Beyond the Dossier: Unlocking Strategic Value in CMC Data

Written By Preeti Desai, Sr. Manager, Client Success and Colin Wood, Strategy & Solutions Leader, Life Sciences

In the world of bio-pharmaceutical development, Chemistry, Manufacturing, and Controls (CMC) is often described as the regulatory backbone of any product submission. Yet, despite its critical role, CMC remains one of the most underutilized, least digitized, and most manually intensive areas in the product development lifecycle.

In recent years, the pharmaceutical industry has shifted focus from merely digitizing documentation to treating data as a core business asset. As regulatory expectations evolve and time-to-market pressures increase, structured CMC data is emerging as the new API — connecting R&D, manufacturing, and regulatory functions. More than just supporting faster submissions, CMC data lays the foundation that has the potential to inform accelerated drug development, enabling companies to learn from prior experiments, optimize processes, and reduce redundancy. When structured properly, this data becomes the substrate on which AI models, ontologies, large language models (LLMs), and knowledge graphs can operate, exponentially increasing its scientific and operational value. 

In part one of this blog series, we will dive into the importance of leveraging CMC data and why it matters now more than ever. 

CMC — and Why It is the Regulatory Backbone 

CMC refers to the comprehensive set of data required by health authorities (like the FDA, EMA) to ensure the quality, safety, and consistency of a drug product. It spans the entire lifecycle — from raw materials and analytical methods to formulation, process development, and manufacturing controls. 

CMC tells the technical narrative — one built on structured evidence. It proves that the product: 

• Is made consistently, batch after batch

• Meets its defined specifications, every time

• Is safe and reproducible at scale, from the lab bench to the manufacturing line

It’s not just a compliance formality — it’s the foundation that gives regulators confidence, manufacturers direction, and patients trust.  

Digitization in Modern CMC Submissions: The Investment Dilemma 

While fully digital regulatory submissions are still several years away — with ICH M4 and related guidelines continuing to favor document-based formats — the industry’s momentum toward digitization is undeniable. This creates a dilemma for many pharmaceutical companies: Should they invest in digital infrastructure now, or wait for regulatory mandates to catch up? 

Reluctance is understandable because, despite being data-rich, the CMC landscape is riddled with inefficiencies. From early-stage discovery to commercial production, teams grapple with: 

In short, CMC data exists, but it is invisible, scattered, and disconnected. 

Missed Opportunities: Data Ignored Beyond Submissions 

What’s often overlooked is that CMC documentation is merely a snapshot — the “final cut” of a much richer, iterative scientific process. In many organizations, once a submission is filed, the underlying data is: 

• Archived and locked away 

• Disconnected from future product lifecycle activities 

• Ignored for cross-product learnings or platform optimization 

• Unavailable for AI/ML model training or decision support systems 

The future of CMC is not a better document. It’s a better data product. Companies that start treating CMC data as a core asset — not just a compliance output — will be the ones ready for the future, long before the future arrives. 

The CMC Data Model – A Game Changer 

AI thrives not on raw data — but on clean, structured, and semantically linked data — which is impossible without a robust data model and a strong Master Data Management (MDM) foundation. That’s what a modern CMC strategy should aim for. While digital submissions are still on the horizon, structured, traceable CMC data creates measurable value today and positions organizations to lead when the regulatory landscape inevitably evolves. 

The shift toward structured, connected CMC data is more than a digital upgrade; it marks a paradigm shift in how pharmaceutical companies can derive scientific and operational intelligence across the value chain. 

At the centre of this shift lies the CMC data model, a foundational framework that organizes and links entities such as materials, processes, test methods, and experiments. When implemented correctly, this model transforms fragmented information into an integrated system of scientific truth.  

Discover how Fresh Gravity helps you streamline, manage, and submit this essential data with accuracy and compliance. 

Each entity is: 

• Structured (machine-readable)
• Linked (e.g., experiment ID connects to ELN records)
• Queryable (can be filtered, aggregated, reported on)
• HL7 FHIR Aligned (supporting future digital submission standards) 

This model becomes a central data hub, enabling: 

• Faster submissions (Regulatory authors auto-generate sections of CTD from verified, structured data) 
• Cross-functional collaboration (R&D ↔ Regulatory ↔ QA) 
• AI assistants to recommend process improvements or analytical methods based on prior outcomes 

Example: Tracking an Experiment ID from LIMS to Manufacturing Using a CMC Data Model 

Step 1: Experiment Creation in R&D (LIMS/ELN) 

• A formulation scientist runs an experiment to optimize pH and excipient concentration for a new oral solid dosage form
• The experiment is logged in LIMS and linked in ELN with a unique Experiment ID: EXP-2025-00321 

• Associated data includes: 
  • API lot number 
  • Excipient types and suppliers 
  • Process parameters (mixing speed, granulation time, drying temperature) 
  • In-process control (IPC) results 
  • Stability data for early formulation prototypes 

The CMC data model captures this under: 

• Entity: Experiment 
  • Attributes: ID, author, timestamp, purpose, related material IDs 

• Entity: Materials 
  • Attributes: API, excipients, batch IDs, specs 

• Entity: Process Parameters 
  • Attributes: equipment, duration, ranges, outputs 

Result: The Experiment ID becomes a unique anchor for linking structured formulation and process development data. 

Step 2: Scale-Up & Manufacturing Transfer 

• The optimized process is transferred to pilot-scale manufacturing. 

• Key parameters from EXP-2025-00321 are used as a baseline for defining: 
  • CPPs (Critical Process Parameters) and 
  • CQA (Critical Quality Attributes) 

• At this point, MES (Manufacturing Execution System) records: 
  • Actual process values (e.g., granulation time, drying profile) 
  • Equipment used 
  • In-process deviations 
  • Batch records and performance metrics 

The CMC data model now links: 

• Experiment ID → Pilot batch IDs → Full-scale batch IDs 

• Shared materials, methods, and parameters across scales 

From Data Product to Decision Engine 

For the above example with EXP-2025-00321, structured CMC data linkage, the organization could explore the following use cases with the CMC data generated and linked accurately. 

While this blog offers only a high-level overview, the data model conceptualized by Fresh Gravity is significantly more detailed and comprehensive — built to support data structure complexity, regulatory alignment, and long-term scalability. If you’d like to explore the full scope of the model and its practical applications, get in touch with us.  

In the next blog, we will dive deeper into how Master Data Management (MDM) systems and IDMP-aligned reference models can enhance this vision — particularly through the lens of ICH M4Q analysis. We’ll explore how aligning M4Q elements with IDMP concepts (like pharmaceutical product, manufactured item, and packaging) creates a more robust, interoperable data model — one that can serve both compliance needs and digital innovation.