When Drug Delivery Fails at Tech Transfer

Key Takeaways

• Drug delivery failures during scale-up and commercialization are more often rooted in technology transfer fragility than in fundamental scientific or formulation flaws.

• Both modified-release oral products and complex parenteral systems depend on tightly coupled formulation–process interactions that are sensitive to manufacturing context.

• Technology transfer exposes conditionally stable delivery attributes that appear robust in development but fail under variation in scale, equipment, and execution.

• Analytical methods frequently act as proxies rather than direct measures of delivery performance, delaying detection of instability during transfer.

• CDMOs do not introduce delivery risk; they reveal whether transferability was treated as a design constraint or deferred as a downstream problem.

When drug delivery systems fail during late development or manufacturing, the failure is often framed — implicitly or explicitly — as a fundamental scientific shortcoming. The formulation is deemed too complex, the delivery concept too ambitious, or the underlying biology insufficiently understood. This framing obscures a more consistent and less comfortable reality: many delivery systems that falter during scale-up or commercialization were never properly designed to survive the conditions of technology transfer in the first place.

From a regulatory and quality perspective, tech transfer is not simply a clerical handoff but a foundational transition in which product and process knowledge must be translated across organizational, physical, and operational boundaries. Fundamentally, the purpose of tech transfer is to move knowledge from development into manufacturing in a way that enables consistent product realization and underpins the manufacturing process, control strategy, and validation approach. In this context, transfer functions as a stress test of how completely a delivery system’s performance has been understood, codified, and made reproducible outside the originating laboratory.

A recurring question is not why delivery systems fail outright but why they often appear to work reliably in sponsor laboratories but degrade, drift, or fail once transferred into a manufacturing environment. These failures are frequently uncovered late in the manufacturing life cycle, emerging during scale-up, process validation, or preparations for commercial supply rather than during early formulation development. By the time they surface, the delivery concept itself is often treated as the liability, even when the underlying issue lies elsewhere.

Delivery performance rarely depends on molecular design alone. It emerges from interactions among formulation attributes, process conditions, analytical measurements, and execution context. Pharmaceutical development guidance emphasizes that product quality must be built into both the formulation and the manufacturing process, and that critical quality attributes (CQAs) and parameters are identified by understanding how variation impacts performance.¹ When that understanding is partial, informal, or narrowly scoped to development-scale conditions, transfer exposes vulnerabilities that were always present but never fully interrogated.

Seen through this lens, delivery failure is less a scientific failure than a systems failure. Technology transfer does not introduce fragility so much as it reveals it, forcing delivery platforms to operate under wider variation, different equipment, new operators, and more formalized analytical scrutiny. For complex delivery systems, survival across that transition is not incidental. It is the true measure of whether the system was ever designed for real-world manufacturing at all.

Tech transfer is often discussed as an operational milestone: a package of documents, a sequence of engineering runs, and a set of acceptance criteria that must be met before manufacturing can proceed. While these elements are necessary, they do not capture what transfer truly demands of a drug delivery system. At its core, tech transfer is a translation event in which biological and physical behaviors observed in one environment must be reproduced in another, often under meaningfully different conditions.

Guidance explicitly frames transfer as the movement of product and process knowledge between development and manufacturing, with the expectation that this knowledge will support the manufacturing process, control strategy, and validation approach.² Implicit in this framing is the recognition that performance does not automatically travel with a formulation. It must be reconstructed through an understanding of how that formulation responds to its environment.

That environment changes along multiple dimensions during transfer. Equipment geometries differ between development-scale and manufacturing-scale systems, altering flow paths, mixing regimes, and residence times. Energy inputs — whether through shear, agitation, or other mechanical forces — are rarely identical across scales or platforms. Operator behaviors, which may be informal or adaptive during development, become standardized and proceduralized. Analytical contexts also shift, as methods are transferred, qualified, and applied under new constraints and expectations.

For delivery systems, these transitions matter because performance is rarely an intrinsic property of composition alone. Product quality must be designed into both the formulation and the manufacturing process, and CQAs and process parameters are defined by how variation affects product quality.¹ This means that delivery behavior is often an emergent property of tightly coupled formulation–process interactions rather than a fixed outcome that can be assumed to persist unchanged.

Experimental literature reinforces this point across delivery modalities. Studies of liposome manufacturing show that attributes, such as size and distribution, depend on a combination of formulation choices and process variables, including flow conditions and solvent systems.³ Work on emulsification similarly demonstrates that physical attributes are shaped not only by chemistry but by the amount of mechanical energy applied during processing.⁴ Reviews of nanoparticle manufacturing highlight the challenges of translating systems developed under constrained conditions into scalable, reproducible processes suitable for broader application.⁵

These observations underscore a central challenge of technology transfer: the gap between how a formulation behaves in its original context and how that behavior is reproduced elsewhere. For delivery platforms, formulation–process interactions are rarely invariant across environments. Transfer therefore becomes the moment at which assumptions about stability, robustness, and reproducibility are tested, often more rigorously than they ever were during development.

Delivery challenges are often framed as a problem unique to complex injectables, while oral dosage forms are treated as comparatively mature and resilient. When viewed through the lens of technology transfer, however, this distinction collapses. Oral and parenteral delivery systems operate under different physical regimes, but both rely on formulation–process relationships that are highly sensitive to changes in manufacturing context. In both cases, transfer exposes how narrowly those relationships were defined during development.

Modified-release oral solid dosage (OSD) forms are designed to control when and where drug release occurs, with performance defined by release kinetics over time rather than by immediate disintegration or dissolution. That performance is typically inferred through surrogate measurements — most commonly dissolution testing — which serve as proxies for in vivo behavior.

The vulnerability of these systems lies in the fact that relatively small shifts in processing conditions can alter the physical microstructure that governs release behavior. Equipment scale and geometry can influence mixing efficiency and residence times. Differences in granulation, coating, or drying operations can change porosity, coating integrity, or particle morphology in ways that may not be immediately apparent from compositional data alone. Because performance is inferred rather than directly observed, these changes may remain undetected until later stages of development or validation.

Regulatory guidance for postapproval changes in modified-release OSD products explicitly recognizes this sensitivity. Frameworks addressing changes in manufacturing site, scale, equipment, or process outline the need for defined levels of testing and documentation to support comparability, reflecting an understanding that manufacturing context is inseparable from product performance.⁶ Analytical comparability across sites becomes a further stressor during transfer, as methods must not only be reproduced but must measure the same performance-relevant attributes with equivalent sensitivity.

Parenteral delivery systems, such as liposomes, emulsions, and nanoparticles, make this dependence on process conditions more visible. In these systems, physical structure is not merely a contributor to performance; it is the product itself. Attributes such as particle size, size distribution, and morphology are directly tied to pharmacokinetics, biodistribution, and stability.

Experimental studies illustrate how tightly these attributes are coupled to manufacturing conditions. Work on liposome manufacturing demonstrates that size and polydispersity depend on a combination of formulation choices and process variables, including flow conditions and solvent systems, underscoring that multiple levers jointly determine critical quality attributes.³ Studies of emulsification further show that final droplet size is influenced not only by formulation chemistry but by the amount of mechanical energy applied during processing, with changes in rotor speed or energy input producing measurable differences in structure.⁴

Nanoparticle manufacturing literature similarly highlights the challenge of translating systems developed under constrained, small-scale conditions into processes that are both scalable and reproducible. Reviews of microfluidic approaches note that non-scalable production methods remain a barrier to translation, even as such systems offer the potential for improved reproducibility when scale-independent strategies are employed.⁵ These observations point to a core vulnerability: performance is tightly bound to shear environment, energy input, flow dynamics, and scale-dependent phenomena that rarely remain constant across transfer.

Oral and parenteral delivery systems thus reveal a shared structural weakness. In both cases, delivery performance emerges from interactions among formulation, process, and measurement that are stable only within a limited operating envelope. Tech transfer widens that envelope. Whether the system is a modified-release tablet or a structured dispersion for injection, it is this expansion, not the inherent complexity of the modality, that determines whether delivery performance endures.

Tech transfer tends to fail not at the level of headline specifications but at the level of attributes whose stability depends on context. These attributes appear robust within the narrow conditions of development, yet prove fragile when exposed to new equipment, scales, operators, or analytical environments. Rather than being inherently unstable, they are conditionally stable: reliable only so long as the conditions that support them remain unchanged.

One category of such sensitivity lies in physical attributes. For many delivery systems, structure is inseparable from performance. In parenteral platforms, key attributes, such as particle size, size distribution, and morphology, are directly linked to function and are shaped by a combination of formulation and processing conditions.^3,4 These attributes may meet specifications under one set of manufacturing conditions, but shift when shear environments, flow paths, or energy inputs change during transfer. What appears to be a formulation issue is often the manifestation of a physical attribute that was never designed to be portable across environments.

A second category involves kinetic attributes: how delivery behavior evolves over time. In modified-release oral systems, performance is defined by release profiles rather than by static composition. That performance is typically inferred through surrogate measurements, such as dissolution testing, which reflect underlying microstructural features shaped during manufacturing.¹ When processing conditions change, the kinetics of release can change as well, even if the formulation appears unchanged on paper. Because these effects unfold over time, they are often detected late, emerging during validation or commercial readiness rather than during early development.

A third and more elusive category consists of hidden dependencies: process conditions that materially influence performance but were never formally identified as critical. Development-stage processes often rely on informal adjustments, implicit operator knowledge, or narrowly tuned conditions that are sufficient for small-scale success. Guidance on pharmaceutical development makes clear that CQAs and parameters are defined by understanding how variation impacts product quality.¹ However, not all influential variables are recognized as such in practice. During transfer, when processes are standardized and scaled, these unrecognized dependencies surface as unexplained variability or loss of performance.

Across delivery modalities, the same pattern repeats. Attributes that seem stable within a single environment fail to travel because their stability was contingent rather than intrinsic. Tech transfer does not create these sensitivities; it reveals them by forcing delivery systems to operate outside the narrow conditions under which they were originally shown to work.

Process robustness and process reproducibility are often treated as interchangeable goals, but they describe distinct capabilities. Reproducibility refers to the ability to perform the same operation in the same way and obtain the same result, typically within a tightly controlled setting. Robustness, by contrast, reflects the ability to achieve the same outcome despite variation in inputs, conditions, or execution. Tech transfer highlights this distinction.

A process that is reproducible under narrowly defined conditions may still be fragile if small, unavoidable sources of variation lead to meaningful changes in performance. Robustness requires that those variations be anticipated, understood, and accommodated within the design of the process itself.

Delivery platforms are particularly effective at exposing the gap between these two concepts. In development laboratories, processes are often tuned to operate within narrow windows that maximize performance under known conditions. Within those windows, reproducibility can be high. The same equipment is used, operators are deeply familiar with the process, and informal adjustments are readily made. Under these circumstances, delivery systems may appear stable and well controlled.

Tech transfer inevitably expands those operating windows: equipment geometry changes, scale increases, and execution becomes more standardized. Even when procedures are followed precisely, the physical and operational context shifts. Guidance on technology transfer underscores that transferred knowledge must support manufacturing, control strategies, and validation across sites and stages of the product life cycle.² This expectation implicitly assumes that processes can tolerate some degree of variation without loss of performance.

For delivery systems whose performance depends on tightly coupled formulation–process interactions, this tolerance is often limited. As transfer expands the range of conditions under which the process must function, reproducibility alone is no longer sufficient. Only processes designed for robustness (e.g., those capable of delivering consistent performance across variation) can sustain delivery behavior beyond the originating environment. Technology transfer does not impose an unreasonable standard; it simply reveals whether robustness was ever part of the design intent.

Analytical transfer is often treated as a procedural exercise: verify that a method can be executed at a new site and demonstrate that it produces results within predefined acceptance criteria. In practice, analytics play a far more central role in determining whether a delivery system survives tech transfer. Failures attributed to formulation or process frequently originate in mismatches between what is being measured and what matters for performance.

Regulatory guidance places analytics at the core of product understanding and control. As stated previously, guidance emphasizes that CQAs are defined by their relationship to product performance and by how variation impacts quality, not simply by their measurability.¹ Tech transfer guidance further states that transferred knowledge must support control strategies and validation across manufacturing contexts.2 These principles imply that analytical methods must not only be transferred but remain meaningfully connected to performance in a new environment.

This requirement exposes several tensions. Method transfer does not guarantee measurement equivalence. A method may be executed correctly at multiple sites while responding differently to subtle changes in sample handling, instrumentation, or operator technique. Sensitivity can also diverge from relevance. Highly sensitive methods may detect differences that are analytically real but clinically or functionally inconsequential, while less sensitive assays may fail to detect early signs of performance drift.

Delivery systems are especially vulnerable to these tensions because many of their defining attributes are indirect. Modified-release oral products rely heavily on surrogate measures, such as dissolution profiles, to infer in vivo behavior. Parenteral delivery systems depend on physical attributes, such as size or distribution, that stand in for downstream biological performance. In both cases, the analytical signal is a proxy rather than a direct readout of therapeutic effect. When those proxies are imperfectly aligned with performance, analytical blind spots emerge.

During development, these blind spots are often masked by stable conditions and limited variability. Tech transfer disrupts that equilibrium. As processes are scaled, standardized, and executed across sites, small shifts in formulation–process interactions may occur without triggering immediate analytical alarms. By the time deviations are detected — during validation, comparability assessments, or stability studies — the underlying instability may already be entrenched.

In this sense, analytical transfer is not a downstream technicality but a central determinant of delivery success. The critical question is not whether the same method is being used, but whether the method continues to measure the same performance-relevant reality after transfer. When it does not, delivery systems can fail quietly, with analytics lagging behind the physical and kinetic changes that ultimately undermine product performance.

Many technology transfer failures trace back not to gaps in execution, but to gaps in what was ever formally known. Development-stage success often relies on a mixture of explicit design choices and implicit understanding: knowledge that is real, functional, and effective, yet never fully articulated. When transfer occurs, only the former reliably survives.

Regulatory guidance emphasizes that tech transfer is fundamentally about the movement of product and process knowledge, and that this knowledge must support manufacturing, control strategies, and validation across the product life cycle.² In practice, however, not all knowledge is captured in protocols, reports, or specifications. Informal process tuning, operator-dependent adjustments, and development-stage “exceptions” frequently play a meaningful role in achieving delivery performance, particularly for systems with narrow operating windows.

These forms of knowledge are often invisible precisely because they work. During development, the same team may run the process repeatedly, making small, experience-based adjustments that are never codified because they are perceived as obvious, intuitive, or insignificant. Over time, these adjustments become embedded in practice rather than documented as critical process considerations. While CQAs and process parameters are defined by understanding how variation impacts product quality, variation that is continuously corrected through tacit intervention may never be formally recognized as such.

Tech transfer exposes this structural weakness. What moves across organizations is what has been formalized: defined inputs, specified parameters, written procedures, and validated methods. However, the assumptions, heuristics, and contextual judgments that shaped development-stage success do not move as easily. For delivery systems that depend on tightly coupled formulation–process interactions, this loss can be consequential. Performance that appeared robust was often being actively maintained through undocumented knowledge rather than inherently stable by design.

As a result, transfer failures are frequently misattributed. The delivery platform is blamed for being fragile, when in truth the fragility lies in the knowledge architecture itself. Delivery systems do not merely rely on materials and processes; they rely on understanding. When that understanding remains tacit, technology transfer becomes an exercise in rediscovering what was once known but never written down.

Contract development and manufacturing organizations (CDMOs) occupy a uniquely revealing position in the life cycle of drug delivery systems. They inherit formulations after key design decisions have been made, often at a point when development success has already created confidence in the delivery concept. At the same time, they operate under formal regulatory scrutiny and are responsible for scaling, validating, and sustaining performance across manufacturing campaigns and, ultimately, commercial supply. This combination places CDMOs at the fault line where assumptions embedded during development are tested against operational reality.

CDMOs are the organizations charged with translating transferred knowledge into durable execution. They must do so across different equipment platforms, staffing models, and production scales while maintaining compliance and reproducibility over time.

For delivery systems, this role is particularly demanding. CDMOs are responsible for operating within — and often expanding — the range of conditions under which those attributes must remain stable. When formulation–process relationships were narrowly tuned or incompletely characterized during development, the resulting fragility becomes visible only when the system is asked to perform under manufacturing conditions.

Importantly, CDMOs do not introduce this fragility; they uncover it. Development environments often mask sensitivity through continuity of personnel, equipment familiarity, and informal adjustments that stabilize performance without being formalized. Manufacturing environments, by contrast, demand explicit knowledge, standardized execution, and defensible control strategies. The transition from one to the other reveals whether delivery performance was intrinsically robust or merely maintained through context-specific expertise.

In this sense, CDMOs are not peripheral to delivery innovation. They are where delivery platforms are proven (or disproven) as manufacturable systems. Success at this stage reflects not only scientific ingenuity, but the extent to which delivery performance was designed to survive translation across organizations, scales, and time.

Delivery innovation is most often evaluated by how convincingly a system performs in development: whether it achieves a desired release profile, improves exposure, or enables a new therapeutic modality under controlled conditions. While these achievements are necessary, they are not sufficient. Development-stage performance alone is a weak predictor of whether a delivery system will survive the transition into manufacturing and sustained commercial use.

Regulatory guidance implicitly points toward a broader definition of success, shifting the evaluative lens away from isolated demonstrations of performance and toward durability across contexts.

Under this reframed lens, successful delivery systems are those that perform not only in the originating laboratory but across sites, scales, operators, and time. They tolerate differences in equipment geometry, energy input, and execution without loss of function. Their CQAs remain stable under the variation that manufacturing inevitably introduces. Their analytical signals remain meaningfully linked to performance as methods and environments change. These are not downstream considerations that can be addressed after the delivery concept has been proven; they are defining characteristics of whether the concept is viable at all.

The implication is that transferability must be treated as an upstream design constraint rather than a late-stage hurdle. Decisions made during formulation development, process selection, and early characterization determine whether a delivery system will later require heroic effort to stabilize or whether it will scale and transfer with predictability. When transferability is deferred, success is defined too narrowly, and fragility is mistaken for sophistication.

Reframing success in this way does not diminish the value of innovation in drug delivery. It clarifies the standard by which that innovation must ultimately be judged. A delivery platform that cannot survive translation across environments has not failed because it lacked ingenuity, but because it was evaluated against the wrong criteria from the outset.

Across delivery modalities, a consistent pattern emerges. Drug delivery innovation is not limited by the absence of sophisticated science but by the realities of execution in manufacturing environments. Platforms that appear compelling in development can unravel when asked to perform outside the narrow conditions under which they were first demonstrated. Technology transfer exposes this gap by forcing delivery systems to operate under variation — across equipment, scales, operators, and time — rather than under idealized laboratory control.

Regulatory frameworks reinforce this perspective by emphasizing that product performance must be built into both the formulation and the manufacturing process and that transferred knowledge must support durable control, validation, and continual improvement throughout the product life cycle. Within this context, delivery success is inseparable from the ability to translate performance reliably across environments. Elegance of design, novelty of mechanism, or precision under constrained conditions offer little protection if the system cannot withstand the demands of transfer.

The most advanced delivery platforms therefore fail not because they are overly ambitious but because their performance was never designed to travel. Attributes that were conditionally stable in development prove fragile when contextual supports are removed. Analytical signals lag behind physical and kinetic changes. Documented knowledge evaporates as processes are formalized. What remains is a delivery concept that showed promise initially but cannot be reproduced consistently.

CDMOs stand at the center of this reckoning. They do not represent the end of delivery innovation, nor do they merely execute what others have designed. They are the proving ground where delivery systems are tested against the conditions they must ultimately survive. Success at this stage reflects whether transferability was treated as a design criterion from the outset or deferred as a downstream problem.

In the end, delivery platforms live or die not on elegance but on their ability to endure translation. Transferability is the measure that separates promising concepts from manufacturable therapies, and it is the standard against which meaningful delivery innovation must ultimately be judged.

1. ICH Harmonised Tripartite Guidance: Pharmaceutical Development Q8(R2). International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. Aug. 2009.

2. ICH Harmonised Tripartite Guidance: Pharmaceutical Quality System Q10. International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. 4 Jun. 2008.

3. Lindsay, Sarah, et al. “Can We Simplify Liposome Manufacturing Using a Complex DoE Approach?” Pharmaceutics. 15: 1159 (2024). 

4. Campardelli, Roberta, et al. “Rotor-Strator Emulsification in the Turbulent Inertia Regime: Experiments toward a Robust Correlation for the Droplet Size.” Langmuir. 39: 18518–18525 (2023).

5. Shepherd, Sarah J, David Issadore, and Michael J Mitchell. “Microfluidic formulation of nanoparticles for biomedical applications.” Biomaterials. 274: 120826 (2021).

6. SUPAC-MR: Modified Release Solid Oral Dosage Forms: Guidance for Industry. U.S. Department of Health and Human Services. Sep. 1997.