- Key takeaways
- What collaborative design centers are and what they provide
- Why does scale-up fail and what does a design center help prevent?
- Scale-down models: the technical foundation of design center work
- What kinds of studies are conducted in a design center before GMP?
- The CDMO design center model: access without capital commitment
- How do digital simulation tools extend the value of physical design centers?
Scale-up is the step in biopharmaceutical manufacturing where processes developed at laboratory scale must perform equivalently at commercial production volumes. It is also the step where the most expensive failures occur. Collaborative design centers, which offer access to pilot-scale equipment, scale-down simulation tools, and process engineering expertise in a shared facility model, provide the testing environment that separates informed scale-up decisions from expensive guesses.
Key takeaways |
|
|
|
|
|
For the regulatory and operational context of scale-up decisions within the CDMO partnership lifecycle, including how technology transfer packages document process characterization data from design center studies, see the related article on negotiating tech transfer and quality agreements. For the broader capital and strategic framework in which design center investment sits relative to internal facility builds, see the related article on the build vs. buy decision in biomanufacturing.
What collaborative design centers are and what they provide
A collaborative design center is a facility that provides shared access to process development equipment, analytical infrastructure, and engineering expertise for biopharmaceutical scale-up work. The facility model distinguishes them from a sponsor's internal process development laboratory: the equipment is operated by or in collaboration with the facility's engineering team, the cost is shared across multiple client programs, and the primary design objective is to generate scale-representative data rather than to discover and optimize the process at the earliest stage.
Equipment vendors that manufacture bioreactors and downstream processing equipment operate design centers as a commercial and technical service, providing clients access to pilot-scale versions of their equipment for process evaluation and equipment fit testing. CDMOs operate design centers as a pre-engagement or early-stage engagement option that allows sponsors to evaluate process performance on the CDMO's equipment before committing to a full GMP program. Academic and industry consortia, including programs supported by organizations such as the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) in the United States, operate shared design centers that provide precompetitive access to advanced bioprocessing infrastructure and expertise.
What design centers provide beyond equipment access is engineering expertise in scale-up methodology: the process engineering knowledge required to design a scale-down model that accurately represents commercial-scale conditions, to conduct process characterization studies that map the relationship between process parameters and product quality, and to interpret the resulting data in the context of regulatory expectations for process development documentation. This expertise is not uniformly available internally at early-stage biotechs and is a significant part of the design center value proposition.
Why does scale-up fail and what does a design center help prevent?
Scale-up is, as described in comprehensive bioprocess scale-up research spanning fluid mechanics and systems biology, notorious for performance losses and delays because not all characteristics of the process can be kept constant as reactor volume increases. The most consequential characteristics that change with scale are mixing time, oxygen transfer rate, and shear force profiles, each of which directly affects cell culture performance and product quality in CHO-based bioprocesses.
Mixing time increases non-linearly with bioreactor volume. At bench scale, a one-liter stirred tank bioreactor achieves uniform mixing in seconds. At 2,000 liters, the same impeller-based agitation system may take minutes to distribute a pH correction pulse uniformly through the culture, creating transient pH gradients that cells in different parts of the bioreactor experience differently. Research on scale-down simulators for mammalian cell culture as tools to assess the impact of inhomogeneities in large-scale bioreactors demonstrated that these mixing inhomogeneities expose cells to varying external conditions based on their location in the bioreactor, affecting cell physiology and process performance in ways that do not appear at bench scale where mixing is effectively instantaneous.
Oxygen delivery is limited by the gas-liquid interface available for oxygen transfer, which decreases relative to culture volume as bioreactor scale increases. At high cell density in commercial bioreactors, sustaining dissolved oxygen above the critical setpoint requires higher gas sparging rates or oxygen supplementation that can introduce shear stress effects not present at bench scale. Carbon dioxide removal, which shares the sparge-and-vent mechanism with oxygen delivery, similarly becomes harder to maintain at large scale, and carbon dioxide accumulation in the culture medium is known to affect glycosylation patterns and cell metabolism.
The scale-down approach to preventing these failures follows four interconnected steps, as established in the scale-up/down literature: detailed analysis of the large-scale bioreactor environment, translation of that environment to a laboratory-scale simulator, testing under those simulated large-scale conditions, and back-translation of findings to the commercial scale. Design centers provide the equipment and expertise to execute this methodology with the specific commercial-scale bioreactor configuration that will be used for GMP manufacturing.
Scale-down models: the technical foundation of design center work
A scale-down model is a laboratory-scale process system that replicates the relevant physical characteristics of a commercial-scale manufacturing process to generate data that predicts commercial-scale behavior. For a stirred tank bioreactor process, the relevant characteristics are primarily the hydrodynamic environment: mixing time, oxygen transfer rate, shear rate at the impeller and sparger, and the dissolved gas profiles that result from the balance between oxygen supply and cellular consumption and carbon dioxide removal. A 2025 comparative analysis of scale-down bioreactor configurations confirmed that effectively applying scale-down bioreactors decreases development time by enabling better understanding of large-scale gradients and their effects on bioprocess performance, as well as enabling higher precision and reliability during scale-up.
Miniaturized bioreactor systems operating at milliliter volumes (ambr-scale systems from 10 to 250 milliliters) provide a high-throughput format for early process characterization: dozens of conditions can be run in parallel with automated sampling, pH and dissolved oxygen control, and integrated analytics. These systems are best suited for media optimization, feeding strategy development, and clone selection, where the goal is to screen a large number of conditions rapidly with limited material.
Bench-scale stirred tank bioreactors at one to ten liters provide the mixing and mass transfer characteristics most relevant to predicting commercial-scale performance. At this scale, instrumentation for dissolved oxygen, pH, carbon dioxide, and nutrient concentration can be run in configurations that mimic the larger-scale environment, including two-compartment scale-down simulators that recreate the spatial gradients present in large production bioreactors by cycling culture between a well-mixed zone and a substrate-limited or low-oxygen zone.
Scale-down format | Operating volume | Primary use case | Key advantage | Key limitation |
High-throughput miniaturized bioreactors (ambr-type) | 10-250 mL per unit; 24-48 units in parallel | Early screening: media, feeds, clones, initial parameter ranges | High throughput with minimal material; automated sampling and control; statistical design of experiments at low cost per condition | Less hydrodynamically representative of commercial scale; some scale effects not detectable at this volume |
Bench-scale stirred tank bioreactor | 1-10 L; typically 2 to 3 L for cell culture scale-down | Process characterization; CPP/CQA relationship mapping; scale-down model qualification | Best hydrodynamic representativeness of commercial scale at this volume; standard industry format with established qualification protocols | Lower throughput than miniaturized systems; requires more material per run; cannot replicate large-scale gradients directly without two-compartment configuration |
Two-compartment scale-down simulator | Total 2-10 L; divided between well-mixed and plug-flow or poorly-mixed zones | Simulating large-scale substrate, pH, and dissolved gas gradients; identifying gradient-sensitive process parameters | Directly replicates the spatial heterogeneity of large-scale bioreactors; exposes cells to transient conditions they encounter at commercial scale | More complex to configure and operate than standard bench-scale STR; requires hydrodynamic characterization of the commercial-scale bioreactor as input for simulator design |
Pilot-scale bioreactor | 50-500 L; intermediate between bench and commercial | Process verification at intermediate scale; engineering runs before first GMP batch; qualification of scale-up parameters | Closer to commercial scale hydrodynamics; suitable for engineering runs that confirm process transfer before GMP commitment | High cost per run; not available in all design center configurations; fewer runs practical per program at this scale |
What kinds of studies are conducted in a design center before GMP?
Process characterization is the primary study type conducted in design centers before a GMP program begins. It systematically maps the relationship between the process parameters that can be varied (pH, temperature, dissolved oxygen setpoint, agitation rate, feed timing and composition, harvest timing) and the product quality attributes that define the drug substance specification (titer, cell-specific productivity, glycoform distribution, charge variant profile, aggregation). The output of process characterization is a process design space: the region of process parameter space within which the process consistently produces product meeting specification.
Scale-down model qualification is a study type that validates the predictive relationship between the scale-down model and the commercial-scale process. It demonstrates that the scale-down model, operating at bench scale under the characterized conditions, produces product with comparable quality attributes to the commercial-scale process run at its operating conditions. Without this qualification, the process characterization data generated at bench scale cannot be used with confidence to set commercial-scale operating ranges in a regulatory submission.
Equipment fit testing is conducted in design centers that operate the same equipment platform as the CDMO's GMP manufacturing facility. It demonstrates that the process, when run on the CDMO's specific bioreactor configuration rather than the sponsor's development equipment, produces equivalent process performance and product quality. This is the practical test that determines whether a technology transfer to the specific CDMO will succeed without process re-optimization at the new site, and it is the most direct risk mitigation available before the formal technology transfer begins.
The process characterization data generated in design center studies directly populates the process development sections of IND amendments and BLA submissions. Regulatory expectations for process characterization, including the relationship between critical process parameters and critical quality attributes, the proven acceptable range for each CPP, and the design space boundaries for commercial manufacturing, are most efficiently met when the process characterization studies are planned from the start with the regulatory submission structure in mind. The relationship between process characterization in design center studies and the technology transfer package that carries this data to the CDMO is covered in the related article on negotiating tech transfer and quality agreements.
The CDMO design center model: access without capital commitment
CDMOs that operate design centers offer sponsor organizations a pre-engagement or early-engagement service: access to the CDMO's equipment and engineering expertise at bench or pilot scale, before the sponsor has committed to a full GMP manufacturing program at that CDMO. The commercial structure varies: some CDMOs provide design center access under a time-and-materials fee arrangement; others incorporate design center access into an early process development program that leads to a GMP manufacturing agreement.
The strategic value of CDMO-operated design centers for sponsors is equipment-specific process development. The bioreactor configuration, sparger design, impeller geometry, and control system at the CDMO's GMP manufacturing facility are specific to that facility, and they affect process performance in ways that a generic scale-down model developed on different equipment cannot fully predict. Running process development and characterization studies on the same equipment platform that will be used for GMP manufacturing produces the most transferable process knowledge.
Equipment vendor-operated design centers offer a different value proposition: access to the vendor's complete equipment portfolio, including both current commercial products and pre-commercial prototype systems, at pilot scale. For sponsors evaluating a capital investment in internal manufacturing infrastructure, design center access at a vendor's facility allows equipment selection decisions to be informed by actual process performance data rather than vendor specifications alone. This is particularly valuable for programs transitioning from external CDMO manufacturing to internal manufacturing and evaluating which bioreactor platform to purchase.
For programs considering internal manufacturing, the build vs. buy analysis of whether to invest in design center-equivalent internal infrastructure or to access design center capabilities through CDMOs and vendors is addressed in the related article on the build vs. buy decision in biomanufacturing.
How do digital simulation tools extend the value of physical design centers?
Digital simulation tools are changing the relationship between physical scale-down experiments and commercial-scale predictions by enabling virtual scale-up studies that can be conducted and iterated much faster than physical experiments. The primary digital tools used in bioprocess scale-up simulation are computational fluid dynamics (CFD) for characterizing bioreactor hydrodynamics, mechanistic process models for predicting cell culture performance under defined environmental conditions, and integrated process simulation platforms that combine upstream and downstream unit operations into a connected process model. Machine learning approaches to bioreactor scale-up are also advancing rapidly, with ML models trained on multi-scale process data used to identify scale-sensitive parameters and predict process behavior at scales not yet physically tested.
CFD modeling provides a detailed map of the hydrodynamic environment inside a specific bioreactor geometry: the mixing time at any agitation rate, the dissolved oxygen gradients that develop at any sparging configuration, the shear rate distribution at the impeller, and the liquid velocity profiles throughout the vessel. This information, which can be computed for any bioreactor geometry without requiring physical experimentation, is the design basis for two-compartment scale-down simulators: the CFD model of the commercial-scale bioreactor defines the environmental conditions the scale-down simulator is designed to replicate.
Mechanistic process models for CHO cell culture describe the biochemical kinetics of cell growth, substrate utilization, product formation, and quality attribute generation as mathematical functions of the environmental inputs (temperature, pH, dissolved oxygen, substrate concentrations). These models, parameterized from bench-scale process characterization data, can be used to predict the effect of scale-up on process performance by combining them with the hydrodynamic environment predicted by CFD for the commercial-scale bioreactor. The combined model predicts what product quality attributes will result from running the process at commercial scale, before any commercial-scale experiment is conducted.
The role of digital twins in commercial-scale biopharmaceutical manufacturing, including the data architecture and MES/LIMS connectivity that enables digital twin-based process monitoring and control, is addressed in the related feature on digital twins and AI in biopharma manufacturing. The design center and simulation tools described in this article represent the process development input to that commercial-scale digital infrastructure.
This article was produced under Drug Discovery News' AI Editorial Guidelines.
