In late January, researchers at BenevolentAI, an artificial intelligence startup in central London, turned their attention to the coronavirus.
Within two days, using technologies that can scour scientific literature related to the virus, they pinpointed a possible treatment with speed that surprised both the company that makes the drug and many doctors who had spent years exploring its effect on other viruses.
Called baricitinib, the drug was designed to treat rheumatoid arthritis. Although many questions hang over its potential use as a coronavirus treatment, it will soon be tested in an accelerated clinical trial with the National Institutes of Health. It is also being studied in Canada, Italy and other countries.
The specialists at BenevolentAI are among many AI researchers and data scientists around the world who have turned their attention to the coronavirus, hoping they can accelerate efforts to understand how it is spreading, treat people who have it and find a vaccine.
Before the pandemic, the AI researchers were part of one of the most hyped and well-funded sectors of the tech industry, pursing visions of autonomous vehicles and machines that can learn by themselves. Now they are simply trying to be helpful working on technology that augments human experts instead of replacing them.
Medical researchers had spent years exploring baricitinib and similar medications as a way to treat viruses. Baricitinib, a pill taken once a day, can help fight extreme and unwanted activity from the body’s immune system, which occurs with both rheumatoid arthritis and viruses like HIV and can damage healthy cells and tissues.
The technology was designed for the development of new drugs not for identifying new uses for existing medications and it had never been used with material related to viruses.
Over two days, a small team used the company’s tools to plumb millions of scientific documents in search of information related to the virus. The tools relied on one of the newest developments in artificial intelligence: “universal language models” that can teach themselves to understand written and spoken language by analyzing thousands of old books, Wikipedia articles and other digital text.
These AI systems are rapidly improving everything from the Google Search engine to automated chatbots designed to carry on a conversation. They can also help machines comb through scientific literature, identify particular pieces of information, organize it and retrieve it on command.
Using its automated language tools, the company’s engineers generated a detailed and intricately interconnected database of particular biological processes related to the coronavirus. Then Richardson, who is 65 and a trained pharmacologist, used additional tools to browse through what the technology had found and understand what it meant.
Drawing on what the technology found in the literature, Richardson could map out the connections between particular human genes and the biological processes affected by the coronavirus. As a multicolored map appeared on his computer screen, two genes leapt out at him.
Once the genes were identified, he and his colleagues could pinpoint the way that existing medications targeted the genes, visualizing the process through a kind of digital flow chart. They identified baricitinib, made by the American pharmaceutical giant Eli Lilly.
Many scientists were already considering similar anti-inflammatory drugs that could reduce a cytokine storm, an extreme response from the body’s immune system that can kill coronavirus patients.
But the BenevolentAI researchers went further. Through their software, they found that baricitinib might also prevent the viral infection itself, blocking the way it enters cells. The company said it had no expectations for making money from the research and had no prior relationship with Eli Lilly.
Through Justin Stebbing, a professor of oncology at Imperial College London, the researchers sent their findings to The Lancet, one of Britain’s oldest and most respected medical journals. Like many other companies and researchers now exploring treatments across the globe, the team wanted to share what it had learned as widely as possible.
The next day, at Emory University Hospital in Atlanta, Dr. Vincent Marconi opened an email from a colleague, Dr. Raymond Schinazi, that pointed him and other colleagues to the paper. They had spent eight years exploring baricitinib and other drugs as a treatment for HIV, and they knew such drugs could potentially help coronavirus patients.
But they had not settled on baricitinib as a viable option, and they had not identified the specific properties that might allow the drug to fight the virus. Nor had the scientists at Eli Lilly. At Emory, the lab researchers were shocked that the paper had come from BenevolentAI.
A month later, Marconi proposed a clinical trial with baricitinib and another drug. As coronavirus cases mounted at his hospital, he and his clinicians administered the pill as a compassionate measure to patients, with encouraging results.
“We normally talk about ‘bench to bedside’,” Stebbing said, referring to moving quickly from laboratory bench research to the treatment of patients. “This is about ‘computer to bench to bedside’.”
Unaware of what was happening in Atlanta, Mario Corbellino administered the drug as a compassionate measure at a hospital in Milan after reviewing the research from BenevolentAI and soon proposed another clinical trial. He and other infectious-disease specialists, he said, feel more comfortable testing this kind of drug if it has the potential to not just reduce an immune system response but prevent the viral infection.
Dr. Dan Skovronsky, chief scientific officer at Eli Lilly, warned that it was still unclear what effect the drug would have on coronavirus patients. Even after the clinical trial, he said, it may not be clear whether the antiviral properties pinpointed by BenevolentAI are as effective as they might seem to be.
He also said those properties were not something his scientists would have discovered so quickly on their own. “There is so much complexity to biology and there is so much information out there, it is hard — if not impossible — for one person to put together the clues that are already there in the literature,” he said.
Source: The New York TimesRelated posts: