Pfizer will scale the SDQ solution across a wider range of global studies to enhance and accelerate data review processes. Credit: Alliance Images / Shutterstock.com.

Pfizer has expanded its multi-year agreement with Saama, intensifying efforts to expedite clinical research.

The strategic development builds on the collaboration that began in 2020, aimed at automating Pfizer’s data review processes leveraging artificial intelligence (AI).

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This collaboration yielded the Smart Data Quality (SDQ) tool, which has substantially reduced the time to database lock.

Pfizer will scale SDQ across a wider range of global studies to enhance and accelerate its data review processes.

The partnership also extends further as Pfizer seeks to expedite regulatory submissions using Saama’s advanced biometrics research and analysis information network.

This move is anticipated to significantly shorten regulatory submission timelines throughout Prizer’s global portfolio.

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Saama’s innovative offering optimises statistical programming and biostatistics workflows, digitises trial specifications and produces tables, listings and figures artefacts ready for submission to regulators.

It also enables ongoing automation and innovation, potentially speeding up trial submissions in the future.

Saama’s suite of AI-enabled SaaS [software as a service] products and solutions supports the entire clinical development spectrum. These technologies unify and standardise data, facilitating medical review activities, enhancing patient safety oversight and enabling the prediction of subject behaviour, among other benefits.

Pfizer data monitoring and management vice-president and global head Demetris Zambas stated: “Building a sustainable framework to accelerate analysis and reporting for clinical trials is fundamental to speeding up the development of breakthrough medicines.

“We look forward to expanding our strategic partnership with Saama across our global portfolio, leveraging their cutting-edge, AI-based technologies to reduce the time and effort required for data review and reconciliation, and to improve the quality and consistency of data across routine and complex clinical studies alike.”