Artificial intelligence “is building highly effective antibodies that humans can’t even imagine,” reports Wired:
At an old biscuit factory…giant mixers and industrial ovens have been replaced by robotic arms, incubators, and DNA sequencing machines. James Field and his company LabGenius aren’t making sweet treats; they’re cooking up a revolutionary, AI-powered approach to engineering new medical antibodies.
In nature, antibodies are the body’s response to disease and serve as the immune system’s front-line troops. They’re strands of protein that are specially shaped to stick to foreign invaders so that they can be flushed from the system. Since the 1980s, pharmaceutical companies have been making synthetic antibodies to treat diseases like cancer, and to reduce the chance of transplanted organs being rejected.
But designing these antibodies is a slow process for humans—protein designers must wade through the millions of potential combinations of amino acids to find the ones that will fold together in exactly the right way, and then test them all experimentally, tweaking some variables to improve some characteristics of the treatment while hoping that doesn’t make it worse in other ways. “If you want to create a new therapeutic antibody, somewhere in this infinite space of potential molecules sits the molecule you want to find,” says Field, the founder and CEO of LabGenius.
He started the company in 2012 when, while studying for a PhD in synthetic biology at Imperial College London, he saw the costs of DNA sequencing, computation, and robotics all coming down. LabGenius makes use of all three to largely automate the antibody discovery process. At the lab in Bermondsey, a machine learning algorithm designs antibodies to target specific diseases, and then automated robotic systems build and grow them in the lab, run tests, and feed the data back into the algorithm, all with limited human supervision. There are rooms for culturing diseased cells, growing antibodies, and sequencing their DNA: Technicians in lab coats prepare samples and tap away at computers as machines whir in the background.
Human scientists start by identifying a search space of potential antibodies for tackling a particular disease: They need proteins that can differentiate between healthy and diseased cells, stick to the diseased cells, and then recruit an immune cell to finish the job. But these proteins could sit anywhere in the infinite search space of potential options. LabGenius has developed a machine learning model that can explore that space much more quickly and effectively. “The only input you give the system as a human is, here’s an example of a healthy cell, here’s an example of a diseased cell,” says Field. “And then you let the system explore the different [antibody] designs that can differentiate between them.”
The model selects more than 700 initial options from across a search space of 100,000 potential antibodies, and then automatically designs, builds, and tests them, with the aim of finding potentially fruitful areas to investigate in more depth. Think of choosing the perfect car from a field of thousands: You might start by choosing a broad color, and then filter from there into specific shades.
The tests are almost fully automated, with an array of high-end equipment involved in preparing samples and running them through the various stages of the testing process: Antibodies are grown based on their genetic sequence and then put to the test on biological assays—samples of the diseased tissue that they’ve been designed to tackle. Humans oversee the process, but their job is largely to move samples from one machine to the next.
In Hungary, doctors are using artificial intelligence to detect cases of breast cancer more effectively, enabling them to remove such cancers before they can metastasize and kill women.
Robotics is fueling other life-saving innovations. Doctors recently did the first robotic liver transplant in America. Robots can fit in small spaces in people’s bodies that a surgeon can’t reach without cutting through living tissue, or doing other collateral damage.
The fact that new technologies are already saving lives in other countries does not mean they can immediately be used to save lives in America. FDA employees commonly take years to approve life-saving drugs and medical devices.The FDA didn’t approve a home test for HIV until 24 years after it first received an application. An FDA advisory committee noted that the test “holds the potential to prevent the transmission of more than 4,000 new HIV infections in its first year of use alone.” That means thousands of people got infected with AIDS as a result of the delay in approving it. As Fortune noted, the FDA’s delay in approving the home HIV test was a “scandal.” Similarly, at least a hundred thousand people died waiting for the FDA to approve beta blockers.
Jake Selliger recently described how he is “dying of squamous cell carcinoma, and the treatments that might save [him] are just out of reach,” due to the FDA, which routinely takes many years to approve life-saving medical treatments. “The FDA is responsible for more deaths on an annual basis than any other government agency. [Selliger is] one of its victims,” notes Paul Matzko of the Cato Institute. Researchers are “curing multiple cancers right now,” yet “the FDA is acting like it’s business as usual” and dragging its feet on approving cures.
