How AI and Big Data Will Disrupt Pharma’s Regulatory Compliance Standards ⚙️
The industry as we know it is changing. Pharmaceutical and life sciences companies across the globe are experiencing more pressure than ever to keep up with increased regulatory standards while moving at a pace that requires them to innovate in order to remain competitive.
With more real-time automation and the steady increase in AI and Big Data sweeping the landscape, what used to be a slow-to-change and risk-averse industry is now expected to see a significant shift towards newer technology that focus on heightened regulatory standards. Here’s how your company can get ahead of what industry experts are calling, Pharma 4.0:
What’s the cause of this shift?
Professional experts at PharmOut explain that Pharma 4.0 plays on the “convergence of people, physical systems, and data within an industrial process to increase quality, productivity and profit by using the power of advanced data analytics”. How does this play into compliance for life sciences?
Continuous, real-time compliance monitoring
Take product quality reviews as an example. Regulators used to acknowledge annual reviews as the acceptable standard, however with the introduction of automation and real-time monitoring, we can expect to see product quality reviews happen much more frequently than on an annual basis. Experts suggest, “Pharma 4.0 technology allows for continuous, real-time monitoring of manufacturing processes”. The goal in mind? To find and predict problems before they occur (which will help you avoid down time, and loss of product in the manufacturing facility).2
AI-powered claim referencing to trial reports
Promotional and Medical, Legal, Regulatory (MLR) reviews are yet another area Big Data is advancing in life sciences. During the review process, auto-linking the safety and efficacy claims back to the originating clinical trials that support them is a new application made possible by machine learning. Currently, referencing claims to trials and clinical study reports is a tedious and time consuming effort for marketing and medical affairs review teams. To compound the problem, the task requires some scientific literacy and familiarity with the clinical research. This is where machine learning comes in. Once the dataset is large enough, you can train the AI to recognize safety and efficacy claims and suggest links to the appropriate section of the related clinical trial report—all within the same platform.
Find out how Papercurve uses AI to suggest references in your workflow: References Powered by Paige AI.
Advanced technologies like big data, artificial intelligence, machine learning, deep learning and natural language processing are bringing forward a new wave of opportunities to disrupt and change the industry. From innovation to operations, this will have the power to significantly impact the processes within the pharmaceutical and life sciences industries. Although more time consuming for companies to adhere to, are these changes all that daunting? Yes. But the important distinction to note is the value it provides back to you—the ability to help you find, predict and fix the issues related to the quality of your product and operational processes.
Are you considering software to audit-proof your promotional review process? Book a demo with Papercurve to see how we can help you easily reference claims and document feedback within your revision cycles.