Too often, the traditional document-centric approach for solving regulatory compliance issues taken by pharmaceutical companies creates barriers in terms of efficiency, collaboration, and compliance.
Because pharma is one of the world’s most regulated industries – with continuously evolving frameworks, guidelines, and reporting requirements – drug developers must keep pace with regulatory changes to avoid compliance issues.
Many of the issues involved with pharma’s traditional approach to maintaining compliance stem from the heavy manual and repetitive workflows that are involved. For example, tasks such as developing regulatory submissions, keeping labels up to date, understanding guidelines, and maintaining compliance with constantly shifting regulations require regulatory teams to access and analyze critical data scattered across vast amounts of documents.
Pharmaceutical companies expend a significant investment in these activities in the form of time, money, and effort, which add up on the cost side of the ledger but do little to enhance revenue. As a result, regulatory teams are looking for digital transformations that will help them evolve from the traditional document-driven approach to more modern, data-driven methods.
Through digital transformations, innovative technologies and systems can help regulatory teams discover essential data within regulatory documents, and then extract and standardize this information for use in downstream processes like reporting, labeling, master data management, or structured content authoring.
To accomplish these objectives, leading pharmaceutical teams are looking to artificial intelligence (AI)-based technologies to automate processes, improve efficiency, and ensure accurate reporting. One of these notable technologies is Natural Language Processing (NLP), which uses algorithms to “read” through unstructured text, and then transform it into structured data that is suitable for analysis and visualization.
NLP: What It Is and What It Does for Pharma
NLP is a subset of AI that pertains to how computers and human language interact. In lay terms, think of it as computers gaining the ability to understand, interpret, and generate natural language. With NLP, computers can process, analyze, and derive meaning from human language from a wide range of text inputs, including emails, social media posts, reports, and reviews, in a way that is similar to humans.
Those capabilities are very beneficial to pharmaceutical companies’ regulatory teams, in particular, which frequently must comb through substantially large collections of documents to discover new information and answer specific research questions. NLP enables teams to identify facts, relationships, and assertions that may otherwise remain buried among hard-to-scrutinize mounds of unstructured data within regulatory documents and other sources of key data.
Once converted to a structured format, this information can be integrated into databases, data warehouses, or business intelligence dashboards and is incredibly valuable for a wide range of use cases pertaining to descriptive, prescriptive, or predictive analytics.
NLP is not a new technology, but it has become widely used in recent years in consumer products, such as Siri, Alexa, and Google’s voice search. More recently, the emergence of Generative AI built on large language models (LLMs), such as ChatGPT, has generated new interest in applications of NLP in a broad range of disciplines, such as medical research, customer care, fraud detection, and risk management.
As it relates to the pharmaceutical industry, NLP has become an indispensable tool aiding in drug discovery, development, and commercialization, largely because it significantly outperforms previous methods of search and information discovery. For example, traditional methods of search merely point to the location of documents, tasking human researchers with the issue of potentially spending hours reading through large amounts of individual documents to pick out necessary data.
Finally, NLP is particularly well-suited for healthcare data discovery due to the prevalence of unstructured data in the industry, from electronic health records to medical images to social media posts. Overall, pharmaceutical companies have used NLP to boost the accuracy and efficiency of the drug development lifecycle by unlocking key information from the many unstructured data sources that are relevant to the industry.
NLP and Compliance: A Winning Combination
NLP delivers value to drug developers across a variety of purposes within regulatory affairs by speeding regulatory affairs and compliance, boosting labeling processes, standardizing regulatory data, mapping to master data management systems, and driving digital transformation in regulatory processes.
NLP offers benefits in many areas, including the following three major regulatory disciplines:
- Regulatory labeling: Access to drug labels from prominent regulatory authorities is important to help labeling teams find reference information for disease and symptom terms, contraindications, adverse events, and special populations.
- Regulatory intelligence: Access to the landscape of regulatory updates, with integrated data flows to consume textual documents, both internal (such as corrective and preventive actions) and external (such as regulatory guidelines and FDA letters) is essential for regulatory teams.
- Regulatory mapping: Compliance teams need a means of finding key data attributes from unstructured text documents and mapping that data to standards, such as Identification of Medicinal Products (IDMP), a set of international standards that define the rules that uniquely identify medical products.
NLP in the Real World: Three Use Cases
The following use cases demonstrate some real-world examples of how life science companies use NLP to support regulatory operations:
Internal and external risk management: A large pharmaceutical company’s product development team sought more efficient ways of understanding internal and external risk management information to optimize the formulations, commercial supply, and post-market regulatory compliance of its products.
The team created a data lake to capture important internal and external feeds. External feeds included FDA warning letters, biological license application review reports, white papers, and industry benchmark repositories, while internal feeds included deviations, corrective and preventative actions, risks, and responses to regulatory questions.
The initiative relied on NLP to structure and generate the data by extracting critical concepts, relationships, and sentiments from the sea of information. User-friendly visualizations enable team members to drill down and navigate the information, driving wider use of the technology among the team. These internal and external data flows are updated automatically to deliver scalable reporting of the regulatory landscape, featuring key risks and recommendations to act upon.
Semi-automated regulatory intelligence tracking: Regulatory and compliance teams often employ manual approaches to monitor regulatory authorities, such as requiring team members to check relevant regulatory websites and subscribe to emails to remain informed of changes to guidelines.
While the process does generate needed intelligence to uncover concerns, deadlines, events, and regulatory decisions for compounds of interest, it is a costly and resource-intensive approach.
To overcome the limitations of manual processes, one leading agrochemical company used NLP to develop a workflow to semi-automate information acquisition and summaries. The company integrated NLP with LLM technology to create a regulatory intelligence assistant, which provided team members with user-friendly question-and-answer access to updated regulatory information, safety alerts, and risk categorization for compounds of interest.
Access to drug labels for more effective authoring: A leading drug developer is using NLP to more efficiently explore and classify drug label data, helping its global regulatory affairs team overcome challenges associated with identifying and accessing label content from diverse sources in multiple languages.
To accomplish this goal, the company uses an NLP-powered labeling intelligence hub, which synthesizes drug label information across key sources such as the FDA and EMA. The hub enables users to compare specific labels through an interactive view and access digitalized and original documents directly. The tool has helped the team streamline processes associated with developing new labels and updating existing ones, expediting regulatory approval.
Conclusion
Regulations are continually shifting and evolving so it’s important for pharmaceutical companies to adapt their approaches to assist with regulatory review and compliance. In the past, many in the industry relied on traditional search methods to uncover essential regulatory data, but with new advances in AI-based technologies such as NLP, there are options that can replace inefficient, laborious, and error-prone methods. NLP transforms internal and external data into high-value, actionable insights, enabling regulatory teams to unlock important supporting evidence to rapidly address critical business issues.
Author Details
Jane Reed, Director Life Science Safety Regulatory Quality - IQVIA
Publication Details
This article appeared in American Pharmaceutical Review:Vol. 27, No. 2March 2024Pages: 56-57
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