AI is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work.

 

Is the Genie. out of the bottle?  

AI is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work.  This according to the MIT Technology Review of February 15, 2023, is a report on the successful use of AI to select a cancer therapy for the treatment of a patient.

AI permits the analysis of innumerable combinations of candidate drugs.  In this case,

With nothing to lose, Paul’s ( a pseudonym) doctors enrolled him in a trial set up by the Medical University of Vienna in Austria, where he lives. The university was testing a new matchmaking technology developed by a UK-based company called Exscientia that pairs individual patients with the precise drugs they need, taking into account the subtle biological differences between people. 

The researchers took a small sample of tissue from Paul (his real name is not known because his identity was obscured in the trial). They divided the sample, which included both normal cells and cancer cells, into more than a hundred pieces and exposed them to various cocktails of drugs. Then, using robotic automation and computer vision (machine-learning models trained to identify changes in cells), they watched to see what would happen. 

The approach allowed the team to carry out an exhaustive search for the right drug. Some of the medicines didn’t kill Paul’s cancer cells. Others harmed his healthy cells. Paul was too frail to take the drug that came out on top. So he was given the runner-up in the matchmaking process: a cancer drug marketed by the pharma giant Johnson & Johnson that Paul’s doctors had not tried because previous trials had suggested it was not effective at treating his type of cancer. 

It worked. Two years on, Paul was in complete remission—his cancer was gone. The approach is a big change for the treatment of cancer, says Exscientia’s CEO, Andrew Hopkins: “The technology we have to test drugs in the clinic really does translate to real patients.

Selecting the right drug is just half the problem that Exscientia wants to solve. The company is set on overhauling the entire drug development pipeline. In addition to pairing patients up with existing drugs, Exscientia is using machine learning to design new ones. This could in turn yield even more options to sift through when looking for a match.

The first drugs designed with the help of AI are now in clinical trials, the rigorous tests done on human volunteers to see if a treatment is safe—and really works—before regulators clear them for widespread use. Since 2021, two drugs that Exscientia developed (or co-­developed with other pharma companies) have started the process. The company is on the way to submitting two more. 

This methodology allows scaling of the process resulting in more choices and quicker results developing a pipeline of therapies.  The model will serve well for all classes of drugs.  It may even reduce the cost of new drug development. Today, on average, it takes more than 10 years and billions of dollars to develop a new drug. The vision is to use AI to make drug discovery faster and cheaper. By predicting how potential drugs might behave in the body and discarding dead-end compounds before they leave the computer, machine-learning models can cut down on the need for painstaking lab work. 

“If somebody tells you they can perfectly predict which drug molecule can get through the gut … they probably also have land to sell you on Mars.”  

(Adityo Prakash, CEO of Verseon) 

Perhaps not so true anymore.

Yet it is still early days for AI drug discovery. There are a lot of AI companies making claims they can’t back up, says Prakash: “If somebody tells you they can perfectly predict which drug molecule can get through the gut or not get broken up by the liver, things like that, they probably also have land to sell you on Mars.” 

And the technology is not a panacea: experiments on cells and tissues in the lab and tests in humans—the slowest and most expensive parts of the development process—cannot be cut out entirely. “It’s saving us a lot of time. It’s already doing a lot of the steps that we used to do by hand,” says Luisa Salter-Cid, chief scientific officer at Pioneering Medicines, part of the startup incubator Flagship Pioneering in Cambridge, Massachusetts. “But the ultimate validation needs to be done in the lab.” Still, AI is already changing how drugs are being made. It could be a few years yet before the first drugs designed with the help of AI hit the market, but the technology is set to shake up the pharma industry, from the earliest stages of drug design to the final approval process.

This new generation of AI companies is focusing on three key failure points in the drug development pipeline: picking the right target in the body, designing the right molecule to interact with it, and determining which patients that molecule is most likely to help.   

Computational techniques like molecular modeling have been reshaping the drug development pipeline for decades. But even the most powerful approaches have involved building models by hand, a process that is slow, hard, and liable to yield simulations that diverge from real-world conditions. With machine learning, vast amounts of data, including drug and molecular data, can be harnessed to build complex models automatically. This makes it far easier—and faster—to predict how drugs might behave in the body, allowing many early experiments to be carried out in silico. Machine-learning models can also sift through vast, untapped pools of potential drug molecules in a way that was not previously possible. The upshot is that the hard, but essential, work in laboratories (and later in clinical trials) need only be carried out on those molecules with the best chances of success.  

Before they even get to simulating drug behavior, many companies are Before they even get to simulating drug behavior, many companies are applying machine learning to the problem of identifying targets. Exscientia and others use natural-language processing to mine data from vast archives of scientific reports going back decades, including hundreds of thousands of published gene sequences and millions of academic papers. The information extracted from these documents is encoded in knowledge graphs—a way to organize data that captures links including causal relationships such as “A causes B.” Machine-learning models can then predict which targets might be the most promising ones to focus on in trying to treat a particular disease.

Applying natural language processing to data mining is not new, but pharmaceutical companies, including the bigger players, are now making it a key part of their process, hoping it can help them find connections that humans might have missed. 

Jim Weatherall, vice president of data science and AI at AstraZeneca, says that getting AI to crawl through lots of biomedical data has helped him and his team find a few drug targets they would not otherwise have considered. “It’s made a real difference,” he says. “No human is going to read millions of biology papers,” Weatherall says the technique has revealed connections between things that might seem unrelated, such as a recent finding and a forgotten result from 10 years ago. “Our biologists then go and look at that and see if it makes sense,” says Weatherall. It’s still early days for this target-identification technique, though. He says it will be “some years” before any AstraZeneca drugs that result from it go into clinical trials.

AI is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work. The basic steps involved in developing a new drug from scratch haven’t changed much. First, pick a target in the body that the drug will interact with, such as a protein; then design a molecule that will do something to that target, such as change how it works or shut it down. Next, make that molecule in a lab and check that it actually does what it was designed to do (and nothing else); and finally, test it in humans to see if it is both safe and effective. 

For decades chemists have screened candidate drugs by putting samples of the desired target into lots of little compartments in a lab, adding different molecules, and watching for a reaction. Then they repeat this process many times, tweaking the structure of the candidate drug molecules—swapping out this atom for that one—and so on. Automation has sped things up, but the core process of trial and error is unavoidable. 

But test tubes are not bodies. Many drug molecules that appear to do their job in the lab end up failing when they are eventually tested in people. “The whole process of drug discovery is about failure,” says biologist Richard Law, chief business officer at Exscientia. “The reason that the cost of coming up with a drug is so high is because you have to design and test 20 drugs to get one to work.”P is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work.