Driving Patient-Centric Innovation in Life Sciences Using Generative AI with Pfizer



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With a mission to transform healthcare for its 1.3 billion patients, Pfizer is always seeking to innovate using cutting-edge technology. To optimize the development of new medicines through cloud-based solutions, Pfizer and Amazon Web Services (AWS) have created the Pfizer-Amazon Collaboration Team (PACT) initiative.

Under PACT, Pfizer has pursued 14 projects, including generative artificial intelligence (AI) and machine learning (ML) to save scientists up to 16,000 hours of search time annually and cut infrastructure costs by 55 percent. Pfizer uses a plethora of tools from AWS to explore new ideas, quickly build prototypes, and drive a passion for innovation in digital drug development.

Opportunity | Supporting Rapid Innovation through the PACT Initiative

Pfizer has focused on scientific innovation as a core value since it was founded in 1849. “To keep up with the pace of technology, somebody would constantly have to learn the technologies that AWS releases,” says Vijay Bulusu, Pfizer’s head of data and digital innovation for Pharmaceutical Sciences Small Molecule (PSSM). “Our collaboration with AWS lets Pfizer remain focused on the science yet use the breadth and depth of new technologies that AWS brings to the table.

In 2021, Pfizer’s PSSM group and AWS launched PACT, applying AWS capabilities in analytics, ML, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. Pfizer teams in pharmaceutical development and clinical manufacturing hatch promising ideas, while AWS provides technical expertise through solutions architects and the AWS Prototyping and Cloud Engineering team.

With Pfizer teams highly focused on ongoing technology projects, PACT addresses challenges around the bandwidth needed for prototyping. “With this collaboration, AWS brings in its prototyping teams to work with business and IT teams directly on relevant challenges,” says Bulusu. “This gave us the ability to prototype rapidly, with a learn, inform, and fail fast cycle.”



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