Doctor with a syringe on the background of DNA.
Steve, a 60-year-old resident of the DC area who works in manufacturing, was always physically fit. In college, he played lacrosse in Division I, the highest level of intercollegiate athletics in the United States. Later, he stayed active by swimming, biking, and running--up until something strange happened around two years ago.
"It was hard for me to even get upstairs. I wasted away."
Steve, who requested that his last name be withheld to protect his privacy, started to notice weakness first in his toes, then his knees. On a trip to the zoo, he had trouble keeping up. Then some months later, the same thing happened on a family hike. What was supposed to be a four-mile trek up to see a waterfall ended for him at the quarter-mile mark. He turned around and struggled back to the start just as everyone else was returning from the excursion.
Alarmed, he sought out one doctor after the next, but none could diagnose him. The disabling weakness continued to creep up his legs, and by the time he got in to see a top neurologist at Johns Hopkins University last January, he was desperate for help.
"It was hard for me to even get upstairs," he recalls. "I wasted away and had lost about forty-five pounds."
The neurologist, Dr. Michael Polydefkis, finally made the correct diagnosis based on Steve's rapid progression of symptoms, a skin and nerve biopsy, and a genetic test. It turned out that Steve had a rare inherited disease called hereditary transthyretin amyloidosis. Transthyretin is a common blood protein whose normal function is to transport vitamins and hormones in the body. When patients possess certain genetic mutations in the transthyretin gene, the resulting protein can misfold, clump and produce amyloid, an aggregate of proteins, which then interferes with normal function. Many organs are affected in this disease, but most affected are the nervous system, the GI tract, and the heart.
Dr. Michael Polydefkis, Steve's neurologist at Johns Hopkins Bayview Medical Center in Baltimore, MD.
(Courtesy of Dr. Polydefkis)
For the 50,000 patients like Steve around the world, the only treatment historically has been a liver transplant—a major, risky operation. The liver makes most of the transthyretin in a person's body. So if a person who carries a genetic mutation for a disease-causing form of transthyretin has their liver transplanted, the new liver will stop making the mutant protein. A few drugs can slow, but do not stop the disease.
Since it is a genetic condition, a regular "drug" can't tackle the problem.
"For almost all of medicine from the 18th century to today, drugs have been small molecules, typically natural, some invented by humans, that bind to proteins and block their functions," explains Dr. Phillip Zamore, chair of the department of Biomedical Sciences at the University of Massachusetts Medical School. "But with most proteins (including this one), you can't imagine how that would ever happen. Because even if it stuck, there's no reason to think it would change anything. So people threw up their hands and said, 'Unless we can find a protein that is "druggable" in disease X, we can't treat it.'"
To draw a car analogy, treating a disease like Steve's with a small molecule would be like trying to shut down the entire car industry when all you can do is cut the power cord to one machine in one local factory. With few options, patients like Steve have been at a loss, facing continual deterioration and disability.
"It's more obvious how to be specific because we use the genetic code itself to design the drug."
A Radical New Approach
Luckily, Dr. Polydefkis knew of an experimental drug made by a biotech company that Dr. Zamore co-founded called Alnylam Pharmaceuticals. They were doing something completely different: silencing the chemical blueprint for protein, called RNA, rather than targeting the protein itself. In other words, shutting down all the bad factories across the whole car industry at once – without touching the good ones.
"It's more obvious how to be specific," says Dr. Zamore, "because we use the genetic code itself to design the drug."
For Steve's doctor, the new drug, called patisiran, is a game changer.
"It's the dawn of molecular medicine," says Dr. Polydefkis. "It's really a miraculous development. The ability to selectively knock down or reduce the amount of a specific protein is remarkable. I tell patients this is science fiction that is now becoming reality."
A (Very) Short History
The strategy of silencing RNA as a method of guiding drug development began in 1998. Basic research took six years before clinical testing in humans began in 2004. Just a few months ago, in November, the results of the first double-blind, placebo-controlled phase III trials were announced, testing patisiran in patients--and they surpassed expectations.
"The results were remarkably positive," says Dr. Polydefkis. "Every primary and secondary outcome measure target was met. It's the most positive trial I have ever been associated with and that I can remember in recent memory."
FDA approval is expected to come by summer, which will mark the first official sanction of a drug based on RNA inhibition (RNAi). Experts are confident that similar drugs will eventually follow for other diseases, like familial hypercholesterol, lipid disorders, and breathing disorders. Right now, these drugs must get into the liver to work, but otherwise the future treatment possibilities are wide open, according to Dr. Zamore.
"It doesn't have to be a genetic disease," he says. "In theory, it doesn't have to be just one gene, although I don't think anyone knows how many you could target at once. There is no precedent for targeting two."
Dr. Phillip Zamore, chair of the RNA Therapeutics Institute at the University of Massachusetts Medical School.
(Courtesy of Dr. Zamore)
Alnylam, the leading company in RNAi therapeutics, plans to strategically design other new drugs based on what they have learned from this first trial – "so with each successive experience, with designing and testing, you get better at making more drugs. In a way, that's never happened before...This is a lot more efficient of a way to make drugs in the future."
And unlike gene therapy, in which a patient's own genetic code is permanently altered, this approach does not cause permanent genetic changes. Patients can stop taking it like any other drug, and its effects will vanish.
How Is Steve?
Last February, Steve started on the drug. He was granted early access since it is not yet FDA-approved and is still considered experimental. Every 21 days, he has received an IV infusion that causes some minor side effects, like headaches and facial flushing.
"The good news is, since I started on the drug, I don't see any more deterioration other than my speech."
So far, it seems to be effective. He's gained back 20 pounds, and though his enunciation is still a bit slurred, he says that his neuropathy has stopped. He plans to continue the treatment for the rest of his life.
"The good news is, since I started on the drug, I don't see any more deterioration other than my speech," he says. "I think the drug is working, but would I have continued to deteriorate without the drug? I'm not really sure."
Dr. Polydefkis jumps in with a more confident response: "If you ask me, I would say 100 percent he would have kept progressing at a fairly rapid pace without the drug. When Steve says the neuropathy has stopped, that's music to my ears."
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.