Researchers at the University of New Mexico Comprehensive Cancer Center are using artificial intelligence (AI), machine learning and big data to discover multi-functional drugs.

Many cancer patients undergo treatment with multiple drugs, each of which attacks cancer in a different way, so the combination fights cancer on many fronts. But more drugs mean higher risks of side effects.

“Most cancer therapy is now a combination treatment,” said Avinash Sahu, assistant professor at the Comprehensive Cancer Center. Sahu joined UNM from Harvard and Dana-Farber Cancer Institute. “We wanted to find drugs that could suppress two cancer-causing pathways at the same time.”

Sahu and his research team created two approaches. The first, called BiopotentR, uses publicly available genomic data to find drugs that can attack cancer in multiple ways and identify genes that the drugs target. The second applies machine-learning methods to this information to predict how people will respond to immunotherapy.

Machine learning is similar to the way people learn. Just as people learn new things – such as riding a bike or driving a car – through lots of experience, computer-driven machine learning assimilates vast amounts of data and gleans patterns that it can then apply to other tasks.

But cancer research data alone were not enough for Sahu and his team to predict how people would respond to a drug. They needed additional biological data that they could then apply to cancer patients and cancer drug responses. In machine-earning terms, they needed to learn from the biological context and apply that knowledge to a cancer context; it’s a technique called transfer learning.

The team partnered with a company to find a compound that would target the top cancer gene candidate they identified using BipotentR. In preclinical testing, they confirmed that their predictions were accurate.

“When tumours have overactive multi-functional drug targets, patients are less likely to respond to immunotherapy,” Sahu said. “However, patients with these types of tumours could potentially benefit from a combination of immunotherapy and multi-functional drugs.”

The team’s work is not limited just to metabolism and immune targets, it can be tailored to explore any two factors to find better multi-purpose drugs. Sahu said this approach thus presented an exciting opportunity for new research in a variety of cancer-focused projects.

And faster drug discovery means more accurate personalised medicine.

Sahu joined the UNM faculty in January 2023. He holds a doctorate from University of Maryland. His research focuses on applications of machine learning and deep learning to improve the understanding and treatment of cancer.