Your Digital Avatar May One Day Get Sick Before You Do
Artificial intelligence is everywhere, just not in the way you think it is.
These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn."
"There's the perception of AI in the glossy magazines," says Anders Kofod-Petersen, a professor of Artificial Intelligence at the Norwegian University of Science and Technology. "That's the sci-fi version. It resembles the small guy in the movie AI. It might be benevolent or it might be evil, but it's generally intelligent and conscious."
"And this is, of course, as far from the truth as you can possibly get."
What Exactly Is Artificial Intelligence, Anyway?
Let's start with how you got to this piece. You likely came to it through social media. Your Facebook account, Twitter feed, or perhaps a Google search. AI influences all of those things, machine learning helping to run the algorithms that decide what you see, when, and where. AI isn't the little humanoid figure; it's the system that controls the figure.
"AI is being confused with robotics," Eleonore Pauwels, Director of the Anticipatory Intelligence Lab with the Science and Technology Innovation Program at the Wilson Center, says. "What AI is right now is a data optimization system, a very powerful data optimization system."
The revolution in recent years hasn't come from the method scientists and other researchers use. The general ideas and philosophies have been around since the late 1960s. Instead, the big change has been the dramatic increase in computing power, primarily due to the development of neural networks. These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn." An AI, for example, can be taught to spot a picture of a cat by looking at hundreds of thousands of pictures that have been labeled "cat" and "learning" what a cat looks like. Or an AI can beat a human at Go, an achievement that just five years ago Kofod-Petersen thought wouldn't be accomplished for decades.
"It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn."
Medicine is the field where this expertise in perception tasks might have the most influence. It's already having an impact as iPhones use AI to detect cancer, Apple watches alert the wearer to a heart problem, AI spots tuberculosis and the spread of breast cancer with a higher accuracy than human doctors, and more. Every few months, another study demonstrates more possibility. (The New Yorker published an article about medicine and AI last year, so you know it's a serious topic.)
But this is only the beginning. "I personally think genomics and precision medicine is where AI is going to be the biggest game-changer," Pauwels says. "It's going to completely change how we think about health, our genomes, and how we think about our relationship between our genotype and phenotype."
The Fundamental Breakthrough That Must Be Solved
To get there, however, researchers will need to make another breakthrough, and there's debate about how long that will take. Kofod-Petersen explains: "If we want to move from this narrow intelligence to this broader intelligence, that's a very difficult problem. It basically boils down to that we haven't got a clue about what intelligence actually is. We don't know what intelligence means in a biological sense. We think we might recognize it but we're not completely sure. There isn't a working definition. We kind of agree with the biologists that learning is an aspect of it. It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn. They can learn specific tasks but we haven't approached how to teach them to learn to learn."
In other words, current AI is very, very good at identifying that a picture of a cat is, in fact, a cat – and getting better at doing so at an incredibly rapid pace – but the system only knows what a "cat" is because that's what a programmer told it a furry thing with whiskers and two pointy ears is called. If the programmer instead decided to label the training images as "dogs," the AI wouldn't say "no, that's a cat." Instead, it would simply call a furry thing with whiskers and two pointy ears a dog. AI systems lack the explicit inference that humans do effortlessly, almost without thinking.
Pauwels believes that the next step is for AI to transition from supervised to unsupervised learning. The latter means that the AI isn't answering questions that a programmer asks it ("Is this a cat?"). Instead, it's almost like it's looking at the data it has, coming up with its own questions and hypothesis, and answering them or putting them to the test. Combining this ability with the frankly insane processing power of the computer system could result in game-changing discoveries.
In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions present themselves before the person gets sick in real life.
One company in China plans to develop a way to create a digital avatar of an individual person, then simulate that person's health and medical information into the future. In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions presented themselves – cancer or a heart condition or anything, really – and help the real-life version prevent those conditions from beginning or treating them before they became a life-threatening issue.
That, obviously, would be an incredibly powerful technology, and it's just one of the many possibilities that unsupervised AI presents. It's also terrifying in the potential for misuse. Even the term "unsupervised AI" brings to mind a dystopian landscape where AI takes over and enslaves humanity. (Pick your favorite movie. There are dozens.) This is a concern, something for developers, programmers, and scientists to consider as they build the systems of the future.
The Ethical Problem That Deserves More Attention
But the more immediate concern about AI is much more mundane. We think of AI as an unbiased system. That's incorrect. Algorithms, after all, are designed by someone or a team, and those people have explicit or implicit biases. Intentionally, or more likely not, they introduce these biases into the very code that forms the basis for the AI. Current systems have a bias against people of color. Facebook tried to rectify the situation and failed. These are two small examples of a larger, potentially systemic problem.
It's vital and necessary for the people developing AI today to be aware of these issues. And, yes, avoid sending us to the brink of a James Cameron movie. But AI is too powerful a tool to ignore. Today, it's identifying cats and on the verge of detecting cancer. In not too many tomorrows, it will be on the forefront of medical innovation. If we are careful, aware, and smart, it will help simulate results, create designer drugs, and revolutionize individualize medicine. "AI is the only way to get there," Pauwels says.
The U.S. must fund more biotech innovation – or other countries will catch up faster than you think
The U.S. has approximately 58 percent of the market share in the biotech sector, followed by China with 11 percent. However, this market share is the result of several years of previous research and development (R&D) – it is a present picture of what happened in the past. In the future, this market share will decline unless the federal government makes investments to improve the quality and quantity of U.S. research in biotech.
The effectiveness of current R&D can be evaluated in a variety of ways such as monies invested and the number of patents filed. According to the UNESCO Institute for Statistics, the U.S. spends approximately 2.7 percent of GDP on R&D ($476,459.0M), whereas China spends 2 percent ($346,266.3M). However, investment levels do not necessarily translate into goods that end up contributing to innovation.
Patents are a better indication of innovation. The biotech industry relies on patents to protect their investments, making patenting a key tool in the process of translating scientific discoveries that can ultimately benefit patients. In 2020, China filed 1,497,159 patents, a 6.9 percent increase in growth rate. In contrast, the U.S. filed 597,172, a 3.9 percent decline. When it comes to patents filed, China has approximately 45 percent of the world share compared to 18 percent for the U.S.
So how did we get here? The nature of science in academia allows scientists to specialize by dedicating several years to advance discovery research and develop new inventions that can then be licensed by biotech companies. This makes academic science critical to innovation in the U.S. and abroad.
Academic scientists rely on government and foundation grants to pay for R&D, which includes salaries for faculty, investigators and trainees, as well as monies for infrastructure, support personnel and research supplies. Of particular interest to academic scientists to cover these costs is government support such as Research Project Grants, also known as R01 grants, the oldest grant mechanism from the National Institutes of Health. Unfortunately, this funding mechanism is extremely competitive, as applications have a success rate of only about 20 percent. To maximize the chances of getting funded, investigators tend to limit the innovation of their applications, since a project that seems overambitious is discouraged by grant reviewers.
Considering the difficulty in obtaining funding, the limited number of opportunities for scientists to become independent investigators capable of leading their own scientific projects, and the salaries available to pay for scientists with a doctoral degree, it is not surprising that the U.S. is progressively losing its workforce for innovation.
This approach affects the future success of the R&D enterprise in the U.S. Pursuing less innovative work tends to produce scientific results that are more obvious than groundbreaking, and when a discovery is obvious, it cannot be patented, resulting in fewer inventions that go on to benefit patients. Even though there are governmental funding options available for scientists in academia focused on more groundbreaking and translational projects, those options are less coveted by academic scientists who are trying to obtain tenure and long-term funding to cover salaries and other associated laboratory expenses. Therefore, since only a small percent of projects gets funded, the likelihood of scientists interested in pursuing academic science or even research in general keeps declining over time.
Efforts to raise the number of individuals who pursue a scientific education are paying off. However, the number of job openings for those trainees to carry out independent scientific research once they graduate has proved harder to increase. These limitations are not just in the number of faculty openings to pursue academic science, which are in part related to grant funding, but also the low salary available to pay those scientists after they obtain their doctoral degree, which ranges from $53,000 to $65,000, depending on years of experience.
Thus, considering the difficulty in obtaining funding, the limited number of opportunities for scientists to become independent investigators capable of leading their own scientific projects, and the salaries available to pay for scientists with a doctoral degree, it is not surprising that the U.S. is progressively losing its workforce for innovation, which results in fewer patents filed.
Perhaps instead of encouraging scientists to propose less innovative projects in order to increase their chances of getting grants, the U.S. government should give serious consideration to funding investigators for their potential for success -- or the success they have already achieved in contributing to the advancement of science. Such a funding approach should be tiered depending on career stage or years of experience, considering that 42 years old is the median age at which the first R01 is obtained. This suggests that after finishing their training, scientists spend 10 years before they establish themselves as independent academic investigators capable of having the appropriate funds to train the next generation of scientists who will help the U.S. maintain or even expand its market share in the biotech industry for years to come. Patenting should be given more weight as part of the academic endeavor for promotion purposes, or governmental investment in research funding should be increased to support more than just 20 percent of projects.
Remaining at the forefront of biotech innovation will give us the opportunity to not just generate more jobs, but it will also allow us to attract the brightest scientists from all over the world. This talented workforce will go on to train future U.S. scientists and will improve our standard of living by giving us the opportunity to produce the next generation of therapies intended to improve human health.
This problem cannot rely on just one solution, but what is certain is that unless there are more creative changes in funding approaches for scientists in academia, eventually we may be saying “remember when the U.S. was at the forefront of biotech innovation?”
New gene therapy helps patients with rare disease. One mother wouldn't have it any other way.
Three years ago, Jordan Janz of Consort, Alberta, knew his gene therapy treatment for cystinosis was working when his hair started to darken. Pigmentation or melanin production is just one part of the body damaged by cystinosis.
“When you have cystinosis, you’re either a redhead or a blonde, and you are very pale,” attests Janz, 23, who was diagnosed with the disease just eight months after he was born. “After I got my new stem cells, my hair came back dark, dirty blonde, then it lightened a little bit, but before it was white blonde, almost bleach blonde.”
According to Cystinosis United, about 500 to 600 people have the rare genetic disease in the U.S.; an estimated 20 new cases are diagnosed each year.
Located in Cambridge, Mass., AVROBIO is a gene therapy company that targets cystinosis and other lysosomal storage disorders, in which toxic materials build up in the cells. Janz is one of five patients in AVROBIO’s ongoing Phase 1/2 clinical trial of a gene therapy for cystinosis called AVR-RD-04.
Recently, AVROBIO compiled positive clinical data from this first and only gene therapy trial for the disease. The data show the potential of the therapy to genetically modify the patients’ own hematopoietic stem cells—a certain type of cell that’s capable of developing into all different types of blood cells—to express the functional protein they are deficient in. It stabilizes or reduces the impact of cystinosis on multiple tissues with a single dose.
Medical researchers have found that more than 80 different mutations to a gene called CTNS are responsible for causing cystinosis. The most common mutation results in a deficiency of the protein cystinosin. That protein functions as a transporter that regulates a lot metabolic processes in the cells.
“One of the first things we see in patients clinically is an accumulation of a particular amino acid called cystine, which grows toxic cystine crystals in the cells that cause serious complications,” explains Essra Rihda, chief medical officer for AVROBIO. “That happens in the cells across the tissues and organs of the body, so the disease affects many parts of the body.”
Jordan Janz, 23, meets Stephanie Cherqui, the principal investigator of his gene therapy trial, before the trial started in 2019.
Jordan Janz
According to Rihda, although cystinosis can occur in kids and adults, the most severe form of the disease affects infants and makes up about 95 percent of overall cases. Children typically appear healthy at birth, but around six to 18 months, they start to present for medical attention with failure to thrive.
Additionally, infants with cystinosis often urinate frequently, a sign of polyuria, and they are thirsty all the time, since the disease usually starts in the kidneys. Many develop chronic kidney disease that ultimately progresses to the point where the kidney no longer supports the body’s needs. At that stage, dialysis is required and then a transplant. From there the disease spreads to many other organs, including the eyes, muscles, heart, nervous system, etc.
“The gene for cystinosis is expressed in every single tissue we have, and the accumulation of this toxic buildup alters all of the organs of the patient, so little by little all of the organs start to fail,” says Stephanie Cherqui, principal investigator of Cherqui Lab, which is part of UC San Diego’s Department of Pediatrics.
Since the 1950s, a drug called cysteamine showed some therapeutic effect on cystinosis. It was approved by the FDA in 1994 to prevent damage that may be caused by the buildup of cystine crystals in organs. Prior to FDA approval, Cherqui says, children were dying of the disease before they were ten-years-old or after a kidney transplant. By taking oral cysteamine, they can live from 20 to 50 years longer. But it’s a challenging drug because it has to be taken every 6 or 12 hours, and there are serious gastric side effects such as nausea and diarrhea.
“With all of the complications they develop, the typical patient takes 40 to 60 pills a day around the clock,” Cherqui says. “They literally have a suitcase of medications they have to carry everywhere, and all of those medications don’t stop the progression of the disease, and they still die from it.”
Cherqui has been a proponent of gene therapy to treat children’s disorders since studying cystinosis while earning her doctorate in 2002. Today, her lab focuses on developing stem cell and gene therapy strategies for degenerative, hereditary disorders such as cystinosis that affect multiple systems of the body. “Because cystinosis expresses in every tissue in the body, I decided to use the blood-forming stem cells that we have in our bone marrow,” she explains. “These cells can migrate to anywhere in the body where the person has an injury from the disease.”
AVROBIO’s hematopoietic stem cell gene therapy approach collects stem cells from the patient’s bone marrow. They then genetically modify the stem cells to give the patient a copy of the healthy CTNS gene, which the person either doesn’t have or it’s defective.
The patient first undergoes apheresis, a medical procedure in which their blood is passed through an apparatus that separates out the diseased stem cells, and a process called conditioning is used to help eliminate the damaged cells so they can be replaced by the infusion of the patient’s genetically modified stem cells. Once they become engrafted into the patient’s bone marrow, they reproduce into a lot of daughter cells, and all of those daughter cells contain the CTNS gene. Those cells are able to express the healthy, functional, active protein throughout the body to correct the metabolic problem caused by cystinosis.
“What we’re seeing in the adult patients who have been dosed to date is the consistent and sustained engraftment of our genetically modified cells, 17 to 27 months post-gene therapy, so that’s very encouraging and positive,” says Rihda, the chief medical officer at AVROBIO.
When Janz was 11-years-old, his mother got him enrolled in the trial of a new form of cysteamine that would only need to be taken every 12 hours instead of every six. Two years later, she made sure he was the first person on the list for Cherqui’s current stem cell gene therapy trial.
AVROBIO researchers have also confirmed stabilization or improvement in motor coordination and visual perception in the trial participants, suggesting a potential impact on the neuropathology of the disease. Data from five dosed patients show strong safety and tolerability as well as reduced accumulation of cystine crystals in cells across multiple tissues in the first three patients. None of the five patients need to take oral cysteamine.
Janz’s mother, Barb Kulyk, whom he credits with always making him take his medications and keeping him hydrated, had been following Cherqui’s research since his early childhood. When Janz was 11-years-old, she got him enrolled in the trial of a new form of cysteamine that would only need to be taken every 12 hours instead of every six. When he was 17, the FDA approved that drug. Two years later, his mother made sure he was the first person on the list for Cherqui’s current stem cell gene therapy trial. He received his new stem cells on October 7th, 2019, went home in January 2020, and returned to working full time in February.
Jordan Janz, pictured here with his girlfriend, has a new lease on life, plus a new hair color.
Jordan Janz
He notes that his energy level is significantly better, and his mother has noticed much improvement in him and his daily functioning: He rarely vomits or gets nauseous in the morning, and he has more color in his face as well as his hair. Although he could finish his participation at any time, he recently decided to continue in the clinical trial.
Before the trial, Janz was taking 56 pills daily. He is completely off all of those medications and only takes pills to keep his kidneys working. Because of the damage caused by cystinosis over the course of his life, he’s down to about 20 percent kidney function and will eventually need a transplant.
“Some day, though, thanks to Dr. Cherqui’s team and AVROBIO’s work, when I get a new kidney, cystinosis won’t destroy it,” he concludes.