As the World Health Organization (WHO) observes World Patient Safety Day on September 17, 2021, we recognize the progress that the life sciences industry has made in protecting patient safety and its continued focus on improving safety for all patients.
This year's theme, which focuses on the safety of women and children during childbirth, is of the utmost importance at a time when 800 mothers in the United States still die during childbirth each year. Mothers and their unborn children can also suffer serious harm in the months that lead up to this most vulnerable time.
WHO's theme also reminds us that it was not until the 1960s that regulators first recognized the importance of data in determining drug safety, and that it was questions of fetal and maternal safety that drove that agenda. After all, the thalidomide tragedy led to the establishment of clinical trials and adverse reporting programs such as FDA's MedWatch in the United States.
Over the past five decades, tremendous gains have been made in our ability to find and mine the data that best support patient safety. First and foremost, the industry and regulatory agencies are shifting from reactive to a predictive approach to pharmacovigilance.
Increasing scrutiny, earlier in development
Today, there is increasing scrutiny of drug safety data that companies must collect and report, and drug developers invest billions of dollars each year to optimize preclinical biomarkers, which enable them to predict risks earlier in the drug development cycle, before a therapy is launched. Companies are turning to automation, and to technologies such as artificial intelligence to help improve their ability to analyze data to predict drug safety problems.
But moving from tracking to improving the prevention of adverse patient responses will depend on the ability to trawl unprecedented volumes of data in different forms, including the huge and growing number of social media, patient websites, and other online sources; longitudinal drug safety data related to post-marketing surveillance (Phase 4) studies; data from regulatory databases such as WHO's Vigibase, FAERS, EVDAs; medical literature monitoring; real-world evidence, including electronic health and electronic medical records; and even drug sales data.
Predictive models and AI
As the pharmacovigilance landscape evolves, there is an urgent need to develop and utilize AI-driven predictive models to analyze all the data. To this end, traditional data-mining algorithms, previously developed to identify causal relationships between an investigational or marketed product and reported adverse events, are being augmented with approaches such as logistic regression, neural models, and machine learning models that can be applied to larger real-world evidence such as electronic health or medical records (EHR / EMR).
At this point, the industry can only get sporadic glimpses into the state of patient safety. An end-to-end approach to drug safety data will be essential to providing a holistic view. It will require significant change, including standardizing data and using a single platform to allow data to flow seamlessly across various functions across the drug development and commercialization lifecycle. A holistic approach will address many of the business pain points from data silos in clinical, regulatory, quality, and safety functions. It will also simplify industry challenges such as product label updates, product complaints, and CAPA tracking. In addition, potential adverse event notifications will, ideally, flow from medical communications to safety.
Cross-functional connections emphasize shared responsibility
The industry is still a long way from achieving this vision, but progress is being made.
At the same time, a new spirit is taking root at more companies, and the awareness that patient safety is not simply the province of the pharmacovigilance department, but a shared responsibility that must involve all R&D functions, as well as manufacturing and quality.
As data begins to flow and content can be more easily accessed across functions, this heightened awareness of collective responsibility for drug safety can only increase. In the end, the responsibility for patient safety continues throughout the drug's life cycle, beginning at the onset of pre-clinical and clinical trial phases to identify safety signals early on and reduce the impact of adverse events in a larger patient population. Better ways to collect, connect, and analyze safety data will be crucial to protecting patient safety in the future. Learn how companies are working to ensure patient safety at the Veeva R&D and Quality Summit on October 14, 2021.
Veeva Systems Inc. published this content on 17 September 2021 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 17 September 2021 17:01:09 UTC.