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.

2
Apr
2020

A Baffling Coronavirus Response, Arch & Flagship Reload, & Merck Passes 2 Phase IIIs

Luke Timmerman, founder & editor, Timmerman Report

The world now has more than 1 million confirmed cases of infection with an insidious virus that spreads from person to person with exponential speed, is spread by people who don’t display symptoms, and kills an estimated 1 percent of people who get infected.

As the US death toll now exceeds more than 1,000 a day from COVID-19, we are heading our way up that horrifying curve we’ve been staring at, and dreading, for weeks. Federal officials looking at the best disease models now publicly say they expect between 100,000 and 250,000 deaths in the US.

We have a good idea of what to expect. Data has been mounting for a couple months, from China, Italy, and other countries. We know COVID-19, the respiratory illness caused by the SARS-CoV-2 virus, sends about 15-20 percent of those infected to the hospital. Patients struggle to breathe, and they often need to stay in the hospital for a couple weeks. We in the US are scrambling. Odds are high that we don’t have enough hospital beds, ventilators, ICU rooms, and skilled staff to grapple with a contagion that moves this fast and has this kind of devastating effect on human health.

Our healthcare workers are now being forced into battlefield triage situations. They themselves are being put at great risk. They’re like soldiers being asked to storm the beaches of Normandy, and we’re asking them to do it without boots.

The great tragedy is that we failed to use our one big advantage over the virus: our superior intelligence as a species.

We in the US wasted precious time in denial, hurling daggers of misinformation and misdirection. Certain leaders displayed incompetence, and then got busy hiding it. Experts knew what we needed to do to #FlattenTheCurve, and yet politicians still haven’t mustered the necessary, unified response as a country to actually make it happen. Early-to-mid-March was the time to get serious to prevent the worst. We didn’t.

We have the money, and technological expertise, to do so much better. Polymerase chain reaction tests are not hard to perform. We have God-like power to collect and analyze the data spitting off those diagnostic instruments. The information could have been used to identify the early cases, so we could isolate them and their contacts in a classic containment strategy. We could have begun widespread testing, quarantining anyone with the virus, not just people who already felt sick. If done right, it’s conceivable we could have avoided the worst of this population-wide mitigation strategy which is forcing so many to stay at home for weeks, maybe months. We could have nipped this pandemic in the bud here in the US.

Yet on Apr. 2, the Governor of Georgia admitted at a press conference that he was unaware until the previous day that the SARS-CoV-2 virus could be spread by people who display no symptoms. Once he realized that, he said he issued a stay-at-home order to Georgia residents.

It’s baffling. And infuriating.

I’m cycling through the same emotions you all are – positive and negative. I admire the heroic frontline workers. I get frustrated and angry with the delays and incompetence. I’m personally grateful to still have a job, to have a healthy family, and to be able to work from home. I feel guilty that I’m not a doctor, that I can’t do more to help the sick or the unemployed.

As the editor of this publication, the main thing I can do is provide useful information. Books will be written for decades about what went wrong. Historians will sift through this story 100 years from now, trying to figure out how it could have happened. For now, I’m seeking to uplift and amplify voices that have something constructive to say about what we can learn from other countries, how we can respond, and how we can get through this difficult time as humans.

To help you absorb the many dimensions of this fast-moving story, I’ve compiled some resources. For starters, I’ve created a landing page for ongoing TR coverage of the pandemic. Everything there is free. So you are all welcome to share this link with friends, family and colleagues who don’t subscribe but who could benefit from this in-depth coverage.

See all TR coverage of COVID-19 here.

I’m proud of this body of work, but also see truly exemplary journalism being done on an hour-to-hour basis by many outlets. Over this past week, I’ve sought to compile some of the most useful articles from a variety of angles – science, policy, human and more – so you can hopefully slow down at some point and absorb the story to achieve some greater understanding.

You can see all of that below, in the usual weekly Frontpoints format. Afterward, you’ll find other biotech news of the week.

Stay well. – Luke

Science of the Pandemic

  • Virological Assessment of Hospitalized Patients with COVID-19. Nature. Apr. 1. (Roman Wolfel et al)
  • Susceptibility of Ferrets, Cats, Dogs to SARS-CoV-2 Infection. (Jianzhong Shi et al.)
  • Dutch Scientists Find Early Warning Signal in Sewage Surveillance. Bloomberg News. Mar. 30. (Jason Gale)
  • Olfactory Dysfunction Outbreak Coincident with COVID19. MedRxiv. Mar. 27. (Seyed Hamid Reza Bagheri et al)
  • What Explains COVID19’s Lethality in Elderly? Scientists Look to Twilight of Immune System. STAT. Mar. 30. (Sharon Begley)
  • Case Series on 24 Critically Ill Patients, Followed 14 Days, in Seattle Area. NEJM. Mar. 30. (Pavan Bhatraju et al)
  • Asymptomatic and Pre-symptomatic SARS-CoV-2 Infections in the Kirkland Nursing Home. Mar. 27. (Centers for Disease Control and Prevention)
  • Transmission Potential of SARS-CoV-2 in Viral Shedding Observed at University of Nebraska. MedRxiv. Mar. 26. (Joshua Santarpia et al)
  • Turbulent Gas Clouds and Respiratory Pathogen Emissions. JAMA Insights. Mar. 26. (Lydia Bourouiba)
  • Why We Should All Wear Masks. New Scientific Rationale. Medium. Mar. 26. (Sui Huang)

Policy / Response

Testing

Vaccines & Therapies

  • Developing Vaccines at Pandemic Speed. NEJM. Mar. 30. (Nicole Lurie et al)
  • Treatment of 5 Critically Ill Patients with Convalescent Plasma. JAMA. Mar. 27. (Chenguang Shen et al).
  • Blood Plasma from Survivors Will be Given to COVID19 Patients. NYT. Mar. 26. (Denise Grady)
  • Renin-Angiotensin-Aldosterone System Inhibitors for COVID19 Patients. NEJM. Mar. 30. ( Muthiah Vaduganathan et al)
  • Novartis and Incyte plan Phase III test of Jakafi for Cytokine Storms in COVID19 Patients. (Incyte statement)
  • Chloroquine/Azithromicin and the Doctor Behind It. In the Pipeline. Mar. 29. (Derek Lowe)
  • Johnson & Johnson announces lead COVID19 vaccine candidate, pledges $1 billion effort with BARDA on manufacturing scale-up, plans to enter clinic in September. (Company statement)
  • What Can Initial Remdesivir Data Tell Us About Covid-19? C&EN. (Lisa Jarvis)

Other Countries

  • India’s Lockdown Leaves Vast Numbers Stranded and Hungry. NYT. Mar. 29. (Maria Abi-Habib and Sameer Yasir)
  • Germany Will Issue Antibody Certificates to Allow Quarantined to Re-Enter Society. Mar. 29. The Telegraph. (Daniel Wighton)
  • Lessons From Italy’s Response. Harvard Business Review. Mar. 27. (Gary Pisano et al)
  • View from the UK. The Lancet. Apr. 4. (Richard Horton)
  • Switzerland: What Went Right, and How to Better Prepare for a Second Wave. Timmerman Report. Mar. 31. (Alex Mayweg)

Health Advice

  • Everyone Thinks They’re Right About Wearing Masks. The Atlantic. Apr. 1. (Ed Yong)
  • What We Need to Know About Asymptomatic Spread. ProPublica. Apr. 2. (Caroline Chen)
  • Wearing Masks Must Be National Policy. NYT. Apr. 2. (Aaron Schildkrout et al)
  • Doctors Recommend Isolation for Those Losing Smell & Taste. NYT. Mar. 22. (Roni Caryn Rabin)

Communication

  • Anthony Fauci Shows Us the Right Way to Be an Expert. Scientific American. Mar. 26. (Gregory Kaebnick)
  • This is Real. Science. Apr. 3. (Holden Thorp)

Regulatory Response

  • Updated FDA guidance on Clinical Trials During the Pandemic. FDA. Mar. 18.

Humanity, at Its Best and Worst

  • How Washington’s Health Care Workers Have Risen to the Pandemic Challenge. New England Journal of Medicine. Apr. 1. ( Lisa Rosenbaum)
  • Deluged System Leaves Some Elderly to Die, Rocking Spain’s Self-Image. NYT. Mar. 25. (Raphael Minder & Elian Peltier)
  • Doctor Who Blew the Whistle on PPE Shortages in Bellingham, WA is Fired. Seattle Times. Mar. 28. (Ron Judd)
  • US Navy Removes Commander Who Wrote Scathing Letter About Coronavirus-Stricken Aircraft Carrier. Reuters. Apr. 2. (Idrees Ali and Phil Stewart)
  • A Million N95 Masks Are Coming From China on the New England Patriots’ Team Jet. Wall Street Journal. Apr. 2. (Andrew Beaton)
  • Taxpayers Paid Millions for a Low-Cost Ventilator for Pandemic. Instead, the Company is Selling Versions of it Overseas. ProPublica. Mar. 30. (Patricia Callahan et al)
  • In March, 60 Choir Members in Washington State Went Ahead With Choir Practice. None Had Symptoms. They Were Careful. Now, 45 Have COVID19. Two Are Dead. Los Angeles Times. Mar. 29. (Richard Read)
  • Ban Alcohol Sales in Pandemic, as Domestic Violence is Rising. Boston Globe. Apr. 2. (Peter Bach)

Epidemiological Modeling

  • The Institute for Health Metrics and Evaluation, University of Washington. The “Chris Murray Model
  • Forecasting Needs for Hospital Beds, ICU Days, Ventilator Days, and Deaths, by State. Institute for Health Metrics and Evaluation / University of Washington. (Chris Murray).

Video

 

In Other Biotech News…

 

Data That Mattered

Merck said its PD-1 inhibitor pembrolizumab (Keytruda) hit its primary endpoint, progression-free survival, in a head-to-head Phase III trial against standard treatment for microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) colorectal cancer. This builds on the company’s strategy to push further into “tumor agnostic” indications based on a cancer’s molecular signature, not its organ of origin. (Subscribers: See related TR coverage by Stacy Lawrence, Mar. 30, 2020)

Merck said it passed a Phase III clinical trial with a heart failure drug candidate. The drug, vericiguat, is an orally administered soluble guanylate cyclase (sGC) stimulator. This was from an outcomes study that looked at heart failure hospitalization and death as a composite endpoint. Bayer is a partner on the program. The data would have been presented at the American College of Cardiology meeting in person, but instead were presented virtually.

San Francisco-based Akero Therapeutics said it met its 12-week primary endpoint for efficacy in a Phase 2b study of patients with NASH. Absolute reductions in liver fat were between 12-14  percent from baseline, and quite consistent across three dose cohorts.

AstraZeneca said it halted a Phase III study because of overwhelming efficacy. It was a study of patients with chronic kidney disease who were getting the SGLT2 inhibitor dapagliflozin (Farxiga). Full results will be released at a future medical meeting.

New Haven, Conn.-based Biohaven Pharmaceuticals said it passed a Phase III clinical trial with its experimental medicine for chronic migraine headaches. The company said its drug candidate reduced migraine days by 3.7 to 4.5 days per month. The Biohaven drug, rimegepant, is a CGRP receptor antagonist.

Personnel File

CureVac, the Germany-based mRNA therapeutic and vaccine developer, named Jean Stephenne as chairman of the board. The company has gone through leadership changes of late after it was reported that the US government tried to seek exclusive access to a program against SARS-CoV-2.

Pfizer named Dr. Susan Desmond-Hellmann to its board. She most recently served as CEO of the Bill & Melinda Gates Foundation. Before that, she was the chancellor of UCSF, and president of product development at Genentech.

Deals

Amgen struck a partnership with Seattle-based Adaptive Biotechnologies on the SARS-CoV-2 pandemic response. Adaptive is the leader in deep sequencing of the immune system’s repertoire of T and B-cells – a unique and valuable set of capabilities in this pandemic. The companies will work together to find neutralizing antibodies against the virus. (See Adaptive CEO blog).

San Francisco-based VIR Biotechnology and Cambridge, Mass.-based Alnylam Pharmaceuticals expanded their partnership to evaluate RNA interference drug candidates against SARS-CoV-2 viral infections. In this case, based on the latest understanding of the biology, they are going after host factors – specifically ACE2 and TMPRSS2. In a joint statement, the companies said ACE2 “is known to be the viral entry receptor for SARS-CoV-2 and other coronaviruses, while TMPRSS2 is believed to cleave the SARS-CoV-2 spike protein to facilitate cellular attachment.”

San Diego-based Fate Therapeutics agreed to work with Janssen Biotech, a J&J company, on CAR-T cell and CAR-NK cell therapies that use Fate’s induced pluripotent stem cell technology. Fate is getting $50 million in cash, and another $50 million via a stock purchase by its new partner at $31 a share.

Financings

Arch Venture Partners raised $1.46 billion for a pair of funds to keep doing what it does – invest large sums in ambitious biotech startups. The news coincided with an announcement from Flagship Pioneering, a stylistically simpatico venture firm. Flagship raised $1.1 billion in new capital for startups. The two firms know each other well, and sometimes syndicate together when their scientific interests and priorities align. 

Cambridge, Mass.-based iTeos Therapeutics, a cancer immunotherapy developer, raised $125 million in a Series B2 financing co-led by RA Capital Management and Boxer Capital.

Legend Biotech, the China and US-based CAR-T cancer immunotherapy company, raised $150.5 million. New investors included Hudson Bay CapitalManagement LP, Johnson & Johnson Innovation, Lilly Asia Ventures, Vivo Capital and RA Capital Management.

France-based Dynacure raised $55 million in a Series C deal to advance its programs for rare diseases, including myotubular myopathies. Perceptive Advisors led.

Cambridge, Mass.-based Pandion Therapeutics raised $80 million in a Series B financing to advance its work against autoimmune disease. Access Biotechnology and Boxer Capital co-led.

San Diego-based Aspen Neuroscience raised $70 million in a Series A financing to develop autologous neuron cell replacement therapy for Parkinson’s disease. OrbiMed led the deal, which included ARCH Venture Partners, Frazier Healthcare Partners, Domain Associates, Section 32, and Sam Altman.

Toronto-based Zucara Therapeutics raised $21 million in a Series A financing to advance work on a treatment for a once-daily treatment for insulin-induced hypoglycemia. Perceptive Xontogeny Venture Fund led.

Waltham, Mass.-based Affinia Therapeutics raised $60 million in a Series A financing to develop gene therapies. F-Prime Capital and New Enterprise Associates (NEA) co-led.

 

1
Apr
2020

What’s the Real Risk of Death from COVID19? It Can Be Deceiving

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

“There are three kinds of lies: lies, damned lies, and statistics,” quote popularized in the US by Mark Twain

This is an almost direct follow-up to my latest article for Timmerman Report (“Let it Rip or Shelter at Home?)

Usual caveats apply: I am not an epidemiologist, and not a virologist. This is still a new virus for us as a species so there is no natural immunity. There is still a ton we do not know about the virus and its biology, blah blah blah…

How bad is this virus, really? As we are spending endless days locked up in our apartments (yes, I am running low on pasta), what is the real risk for the individual? This is especially an issue in countries, like the US and some other Western democracies, where individual rights are prized above all, and where there is a natural tendency to be skeptical of government intervention.

Erring on the side of caution (precautionary principle) might sound good in theory, but how can we go on as a society for more months when entire swaths of the economy are basically veering towards bankruptcy as I write?

What is, then, the “real” fatality rate (number of people dying after being infected)? Should we expect a 0.04% fatality rate, or a horrific Northern Italian / spaghetti western-style situation with a crude case fatality rate (CFR) of almost 12%?? (More on these numbers below).

Obviously, there is potentially a very wide range of policy actions depending on this “real” fatality rate. For a great discussion of this (and of everything coronavirus), please follow Kai Kupferschmidt (@kakape, a German journalist who writes for Science and has been following this outbreak from the beginning).

Starting from the premise that every death (according to my Roman Catholic upbringing, at least) should be avoided, especially if in excess to what a “normal” fatality rate is, I would like to offer, first of all, some important clarifications.

The fatality rate for any given infectious pathogen is, as for everything, context-dependent: population demographics (age structure) and overall health conditions (rate of pre-existing conditions, including obesity, diabetes, smoking rates, etc), as well as status of healthcare infrastructure (ICU beds, ventilators, number of nurses, availability of drugs, etc) are all huge factors. This is not a good time to be infected with SARS-CoV-2 if you are an elderly obese male smoker with heart conditions, to be honest.

So, it is to be expected that different countries’ populations, especially since they might well be at different stages in their own outbreak, might see different fatality rates.

I think it might be useful to describe some basic terminology used in the field below.

IFR: Infection Fatality Rate, also referred to above as “real” fatality rate: this is a “simple” calculation (expressed as a percentage): It’s expressed as the number of fatalities divided by the number of infected individuals. NOTE that infected individuals = detected + undetected (asymptomatic / other infected but not tested) infections. The obvious problem in calculating this “real” fatality rate is the denominator: knowing how many people have been infected. When you don’t adequately test across the population, you don’t really know the denominator.

CFR: Case (or “Crude”) Fatality Rate: also a percentage. It is often calculated by simply dividing the number of deaths by the number of confirmed cases. This is obviously a much rougher (hence “Crude”) measure since not every infection leads to disease and not every infected individual is identified and counted (see above about the number of asymptomatic carriers).

Because a limited number of tests have been performed, it is presumable, and even very likely, that a large number of people have been infected with the virus but have not been tested. In this case, the denominator should be MUCH larger, and therefore this virus that you are all worried about is really. Not. That. Bad!!! The latest data published in The Lancet Infectious Diseases on March 30 pegs the death rate from confirmed COVID19 cases at 1.38 percent, and the overall death rate, including unconfirmed cases, at 0.66 percent.

Indeed, if we are overestimating the eventual IFR, (by relying on CFR, and correspondingly underestimating the number of infected patients, the denominator, due to the lack of testing) then CFR will appear much higher than the real one. There has indeed been a recent “surge” in pundits discussing various epidemiology models which suggest the overall infection rate is far in excess of the official numbers (everybody is an epidemiologist lately). If there were so many more people infected than have been detected, fatalities across the entire population would then appear less dramatic and might lead one to reconsider some of the current strict stay-at-home policies.

To be fair to this argument, there are a number of studies, including in the town of Vo’ in Italy as well as from many other studies, that establish that ~50-55% of people infected are asymptomatic (see Timmerman Report “8 Days Later”). It is also true that most countries, with the possible exception of Korea and Germany, have not yet implemented a broad testing program across their population (Germany just announced a study testing 100,000 individuals at random from the community to assess more concretely these very same prevalence statistics).

That said, current very crude CFR rates in Germany are close to 1.2% (and starting to really go up, as the virus starts reaching more of the elderly population). For South Korea the numbers are higher, ~1.7%. Assuming (big assumption here) ~50% of infected are asymptomatic, we might end up with an IFR here of between 0.7-0.9% or close to 1%.

There are a few sources of additional information: the Princess Diamond cruise ship had ~1% fatality rate (7 deaths / 619 infections: I have been REALLY trying to find out if they had tested everybody in the ship, but I could not ascertain that). These are very small numbers, obviously.  

Let’s now look at the numerator, the number of deaths. Yes, that should be not much of a discussion, really. After all, “in this world nothing can be said to be certain, except death and taxes” (Benjamin Franklin, in a letter to Jean-Baptiste Le Roy, 1789).

Well, I beg to differ on the former (and yes, I do pay my taxes in the US, thank you very much).

I will not comment on the recent multiple media reports on China having possibly concealed the extent of the coronavirus outbreak, under-reporting both total cases and deaths suffered from the disease. However, there are other statistics that are worth quoting and discussing.

Starting in the UK: the government (finally…) announced today, April 1, the criteria for counting a fatality as related to COVID (2,352 as of 5pm GMT on March 31): only people who tested positive and died subsequently to hospitalization were counted.

Obviously, this undercounts (possibly substantially) the real number of fatalities attributable to the virus (even more so as testing is still not provided at scale in the UK). (source: www.gov.uk). As of 9am GMT on April 1, 152,979 people have been tested, of which 29,474 have tested positive (~19.2%). Using the government criteria, this is a CFR of ~8%. I would really like to know what the number of fatalities in assisted living facilities, as well as homes, has been during the same period versus, say, last year. I think it is very fair to say that the denominator should be higher, but then, perhaps, the numerator as well should be.

If only there were a way to compare overall fatality rates in a defined place affected by the virus now versus, say, a year ago when we were presumably dealing with seasonal flu and other pathogens we have tools to deal with. Interestingly enough, there is: Italy might provide once again some perspective and interesting statistics.

Bergamo (source: ecodibergamo.it) is one of the most severely hit municipalities in Northern Italy (Lombardia). In March 2020, more than 5,400 people have died in the province (6x more than in March 2019), 4,500 apparently due to the novel coronavirus, SARS-CoV-2. Of those, only 2,060 deaths have been officially certificated as caused by the respiratory illness we now call COVID-19 (the entire number of fatalities in Italy officially attributed to the virus is 13,155 as of right now, ~7 pm ET, Apr. 1).

Many of the additional fatalities are in the demographics most susceptible to the infection (elderly), who died at home, or in assisted living facilities. They were never tested for the virus, despite exhibiting the telltale symptoms. The official death cause is reported as interstitial pneumonia (the virus causes pneumonia by invading the lungs: the immune system response to the invasion then causes a severe inflammatory reaction leading to death).

Another source of information, from ISTAT (the Italian statistics agency), highlighted by Matteo Cavallaro (@matheusagaso), reports comparative fatalities data from 1,084 Italian municipalities in March 2020 vs 2019: March 2019: 8,054 deaths; March 2020: 16,216 (~2x).

Those excess fatalities were highly concentrated (4,079 of 8,162 excess deaths) in 4 provinces: Bergamo with 2,043, which roughly tallies with the Eco di Bergamo stats above; Brescia (+879), Milano (+639) and Cremona (+518), all of them in Lombardy, the epicenter of the Italian outbreak.

The increase in fatalities is also more frequent in men (+144% vs March 2019) than women (+79%), and in the elderly (in Bergamo, 1,949 of the 2,043 excess deaths are in the population >65 years old). These excess deaths are largely, and clearly, attributable to the new coronavirus.

Keep in mind: People in Italy, and especially in the provinces hardest hit, were in lockdown since March 9 to flatten the curve as much as possible. So, the overall number of car accidents, heart attacks, etc, which are other causes of mortality, are actually DOWN substantially in March 2020 vs 2019. I could not find the numbers for those municipalities, but I did find out that in Los Angeles (yes, they drive almost as badly as Italians) March car accidents were down more than 26% versus March 2019. For the week ending March 27, when people really started taking the stay-at-home instructions seriously, accidents were down 60%! Simple explanation: when people stay at home, they drive less, and get into fewer traffic accidents. However, at the same time, hospitals in the region were unable to withstand the onslaught of new patients: people were counseled to stay home, until the most severe symptoms emerged. As a result, most people dying in houses or nursing homes will not be tested for the virus.

Finally, there are factors to consider: the concept of “excess deaths” versus a period pre-pandemic. Excess deaths are caused not just by the virus itself, but also by the lack of normal standards of care which are suddenly not available any more to any patients suffering from other illnesses. If the hospital is completely full, and you have a stroke, you could be in a tough spot. There are indeed signs that excess overall deaths are climbing in Italy, especially (again) for demographics over 65.

In conclusion, many people want to know — exactly how bad is this outbreak? Is it worse or better than we thought a few weeks ago? (Note, the word “we” is doing a lot of work here, as there is a lot of variability in outbreak severity and response between  countries and even different states / regions within countries).

The simple, terrible math is driven by the number of deaths. They are mounting and increasing the numerator. We are getting a clearer sense now that we know some deaths haven’t been officially attributed to COVID19, but would have been if proper testing had been in place. Now that we can begin to get at the true death toll, and we’re beginning to roll out large-scale testing, a clearer picture is emerging of the infection fatality rate and case fatality rate.

For the time being, I’m basing my answer on the following: 1 case of death not officially associated with COVID-19 “offsets” 50-100 non-tested / asymptomatic individuals (if we believe the real IFR is between 1-2%, which is probably not too far from the truth). So, the numerator and denominator effects described above are not equivalent. Each increase in the numerator offsets A LOT of increases in the denominator.

The realization that a large number of virus-associated deaths went un-reported in several countries like UK, Italy, now France and possibly China should strengthen even more our resolve. We need to stick to the physical distancing orders in place to limit the virus spreading as much as possible, at the same time we work at breakneck speed to increase capacity for testing and manufacturing of protective equipment for healthcare workers struggling with the surge of COVID-19 patients.

I would like to leave you with a quote (translated by GoodQuotes from the French):

“But again and again there comes a time in history when the man who dares to say that two and two make four is punished with death. The schoolteacher is well aware of this. And the question is not one of knowing what punishment or reward attends the making of this calculation. The question is one of knowing whether two and two do make four.” –Albert Camus, La Peste

Hope all of you are safe and sound.

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.

31
Mar
2020

Diagnostic Test Developer Points to Academic Blind Spot That Hampers Translation

David Shaywitz

The COVID-19 pandemic has highlighted the need for reliable diagnostic tests for the new  SARS-CoV-2 virus, and treatments that can cure or mitigate its devastating effects. 

To have an impact, these diagnostic tests can’t just work brilliantly in a single academic lab. They ultimately need to be rapidly deployable across this large country with 330 million people. Developing an approach that can function at this scale is an aspect of innovation that can easily be overlooked — and often is.  After all, some argue, the role of academic science is to discover new things, and publish the findings; what happens next is often viewed as an intellectual derivative, tedious-but-necessary crank turning task that can and should be relegated to commercial organizations.

This mindset seems both surprisingly common and dangerously short-sighted. It adversely impacts not only the translation of academic science, but also the quality of the science itself, depriving researchers the opportunity to pressure-test assumptions in real-world situations, and gain fresh insights from these observations.

I’ve written about the value of a commercial perspective in driving translation (here). The key point: there is value in an intellectual approach that actively contemplates real-world usability. This examination can often sharpen the question and better guide the science. 

Bringing real-world considerations into the innovation process early and often has been championed by many experts. Leading proponents include MIT’s Eric von Hippel, who’s highlighted the critical value of lead users. Lean Startup guru Steve Blank advocates entrepreneurs get real-world feedback from potential users as early as possible. He has said this is especially challenging to do in healthcare, as academic biomedical innovators often regarded themselves as the market experts. Since they see themselves as the authority already, they often see little need to spend time truly testing their assumptions in the marketplace. 

The importance of getting the translation piece right, and more generally, ensuring university research (where appropriate) finds expression in the real world and helps patients (not just academic CVs) has been a focus of several courageous academic leaders who prioritize diverse engagement over perceived intellectual purity. Some of the leaders include Sue Desmond-Hellmann, former CEO of the Bill & Melinda Gates Foundation and chancellor of UCSF, Atul Butte, a distinguished biomedical data scientist at UCSF, and Bob Langer, the prolific bioengineering professor at MIT.

The gap between sexy science and implementable technology was brought to the fore this week, on the podcast Marketplace Tech, produced and distributed by the non-profit American Public Media. On Monday, host Molly Wood interviewed Purdue University bioengineering professor Jacqueline Linnes, who’s working on developing and implementing a useful diagnostic test for the virus. 

The entire discussion is fascinating; I was especially struck – as was Wood – by the translational challenges. As Wood put it, “the science is complicated, but getting from lab to market is even harder.”

As Linnes tells Wood:

“There’s a difference between getting a test to work in the lab 5, 10 or even 100 times, and then getting it to a point that’s manufacturable at the scale that we need. A lot of times when these are in development, the exciting part is getting the actual sensing to work. There’s lots of really cool sensing techniques that are out there that actually are not deployable at all, so there’s a big disconnect between what is manufacturable at scale and what is actually getting made in the lab. For us, if we are developing a device and we’re able to publish a paper on it, we’re super-excited. It’s only after that, that we come back and look at it and realize, ‘This has way too many layers and alignment issues.’ Getting that discussion with manufacturers in the process is really critical.”

Linnes goes on to explain how in general, manufacturing considerations should enter the conversations earlier than they often do.  This mindset, including engagement with manufacturers, “has certainly sped things up in my lab as far as developing technologies with manufacturing in mind,” Linnes said, adding, “It’s been beneficial, I think, so that we can reframe how we’re developing these devices and focus on how we’re not just going to make a cool device, but how it’s actually going to go from the lab into usability.”

Part of the challenge in academia, Linnes says, is the way projects are funded.

She explained:

“[W]hen we’re submitting proposals, and they undergo peer review, people get really excited about the innovative new biosensor and they’re not very excited about the mundane development processes to get things out into the world. The way that we’re even reviewing each other is contributing to that.”

Finally, Linnes points out that part of the challenges is that the lab to real world transition represents the “valley of death,” and is “something that nobody’s quite figured out whose job it is. Companies are out there scouting for potential technologies, but the further along the universities can get them, the better, because it’s a risk from them [i.e. companies] to translate something out of the lab into the company.”

Bottom line:

A model of innovation that enables greater continuity and mutual feedback between discovery scientists, including – especially – at universities, and industrial scientists focused on implementation and scaling, could sharpen science and catalyze translation. 

Conversely, efforts seeking to construct walls between academia and industry, ostensibly to protect the putative purity and sanctity of the research within academia, are likely to adversely impact translation; make it harder for academic researchers to learn from the real world experiences of industry colleagues; and ultimately harm the patients who most of the academic research was funded to serve.