Pfizer, Saama partner on AI clinical trial platform

By Jenni Spinner

- Last updated on GMT

(Image: Getty/LeoWolfert)
(Image: Getty/LeoWolfert)

Related tags Saama Technologies Pfizer Data management Clinical trials

The two firms plan to use Saama’s Life Science Analytics Cloud platform to streamline clinical trial data management, using artificial intelligence technology.

Pfizer and Saama Technologies will work together to develop and deploy an artificial intelligence (AI) powered analytical tool geared toward clearing many of the obstacles faced by study data managers and monitors. The Saama Life Science Analytics Cloud (LSAC) platform will aggregate, analyze, model and predict data via 'deep learning'.

Pfizer’s role is to provide the required clinical data and domain knowledge to train Saama models to obtain the accuracy needed.

Demetris Zambas, VP and head of data monitoring and management at Pfizer, told Outsourcing-Pharma that Saama was invited to work with his company on the AI project after a 'hackathon', in which a number of companies competed with each other. Saama initially was invited to participate based on their core competencies.

“Our decision was simply to connect potential partners into the hackathon. From that point on, deciding to work with Saama was based on their performance in that competition,”​ he said.

Sagar Anisingaraju, chief strategy officer for Saama, told Outsourcing-Pharma that conventional clinical trial data solutions and processes can lead to a number of problems.

“The industry’s ability to validate clinical data integrity is limited to anticipated edit checks and significantly manual data review processes, which are very laborious, inconsistent, and prone to human error,”​ he said. “Furthermore, the current processes do not intrinsically improve or self-learn as more data is processed.”

Anisingaraju told us Saama sees AI benefiting the clinical trial arena in three ways:

  • Connections: Through harnessing AI, clinical trials can 'leapfrog' in its connecting, standardizing and understand data, helping change the course of the pharma industry.
  • Context: Whereas traditional methods direct attention to scores of parameters and insights that need to be manually adjusted, an AI-driven approach enables greater focus.
  • Conversation: AI allows the use of virtual assistants and other tools that enable them to experience clinical outcomes, without the need for complex programming.

AI ultimately will bring about process improvements, greater accuracy and time savings, he told us.

Zambas said harnessing AI will empower clinical trials to greatly improve upon “manual, inefficient data review processes”​ to validate clinical trial data.

“Through our strategic collaboration with Saama Technologies, we’ve identified efficiencies to improve processes and experiences for our clinical research partners,”​ he said.

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