From production to bedside: A tiered QC framework for regulatory alignment and analytical comparability in decentralized CGT

Abstract

Decentralized and point-of-care (PoC) manufacturing is reshaping cell and gene therapy (CGT) by bringing production closer to patients. This model offers advantages, including reduced vein-to-vein times, streamlined logistics, and greater flexibility for patient-specific treatments, with the potential to improve affordability and broaden access. However, it introduces new quality control (QC) challenges that are central to ensuring product safety, potency, and consistency. Autologous CGTs, typically produced as single patient lots with limited shelf lives and compressed release timelines, require QC systems capable of delivering reliable results within hours for critical quality attributes (CQA) such as identity, potency, sterility, and safety. In both autologous and allogeneic settings, variability in starting material, shortages of trained personnel, and a lack of standardized assays across sites complicate consistency and hinder data pooling in multicenter trials. Hospital-based facilities also require enhanced infrastructure, harmonized procedures, and digital oversight to sustain network-wide comparability. Emerging solutions emphasize automation, validated rapid-testing platforms, and methods that minimize reliance on highly specialized expertise to accelerate batch release and improve reproducibility across distributed networks. Regulatory agencies are updating frameworks for modular and Point-of-Care (PoC) models, emphasizing flexibility while maintaining rigorous standards. Here, we outline a tiered QC model designed to maintain product comparability and enable timely release. QC has become the defining challenge of decentralized CGT manufacturing. However, implementing robust strategies, assay harmonization, and validated rapid-release methods can ensure uniform product quality across distributed sites, so that every patient, regardless of treatment site, receives safe, effective, and timely therapy.

1. Introduction - Decentralized CGT manufacturing: Quality control challenges and implications for patient access

For patients receiving CGTs, timing is critical [1,2]. Delays can postpone infusion [2,3], prolong hospitalization [3,4], and increase reliance on bridging chemotherapy [3,4] while reducing overall treatment efficacy [2,4]. In this context, QC is not only a technical safeguard but also a determinant of therapeutic timelines and outcomes [5].

Decentralized, distributed, and PoC manufacturing models are reshaping CGT production by moving manufacturing activities closer to the patient [[6], [7], [8]]. Decentralized manufacturing refers to the production process that spans multiple qualified and controlled facilities [8,9]. To operationalize these definitions within the broader CGT manufacturing landscape, the key attributes of each model are summarized in Table 1 and list of abbreviations can be found in Table 2.

Table 1.

Manufacturing Model Definitions. This table outlines core manufacturing models and their defining features, supported by key references.

Manufacturing Model	Definition/Key Points	References
Decentralized	Production across multiple sites or regions, rather than a single central site, can bring manufacturing closer to patients.	[8,10]
Distributed	A subset of decentralized manufacturing: manufacturing is replicated across multiple sites using standardized processes and automation; geographically dispersed to improve scalability and supply reliability.	[11,12]
Point-of-Care (PoC)	Manufacturing occurs at or immediately adjacent to the treatment center, often as a subset of decentralized/distributed models, designed to reduce logistics complexity and time-to-patient.	[13,14]

Table 2.

List of Abbreviations. This table provides the abbreviations used throughout the article.

Abbreviation	Full Spelling
ALCOA+	Attributable, Legible, Contemporaneous, Original, Accurate, + indicates Complete, Consistent, Enduring, Available
CQA	Critical Quality Attribute
DMS	Document Management System
LIMS	Laboratory Information Management System
eBR	Electronic Batch Record
ELN	Electronic Lab Notebook
MM	Modular Manufacture
PAT	Process Analytical Technology
PPQ	Process Performance Qualification
QP	Qualified Person
RCR	Replication Competent Retrovirus
RMM	Rapid Microbial Methods
VCN	Vector Copy Number

Local production offers clinical and logistical advantages, including reduced vein-to-vein times, simplified supply chains, and, in some settings, the ability to obviate cryopreservation, thereby improving scheduling flexibility for patient-specific therapies [15,16,22]. These benefits are particularly relevant for autologous therapies, which require tightly controlled supply chains to maintain product quality and safety [17,18,73]. All currently approved Chimeric Antigen Receptor T-cell therapies (CAR-T) are autologous, underscoring the importance of robust local manufacturing models [19,20]. At the same time, the high costs and limited capacity of centralized facilities remain major bottlenecks, driving interest in distributed manufacturing approaches to expand access [[21], [22], [23], [24]]. These advantages, however, come with unprecedented QC demands. Autologous cell therapies, including gene-modified cell products such as CAR-T cells, are living patient-specific products manufactured as single lots with batch identity and release tied to each treated patient [17,25,26]. Decentralization amplifies these challenges by mandating rapid release timelines and exacerbating the risk that inherent patient-to-patient variability in starting materials poses to validation consistency across site networks [17,27]. This latter point necessitates extensive comparability studies across the decentralized network. Thus, QC may shift from a supportive function to the defining bottleneck of decentralized CGT manufacturing (see Table 3). However, when decentralized manufacturing sites are set up as identical units by implementing prefabricated modular units, a similar setup of local operations, and comparable technical documentation, comparability exercises can be streamlined.

Table 3.

QC Challenges Unique to Decentralized Manufacturing of Cell and Gene Therapies. The table summarizes key QC challenges specific to decentralized and PoC manufacturing, highlighting constraints that are less pronounced or absent in centralized models. Decentralization introduces operational and analytical complexities that demand harmonized SOPs, network-wide digital QMS, standardized assay transfer, and structured training programs to maintain comparability, traceability, and regulatory compliance across sites.

Domain	Decentralization-Specific Challenge	QC Implications
Fresh product timelines	Shelf-life measured in hours to days	Necessitates rapid/real-time release testing; traditional sterility assays unusable
Biological variability and limited batch size	One-patient, one-batch production with very small sample volumes and high variability	Restricts QC sampling; limits statistical power of comparability studies; analytical variability impacting assay reliability, need for QbD-driven assay design and advanced technologies for reproducibility
Hospital-based constraints	Sites often lack purpose-built GMP infrastructure, cleanrooms, or digital traceability	Delays in QC release; higher compliance risk compared to centralized GMP facilities
Site-to-site variability	Multiple sites operating under different conditions and expertise levels	Risk of inconsistent analytical results; requires harmonized SOPs, uniform training programs, assay transfer, transparent data reporting, and centralized digital QMS for traceability
Expertise distribution	Shortage of trained personnel across all PoC sites; high turnover	Increases operator-dependent variability; hinders consistent troubleshooting demanding continuous training and remote quality supervision
Comparability burden	All-to-all site comparability is infeasible; local regulations and supply chain variability differ across regions	Drives adoption of reference-site models and standardized technology transfer packages for regulatory acceptance

While the manuscript uses a vein-to-vein CAR-T workflow as an illustrative example of a suspension-based manufacturing process, not all autologous CGTs share the same manufacturing architecture. Process-specific controls must be tailored to the modality, reflecting the nature of the starting material, the mode of cell culture, and the degree of closure and automation across unit operations; these factors directly determine CQA control strategies, contamination risk, facility design, and environmental classification. Adherent cell therapies such as MSC-based products impose distinct manufacturing constraints, including substrate-dependent expansion, stricter cleanroom classification, enhanced environmental monitoring [28], and elevated risks of identity drift and replicative senescence with successive passaging. Although emerging closed systems are improving process closure and scalability for adherent workflows [29], process validation requirements and facility qualification burdens at decentralized or PoC sites remain substantially greater than those associated with suspension-based CAR-T manufacturing. Accordingly, subsequent sections identify where adherent-cell processes face additional operational and QC constraints, recognizing that the feasibility of decentralized manufacturing is modality-dependent and cannot be generalized.

1.1. Regulatory & standards landscape

Regulatory frameworks play a decisive role in shaping how QC is implemented in decentralized and PoC manufacturing models, especially as traditional CGT manufacturing approvals have been site-specific. While these emerging manufacturing approaches offer compelling clinical and logistical advantages, regulators emphasize that distributed networks must meet the same current Good Manufacturing Practice (cGMP) standards as centralized facilities. Oversight therefore remains anchored in harmonized systems of governance, with consistent expectations for product quality, safety, and efficacy, regardless of manufacturing location [9,12,17,25,27].

In the European Union, the 2022 revision of EudraLex Volume 4, Annex 1 [30] emphasizes contamination control strategies, environmental monitoring, and rapid microbiological methods, all of which are directly relevant to hospital-based or modular Advanced Therapy Medicinal Products (ATMP) facilities [31]. In parallel, the EMA Guideline on the Quality, Non-Clinical and Clinical Aspects of Gene Therapy Medicinal Products [25] outlines expectations for identity, purity, potency, and comparability, underscoring that validated analytical approaches must support any manufacturing changes.

In the United Kingdom, the Medicines and Healthcare products Regulatory Agency (MHRA) framework for modular and PoC manufacture established new types of manufacturers’ licenses, Control Sites, and Master File requirements in 2025, providing mechanisms for harmonized oversight of distributed manufacturing networks [13]. The continuing regulatory trend is to formalize the governance architecture required for multisite production and establish expectations for centralized ownership of methods, documentation, and release decision-making.

In the United States, the Food and Drug Administration (FDA) applies cGMP principles through CBER and CDER guidance on CMC expectations for gene therapy products, analytical method validation, potency assay development, comparability, and lifecycle control. Key documents including FDA's CMC Guidance for Human Gene Therapy INDs (2020) [17], Analytical Procedures and Method Validation Guidance (2024) [53], Potency Tests for CGT Draft Guidance (2023) [32], and Comparability Protocols Guidance (2016) [33] reinforce the need for validated analytical methods, well-defined control strategies, assay transfer, and robust comparability packages for multisite or evolving manufacturing processes [17,34,35].

In Japan, the regulatory framework of the Pharmaceuticals and Medical Devices Agency (PMDA) and the Ministry of Health, Labor and Welfare (MHLW) emphasize centralized oversight and site-specific validation, with compliance based on the Ministerial Ordinance on Good Gene, Cellular, and Tissue-based Product Manufacturing Practice (GCTP) [36]. The Act on the Safety of Regenerative Medicine (ASRM) specifically allows hospitals to conduct cell processing within licensed facilities under a notification-based system and robust facility standards, rather than full commercial marketing authorization [37]. Manufacturing and quality control remain under strict oversight, including environmental control, documentation, batch traceability, and extensive product testing during manufacturing and release [38]. When manufacturing sites, processes, or controls differ, PMDA/MHLW generally require comparability assessments or other product-specific evidence to confirm consistent product quality and safety [36], which may limit flexibility for distributed manufacturing or QC networks. Japan's Pharmaceuticals and Medical Devices (PMD) Act also provides conditional and time-limited approval pathways for regenerative medicine products, enabling case-by-case flexibility and post-marketing evidence generation [39].

In South Korea, the Act on the Safety of and Support for Advanced Regenerative Medicine and Advanced Biopharmaceuticals (ARMAB Act 2019/2020) establishes a joint regulatory framework between the Ministry of Health and Welfare (MoHW) and the Ministry of Food and Drug Safety (MFDS) for advanced regenerative medicine [40]. The risk-based system uses a dual-track structure distinguishing Clinical Research Institutions (CRIs) from commercial product manufacture, or Commercial Cell Processing Establishments (CPEs); designated CRIs may conduct clinical research using approved research cell-processing facilities, while commercial manufacturing must occur in licensed, GMP-compliant facilities [40]. Manufacturing, facility control, recordkeeping, and lifecycle oversight are grounded in the ARMAB Act, with product approval, quality evaluation, and comparability review further governed by the Regulation on Approval and Review of Biological Products [41]. South Korea's approach provides strong centralized regulatory control and robust quality and safety oversight, thus remaining primarily oriented toward centralized manufacturing and institution-based clinical research, with only limited explicit support for decentralized or point-of-care manufacturing networks [42].

Collectively, these regulatory frameworks reflect increasing international convergence around cGMP expectations, emphasizing that decentralized and PoC manufacturing models must be supported by standardized analytical validation, comparability assessments, and lifecycle quality management to ensure consistent product quality and patient safety across jurisdictions.

Combined with parallel expectations in other ICH-aligned regions, the global regulatory landscape emphasizes that decentralized CGT networks must operate within harmonized global standards to ensure consistent product quality, safety, and efficacy. Together, these frameworks underscore that decentralized CGT manufacturing is not exempt from established GMP expectations; instead, they intensify the need for harmonized procedures, validated assay transfer, and robust oversight to ensure consistent product quality and regulatory confidence across networks.

Decentralized and PoC manufacturing are transforming advanced therapy production by shifting QC from a downstream safeguard to the pacing constraint for therapy delivery. Distributed networks, often operating with heterogeneous infrastructure and variable expertise, must still demonstrate that identity, purity, potency, safety, and stability are controlled to the same standard as centralized models under a unified Quality Management System (QMS) with clear oversight and data integrity expectations. In practice, this underscores the need for structured governance (e.g., Control-Site ownership of methods and release oversight, with regional QC support) and for the implementation of tightly integrated digital systems to ensure all manufacturing sites adhere to harmonized procedures and specifications.

1.2. Comparability frameworks in QC for decentralized cell and gene therapy manufacturing

The increasing reliance on decentralized or PoC manufacturing models for autologous CGTs aims to expand patient accessibility and operational scalability [8,9,22,24]. However, these models demand rigorous QC comparability across diverse manufacturing sites. Demonstrating comparability requires evidence that products manufactured at different locations are equivalent with respect to safety, purity, identity, and potency [32,43]. Such evidence is indispensable for regulatory acceptance and patient safety, as explicitly required by agencies including the FDA, EMA, and MHRA [8,9,27,34,44]. ICH Q12 complements these requirements by defining risk-based, life-cycle-oriented mechanisms for managing manufacturing or site changes within an established control strategy [45]. In this context, comparability is distinct from biosimilarity; it reflects a regulatory determination that manufacturing at different sites does not adversely impact quality, safety, or efficacy.

Comparability principles are also increasingly reflected in Asian regulatory frameworks [36,[46], [47], [48]]. In Japan, the PMDA Act mandates MHLW to evaluate equivalence across key product attributes, including assessment of cells, components, dosage, and performance against approved products [36,46,47], establishing the foundational basis for demonstrating analytical and clinical comparability across decentralized manufacturing nodes. In South Korea, multi-site products within a network are treated as a single product under unified approval when the manufacturing processes are comparable to the reference product through comprehensive analytical, non-clinical, and clinical evidence, including validated QC assays with multi-lot data [41,48]. This provision directly supports hub-and-spoke configuration, reinforces assay validation, and establishes batch-to-batch consistency at each decentralized node when process comparability is maintained.

Achieving analytical comparability in decentralized CGT settings is challenging for several reasons. First, autologous therapies depend on patient-specific starting materials, introducing inherent variability in the input cells and consequently in the final product [8,27,34]. Second, the extremely small batch sizes, often a single patient dose, limit the amount of material available for QC testing and comparability assessments, thereby limiting statistical power and the robustness of conclusions [34,43,49]. Consequently, regulators advocate a risk-based, stepwise strategy in which the extent of comparability studies aligns with the criticality of the manufacturing change and the volume of available sample material [17,50,51]. Third, decentralized networks frequently rely on local QC laboratories with varying capabilities; differences in equipment, personnel training, and adherence to standard operating procedures (SOPs) can introduce significant analytical variability [8,52]. Among quality attributes, potency testing remains the most variable and challenging to standardize, frequently presenting as the focal point of comparability discussions. Because exhaustive pairwise site-to-site comparability studies are both statistically underpowered and operationally impractical, a Control Site–anchored framework offers a more efficient and regulator-endorsed alternative [27,34,43]. In response to these challenges, the Control-Site model is increasingly recognized as a practical and regulator-endorsed framework for ensuring comparability across decentralized networks [8,9,27,44]. This central oversight ensures consistent quality standards, while comparability between sites can be reinforced through systematic checks, such as inter-laboratory comparability exercises in which the reference site and selected decentralized sites process shared starting material and compare analytical results [34,44]. Typically, the original manufacturing facility serves as a standard designated reference (control) site, providing a stable analytical and procedural benchmark for the network.

Robust comparability within this framework relies on specific technical enablers. Validated assay transfer protocols are critical to ensuring that non-compendial QC assays are reproducible and fit for purpose across multiple testing sites [32,52,53]. Bridging studies that use side-by-side testing of identical samples, including split patient samples, can directly demonstrate equivalence between assays or sites while accounting for patient-specific variability [8,32,52]. Harmonized SOPs, shared reference materials, and qualified reagents minimize analytical variability and support inter-laboratory assay validation [43,[54], [55], [56]]. Finally, digital tools enabling centralized oversight and real-time data sharing, such as cloud-based laboratory information management systems (LIMS), blockchain-enabled traceability, and automated closed processing systems, provide traceable, auditable datasets across the networks [8,9,49]. Collectively, these measures establish a robust framework for analytical comparability that mitigates site-driven variability, ensures regulatory confidence, and preserves the ability to pool outcomes across centers.

1.3. Analytical method validation and transfer in decentralized manufacturing

Analytical method validation is not only a regulatory requirement but also a practical challenge in decentralized and PoC manufacturing. In contrast to centralized facilities, where validation is performed under tightly controlled conditions, decentralized networks must ensure that validated assays maintain robustness across sites with differing infrastructure, staff expertise, and patient-specific inputs [17,57,58]. As a result, validation becomes a recurring issue, as methods proven reliable in one context may not perform consistently in another [8,14].

During early clinical phases (Phase I/II), limited patient material and high biological variability may necessitate qualified or partially validated assays that provide scientifically sound, reliable, and reproducible results without full validation. As development progresses into pivotal studies (Phase III) or commercial manufacturing, full analytical validation should be implemented to ensure comprehensive assessment of product quality, safety, and efficacy across all sites. This stepwise approach allows comparability and regulatory confidence to evolve in line with product and process understanding, while balancing the constraints of decentralized operations [57].

Method transfers compound this challenge. USP <1224> (2012) outlines structured approaches for transferring analytical procedures between laboratories, including co-validation, partial validation, and the use of justified waivers [59]. Each approach requires predefined acceptance criteria and a risk-based study design [14,59]. In decentralized manufacturing, inter-laboratory variability, differences in instrumentation, and limited on-site expertise make the design of transfer protocols and establishment of acceptance ranges especially critical to ensuring data integrity.

Ultimately, both validation and method transfer in decentralized manufacturing must be treated as continuous, adaptive processes rather than one-time events. Risk-based frameworks, central QMS oversight, and proactive regulatory engagement are essential to verify that analytical results remain accurate, reproducible, and clinically meaningful across all participating sites. Key documents such as Implementation of a Quality Management System for Decentralized Manufacturing [8] and Distributed Manufacturing and Point-of-Care Manufacturing of Drug [35] propose a centralized quality system model to oversee networked decentralized manufacturing sites.

1.4. Key QC challenges in decentralized manufacturing

1.4.1. Fresh materials and products: Escalating QC demand

In decentralized manufacturing, proximity to the hospital often eliminates the need to cryopreserve either starting materials (e.g., leukopaks) or final products [22]. Fresh manufacturing pathways offer significant clinical and logistical benefits:

• •Avoiding cryoprotectants and infusion-related toxicities.
• •Reducing the need for prolonged bridging chemotherapy.
• •Simplifying logistics.
• •Minimizing storage and transport costs.

Despite these benefits, fresh manufacturing substantially intensifies QC requirements. Identity, viability, and potency must be confirmed the same day or the next day of production, while sterility assurance relies on validated rapid methods and in-process controls. In some cases, where complete release testing cannot be finalized before the product is administered to the recipient, risk-based release may be necessary under pre-approved Real Time Release Testing (RTRT) or similar regulatory frameworks to ensure timely administration [[60], [61], [62]]. Conversely, cryopreservation simplifies scheduling, provides extended shelf life, and eases scheduling but requires post-thaw validation studies, validated freeze/thaw procedures, post-thaw testing, and dimethyl sulfoxide (DMSO) risk mitigation. This means that regardless of whether the product is fresh or cryopreserved, assay transfer packages must address pathway-specific risks, while chain-of-identity/custody and digital traceability remain essential to ensure product integrity [44,54,63].

1.4.2. Compressed timelines and sterility bottlenecks

Fresh (non-cryopreserved) products impose stringent time constraints on QC. Fresh (non-cryopreserved) formulations have a limited time window of stability during which QC assays must be conducted to support final product release [26,54,64]. Traditional culture-based sterility tests requiring 7–14 days are incompatible with these accelerated timelines. This limitation necessitates validated rapid microbial methods, enhanced in-process monitoring, and pre-approved, risk-based release approaches [13,26,54,65]. Cartridge-based rapid endotoxin systems and qPCR-based mycoplasma detection assays have emerged as viable solutions, reducing turnaround times from weeks to 24-48 h. These rapid methods, when validated within a harmonized multi-site framework, are critical enablers of decentralized and PoC manufacturing models [66,67].

1.4.3. Biological variability and limited sampling

Autologous therapies rely on patient-derived materials (e.g., leukapheresis, tumor-infiltrating lymphocytes) that exhibit substantial variability in cellular composition, activation state, and proliferative potential. This intrinsic biological heterogeneity propagates through the manufacturing process, affecting both process performance and final product quality [68,69]. Because each batch corresponds to a single patient, the quantity of material available for QC testing, characterization, and bridging studies is inherently limited, restricting opportunities for statistical robustness. Regulators, therefore, expect risk-based, stepwise evidence focused on critical quality attributes (CQAs) [12,43,50], particularly potency. To address variability and strengthen comparability across decentralized networks, automation and digital analytics provide promising tools to improve technical precision, reproducibility, and interpretability of potency assays.

1.4.4. Operational constraints at PoC site

Hospital-based PoC sites often lack purpose-built GMP space, harmonized SOPs, and digital traceability to manage batch data, chain of custody, and supply chain. Additionally, shortages and high turnover of trained personnel in microbiology, immunology, and molecular biology further increase operator dependency and delay investigations [8,24,70,71].

These operational challenges are amplified for adherent autologous CGTs such as MSC therapies, where upstream tissue-processing and adherent culture operations introduce open or semi-open manipulations that limit full reliance on closed, suspension-based systems [28,29] and demand higher-grade cleanrooms and intensified EM oversight [28]. Compared with CAR-T systems, automation for adherent workflows is less mature, and deviations in gas exchange, shear stress during passaging, or manual handling can directly impact identity, potency, and senescence profiles [29].

While closed bioreactor platforms may reduce cleanroom requirements, regulators still mandate validation of the entire manufacturing process, including justification of parameter selection, acceptance criteria, and in-process controls [28].

These factors widen the gap between the “ideal” decentralized setup and real-world PoC environments, reinforcing the need for modality-tailored facility qualification, EM programs, operator training, and digital traceability for real-time process monitoring when deploying MSC or other adherent cell processes across decentralized networks.

Consistent alignment of procedures, instruments, and training across sites is therefore a prerequisite for reliable inter-site performance and timely release. While staff may have relevant scientific expertise and skills, formal GMP training ensures understanding of regulatory requirements and documentation standards execution, whereas operator competence reflects the ability to perform tasks accurately within validated processes. Both elements are essential for maintaining compliance, consistent quality, and timely product release under the site's QMS.

1.5. Key QC solutions in decentralized manufacturing

1.5.1. Tiered quality control framework in decentralized manufacturing

An effective strategy for managing QC in decentralized manufacturing is the adoption of a tiered oversight model [8,22]. Unlike traditional centralized paradigms, where a single facility retains complete QC responsibility, decentralized models require distributed oversight [72,73]. Within a unified QMS, geographically distinct sites may simultaneously produce the same product for different patients, all operating under harmonized global quality standards [8,12]. To maintain comparability, differentiated levels of oversight, compliance, and operational efficiency across the network are essential [8,22].

The model described here introduces three tiers: a central Control Site, Regional QC Hubs, and PoC Sites (Fig. 1) [8,22]. At the top of the hierarchy, the Control Site serves as the regulatory and Quality Assurance (QA)/Qualified Person (QP) nexus, providing unified governance, approving QC protocols, and ensuring consistency across the network [8,13]. It also serves as the primary location for analytical development, qualification, and validation, generating the authoritative methods, specifications, and acceptance criteria that benchmark network-wide performance. Regional QC Hubs act as intermediaries, harmonizing analytical testing, conducting comparability studies, managing assay transfer, and supporting lot release [8,23,34]. Following readiness for clinical-grade manufacturing, a CGT product's process and validated analytical methods are transferred to a qualified PoC site, which becomes the designated reference site for decentralized operations. As the operational benchmark for the network, the reference PoC site provides the comparative standard against which subsequent PoC sites are evaluated. Finally, PoC/reference sites conduct in-process monitoring, rapid patient-specific QC, and rapid release testing within the clinical environment. This tiered structure integrates with eQMS, pharmaceutical QA principles, and global regulatory expectations for method validation, comparability, lifecycle control, and multi-site manufacturing oversight [12,74,75]. By balancing centralized oversight with local responsiveness, scalable regulator-aligned quality operations are enabled that preserve product comparability and patient safety across decentralized manufacturing networks.

Fig. 1.

Decentralized QC: Control and Oversight Mechanisms. The schematic illustrates a tiered QC framework consisting of three interconnected levels enabling harmonized oversight, data flow, and assay standardization across distributed manufacturing sites. A designated PoC site may function as the reference site within the decentralized network. Batch release across the network is finalized by the QP which could function either at a regional site or the central Control Site. Bidirectional data exchange and sample flow between tiers are supported by feedback, training, and oversight mechanisms, ensuring continuous improvement and alignment across the decentralized QC network.

1.5.2. Tier 1: Control Site

The Control Site functions as the regulatory and quality anchor of the decentralized network. Its responsibilities include documentation, regulatory liaison, QA/QP oversight, analytical governance, digital infrastructure integration, and continuous performance monitoring [22,35]. A key function is maintaining PoC and Modular Manufacture (MM)/PoC Master Files that define manufacturing processes, QC strategies, supervision, and pharmacovigilance [9,76]. The Control Site also serves as the primary regulatory contact, with the QP retaining ultimate authority for product release, inspections, and compliance [8,9,74]. While the QP designation is a specific legal requirement primarily in the EU/UK, this paper uses QP to represent the individual(s) authorized within an organization's Quality Unit(s) to perform final product release.

Centralized QA/QP oversight is implemented through a unified QMS that governs deviations, Out-Of-Specification (OOS)/Out-Of-Trend (OOT) events, Corrective and Preventive Actions (CAPAs), batch disposition, and change control. Integration of digital and Artificial Intelligence (AI)-enabled batch review systems strengthens compliance with cGMP requirements and enhances audit readiness [12,74]. Annual product quality reviews verify consistency and drive continuous improvement [74].

Analytical governance is another critical responsibility of the Control Site (often functioning as the reference site). It leads to the development, qualification, and validation of potency assays and other critical analytical methods, defining specifications for reagents, critical materials, controls, replicates, and performance parameters. Assay transfers to each manufacturing site require full or partial validation, and reference material stability is monitored to ensure assay reliability [8,53,58].

Digital integration further extends the Control Site's oversight capacity. Core platforms, such as eQMS, Document Management System (DMS), LIMS, and eBRs, ensure data traceability and harmonized quality operations across the network [12,26]. Rapid microbial methods (RMMs) embedded within LIMS facilitate real-time microbial detection and accelerate product release workflows [65,77]. Structured training programs ensure harmonized GxP education, competency tracking, and consistent QMS implementation across decentralized sites. Collectively, these mechanisms establish the Control Site as the nucleus of regulatory accountability, analytical consistency, and digital integration, providing the foundation for quality assurance and comparability throughout the decentralized manufacturing network.

1.5.3. Tier 2: Regional QC hubs

Regional QC Hubs serve as the operational and analytical backbone of decentralized manufacturing, bridging the Control Site and PoC sites [8,22]. These hubs consolidate analytical testing, standardize method execution, integrate environmental and quality monitoring programs, and ensure both product comparability and supply continuity across geographically distributed operations [12,23].

Their key functions include real-time release with QP oversight and routine quality control testing focused on identity, potency, viability, and sterility, using harmonized protocols to minimize inter-site variability [23,78,79]. Regional hubs implement advanced analytics and process monitoring technologies, such as Process Analytical Technology (PAT) platforms and RMMs. PAT platforms provide real-time monitoring of culture dynamics, such as nutrient consumption, metabolite generation, and cell growth kinetics, supporting proactive quality assurance. RMMs further mitigate sterility-testing bottlenecks and significantly shorten turnaround times, directly contributing to timely product availability for patients [49,79].

Environmental monitoring programs are also coordinated at the Regional QC hub level, tracking particulates, microbial burden, temperature, and humidity, with data digitally captured, trended, and reported to the Control Site for centralized oversight and prompt deviation management. They are responsible for analytical method qualification and validation when onboarding new PoC sites, including comparability studies and stability testing under predefined acceptance criteria, ensuring assays remain reliable across diverse operating environments [8,52,53]. Standardized training frameworks deployed by regional QC hubs further support harmonized GxP execution, ensuring consistent and efficient method adoption across regional QC hubs by providing uniform training programs. This reduces variability and facilitates smooth implementation of new methods while maintaining compliance with global quality standards.

In addition, Regional QC hubs coordinate with external laboratories for specialized assays that cannot be performed locally, ensuring data integrity, traceability, and compliance requirements. They maintain contingency testing and release capacity, safeguarding product availability and supply continuity during local disruptions or surges in clinical demand.

1.5.4. Tier 3: PoC/reference sites

PoC Sites represent the execution point of decentralized manufacturing, operating in proximity to patients and handling autologous starting materials [13,23,80]. These sites perform localized GMP operations and in-process controls to ensure that product quality, identity, and safety are preserved throughout the workflow [8,12,44,78].

At PoC sites, the manufacturing team typically performs localized QC functions, integrating in-process controls into their workflow, and sometimes supported by embedded QC specialists. Their primary QC responsibilities include real-time CQA monitoring such as cell viability, proliferation, and metabolic activity, as well as environmental monitoring focused on particulates and microbial burden to support aseptic conditions. Digital trending enables rapid escalation and remediation when excursions occur. PoC Sites also ensure bidirectional traceability of materials, consumables, and intermediates to prevent mix-ups and contamination [26,74,81].

Digital integration connects PoC sites with Control Sites and Regional QC Hubs through eBRs, deviation management, and automated data exchange [8,12,13]. Training programs reinforce harmonized SOPs and GxP practices within clinical environments, enabling consistent adoption of standardized methods and quality expectations even in highly variable conditions while adapting to patient-proximal settings [14,23,82].

Through these roles, PoC sites act as the execution point of the tiered QC framework, transforming standardized procedures into patient-ready therapies. Their integration with higher tiers ensures decentralized manufacturing maintains quality, safety, and comparability across networks.

Another essential duty within the PoC network is to designate one qualified PoC site as the Reference Site (RS). In this role, the site is the first to receive the finalized manufacturing process and analytical methods once a CGT product is deemed suitable for clinical-grade production [8]. Following approval of the technology transfer protocol by regional QA and the Control Site, the Reference Site manufactures at least three verification batches that include full In-Process Control (IPC), release testing, and stability studies as defined in the end-of-process-development report. These batches establish the foundational data set against which all additional PoC centers are compared; an alternative number of batches may be justified considering whether standard methods are used and whether similar products or processes are already used at the site [8,70].

Acting as the benchmark for decentralized manufacturing, the RS enables consistent evaluation of process performance, analytical method suitability, and product quality across the network. The Reference Site therefore ensures a stable reference standard for ongoing comparability activities, including the annual comparability check in which selected PoC sites manufacture and test products from shared starting material to confirm continued equivalence [8].

1.6. Expanded QC solutions: Automation, analytics, and governance

In addition to the tiered QC framework, manufacturing can be further strengthened through complementary digital and automation solutions, as outlined below, providing the necessary infrastructure for efficient, high-quality production and centralized oversight across decentralized sites.

1.6.1. Environmental monitoring as a digital integrated release gate

Maintaining asepsis in small, distributed units requires robust environmental monitoring (EM) programs integrating viable air and surface sampling, non-viable particulates, differential pressure, temperature, and humidity with pre-release review and trend analysis under defined alert/action limits [30,83]. EM data generated at regional QC hubs or PoC sites should be digitalized at the source through automated sensor systems, trended directly into a centralized monitoring platform, and integrated into LIMS/Total Lab Automation (TLA) infrastructure so that excursions automatically trigger deviation/CAPA workflows and feed network-level risk management. This approach enables continuous environmental surveillance, improves traceability, and facilitates real-time detection of deviations by turning EM into an active, digital release gate that supports product disposition decision and comparability assessments across the network [84].

1.6.2. Analytical method validation and transfer

Inter-site consistency depends on fit-for-purpose methods, clear specifications (reagents, controls, replicates, performance parameters), and lifecycle management. Transfer strategies should use co-validation or partial validation with common samples, predefined acceptance criteria, and risk-based protocols [34,58,59,85]. Where sample is scarce, split-source designs and matrix-equivalency checks ensure that non-compendial assays perform reliably at each site [8,54]. Standardized training, reference materials, and assay kits further reduce inter-laboratory variability and strengthen comparability packages.

1.6.3. Digital traceability and data integrity

Robust digital infrastructure is foundational to QC oversight in decentralized manufacturing models. A unified QMS supported by digitized tools such as electronic QMS (eQMS), LIMS, electronic Batch Record (eBR), and Electronic Lab Notebook (ELN) is essential to prevent mix-ups and to enable real-time data capture, traceability, and centralized review. These systems provide granular chain-of-identity and chain-of-custody controls to prevent mix-ups and support auditable, near-real-time batch release. Barcode-based systems and, in some programs, blockchain-backed logs strengthen end-to-end traceability. ALCOA + expectations should apply to all electronic records, analytics, and algorithm outputs [22,49,86,87]. Cloud-based LIMS architectures extend this further by enabling secure, near-real-time transmission of instrument data from PoC sites to a centralized data repository, where automated checks flag out-of-specification (OOS) or out-of-trend (OOT) values and trigger deviation workflows without manual re-entry, directly supporting data integrity under compressed release timelines [88]. As the field adopts Industry 4.0 technologies, the risks to data integrity posed by advanced automation, PAT, and machine learning outputs must be systematically addressed through validated computerized systems governed by ALCOA+ and Good Automated Manufacturing Practice, 5th Edition(GAMP5) standards [89]. Beyond infrastructure, AI and Machine Learning(ML) are increasingly being explored as powerful tools for QC analytics in CGT manufacturing, including automated data gating, deviation detection, and predictive quality assurance; the International Society for Cell & Gene Therapy (ISCT) has highlighted the importance of standardized validation frameworks and regulatory alignment before clinical deployment of such systems [84]. Looking further ahead, digital twins, continuously updated virtual process replicas fed by real-time sensor and analytical data, hold significant potential for enabling model-informed release strategies and continuous process verification across decentralized networks, with early biopharmaceutical applications demonstrating their capacity to predict deviations and optimize operating conditions before they affect product quality [90,91].

1.6.4. Automation, Process Analytical Technology (PAT), and real-time analytics

Closed/automated platforms reduce contamination risk and operator variability, while PAT tools (Raman/NIR/MIR spectroscopy, bio-capacitance, microfluidics, machine-vision) provide in-/at-line insight into cell state and media dynamics, enabling faster, more reproducible decisions within tight release windows [77,79,[92], [93], [94]]. When integrated with Quality by Design (QbD) and continued process verification, these systems enable predictive QC and accelerate investigations [95,96].

1.6.5. Automated, remotely readable assays and smart QC workflows

Automated assays are emerging as a cornerstone of decentralized QC [6,8,10,23,97] because they combine simplified workflows with digital oversight [49,94], enabling consistent performance across sites with diverse infrastructure and expertise [7,98]. Examples of such technologies include automated flow cytometry with remote readout for identity testing [99,100], qPCR/dPCR platforms with integrated analysis for mycoplasma detection, replication-competent virus (RCV) testing, vector copy number (VCN) quantification [[100], [101], [102]], and transgene confirmation, image-based functional assays for potency [103] and impedance-based functional assays for real-time cell activity monitoring [104].

These automated and remotely readable platforms reduce operator variability, shorten training curves, and enhance reproducibility. A defining feature of these systems is their ability to generate remotely interpretable data that enables real-time decision-making. Results are uploaded into LIMS and eBRs, enabling near real-time review by Control Sites and Regional QC Hubs. This not only preserves data integrity but also allows centralized QA/QP to provide oversight while ensuring patient samples remain at the PoC site [13,27]. The next frontier is the incorporation of AI-enabled QC interfaces, which can automate gating strategies, detect deviations, harmonize data interpretation across sites, and support predictive analytics [49]. Such systems extend the value of remote-read assays by embedding predictive QC, reducing review times, and ensuring consistent decision-making across networks. Combined with robust connectivity to LIMS and centralized oversight, these assays exemplify how automation, digital integration, and AI can jointly enable timely, regulator-ready QC in decentralized manufacturing [8,17].

1.6.6. QMS governance and workforce

Decentralized manufacturing networks heighten the need for consistent governance and workforce standards. A robust QMS must provide harmonized definitions for deviations, OOS/OOT events, root-cause investigations, and CAPA management to ensure comparability across sites [51,75,95]. Clear, network-wide governance structures also support product and process lifecycle management, continuous verification, and alignment with regulatory expectations for distributed CGT operations [8,32,34].

Digital oversight, through eBRs, cloud-based QMS platforms, and AI-enabled monitoring, facilitates real-time review, traceability, and continuous inspection readiness across distributed facilities [49,87]. Within this framework, QPs maintain responsibility for batch release, using standardized electronic documentation and validated data generated by trained personnel at each manufacturing site [74].

A harmonized and competency-based workforce strategy is equally critical. Standardized training programs covering GMP principles, aseptic practices, and quality risk management are essential for sustaining quality performance. Integrated competency tracking and retraining triggers strengthen workforce readiness and mitigate operator-dependent variability [74,83]. As automation and digital systems reduce manual interventions, operator roles shift toward oversight and specialized skills, which require targeted training regimens [49,97].

1.7. Overarching standards and regulatory considerations for decentralized manufacturing

QC in CGT manufacturing remains firmly anchored in stringent global standards, even as manufacturing increasingly shifts from centralized facilities to decentralized networks. The distributed nature of PoC and modular manufacturing (MM) increases variability and operational risk, amplifying the need to consistently preserve product safety, purity, and potency. Core international frameworks, such as ICH Q8(R2) for pharmaceutical development, ICH Q9 for quality risk management, and ICH Q10 for lifecycle-based quality systems, provide the foundation for cross-site harmonization [51,75,96]. ICH Q12 further supports these frameworks by providing a structured approach to lifecycle management and post-approval change control, which is particularly relevant for multi-site or evolving decentralized CGT manufacturing [45]. Following QbD principles [96] (ICH Q8(R2)) for CQA identification coupled with analytical procedure development best practices (ICH Q14) [105] further supports a consistent cross-site approach to product and process quality. Ensuring comparability studies and validated potency assays is indispensable to achieve and confirm that products manufactured at different locations are equivalent in quality and clinical performance [8,32,34] (see Table 4).

Table 4.

Dimensions of Comparability in Decentralized Manufacturing. This table outlines the major domains where comparability must be demonstrated to ensure consistent product quality across distributed manufacturing networks. Product quality comparability focuses on CQAs such as safety, identity, purity, stability, and potency. Analytical comparability addresses assay transfer, validation, and robustness across operators and sites. Process comparability involves alignment of SOPs, equipment, and closed-system workflows, typically confirmed through process performance qualification (PPQ) and technology transfer packages. Documentation and training comparability are essential for regulatory compliance, with harmonized QMS, deviation tracking, and CAPAs ensuring oversight and accountability across sites. Together, these dimensions form the foundation of regulatory acceptance for decentralized manufacturing models [8,27,36,48,54,76].

Dimension	What Must be Comparable	Example Metrics/Critical Quality Attributes (CQAs)	Methods of Demonstration	References/Resources
Product Quality	➢Identity, strength, purity, potency, safety, viability, and stability of the product. ➢Products manufactured at decentralized sites must be comparable to a reference site and maintain lot-to-lot consistency.	➢Potency (functional assays) ➢Identity (cell phenotype, CAR transgene expression by flow cytometry/PCR) ➢Purity (residuals, Adventitious agents, RCR) ➢Viability (≥70%) [112,113] ➢Strength/Dose (genetically modified cells, viral titer, viable cell count) ➢Sterility and stability (hold times, storage impact)	➢Comparability protocols ➢Split-lot or side-by-side testing ➢PPQ and continued process verification ➢Predefined acceptance criteria ➢Quality Risk Management (QRM) based assessment	[13,17,27,32,33,36,40,41,45,47,48,50]
Analytical Assays	➢Assay validation, transferability, reproducibility, and robustness across testing sites. ➢Methods must be suitable for intended use.	➢Assay precision ➢Sensitivity (LOD/LOQ) ➢Specificity ➢Reproducibility ➢Inter-laboratory variability ➢Assay drift monitoring	➢Assay transfer protocols ➢Side-by-side validation using common reference standards ➢Method qualification/validation per ICH Q2(R2) ➢Compendial method verification ➢Harmonized SOPs and reagent control across sites	[13,17,27,35,36,42,52,58]
Processes	➢Manufacturing SOPs, equipment qualification, reagents, aseptic procedures, and IPCs must be consistent across sites.	➢Process parameters (PPs) ➢IPCs ➢Cell expansion kinetics ➢Yield and batch uniformity ➢Sterility assurance and contamination rate ➢Environmental monitoring data	➢Lifecycle process validation (Design→ PPQ → Continued verification) ➢Comparability studies across sites ➢Equipment qualification ➢Aseptic media fills ➢Standardized SOPs and training.	[17,30,36,40,41,50,70,74,75,95,114]
Documentation & Training	➢QMS harmonization, deviation and CAPA management ➢Training consistency ➢Robust documentation practices ➢Change control and oversight mechanisms across sites.	➢Deviation frequency ➢CAPA closure timelines ➢Audit findings ➢Consistency of SOPs and assay description ➢Training completion rate and competency scores	➢Quality system-governed procedures ➢Standardized training programs ➢Quality Unit oversight ➢Risk-based quality review ➢Centralized QMS model ➢Regulatory inspections ➢Comparability protocols for planned changes	[30,33,40,41,46] [51,70,74,75,87]
Raw Materials	➢Quality, grade, and traceability of starting and ancillary materials (cells, reagents, viral banks, equipment) must be consistent.	➢Identity ➢Grade (clinical vs. research) ➢Pathogen-free status ➢Certificates of Analysis (CoA)/Certificate of Conformance (CoC) ➢Cell viability ➢Cell source characterization ➢Donor variability	➢Supplier/vendor qualification ➢Material testing (sterility, adventitious agents) ➢Master Cell Bank (MCB)/Master Viral Bank (MVB) qualification, ➢Traceability systems, ➢Harmonized acceptance criteria and storage conditions ➢Risk-based qualification strategies	[9,17,36,40,51,74,115,116]
Data Integrity & Digital Systems	➢Accuracy, reliability, and traceability of QC data supported by standardized chain-of-custody, chain-of-identity, electronic data management systems, and cybersecurity controls across distributed sites.	➢Accuracy of assay readouts ➢Electronic QC records traceability ➢Validated predictive models ➢Audit trail compliance	➢Validation of automated/electronic QC systems ➢Audit trail review ➢Model-in-use verification ➢Quality Control Unit (QCU) oversight of digital records ➢Compliance with ALCOA + principles.	[30,46,47,51,58,70,84,[86], [87], [88], [89], [90], [91],95,114,117]

Decentralization does not lessen cGMP expectations; rather, it requires strengthened oversight and standardized practices. Both licensed and investigational CGT products must meet cGMP requirements regardless of production setting [44,106]. Regulatory authorities are adjusting their frameworks to support these emerging models. For example, the UK MHRA has introduced PoC and MM Master Files to centralize process descriptions, QC strategies, and pharmacovigilance plans [9]. In parallel, FDA guidance reinforces that analytical procedures, potency assays, and reference material management must be validated with the same rigor applied in traditional centralized settings [32,52,58]. The growing use of digital platforms and AI-enabled oversight reflects that decentralized models demand systemic, technology-driven approaches to assure data integrity and compliance [49,107].

Nonetheless, regulatory challenges remain. Traditional definitions of manufacturing sites remain tied to fixed street addresses, an approach poorly suited to MM, creating uncertainty in registration and oversight [6,12,108]. Variability in starting cellular materials complicates comparability assessments, underscoring the need for regulatory convergence on study design, statistical methodology, and acceptable variability thresholds, providing a complementary layer of assurance.

Accreditation systems complement regulation in filling these gaps. Independent accreditation of laboratories and manufacturing units, through FACT-JACIE for cell therapy programs, ISO 15189 for analytical or clinical testing laboratories, or ISO 9001 (Quality Management Systems – Requirements) or ISO 13485 (Medical Devices – Quality Management Systems – Requirements for Regulatory Purposes) for quality management, provides an external layer of quality assurance. While ISO 9001 for Quality Management Systems (QMS) and ISO 13485 are site-specific and typically require a physical site audit, decentralized PoC sites can be included under the parent Control or Regional QC Hub site's QMS if they meet audit and procedural requirements. These accreditation systems reinforce standardized local laboratory practices, promote comparability of analytical methods, and strengthen data integrity in digital platforms, supporting consistency across distributed networks [12,22,23]. In certain jurisdictions, accreditation frameworks also help streamline oversight, facilitate mutual recognition between regulators and clinical institutions, and assure patients and payers of product quality [6,22].

Taken together, the evolving regulatory landscape underscores that decentralized manufacturing requires more structured and multilayered oversight than conventional centralized production [8,12]. A promising way to operationalize these requirements is through a tiered QC model, in which responsibilities are distributed across the Control Site, Regional QC Hubs, and PoC facilities [8,13,22]. By embedding global regulatory standards within this structured framework, supported by accreditation and digital integration, decentralized networks can achieve both flexibility and consistency in delivering high-quality therapies, while maintaining regulatory confidence and safeguarding patient safety [21,107].

1.8. Future directions for QC in decentralized manufacturing

QC in decentralized manufacturing is transitioning from foundational systems into fully digital, automated, and data-driven networks. Emerging platforms integrate advanced automation, real-time analytics, and AI to reduce manual intervention, enhance process robustness, and streamline regulatory reporting across distributed facilities [12,49,109]. These platforms are expected to enable predictive QC, where critical deviations are identified and mitigated before impacting product quality, and release decisions are supported by continuous, real-time monitoring rather than solely retrospective testing.

Regulatory frameworks are gradually evolving to accommodate these technologies, with risk-based batch release emerging as a pathway to reduce turnaround times. This approach allows patients to dose while compendial results are pending, relying on robust control strategies that encompass validated in-process controls, comparability protocols, and continuous monitoring systems, provided formal regulatory acceptance is secured [60,61,110,111].

Embedding these approaches into unified digital infrastructures and harmonized QMS represents the next step in decentralized manufacturing, enabling efficient delivery of patient-specific therapies while maintaining stringent quality standards, regulatory confidence, and patient safety.

2. Summary

Decentralized manufacturing of CGTs offers clear clinical and logistical advantages, including faster vein-to-vein delivery and reduced transport risks but also introduces significant QC challenges. Ensuring product consistency across distributed sites requires harmonized standards, rapid testing, and comparability frameworks to ensure equivalent product quality irrespective of manufacturing location [21,23]. Centralized coordination, standardized assays, and unified QMSs are essential to maintain regulatory compliance [8,12].

The short shelf life and patient-specific nature of autologous products demand validated release assays and PATs capable of supporting real-time decision-making at PoC [22,49,68]. Although regulatory agencies, including the FDA, EMA, and MHRA, are actively advancing frameworks for PoC and modular models, critical gaps persist in comparability expectations and oversight mechanisms for distributed operations. Emerging tools such as automation, digital platforms, and AI offer opportunities to strengthen QC robustness and scalability but require rigorous validation and harmonization [12,22,49,107]. Ultimately, QC remains central to decentralized CGT manufacturing, ensuring timely patient access balanced with robust safeguards for product quality, safety, and efficacy supported by flexible, integrated QC strategies that maintain regulatory confidence and, above all, protect patient safety.

Author contributions (CRediT)

Sagi Nahum: Conceptualization, Methodology, Formal analysis, Visualization, Supervision, Project administration, Writing – original draft, Writing – review & editing. Devanshi Doshi: Methodology, Investigation, Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing. Kristine Nishida: Methodology, Investigation, Validation, Data curation, Writing – review & editing. Cris Kosnik: Methodology, Validation, Resources, Writing – review & editing. Heiko von der Leyen: Conceptualization, Supervision, Writing – review & editing. Vered Caplan: Conceptualization, Resources, Funding acquisition, Writing – original draft, Writing – review & editing. Emiko Jeffries: Investigation, Writing – review & editing.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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