed ~21% of ALL tests in the country (worldometers.info/coronavirus). So, we have a long way to go. If you want to really geek out on what does it mean to “scale testing”, I have written a few words in a little appendix at the bottom of this post. (See “Addendum”)
  • Contact Tracing: this piece of the puzzle is extremely important, as ramping up testing capacity is necessary but not sufficient to contain the pandemic. What should you do once somebody has tested positive to the RNA test? They should be isolated immediately, ideally away from their family, and everybody they have been in contact with should also receive the test: all their “contacts” need to be “traced” and tested. This is all the more important as there is now substantial evidence that: a) there is a substantial (probably ~50%) number of people who are infected and contagious but asymptomatic, BUT ALSO b) that people who will go on to develop symptoms start spreading the virus starting ~2 days AHEAD of showing symptoms. Contact tracing is equivalent to old shoe-leather detective work, and involves extensive interviews with the people who tested positive, their families, their co-workers. Therefore, it requires a large number of trained individuals to be performed correctly. It is very labor intensive. There are initiatives for automating some of that hard task by using phone apps to supplement human operators (S. Korea and Singapore have used similar apps with great success): Google and Apple are joining forces to use their combined dominance of mobile phone operating systems to develop such an app. Salesforce is also joining the fray. There are obvious privacy concerns associated with such an app and its use of location and contact data: those are not for me to address, here or elsewhere, and they are important concerns. That said, I do not personally believe, seeing the level of spread of the contagion in the US, that there will be a sufficient number of qualified trained human operators that could perform this task without some technology support.
  • PPE / Masks: we need to continue (start??) ramping up manufacturing of protective equipment and masks for healthcare workers (and eventually for the general population). Data reported from Italy are staggering and scary: roughly 10-20% of all healthcare workers in Lombardia tested positive to the virus. These selfless warriors are literally sacrificing their lives to care for patients. No effort should be spared to provide them and the hospitals they work in with equipment and relief. We are talking multiple hundreds of millions of masks needed per month: healthcare workers need to be able to use multiple (3-5) N95 masks / day to take a break, drink and eat, and not have to recycle the same mask, which is sadly the case right now. It is also important, since there is evidence of aerosol transmission with this virus, that people use masks in general if/when leaving their homes. Since we do not know if a person is spreading the virus or not, masks help “contain” the radius of infectious particle spread, as well as provide some protection for at-risk demographics (especially if masks are N95 or N99 type).

Until we see ALL these fundamental steps in place, NATION WIDE, it is very hard to start thinking about “when.” If only New York were to follow these steps, then it’s only too easy for people to spread the virus elsewhere as people travel around the country by planes, trains, buses and cars. What we are doing right now with social distancing measures, and at incredible, horrific economic costs, is lowering R-naught (R0), the rate of infection of the virus, below 1. This is essential if we are going to catch up.

There is evidence it’s working. Every day there is a lower number of new people who become infected. This is a prerequisite step to be able to reduce the number of infected people to manageable numbers.  The painful work of social distancing to bring down the rate of transmission has to be done first. Without that step, testing and contact tracing are impossible: there are just too many people infected to trace all their connections.

Think of New York City, with roughly ~50% tests coming back positive: how do you trace contacts in a densely populated urban area, which necessarily requires the use of public transportation to function, when there are so many cases? Also do not forget, New York was very much part of the first wave: the same will apply to any other large metro area as soon as we relax restrictions, without having a commensurable capacity of performing tests/contact tracing (and provided we have rapid assay formats).

So, it is sensible, in the government’s guidelines above, to stress a “reduction” in the number of positive tests over 14 days (roughly the period required for a person to not infect others any more). This should allow the current pool of people infected to shrink, perhaps even very substantially, to numbers low enough to be traced if tested positive. I also encourage you (if you have not gotten to watch the movie yet) to do a mental exercise of how many people we interacted with in close proximity, and how many surfaces we used to touch during the “old normal” days we used to live in. It is literally hundreds, at least in my case (Italians are eminently social and we are also quite tactile in our social interactions). Again, watch “Contagion” (Disclosure: I do not receive royalties on the movie streams…). 

So, now to the (blind) guessing part: “when” do we think we might have figured out the “how” well enough to hazard some projections for when Phase I will start?

Spoilers: I do not think it is going to be before June, at the earliest. This is not a pessimistic projection, I believe. PLEASE take that with a “couple of mines” worth of salt. I would really like to see some random RNA and serological testing in a couple of places (outside of NY or NJ or Michigan) to understand more how diffused the virus is in the country. As of this writing on Apr. 17, we are still flying blind. I cannot wait to see the results of the serological random survey of 100,000 people that Germany is performing.

Some other random data points to support the very rickety framework I used to come to that “June” guess:

On Apr. 14, Anthony Fauci (in the impossibility that anybody who reads this does not know who he is, “Tony” is director of the National Institute of Allergy and Infectious Diseases (NIAID) and member of the White House Coronavirus Task Force), told the Associated Press that the May 1 reopening projection was “a bit overly optimistic” for many areas of the country. That’s because a strong testing and tracing system would be needed before social distancing measures were lifted. I guess we all agree with the good doctor here, especially since Fauci is an Italian name (which, hilariously enough, means “Jaws” in Italian: the comic implications / associations with the movie of the same title and the various decisions made by elected officials in said movie are too funny to mention). “Overly optimistic” feels a bit “overly kind” to that prediction statement, but, you know, some people have to be mindful of their audience.

Yesterday, Apr. 16, New York Gov. Andrew Cuomo said that his state would remain under stay-at-home orders until May 15. New York is likely beginning to descend the slope of the infectious spread. Note that a number of other countries (from Italy to the UK) have repeatedly pushed off the timing of a potential “re-opening” due to the scale of the infection spread being detected as more testing has gone online. I am guessing the same thing will happen here, especially since New York is detecting a positive testing rate of ~50% of tests – an incredibly high rate. The statistic can be biased since they are only testing people at high probability of being infected, but still.

Germany will reopen schools on May 3, with Austria even earlier than Germany. Both countries did a great job of containing the infection early on, have first-class and abundant hospital critical care units (Germany has the highest ICU bed concentration probably in the world) and both scaled up testing very early. So, even states and cities which were very much hit in the first wave might need to wait longer to get there. Not every city / state hit in the first wave is in the same spot: The San Francisco Bay Area and all of California are way ahead of the curve. So is Washington state: both acted early and ramped up social distancing / testing early, and have also much lower population density than New York City.

The issue is then, again, the “where” to decide the “how” that gets us to “when”: you can see here when different states issued stay-at-home orders. The differences are very wide, and frankly, puzzling. How do we solve for interstate travel/traffic to avoid a large second wave, at the same time as summer kicks in and people want to escape their home confinements and rejoin families/ friends as soon as restrictions are lifted?

This is a huge problem, and has massive implications for how we behave. I do not think we can simply rely on individual self-policing of behavior. I know this does not jibe with the ruggedly individualistic, “frontier” culture of the country. But, guess what: the virus does not care. We should.

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.

Addendum: More on Testing

“Testing capacity”, as well as “contact tracing” are very vague terms. As I am not sure everybody is familiar with what they actually mean, here are some thoughts: to test and contact trace effectively, at the scale we are talking about here (literally hundreds of millions of test), you need to think and coordinate a huge number of factors (each of which is very likely a rate-limiting factor). Some of these factors are: type of testing (batch testing in a central laboratory, which can process at high throughput a large number tests but has slow turnaround, vs localized testing in city hospitals?); number of locations where tests are available (dense cities vs rural areas: you do not want people piling up in a queue to get tested); number of total test kits; reagents needed to produce the tests (which are in very scarce supply right now); personnel trained to extract the material needed to perform the test; personnel trained to perform the test (they are not necessarily the same); protective equipment needed for all this personnel. Same thing for contact tracing: it is a process that needs to be taught, requires a certain amount of psychological expertise and understanding of what is going through the head of the person in front of us, who might well be under a certain amount of distress. So it’s hard to train thousands of people to perform this without some technical tools to aid in contact tracing. So this is not simple. And every US state should not have to go through a steep learning curve to develop all this infrastructure, in all its complexity and inevitable mistakes, on their own. This is where it pays to adopt best practices in a coordinated fashion across the country.

17
Apr
2020

Getting it Right: Walking the Diagnostic and Serologic Testing Tightrope

Ruth Etzioni, Full Member, Division of Public Health Sciences, Fred Hutch Cancer Center

Last week I felt like I was coming down with something. My throat hurt and I had a bit of a dry cough.

A dreadful feeling set in. Obviously, I had COVID-19.

My mother and her partner, both in their 70s, are currently living with me in Seattle. My friend’s son had recently returned from New York. A few days earlier, I had chatted briefly with him (outside, keeping my distance) not realizing he would be diagnosed shortly thereafter. These factors made me eligible for testing through my employer, Fred Hutch.

Thankfully, I was negative.

I never thought that my test might have been a false negative, that I might actually be harboring the virus and still capable of spreading the virus to those around me. No, in this case I had confidence in the accuracy of the result. I heaved a sigh of relief and returned to my socially distanced life. When my symptoms cleared up quickly, it seemed to confirm that the test had been accurate.

But many people who get tested for COVID-19, and are told they do not have it, are not so lucky. It has been estimated that as many as 30 percent of patients with negative test results are “false negatives.” That means they actually are infected, and able to infect others.

Many of us trust the tests we take, and believe the yes-or-no results they return. But we need to be more curious.

In the fraught deliberations about the optimal path to re-opening the US, one theme has emerged louder and clearer than any other: testing, testing, testing. In practically every forum and on practically every stage a consensus has emerged that if widespread rapid testing is not made readily available, and easily accessible, the US cannot reopen for the business of everyday life. But it’s not that simple.

There are two main types of tests. Diagnostic tests are designed to tell you whether you have the virus: are you sick now? Antibody tests, also called serologic tests, are designed to tell you whether you had the virus in the past: were you sick and are you now immune?

It is not altogether clear which test is the magic bullet that will return our lives to normal. Right now in Wuhan, both are being used; diagnostic tests to find existing asymptomatic cases and serologic tests to learn how much of the population still remains susceptible.

There are at least 95 antibody tests and the number keeps going up. Yet, we have little information about how well they work. This is most certainly not front and center in press releases and advertisements.

We need to be much more curious about this.

No test is completely accurate. There are false negative results, meaning that the test is negative, but you are sick. There are also false positive results, meaning that the test is positive, but you are just fine. And some tests have much better performance than others; their error rates are lower.

Development of new tests involves rigorous assessment of these two types of testing errors, by examining the test results for people known to have the disease and known to be disease-free. The process of actually creating the test involves a lot of tweaking and refining to produce error rates that are acceptable. It’s a little like having a custom jacket tailored to your size and build. The jacket fits you well, but how will it fit the average person?  

To assess this, the test performance needs to be confirmed, or validated, in a whole new sample, with no tweaking allowed. This is like seeing how well your tailored jacket, sitting in a boutique, fits folks coming in off the street. Many of the new antibody test have not been validated. This means that the error rates cited (if you can find them) are likely to be too low, making the test performance seem overly optimistic.

Different types of testing errors have different consequences. Sometimes people worry more about a false negative than a false positive. Women going for their annual mammogram are far more concerned about a false negative – not finding a silent tumor – than a false positive – having an unnecessary biopsy for something that looked suspicious but was actually nothing.

What are the consequences of testing errors in the case of COVID-19 tests?

For diagnostic tests, a false negative test has two potentially fatal consequences. First, it means that an infected person can continue in principle to keep spreading the virus; second it may delay the opportunity to offer potentially life-saving treatment. A false-positive test, on the other hand, is less consequential; it will increase the likelihood of self-quarantine for a time. Thus, for diagnostic testing, it is clear that we need tests that are highly sensitive, meaning that they almost never miss a case of the virus.

Interestingly, the opposite is true for antibody tests. Let’s first note that these tests are really only useful for asymptomatic individuals who do not know whether they had COVID-19 in the past. A false negative antibody test means that a person who has had the virus and recovered gets told they never had it. This is unfortunate for the individual who is immune and does not know it, and will certainly put a damper on their lifestyle. If the opportunity exists to do serial testing, then requiring more than one negative test result could bring down the fraction of people so affected.

In the case of antibody tests, the real problem is the false positives. The false positives will be told that they have antibodies but in fact they don’t. They will likely resume life with a vengeance (I know I would), distancing themselves from the memories of social distancing, potentially exposing themselves to becoming infected and then spreading the virus to others. So, in the case of antibody tests, we really need to focus on making sure that the false positive error rate is exceedingly low.

How low is low enough? It turns out that we can actually answer this question, but it requires knowing what fraction of the population is still susceptible. Even in Wuhan, only 2-3 per 100 people tested had antibodies, so the vast majority were still susceptible. In the state of Washington, the early center of the US outbreak, the fraction of people with antibodies is closer to 1 per 1,000. This means that even if the false positive rate is very low at 1%, 9.9 people per 1,000 tested will be wrongly informed that they have antibodies; scaling up, this would amount to 9,900 per million people tested going about their lives under the mistaken impression that they were immune. It is easy to see how this could provide sparks for new clusters of infection in the absence of a vaccine or other means of eradicating the virus.  So, when the prevalence of true cases is low, the antibody test has to have a vanishingly small false positive rate.

When we think about getting a test, particularly for a disease that scares us, like COVID-19 or mammography, we naturally think about sensitivity – if I have it, will the test detect it?  We want to know that there will be no false negatives. But false positives can have enormous costs, not only for individuals but for society. A study of women getting mammograms estimated that, over ten years, at least half of women screened annually received at least one false-positive recall. Many went on to have unnecessary biopsies, with all the pain, cost, and anxiety these create.

There are many other questions about the new antibody tests, including the extent to which they actually measure immunity. This only reinforces the message: Be curious; Trust less.

I should have heeded my own advice when I went for my own COVID-19 diagnostic test. It’s human nature to breathe a sigh of relief, like I did with that negative result. But it’s not too late to be skeptical. I’ll do better when I get tested for antibodies one day. First, I’ll ask which test is being used, and whether it is from a reputable source. Then I’ll read the fine print about the testing error rates.

17
Apr
2020

Hunting for Antibodies Against COVID19: George Scangos on The Long Run

Today’s guest on The Long Run is George Scangos.

George is the CEO of San Francisco-based VIR Biotechnology.

VIR has been all over the news the past couple months. It’s at the forefront of companies racing to discover and develop broadly neutralizing antibodies against the SARS-CoV-2 virus that’s sparked a global pandemic.

George Scangos, CEO, VIR Biotechnology

VIR was founded in early 2017 by some deep pocketed venture investors who saw big strides being made in immunology for cancer, but a gap in how that immunology was being applied against infectious diseases.

VIR was able to pull together a lot of money, a variety of technology platforms, and a lot of smart people over the past three years. If you have time for a deeper backstory, listen to a previous episode of The Long Run from a year ago.

The company has been making progress, but the value of its work became more abundantly clear as the world came to grips with the seriousness of COVID19 in January 2020. It has an antibody discovery platform that could be quite useful in the near term. VIR has working intensely with partners that can help with various aspects of the effort – GSK, Alnylam Pharmaceuticals, WuXi and Samsung Biologics among them.

In this conversation, George spoke about the scientific rationale for what VIR is doing, and the changing approach to collaboration and regulation necessary to operate at pandemic speed.

Now, please join me and George Scangos on The Long Run.

The Long Run is sponsored by RareCyte

RareCyte delivers Precision Biology solutions for circulating tumor cell analysis designed to accelerate the pace of your cancer research. RareCyte leverages a world-class assay design team and end-to-end platform with biomarker-enabling technology to provide CTC assays that are rigorously validated for accuracy and reproducibility.  

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17
Apr
2020

Bridging the Social Divide in the Pandemic

Alex Harding, MD, associate, Atlas Venture

Halfway through an explanation to a patient I am treating for a suspected case of COVID-19, I realize I don’t know the Spanish term for “sanitizing wipes.”

I’m in my new clinical home base, where Massachusetts General Hospital has cordoned off special care for COVID-19 patients.

There are modular plastic walls and bright fluorescent lights. A few weeks ago, this space was converted from a parking garage for ambulances into a fully functional medical clinic for patients with respiratory illnesses.

The floor still has painted lines to indicate where the ambulances are supposed to park. Drops of motor oil from a leaky ambulance engine have stained the concrete. I’m sitting on a stool in front of a computer on wheels. The only other items in the room are three boxes of rubber gloves, a wall-mounted hand sanitizer dispenser, a floppy disposable stethoscope, and an exam table where my patient is perched, hands on her knees, waiting for me to finish my sentence.

I fill in the blank with “paper towels with Lysol.”

She nods, and we move on.

She almost certainly has COVID-19. She has a high fever and a cough, muscle aches throughout her body, and a sensation of tightness in her chest, like she can’t take a full breath. She also has an important risk factor: She lives in Chelsea, MA.

Chelsea, a community just outside Boston with 40,000 residents, most of whom identify as Hispanic or Latino and many of whom are below the poverty level, has been hit harder by COVID-19 than any other town in Massachusetts. Although it is separated from Boston by just a short bridge across the Mystic River, it nonetheless has an infection rate nearly triple that of Boston.

There has been some speculation that this discrepancy in infection rates is because of biological differences that may place Hispanic people at higher risk of infection.

I favor a simpler explanation—that low socioeconomic status is a risk factor for COVID-19.

My patient had a retail job and was still required to go into work while the pandemic ripped through Massachusetts. Being at work in a retail setting, coming into close contact with many customers per day, put her at higher risk for exposure than people in white collar jobs that can be done from home.

She also lived in a three-bedroom apartment with her husband and kids, parents, and her brother-in-law’s family. Her nephew had been sick with fever for several days. Any of those relatives, and all the people they came into contact with, could potentially have spread the virus to her.

Based on her symptoms and risk factors, I advised her to isolate herself in a room to avoid getting her family members sick. She asked me how she could do that when her nephew was already self-isolating in another room, leaving just one room for 8 other people. The math didn’t work out.

People from lower socioeconomic groups are less able to perform many of the protective measures that reduce the risk of COVID-19. They often don’t have jobs that can be done from home and can’t afford to give up hours. Call in sick day after day, and you might lose your job. They sometimes live in close quarters with large extended family units, making it hard to perform home isolation. And they lack certain amenities that facilitate social distancing. People without laundry machines at home have to use laundromats, for example, which means potentially coming into contact with other people, or touching surfaces that have been touched by many other people.

Lower income patients also have a more difficult time accessing healthcare. They may lack insurance and fear getting a large medical bill. They may not have a primary care doctor. And in the case of immigrants, like many of the people in Chelsea, they may fear deportation if they seek medical help and are discovered to be undocumented. As a result, we have been seeing many patients wait until their illness is severe before seeking care. Sadly, some have died because they sought help too late.

In theory, coronavirus is indifferent to someone’s ethnicity and bank statement. But in practice, social factors play a powerful role in determining a person’s health outcomes. Chelsea, as a low-income community, has felt the consequences of that imbalance during this pandemic.

While all our attention is turned to COVID-19, this is a lesson that applies to drug development in general. Basic science might happen in test tubes, but human disease happens in the real world and is just as complex as human society itself.

Unfortunately, biopharma’s clinical trials have not geared toward the people in greatest need. In a review of 230 oncology trials from 2008 to 2018, for example, only 6.1% of patients were Hispanic and only 3.1% were black—far below national averages for both groups. The result is that our future drugs are being studied in distorted populations that do not represent all the patients who could be treated with those drugs in the future. We are not accurately capturing the biological differences between ethnic groups, but, just as importantly, the social differences between us that affect how we experience disease.

Mass General Hospital and the local government are working together to counteract the disadvantages facing Chelsea during this pandemic. The hospital has given away thousands of ‘quarantine kits’ containing masks, soap, and educational information to residents. A hotel is now accepting COVID-19 patients who cannot effectively quarantine themselves at home. Mass General is undertaking mass testing campaigns to identify cases in the community early. And the hospital has opened the doors of its clinic in Chelsea to everyone who comes in, regardless of whether they have a Mass General doctor.

We’re starting to realize that we need to do more to support vulnerable communities like Chelsea during the COVID-19 pandemic. We should take this lesson and apply it to all aspects of medicine and drug development. People respond to diseases and therapies differently based on socioeconomic factors.  We need to make sure not to leave any groups behind.

14
Apr
2020

Why COVID-19 Can’t Be Directly Compared With the Flu

Ruth Etzioni, Full Member, Division of Public Health Sciences, Fred Hutch Cancer Center

A few years ago, I was preparing for a live radio interview about prostate cancer screening, my main area of research for the past 20 years.

As a statistician focused on getting the numbers right, I disagreed strongly with the new national recommendation from an influential task force that guides practice and reimbursement. Members of that task force argued against routine population-wide screening for this most common cancer in men, saying essentially that there wasn’t any discernable benefit.

In the last few seconds before we went on air, the host whispered to me, “just don’t talk about numbers please!”

The problem was not with the numbers, I told him. It was with a faulty comparison.

To support their recommendation, the panel had relied heavily on data from a national, randomized clinical trial that showed similar numbers of prostate cancer deaths among men screened and not screened.

This was the obvious comparison to make, the one mandated by established research principles, and the one for which the data was readily available.

But it was the wrong comparison. 

It turned out that the national trial had started late, after prostate cancer screening was already commonplace. It was not a comparison of screening versus no screening; rather, the trial compared a group that had been screened against a group that had been screened almost as much. 

As we face critical decisions about whether and how to resume our daily lives in the midst of the COVID-19 pandemic, we are being tempted into making the wrong comparisons again. This may be dangerous for individuals, but it could be catastrophic for national and state policies.

Comparison 1: Number of lives lost (so far) because of COVID-19 versus the seasonal flu. Even as the numbers change, the current projections of COVID-19 deaths (estimated at approximately 60,000 as of yesterday) are being compared with the reported deaths from flu in the current 2019-2020 season.

The CDC’s latest estimates are that 24,000 to 62,000 US deaths can be attributable to the flu in the season stretching from Oct. 1, 2019 to Apr. 4, 2020.

If all you did was look at the current COVID-19 projection, and compared it with the upper-end worst estimate of this year’s flu season, you could say they are in the same ballpark.

But this is a faulty comparison. The deaths from these two illnesses are occurring under completely different circumstances.

The COVID-19 deaths are in the presence of a nearly national shutdown, with school and university closures and widespread practicing of social distancing. The flu deaths from last season occurred largely in the absence of all of these interventions. What would the number of flu deaths be if every winter we locked down early in the season, in October or November, like we are doing now for COVID-19? That is the question to ask.

The takeaway here is that if we want to compare COVID-19 deaths with flu deaths, we need to do so under the same prevailing policies. We could compare reported COVID-19 deaths with the flu deaths expected under current work/school closures and social distancing policies. Alternatively, we could compare COVID-19 deaths expected in the absence of any of these interventions with the flu deaths actually reported.  Either way, we would not have hard data from past experience to work with. We would have to resort to modeling. So let’s talk about models.

Comparison 2: Numbers of COVID-19 deaths reported or projected today versus those projected by earlier models.

Much has been made recently of updated projections of COVID-19 deaths that are considerably lower that what was predicted based on earlier model reports. A highly influential model from Imperial College London, early in the outbreak response, predicted upwards of 2.2 million deaths in the United States if policymakers did nothing to mitigate the spread of the virus. Subsequently, a US-developed model from the Institute for Health Metrics and Evaluation (IHME) had projected between 100,000 and 240,000 deaths, taking into account the ongoing mitigation policies. This model is the one most frequently cited by the Trump administration, but its predictions have changed considerably as the pandemic has evolved, and now stand at approximately 60,000 COVID-19 deaths in the US by the end of August.

Some people are using the changing numbers to argue that our efforts to mitigate the pandemic have been wildly successful. Some are arguing that the numbers are proof that we overreacted and intervened too dramatically. This comparison of current model projections with earlier ones is even fueling calls to rapidly unwind social distancing policies and reopen the economy.

This is the wrong comparison to be driving any decisions about relaxing the current restrictions.

We can’t compare current observations or predictions from the model with earlier predictions as if they were set in stone. This gives far too much credibility to earlier versions of a model that could not have been expected to provide accurate predictions in the first weeks of COVID-19 spread in the US.

Originally, the IHME model was constructed to replicate the increasing and subsequently declining pattern of deaths officially reported by China. To port this model to the US setting, the modelers had to compress or stretch the China curve to match the accumulating US data. In the early days of the pandemic in the US in early March, the IHME modelers had about 70 days of data from China to work with. But there was very little data to tailor the model to the US experience. The current version of the model is privy to a more complete, but still evolving, picture of COVID-19 in the US; it also incorporates data from Europe. No wonder the results keep changing. 

The takeaway here is that comparing different versions of a model to learn about facts on the ground is a mistake. If we want to discern the likely impact of mitigating policies, we need to be using the same version of the model to do the comparison.  This could help us to understand how well current polices might be working and what to expect if in their absence. We should not lose sight of how limited models are and how uncertain their predictions can be, but we should at least use and interpret them in a defensible way.

And what of prostate cancer screening?

Ultimately, the question of prostate screening benefit was put to rest by a trial conducted in Europe before screening became widespread there, along with a set of related modeling studies based on all of the accumulated data. My team published that work in 2017, confirming that screening saves lives.

The general lesson here is not about models, or cancer, or even COVID-19. It is about thinking clearly when using statistics to make life-or-death decisions. The numbers do matter, but first you have to get the comparison straight.

9
Apr
2020

GSK Partners With Vir on Antibodies, Pfizer With BioNTech on Vaccines, & the Testing Fiasco Continues

Luke Timmerman, founder & editor, Timmerman Report

Here we are heading into Easter Weekend. We’re the wealthiest country in the world, and the undisputed superpower of biomedicine.

Yet we’re still playing a game of catch up against SARS-CoV-2, which apparently our intelligence community knew was emerging around Thanksgiving.

Even after months to get our act together, we still don’t have a coordinated national strategy on exactly how to do the large-scale testing, contact tracing, and the kind of surveillance necessary to allow a reasonably safe return to normal life and economic growth.

The pandemic crosses international boundaries easily, but it could end up hitting the US harder than any other country by most measurements. We currently have the most confirmed cases of any country in the world by far – about 3 in 10 people worldwide with COVID19 are in the US. Latest death count: 16,327, and with about 2,000 more adding up every day at the current rate. The death rate on a per capita basis was less than 20 per million last week. It’s at 50 now.

How are we doing on testing to locate the virus, isolate carriers, and tamp down the community and nationwide spread?

Terribly.

See the dashboard below, which receives data feeds from Johns Hopkins and the World Health Organization. Look especially at the far-right column. On a per capita basis — the number of tests per million residents — the US still lags WAY behind Germany and Italy.

Scott Gottlieb and colleagues at the AEI estimate we need to run 750,000 tests a week. Yesterday, we ran about 43,000. That’s less than halfway where we need to be.

We could be doing so much better. We must get our act together.

Industry can’t do it all alone, because it can’t perform the essential coordination function that only the US federal government can. But industry has a big part to play with rolling out existing diagnostics, and developing technology for tracing, surveillance, treatments and vaccines.

Those of us who can’t directly work on projects can still do a lot. I encourage Timmerman Report readers to go to the new Community Action page on this site, where you’ll see two excellent ways to help. One is by donating your time and money through Life Science Cares, to support the most vulnerable members of our society. Another is by giving to Fred Hutch, to support science. Fred Hutch is best known for cancer research, but it’s strength in infectious disease has been building for decades, and it is evident for all to see today. Trevor Bedford and colleagues blazed a trail with the NextStrain.org resource for sharing viral genomes, and it’s been immensely useful in the fight.

Go to the TR Community Action page to learn more, and see how you can give to Life Science Cares and Fred Hutch’s world-leading COVID19 research.

Now, please review the big events of the week in biotech via Frontpoints.

TR Pandemic Coverage

Science From Around the Web

Policies

  • A $30 Billion Gamble. Pandemic Expert Calls for Making Vaccines Before We Know They Work. Apr. 7. STAT. (Helen Branswell)
  • Ventilator Shortage is Here. Essential Generic Meds Are Next. Apr. 9. Washington Post. (Josh Resnick)
  • Preparing for the Next Pandemic. Apr. 3. WSJ. (Susan Desmond-Hellmann)
  • Hospital Experiences Responding to the Pandemic, Survey of 323 Hospitals, Mar. 23-27. Office of the Inspector General, US Health and Human Services Department. (Christi Grimm)
  • How SF’s Experience with HIV Shaped Its Coronavirus Response. Apr. 5. SF Chronicle. (Ryan Kost)
  • Americans Are Paying the Price for the President’s Failures. Apr. 7. The Atlantic. (David Frum)
  • Trump Team Failed to Follow National Security Council’s 69-page Playbook from 2016. Mar. 25. Politico. (Dan Diamond and Nahal Toosi)
  • Drugmaker Caps Insulin Costs at $35 To Help Diabetics, Who Are at Elevated Risk of Infection, During Pandemic. Apr. 7. The Hill. (Zack Budryk)
  • These Factors Will Determine How Bad It Gets in Each US Community. Apr. 1. STAT. (Andrew Joseph)

When to Reopen America?

The World

  • Why the Low Death Rate in Germany? Apr. 6. NYT. (Katrin Bennhold)
  • New Zealand Isn’t Just Flattening the Curve. It’s Squashing It. Apr. 7. Washington Post. (Anna Fifield)
  • US Accused of Modern Piracy, After Diverting Shipment of N95 Masks Meant for Europe. Apr. 3. The Guardian. (Kim Willsher et al)
  • US Military Medical Intelligence Spotted, and Warned Officials, of Budding Epidemic in Wuhan, China in November. Apr. 8. (ABC News)
  • Who Are the Heroes of the Pandemic in Europe? Scientists. Apr. 5. NYT. (Matina Stevis-Gredneff)

Communication

  • CDC Director Reaches Out to Groups Skeptical of the Pandemic Response. Apr. 9. Politico. (Darius Tahir)
  • What We Pretend to Know About Coronavirus Could Kill Us. Apr. 3. NYT. (Charlie Warzel)
  • Biologist Carl Bergstrom on Coronavirus Misinformation and Why We Weren’t Prepared. Apr. 9. CNBC. (Chrissy Farr)

Testing and Tracing

  • Germany Launches Smartwatch to Monitor Coronavirus Spread. Apr. 7. (Reuters)
  • Can Smartphone Apps Help with Contact Tracing to Beat Pandemics? Apr. 9. NIH Director Blog. (Francis Collins)
  • The Test That Might Exempt You From Social Distancing. If You Pass. Apr. 2. MIT Tech Review. (Neel Patel)
  • Blood Tests in One German Town Show 15 Percent are Immune. Apr. 9. MIT Tech Review. (Antonio Regalado)
  • Testing Survivors’ Blood Could Help Reopen US. Mar. 31. Washington Post. (Carolyn Johnson)
  • Thousands of Tests Are Going Unused at Academic Labs Operating Below Capacity. Apr. 9. Nature. (Amy Maxmen)
  • FEMA Ends Community-Based Testing Program. Apr. 9. CNN (Priscilla Alvarez)

Drugs and Vaccines

  • We Need a Vaccine. Let’s Get it Right the First Time. Apr. 8. Wired. (Maryn McKenna)
  • Therapeutic Ideas: Too Much Inflammation? Apr. 9. In the Pipeline. (Derek Lowe)
  • Drug Repurposing in SARS-CoV-2 / COVID-19, Preventing Maladaptive Immune Response. Apr. 8. (Robert Plenge Blog)
  • Former FDA Leaders Decry Emergency Authorization of Malaria Drugs for Coronavirus. Apr. 7. Science. (Charles Piller)
  • Remdesivir Data Preview, What to Watch For. Apr. 6. (STAT)
  • An Update on COVID19 From CEO Daniel O’Day. Apr. 4. Gilead Sciences.
  • Inside the Race for a Coronavirus Cure. Apr. 6. WSJ. (Joseph Walker, Peter Loftus, and Jared Hopkins)
  • Bet Big on Treatments for Coronavius. Apr. 5. WSJ. (Scott Gottlieb)

Financial Markets

  • Booming VC-Backed Biopharma, Despite Pandemic. Apr. 8. LifeSciVC. (Bruce Booth)

The Human Toll

Quote of the Week

“It’s very feasible we’re going to see multiple city- or state-level outbreaks across the country in the next few weeks and months,” said Maia Majumder, a computational epidemiologist at Boston Children’s Hospital. “They will start at different times and they will peak at different times.” – Apr. 1, STAT article by Andrew Joseph.

Worth Watching

Britt Glaunsinger, a molecular virologist at UC Berkeley, delivers an excellent one-hour lecture on the basics of SARS-CoV-2 virus. Includes its origins, how it gets into cell, replicates, and causes immune havoc.

Annals of Distrust

Who do you approve or disapprove of during this coronavirus crisis – corporations in general, the drug companies, the media?

See these results from CNBC poll.

Meg is a friend and respected colleague, but these kind of polling questions annoy me for multiple reasons.

For starters, not everyone in “pharma” is the same. There’s Hal Barron and a bunch of people I could name on one end, and there’s Daraprim price-gouger on the other end. There’s a lot of in between.

Same thing in “the media.” It’s not a monolith.

Partly for this reason, I don’t like the term “the media.” It’s too vague to have any meaning at this point. “The media” should be more precisely defined to create a distinction between professional journalists and info-tainers with large audiences (Rush, Hannity) or popular TV entertainers (Stephen Colbert, Trevor Noah).

When you look at actual journalists at responsible outlets, the media has performed extraordinarily well in this pandemic. Often, serious reporting and analysis is occurring under the extraordinary pressure of an advertising collapse. Even so, according to a CNBC poll, “the media” appears to have been effectively demonized.

Scapegoating mission accomplished.

While We’re At it, Who Exactly is ‘The Media’?

Does this guy with an opinion, and 565,000 followers, cited below count as “the media”?

God bless @andybiotech for stepping up to the plate to debate this guy, but in an all-out propaganda war, he who commands the most trolls wins the day, not he who makes the most persuasive argument rooted in facts.

Deals

GSK and VIR Biotechnology agreed to work together on antibodies for treatment against SARS-CoV-2 and other coronaviruses. The plan is to take VIR’s antibody discovery capabilities, and leverage them further with GSK’s functional genomics and CRISPR screening assets. GSK agreed to make a $250 million equity investment in San Francisco-based VIR, buying shares at a 10 percent premium. VIR has identified a couple of antibody candidates that appear to be good at neutralizing the virus in cellular assays in the lab. The companies hope to start Phase II studies in three to five months.

Pfizer and BioNTech provided further details on their mRNA vaccine collaboration against COVID-19. Germany-based BioNTech will pocket $185 million upfront (mostly via equity). If the vaccine is found safe and effective, there’s potential to scale up to “millions” of doses by the end of 2020, the companies said. Initial trials will be conducted in the US and Europe, starting this month, April 2020. If the vaccine truly works, the companies say they could scale up to meet demand with “hundreds of millions” of doses of the mRNA vaccine in 2021. The companies said they have “multiple” vaccine candidates to evaluate, meaning it hasn’t selected a single lead candidate to put all possible corporate firepower behind.

Waltham, Mass.-based Arrakis Therapeutics pocketed a $190 million upfront cash payment from Roche. The big company will now gain access to Arrakis’ platform, which adapts drug discovery tools to predict structures on RNA that are amenable to intervention with small molecule drugs. The new partnership provides Roche with access to a broad set of RNA targets “across all of Roche’s R&D areas.” (See TR coverage from the Arrakis early days, Feb. 2017)

South San Francisco-based Second Genome secured a $38 million upfront payment from Gilead Sciences. The companies will now work together to look for useful biomarkers for as many as five Gilead pipeline programs for inflammation, fibrosis and other diseases.

A pair of RNA interference companies that have clashed in the past, Alnylam Pharmaceuticals and Dicerna Pharmaceuticals, are now working together. Dicerna will lead a development program for siRNA drug candidates from each company against alpha-1 liver disease. At the end  of Phase III, Alnylam can opt-in to co-commercialize. The companies also agreed to cross-license some intellectual property, on a non-exclusive basis, for the treatment of primary hyperoxaluria. In a statement, the companies said the deal “puts the needs of patients and the patient community first,” presumably by directing more time and energy to drug development, and less toward IP litigation.  

Data That Mattered

Ann Arbor, Mich.-based Millendo Therapeutics said its Phase 2b study of its most advanced asset in development, livoletide, failed to meet its primary endpoint in the treatment of Prader-Willi Syndrome. This was a randomized, placebo-controlled study looking at hyperphagia (overeating) and food-related behaviors. Millendo said it’s shutting down the livoletide program, and shifting its resources to work on a couple of other pipeline programs.

Personnel File

Cambridge, Mass.-based Sage Therapeutics cut the jobs of 340 employees — 53 percent of its workforce. Most of the cuts are coming in the commercial group that’s been marketing brexanalone (Zulresso), a first-of-its-kind treatment for postpartum depression. The company said it will continue to supply existing prescribing sites, but no longer actively focus on new geographies. The company said the cuts will save $170 million a year, and it has $1 billion in cash left in the bank to invest in development programs for depression, neurology, and neuropsychiatry.

The Parker Institute for Cancer Immunotherapy named Frederic Pla as chief operating officer. Former CEO Jeff Bluestone left to join a startup, Sonoma Biotherapeutics. (TR coverage, Feb. 2020).

Waltham, Mass.-based Morphic Therapeutic hired Marc Schegerin as chief financial and chief operating officer. The company is developing oral small-molecule integrin inhibitors.

Redwood City, Calif.-based Bolt Biotherapeutics, a cancer immunotherapy company, named Edith Perez as chief medical officer.

Financings

Cambridge, Mass.-based Tango Therapeutics, the developer of synthetic lethal cancer drugs, raised a $60 million Series B financing. Boxer Capital led. (TR coverage of Tango’s Series A, Mar. 2017

venBio raised a new $394 million life sciences venture fund.

Lexington, Mass.-based Keros Therapeutics raised $96 million in an IPO of 6 million shares priced at $16 apiece. The company is working on treatments for hematological and musculoskeletal disorders. Few people noticed, but the stock traded up in its first two days, closing at $21.57 yesterday.

New York and San Diego-based Zentalis Pharma raised $190 million in an IPO of 10.6 million shares priced at $18. It’s a developer of small molecule drugs for cancer. It traded up steadily as well, ending its fifth day on the NASDAQ at $24.31.

Regulatory Action

The FDA cleared luspatercept-aamt (Reblozyl) for the added indication of myelodysplastic syndrome (MDS). It’s from Bristol-Myers Squibb and Acceleron Pharma. The drug was previously approved for treating anemia in beta-thalassemia patients.

European regulators cleared Takeda’s brigatinib (Alunbrig), as a treatment for first-line ALK-mutated forms of non-small cell lung cancer.

Pfizer got the FDA green light to market encorafenib (Braftovi), with cetuximab for patients with metastatic colorectal cancer that have BRAFv600E mutations.

 

7
Apr
2020

George Yancopoulos, That Rarest Of Species – A Physician-Scientist Still In Charge Of A Pharma

David Shaywitz

Growing up in an academic household (my parents are both professors at Yale Medical School, still engaged, as ever, in dyslexia research), it was perhaps inevitable that, outside of my parents, my first role model was the brilliant President of Yale University, the late Bart Giamatti (you know- Paul’s dad). 

The elder Giamatti inspired me so much as a teenager (his wife was my favorite high-school English teacher), I borrowed an excerpt from his wonderful “Earthly Use Of A Liberal Education” address as one of my high school yearbook quotes. It was right next to quotes from Watson, Crick, and Monod, lifted from Horace Freeland Judson’s magisterial history of early molecular biology, The Eighth Day Of Creation.

Bart Giamatti signature in David Shaywitz’s high school yearbook.

In college, as I pursued laboratory research in immunology, I first became familiar with the work of another intellectual superstar, who also became a role model — in this case, inspiring not only me, but also an entire industry: George Yancopoulos. When I was interviewing for MD/PhD programs in 1988-89, one of the highlights of the process was the chance to meet Yancopoulos (who I think was still a legendary postdoc in Fred Alt’s lab at Columbia University, though Yancopoulos may have stepped up to the faculty by that time). It was probably a smart decision not to go there to work with him (I had opted for Boston), because he shortly decamped in 1989 to become scientific founder of a tiny startup Leonard Schleifer had founded the year before. It was called Regeneron Pharmaceuticals.

I should say that I’ve never worked directly for Regeneron – the closest I came was when I joined DNAnexus as chief medical officer, and had the opportunity to participate in the early days of the Regeneron Genetics Center, for which DNAnexus was (and, I think, still is) providing key aspects of the cloud data infrastructure and analytics. It was DNAnexus’s participation in this project that played a key role in attracting me to the startup, and the project proved even more exciting than I could have ever anticipated.

I was thinking about Yancopoulos yet again this week while reading a Wall Street Journal article about emerging medicines in the pipeline for COVID-19. It was a characteristically well-reported Journal article, discussing projects from many companies, but Yancopoulos was, shall we say, not underrepresented in the proceedings. While other companies seem to have shared updates in the usual ultra-cautious how-are-the-media-going-to-screw-pharma-this-time kind of way, with Yancopoulos, you could feel his personal passion for the science jump off the page. It was a sense aided and abetted by screenshots of critical internal communication (photo credit: George Yancopoulos) and of a happy team celebrating a research milestone (photo credit: George Yancopoulos). 

It was the sort of enthusiasm and evangelism (don’t get him started on the “magic mice”) you might expect from a startup founder. Which, of course, he is. His brilliance, and unmistakable enthusiasm, for the science shines through in every interaction he has – with partners, investors, employees, and even with the media. (Listen to him on The Long Run podcast, October 2017.)

Maybe the best way to appreciate the distinct respect Yancopoulos has earned from me and so many others in the biopharmaceutical industry is to consider the contrast between leading tech companies and top pharmas. Many of the top tech companies are – or were, until recently — led by their engineer-founders: Zuckerberg at Facebook, Bezos at Amazon, Brin and Page at Google, Gates at Microsoft. Even after the founders departed, most top tech companies continued to be helmed by card-carrying engineers – Nadella at Microsoft, Pichai at Google, for example. (As for Apple – well, good luck figuring out what box Steve Jobs belongs in – or Tim Cook, for that matter.)

But move over to Big Pharma, and you see an entirely different set of individuals in the C-suite. You see no founders — understandable because in most cases, the companies were launched many years ago. But you rarely see physicians or scientists as CEOs or Presidents. Big pharmas are led instead by adept corporate managers.  To be sure, each of these companies has internal R&D, yet they have all become increasingly reliant on innovation that occurs elsewhere in the startup ecosystem, and which the big companies must someday pay a premium to acquire. 

Even more to the point, business success in biopharmaceuticals would seem to depend less on the understanding of medicine and science, and more on the ability to manage massive corporate structures. The thinking of investors is clearly that sophisticated corporate mangers can deliver shareholder value in a way that physicians and scientists generally cannot. Perhaps this is also why pharma doesn’t see a lot of acquihires – startups acquired just for the talent. In biopharma, intellectual property is the coin of the realm.

In tech, great software engineers are seen as likely to create something valuable time after time. There’s even a term in Silicon Valley around ‘10X’ engineers who some believe are worth 10x the average engineer and therefore must be aggressively recruited and earnestly retained. While experienced drug hunters are valued, you just don’t see same kind of human resources attitude in biopharma that you see in tech. Drug discovery is such a capricious and uncertain business that while you can certainly do it conspicuously badly, it’s hard to be consistently successful – too much seems beyond anyone’s control. The key job of pharma executives is figuring out how to keep a biopharmaceutical company going despite the inability to count on any specific research initiative leading anywhere. As I’ve discussed, this is a key challenge of running a “miracle-based business.

Which brings us back to Regeneron and Yancopoulos. It’s true Yancopoulos is technically not the CEO (that’s Schleifer’s job) – Yancopoulos is the President, head of R&D, and a member of the board of directors. The head of R&D is a position that exists at all biopharma companies and is generally staffed by distinguished scientific leaders like Mathai Mammen at Johnson & Johnson’s Janssen Pharmaceutical Companies, Hal Barron at GSK, and Roger Perlmutter at Merck. In practice, Yancopoulos has even more clout in his organization – he has the authority of the co-founder and President he is. 

The fact that a savvy physician-scientist is in charge shows. The company, now valued around $55B, behaves like a science-driven startup, with priorities set almost exclusively by internal research. While most of the pharma industry has embraced Joy’s Law – no matter who you are, most of the smartest people work somewhere else – Yancopoulos doesn’t behave like he believes this; he thinks he can lead the best biopharmaceutical drug discovery team in the world, and that this represents the company’s unfair advantage. (It has also attracted criticism from others in the industry who see this as hubris, and not very long ago questioned the company’s capability in late clinical development and effective commercialization; I’m not sure where Regeneron stands in these essential areas today.)

In some ways, I look at Yancopoulos the way you might have looked at the first molecularly-targeted oncology medicine, Novartis’ imatinib (Gleevec), from almost 20 years ago. Gleevec is an example that’s held up as representative of biomedical science, yet which turned out – at least for a while – to be far more the exception than the rule. As a physician-scientist with a passion for making medicines, I look at Yancopoulos as accomplishing what I and so many doctors and researchers hope to achieve. He has managed to take his personal passion for driving science into application and turn this into a successful, science-driven company that he continues to lead and drive, goad and inspire.

The question is why aren’t there more large companies like Regeneron, with more leaders like Yancopoulos? (Vertex may be another example.) Perhaps if Yancopoulos continues to lead the company to further achievements, there will be. In the same way ambitious engineers may be attracted to an organization built by Zuckerberg or led by Pichai, physicians and scientists would unquestionably gravitate to successful drug development organizations that hadn’t descended into corporate doublespeak, endless committees, and matrix management, but rather were informed by the scientific vision and founder’s passion – biopharma companies that were doing outstanding science and consistently winning because of it. Many eager researchers seek these qualities in startups – but imagine how wonderful it would be if you could find such a stimulating, challenging environment in an organization with enough resources to drive this vision forward through commercialization at scale.

Hopefully, this model will become more common – I would love to see the industry more beholden to medical science than to management “science,” and led by physicians who know what it’s like to take care of sick patients, and scientists who are daring, yet humble, because they fully appreciate the complexities of human biology.

But until then, at least we have Yancopoulos, continuing to run his company and speak his mind, in a spirit perhaps best evoked by Frank Sinatra:

Yes, there were times, I’m sure you knew
When I bit off more than I could chew
But through it all, when there was doubt
I ate it up and spit it out
I faced it all and I stood tall
And did it my way

7
Apr
2020

COVID-19 Models: What Makes Them Tick?

Ruth Etzioni, PhD, Full Member, Division of Public Health Sciences, Fred Hutch Cancer Center

As the COVID-19 pandemic unfolds, questions about its likely course are much on our minds.

How long will it last? How bad will it get? And are we doing enough to flatten the curve?

These questions are not about the past, but about the future. Models are now frequently cited in public by elected leaders to inform expectations and justify policy decisions.

But the models themselves are poorly understood. To many, the models appear to be black boxes which somehow combine biology, mathematics, and behavioral factors to miraculously produce precise projections of disease and its outcomes.

But all models are not created equal. There are key differences between the models that are currently being used to guide the national conversation — differences that should be understood by the public.

To illustrate just how different models can be, consider two of the most publicly cited COVID-19 models – one from Imperial College London (ICL), and another from the Institute for Health Metrics and Evaluation (IHME).

Both models project what would happen under actions to mitigate transmission, such as physical distancing over a period of time. Both models produce similar looking graphs that show infections, and deaths, increasing and subsequently declining, over the course of the pandemic. But their innards could not be more different.

The ICL model is a mechanistic model. This means it replicates the person-to-person process by which disease is transmitted between individuals as they interact within their households, schools and communities.

Remember SimCity, the video game in which players designed cities, populated them with simulated people, and let them run under given budgets and social policies? The ICL model is SimCity plus an infectious disease. The “city” is the country or location being modeled.

Now, imagine trying to model a country like the UK with almost 68 million people, or a state like Colorado with some urban pockets of density along with large geographic expanses with different economic and behavioral patterns. Building a model of this nature requires a dizzying array of data including the population age structure and density, travel and commuting patterns, and school and workplace sizes. The ICL model sources all of these and more.

Once the population infrastructure is specified, the infection is added to the mix. Here’s where the new — and shifting — biological understanding of COVID-19 comes in. Modelers input its incubation period and transmission rate and generate anticipated growth in cases over time. These trigger further outcomes based on assumed rates of hospitalization, ICU needs, and other inputs  including fatality and recovery rates. Interventions to mitigate infection alter the model’s settings in multiple directions; for example, school closures eliminate interactions associated with school attendance, but increase household transmission risks. As more data becomes available, whether it’s new biological learning about the transmission rate, the possibility of re-infection, or compliance with physical distancing – these new data can further refine the model over time.

In contrast, the IHME model is an empirical model. An empirical model does not attempt to capture the mechanistic process by which disease spreads. It doesn’t get that deep into the details. Instead, it uses a mathematical formula to summarize the pattern of deaths in a location that has already crested the wave of the pandemic, and extrapolates it to other locations. Empirical models of COVID-19 have looked at the data from China and Italy to inform their projections of what is likely to happen in other countries. In terms of the SimCity analogy, the empirical model is about what happens in the game rather than how it happens. 

To model a new location, the IHME model squeezes or stretches the pattern of deaths so that its projection matches whatever data has accumulated there. Mitigating interventions can change the height of the peak or its timing. The model assumes that mitigation will have a similar effect on deaths as in other locations. Then, it projects further outcomes like hospitalizations or ICU beds based on estimates of the fraction of deaths among patients hospitalized or in intensive care.

The original IHME model was based on the experience of Wuhan, but updates have incorporated data from parts of Spain and Italy. While the model continues to evolve, it remains reliant on a specific form for the pattern of rise and fall in deaths that is data — rather than process — based. The IHME model cannot explicitly incorporate new learning about transmission rates or the possibility of re-infection. To the extent that these are reflected in the data, they are already accounted for. Similarly, the model cannot change behavior patterns to match a target level or duration of social distancing; it is just not that granular.

It is natural to ask which approach is to be preferred — a model that is more mechanistic or a model that is more empirical. 

A more salient question is whether we can trust any of the models to deliver accurate, quantitative predictions of the future course of the pandemic.  

All models simplify reality, make assumptions, and require reliable data. But different models depend on different input data streams. Thus, misestimated disease transmission rates will reduce the accuracy of the ICL model, while under-reported death rates will reduce the reliability of the IHME model.

At this point we might find ourselves feeling that all model predictions of the future of COVID-19 are likely missing the mark. But if we expect models to accurately predict numbers of cases, hospitalizations and deaths, we may be asking too much. No single place’s experience is the same as another, and responses — by individuals and countries — vary significantly. Even as we use the models to plan for the next weeks and months, we need to recognize what they can and cannot do. Knowing what goes into each model is the first step. 

Ruth Etzioni has spent the last twenty years developing and comparing models of cancer prevention, detection and treatment. Thanks to Roman Gulati and Eli Etzioni for their help with this report.

4
Apr
2020

Diagnostic Dysfunction for COVID19: Mike Pellini on The Long Run

Mike Pellini, managing partner, Section32

Today’s guest on The Long Run is Mike Pellini.

Mike is a managing partner with Section32, a venture firm that invests in biotech startups.

Mike is an expert in diagnostics. He was previously the CEO of Foundation Medicine, a company that looks at a wide range of gene mutations in tumor samples to can act as molecular drivers of cancer. I’ve known Mike for about 10 years. It was cool to see that company grow up, and mature. It was acquired by Roche in 2018 for $2.4 billion.

Lately, Mike has been putting his 20 years of experience in diagnostics to work in helping leaders in industry, and policymakers, think about how to better meet the urgent need for national testing in the COVID19 pandemic.

The federal government’s initial testing plan was an epic failure, but he and I didn’t spend time talking about that today. We’re both more interested in what can be done going forward to create a new testing system – with the people, the supplies, the coordination, and the resources they need – to test all 330 million Americans. Getting that much data, and aggregating that (along with a few more kinds of data we talk about) into a national shared surveillance system is the kind of thing we need to think about creating over the coming months. It’s what we’ll need to get the country back to something approximating normal life.

For background, you can read a guest editorial Mike wrote for Timmerman Report on Mar. 28. Headline: Knowledge is Power.

Now, please join me and Mike Pellini on The Long Run.