What helped them develop the vaccine at a record-breaking speed was an innovative artificial intelligence (AI) technology geared toward clearing study data analysis and prediction obstacles. The technology enabled the Pfizer team to review clinical trial data in a mere 22 hours after meeting the primary efficacy case counts. The tool not only saved an entire month that they had not anticipated but also enabled Pfizer to maintain a high level of data quality throughout the trial.
The revolutionary technology was Smart Data Query (SDQ) in the Life Science Analytics Cloud (LSAC) platform from Saama Technologies, the AI-driven Intelligent Clinical Cloud company that enables the life sciences industry to conduct faster and safer clinical development and regulatory programs.
“Through SDQ, LSAC was able to process up to 200 million clinical data points per day, cleansing the data as it appeared from patients around the world. As a single, unified data aggregation and analytics platform, Saama’s LSAC is designed to allow drug development teams—including clinical operations, medical, and data management—to make more informed decisions,” says Suresh Katta, CEO, Saama Technologies. The company enables pharma companies in bringing their drugs to the market faster.
At its core, LSAC is an AI platform that implements smart algorithms that can connect with all types of data sources. The platform can then quickly contextualize the data, enabling clients to save several hours rather than waste time manually cleaning the data which is highly error-prone. Finally, LSAC turns the contextualized connected data into conversational data, which can communicate with all types of devices to gain insights.
With all the data in one place, clients can ensure security, scalability, and quality across the drug development lifecycle
The platform brings client’s heterogeneous data—from EDC, CTMS, financials, ePRO, IVRS, central/local labs, multiomics, biomarkers, real-world data—together into a unified model that accelerates clinical development outcomes. The data aggregation is agnostic to source, structure, and existing infrastructure. “With all the data in one place, clients can ensure security, scalability, and quality across the drug development lifecycle, while gaining workflow efficiencies such as automated alerts, a collaborative task management system, and AI-powered predictive analytics and search,” says Katta. By offering oversight across more than 1,500 clinical studies, LSAC has become the platform of choice for more than 50 biopharmaceutical companies worldwide.
Rather than replacing the client’s existing IT infrastructure, Saama’s LSAC works to enhance the systems. Since LSAC is compatible with all IT and data infrastructure, Saama can easily serve the needs of a wide variety of clients. “We cover everything including cloud and hybrid infrastructures; we also help clients in their journey from traditional relational databases all the way to Snowflake and other technologies,” adds Katta. In addition, the technology's impeccable implementation speed adds a silver lining to LSAC. Rather than bringing large implementation teams that take months to deploy the technology, Saama’s LSAC can be configured in a few days to weeks.
With such exemplary features, Saama is all set to equip the pharma industry to leverage the maximum benefits of AI technology. “Till now, modern medicine has only been able to introduce therapies for less than 10 percent of known illnesses in the human body because it is extremely costly to develop them. We want to enable pharma companies to build a smart platform for cost-effective clinical development processes,” says Katta. The company also aims to bring the ability to intelligently share research across the industry to further augment and fast-track the drug development process.