Saama Technology’s newly launched Active Safety Analytics for Pharma (ASAP) platform reportedly is the first validated pharmacovigilance technology to use the US Food and Drug Administration (FDA) Sentinel Common Data Model and TreeScan methodology for detecting of safety signals. The platform, which leverages real-world data for pharmacovigilance) was co-built with pharma firm Gilead Sciences, harnessing scientific advice from the Reagan-Udall Foundation.
Benzi Mathew, vice president and partner with Saama, told Outsourcing-Pharma that use of RWD expands the possibility of pharmacovigilance.
“Pharmacovigilance, when not complemented by using RWD, is limited by the individual and mostly subjective instances of adverse events reported by physicians and patients; this is also severely limited since it is a point-in-time incident that is reported and does not have any history or longitudinal information associated with it,” he explained. “Use of RWD contributes to these limitations being overcome.”
Additionally, Mathew said the platform’s use of Sentinel Common Data Model and TreeScan is notable, with the two being “core elements” of the initiatives the FDA is relying on for safety surveillance of drugs circulating in the US market.
“The same data model and TreeScan methodology is now available for pharma companies to use in order to not only further enhance their safety surveillance capabilities, but also improve meaningful discussions with FDA since everyone is using the same data model and methodology,” he told OSP.
Active surveillance, Mathew explained, offers a more effective, proactive method to confirm the safety of their products.
“The alternate option is passive, or spontaneous, safety surveillance; in such cases pharma companies use patient or physician-reported cases of safety incidents for safety surveillance and analysis,” he told us. “In the case of passive or spontaneous safety surveillance, the pharma companies respond to safety incidents reported to them. In the case of active safety surveillance, the pharma companies are proactively scanning the marketplace to detect any safety signals within a patient population.”
“Since the volumes of data used in active analytics are significantly higher, it provides much better empirical evidence of a safety signal,” Mathew added.
Suresh Katta, CEO and founder of Saama, said, “ASAP was borne of a collaboration between the great scientific minds of Gilead and the great engineering minds of Saama. Together we have created a new bar by which to monitor and evaluate safety through the post-approval phase of the drug development life cycle.”
The TreeScan models were adopted to various study designs incorporating scientific advice from two experts—Robertino Mera, executive director of epidemiology, Gilead Sciences; and Judith Maro, operations lead of the FDA Sentinel Operations Center—through a collaboration with the Reagan-Udall Foundation (RUF) for the FDA.
“Reagan-Udall Foundation for the FDA has the charter to drive clinical and safety innovation as part of FDA; in that context, they are driving several real-world evidence programs, including the Sentinel initiative and the COVID-19 related Evidence Accelerator initiative,” Mathew explained. “Towards the end of 2019 and early 2020, Saama had approached RUF for their advice in building out ASAP to adopt the same Sentinel Common Data Model 7.0 and TreeScan that RUF had leveraged as part of the Sentinel Initiative for the benefit of the Pharma industry; since then, Saama and RUF have partnered in enabling enhanced active safety surveillance capabilities for improved patient safety for the pharma industry.”
“Gilead’s commitment to advances in pharmacoepidemiology is reflected in our partnership with Saama to create ASAP to push the boundaries of safety for new and existing medicines,” said Mera. “ASAP can effectively complement existing spontaneous safety surveillance capabilities to provide biotech and CROs with a comprehensive safety surveillance framework that will help enable the next generation of life-changing medicines.”
