he new eBook Achieving Excellence in Regulatory Information Management compiles industry knowledge and experience representing the spectrum of Regulatory Information Management (RIM) capabilities. For mature regulatory teams, the eBook provides best practices across the capability areas. For a young company investing in RIM for the first time or bringing its first product to market, there’s no better way to get educated on the scope of RIM processes, data, and documentation and how RIM capabilities leverage these assets. The eBook also gives an idea of the scale and magnitude of what is required to effectively implement a regulatory information management solution, including cross-functional interfaces.

This eBook was written not by a single author but by 24 regulatory professionals who work in various roles within Regulatory Affairs and Regulatory Operations and have been living regulatory work for most of their careers. It reflects the broadening role of regulatory information management within the life sciences and the input of multiple DIA working communities and groups: the RIM Whitepaper Working Group, the RIM Reference Model Team, and the Intelligent Automation Working Group, all within the overarching DIA Regulatory Affairs Community.

RIM’s original focus was registration management. But it has evolved to include 15 capability areas (e.g., regulatory planning and tracking, correspondence management, and labeling) as well as 14 cross-functional connection points within the typical life sciences company, as the Gens and Associates biannual World Class RIM Survey has identified. This new eBook reflects the essential role that Regulatory Information Management plays in making pharmaceutical, biologic, and medical device products available to patients.

Origin and Evolution

In 2015, there was a call to create a DIA community for Regulatory Information Management and to create a standard RIM data model or common reference model (see RIM Reference Model below). But this group soon realized that RIM means different things to different people. The authors from this new DIA RIM Working Group set out to write what was framed as a consensus White Paper on the definition of RIM, which was published in 2019 as V1.0. RIM White Paper V2.0, an extension of this first consensus White Paper, was published in 2021.

With the maturing of the industry’s regulatory capabilities, advancement of technologies (including artificial intelligence [AI] and machine learning [ML]), and new paradigms of collaboration with and among health authorities, the RIM Working Group authors recognized the need to dig more deeply into the original topics as well as expand the scope of topics from White Paper 2.0. What was originally expected to be an expanded White Paper (3.0) evolved into this new 240-page eBook.

A Regulatory Sea Change

This new eBook also reflects a sea change in regulatory science: the prospect that a single submission can address multiple markets near-simultaneously. Many people know about concurrent submissions and review through Project Orbis. In one recent regulatory reliance pilot, the sponsor filed one variation to one authority for a CMC change, but the Q&A period and review were conducted collaboratively across 48 regulatory authorities, supported by a platform developed by the nonprofit Accumulus Synergy, to approve that variation. Managing data is important, but as these data are managed for a changing world, it’s important to ensure that RIM systems and data can support the new operating paradigms.

This sea change also reflects industry’s ability to harness the data within their company systems. Companies are beginning to focus on more effective dialogue among affiliates and distributors to capture unpublished submission requirements and Health Authority (HA) expectations.

When we consider regulatory intelligence, we must also think of the content embodied within the RIM system (data, metadata, and narrative) as the keys to the kingdom—IF we apply AI and other tools to extract that information to accelerate regulatory activity. The regulatory function is often viewed in drug development as a “stage gate.” However, as keeper of information beneficial to the entire enterprise, the regulatory function can become a powerful enabler which accelerates access to therapies.

Drug-Device Combination Products

The eBook addresses another trend within the pharmaceutical industry: the growing prevalence of drug-device combination products. In his organization’s most recent biannual industry survey on RIM capabilities and adoption, Steve Gens found that 60% of the respondents—historically, pharmaceutical companies—now have some form of drug-device combination product in development or on the market. These vary from simple medication delivery to devices that have a pharmaceutical component. However, most pharmaceutical companies have no experience working with the Center for Devices and Radiological Health (CDRH) or with FDA’s device regulatory pathway. As medical device regulations evolve, even seasoned pharmaceutical regulatory professionals need to understand just how their drug-device combination product impacts their global regulatory submissions activity as well as post-market lifecycle activities.

Intelligent Automation

Intelligent automation refers to several advanced technologies: Robotic process automation (RPA), Natural Language Processing (NLP), artificial intelligence (AI), and Generative AI all fall under the umbrella of intelligent automation. Intelligent automation is not only about back-end computing but also about providing a user interface that delivers more intelligent end-user interaction, helping the end user understand what’s really going on behind the scenes while, at the same time, deploying capabilities to provide feedback and fine-tune the outputs. Industry seems to be struggling to identify high-value use cases. Many people now think that Gen AI means that we can reduce the amount of human input to create better submissions faster, but this has not happened. Some early use cases were more about using intelligent search, for example, to identify requirements and better understand HA expectations.

RPA has been implemented in various industries for some time. But now, AI and Gen AI increase its potential and complexity. The eBook illustrates different use cases that intelligent automation can potentially address and how AI and Gen AI come into the mix. The RIM Intelligent Automation subgroup will soon release a survey to figure out where things are headed: How are companies using AI? Where is the success? What are the lessons learned?

Finally, intelligent automation isn’t limited to narrative searches in text but can also apply to analytics. For example, AI would be well-served to look at the metadata within a RIM system to allow better forecasting and planning of submission activities. There’s a world of opportunity in these early days of really extracting value out of the application of AI.

Functional Intersections

Regulatory activity does not exist within a vacuum. It is, in fact, the nexus of all the functional areas that are relevant to health authorities. Regulatory takes information from preclinical, clinical, quality, CMC (chemistry, manufacturing, and controls), and other groups and distills it into a cohesive submission. But it doesn’t end with the original NDA, MAA, or BLA (New Drug Application, Marketing Authorization Application, or Biologics License Application). Maintaining a product’s regulatory lifecycle is an ongoing process.

Regulatory also contributes its own information, such as conditions of approval and milestone dates, which other functions consume throughout the product lifecycle. For example, to file Annual Reports or PSURs (Periodic Safety Update Reports), Quality and Pharmacovigilance teams need to know when the product was approved in which country. Manufacturing needs to know what forms of each product are approved in each country. The eBook explores solutions for sharing information through connections, connectivity, and integration.

Each functional area can own their respective data and information, but how do they interoperate without duplicating data? Data standards are part of the solution. Data governance, including data ownership and good data citizenship, enables each function and/or individual in the value chain to generate and own their data but at the same time share their data with other functions. Because each of these systems is owned by different organizations with different core purposes, the challenge of seamlessly executing workflow or sharing data between them seems almost insurmountable. Industry will continue to face these challenges until it creates a connected environment where ownership is shared instead of discretely assigned to a single group.

Change control is a good example of an industry challenge: Whether it’s a label change or a CMC change, regulatory must communicate with distributors and affiliates around the world to understand local health authority expectations and requirements on maintaining that product. Regulatory also needs to intersect with quality, because quality owns the change control process. If it’s a CMC change, regulatory works with supply chain to manage the pace of change. In many cases where prior approval is required, the whole change process depends upon regulatory determining in which markets that change can ultimately be implemented and when. Regulatory is at the center of the change process.

RIM Reference Model

The eBook also discusses the RIM Reference Model (download), which was released two years ago. In 2017, a team worked on a conceptual model based on an analysis of RIM processes, how information flows through those processes, information required for the regulatory function, and information required by other functional areas. This Reference Model was originally an Excel workbook posted on the DIA website along with mind maps and process maps. Soon after it was released, this group realized that the model was not easy to use. Excel is two-dimensional, but this information is more complex and didn’t fit well into a single Excel spreadsheet.

The group began to explore an object-oriented architecture for the Reference Model. The revised RIM Reference Model is more logical, using entity-relationship constructs, and is available as a Lucid Chart. It will also be available as an Excel workbook, with numerous tabs for each entity, including properties or data elements such as product data, approval data, application information, etc. This expanded model includes approximately 55 such objects and relationships between them.

The RIM Reference Model is data-centric, another growing trend in the regulatory field, which gives this group opportunities to figure out relationships to the Identification of Medical Products (IDMP) and Fast Healthcare Interoperability Resources (FHIR) standards. It can also provide a data migration backbone, mapping the capabilities and requirements when a company wants to go from system vendor A to system vendor B.

This group is vetting the new RIM Reference Model to present at DIA’s Regulatory Submissions, Information, and Document Management (RSIDM) Forum 2025. A few sponsors will share how they have used it to spearhead some of their internal RIM transformation efforts and in the process validated the model itself. We view this Reference Model as the data requirements starter kit for any system implementation. After its release, our biggest call for action will be for sponsors and vendors to adopt it into their internal processes and provide feedback.

Roadmap to Future RIM

The authors know that this eBook will need to evolve because the regulatory science and technology ecosystem is changing rapidly, and they hope that others will join them as this community of practice reaches up to the next level.

A summary of the eBook is available through DIA’s recent (November 2024) webinar. Another team is preparing the eBook content as a series of DIA training courses for 2025.

The eBook is a foundational text that needs to be revisited annually or biannually to make sure that the content continues to represent best practices across the spectrum of capabilities. Feedback from people reading and using the eBook can provide the basis for fine-tuning topics or adding new topics to improve it. It could also provide a solid educational text that supports a post-graduate regulatory curriculum and brings new candidates into the regulatory profession.