Designing better antibody drugs with artificial intelligence



IMAGE: Machine learning helps develop optimal antibody drugs.
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Credit: ETH Zurich

Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a few decades now, medicine has also been using antibodies produced by biotechnology as drugs. This is because antibodies are extremely good at binding specifically to molecular structures according to the lock-and-key principle. Their use ranges from oncology to the treatment of autoimmune diseases and neurodegenerative conditions.

However, developing such antibody drugs is anything but simple. The basic requirement is for an antibody to bind to its target molecule in an optimal way. At the same time, an antibody drug must fulfil a host of additional criteria. For example, it should not trigger an immune response in the body, it should be efficient to produce using biotechnology, and it should remain stable over a long period of time.

Once scientists have found an antibody that binds to the desired molecular target structure, the development process is far from over. Rather, this marks the start of a phase in which researchers use bioengineering to try to improve the antibody’s properties. Scientists led by Sai Reddy, a professor at the Department of Biosystems Science and Engineering at ETH Zurich in Basel, have now developed a machine learning method that supports this optimisation phase, helping to develop more effective antibody drugs.

Robots can’t manage more than a few thousand

When researchers optimise an entire antibody molecule in its therapeutic form (i.e. not just a fragment of an antibody), it used to start with an antibody lead candidate that binds reasonably well to the desired target structure. Then researchers randomly mutate the gene that carries the blueprint for the antibody in order to produce a few thousand related antibody candidates in the lab. The next step is to search among them to find the ones that bind best to the target structure. “With automated processes, you can test a few thousand therapeutic candidates in a lab. But it is not really feasible to screen any more than that,” Reddy says. Typically, the best dozen antibodies from this screening move on to the next step and are tested for how well they meet additional criteria. “Ultimately, this approach lets you identify the best antibody from a group of a few thousand,” he says.

Candidate pool massively increased by machine learning

Reddy and his colleagues are now using machine learning to increase the initial set of antibodies to be tested to several million. “The more candidates there are to choose from, the greater the chance of finding one that really meets all the criteria needed for drug development,” Reddy says.

The ETH researchers provided the proof of concept for their new method using Roche’s antibody cancer drug Herceptin, which has been on the market for 20 years. “But we weren’t looking to…

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