Safety, efficacy, speed and costs must all be prioritized and balanced in the delivery of life-changing therapies to patients. A drug that's quickly and cost-efficiently delivered to market, but isn’t effective and safe is unacceptable. An effective, safe drug that doesn’t get to patients in time to save lives has failed those who needed it most. When it comes to patient health and safety, there can be no compromises.
Fortunately, in a world with abundant data and advanced analytics, we have more tools than ever before to optimize this balance for the betterment of patient safety and outcomes.
In this week’s DIA 2021 Exclusive, SAS Principal Solution Architect Tom Sabo will join a panel of leaders from the pharmaceutical industry and the FDA to discuss "Artificial intelligence: Real applications and regulatory perspectives."
Deep learning and machine learning in action
Sabo's expertise is in using deep learning and machine learning to improve efficiency in identifying drug safety signals in adverse event (AE) narratives. Patient narratives reported in clinical study reports provide clinical evidence of AEs that occurred to a patient and help scientific safety reviewers. The manual review of these narratives is a daunting task because it is time consuming and resource intensive.
Sabo is exploring the use of deep learning to scale back on manual reviews of patient narratives. In a recent study, he implemented deep learning technology on freeform AE narrative data with the goal of accurately categorizing one AE: Serotonin Syndrome. The results of the study are exciting because they indicate that deep learning and machine learning can improve the speed, accuracy and interpretability of medical coding for adverse events.
I recently had the opportunity to chat with Sabo about AI and the future of drug safety. “AI gives us the opportunity to get closer to the optimal balance between efficacy, which includes safety, speed and costs in drug development,” he said.
We also talked about the importance of ethics in AI and the need for human experts to be able to understand the results. “It’s critical for non-technical experts to be able to review the results of AI models and ensure that they make sense. This is one of the key strengths of SAS® Viya®. It makes the AI modeling process accessible and understandable for all stakeholders, which empowers ethical AI,” he said.
Sabo’s recent work is just one example of an area of exploding growth for the application of AI in drug safety for the pharmaceutical industry and regulatory bodies alike.
Expanding partnership with the FDA
SAS recently announced a major expansion in our 40-year partnership with the US Food and Drug Administration (FDA) that will give the agency access to new capabilities in natural language processing, AI and machine learning through SAS Viya. The FDA’s Center for Drug Evaluation and Research (CDER) will use SAS to advance digital transformation efforts such as modernizing regulatory programs and driving analytical manufacturing facility surveillance.
- Learn more about the evolving role of analytics in health care and life sciences by viewing on-demand episodes in the Analytics in 20 webinar series.
- Or download this free e-book: What's Next for Life Sciences? 5 trends building resilience and driving innovation beyond COVID-19.