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Jeffrey Lu uses big data tools to accelerate discovery of new drugs

The CEO of Engine Biosciences leverages AI, network biology, and gene editing in the fight against debilitating diseases.

Sitting in his 23rd-floor office located in the CBD, Jeffrey Lu is a picture of confidence. The 34-year-old is bullish that his start-up, Engine Biosciences, will discover drugs to treat some of the deadliest diseases, especially liver, breast and ovarian cancers.

His upbeat mood is not without some basis. Since founding the company in 2015, the American of Taiwanese descent has put together a team of experts in the medical, biotech, data science and technological fields to speed up the discovery of those drugs.

Current methods, the Singapore permanent resident says, are laborious as the human genome has about 20,000 genes. Before a drug can be found to treat a disease, genes have to be examined individually or in pairs. Theoretically, this means over 20,000 tests have to be done for single gene, and an additional 400 million tests for every pair. To discover the right drug and get it approved, the process may take up to 15 years and require US$2.7 billion (S$3.7 billion), he says.

  • Jeffrey Lu

    JEFFREY LU

    CEO, Engine Biosciences

Engine Biosciences, he believes, will slash time and costs exponentially. With researchers from establishments such as Massachusetts Institute of Technology and Harvard University, his team has developed the technology, Combigem, and patented it. “It’s like a combination lock,” Lu says. “We want to crack the biological code of the disease because without it, you can’t develop a drug that will stop it. So, like the lock problem, there are all these potential combinations you have to hit before cracking it. The traditional way is to try each, one by one, and it will take forever. Our technology can test all the combinations at once.”

Using artificial intelligence, network biology, and gene editing, the platform can analyse countless genes and gene combinations and can run hundreds of thousands of tests on cells simultaneously, including studying genes in pairs, triplets, or higher-order combinations.

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His foray into biotech came about after he started Goodman Capital, a company that invests in early stage ventures, with wife Daphne Teo, a former investment banker. It was his prior work as head of strategy with Air Asia, applying data science tools to grow its joint venture with travel firm Expedia, that triggered the desire to use big data in biotech.

“It was a personal thing for me,” Lu confides. “My grandfather died of pancreatic cancer just after I graduated from University of Pennsylvania. So I always thought that if I were to spend the next decades of my life working on something, it should have a human impact. That’s why I am applying the latest technologies in health care.” Although Combigem is still at the lab bench stage, it has made headway not only in cancer, but also in neurodegenerative diseases.

Says Lu: “In liver, breast and ovarian cancers, we have identified new critical genes and gene combinations that appear to be causing those diseases. We have potential drugs against these genes, and the ones that we predicted would work better against the cancer cells are doing just that. We also have drugs for diseases like ALS and Alzheimer’s, and in mice models, these have been able to reverse some of the neurodegeneration.”

He has set a target for the drugs to be ready for human clinical trials in 2021-2022. His team’s confidence in its work raised US$10 million in seed funding last January. It was the largest seed amount for a biotech start-up in South-east Asia.

He is pragmatic about the risk of failure – 90 per cent of start-ups tank – but he learnt entrepreneurial skills from his father, who grew technological businesses. His brother and Engine Biosciences co-founder, Timothy, an associate professor at MIT, also started companies that are successful today.

“Building a company is about the process and not the sexy bits,” he says. “The core challenge is to remain focused and not to do too many things.”

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