Spray-Drying Technology Transfer of Amorphous Solid Dispersions

However, despite its industrial maturity, technology transfer of spray-drying processes remains a critical source of variability, cost, and delay in pharmaceutical development given limited process understanding, equipment dependency, and scale-up complexity.

An Alternative Paradigm

This article examines spray-drying technology transfer through the lens of process systems engineering and ISPE Pharma 4.0™ principles. It highlights how incomplete mechanistic understanding and reliance on empirical design of experiments (DoE) strategies contribute to inefficient scale-up and reduced right-first-time (RFT) performance. A digital transformation framework combining process analytical technology (PAT), mechanistic modeling, and historical data integration is presented as an alternative paradigm.

By linking critical process parameters (CPPs) to critical quality attributes (CQAs) through predictive models, technology transfer can shift from empirical replication to knowledge-driven process reproduction. Case-based evidence demonstrates that this hybrid approach can reduce experimental effort by approximately 30% to 50%, shorten timelines to good manufacturing practice (GMP) readiness, and improve process robustness. The integration of Pharma 4.0™ principles into spray-drying tech transfer provides a path toward more predictive, scalable, and reproducible manufacturing of complex ASD formulations.

Background

As pharmaceutical pipelines increasingly focus on high-potency and structurally complex molecules, poor aqueous solubility remains one of the most significant barriers to oral drug development. A large proportion of new chemical entities (NCEs) fall into Biopharmaceutics Classification System (BCS) Classes II and IV, in which dissolution and solubility limitations restrict oral bioavailability. To address these challenges, formulation strategies such as lipid-based systems, particle-size reduction, and particularly ASDs have become widely adopted.

Spray drying has emerged as one of the most important manufacturing platforms for ASD production because of its scalability, reproducibility, and ability to generate molecularly dispersed drug-polymer systems. However, despite its established industrial use, spray drying remains a highly sensitive unit operation governed by tightly coupled thermodynamic, fluid dynamic, and material interactions. Small variations in formulation composition or operating conditions can significantly affect CQAs such as particle morphology, bulk density, residual solvent content, and solid-state form.

This sensitivity becomes particularly critical during technology transfer, where processes are transitioned from development to commercial manufacturing or between sites. Technology transfer is formally defined by ICH Q10 as the transfer of product and process knowledge to ensure consistent product realization across the life cycle. In practice, however, it is often constrained by incomplete mechanistic understanding, equipment variability, and reliance on empirical scale-up approaches.

Within spray drying, these limitations are amplified, as laboratory-scale robustness does not guarantee commercial-scale equivalence. As a result, tech transfer becomes a major influencer of development time, cost, and risk. This creates a need for more structured, predictive, and knowledge-driven approaches aligned with ISPE Pharma 4.0™ principles, integrating digital tools, mechanistic understanding, and cross-site data continuity.

Figure 1: A) BCS for NCEs based on solubility and permeability, and B) typical solubility enhancement strategies.

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This article proposes a structured framework for digitally enabled technology transfer of spray-dried amorphous solid dispersion processes. The approach focuses on preserving and transferring mechanistic process understanding across development, scale-up, commercial manufacturing environments, particularly within multisite and contract development and manufacturing organization (CDMO)-based production networks.

By integrating mechanistic process knowledge with experimental and historical manufacturing data, the framework strengthens the continuity of process understanding across organizational and scale boundaries. Rather than relying solely on static documentation, it establishes a more structured relationship between formulation attributes, process parameters, and equipment-specific behavior. The objective is to improve scale-up predictability, reduce iterative troubleshooting during process qualification, and support more robust and reproducible commercial manufacturing outcomes.

Spray Drying: A Powerful Approach to Overcoming Solubility Challenge

As pharmaceutical pipelines increasingly target high-potency and structurally complex molecules, poor aqueous solubility continues to represent a major limitation to successful oral drug development, primarily given its effect on dissolution, absorption, and ultimately bioavailability. Based on key physicochemical attributes such as melting point, logP, molecular weight, and solubility, NCEs are broadly classified as either “brick-dust” or “grease-ball” compounds.1^, 2

It is estimated that approximately 40% of marketed drugs and up to 90% of pipeline candidates exhibit poor aqueous solubility, placing them predominantly within BCS Class II or IV.3^, 4 Among these, Class II compounds are most prevalent in development and present challenges for achieving adequate oral absorption. These can be further subdivided into Class IIa (dissolution-rate-limited) and Class IIb (solubility-limited) compounds, as shown in Figure 1.

According to the World Health Organization’s Model List of Essential Medicines, between 2000 and 2011, 41.8% of approved BCS-classified drugs were Class I, 20.9% Class II, and 37.3% Class III, with none categorized as Class IV. More recent analyses show a continued increase in poorly water-soluble compounds within pharmaceutical development pipelines, with approximately 40% of marketed drugs and nearly 90% of drug candidates exhibiting low aqueous solubility, predominantly within BCS Classes II and IV categories.

BCS Class II compounds remain particularly challenging given their high permeability but dissolution-limited absorption behavior.5^, 6 To address these limitations, formulation scientists have increasingly adopted enabling formulation strategies (see Figure 1), including cocrystals, cyclodextrin complexes, lipid-based formulations (LBFs), particle-size reduction, and ASDs. Among these, ASDs have gained particular prominence, supported by a growing number of US Food and Drug Administration (FDA) approvals in recent years.

First introduced by Keiji Sekiguchi and Noboru Obi in 1961, 7 the ASD concept is based on stabilizing the drug in a high-energy, noncrystalline state to enhance dissolution and absorption. In ASDs, the active pharmaceutical ingredient (API) is molecularly dispersed within a polymeric carrier in its amorphous form, eliminating the crystal lattice energy barrier and improving both apparent solubility and dissolution rate. This approach is well described by the “spring-and-parachute” model proposed by Hector Guzmán, where rapid generation of a supersaturated state (“spring”) is followed by stabilization via precipitation inhibitors (“parachute”), thereby maintaining supersaturation and enhancing oral bioavailability.7

Since the first spray-dried ASD product, Cesamet, received US FDA approval in 1985, spray drying has become a cornerstone of oral formulation development. As of 2023, the US FDA had approved 55 ASD-based drug products indications (see Table 1), reflecting the maturity and sustained relevance of ASD technology in addressing solubility-limited drug development. 8

The period from 2018 to 2023 represents the most significant growth phase, accounting for 44% of all US FDA-approved ASDs (see Figure 2); 18% of total approvals came in 2018 alone. Since the first approval in 1985, the average rate of new ASD introductions has been approximately two per year, with roughly 54% of all approved products manufactured via spray drying. This predominance underscores spray drying’s role as the principal platform for ASD manufacturing and sets the stage for continued digital transformation to further improve process understanding, scale-up, and technology transfer.

Tech Transfer: The Unsung Hero of Product Success

Definition and Regulatory Context

According to the International Council for Harmonization (ICH), technology transfer is a structured process encompassing the transfer of product and process knowledge, supported by appropriate documentation and technical expertise. The ICH Q10 Pharmaceutical Quality System guideline defines its objective as ensuring effective knowledge transfer from development to manufacturing, and between or within manufacturing sites, to enable consistent product realization across the product life cycle.9^, 10 In this context, technology transfer is not a discrete activity but a controlled life-cycle function linking pharmaceutical development to commercial manufacturing. It provides the foundation for process validation, control strategy implementation, and continued process verification.

Within biopharmaceutical manufacturing systems, technology transfer is executed across multiple life-cycle stages, from late development through post approval life-cycle management. Transfers may be classified as internal or external, depending on the manufacturing strategy. Internal transfers typically support scale-up, capacity expansion, or life-cycle risk mitigation across sites. External transfers involve the structured handover of a fully defined process from a sponsor organization to a contract manufacturing organization (CMO) or CDMO, either during clinical development or at commercial scale.11

Within this framework, the sending unit (SU) is responsible for defining and characterizing the process, including CPPs, critical material attributes (CMAs), CQAs, and associated analytical controls. The receiving unit (RU) is responsible for applying the process under qualified equipment and validated operating conditions, ensuring equivalence of performance and compliance with regulatory expectations.12

Figure 2: Summary of US FDA-approved ASD formulations by year (top). Breakdown of FDA-approved ASD by technology (bottom).

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Tech Transfer as a Control Strategy Translation Problem

From a systems engineering perspective, technology transfer is fundamentally the translation of a validated control strategy from one operational environment to another. Although often perceived as documentation transfer, its actual complexity lies in preserving process capability across changes in scale, equipment design, facility constraints, and operator interaction.

Successful transfer, therefore, depends on the robustness of the process definition space and the completeness of process understanding generated during development. The fidelity of transferred knowledge directly determines manufacturing reproducibility, batch-to-batch consistency, and regulatory defensibility. Despite this, technology transfer is frequently underweighted relative to process innovation, even though it represents the point at which process understanding is stress-tested against industrial variability.

Spray Drying as a High-Sensitivity Transfer Case

These challenges are particularly pronounced in spray-drying processes used for ASDs, in which product quality is governed by tightly coupled thermodynamic and transport phenomena. Process outputs such as particle morphology, residual solvent content, bulk density, and solid-state form are highly sensitive to coupled variations in inlet temperature, atomization energy, feed solids content, and drying kinetics.

As a result, nominal parameter equivalence between laboratory and commercial scale does not guarantee process equivalence. Instead, scale-dependent effects, particularly heat and mass transfer limitations and residence time distribution, must be explicitly accounted for within the transfer strategy. Effective tech transfer in spray drying requires explicit mapping between material attributes (CMA), equipment configuration, and process parameters, supported by mechanistic or semi-mechanistic understanding where possible. Without this, scale-up becomes empirical rather than predictive, increasing the risk of deviation in CQAs.

Cost, Industrial Drivers, and Capacity Constraints

Successful technology transfer enables (bio)pharmaceutical organizations to transition products from clinical development to commercial manufacturing while maintaining a validated state of control. In practice, this requires coordinated execution across development, engineering, quality, and manufacturing functions to ensure reproducibility across sites and scales.

However, increasing structural pressure on the pharmaceutical manufacturing ecosystem has accelerated reliance on external manufacturing networks. CMOs and CDMOs now represent a rapidly expanding segment of the global pharmaceutical value chain, with the CDMO market valued at more than US $200 billion in the early 2020s and projected to grow at a double-digit compound annual growth rate, approaching the mid-US $300 billion range by 2030.13^, 14

In parallel, outsourcing of pharmaceutical manufacturing activities is estimated to account for approximately 40% to 45% of total global production volume, increasing from roughly 30% to 35% in the early 2010s. The capital intensity of biologics manufacturing, increasing process complexity, and the need for flexible, scalable capacity allocation across global supply networks are driving this structural shift.13^, 14 Meanwhile, development and commercialization costs have increased significantly industrywide. From 2010 to 2020, average development costs approximately doubled compared with the 2000s and quadrupled relative to the 1990s, reflecting increased clinical complexity, regulatory requirements, and manufacturing sophistication.15^, 16

Within this environment, technology transfer becomes a critical cost and timeline determinant rather than a downstream operational step. Industry benchmarks show that a single (bio)pharmaceutical tech transfer can exceed US $5 million, require 18 to 30 months to complete, and involve multidisciplinary teams spanning process engineering, quality assurance, analytical development, and manufacturing operations. These figures exclude subsequent process validation, continued process verification, and life-cycle optimization activities.17

Common Pitfalls in Spray-Drying Tech Transfer

Despite structured frameworks and regulatory guidance, technology transfer remains a frequent source of late-stage variability and operational risk. Common failure modes include incomplete definition of process design space, insufficient characterization of raw material variability, lack of equipment equivalency assessment, and limited understanding of scale-dependent transport phenomena.

In spray drying specifically, mismatches between lab-scale and commercial-scale dryer configurations can result in deviations in particle formation kinetics, drying efficiency, and solid-state stability. These issues are often compounded by differences in nozzle design, atomization regime, and thermal-hydraulic conditions. Table 2 summarizes typical failure modes observed in spray-drying technology transfer, highlighting the importance of integrating process engineering principles early in development rather than treating transfer as a downstream documentation exercise.

Table 2: Typical pitfalls in spray-drying technology transfer

Pitfall	Description
Image Incomplete process understanding at development stage	Spray drying is governed by tightly coupled thermodynamic, fluid dynamic, and material transport phenomena. Process outputs such as particle morphology, bulk density, residual solvent content, and solid-state form are highly sensitive to multivariate interactions among inlet temperature, atomization energy, feed concentration, and solvent composition. In early development, these relationships are frequently established empirically, with limited mechanistic characterization of process behavior or design space boundaries. This results in incomplete definition of CPPs and weak linkage to CQAs, reducing process robustness during scale-up and transfer.
Image Equipment-specific variability	Spray-drying systems exhibit significant equipment dependency because of differences in dryer geometry, gas flow distribution, atomization technology, and control system architecture. Even nominally equivalent equipment platforms can generate non-equivalent process conditions due to variations in residence time distribution, heat and mass transfer efficiency, and droplet formation dynamics. As a result, process transfer based solely on batch history or static process descriptions is insufficient to ensure equivalence across sites or scales without explicit equipment characterization and performance mapping.
Image Limited knowledge transfer mechanisms	Traditional technology transfer workflows remain largely document-centric, relying on batch records, development reports, and experiential knowledge exchange between sending and receiving units. This approach often fails to fully capture process context, including sensitivity to operating ranges, transient behavior during start-up and shutdown, and interactions between material attributes and equipment response. For complex unit operations such as spray drying, this results in incomplete transfer of process understanding and increased reliance on empirical re-optimization at the receiving site.
Image Reactive, rather than predictive, risk mitigation	In many cases, technology transfer issues are identified only after commercial or clinical batches deviate from predefined specifications. This reflects a predominantly reactive control paradigm, where process variability is detected through deviations rather than predicted through prior characterization. Such an approach increases reliance on batch rejection, deviation investigations, and corrective actions and may delay regulatory closure when root causes are linked to insufficient upstream process understanding or inadequate definition of the control strategy during transfer.

Pharma 4.0™ in Spray-Drying Technology Transfer

The transition from development to commercial manufacturing remains one of the pharmaceutical product life cycle’s most vulnerable phases. This risk is amplified in particle-engineering-intensive processes such as spray drying, in which coupled interactions between formulation properties, atomization behavior, and drying thermodynamics determine final product quality. In such systems, even minor misalignment in process understanding or equipment behavior can spark deviations in CQAs, resulting in batch failure, regulatory delays, or extended scale-up cycles.

To address these challenges, the industry is increasingly adopting the ISPE Pharma 4.0™ framework, which extends Industry 4.0 principles into the regulated pharmaceutical environment. Rather than functioning as a collection of digital tools, Pharma 4.0™ defines a system-level approach for integrating data, digital infrastructure, governance, and organizational capability across the product life cycle, enabling consistent and knowledge-driven manufacturing execution.18

Digital Enablers for Process Control and Life-cycle Understanding

Within this framework, PAT and multivariate data analysis (MVDA) provide the basis for real-time process monitoring by linking CPPs to CQAs during operation. These tools enable earlier detection of process drift, structured root-cause analysis, and strengthened adoption of quality by design (QbD) principles through continuous data acquisition and interpretation.

Complementing this, digital twins and mechanistic modeling approaches enable simulation of spray-drying behavior prior to physical execution. These models capture coupled heat and mass transfer phenomena, droplet formation dynamics, and particle-drying kinetics, allowing virtual exploration of process behavior across operating conditions and scales. Artificial intelligence (AI) and machine learning (ML) further extend this capability by identifying nonlinear relationships within historical and multisource data sets. When embedded within tech transfer workflows, these methods support predictive risk assessment and facilitate more robust definition of operating design space before GMP execution.

From Empirical Development to Knowledge-Integrated Tech Transfer

Historically, spray-drying process development and technology transfer have relied on sequential DoE campaigns supported by lab-scale trials and offline characterization. Although effective for defining operating ranges, this approach is inherently empirical, requiring repeated experimental iteration to resolve scale-dependent uncertainties. This results in extended development timelines, high material consumption, and significant dependence on expert judgment during scale-up decision-making. In commercial contexts, these limitations frequently translate into delayed GMP readiness and increased project cost and risk.

To address these challenges, the industry is increasingly adopting the ISPE Pharma 4.0™ framework, which extends Industry 4.0 principles into the regulated pharmaceutical environment.

To overcome these constraints, CDMOs are increasingly adopting Pharma 4.0™–aligned, data-driven development strategies. These approaches integrate historical process data across scales, sites, and product types with mechanistic modeling frameworks to establish transferable process knowledge. In spray drying, this enables the integration of formulation variables (e.g., solvent volatility, polymer viscosity), atomization conditions, and thermal process parameters into predictive models that describe their combined impact on CQAs such as particle-size distribution, bulk density, and residual solvent content.

Model-Driven Technology Transfer in Practice

Digitally enabled process development introduces an integrated workflow that reduces reliance on purely empirical scale-up strategies. Mechanistic and semiempirical models, often supported by computational fluid dynamics (CFD) and single-droplet simulations, allow virtual evaluation of the process design space before material-intensive experimentation.

A recent spray-drying technology transfer project for a client highlighted the limitations of traditional approaches, including extensive DoE cycles and incomplete process understanding, which led to unsuccessful scale-up attempts that failed to meet product specifications (see Figure 3). By leveraging historical spray-drying data across multiple sites, scales, polymer systems, and solvent environments, combined with a mechanistic digital tech transfer model, a structured predictive framework was adopted to guide process transfer and scale-up strategy. A targeted laboratory-scale DoE was then used primarily for model calibration and process confirmation rather than full design space exploration.

This hybrid digital–mechanistic approach effectively converts what is traditionally an extensive experimental campaign into a focused process familiarization and validation exercise. In this case, it reduced experimental workload by about 30% to 50%, accelerated progression to GMP manufacturing, and improved RFT success rates, ultimately reducing development cost and time-to-market for the manufacturer.

Pharma 4.0™ Integration Across the Operating Model

The implementation of such approaches directly reinforces the core dimensions of Pharma 4.0™.

• Data and information systems: Centralized and structured data environments enable continuity of process knowledge across sites and life-cycle stages
• Processes: Model-informed workflows reduce variability introduced by empirical iteration and improve reproducibility of transfer outcomes
• Resources and workforce: Enhanced capability in data interpretation, modeling, and digital tools strengthens decision-making during transfer execution
• Culture: Transition toward predictive, data-driven process understanding supports continuous improvement and risk-based manufacturing strategies
• Integrating Pharma 4.0™ principles into spray-drying technology transfer enables a shift from experience-based execution towards predictive, model-informed manufacturing. This transition improves robustness of scale-up, reduces experimental burden, and strengthens regulatory confidence by embedding process understanding directly into transfer strategy rather than treating it as a post-development activity.

Figure 3: Comparison of A) traditional and B) digital transformation approaches to laboratory process development and manufacturing scale-up.

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Conclusion

Technology transfer in spray drying remains a critical determinant of development efficiency, manufacturing robustness, and time-to-market for ASD products. As regulatory expectations increasingly emphasize life-cycle understanding and control strategy robustness, pharmaceutical development is shifting toward data-driven and model-informed approaches aligned with the ISPE Pharma 4.0™ framework.

The integration of mechanistic modeling, PAT, and structured historical data enables a transition from empirical DoE-driven scale-up toward predictive, knowledge-based process transfer. In spray drying, this allows earlier identification of relationships between CPPs and CQAs, reducing reliance on large experimental campaigns and enabling targeted, model-informed validation studies. This shift can reduce experimental effort by approximately 30% to 50%, improve RFT performance, and shorten timelines to GMP readiness.

Beyond efficiency gains, this approach fundamentally redefines technology transfer as a continuity of process understanding rather than a replication exercise. Digital twins, mechanistic models, and integrated data infrastructures enable process knowledge preservation and transfer across sites, scales, and life-cycle stages, reducing variability introduced by organizational and equipment differences.

In this context, Pharma 4.0™ provides the structural framework for transforming spray-drying technology transfer from a document-driven activity into a predictive manufacturing capability. For CDMOs and pharmaceutical developers alike, this represents a shift toward more resilient, scalable, and scientifically robust manufacturing systems, ultimately improving the speed and reliability with which complex therapies reach patients.