There is one application in addition to vaccines, however, where brief exposure to messenger RNA could have effects lasting years, or even a lifetime.
In late 2019, before covid-19, the US National Institutes of Health and the Bill and Melinda Gates Foundation announced they would spend $200 million developing affordable gene therapies for use in sub-Saharan Africa. The top targets: HIV and sickle-cell disease, which are widespread there.
Gates and the NIH didn’t say how they would make such cutting-edge treatments cheap and easy to use, but Weissman told me that the plan may depend on using messenger RNA to add instructions for gene-editing tools like CRISPR to a person’s body, making permanent changes to the genome. Think of mass vaccination campaigns, says Weissman, except with gene editing to correct inherited disease.
Right now, gene therapy is complex and expensive. Since 2017, several types have been approved in the US and Europe. One, a treatment for blindness, in which viruses carry a new gene to the retina, costs $425,000 per eye.
A startup called Intellia Therapeutics is testing a treatment that packages CRISPR into RNA and then into a nanoparticle, with which it hopes to cure a painful inherited liver disease. The aim is to make the gene scissors appear in a person’s cells, cut out the problem gene, and then fade away. The company tested the drug on a patient for the first time in 2020.
It’s not a coincidence that Intellia is treating a liver disease. When dripped into the bloodstream through an IV, lipid nanoparticles tend to all end up in the liver—the body’s house-cleaning organ. “If you want to treat a liver disease, great—anything else, you have a problem,” says Weissman.
But Weissman says he’s figured out how to target the nanoparticles so that they wind up inside bone marrow, which constantly manufactures all red blood cells and immune cells. That would be a hugely valuable trick—so valuable that Weissman wouldn’t tell me how he does it. It’s a secret, he says, “until we get the patents filed.”
He intends to use this technique to try to cure sickle-cell disease by sending new instructions into the cells of the body’s blood factory. He’s also working with researchers who are ready to test on monkeys whether immune cells called T cells can be engineered to go on a seek-and-destroy mission after HIV and cure that infection, once and for all.
What all this means is that the fatty particles of messenger RNA may become a way to edit genomes at massive scales, and on the cheap. A drip drug that allows engineering of the blood system could become a public health boon as significant as vaccines. The burden of sickle-cell, an inherited disease that shortens lives by decades (or, in poor regions, kills during childhood), falls most heavily on Black people in equatorial Africa, Brazil, and the US. HIV has also become a lingering scourge: about two-thirds of people living with the virus, or dying from it, are in Africa.
Moderna and BioNTech have been selling their covid-19 vaccine shots for $20 to $40 a dose. What if that were the cost of genetic modification, too? “We could correct sickle-cell with a single shot,” Weissman says. “We think that is groundbreaking new therapy.”
There are fantastic fortunes to be made in mRNA technology. At least five people connected to Moderna and BioNTech are now billionaires, including Bancel. Weissman is not one of them, though he stands to get patent royalties. He says he prefers academia, where people are less likely to tell him what to research—or, just as important, what not to. He’s always looking for the next great scientific challenge: “It’s not that the vaccine is old news, but it was obvious they were going to work.” Messenger RNA, he says, “has an incredible future.”
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
—June Kim
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
Solar geoengineering
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.
