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AstraZeneca places digital tech at core of blueprint for growth

AstraZeneca PLC

AstraZeneca places digital tech at core of blueprint for growth

Pharma group hopes push will speed up drug discovery and show value of its medicines

AstraZeneca is mounting a big push into digital technologies, which includes hiring a former Nasa artificial intelligence expert, as it seeks to accelerate drug discovery and show the value of its medicines in an increasingly tough pricing environment.

The approach was mapped out at a meeting of about 200 senior leaders at its headquarters in Gothenburg, Sweden, late last month. It forms part of a blueprint for future growth that its chief executive, Pascal Soriot, has internally dubbed “AZ2025”.

The pharma industry is facing unprecedented levels of scrutiny over its pricing, particularly in the US. At the same time, the emergence of highly expensive potential cures for diseases such as cancer has put more pressure on drug companies to show their prices represent value for money.

Mr Soriot, who in November announced the Anglo-Swedish company’s return to sales growth for the first time since 2014, suggested Astra would aim to expand the range of drugs it provided where payment was dependent on the results achieved.

In an interview, Mr Soriot singled out a deal struck over Brilinta, a heart disease drug, under which health systems or insurers receive a rebate if more than a certain percentage of patients taking the medicine suffer a second heart attack. Such medicines could be part of the solution for reducing health costs overall, he argued.

He said: “The point is that, if someone goes back to a hospital with a heart attack, they cost a fortune and, of course, it’s not nice for them to have a heart attack in the first place.

“We have to get to a point where you can price a product in the context of the total cost of treatment and disease.”

However, striking such contracts successfully can depend on the availability of data covering all aspects of a patient’s contact with the health system to produce so-called real-world evidence of its effects.

Mr Soriot said that in order to spread the outcomes-based pricing model more widely, it was necessary “to build capabilities to collect real-world evidence [and] partner with payers on ensuring this data is collected, developing contracts that enable you to develop these metrics”.

He suggested Britain’s National Health Service, with its cradle-to-grave relationship with patients, could give the country an edge.

“In a post-Brexit world, if you look for competitive advantages for the UK as a country, certainly the ability to collect data, integrate it and run clinical trials and provide data to people . . . is a competitive advantage that will attract more research and more investment,” Mr Soriot said.

It’s not a world where everybody can stay in their little camp and do their work, it’s a world where everybody has to come together to manage those costs

Pascal Soriot, CEO, AstraZeneca

In January, the company hired Richard Dearden, who worked on the Nasa Mars Rover project, to boost its capabilities in machine learning. He will have a particular focus on improving and speeding up Astra’s clinical trials.

Mr Soriot said the future would be “a world of partnerships with small companies, digital companies that have tools that enable us to do these kind of things . . . so you have to partner with hospitals, with payers”.

He added: “It’s not a world where everybody can stay in their little camp and do their work, it’s a world where everybody has to come together to manage those costs.”

As it seeks to improve its research productivity and reduce the time taken to bring a drug to market, AstraZeneca is starting to deploy new technology that will allow it to analyse 1.5m chemical structures in about 20 minutes, in search of the 20 or so that hold promise of delivering a breakthrough. Currently Astra scientists are able to look at just 1,000 over a far longer period.

This emphasis on digital technologies will require the recruitment of people with a particular blend of skills, Mr Soriot said. “It’s complicated because you’ve got a lot of people who understand artificial intelligence and maths and how to manage data, then you have other people who understand biology.

“You need to find people who understand both and find a way to kind of bridge the two.”

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