This piece originally appeared in The Nation on December 22, 2021 and has since been updated to reflect developments in the case.

It’s understandable why millions of people would like to see Elizabeth Holmes’s head on a pike.

She has become, conveniently if involuntarily, the poster child for a wide variety of sins: the excesses of Silicon Valley, commonplace corporate lies, a thorough lack of executive accountability. The jury just found the Theranos founder guilty on four of the 11 charges, including multiple counts of wire fraud (that quaintly named crime, suggesting that American statutes have not caught up to the 21st century). Holmes, at one point the world’s youngest self-made female billionaire, could go to prison for 20 years or more, having defrauded the investors who put a billion dollars or so into her company.

The trial and the Theranos saga have been an object lesson on what is wrong with health care and American capitalism.

It’s impossible to fault Holmes or Theranos for trying to produce a quick, pinprick blood test that would make so much of the world’s health care delivery easier, faster, and putatively cheaper. True, some or perhaps most of Theranos’s champions may have had less-than-pure motivations; John Carreyou’s vital book Bad Blood depicts Walgreens executives, for example, as conspicuously incurious observers of Theranos’s technology, lest there be any obstacle on the path to the cash register. In 2010, the Walgreens CFO visited Theranos’s Palo Alto headquarters and was, according to Carreyou, unfazed by lack of access to the laboratory, happy instead to accept an autographed American flag that Holmes said had flown over an Afghanistan battlefield.

Still, we want entrepreneurs to swing for the fences, don’t we? If venture capital and other investors are going to pump their billions into start-ups, it’s better that they support health care innovation than, say, Juicero, the VC-backed $400 kitchen device for squeezing fruit that quickly went the way of all peels and rinds.

Virtue of purpose is, of course, no excuse for fraud. Theranos, depending on your perspective, committed fraud for many years, almost immediately from its 2003 launch. Holmes’s intensity and gender-pioneer status bamboozled not only investors but a small army of journalists. I was among them: As the editor of Inc. magazine, I put Holmes on our October 2015 cover. (I’ll still defend the story, but regret calling Holmes “the next Steve Jobs” on the cover, not that Jobs was a CEO without flaws.)

More surprising and disturbing than the seduction of journalists is that Theranos’s top-tier investors didn’t smell something unpleasant years ago. This has been an essential discovery during Holmes’ trial; Bloomberg reported: “Theranos forecast revenue of $140 million in 2014, and almost $1 billion for 2015. Holmes’s top financial officer testified that Theranos posted just $150,000 in revenue in 2014. Evidence at trial shows it was even less in 2015.” This kind of shortfall should have triggered nuclear alerts, from major investors and board members; the lack of public flares vividly illustrates the cynical corruption of America’s financial leadership.

But the broader indictment should be of the for-profit health care system that made Theranos attractive in the first place. One of the trial’s most telling threads involved Theranos not disclosing basic information to Walgreens because it was trying to protect trade secrets.

Whether it’s quick blood tests or mRNA Covid vaccines, health care technology is an obvious public good, and the Theranos debacle—regardless if Holmes is convicted or acquitted—shows the predictable perils of entrusting those technologies to a patent-enabled private sector. The public sector is hardly immune to missteps or corruption, but a blood-test technology under the supervision of the government would not have needed to fake revenue results, and would likely not have produced fraud on a Holmesian scale.

That juxtaposition is especially ironic because, as the flag-over-Afghanistan anecdote reminds us, Theranos was in a critical sense a creature of the military-industrial complex. The US military was highly interested in Theranos’s technology, because it believed that this technology could be supremely useful in a battlefield setting. One of Theranos’s board members was former secretary of state George Shultz (in a wonderfully Oedipal twist, his grandson Tyler, briefly a Theranos employee, was a major source for the Wall Street Journal series that destroyed Theranos and ushered in the federal indictment).

I can’t help wondering: If Theranos had been indirectly working on behalf of a taxpayer-funded military, what would have happened if taxpayers had directly funded the blood-prick research? Might we have achieved the science without the fraud? It seems like a useful experiment.

James Ledbetter is the publisher of FIN and chief content officer of Clarim Media.

Clinical trials, a crucial phase of drug development, continue to evolve as new technologies allow researchers to manage them without participants coming to a physical location. Historically, they had to go somewhere to get medications and report results. Now reporting is done online and drugs are delivered by mail, in what are called decentralized trials (DCTs). The trend began before the Covid-19 pandemic, but it has significantly increased drug companies’ need to use this approach.

The very first DCT was done by Pfizer in 2011. It was known as the REMOTE (Research on Electronic Monitoring of Overactive Bladder Treatment Experience) trial, part of a so-called Investigational New Drug Application with the FDA.

“The scientists managing the study recruited participants on the Internet, relied on online questionnaires and electronic diaries, and delivered the drug under investigation directly to participants’ homes,” says Rob Goodwin, vice president and head of operations for global product development at Pfizer. Recruiting participants was a challenge. “It wasn’t easy to bring patients in through the Web,” continues Goodwin. “Not having a central study site presented a logistical challenge. But we learned a lot. It was a springboard for several other trials, and it set us up well for the pandemic.”

A few years later the FDA officially called for “new strategies to modernize clinical trials to advance precision medicine, patient protections, and more efficient product development.” In a 2019 statement, then-FDA chairman Scott Gottlieb said that critical medical innovations could be delayed or completely lost because the cost of testing had gotten so high. He said the U.S. needed  “a more agile clinical research enterprise capable of testing more therapies or combinations of therapies against an expanding array of targets more efficiently and at a lower total cost..”

Traditionally, drug trials have been performed by clinicians or researchers affiliated with an academic medical center or hospital. Participants come to the facility, where drugs are dispensed, vital signs measured, and tests carried out. Such trials remain the gold standard, says Yosef Khan, principal for clinical trials and real-world evidence at Premier, a healthcare improvement company, and remain necessary for products such as surgical and medical devices. But in many cases they drain sponsor resources and impose a burden on participants, many of whom might not live nearby and/or face transportation and time challenges.

Three factors, says Khan, have led DCTs to evolve during the past 20 years. The first is frustration on the part of clinical researchers about the length of trials and the resulting attrition among participants. Then there is the rapid growth of technologies that allow researchers to conduct remote trials or access clinical registries, which are vast databases containing real-world data about medical conditions and how doctors are treating them. Finally, the government has given a push. The federal 21st Century Cures Act was passed in 2016 to accelerate the “discovery, development and delivery” of medical therapies. It encouraged biomedical research investment and facilitated more rapid innovation in review and approval processes, among other things. Khan says additional regulation remains necessary to ensure that the data collected in DCTs going forward is accurate and reliable.

Decentralized trials offer a range of benefits, including reducing the needed size of research teams and lowering costs for drug companies. More participants may be attracted to trials that eliminate worries about travel and time demands. And perhaps most importantly, because physical boundaries disappear online, researchers can broaden their participant pool to more accurately include people of diverse ethnicities, genders, and ages.

This addresses real problems with how medical science has proceeded until now. “Studies show persistent minority underrepresentation in clinical trials, even for conditions that affect them disproportionately,” reports the Journal of the American Medical Association. “Black people accounted for only four percent of 300,000 participants in trials of cardiovascular and diabetes medications approved by the FDA from 2008 to 2017, and Hispanic individuals accounted for 11 percent.” (Black Americans represent about 12.8% of the population, and Latinos 18%.) Recruiting minority participants for Covid-19 vaccine efforts in the past two years has been a challenge, according to some researchers. Improving the diversity of trials is critical, says Khan: “If I have only white men in the trial, can I really say the drug will work for all demographics?”

“There is great hope that DCTs will increase diversity, bring in unmet populations, and increase retention,” says Pfizer’s Goodwin. “I would say it’s too early to determine whether these efforts are panning out…You’re trying to create the best experience for the patient, but some populations have concerns and simply won’t join a trial. The challenges aren’t simply related to technology. We must continually focus on making patients comfortable.”

Despite the clear benefits DCTs offer—including, says Khan, the ability for small biotech startups to more easily carry out clinical trials—they come with challenges. Sending drugs to individuals by mail can be complex and even pose a risk if shipments are lost or compromised. Wearable biometric devices, often required for modern trials, are still relatively finicky, and troubleshooting them remotely can be difficult. There are also privacy concerns about online reporting, though blockchain technology offers some ways to protect personal healthcare data.

“When the pandemic hit, we weren’t new to DCTs, which gave us the ability to move quickly,” says Goodwin. “To develop the coronavirus vaccine, we were able to take advantage of existing technology such as telemedicine, and work with local labs, so patients didn’t have to travel. We used e-diaries and a range of apps that people could download to their mobile devices, though some people chose to use physical diaries to record signs and symptoms. We taught people to swab themselves, and had the swabs picked up at their homes.”

Pfizer doesn’t rely on in-house technology for its DCTs—it partners with third-party companies that handle many aspects of remote trials. Medable, umotif, and snapIoT are among the companies operating in this industry.

Ultimately, says Goodwin, “a hybrid approach is the way of the future.” In such cases, participants might show up in person at the beginning of the study for necessary lab tests and paperwork, and return only if the study required it or if they experience an adverse event.

Despite hurdles, remote trials are likely to become standard practice. “Change is difficult,” says Khan. “But we have to continue accelerating the use of DCTs.”

Amyris, a small but fast-growing synthetic biology company, first created the position of chief sustainability officer (CSO) just this year, but the company has environmental sustainability in its DNA going back to 2003 when it was founded. CSO Beth Bannerman is now helping guide and maintain that responsible heritage.

Amyris uses synthetic biology to develop molecules that are analogous to those found in nature for a wide range of uses. Applications range widely, including anti-wrinkle skin cream, fragrances, a zero-calorie sweetener, life-saving medications, and polymers for manufacturing industries. The company says it is using biology to make abundant that which is scarce in nature. I interviewed Beth on a rainy day under the roof of a bicycle shelter across the River Clyde from the COP26 conference in Glasgow, Scotland. This is an edited version of our conversation.

Steve: For those who don’t know what synthetic biology is, please describe it.

Beth: Synthetic biology is a discipline within biotechnology that synthesizes engineering, design, and biology. Our technology enables us to produce novel molecules in addition to molecules that already exist in nature. In order to produce novel molecules, we first produce known molecules that exist in nature via fermentation and then use chemistry to convert them into novel molecules. We can also produce novel molecules by combining naturally occurring enzymes and molecules that wouldn’t typically interact together in their natural state, but our technology allows us to bring them together in a unique fashion.

Steve: One of your company’s tag lines is that you’re using biology to make what is scarce in nature abundant for all. Please explain. How does that make society more sustainable?

Beth: That comes from the idea that we can create molecules that exist in nature while avoiding depleting Earth’s resources, whether that be animals or vulnerable plants. We do this by using sugarcane as a feedstock – it’s responsibly sourced and sustainably grown from Brazil, where there’s abundant sunshine and rain water minimizes the need for additional irrigation. We can create molecules with yeast, which we program just like you program a computer. You feed the yeast sugarcane and it excretes the target molecule. We use artificial intelligence to design the target molecule. Through this process you can see how we’re able to protect the scarcity of Earth’s resources, because through synthetic biology we’re able to create an abundance of bioidentical molecules rather than harvesting them from raw sources. We have a proprietary Lab-to-Market system that enables us to scale from two liters to 200,000 liters of our target molecule. Access and scalability are key. The world needs to shift to more sustainable consumption and synthetic biology is a critical part of accelerating that transition.

Let me give you an example: One of our molecules, Squalane, is commonly used in skincare products as a moisturizing ingredient. With our fermentation-based approach, it requires the size of an 8x10ft. rug of sugarcane to produce one kilogram of Squalane. To harvest the same amount of Squalane in nature would require killing three sharks because Squalane is naturally found in the livers of sharks.

Steve: How does your high-throughput system work?

Beth: It started back in 2003 when we received a $45 million grant from The Bill and Melinda Gates Foundation to develop our first molecule. That molecule was a replica of the artemisinin, which is found in the sweet wormwood tree of China and is used to treat malaria. We partnered with Sanofi to commercialize our artemisinin molecule and it went on to save over a million children’s lives. We learned a lot from that project, and since then we have improved and optimized our platform through high-throughput screening and artificial intelligence to increase the speed of the process. In the last five years, we’ve reduced the speed of taking a molecule to market by 80% and reduced our cost by 90%. Before we made these improvements, it would take several years to develop one new molecule. We’re now at a stage where we are able to commercialize 4 to 6 molecules per year.

Before, the process was extremely manual, with scientists relying on handheld pipettes, for example, which is resource-intensive, leaves more room for inconsistencies, and doesn’t easily scale. So with over $500 million of investment in our technology, including both AI and machine learning and robotics that were designed by Amyris in house, we have significantly improved our speed. Now, we can put hundreds of thousands of yeast strains through high-throughput screening and are able to test those strains at a dramatically faster rate. The other thing that the company did was invent a Genotype Specification Language (GSL), which is a DNA programming language-based design tool invented at Amyris to accelerate the design of molecules.

Steve: What are you doing at COP26?

Beth: This is the company’s first visit to a COP summit. Ahead of the conference, they hired me as the first chief engagement and sustainability officer, and earlier this year we published our first ESG report, which many companies now do to inform investors about their environmental, social, and governance programs. The timing is good for us because we have built out a family of nine consumer brands and our consumer revenue is going to outpace our B2B ingredients revenue this year.  This is an opportunity to get the attention of manufacturers and to help them disrupt the way they make ingredients. We have a climate crisis that’s not going away. Consumers are demanding sustainability – they’re much more comfortable with food and fashion and skincare that is made with biology. And, finally, we have a stage to have a conversation.

Steve: What have you achieved here that you can talk about?

Beth: We have had meetings with a variety of interesting folks who we sought out. These are manufacturers and NGOs (non-government organizations) that we’re talking to about future long-term partnerships. The surprising development is that we have received attention from manufacturers that we didn’t reach out to before the conference. We can’t claim success yet, but it starts with a conversation.

Steve: Among the people in Glasgow who are not allowed inside the gates of the official Blue and Green zones, there’s a lot of skepticism about both government and business. We hear a lot of pledges, but will action really follow? Do you think that the business community is doing enough about making the world a safer and more survivable place?

Beth: The answer is unequivocally “No.” Businesses can do a lot more than they’re doing right now. Before we published our ESG report, we were concerned that it might be seen as a veneer trying to get approval for work that actually wasn’t getting done. Potentially, it was a vanity exercise. But after talking to a number of ESG investors and listening to what consumers are demanding in the way of transparency, we decided that in order for us to tell our great story, we needed to back it up with data. So we have done that. We not only set our three goals but laid out a road map for achieving them. One of our goals is to get to net zero carbon emissions by 2030. We spelled out all the steps we need to take to get to that place.

Another thing we did is launch our first ESG council, which is made up of executives from across the enterprise. This is a group of folks who have very clear accountabilities to help drive toward the goals. This is our opportunity, win or lose.

Steve: Are you saying that other companies should do the same?

Beth: Yes. We have a market cap of $3.7 billion. We’re not one of the big players yet. It’s difficult for a small company to split its focus and focus on sustainability. That said, I would encourage small companies to do this. Investors are no longer just looking at growth plans. They want to see sustainability plans, and these things can’t just run in parallel; they have to be integrated and woven together.

—

Steve Hamm is a freelance writer and documentary filmmaker based in New Haven, Connecticut, USA. His new book, The Pivot: Addressing Global Problems Through Local Action, about the journey of Pivot Projects, was published in October by Columbia University Press. This is one of a series of dispatches from COP26.

Read more from Steve Hamm’s COP26 Dispatches

October 29th: COP26: Let’s Pivot to Save the Planet

November 1: SustainChain: a Collaboration Platform for Do-Gooders

November 3: How Oil-Rich Aberdeen is Pivoting Away from Fossil Fuels

November 5: Glasgow Dispatch: Startup Funding Encourages Sustainability

November 8: Can Better Town-Gown Relations Help Save the Planet?

November 8: Young People Are Watching, and They’re Pissed Off

November 9: Piloting Big Technologies On a Tiny Scottish Island

Suicide is a global epidemic, taking nearly 800,000 lives each year. And in the U.S., where suicide rates rose by 33 percent between 1999 and 2019, getting support and resources to the right people at the right time is a pressing need.

The problem is so critical that large groups of scientists around the world have banded together to study it. Recently, they mined genomic data from nearly a million people and identified specific genetic variants that appear to be associated with increased risk of suicide. Those discoveries could have important implications for getting useful medications to at-risk individuals, or for predictive diagnostic tools that would flag people who might need help.

Suicide is an incredibly complex phenomenon, and scientists don’t believe that genetic risk factors tell the whole story. Accurate prediction of which people would attempt suicide in a particular situation would require a sophisticated understanding of genetic, environmental, psychological, sociological, and other factors.

Still, the new genetic findings could be a major step forward. Results from in-depth analyses of attempted suicides in two large populations — including nearly 30,000 people tracked by the International Suicide Genetics Consortium and more than 14,000 in the Million Veteran Program, plus more than 900,000 people who had not attempted suicide as controls — were presented this month at the annual meeting of the American Society of Human Genetics. By comparing genetic differences between people who had made suicide attempts and those who hadn’t, scientists were able to create a list of DNA variants found only in individuals with recorded suicide attempts. Because veterans attempt suicide more often than civilians, including their data was key to these discoveries.

The scientists looked at which variants occurred most often among people who had attempted suicide, and investigated their biological function. Many were associated with traits known to be linked to suicide risk, such as oxytocin signaling, which is important for social bonding, or circadian rhythm, which could explain the higher sleep dysfunction reported in people who attempt suicide.

A particularly revealing sign to the scientists was that many of the findings dovetailed between the veteran and the civilian populations studied. “We’ve seen the same results in two large data sets,” says Elizabeth Hauser, a professor and biostatistician at Duke University who helped crunch the numbers. “That really gives us confidence [in these results].”

The genetic data offer some hopeful possibilities. By highlighting certain biological pathways that may be associated with suicide risk, the data could lead to new clinical treatments. Currently approved drugs that work on those same pathways might be repurposed for use in at-risk patients, and in the longer term, drug developers could create new treatments based on specific genetic variants.

In addition, further validation of the genetic variants could pave the way for a diagnostic test that would help to identify people who are at increased risk for attempting suicide — understanding, of course, that genetics is just one piece of the puzzle. “Just because you might have a particular genetic risk factor, that doesn’t necessarily mean that you will in fact go on to develop these suicidal behaviors,” says Allison Ashley-Koch, a professor and genetic epidemiologist at Duke who participated in these studies. “It just means you may benefit from some additional targeted interventions to help prevent it.”

Nate Kimbrel, a clinical psychologist at Duke and the Durham VA Medical Center who helped lead these studies, hopes that genetic data will ultimately be incorporated alongside more traditional risk factors to hone predictions of which people are most at risk. He and his colleagues previously established the Durham Risk Score, a checklist that helps clinicians identify chronic risk factors to understand a patient’s likelihood of attempting suicide. When high-risk people face acute stressors, such as a romantic breakup or sudden financial hardship, their clinician can offer more intensive support than might be needed for someone at low risk of suicide. If genetic data could be used to pinpoint a person’s risk with greater accuracy, it could give psychologists and psychiatrists a better opportunity to tailor interventions for each patient.

Of course, the possibility of using genetic data for this purpose raises concerns about privacy and each person’s willingness to get tested. “I would view it as something that people would always need to have control over,” Kimbrel says. “People need to be able to make that decision. I would see it being part of a conversation with your provider.”

Years from now, our current age of genetic testing will be remembered as one of innovation and excitement — as well as a time when a host of misleading claims about genetic tools were targeted at consumers. Unfortunately, that last trend applies to pandemic-related marketing too.

This story begins, like so many other COVID-19 stories, on social media. Early this spring Robert Pyatt was browsing social media channels when he noticed ads popping up for genetic tests that would tell him how susceptible he was to the SARS-CoV-2 virus, or how severe he could expect his case to be if he did get infected. As a genetics expert at Kean University, Pyatt knew enough to be skeptical.

But he didn’t have to just wonder about the value of these tests. Pyatt serves as research coordinator for his university’s genetic counseling program, and helped develop the campus diagnostics laboratory that tests faculty and students for COVID-19. Working closely with two Kean students training to be genetic counselors, Maya Briskin and Esther Choi, Pyatt decided to order these susceptibility tests to see what kinds of results they delivered. He wanted to answer two key questions, he told me later: “What’s the genetic and medical background for these tests? And what kind of information are they really relaying to [customers]?”

At the recent annual meeting of the National Society of Genetic Counselors, Choi presented results from five different tests. They showed conflicting outcomes, depending on which company offered the test. All five tests were run on existing consumer genetic data rather than on new saliva samples — for example, a 23andMe customer could upload their genetic data to these services for analysis.

For this study, the team aimed to make results as comparable as possible by using 23andMe data from the same person — a participant in Harvard’s Personal Genome Project whose genetic data is freely available for research purposes. The same data was uploaded to each direct-to-consumer test provider (GeneInformed, LifeDNA, SelfDecode, Sequencing.com, and Xcode). When all the test results came back, the researchers compared the outcomes and reviewed the scientific basis each company used for its test.

They found what any scientist might expect in a field evolving so rapidly. With no clear consensus in the scientific community about which genetic variants make someone more or less susceptible to COVID-19, or more or less likely to suffer severe disease, the test results were predictably hazy. There have been many published reports of human genetic variants associated with that person’s COVID-19 response — but none of those has been validated in the kinds of large studies needed to translate findings into actionable, consumer-ready information. So it’s no surprise that test companies looked at different subsets of human genome variants, basing results on certain reports and ignoring others.

“Our main takeaway from this study [is] ‘buyer beware,’” says Briskin. “We did see an overlap of certain genes and variants across the tests, but what we’re not seeing is consistency of the results they’re producing.”

To be clear, there’s no indication that any of these services conducted its test badly; it doesn’t seem that the conflicting results were caused by erroneous genetic data or by evaluating the wrong variant. There’s even a disclaimer in each company’s report saying the results are not intended for medical use.

The problem highlighted by the Kean team is that inconclusive scientific findings are being presented to consumers as reliable and meaningful. And, really, won’t people who are told by one of these testing companies that they have a lower risk of getting COVID-19 make some health-related decisions based on that information?

In this study, all five providers offered disease severity tests, covering five to 52 genetic variants (with two providers not revealing how many variants their tests analyzed). In the results, two companies said the genetic data indicated higher risk, two predicted average risk, and one interpreted the data as showing lower risk. Remember, this is all based on the exact same person’s genetic data.

Three providers also ran susceptibility tests, which analyzed as few as two variants and as many as 31 variants to make a call. In this case as well, results were inconsistent depending on which company produced them.

“As genetic counseling students, we’ve been taught to view everything with a critical lens,” says Choi. “It wasn’t particularly surprising to me that the results came back as what they were.”

Also not surprising: these tests aren’t going away. Months after identifying the five testing companies included in this study, Pyatt searched online again and this time found that even more companies were offering tests with similar claims. “This is an area of consumer testing that we’re going to see grow,” he says. “Our fear is that people are being directly advertised to. They’re being sold either inaccurate information or information that hasn’t been scientifically proven.”

Data scientists took a tool originally developed for Uber and built a new prediction model to help make sense of emerging variants.

Until recently, there was no scientific way to predict which COVID-19 variants would be the most transmissible, and therefore no way to guide public policy as new strains emerged. For the past year and a half, public health experts have had to base their planning on simple observation, and to some extent, guesswork: which variants were becoming dominant in other regions or countries, and how soon could we expect to see them here?

Now, though, a collaborative team of data scientists, biologists, and infectious disease experts has applied machine learning advances originally designed, believe it or not, for the ride-sharing industry to this challenge. The result: a new tool that can actually predict the transmissibility of variants well ahead of time, accurately forecasting variant transmission patterns for the next one to two months. (Note: this tool and a description of its scientific validation have been posted as a preprint, which is a scientific paper that has not yet undergone the rigors of peer review.)

The tool would not have been possible without an unusual pairing. In the summer of 2020, data scientists who had previously worked for Uber joined one of the world’s leading genomic institutes, teaming up with scientists dedicated to fighting the COVID-19 pandemic. Last year, the Broad Institute (in this case, Broad rhymes with “rode”) in Cambridge, Mass., quickly converted some of its industrial-scale genomics lab capacity into a pandemic testing facility. In addition to determining whether samples were positive or negative for the SARS-CoV-2 virus, the team also sequenced tens of thousands of viral genomes.

Around the world, many laboratories are contributing to the database of viral genomes as well; the GISAID repository has had 3.7 million submissions. That’s a wealth of data, but running any kind of comparison across so many genomes is prohibitively costly in computational terms.

At the Broad, scientists wanted to do more with this data, and they had just the team to make it happen — three data scientists recruited from Uber’s AI team who had created a machine learning tool called Pyro to help customize models of traffic patterns and other elements for cities or regions. The tool was particularly good for building new models that contained many uncertain variables. When it was publicly released by Uber as an open-source platform, it got a surprising amount of uptake in the life science community, where it could be used for probabilistic modeling of biological experiments. “It’s actually more useful for science than it is for a ride-sharing company,” says Fritz Obermeyer, one of the developers who formerly worked at Uber.

At the Broad, Obermeyer and his colleagues quickly took up the challenge of mining the millions of available SARS-CoV-2 genomes to try to forecast the transmissibility of new variants. Rather than comparing every genome to every other genome, they streamlined the process by analyzing clusters of closely related variants. Their preprint describes the analysis of 2.1 million genomes, clustered into nearly 1,300 lineages representing more than 1,000 different regions around the world.

The machine learning tool they built is based on the original Pyro framework — this one is called PyR₀, a play on the R₀ metric used to assess disease transmissibility. It models variant patterns based on specific mutations in the viral genome. “The predictive capability of this model relies on the repeated emergence of the same mutation in different strains independently,” Obermeyer says. “That allows us to predict the growth rate of a particular strain based on the new mutations it has acquired.”

While the model relies on mutations that have been seen before, one of its most important features is that it does not need to know what any given mutation does. Typically, scientists seeking to assess transmissibility of a variant have to perform a series of lab experiments to tease out the precise function of each new mutation. For Obermeyer’s tool, those time-consuming functional tests aren’t necessary for forecasting. The model has access to all of the mutations from genome sequence data, and can infer from the data which ones are associated with increased transmissibility. That is a huge leap in capability for epidemiological researchers focusing on the COVID-19 pandemic.

According to Bronwyn MacInnis, an infectious disease scientist at the Broad who described this work in a presentation at the recent AGBT Precision Health conference, the PyR₀ tool accurately predicted both the explosive growth of the Delta variant and the relatively minor emergence of the Mu variant (originally detected in Colombia earlier this year), long before conventional scientific approaches could have. Using genomic data for epidemiology and infectious disease has “really come of age” in the pandemic, she said. But genomic tools were not built for this kind of use. “The field really needs some great and quick innovation to keep up with the data,” she added, pointing to the former Uber team’s work as a great example.

Obermeyer points out that the model only works as well as it does because it has access to such an enormous trove of genomic data collected around the world. “It’s really important to be able to share observations [of mutations] across countries and across cities,” he says.

Now that the tool is available, public health experts have one more arrow in the quiver to help guide the pandemic response. Mask mandates, indoor capacity limits, and other measures can all be used in a more targeted manner if we can predict the likelihood of the spread of specific new COVID-19 variants. “As soon as we see that there’s a more highly transmissible strain in a particular region, then we [can] react to that by changing these intervention measures,” Obermeyer says.

We have learned many lessons from the pandemic – but three stand out as drivers for how to improve healthcare. First, it is painfully apparent that certain segments of the population lack access to care, resulting in huge gaps in healthcare outcomes. Second, it is clear that technology has fundamentally impacted all industries and changed business models. That’s true especially in healthcare, which has been a laggard in adopting tech. Third, unorthodox partnerships and collaborations have greatly accelerated our ability to move. They have allowed us to introduce new solutions to health challenges as pandemic pressures and waves of COVID traveled around the globe. Recent pilots undertaken by Sanofi, where I serve as CEO, along with partners, shed light on how we can retain the lessons of the pandemic and build on its successes.

Work we’ve done in China has given us deep insights into how to tackle these issues – which many areas across the world also face. Here’s more on several efforts we have worked on in China that have yielded significant results:

Reaching those without access

Our first observation was the indisputable need to reach marginalized populations. It is well documented that minorities, and those in remote areas, have been most heavily impacted by COVID as well as other chronic diseases. These populations suffer from high rates of diseases such as diabetes and may live where healthcare providers and proper therapies are inaccessible. In China, like many parts of the world, the best doctors and specialists are often in large hospitals in urban areas like Shanghai and Beijing.

China, however, is taking measures to increase access to these specialists in rural parts of the country. China’s Healthy China plan is working to increase healthcare capacity while extending its reach to more of the population. Nearly 64% of China’s population live in counties many kilometers from the nearest hospital. The challenge is how to reach this remote population. One effective strategy has been to integrate with what China calls its “internet hospital” system. It offers a closed loop, using digital technology, from consultation to prescription to payment to medicine delivery. Now, internet hospitals are emerging as a new channel with significant potential. In 2020 alone, nearly 49 million Chinese used them for online diagnosis and treatment.

The urgency of extending healthcare access is especially clear when managing chronic illnesses. Such illnesses are on the rise worldwide, due to an aging population and changes in societal behavior. Now, approximately 33% of Chinese citizens over the age of 50 suffer from a chronic illness. For example, diabetes affects over 129 million people there, accounting for about 11% of Chinese adults. In response, one chronic disease care project launched last October was designed to build a coordinated management system for chronic diseases targeting ordinary situations and emergencies, provide patients with full-life-cycle health care and medical support, and ultimately improve comprehensive chronic disease management in China. In a first step, six county-level hospitals followed standards and processes for chronic disease management formulated by experts and obtained front-line data as well as management experience. This will serve as a pilot, to be rolled out to county-level hospital clusters. As an important milestone, an analysis report is expected to be published at the end of this year, focusing on the current situation of chronic disease diagnosis and treatment in county-level hospitals in China.

AI and IoT can enable better healthcare outcomes

The rapid adoption of new technology tools has also had a strong impact in improving health outcomes. By using an artificial intelligence (AI)- and internet of things (IoT)-enabled app, we established a standardized process for out-of-hospital management of diabetic patients. It included systemic implementation of a management/care concept through a smart three-party interaction system–including a physician, nurse, and patient. Patients were guided through a personalized treatment journey, with different intervention frequencies, methods, and contents. Over the past 7 years this system has been used by more than 500 hospitals, reaching 780,000 diabetic patients. Within three months it raised adherence to treatment regimens from 49% to 78-82%.

Unorthodox collaborations

It is deeply important to think of an effective healthcare system as an integrated closed loop. This means linking local doctors and clinics, specialty hospitals and experts, families and patients, and insurers into one service for patients. It is imperative to connect in-hospital medical treatment, out-of-hospital management, and payment scenarios, so that the system works seamlessly. To close existing gaps between these players, partnerships between stakeholders from various industries are key. That will enable us to improve diagnosis, treatment norms and standards through large-scale medical education and training, as well as to explore various forms of commercial insurance innovation.

One example is a Sanofi partnership with Ping An Smart City to develop and provide innovative solutions for patients and healthcare professionals in diabetes management. Another is a strategic partnership with Atman, a pioneering company in medical language intelligence, which creates a bilingual (English and Chinese) medical information platform. With the help of AI and natural language processing, the platform becomes a medical communication engine. Most recently, Sanofi announced a strategic partnership with JD Health, one of the largest digital healthcare platforms in China. The two companies will leverage complementary strengths to promote a full range of strategic initiatives in five areas – prescription drugs, vaccines, consumer health product, medical services, and commercial insurance. This will cover a full-service cycle–before, during, and after diagnosis. The companies will also together explore innovative payment methods, aiming to improve the patient journey through online consultation, drug prescription, purchase, delivery, and disease management.

Pandemic key learnings

During the pandemic, we witnessed how people struggled with COVID even as many simultaneously sought help for preexisting conditions and chronic illnesses. By closely examining where we could innovate and move quickly, we gleaned learnings that have the potential to benefit other industries as well as transform healthcare in many parts of the world.

Digital technology in healthcare will ultimately lead to medical care that’s faster, less expensive, and offers greater access. People living in rural areas and disadvantaged populations will be able to get equal access to quality medical treatment as digital solutions cut time spent traveling to doctors and obtaining prescriptions. An integrated closed-loop system that allows for a streamlined diagnosis, treatment, and commercial insurance pathways appeals to both patients and physicians. By forming unexpected partnerships, we can help break out of old ways of operating and speed up innovation. These strategies will help us address many underlying challenges that the pandemic made painfully apparent.

Paul Hudson is CEO of global pharmaceutical company Sanofi.

Even as confusion about third vaccine doses deepened, recent actions by the U.S. Centers for Disease Control and Prevention increased the likelihood that such booster shots will be inevitable for Americans trying to protect themselves against COVID-19. But questions abound.

Has the third dose decision been finalized?

No. While the CDC has put forth a plan to begin delivering third doses to Americans this fall — recommending that people aim to get another jab eight months after their second dose — the strategy will not be finalized until the FDA conducts its own evaluation and weighs in. (Some reports suggest the CDC might shift to recommending booster shots after six months, but it has not confirmed that.)

Who needs a third dose?

At this point, the only people for whom there is wide agreement that a third dose is necessary are the immunocompromised. That means certain cancer patients, organ transplant recipients, and some people with immune deficiencies. Patients with certain disorders, for example, take drugs to suppress their immune system, and those drugs sometimes also reduce the effectiveness of the vaccines. It’s not uncommon for such patients to produce few or no antibodies to the COVID-19 virus even after two shots. But in a number of studies, immunocompromised patients who previously received two doses of the Pfizer/BioNTech or Moderna vaccines responded to a third dose by producing significantly higher levels of antibodies. That increase could offer more protection against the virus.

What about people who received the Johnson & Johnson vaccine?

Since far fewer Americans received the J&J vaccine, it may take longer for scientists to generate enough information about how their vaccine-induced immunity holds up over time. Those studies are currently being conducted. In announcing the third dose plan, the CDC said that it anticipated that booster shots would likely be necessary for J&J vaccine recipients. The company recently announced that a booster shot six months after the initial dose increased antibody levels nine-fold.

Is the third dose specific to the Delta variant?

At this point, the third doses being administered to people are the same formulation as the first two doses. Both the Pfizer and Moderna vaccines give strong protection against the Delta variant, even though they are based on the genetic code for the original strain of the virus. BioNTech, the company that partnered with Pfizer to create the first COVID-19 vaccine, has now designed a Delta-specific vaccine and is now starting clinical trials to evaluate it. But it’s highly unlikely that Americans getting a third dose in coming months will get anything other than one of the original vaccines.

What’s the scientific evidence behind the third dose recommendation?

Unlike the bulletproof evidence for the first two doses, the science for a third dose is still evolving. Public health officials and scientists have mixed views on whether a third dose is needed, and some critics say the Biden administration jumped the gun on announcing that third doses would be administered. The challenge lies in interpreting real-world data. While clinical trial results are highly controlled and therefore fairly straightforward, real-world data includes a huge number of known and unknown variables. Some experts look at the data and see signals of a worrisome decline in immune protection over time; others reviewing the same data say that the ramp-up of the Delta variant in different populations, as well as other statistical factors, is muddying the waters.

What’s the argument for a third dose, then?

While there is not yet a consensus among scientists about whether a third dose is needed to prevent severe COVID-19 cases, it is clear that waning immunity from the first two doses leads to more patients having cases with mild symptoms. While some public health officials note that the point of vaccines is not to eliminate such mild symptoms, others believe that today’s mild symptoms could be tomorrow’s moderate symptoms and the next day’s severe symptoms. The third dose is being pushed as a way to prevent that deterioration. “Our top priority remains staying ahead of the virus and protecting the American people from COVID-19,” said the CDC in a statement.

What are the ethical implications of a third dose?

This is where things really get thorny. Since nearly everyone in the world needs to get vaccinated and there is a finite supply of vaccines, any third doses given without strong scientific evidence could have instead been given as urgently needed first or second doses to people in countries that have not yet had broad access to the vaccines. While North America and Europe have strong vaccination rates, countries in Africa and the Middle East have so far vaccinated just a tiny fraction of their populations. The World Health Organization’s Director-General, Tedros Adhanom Ghebreyesus, has for weeks been criticizing Western nations for moving toward a third dose even as much of the world has seen no inoculation. The real challenge is ramping up vaccine production globally so there will be enough doses for all. So far, the world has failed to meet that challenge.