CRACKPOTTERY:

THE POSSIBILITIES IN early 2020: Hydroxychloroquine might help. Or it might not. Or it might make people worse. No one knew.

One of the first people to leap into that breach was David Boulware, a diligent infectious disease researcher and professor of medicine at the University of Minnesota. Back in 2015 he'd worked on an Ebola drug trial with the National Institutes of Health, and he quickly raised his hand to work on trials of treatments for the new virus.

In early March, he and his team were supposed to be at an HIV conference in Boston, but by that point nobody was traveling anywhere. "We all had four days free to totally focus on this task," Boulware told me then. His group used the time to put together a plan to study hydroxychloroquine.

Right here--the stage where scientists come up with these "research protocols"--is where how-to-know starts getting complicated. It's a cliché because it's true: The answers you get depend on the questions you ask. In this case, Boulware's team decided not to test the drug on hospitalized patients, when the disease becomes severe. "If it was going to work, you'd have a better chance to alter the disease course early on," Boulware said.

They hoped it worked. But they didn't know. To find out, they proposed a classic structure: A couple hundred people would get the drug; a similar number would get a placebo--an inert fake. The ones getting the placebo would be the "control group," experiencing all the same things except for the drug, to isolate its effects. Neither researchers nor participants would know who got which until the end; that's called a "double-blind" study. And people would be assigned to the groups at random, to avoid even unconscious bias on the part of the researchers and prevent differences between groups of humans--socioeconomic, demographic, and so on--from throwing off the results.

That is, in other words, a large, double-blinded, randomized controlled trial. Boulware's team proposed two. One would look at whether hydroxychloroquine could prevent illness in people with exposure to an infected person--"post-exposure prophylaxis"--and another would see if taking the drug close to the onset of symptoms could keep those symptoms from getting worse. That was "early treatment." On March 13, the US Food and Drug Administration approved the study, a blisteringly fast green light from a typically cautious, plodding agency. The responses of the federal government's scientific policymaking would falter in key ways over the next few months, but this wasn't one of them.

Boulware started enrolling people almost immediately. For statistical validity, they'd need enough people so that some in the experimental groups and some in the controls would get Covid-19. The researchers would run the numbers, ask who got what, and they'd have an answer in weeks. They'd write up the results, publish in a journal, and it would be science.

Except Boulware's reasonable expectation that things would work the way they were supposed to didn't take into account the viral social-media blender that was spinning up its blades--making a viscous gazpacho out of Silicon Valley opportunism and the hottest of hot takes from the president of the United States.

EVEN THE STODGIEST of scientists don't believe that waiting months or years for a formal write-up of an experiment to penetrate a wall of skeptical reviewers, receiving an inscrutable thumbs-up to get published--in ink! on paper! that gets mailed! to libraries!--is an ideal system for disseminating new knowledge today. Yet that's still mostly how things work, despite the existence of the online version of most journals. But the Covid-19 pandemic came at a weird moment in the history of how information spreads. For one thing, that formal system was already in the process of breaking down. Due to the pressures of publication and academic seniority, some of the science that gets into peer-reviewed journals doesn't hold up to scrutiny, and many scientists are internalizing the hard truth of that " reproducibility crisis." Formal peer review and publication doesn't make something true. That's part of the reason the biomedical sciences were embracing a newer approach, one that their colleagues across the quad in the physics and math buildings had arrived at years before: "prepublication" or "prepress" articles that could go online as soon as their authors finished typing them.

That's good; it means faster, freer information and a more egalitarian kind of review. But rethinking the gatekeeping in the ways nominal experts disseminated nominal knowledge opened the door to other people playing the game. Thanks to widespread access to publishing tools and social media, pretty much anyone can marshal the trappings of expertise. The crisis of the global pandemic intersected with a crisis of belief, with opposing scientific ideas somehow getting tethered to political ideologies. With just a bit of Googling, anyone can find things that look like truth, that are what that person was hoping to hear in the first place. If one of those things goes viral, and if the science behind it is difficult or undercooked, pretty soon everyone starts nodding along.

Which is what happened on March 13--the same day the FDA approved Boulware's well-thought-out trial. A physician named James Todaro tweeted that chloroquine could fight Covid-19, and he'd written a paper that proved it. Now, this wasn't a "paper" from a peer-reviewed journal, or even a preprint. It was a Google Doc, coauthored by a lawyer named Gregory Rigano and a biochemist named Thomas Broker, identified as a Stanford PhD. It was a pretty good summary of all the research on chloroquine up to that point. It even cited the work of a French researcher named Didier Raoult, a controversial infectious disease specialist who, a few days later, claimed he had results showing that hydroxychloroquine worked against Covid-19 in human beings.

A steady rain of likes and retweets turned into a viral downpour. The influential Silicon Valley blog Stratechery linked to the Google Doc. Rigano went on Fox News. Elon Musk tweeted about the document with the link. Musk, who said he'd taken chloroquine for malaria, also tweeted a link to a video on hydroxychloroquine and Covid-19 produced by a small medical-education company called MedCram. The company had started doing brisk traffic covering the coronavirus; the hydroxychloroquine episode took off.

The original Google Doc made a good case for chloroquine being of interest--attempted use in prior pandemics, studies in cells and in animals, preliminary results from China. Not proof, to be sure, but tantalizing hints. But, as it turned out, the creators were not all that they appeared.

Rigano had done most of the initial work. According to his LinkedIn bio, Rigano was on leave from a master's program in bioinformatics at Johns Hopkins and was an adviser to a drug development program at Stanford. But the head of the bioinformatics program at Johns Hopkins told me Rigano wasn't really on leave from the program; he had only taken one class. And the codirector of the Stanford program told me that, while he'd met Rigano, he was in no way an "adviser." Todaro, whom Rigano met via Twitter, was a former ophthalmologist turned professional bitcoin investor. And Broker was not, it turned out, a Stanford biochemist. He attended Stanford but now was a retired virologist at the University of Alabama who studied not coronaviruses but an entirely different family of viruses. Broker disavowed any involvement in the paper, and Todaro and Rigano soon removed his name from it.

None of which is to say they were necessarily wrong. But none of which is to say they were necessarily right, either. Yet the idea rippled through Silicon Valley like photons through an optical cable. Facebook, Amazon, Apple, and Google had sucked up most of the disruption oxygen in tech, and entrepreneurial types were already interested in biotech as a thing to pour money on. And their libertarian bent means they're always looking for an institutional eyeball into which they can shove a venture-capital finger. The medical establishment, with its elitist reliance on the plodding, 20th-century model of clinical trials in the midst of a raging pandemic, seemed like a fat target.

The need for speed was real, and it played into the baser, basic instincts of the Valley. Those hold that all a technologist needs is a dream, a minimum-viable product, and the will to build a company. (A Stanford undergraduate degree doesn't hurt.) If you're trained to see your successes as the result of genius and instinct rather than luck, you might not be able to readily distinguish between the rigors of testing a drug's efficacy and the travails of bringing a product to market. But they are different processes with different goals. In the Valley, whether something works is different from, maybe even disconnected from, whether it sells.

Combine that with the quantified-self, n-of-1 approach to health and wellness that some of the same people also embrace, and you get not science but pseudo-science touted by the four-hour-body crowd that gets rejuvenating transfusions of young people's blood and rebrands nutritional diet shakes as food from a dystopian science fiction movie. "Tech, and especially Silicon Valley, has this belief that all you have to do is disrupt things and try shit and make it stick to the wall, and it will work and change everything," says one investor with a long history in health care. "We've had a tried-and-true method of getting vaccinations and drugs approved in the US that is absolutely antithetical to everything the tech industry believes and has found to be true."

As deaths in the US mounted and the economy went into a lockdown-induced spin, some rich and successful venture capitalists started arguing that the whole system was nonsense. As noted contrarian, investor, and former PayPal, LinkedIn, and Square executive Keith Rabois tweeted, "Randomized controls are horrible ideas. Largest impediment to progress in health spans." (Rabois agreed to consider answering emailed questions but didn't respond to the ones I sent.) Randomized, controlled trials not only take too long, Rabois and his ilk said, but were in this case unnecessary. You could instead use "real-world data," like the experience of the tens of thousands of people who were actually taking hydroxychloroquine, and do some kind of data thing on it.

"We've had a tried-and-true method of getting vaccinations and drugs approved in the US that is absolutely antithetical to everything the tech industry believes."

It's not crazy. Randomized controlled trials are, as the scientists say, the gold standard. But that method isn't the only way to figure out causality, or at least to start to get a sense of it. Sometimes double-blind studies are impractical. Sometimes nature and circumstance offer a great opportunity to see how changes in conditions have different effects. Observational studies, retrospective analyses of existing data, meta-analyses of grouped smaller studies--they're all useful, and certainly better than throwing biotechnological spaghetti against a pandemic to see what sticks. But look what happened months later, after similar hopes for convalescent plasma as a therapy turned into widespread use. After giving it to nearly 100,000 people, plasma appeared to be safe, but there was only limited evidence of its effectiveness.

If it's possible to characterize an entire swath of opinions, though, what the techfluencers seemed to be pitching was not a study where the parameters of observation were defined in advance, but one where all sorts of casually collected data, the flotsam and jetsam of our digital lives, might somehow be tabulated and correlated to whether, when, and how a person got hydroxychloroquine. Quantified self, but applied to everyone--quantified other.

To be fair, the ethics of demanding rigorous, time-consuming tests during a pandemic are worth debating. In a sense, this is about medicine now versus science later. Correctly administered, hydroxychloroquine only rarely has serious side effects; it's a well-understood, mostly safe drug. Why not just give it to everyone and monitor their outcomes? That's a very Silicon Valley approach--intermediate risk, high reward. "I appreciate some of the tech people coming to health care, because I do think we should be thinking about some things differently. Having fresh thinking is great. But fresh thinking is different from illogical thinking or uncaring thinking," the investor tells me. "If you're a tech guy flacking hydroxychloroquine to people who shouldn't use it, what the fuck? People can get really sick."

Even if they don't get sick, that plan still has problems. Giving people a drug that may or may not work is ethically dicey. And who would actually keep track of those outcomes? "Big data" approaches to medicine are susceptible to the distortions and bias of anecdotal evidence and intuition, exactly the mistakes that rigorous, large-scale, randomized controlled trials are designed to avoid. But over decades, those trials have gotten more and more complicated and expensive--just as government funding of them has plateaued. The main consequence has been that pharmaceutical companies fund their own trials, and the companies are highly incentivized to focus on drugs with huge potential markets. That often means more expensive lifestyle drugs and fewer worthy public health solutions or medicines with population-scale benefits--more Viagras, fewer Vancomycins. Little wonder, then, that researchers running trials for the unpatented drug hydroxychloroquine had such trouble gaining traction, while the expensive antiviral remdesivir, with the transnational pharmaceutical company Gilead Sciences pushing it, found support for a trial in the NIH and in the White House--and is now standard in US Covid-19 treatment. The foxes all run their own chicken-coop businesses.

THE SAME WEEK the mania for the drug took hold in Silicon Valley, Larry Ellison, the chair of Oracle and the fifth-richest person on earth, started talking with Donald Trump. According to The Washington Post, Ellison wanted to pitch a widespread study of chloroquine and hydroxychloroquine as a treatment. Ellison proposed that Oracle could develop a website to track people's use of the drug along with their health outcomes, and the data would anticipate whatever a slow, expensive randomized controlled trial might eventually reveal. (Through a spokesperson, Ellison declined to answer my questions about these discussions, as did a White House spokesperson.)

Ellison seemed to make an impression. Shortly after that conversation, the Post reported, Trump met with his senior advisers on the coronavirus pandemic and asked if the government could expedite the approval process for hydroxychloroquine, chloroquine, and, for good measure, remdesivir. Emergency use authorizations had been employed during pandemics in the past, to allow treatments with potential to jump the line in times of urgent need. Remdesivir was in the midst of a large-scale randomized trial sponsored by the National Institutes of Health. Hydroxychloroquine didn't have the same backing.

The president's urgency wasn't just a matter of public health. Trump had promised Covid-19 would just disappear, but the US response to the disease was going entirely off the rails. During a disastrous visit to the CDC on March 6, Trump touted his own scientific acumen--"I like this stuff. I really get it. People are surprised that I understand it"--but behind the scenes he was obstructing programs to begin widespread testing for the disease. The failure to do those tests meant that as March ticked onward, thousands of Americans were already infected. Trump acknowledged privately to the journalist Bob Woodward that Covid-19 was a dangerous, plague-level disease even as he railed against the press on Twitter and elsewhere, hoping to bolster a plummeting stock market. ("I don't want to create panic," he said in September when asked about why he had downplayed the severity of the pandemic.) And meanwhile every model, every infectious disease researcher, every epidemiologist was looking at case and fatality curves on the cusp of exponentiality, with worst-case fatality estimates in the millions.

A miracle cure must have sounded pretty good.

On March 19, the president conducted a press conference, and it was really weird.

This is where he started pitching hydroxychloroquine. "It's shown very encouraging--very, very encouraging early results. And we're going to be able to make that drug available almost immediately," the president said. The FDA was all in too: "They've gone through the approval process; it's been approved."

This was untrue in most respects.

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