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Life sciences companies confront increasingly stringent requirements on safety signal detection and risk management. Regulatory authorities are taking steps to further safeguard patients by requiring companies to take more comprehensive steps to assess and monitor the benefit-risk balance of their products.

As noted in our previous blog , a leader in this regard is the European Medicine Agency (EMA), whose EudraVigilance system is used to report and evaluate suspected adverse drug reactions.

The EMA is particularly tough on signal management, and many companies have received non-compliance reports (NCs) on signal management, typically because they don’t have the controls in place to manage the signals on their products.

Companies also need to follow U.S. adverse event reporting requirements to the FDA’s Adverse Event Reporting System (FAERS).

Being Prepared

In this increasingly vigilant regulatory environment, having a manual error-prone approach to risk management will inevitably put companies at greater risk of scrutiny, and more than likely of receiving NCs (Non Conformance).

For companies to minimize risk, address compliance and manage inspections, while also being able to analyze data without overly burdening safety teams, automation is key. Cognitive computing capabilities, including machine learning, built into decision support systems help to improve operational efficiencies, address compliance issues and increase control by streamlining business process downstream or upstream.

In order to monitor all potential sources of data for safety concerns, companies need to adopt an integrated signal detection and risk management solution as part of their safety solution suite that not only aggregates data from disparate sources and produces quality signals but also captures and produces periodic safety assessment reports and relevant documentation as and when required.

For example, portfolio-driven lifecycle management can help life sciences companies to characterize and monitor safety profiles of their products from development to post-marketing. In addition, real-time safety profiling that captures information simultaneously from multiple sources lets companies manage both traditional signal detection – such as from individual case safety reports – and signals coming from post marketing safety studies, literature, unstructured safety sources and social media.

To stay ahead of the authorities on signal detection and risk-management, companies need full traceability from sources to actions taken or required, and they need to be able to track and communicate the changing benefit-risk to all stakeholders. Having a solution that lets you perform gap analysis between pharmacovigilance and regulatory affairs helps to ensure nothing is missed.

Tools that enable qualitative and quantitative insights for data analysis, reporting and signal detection, and that reconcile data across sources to make it easier to identify missing cases and cross reference cases are also invaluable.

The pressure is on companies to improve regulatory responses and address compliance concerns, and at the same time to streamline business processes. Taking a next-generation approach to risk management is the way forward.

This is Part 2 in a blog series on signal detection and risk management. Part 1 discussed the business and regulatory drivers that are causing companies to explore automated solutions.

Learn more about LifeSphere Signal and Risk Management and how it puts the company in control when it comes to the detection and management of safety signals.