harmaceutical labeling is continuously in the spotlight, due to FDA regularly issuing new guidelines, including these in May 2022, to which manufacturers must adhere. Historically, the management and tracking of labeling required a “mishmash” of different solutions for various aspects of the labeling operation, including programs like Microsoft Project for planning and monitoring. The regulatory agency also requires drug companies to create a risk minimization plan, particularly for sensitive medications such as those that can affect pregnancy.
The aim of aRMMs is to prevent or reduce the risk of adverse reactions associated with exposure to a medication. In the event of an adverse reaction, the aRMM should limit the severity or impact on the patient or optimize the product’s benefit. While aRMMs are currently more common in Europe than in the US, the FDA’s Risk Evaluation and Mitigation Strategy (REMS) is very similar.
Modern technology offers unique opportunities to combine aRMMs with end-to-end labeling to support patient safety across organizations and geographical locations. This process provides controlled version management, consistent information across all systems, and a single source of truth for all drug label-related data.
Integrating Labeling with aRMMs
Such programs enable manufacturers to obtain approvals, identify resources, and create and distribute label data to their document-management team. The team can then start building the approved assets.
The advantages of this type of solution include:
- Fewer errors in drug prescription and usage;
- Less confusion in the manufacturing and packaging environment; and
- Reduced liability for pharmaceutical companies.
After distribution of updated information to stakeholders, the solution can also collect and process responses to generate a 360-degree view.
The type of technology used to plan, track, create, and distribute pharmaceutical product information consistently and quickly impacts patient safety. In end-to-end labeling, optimizing these functions begins with basic workflow technology. Intelligent automation (IA) helps assess and identify labeling changes necessary and determines the best way to respond. Natural language processing (NLP) addresses semantic translation for multiple different marketplaces.
These technologies also support taking existing information and “componentizing” it into pieces, creating structured assets and preparing them for reuse with the possibility of having to navigate each component in a different language.
Benefits of Optimization
In terms of recording responses, companies would previously track email opens or use SharePoint portals, which were disconnected from the labeling or information system. It’s now possible to capture metrics in the same central hub as the labeling versions, confirming the version each response applies to.
Pharmaceutical companies considering deploying a labeling solution that optimizes aRMMs should begin by examining their business processes. Once they identify inefficiencies resulting from using multiple applications, they can re-engineer these processes based on the use of a central software solution.
The process also requires a cultural change to encourage buy-in for the use of new technology. One of the biggest challenges companies are likely to face will be getting their personnel to think differently and embrace componentization. Ideally, companies should take an incremental approach that allows users to gradually become comfortable with the solution.
Minimizing the Risk
With new smart-label technology, companies no longer need to juggle multiple data sources that might not be aligned. They simply have one consistent core of information from which components get reused and repurposed in any assets downstream. This factor eliminates the need to modify multiple documents. Users have a full view from the starting point right through to maintenance, including details of the assessment, the changes made, approval and distribution dates, and the response rate from recipients.
In the event the response to a labeling change fails to meet the regulatory agency’s requirement or another update is necessary, users can pull historical data from the system to inform the process and help guide further actions.