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Business News/ Companies / J&J Hired Thousands of Data Scientists. Will The Strategy Pay Off?
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J&J Hired Thousands of Data Scientists. Will The Strategy Pay Off?

wsj

The 137-year-old pharmaceutical and medical-device company is taking a new direction in its efforts to discover drugs

A Johnson & Johnson drug-discovery laboratory in Spring House, Pa., uses automation to screen millions to trillions of compounds, generating data that can later be analyzed by artificial intelligence to find molecules with potential to treat disease.Premium
A Johnson & Johnson drug-discovery laboratory in Spring House, Pa., uses automation to screen millions to trillions of compounds, generating data that can later be analyzed by artificial intelligence to find molecules with potential to treat disease.

Johnson & Johnson is making one of the biggest bets in the healthcare industry on using data science and artificial intelligence to bolster its work.

The 137-year-old pharmaceutical and medical-device company has hired 6,000 data scientists and digital specialists in recent years, and spent hundreds of millions of dollars on their work, such as using machines to scour massive health-record datasets. Last year the company opened a state-of-the-art research site near San Francisco that houses advanced data science.

Some early efforts focus on diagnostics, like an algorithm that analyzes heart tests to spot a deadly type of high blood pressure much sooner than humans can, and voice-recognition technology to analyze speech for early signs of Alzheimer’s disease. There’s a virtual-reality goggle set to help train surgeons on procedures like knee replacements.

The long game, though, is a goal that has seen a lot of hype but less concrete proof that it will become a reality: using AI for drug discovery.

Startup biotechs are in the early days of human testing of AI-discovered drugs. Google this year introduced cloud-based AI tools to assist drugmakers in finding new treatments. But it could still be years before an AI-discovered drug is approved for sale by regulators.

Some pharmaceutical leaders have expressed skepticism that AI could ever discover new drugs any better than humans can.

J&J says it has an edge: a massive database called med. AI that it can sift for patterns to help speed up drug development. The info includes “real-world data"—anonymized information collected from everyday patient visits to doctors and hospitals—and years of clinical-trial results.

“AI and data science are going to be the heart of how we are transforming and innovating," says Najat Khan, chief data science officer and global head of strategy and operations for J&J’s pharmaceutical research unit. “The amount of data is increasing, the algorithms are getting better, the computers are getting better."

J&J says it has already used machine learning to help design an experimental cancer drug that is scheduled to start human testing next year.

A few things make J&J’s effort different, Khan says. Its data-science workers are tightly integrated into the company’s strategic decisions on drug research. The company’s massive datasets—med. AI has more than three petabytes of information—are made available to tens of thousands of employees. And it has hired people who aren’t just data scientists but also have skills in chemistry, biology or drug development.

Khan, who has a doctorate in organic chemistry, joined J&J in 2018 after working for Boston Consulting Group advising drugmakers on research and development strategies. She was tapped to spearhead the use of data science in the pharmaceutical R&D operation, and also works alongside the scientists.

Analysts consider J&J to be one of the most active large drugmakers in its commitment to AI. Market intelligence firm CB Insights recently ranked it third of 50 companies in its Pharma AI Readiness Index, which tracks companies’ patent applications, investments, deal making and other efforts related to AI.

J&J’s sprawling business—more than 130,000 employees and $80 billion in annual global sales—has had data-based projects for years, but the company’s leaders began to take a more coordinated approach about a decade ago, and ramped up investments around four years ago.

Today, most of the company’s drug-development projects incorporate some aspects of data science, up from just a handful five years ago. Its San Francisco-area research site in Brisbane, Calif., places data-science projects alongside R&D focused on finding treatments for retinal and infectious diseases. Many of J&J’s data workers are spread across multiple company locations including in the U.S., China and Belgium.

The hunt for precision medicine was one impetus, says Mathai Mammen, who helped build up J&J’s data-science capability as head of its pharmaceutical R&D operation between 2018 and earlier this year. In precision medicine, treatments are personalized to match genetic or other variations in an individual’s disease. J&J’s hope is that smart use of its data will yield answers about the molecular traits of disease and how to target drugs that exploit those traits.

“The patients are more apt to be found in the world and matched with the medicine that matters," says Mammen, who is now CEO of the biotech company FogPharma.

In one recent project, J&J scientists led a collaboration of 13 drug companies that analyzed blood samples collected from more than 50,000 people in the U.K. as part of a national database called UK Biobank. They identified thousands of genetic variants that influence levels of certain blood proteins, about 80% of which weren’t previously known.

J&J plans to analyze the dataset using AI and machine learning to help spot patterns. This in turn could lead to new drugs or diagnostics that target the gene-protein links to various diseases. In the past, industry scientists would look for such molecular drug targets by scouring academic papers. The AI-enabled approach could spot many more targets, more quickly.

The company also is using an AI algorithm to study digitized images of biopsies to detect subtle differences between tumors, which could lead to identifying genetic subtypes for certain tumors. Researchers could use this information to make a medicine that specifically targets the genetic subtype.

One hallmark of J&J’s approach is collaborations—more than 50 external partnerships with data-science startups and others. “They seem to be making more investments in other companies, startups and initiatives, in more of a venture-feeling sort of way than some of the other life-sciences firms," says Daniel Faggella, CEO and head of research at Emerj Artificial Intelligence Research, a Boston firm that conducts market research on corporate use of AI.

One project—with the Mayo Clinic and a health-technology company called Anumana based in Cambridge, Mass.—aims to speed up diagnosis of high blood pressure in the lungs, or pulmonary hypertension. It now can take two or more years to identify the life-threatening disease.

J&J and its partners amassed six million patient records, stripped of individuals’ identities. including more than eight million electrocardiogram readouts. An electrocardiogram, or ECG, is a procedure to record the electrical signals in the heart. They fed the records into a software algorithm to teach it to spot patterns in the electrical readings that were present in patients later diagnosed with pulmonary hypertension. Using the algorithm in conjunction with ECG’s can shorten the time to a pulmonary hypertension diagnosis by 12 to 18 months, J&J says.

The Food and Drug Administration has granted “breakthrough device" designation to the algorithm, for products that could improve diagnoses or treatment of serious diseases. The FDA hasn’t approved the algorithm but a decision could come next year.

Write to Peter Loftus at Peter.Loftus@wsj.com

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