---
title: "Pacing the Journey: A Framework to Commercial Readiness"
description: As gene therapy programs advance toward commercialization, the discipline required starts to look less like a sprint and more like a marathon.
image: https://insights.forgebiologics.com/hubfs/Endpoints_Pacing__Anchor1800x800%20for%20blog.png
---
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# Pacing the Journey: A Framework to Commercial Readiness
[By Forge Biologics](https://insights.forgebiologics.com/author/forge-biologics)
9/14/26, 8:00 AM
## **Pacing the Journey: A Framework to Commercial Readiness**
*This article was first published in [Endpoints News](https://endpoints.news/sp/pacing-the-journey-a-framework-to-commercial-readiness/).*
#### Authors
 
Jey Bouc, Ph.D. Keerthana Subramanian
Senior Manager of Associate Director of
Technical Sales & Downstream
Scientific Advisory Process Development
#### As gene therapy manufacturing continues to mature, the focus is shifting. Most gene therapy developers begin this work running a sprint, racing to reach first-in-human data as quickly as possible. But as programs advance toward commercialization, the discipline required starts to look less like a sprint and more like a marathon. Early development once focused primarily on demonstrating that a vector could be produced reproducibly and at sufficient yield. Today, as programs advance toward pivotal studies and commercialization, the challenge shifts to a thorough, steady journey. Regulatory agencies no longer ask whether a product can be manufactured. They ask whether the process and product are truly understood in a way that supports consistency and safety. Process characterization (PC) provides the scientific foundation needed to build the data set that enables developers to identify critical process parameters, establish robust manufacturing control strategies and demonstrate process understanding as programs advance toward commercialization. Like training for a marathon, PC is not about a single explosive effort; it’s the sustained, disciplined work, mile after mile, that builds the endurance a program needs to go the distance to commercial launch. It transforms a process that operates reliably in the hands of its developers into one that can be justified, reproduced, and trusted by health authority reviewers. When executed well, PC reduces downstream risk by strengthening validation strategies, supporting PPQ readiness, and enabling a smoother path to commercial launch. When treated as both a regulatory requirement and a scientific exercise, it can become a foundational source for information requests during BLA or MAA review.
Within the development lifecycle, process characterization serves as the critical link between process development and commercial process validation:
It is where the empirical, exploratory work of early development is formalized into a defensible control strategy, grounded in statistical evidence rather than institutional knowledge. This is precisely why PC draws such close regulatory scrutiny. Regulatory bodies collectively expect that critical process parameters (CPPs), proven acceptable ranges (PARs), and the resulting control strategy are not asserted, but demonstrated.
At Forge, our approach to process characterization aligns with the Alliance for Regenerative Medicine’s (ARM) Project A-Gene framework, which emphasizes a connected scientific narrative rather than a collection of independent studies. Every stage builds upon the previous one, beginning with structured risk assessments, progressing through qualification of representative scale-down models, into Design of Experiments (DoE), and ultimately supporting process validation and commercial readiness. Together, these elements support regulatory expectations while building the scientific basis for a robust and scalable manufacturing process.
#### Process Characterization Begins with Understanding Risks
Starting characterization early on is what separates programs that pace themselves for the marathon ahead from those left sprinting to catch up later.
Before a single DoE run is executed, the question must be answered: what are the greatest risks to product quality? This is the role of the Parameter Risk Assessment (PRA) and process Failure Mode and Effects Analysis (pFMEA). These tools provide a structured framework for holistically evaluating process parameters including setpoints, hold times, and material attributes across each unit operation, and systematically ranking their potential impact on critical quality attributes (CQAs), which are the measurable property or characteristics that must be controlled to ensure product quality2. In gene therapy products, relevant CQAs may include attributes such as purity, potency, and infectivity.
The quality of a risk assessment depends on the breadth of expertise involved in its development. A scientifically sound and defensible risk ranking requires cross-functional input from Process Development, Analytical Development, Manufacturing Science and Technology (MSAT), Validation, Quality and Regulatory teams. Each team brings a distinct perspective to the assessment of process risk. For developers working with a CDMO, this is also the moment to bring that partner to the table, as a marathon is best paced with the full team assembled before race day, not recruited mid-course. A parameter that may appear low risk from a process performance perspective may present greater risk when evaluated through the lens of analytical robustness or clinical impact.
Importantly, risk assessment is not a one-time milestone completed early in development. It should be treated as a living document that evolves alongside the program, incorporating new process knowledge, characterization data, and manufacturing experience as the product advances toward commercialization.
#### Qualifying the Scale-Down Model
Once the right questions are identified, they must be evaluated using a model that reliably represents commercial manufacturing. This is where scale-down model (SDM) qualification becomes essential.
Think of the SDM as the practice course: it must mirror race-day conditions (i.e., commercial-scale manufacturing) closely enough that a strong training run predicts a strong result on the day that matters.
Regulatory expectations require characterization data generated at laboratory or pilot scale to be representative of commercial-scale manufacturing. Common qualification approaches include statistical equivalence testing (e.g., TOST), comparability assessments, and multivariate analyses evaluating multiple quality attributes simultaneously.
To ensure SDM qualification proceeds smoothly, several considerations should be addressed upfront. These include confirming the availability and suitability of representative commercial-scale batches for comparison, accounting for expected batch-to-batch variability, and defining scientifically justified acceptance criteria and data inclusion/exclusion criteria before execution of the qualification study. The qualification strategy should also establish in advance which process parameters and CQAs will be compared, the statistical approach for assessing scale-dependent differences, and the rationale for determining equivalence between the SDM and commercial-scale processes. Establishing these elements prospectively helps distinguish true scale-dependent effects from normal process variability and provides a clear, defensible framework for qualification. When supported by representative data and predefined criteria, a qualified SDM can then be confidently leveraged for characterization of multiple unit operations.
#### Designing Experiments that Build Process Understanding
With confidence that the scale-down model is representative of manufacturing, the next challenge is designing experiments that generate meaningful process understanding rather than simply confirm existing assumptions.
This is where DoE provides a critical advantage over one-factor-at-a-time (OFAT) approaches. While OFAT studies can demonstrate how an individual parameter behaves in isolation, they are unable to capture interactions between parameters, which are often where the greatest risks to product quality exist. DoE establishes PARs, characterizes parameter interactions, and establishes a control strategy reflecting how the process behaves across the full operating space.
The choice of study design plays a critical role. Screening designs narrow a large parameter list early; optimization designs like full factorials or Response Surface Methodology (RSM) then characterize curvature and interactions for parameters that survive screening.
Consider an affinity chromatography step characterized via a factorial design evaluating mass challenge and elution pH, with yield, purity, and impurity clearance as outputs. When the data reveals curvature or meaningful interactions, it informs the design space boundaries and the PARs carried into the control strategy. Where the response is flat, that’s equally valuable because it supports a wider operating range.
#### Analytics Provide the Data Foundation for Process Understanding
None of this can be effectively executed without analytical capabilities that can keep pace with the experimental design. A well-designed process characterization study can generate hundreds of in-process samples across multiple matrices including cell lysate, clarified pool, chromatography load and elution fractions. Each matrix can introduce unique effects that may confound an assay developed or qualified using a simpler reference material. Characterizing a process at this resolution therefore requires analytical methods that are both sensitive enough to detect meaningful differences across a narrow operating range while remaining robust across diverse in-process matrices. Just as importantly, analytical workflows must be capable of handling the volume of samples generated by a characterization study. Automated or miniaturized assays and plate-based workflows can provide the throughput needed to prevent analytical testing from becoming a rate-limiting step in the overall study. This makes early engagement with Analytical Development critical. Analytical strategy needs to be developed in parallel with process characterization study design, rather than after the experimental plan is established. Early alignment on which assays require matrix-specific qualification, where throughput bottlenecks are likely to occur, and which methods may need to be developed or adapted helps ensure the analytical strategy is ready before study execution, rather than becoming a constraint discovered mid-execution.
#### Bringing the Story Together
Risk assessments, SDM qualification, and DoE studies are important components of process characterization, but their value increases when they are linked. Each piece may be technically sound on its own, but it’s the connected story that carries a program across the finish line. Regulators aren’t just confirming the miles were run, they want to see how each one led to the next. They evaluate them as a connected narrative, not independent studies. The risk assessment should explain why parameters were selected; the SDM qualification, why the data can be trusted; the DoE study, how the PARs and control strategy were derived. When these threads are visible and consistent, the filing can be understood as a coherent story of process understanding.
The most common pitfall is treating these activities as siloed deliverables with no connective tissue. For example, parameters that appear in a risk assessment but are never addressed in DoE studies or PARs leave a gap with no clear trace back to supporting data. Building traceability from the start, rather than reconstructing it under filing pressure, is one of the highest-leverage things a program can do.
#### Process Characterization Is an Investment in Commercial Success
Process characterization is not simply a regulatory requirement to satisfy, it’s the scientific foundation for manufacturing throughout the product lifecycle. The objective is not only to demonstrate that a process can perform consistently at commercial scale, but to build the process knowledge and understanding needed to make informed decisions as the program evolves from early development through commercialization and beyond.
A marathon is not a distance anyone covers in a single sprint, but one paced mile by mile, with the finish line in sight the whole way. Commercial readiness for gene therapy manufacturing asks the same of a program: the work is hard, and while the eye stays on the prize, it’s the discipline of the journey itself, not just the finish line, that determines whether a program gets there.
The value of process characterization therefore extends well beyond a single campaign. Early-stage development can establish the risk framework, analytical approaches, scale-down models, and process understanding that inform late-stage characterization. In turn, data generated during late-stage process characterization can strengthen process validation, support inspection readiness, and provide a deeper understanding of continued process performance during commercial and post-registrational manufacturing.
Programs that invest early and rigorously in risk assessment and SDM qualification see that investment pay off downstream, in more efficient validation and smoother inspection readiness. Just as importantly, the platform knowledge generated through PC work like qualified models. Characterized unit operations and mature risk frameworks can be leveraged across programs, providing a foundation for more efficient and consistent process development. The knowledge generated creates a feedback loop across the product lifecycle. Findings from late-stage characterization can inform continued process monitoring, support investigation and change-management strategies, and strengthen inspection readiness. Commercial and post-registrational manufacturing, in turn, generates additional process performance data that can be fed back into the risk assessment and process understanding framework. New programs can then benefit from the analytical methods, qualified SDMs, characterized unit operations, risk frameworks, and platform knowledge established through earlier programs.
This is where process characterization becomes an investment rather than a one-time activity. A rigorous PC strategy can reduce uncertainty not only for the current program, but for the programs that follow it. Knowledge compounds across the portfolio: lessons learned from one program can sharpen risk assessments in the next, mature analytical platforms can accelerate method development, and qualified models can provide a foundation for future characterization studies.
Training for one marathon builds the strength for the next. The same is true here: the pacing, discipline, and platform knowledge built through a program’s process characterization carry forward, making the next race easier to run. The finish line may be commercial readiness, but the real return on the investment is a stronger, more knowledgeable manufacturing platform that is prepared for the next program, the next batch, and the next stage of the product lifecycle.
#### References
- ARM/NIIMBL. Project A-Gene: A Case Study-Based Approach to Integrating QbD Principles in Gene Therapy CMC Programs. Alliance for Regenerative Medicine: Washington, DC, 2021
- ICH Q8 (R2) – Pharmaceutical Development, September 2009
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