Data campaign

AstraZeneca Customer Story

Issue link:

Contents of this Issue


Page 2 of 2

"While continuing to adhere to all required privacy and regulatory requirements, we ultimately want to get to the point where we can share data with research centers, universities and partners where their use of data will benefit our patients," Haskill said. "FAIR does that for us." In just a few years, AstraZeneca had completely transformed significant portions of its R&D organization—including both the automating or augmenting of key processes. And a key contributor to this effort was AWS, simultaneously serving as both strategic thinker and reliable platform provider. "AWS is a key partner in our R&D data strategy, because it allows us to move at speed," Åsberg said. "It's critical to helping our R&D colleagues test new ideas and scale—and bring value." Theory into practice One of AstraZeneca's core AI implementations was something called AI Bench. Originating in the early days of the company's data transformation, it utilized Amazon SageMaker (AWS' fully managed machine learning service) and Amazon EMR (next generation cloud to leverage enormous amounts of data) to provide data scientists with a unified data science workstation. Whereas data scientists embedded in teams scattered across the organization had previously operated in siloed environments, AI Bench brought their work into a central system— meaning individuals could tap into and borrow from the models and innovations of their peers while still protecting sensitive data. AI Bench's 1.0 iteration was valuable. Its 2.0 iteration, which incorporated AWS cloud technologies to help expedite and automate software and application releases, was invaluable. It also carried a GxP validation, an FDA distinction that ensures pharmaceutical products are safe and in alignment with key processes. That meant data scientists could use AI Bench far later in the R&D process and on increasingly complex problems. AstraZeneca has even found success in AI- assisted literature surveillance, turning to natural language processing—also powered by SageMaker—to review hundreds of thousands of abstracts each year for key signals. This means potential data trends or patterns in how medicines are performing in-market are identified faster and more accurately than ever before. That's the kind of speed and efficiency that translates to tangible patient impact: in some cases, AstraZeneca projects might be able to reduce as much a year from certain product development timelines. The list of implementations goes on—and, more importantly, will continue to grow. By leveraging AWS tools and with the Growth Through Innovation corporate strategy as a north star, AstraZeneca is continually expanding its investment in data, AI, and ML—surgically identifying use cases, then building scalable solutions that are designed to outgrow them. It's modern innovation, transforming one of the world's most impactful biopharmaceutical companies. "We're thinking big, starting small, and scaling fast," said Åsberg. "It's exciting to see how AI can enable our scientists to push the boundaries of science to deliver life-changing medicines." branded content by This story was produced by WIRED Brand Lab for AWS.

Articles in this issue

Links on this page

view archives of Data campaign - AstraZeneca Customer Story