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Highlights
- The pharmaceutical and medtech industries have evolved significantly over the past few decades.
- With innovative product portfolios, breakthrough treatments, and changing regulations, organizations find it challenging to stay compliant and make the right strategic decisions.
- What we need is a regulatory intelligence (RI) solution that can readily capture, process, and analyze information and disseminate insights using the latest digital technologies and semantics.
In this article
Adopting an agile approach
The pharmaceutical and medtech industries are rapidly evolving.
In keeping pace with the changing regulations, breakthrough treatments and innovative product portfolios, organizations need an agile approach to capture, analyze, and generate insights in order to stay compliant and make the right decisions. A regulatory intelligence solution that uses digital technologies and semantics can disseminate valuable insights.
AI and ML-enabled scripts will monitor various regulatory and standard authority sites for regulatory changes, extracting concepts and mapping the applicable changes with relevant functions and semantic capabilities (using tagging, semantics, and available knowledge base). This will predict the behaviors of regulatory change and determine its impact on shaping the strategy. It will also create responses based on past events and expert opinions, thereby improving agility in regulatory intelligence. Building a regulatory knowledge base further helps in acquiring regulatory information and extracting knowledge for faster contextualization.
As a process, information captured is analyzed to generate intelligence that can be used to build new strategies and modify and change processes. The user can get auto suggestions, alerts, and notifications that automatically summarize the data and contextualize it as per new standards and regulations. To build a next-gen regulatory intelligence system, organizations need to understand the changes in regulations, draft them, and affix responsibility and accountability.
Our article on modernization of knowledge management in pharma regulatory affairs illustrates the knowledge management loop, involving processes like acquisition, curation, analysis and intelligence embedment, and performance measurement. We now present the process of deriving regulatory intelligence through semantic enablement, ensuring it is readily available for compliance, and to achieve a high probability of regulatory success (PRS) rate.
Data is becoming a strategic asset, and its value will substantially increase with the insights it generates. These insights can be used by different people (regulatory roles in various functions like submission manager in the submission process and labeling SME in the labeling process) to direct activities and achieve business outcomes. These include reducing the submission filing time and the time it takes to respond to queries by health authorities and improving the quality of submissions and compliance and risk management.
Key regulatory intelligence processes
The regulatory intelligence system includes four essential processes.
These are –
- Acquisition: Regulatory knowledge base can be built by acquiring information from varied sources—both structured (enterprise data) and unstructured (regulatory guidelines and filing documents). These are drawn from regulatory information management (RIM) and connected business applications, regulatory agency sites, communities, social sites, and so on. With the increase in frequency, volume, and variety of regulatory data being generated, it needs to be collected and processed with agility.
- Curation and analysis: An essential aspect of generating RI is the ability to capture information and extract insights.
- Intelligence embedment: The RI solution analyzes the data that can be linked with historical enterprise data to assess the impact. Based on that, the solution can apply the rules and workflows while assigning the following action. For example, the system notifies the responsible local or regional affiliate for review and comments. Post review, the system can trigger the subsequent workflow to keep the submission plan up-to-date and automate regulatory filing.
- Applying insights: After applying intelligence to operations and strategies, the performance evaluation of regulatory processes based on the improvement in key performance indicators (KPIs) can be fed back into the knowledge base for continuous improvement.
The next in regulatory intelligence
A knowledge-driven regulatory intelligence hub will allow us to derive actionable insights.
Table 1 determines how the solution captures regulatory knowledge and derives intelligence for embedment at the right time for the right stakeholder, using semantics and other digital technologies.
Table 1- The technical components of the next-gen, knowledge-driven RI hub
In Figure 1, a reference architecture for the proposed RI solution illustrates various components for acquiring knowledge from numerous sources. It applies built-in ontologies and taxonomies, driven by robust governance processes before it is organized and stored in a regulatory knowledge base. A regulatory knowledge base is a repository where the regulatory data is standardized, tagged, linked, and connected to other data in context. It is made available for faster search, analysis, and reporting while leveraging other business capabilities like workflow management, impact assessment, collaborative authoring, recommendations, and the like.
Figure 1- A reference architecture for the RI solution
The flow of information
The solution captures new ICH guidance release.
As per the flow of activities in the case of ICH (International Council on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use), the proposed RI solution will manage the guidance change capture process, as demonstrated in Figure 2.
Figure 2 - The information flow between various solution components.
The data from this release is ingested for tagging and content analysis (title, date of issue, status, among others) and further segmented into types of products, procedures, tests, and strategies.
The SME is identified for curation in case of manual curation or addition of data (applicability groups, type of products relevant to the company). The curated data is then updated and stored in the knowledge repository. For the workflow triggered for impact assessment (manual or auto), the strategy gets updated as part of the enhanced analytical testing approach. The assessed impact is linked to the newly captured information in the repository.
A new workflow is triggered to create an author report or summary. The recommendation engine is used to find a recommendation based on the assessed impact, past events, and expert opinions. The newly curated regulatory information will be available for querying, searching, analysis, and creation of reports.
The availability of ready-to-use knowledge that leverages scalable ontologies is crucial. It grows in alignment as it captures more data. Semantically aligned data can potentially boost data democratization in pharma, enabling faster decision-making and early product-to-market.
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