Reimagining Drug Regulation: The Promise of Structured Data Management

The Future of Regulatory Science: SDM in CMC Submissions

In an era where industries from finance to aerospace have embraced automation, artificial intelligence (AI), and big data analytics, the pharmaceutical sector remains bogged down by legacy processes. The sheer complexity of regulatory requirements has long hindered innovation in chemistry, manufacturing, and controls (CMC) data management. Traditionally, regulatory submissions for drug approvals involve an exhaustive cycle of document authoring, manual data input, and narrative-based decision-making—leading to inefficiencies, human errors, and delays in bringing life-saving therapies to patients.

As regulatory bodies like the U.S. Food and Drug Administration (FDA) introduce accelerated approval pathways, pharmaceutical companies are under increasing pressure to streamline their submissions while maintaining rigorous quality and safety standards. One promising solution is Structured Content Management (SCM), a data-driven approach that transforms how pharmaceutical companies organize, compile, and submit regulatory information. By centralizing and automating content blocks, SCM offers an unprecedented opportunity to enhance efficiency, improve data integrity, and lay the foundation for AI-powered regulatory assessments. However, the transition to this digital paradigm is fraught with challenges, requiring close collaboration between global health agencies and industry stakeholders.

The Bottlenecks in Traditional CMC Submissions

For decades, the pharmaceutical industry has relied on fragmented, document-heavy workflows for regulatory submissions. The Common Technical Document (CTD) format, established by the International Council for Harmonisation (ICH), remains the global standard for organizing CMC data. However, despite the adoption of the electronic CTD (eCTD), much of the submission process still involves manual data entry, reformatting, and redundancy across multiple documents.

A typical regulatory submission follows a labyrinthine process. Data from experimental studies, batch validation, and quality testing are compiled into internal reports, repurposed into Product Quality System (PQS) documentation, and formatted for submission to global health authorities. Each regulatory agency—whether the FDA, European Medicines Agency (EMA), or Pharmaceuticals and Medical Devices Agency (PMDA) of Japan—has its own version of these requirements, leading to a duplicative, time-consuming process for pharmaceutical companies.

Furthermore, post-approval modifications to manufacturing processes introduce additional layers of complexity. Regulatory agencies require different levels of notification based on the perceived risk of the change, ranging from major changes requiring prior approval to minor modifications that can be reported in annual updates. Inconsistencies in regulatory expectations across global markets add further strain on industry professionals, increasing the likelihood of errors, delays, and noncompliance risks.

The inefficiencies of this system are particularly problematic in the context of expedited regulatory pathways, which have become increasingly prevalent in response to urgent public health needs. Programs like the FDA’s Priority Review, Breakthrough Therapy Designation, and the EMA’s PRIME (Priority Medicines Initiative) aim to accelerate drug approvals, but they often necessitate real-time updates to CMC data—something that the current document-based system struggles to accommodate.

Structured Content Management: A Paradigm Shift

The adoption of Structured Content Management (SCM) represents a seismic shift in how pharmaceutical companies manage regulatory submissions. Unlike traditional document-based systems, SCM organizes regulatory content into reusable, centrally maintained content blocks. These blocks can be dynamically updated, linked across multiple documents, and repurposed for different submission formats—reducing redundancy and enhancing consistency.

At the core of SCM is eXtensible Markup Language (XML), a widely used format that enables both human and machine readability. XML-based frameworks provide meta-descriptions for content elements, improving searchability, standardization, and automation. Pharmaceutical companies such as Sanofi and Medtronic have already pioneered XML-driven SCM solutions in areas like drug labeling and clinical data management, demonstrating significant efficiency gains.

In the context of CMC data, SCM offers several transformative advantages:
• Enhanced Data Integrity: Changes to a single content block are automatically reflected across all documents in which it appears, eliminating inconsistencies and reducing the risk of human error.
• Automation of Regulatory Submissions: Machine-readable content allows for automated extraction, validation, and compilation of data, significantly reducing manual labor.
• Real-Time Regulatory Compliance: SCM enables companies to track, audit, and update regulatory information dynamically, ensuring alignment with evolving health authority requirements.
• Seamless Integration with AI and Machine Learning: Structured data opens the door for predictive analytics, automated risk assessment, and regulatory decision support.

FDA’s KASA Initiative: The Future of AI-Driven Regulatory Review

One of the most ambitious efforts to modernize regulatory submissions comes from the FDA’s Knowledge-Aided Assessment and Structured Application (KASA) Initiative. Announced in 2018, KASA seeks to shift regulatory review away from traditional, narrative-driven summaries toward a structured, data-centric approach.

Under the current system, FDA reviewers must manually interpret CMC data, often relying on experience-based judgment to assess manufacturing risks and product quality. The lack of standardized formats makes cross-product comparisons challenging and increases the likelihood of subjective decision-making.

KASA introduces an algorithm-driven, structured data framework that enhances regulatory decision-making by:
• Automating Risk Assessment: By leveraging historical data and predefined risk parameters, KASA enables automated evaluation of CMC submissions.
• Enhancing Review Consistency: Standardized data structures ensure uniform assessment criteria, reducing inter-reviewer variability.
• Integrating with SCM Systems: KASA’s machine-readable format aligns with SCM, enabling seamless data exchange between pharmaceutical companies and regulators.

While KASA is currently focused on Abbreviated New Drug Applications (ANDAs) for generic drugs, its principles could be extended to New Drug Applications (NDAs) and Biologics License Applications (BLAs) in the future. However, the success of KASA hinges on global regulatory harmonization—without industry-wide adoption of structured data standards, pharmaceutical companies may face increased complexity in meeting divergent regional requirements.

The Role of Fast Healthcare Interoperability Resources (FHIR)

As the pharmaceutical industry moves toward structured regulatory submissions, interoperability with healthcare data systems becomes increasingly important. The Fast Healthcare Interoperability Resources (FHIR) framework, developed by Health Level Seven (HL7), offers a standardized approach to exchanging health-related data.

The FDA’s Pharmaceutical Quality/Chemistry, Manufacturing, and Controls (PQ/CMC) initiative is piloting the use of FHIR to structure CMC data, creating potential linkages between drug manufacturing, quality control, and real-world patient outcomes. By integrating SCM with FHIR, pharmaceutical companies could:
• Enhance Pharmacovigilance: Structured data could facilitate automated tracking of adverse events, linking manufacturing quality data with post-market safety monitoring.
• Enable Real-Time Manufacturing Adjustments: AI-driven insights from real-world patient data could inform continuous process improvements in drug production.
• Support Global Regulatory Collaboration: Standardized data exchange could facilitate multi-region approvals, reducing duplication of regulatory efforts.

Overcoming Barriers to SCM Adoption

Despite its promise, the widespread adoption of SCM for CMC data management faces significant hurdles. Key challenges include:
• High Initial Implementation Costs: Transitioning from legacy document-based systems to structured content management requires substantial investment in IT infrastructure and personnel training.
• Resistance to Change: Regulatory affairs professionals accustomed to traditional workflows may be hesitant to adopt new methodologies, necessitating comprehensive change management strategies.
• Lack of Global Standardization: Divergent regulatory expectations across regions complicate the development of a universally accepted SCM framework.

To address these barriers, industry leaders and regulatory agencies must work collaboratively to establish consensus-driven standards. Initiatives like ICH Q12 (Lifecycle Management Guidelines) and global harmonization efforts led by the EMA and PMDA will play a pivotal role in shaping the future of SCM adoption.

A Digital Future for Drug Regulation

The pharmaceutical industry stands at a critical juncture—embrace structured data management or remain constrained by outdated, inefficient regulatory processes. SCM offers a transformative solution for streamlining CMC submissions, reducing compliance risks, and accelerating drug approvals. With initiatives like FDA’s KASA, PQ/CMC, and global FHIR integration, the future of regulatory science is rapidly evolving toward a digital-first, AI-powered ecosystem.

However, achieving this vision requires coordinated effort from industry stakeholders, regulatory agencies, and technology innovators. If successful, structured data management will not only enhance regulatory efficiency but also pave the way for faster patient access to high-quality, life-saving therapies—a goal that transcends borders, industries, and bureaucracies.

Study DOI: https://doi.org/10.1016/j.xphs.2020.01.020

Engr. Dex Marco Tiu Guibelondo, B.Sc. Pharm, R.Ph., B.Sc. CpE

Editor-in-Chief, PharmaFEATURES