Industry Insights

Digital CMC Maturity Starts
with Structure: Rethinking
QTTP and Control Strategy

 

Abstract

The transition to digital CMC strategies requires a fundamental shift in how product
and process understanding is created, structured, and managed across the product
lifecycle—from early development through commercial operations. As products
move through increasingly accelerated clinical timelines, CMC teams must complete
process characterization, analytical validation, scale-up, facility readiness,
comparability and stability assessments, and process validation strategies while also
addressing regional regulatory expectations in Module 3 submissions.

In this environment, time to market is critical, but regulatory readiness cannot rely
on fragmented documents, disconnected systems, or late-stage consolidation of
CMC knowledge. An effective knowledge management system, grounded in scientific
evidence and risk-based decision-making, is essential to manage product and
process complexity while enabling global cross-functional teams to operate with
both global alignment and local regulatory specificity.

Building on previous Industry Insights on digital QbD, this paper explores why
digital CMC maturity starts with how knowledge is structured—not merely digitized.
It examines the technical, logistical, and regulatory challenges that prevent
organizations from fully realizing the value of their CMC knowledge and proposes
a more integrated approach to connecting QTPP, CQAs, CPPs, CMAs, analytical
strategy, control strategy, validation, technology transfer, and lifecycle management.

Ultimately, digital CMC maturity is not achieved by digitizing existing documents; it
requires a shift toward structured, science- and risk-based knowledge management.
Organizations that make this transition will be better positioned to accelerate
development, improve Module 3 readiness, manage post-approval changes, and
sustain product quality across global manufacturing networks.

 

Introduction

This Industry Insight builds on previous publications, Integrating Digital Transformation
and Quality by Design for Enhanced Pharmaceutical Development and Transforming
Pharmaceutical Development: Combining Digital Platforms and QbD Principles, which
explored QbD and analytical quality by design (aQbD) development using digitally
enabled methodologies. These papers highlighted the challenges of paper-based
processes and the value of a digital ecosystem to streamline risk assessments, accelerate
regulatory submissions, and improve knowledge management. Real-world case studies
demonstrated how embedding digitalization throughout the development lifecycle can
strengthen compliance, increase agility, accelerate innovation, and foster a culture of
continuous improvement.

This paper extends the discussion further by exploring how CMC strategies can be
revolutionized—not just digitized—through approaches that address the technical,
logistical, and regulatory challenges preventing organizations from fully realizing the
value of their CMC knowledge.

Even when organizations adopt structured QbD development processes, limited
digital maturity can still create significant challenges, including gaps in traceability
between development decisions and downstream activities; inconsistencies during
technology transfer and scale-up; and misalignment across development, validation,
and commercial operations. Perhaps most importantly, many organizations still fail to
treat the QTTP and control strategy as evolving lifecycle assets. Without an integrated
and interconnected approach from the outset, organizations risk losing the relationships,
criticality assessments, and risk logic needed to maintain continuity across development,
PPQ, and commercial manufacturing.

Organizations that structure and connect CMC knowledge at the point of creation
are better positioned to improve consistency, scalability, user adoption, and lifecycle
management readiness. Consistency is key to reducing reliance on manual interpretation,
knowledge reconciliation, and rework.

Establishing a systematic approach to align clinical outcomes with CMC strategy is
particularly important in accelerated programs. Clinical teams may push for rapid
pivotal studies or accelerated filings while CMC teams still need to complete process
characterization, analytical validation, scale-up, facility readiness, comparability,
stability, and process validation strategy (FDA, 2024; Fontanillo et al., 2022).

Digital CMC maturity enables organizations to address these challenges more
proactively while reducing the time and effort required across the product lifecycle—from
development scientists to MS&T technicians and CMC specialists. Organizations can also
mitigate the risk of delays and incomplete CMC information in Module 3 submissions,
which, as stated above, are likely to require an understanding of the nuances of regions-specific
regulatory commitments.

 

Driving the Next Level of CMC Submissions

Where We Stand: Digital Maturity

The pharmaceutical industry is experiencing a
significant digital transformation driven by the need
for faster, more efficient, and compliant product
development. Regulatory expectations, particularly
those outlined in ICH Q8–Q14 guidelines, are pushing
organizations toward systematic, science- and risk-based
approaches, while business drivers are pushing
companies to become more digitized (Figure 1).

However, many companies still rely on manual, paper-based
processes that create inefficiencies, increase
data integrity risks, and limit opportunities for innovation.
Others have begun taking the first steps toward a more
systematic digital transformation in how CMC strategies
are developed. One widely recognized framework
for assessing the digital maturity of pharmaceutical
companies is the ISPE Digital Maturity Model, developed
as part of the Pharma 4.0™ initiative. The framework
provides a structured approach for pharmaceutical
organizations to assess their current level of digital
capabilities and define a roadmap toward a fully
integrated, data-driven operating model (ISPE, 2023).

 

 

The model reflects a shift from traditional, document-based,
siloed operations toward end-to-end digital
integration across the product lifecycle, encompassing
development, manufacturing, quality, and regulatory
functions. It defines five levels of digital maturity, ranging
from pre-digital, paper-based operations to adaptive,
autonomous organizations operating in real time with
seamless data flows across the enterprise and across
lifecycle stages (Figure 2).

Figure 2. Schematic of the Digital Maturity Scale adapted from the ISPE Digital Maturity Model
Source: Pharma 4.0™ Baseline® Guide: Volume 8, Pharma 4.0™ (ISPE, 2023).
The assessment framework considers dimensions
that align closely with the “triple constraint” triangle
of processes (“organizational processes”), data and
technology (“data integrity by design” and “information
systems”), and people (“culture” and “resources”).

 

 

Empirical data suggests that most pharmaceutical
companies are still in the early to intermediate stages of
digital maturity, indicating that the industry is not lacking
awareness but still running proof-of-concept (PoC)
and pilot programs as scattered initiatives rather than
systematic, scaled enterprisewide execution. Previous
Industry Insights explored how companies in these earlier
stages of digital transformation are using structured
digital frameworks to build science- and risk-based
control strategies (ValGenesis, 2025a; ValGenesis, 2025b).

While many companies have invested in digital tools to
operationalize QbD adoption, digital CMC maturity is
still constrained by how QTTP and control strategies are
structured at the source or at discrete points as product
knowledge evolves. Figure 3 illustrates how control
strategies evolve across the product lifecycle, from
pharmaceutical development through post-commercial
manufacturing.

Building a control strategy is an essential part of the CMC
regulatory dossier and the foundation of commercial
manufacturing to ensure ongoing product quality
assurance. During the product lifecycle, the control
strategy may evolve based on feedback from process
monitoring and the change management process.

Control strategies are typically solidified during the
final stages of pharmaceutical development. However,
even organizations with digital platforms supporting
integrated QbD workflows often still rely heavily on
manual data entry as more process and product
knowledge becomes available. Clinical data generated
during accelerated development programs frequently
must be manually assessed and consolidated with the
desired QTPP. Likewise, development data from DoE
and other exploratory studies are often stored across
disconnected systems, such as ELN and LIMS, requiring
manual consolidation that introduces both data integrity
risks and efficiency bottlenecks.

As a result, many current digital approaches can support
the technical aspects of designing a CMC strategy but
still fall short of achieving the broader “north star” goal of
having a single product with one process recipe built on
fully traceable and connected product knowledge.

Another important dimension of digital CMC maturity
involves post-approval CMC changes to existing
regulatory submissions. These changes are essential
for capacity expansion, site additions, process
improvements, analytical updates, and supply continuity.
However, industry studies report that divergent national
requirements and review timelines can delay approvals
by three to five years in some cases (Deavin et al., 2024;
Mangia et al., 2024).

Global post-approval CMC change timelines indicate a
90% probability of country approval made over 24 months
in 63% of countries studied and over 36 months in 15% of
countries studied (Harris et al., 2023). These delays mean
companies may need to operate multiple parallel supply
chains, inventory strategies, and registered process
versions for the same product while waiting for approvals
across markets (Harris et al., 2023; Deavin et al., 2024).

Together, these challenges highlight why organizations
must move beyond simple CMC digitization toward more
integrated and structured digital CMC maturity models.

 

Moving Beyond Digitization of CMC:
Challenges
Legacy processes, unstructured data, lack of
standardization, and siloed data/system architectures
continue to prevent companies from realizing the full
value of digital tools and advanced analytics (Fontanillo
et al., 2022). The path to true digital CMC maturity,
where knowledge is embedded into a harmonized
CMC submission, can be hindered by several technical,
regulatory, and logistical hurdles.

Technical challenges
In digitally mature companies, process performance
assessment requires advanced data and computing
infrastructures capable of rapidly acquiring data and
transforming it into meaningful process and product
information. One of the key challenges associated with
large-scale data environments is determining how best
to use the knowledge and insights generated, whether for
internal auditing purposes, product release decisions, or
the development of regulatory CMC strategies.

For CMC purposes, data volumes continue to increase
due to automation, equipment-generated data, real-time
process monitoring, and data modeling, making data
integrity and compliance with FAIR principles—findability,
accessibility, interoperability, and reusability—more
difficult to attain (Fontanillo et al., 2022). By applying
FAIR principles within a centralized tool, companies
can achieve more structured and traceable knowledge
management, reducing compliance risk, streamlining
analytics, and strengthening regulatory submission
readiness. This means that when development
laboratories, pilot plants, commercial manufacturing
sites, CDMOs, and global regulatory groups operate using
integrated systems and common data standards, CMC
decisions and submissions become faster and easier to
defend (Fontanillo et al., 2022; Algorri et al., 2026).

There are additional technical challenges beyond data
governance that continue to limit progress toward digital
maturity. These include inconsistent communication
protocols and limited interconnectivity across digital
ecosystems, particularly in hybrid system landscapes
where legacy equipment and facilities may not support
modern communication standards (ISPE, 2025).

As organizations become increasingly digitized,
cybersecurity risks also become more significant and
may ultimately create vulnerabilities that affect product,
process, and operational security. Companies will need
to develop robust risk mitigation strategies to reduce or
eliminate network vulnerabilities and strengthen
system resilience.

Regulatory challenges
Limited regulatory precedent can discourage
organizations from adopting new approaches, even
when those approaches may reduce regulatory burden
and improve product quality over the long term. Another
major challenge is the complexity of filing regulatory
submissions across multiple global jurisdictions
with differing regulatory expectations, particularly
for emerging manufacturing technologies. Greater
international regulatory convergence on advanced
manufacturing technologies could potentially lessen this
uncertainty for manufacturers.

As mentioned previously, the pharmaceutical industry
remains in a transitional phase in which novel and legacy
systems must co-exist. This reality requires regulatory
frameworks that are flexible enough to accommodate
different technology paradigms.

A major industry-reported challenge is that the same
CMC data and control strategy may be interpreted
differently by different health authorities, even across
established ICH regions. An ISPE/IQ study covering 112
marketing applications submitted by 11 companies found
an overall core-document acceptance rate of 54% across
the United States, Europe, Canada, and Japan. This
translated to only an 8.7% probability that core control
strategy documents would be accepted by all four
regions without modification.

This creates a practical challenge for global companies.
While manufacturing operations are generally run as
“one product, one process” globally, registered CMC
commitments can evolve into region-specific variants.
Companies may therefore be required to manage
different specifications, process descriptions, material
controls, or testing expectations by market. Global CMC,
regulatory, manufacturing, and quality teams must then
spend significant effort reconciling local regulatory
commitments with global process realities.

Ultimately, CMC strategy fails when it is treated as a
late-stage regulatory writing exercise rather than an
integrated product, process, control, facility, supply, and
lifecycle strategy (Fontanillo et al., 2022; FDA, 2024).

Logistical challenges
Logistical challenges are closely tied to organizational
culture, workforce readiness, and cross-functional
collaboration. Global organizations routinely
transfer processes from development to commercial
manufacturing sites, between commercial sites, or from
internal sites to CDMOs. Any gap during these transfers
can trigger CMC comparability concerns, validation
delays, deviation trends, or region-specific regulatory
commitments (Kaczanowski, 2024; Harris et al., 2023).

Change management and workforce capabilities:
Companies must embrace change management
programs to upskill existing employees, recruit new talent,
and support the transition away from static, paper-based,
or basic digitization. At the same time, organizations must
create conditions that empower employees to adapt to
new ways of working and address concerns related to
increasing automation and potential job loss (ISPE, 2025).

Cross-functional collaboration: Technological
advancement must be accompanied by a shift away
from siloed functions toward greater transparency
and cross-functional collaboration. This is particularly
important when building CMC strategies that rely
on multiple functions and areas of expertise. Crossfunctional
collaboration becomes especially important
during technology transfer activities, which require
more than the transfer of documents alone. Successful
technology transfer depends on the effective transfer of
product knowledge, process understanding, analytical
capabilities, control strategy, equipment fit, and tacit
operational know-how (Kaczanowski, 2024; Fontanillo
et al., 2022).

 

Shaping the Future of Digital CMC
Figure 4 presents a high-level view of the digital
elements that characterize increasing levels of CMC
digital maturity. The figure distinguishes between a set
of foundational capabilities, referred to as standard
elements, and a set of more advanced capabilities,
referred to as enhanced elements, that support the next
generation of digital CMC maturity.

In this context, the standard elements represent
capabilities typically found in organizations that have
already begun their digital transformation journey. Using
the ISPE Digital Maturity Model as a reference, these
organizations generally operate around Level 2 maturity
(“Digital Silos”), as illustrated in Figure 2, and demonstrate
intermediate maturity across data- and technology-related
dimensions.

The enhanced elements shown in Figure 4 are intended
to address some of the technical challenges discussed in
the previous section by establishing clearer frameworks
for data acquisition, system integration, and workflow
automation. These capabilities can improve how crossfunctional
teams share information, manage knowledge,
and collaborate across the product lifecycle.

The following section presents a proposed approach for
achieving greater digital CMC maturity.

 

 

Global Implementation of an Integrated Digital
CMC Strategy: An Approach

Typical Problem Statement
• Geographical dispersion: Global organizations
with geographically dispersed development and
commercial manufacturing sites require a unified
approach to QbD-based process development,
tech transfer, and commercial operations.

• Inefficient data analysis: Manually managing
large datasets through spreadsheets makes risk
management activities time-consuming and
error-prone.

• Poor knowledge sharing: Disparate systems hinder
effective communication across teams and limit
access to historical product and process data.

Proposed Solution
• Implementation of a digital platform that
streamlines and standardizes activities from Stage 1
through Stage 3 of the product lifecycle.

• Development of an interoperable digital ecosystem
in which QbD knowledge flows across systems and
functions, supporting structured CMC data and
a unified global control strategy while avoiding
fragmented standards and platforms.

• Development of integrated QbD workflows
supporting process development through PPQ.

• Development of workflows that support effective
knowledge management during tech transfer
activities.

• Program management supported by a
consistent delivery model that uses agile
methodologies to enable adaptability and speed
through a collaborative, consultative,
and outcome-driven approach.

In the schematic below, the proposed approach is
illustrated in a layered framework, beginning with the
identification and mapping of data sources and the
establishment of data governance at its core. The
expected gains and outcomes associated with this
approach are also outlined.

 

 

Data sources and governance
The identification of data sources (for example, ELN, LIMS,
and MES) is followed by data mapping activities and
engineering assessments focused on system connectivity
and integration between external data sources and
a unified digital platform. Within this framework, QbD
workflows are supported by the required process and
quality data points. Clinical outcomes may still be entered
manually into the workflow to support assessment
against the QTPP.

A common problem in QbD development faced by
organizations is not the absence of data, but rather the
lack of usable, contextualized data. Information is often
distributed across ELNs, LIMS, chromatography systems,
historians, spreadsheets, MES platforms, and statistical
software without consistent metadata or traceability.
Data contextualization is particularly important in QbD
because process understanding depends not only on the
underlying data, but also on being able to explain why the
data were generated, under what conditions they were
generated, and how they support process understanding.

Data integration and system connectivity
Instead of keeping QTPPs, CQAs, CPPs, CMAs, DoE
outputs, analytical data, batch records, and risk
assessments in disconnected files, the platform can
establish a connected data model where each item is
linked and traceable.

This approach is particularly important because many
CMC organizations continue to operate with legacy
processes, unstructured data, inconsistent standards,
and siloed systems that limit the effective use of digital
tools and advanced analytics. This helps create a
structured “digital thread” linking development decisions
to regulatory commitments, aligning with the broader
industry push toward a single global digital CMC dossier
and structured CMC data exchange (Algorri et al., 2026).

Within this framework, QbD risk assessment can evolve
into a more dynamic process. As new DoE, scale-up,
engineering, clinical, stability, or PPQ data become
available, the platform can prompt reassessment of risk
scores and control assumptions.

QbD workflows
As discussed in the previous Industry Insights, QbD
workflows can provide a structured approach to process
development beginning with QTPP and extending
through the establishment of the control strategy. Figure
6 provides a high-level schematic illustrating a typical
QbD workflow across the different stages of the product
lifecycle (Figure 6).

Data analytics
Digital platforms can help incorporate DoE and modeling
outputs into a structured product knowledge base
by capturing key model conditions, assumptions, and
known limitations. Process observations can then be
contextualized to strengthen process knowledge and
scientifically justify process ranges.

This becomes increasingly important as future CMC
development continues to rely more heavily on in silico
tools, advanced analytics, digital twins, high-throughput
experimentation, and automated reporting (Fontanillo
et al., 2022).

Instead of searching through multiple reports, teams
can trace a registered process parameter back to the
supporting DoE model, associated batches, analytical
data, risk assessments, and the decision record.

Dashboards and reporting
Once data are structured and connected, digital
platforms can generate dashboards that support both
scientific and portfolio-level decision-making, including:

• QbD dashboards: Product and process
understanding metrics to gauge the level of
knowledge/uncertainty of each quality attribute,
CPP/CMA–CQA relationship strength, open
knowledge gaps, model confidence, and process
capability.

• Control strategy dashboards: CQA coverage
through release testing or IPC/PAT controls,
specification justification status, analytical method
readiness, and validation readiness.

• Technology transfer reporting: Transfer package
completeness, site gap assessments, open
deviations and investigations, engineering batch
status, and PPQ readiness.

• Regulatory readiness reporting: Module 3 sourcedata
completeness, established conditions,
regional requirement mapping, stability package
readiness, process validation maturity, and
facility readiness.

This aligns with broader industry trends toward real-time
data visibility, advanced analytics, automated reporting,
and interactive visualization supporting project- and
portfolio-level decision-making.

Expected outcomes
Global alignment on control strategy
One of the major challenges for global organizations is
maintaining a unified control strategy while managing
regional regulatory differences. This approach can
support the development of a master control strategy
linked to regional variants, allowing organizations to
better respond to questions such as which process
parameters are globally registered, which ranges differ
by market, which tests are release tests versus internal
controls, and which commitments are site-specific.

Change management can also become more
integrated and traceable across sites and regions by
helping organizations assess which markets are
affected by a proposed change and whether a change
can be implemented globally or requires a phased
regional approach.

Knowledge transfer becomes evidence-based rather
than document-based
Technology transfer is not simply the transfer of
documents or assets; it requires the transfer of process
understanding, product knowledge, operational
expectations, and control strategy. Traditional
technology transfer activities often depend heavily on
reports, meetings, checklists, and tacit knowledge held
by experienced scientists. A more structured digital
approach can help to transform technology transfer into
a structured knowledge package in which product- and
process-specific information is more easily accessible.

Better lifecycle management after approval
QbD should not end at regulatory filing. The same digital
backbone should support continued process verification
(CPV) based on commercial batch data, product quality
review, process monitoring, change control, deviation
investigations, and post-approval improvements.
Through CPV activities, organizations can reassess CQA/
CPP relationships, confirm or update process models,
and reevaluate control strategies through ongoing risk
assessment activities. This aligns closely with the lifecycle
management principles outlined in ICH Q12, which
aim to support more predictable and efficient postapproval
CMC changes through stronger product and
process knowledge, risk management, and an effective
pharmaceutical quality system (ICH, 2019).

 

Conclusion
Digital CMC maturity is not achieved by simply converting documents into digital files; it
begins when product and process knowledge are structured, connected, and governed
from the point of creation. For organizations developing complex products, operating across
global sites, and navigating accelerated clinical and regulatory timelines, the ability to trace
decisions from QTPP through CQAs, CPPs, CMAs, analytical strategy, process validation, and
control strategy is becoming a defining capability.

A mature digital CMC strategy enables companies to move beyond static submissions and
fragmented knowledge repositories toward a connected, evidence-based product knowledge
ecosystem. This supports faster and more consistent Module 3 preparation, more robust
technology transfer, stronger regulatory justification, and improved lifecycle management
after approval.

The future of CMC will depend less on isolated digital tools and more on an organization’s
ability to create a connected digital thread across development, validation, manufacturing,
quality, and regulatory functions. By treating QTPP and control strategy as evolving lifecycle
assets rather than late-stage documentation outputs, companies can reduce rework, improve
global alignment, and respond more effectively to post-approval changes.

Ultimately, digital CMC maturity represents a shift in operating model—from document-centric
compliance toward structured, science- and risk-based knowledge management.
Organizations that make this transition early will be better positioned to accelerate
development, defend regulatory decisions, and sustain product quality across the full
product lifecycle.

 

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