Unlocking analytics in cell and gene therapy: from complexity to control

25 Jul 2025 26min read

Cell and gene therapies (CGTs) have redefined the treatment landscape, but their development and manufacturing remain uniquely challenging. They involve variable starting materials and complex, small-scale processes. As the field pushes for faster development, scalable manufacturing and greater cost-efficiency—without compromising quality or safety—there is a clear shift toward automated, closed-system platforms. However, automation alone is not enough.

Extracting insight, supporting decision-making, enabling process control and facilitating rapid, cost-effective release testing is crucial to ultimately accelerate delivery to patients. This, however, depends on effective analytics, positioned at the heart of this transformation.

Why analytics are important for cell and gene therapy development
Unlocking analytics in cell and gene therapy development
Insight from cell and gene therapy experts across the industry
Input material characterisation
Process characterisation and Process Analytical Technologies
Release quality control
Reflections

Why analytics are important for cell and gene therapy development

The inherently limited biological understanding of cell and gene therapy processes, coupled with high variability in input materials and overall product complexity, necessitates a strategic and integrated approach to analytics. Variability in starting materials complicates process understanding, making it challenging to consistently link inputs to product performance. Simultaneously, the lack of deep biological insight into CGT mechanisms demands extensive in-process data to effectively characterise and control manufacturing.

The unique nature of cell and gene therapy products makes release quality control particularly complex requiring rigorous analytical testing. This is due to modality-specific critical quality attributes, therapy-specific specialised potency assays, the inability to filter products without compromising their function, and other factors.

To help frame the spectrum of analytics used in CGT development, they can be broadly categorised into three functional groups:

Input material characterisation
In-process analytics (or Process Analytical Technologies – PAT)
Release quality control (QC) analytics

These analytic tools serve different purposes at each stage of the product lifecycle: discovery and process development, clinical manufacturing and commercial manufacturing.

Diagram of analytic tools used a different stages of cell and gene therapy development

Diagram of analytic tools used at different stages of cell and gene therapy development

Discovery and process development

Developers often lack clarity on which inputs or parameters are critical, making it essential to measure and monitor many variables with high fidelity. Capturing detailed data on input characteristics, process parameters, noise sources and product outcomes helps build models that reveal key input–output relationships. This enables early control implementation to reduce variability and identify poor runs, shortening development timelines and lowering costs to achieve a robust, reproducible clinical process.

In a previous article, we explored a methodological approach to process understanding in CGT using the Process Analytical Technology framework.

Clinical manufacturing

Efficient production and variability control become critical due to the high cost and scale-up for trials. The focus shifts to identifying and refining the most predictive critical quality attributes (CQAs). A comprehensive analytics dataset linking starting material, process parameters and quality attributes helps uncover connections to clinical outcomes, improving CQA selection and prediction of product performance.

Commercial manufacturing

Analytics refine the most relevant critical process parameters (CPPs) and CQAs, with fit-for-purpose tools supporting real-time monitoring and predictive insights. PAT enables manufacturing based on cellular activity instead of fixed schedules, which is especially important for autologous therapies. Robust, scalable release QC ensures identity, potency and safety for each batch.

Diagram of process development for cell and gene therapy production

Unlocking analytics in cell and gene therapy development

While significant progress is being made to leverage analytical methods for cell and gene therapy development, substantial technological advancements are still required to unlock their full potential.

A major challenge is the lack of analytical technologies fit for CGT processes. Many developers rely on legacy tools from traditional biologics or academic research, which aren’t designed for CGT’s unique demands. These assays often require large sample volumes and long turnaround times, both are problematic for CGT’s small batch sizes and time-sensitive treatments. Several additional challenges remain including:

Most existing tools are manual and difficult to integrate into cell and gene therapy manufacturing workflows.
Real-time, in-line analytics for critical cell attributes remain limited, restricting process control.
Release testing is complex and labour-intensive, often delaying product availability by 7–10 days. It frequently dominates manufacturing turnaround time and may be incompatible with products that have short shelf lives.
Usability is a barrier as many methods are complex, prone to variability and dependent on specialist skills.
Sampling adds further strain. With batch volumes as low as 50–150 mL, offline testing can consume significant portions of the product, increasing costs and reducing yield.

Perhaps the most significant hurdle is converting measurements into actionable insights. Building predictive models requires large datasets, advanced analytics and robust IT infrastructure. These resources are expensive and not widely accessible.

Unlocking the full promise of analytics in CGT will require the development of fit-for-purpose tools that are fast, low-volume, automated, easy to use and can be integrated into manufacturing workflows. These tools should offer adequate specificity, sensitivity, robustness and reproducibility, enabling actionable insights at every stage of development.

Insight from cell and gene therapy experts across the industry

To explore how the full potential of analytics can be realised in advancing cell and gene therapy development and manufacturing, we engaged with key stakeholders and thought leaders across the field.

Sharon Barkatullah is the Head of Analytical Development at Cell and Gene Therapy Catapult (CGT Catapult) – an independent, not-for-profit organisation dedicated to accelerating the industrialisation of advanced therapies, among its various initiatives, the CGT Catapult leads collaborative innovation programmes, including several with a strong focus on solving analytical challenges within the cell and gene therapy industry.
Rachel Legmann is the Senior Director of Technology, Gene Therapy at Repligen – who provide advanced bioprocessing technologies and analytics for plasmid DNA, viral vector and mRNA production, enabling scalable, high-yield and quality-controlled manufacturing from upstream to downstream processes.
Antoine Espinet is the CEO of MFX – an early-stage start-up developing automated bioreactor systems with integrated analytics, which enables scalable cell therapy manufacturing by providing reproducibility from their process development stage instrument through to clinical and commercial stages.

In the following section, we highlight key areas of ongoing innovation spanning enabling technologies, strategic implementation approaches and critical technological advancements, that aim to unlock the transformative role analytics can play in driving greater efficiency, control and insight across the CGT lifecycle.

Image depicting getting the correct storage temperatures for cell and gene therapies

1. Input material characterisation

Autologous cell therapy donor material benchmarking

In autologous therapies, the donor material—sourced from the patient themselves—is foundational. Variability in this starting material affects every step of the process, from manufacturing success to clinical efficacy. Factors such as donor age, disease burden, immune history and leukapheresis collection methods (e.g., duration, access type, patient tolerance) all influence the composition and quality of the harvested cells. If this variability cannot be normalised, the process may need patient-specific adjustments to ensure consistent product quality.

However, it remains unclear which attributes of the input material matter most and how they impact outcomes. This challenge is amplified by the high noise in small, patient-specific datasets, making statistical correlations difficult.

Deep characterisation tools like flow cytometry, next-generation sequencing, mass cytometry and multi-omics can assess T cell subsets, surface markers and phenotype. While powerful, these tools are expensive, complex and require skilled operators, limiting routine use.

One solution is the creation of consortia to generate large, shared, IP neutral datasets linking input material attributes with process and clinical outcomes. The CGT Catapult is exploring such an initiative to define what constitutes “high-quality” donor material. This could eventually enable targeted, one-off assessments to improve viability and optimise individualised manufacturing strategies.

Plasmid DNA quality control

Beyond using analytics to understand donor material variability in autologous therapies, applying the same principles to other raw materials is also valuable. For example, in gene therapy, plasmid DNA is a foundational component used in viral vector production. Variability in plasmid quality can affect transfection efficiency, viral yield and overall product consistency.

While suppliers often provide a certificate of analysis (CoA), some CDMOs choose to develop plasmids in-house and perform internal quality control to ensure consistency. Rachel Legmann from Repligen recommends conducting independent QC despite a CoA—especially to avoid troubleshooting failures later in the process. Addressing issues proactively can save time and cost downstream.

“Some developers just rely on the certificate of analysis and move forward—but if something goes wrong, it delays everything. Evaluating upfront reduces the risk of failure.”

Rachel Legmann, Senior Director of Technology,
Gene Therapy at Repligen

QC of plasmids typically includes purity, supercoiled percentage, identity confirmation and residual impurities using tools like gel electrophoresis, HPLC, UV spectrophotometry and sequencing. Since this QC happens before the process begins, speed is less critical and current analytical tools are generally adequate.

Rachel also recommends viral clearance validation for all raw materials entering the process, including media, transfection reagents and cell activators. While many reagents can be tested with existing technologies, some—like transfection reagents—remain challenging due to complex compositions or proprietary formulations and current tools may not fully assess their impact, highlighting a need for better analytical strategies in this area.

“Raw material is extremely important—not only at the beginning, but all along the process. That’s why you have to build a risk mitigation strategy to monitor and control every material going into the process, including plasmids, media and transfection reagents.”

2. Process characterisation and Process Analytical Technologies

Process development involves measuring key parameters like pH, dissolved oxygen (DO), temperature, metabolites and product attributes (such as cell count, viability, yield and potency). These measurements—taken off-line, at-line, in-line or online—guide iterative protocol adjustments.

Bioprocessing discovery instrumentation

The bioprocess involves multiple complex steps where analytical data can be collected and assessed. However, establishing meaningful correlations between process parameters and outcomes remains complex and time-consuming. Two key limitations that slow down process development are:

Inflexible bioprocessing tools – many are manual, slow or lack automation and parallelisation. There is limited integration of rapid, real-time analytics to capture detailed, high-frequency data.
Developers may optimise processes based on attributes that do not significantly influence clinical outcomes, meaning the identified critical quality attributes may not be the ‘true’ CQAs. This can lead to suboptimal processes and missed opportunities for improvement.

To address the limitations of traditional bioprocessing tools, MFX developed a scalable bioreactor platform that supports high-throughput parallelisation and flexible control of process parameters. It integrates online and in-line analytics, such as live imaging of cells and measurement of metabolites, allows small-volume sampling for external analysis and is compatible with rapid at-line measurements. This system—designed as a “DoE machine”—enables systematic adjustment of inputs and measurement of outputs in small-volume bioreactors which are wholly representative of manufacturing scale, to streamline design of experiments and accelerate process development. In a Treg expansion study with AstraZeneca, MFX demonstrated how real-time analytics and control of agitation and feeding strategies enabled adaptive responses based on metabolic and phenotypic data, leading to more efficient and data-driven optimisation of the bioprocess.

“Once you know what you’re looking for, you can really start to zoom in on the analytics that are required to control that quality attribute—and use that to control your manufacturing. Control loops that drive manufacturing towards your product quality targets which ultimately results in the best outcomes for patients.”

Antoine Espinet, CEO of MFX

Another hurdle in implementing PAT is that, during early process development, developers often don’t know which parameters or CQAs are most predictive of clinical outcomes and as a result, these may change over time. Sharon from the CGT Catapult highlighted a shift in the industry toward building deeper process understanding and in-process control through more frequent—and ideally real-time—monitoring using at-line, online and in-line analytics.

The CGT Catapult’s Analytical Development group is tackling this challenge by creating a comprehensive in-process deep analytics database. Using tools like transcriptomics, metabolomics, proteomics and protein expression profiling, they aim to identify biomarkers that can predict batch quality and ultimately product efficacy.

Once predictive biomarkers are identified, the goal is to develop fit-for-purpose analytics systems capable of directly or indirectly (through surrogate metrics) measuring these markers—either online/in-line or rapidly at-line. The team can then pursue targeted assay development to create rapid, robust and scalable QC tools for batch release. This work aims to de-risk adoption of advanced analytics by generating tools and insights for industry-wide use.

“This is an approach that we have been working on for several years, establishing the fundamental capabilities, working with various therapy and tool-development companies. We are now moving into the next collaboration phase and the sector is really embracing this approach, due to the clear need. Once biomarkers are identified, they could move from characterisation tools into release criteria—helping to eventually predict whether a batch will perform well clinically.”

Sharon Barkatullah, Head of Analytical Development at Cell and Gene Therapy Catapult

2.1 Towards continuous closed-system processing

The monoclonal antibody (mAb) manufacturing industry, being more mature, serves as a strong precedent for the benefits of continuous processing—defined as an integrated, non-stop production method. Recent studies show that continuous processing can reduce costs by up to 35% at lower annual production scales.

The integration of machine learning and advanced analytics has enabled real-time monitoring and process control, enhancing consistency, efficiency and product quality. These advances not only reduce production costs but also improve access to mAb therapies. As continuous processing and analytics capabilities evolve, they offer a scalable pathway to meet rising global demand while maintaining economic viability.

For cell and gene therapies – characterised by higher variability and greater complexity – the potential benefits of continuous processing are even more significant. This approach could be well suited to gene therapies and allogeneic cell therapies, but application becomes more challenging for autologous cell therapies, which by their nature require small batch production. Realising this potential requires purpose-built, closed, sterile and automated systems, incorporating modular single-use technologies. Integrated online/in-line/at-line analytics fast enough to control manufacturing based on feedback, seamless data acquisition and Good Manufacturing Practice (GMP)-ready control systems are essential to support adaptive operation and drive the transition to continuous processing in CGT manufacturing.

Continuous mRNA LNP encapsulation

Repligen’s PAT solutions are designed to optimise performance across both Upstream and Downstream workflows. By using perfusion for example for lentivirus production as well as other envelope viruses enabling continuous upstream production and continuous clarified feed into the downstream process steps with integration of online metabolic analysis.

A compelling example of PAT-enabled continuous processing comes from a Repligen collaboration with a CDMO manufacturing mRNA encapsulated in lipid nanoparticles—a growing alternative to viral vectors due to its cost-efficiency. Repligen integrated the FlowVPX™ variable pathlength UV-Vis system directly into the production line to enable real-time quantification of mRNA concentration and in-process impurities. This online measurement supports continuous processing and reduces reliance on traditional offline testing. Crucially, FlowVPX™ also enables assessment of encapsulation efficiency at the end of the process. By embedding this analytic capability online, the CDMO can proactively manage and reduce the risk of batch failure at release.

Another compelling example is PAT-enabled continuous optimised upstream viral vector production comes with integration of Repligen’s MAVEN™ and MAVERICK™, bioprocessing analytics tools acquired from 908 Devices. MAVEN™ is a real-time glucose and lactate monitoring system, while MAVERICK™ is a Raman spectroscopy-based in-line bioprocess analyser and control instrument, therefore providing real-time insights into critical process parameters.

“If we want to reduce cost of this complex product without compromising safety there is no doubt that the field will move faster into continuous processing than monoclonal antibodies did.” – Rachel Legmann, Senior Director of Technology, Gene Therapy at Repligen

Advancing PAT integration

Progress toward continuous processing in CGT relies on seamless, non-destructive integration of Process Analytical Technologies. While existing sensors for pH, dissolved oxygen and metabolites are adapted from traditional bioprocessing, they often require large sample volumes, frequent calibration and complex modelling—making them poorly suited for small-scale, aseptic CGT workflows.

To address these barriers, modest redesigns and emerging innovations are improving integration.

Sterile connectors, pre-sterilised single-use probes and non-invasive sensors are enabling closed-system compatibility. For example, PreSens® offers single-use, non-invasive optical sensors for oxygen and pH that weld directly into cell culture bags. Similarly, single-use flow-through cells can be added to perfusion lines for real-time monitoring.
Repligen’s MAVEN™ focuses on real-time single use monitoring of glucose and lactate levels, crucial for cell culture processes. It integrates with bioreactors to provide data for process optimisation and control.
Raman spectroscopy is gaining traction as a non-invasive, in-line tool for tracking nutrients and metabolites like glucose, lactate and ammonium via chemometric models. However, adoption is still limited by calibration needs and data complexity. Solutions like Merck’s ProCellics™ simplify Raman monitoring through integrated software and technical support. Repligen’s MAVERICK™ utilises Raman spectroscopy to measure multiple parameters in real-time, enabling continuous monitoring and control of bioprocesses across various bioreactors and cell lines.

2.2 The gap in real-time analytics – cell characterisation analytics

Many CGT-specific attributes of identity, potency and function still lack suitable sensing solutions, particularly for online or in-line cell characterisation, which could yield major cost and efficiency gains. There are currently no tools capable of providing real-time, non-invasive analytics for key metrics such as total and viable cell counts, CD4/CD8 ratios, cell identity, vector titre, full/empty capsid ratio and impurities. Cell characterisation currently relies on complex, offline assays, such as flow cytometry, PCR/ddPCR and enzyme-linked immunosorbent assays (ELISA), which require manual sampling, are slow and labour-intensive and disrupt the manufacturing process. These methods are difficult to miniaturise or automate for in-line or real-time use.

“It’s about streamlining the whole development life-cycle and manufacturing workflow through data. Once you do that, you can shave days off production, reduce costs, and accelerate time to market – that’s why analytics will be a game changer.” – Antoine Espinet, CEO of MFX

‘PATable’ surrogate parameters

A promising strategy for real-time monitoring of cell attributes involves the use of PATable surrogate markers – measurable indicators that correlate with cell identity, function or quality. A strategy proposed by the CGT Catapult is to combine high-throughput, high-content screening methods such as flow cytometry, gene expression profiling, liquid chromatography-mass spectrometry (LC-MS) and sequencing with PAT-compatible technologies like Raman spectroscopy to enable the development of robust correlations between complex, offline biomarkers and simpler, real-time measurements using in-line, online or at-line sensors. CGT Catapult are using these outputs to develop digital twins and enable advanced process control strategies.

Another example is the use of cytokine expression as a surrogate for infectious titer, a critical metric in viral production that is traditionally assessed via slow, manual assays. A recent study showed that cytokine expression within hours of infection correlates strongly with infectious titer. Platforms like Bio-Techne’s Ella system, a rapid, automated ELISA tool, enable fast, at-line cytokine measurement to support timely process control based on early infectious titer insights.

Leveraging enabling technologies (contactless, label-free)

To further overcome limitations in online, in-line or rapid at-line cell characterisation, the field is increasingly turning to enabling technologies inherently suited for real-time integration.

Typically label-free, non-invasive, non-contact and rapid sensing modalities – such as imaging (including holographic imaging), electrical impedance, Raman spectroscopy, UV-Vis spectroscopy and NIR spectroscopy and laser force cytology – offer potential for in-line, online or rapid at-line deployment. With approaches like microfluidic device integration, flow cells and dynamic sampling platforms, their potential is enhanced.

Imaging and holographic imaging

AI-based methods for interpreting images including the combination of 2D imaging, microfluidics and computer vision, has enabled low-cost, real-time cell characterisation. Microfluidic devices allow precise, high-throughput manipulation of cells, while 2D imaging captures key morphological features. Computer vision enables deeper analysis, supporting rapid cell counting, identity classification and morphology assessment.

We explored this setup for in-process cell identification and sorting. Holographic imaging extends these capabilities, providing 3D, label-free cell maps based on light phase shifts. Ovizio’s iLine F PRO is an online holographic microscope that enables continuous, non-invasive monitoring of critical quality attributes in GMP settings and integrates with wave bags, stirred tanks and single-use bioreactors.

technician with low-cost, label-free cell sorter prototype

A low-cost, label-free cell sorter prototype, developed by Team Consulting

Optical spectroscopy techniques

Optical spectroscopy techniques are of growing interest in CGT analytics due to their label-free, non-contact, rapid and accurate measurement capabilities – making them well-suited for in-line or online deployment.

Raman spectroscopy is the most common example, enabling real-time monitoring of nutrients and metabolites. Another promising approach is variable pathlength UV-Vis spectroscopy, which dynamically adjusts the optical pathlength to directly measure concentration, eliminating the need for dilution. Repligen has demonstrated that its FlowVPX™ system – an in-line variable pathlength spectrophotometer – can automate tangential flow filtration (TFF) by using concentration as a feedback loop, replacing traditional balance-based monitoring. This improves process precision and reduces human error. The same platform is also being explored for in-line full/empty capsid analysis.

Laser force cytology by Lumacyte

Proprietary sensing technologies like LumaCyte’s Laser Force Cytology™ (LFC™), found in their Radiance® instrument, are also emerging as powerful tools for cell characterisation. LFC™ infers intrinsic biochemical and biophysical properties by measuring how individual cells respond to precisely controlled laser light and microfluidic flow. It uses optical and fluidic forces to capture parameters such as cell velocity, size, shape, optical force index, cellular deformability, that depend on cell morphology, refractive index, internal density, complexity and membrane properties. This enables the detection of subtle phenotypic changes, rapidly quantifying early indicators of cellular responses to viral infection, activation, transfection, transduction and differentiation — without the need for labels or reagents. The technology has been used in both descriptive and predictive analytics for vaccines, gene and cell therapies.

Electrical impedance sensing

Electrical impedance-based measurements offer rapid, label-free characterisation of single cells by assessing their electrical properties as they pass through a microfluidic chip. When an electric field is applied across embedded electrodes, each cell disturbs the field, enabling measurement of resistance and capacitance without the need for stains or labels. Sophisticated analysis of impedance signals can reveal subtle phenotypic or functional changes.

Cellix’s Inish Analyser, an impedance-based flow cytometer, provides cell counting, viability and transfection efficiency data. Cytomos is developing a dielectric spectroscopy-based platform for rapid at-line analytics. Agilent’s xCELLigence RTCA eSight combines impedance sensing with live-cell imaging to monitor cell behaviour in real time.

3. Release quality control

Accelerating release quality control (QC) is essential to reduce delays and ensure product consistency. Traditional QC methods – such as qPCR, flow cytometry, ELISA, sequencing and culture-based assays – are often manual, complex and slow, with testing timelines extending to weeks.

Rapid analytics

A promising strategy is shifting toward rapid analytics with short turnaround times, automated for ease of use and validated against established compendial methods. For example, sterility and mycoplasma testing are moving from days-long culture assays to automated molecular qPCR, rapid nanopore sequencing and sensitive optical microbial detection, significantly cutting time to results. These rapid methods can be adopted in regulated environments when validated as comparable to established compendial methods.

Furthermore, by identifying CQA biomarkers that correlate strongly with batch quality and eventually clinical outcomes, targeted rapid assays can be developed and validated. The Analytical Development group at CGT Catapult, led by Sharon, is actively working on combining insights from CQA biomarker studies with diagnostic industry experience to identify and adapt proven diagnostic technologies for cell and gene therapy in-process monitoring and release testing.

The diagnostics industry offers valuable lessons in developing rapid analytics – particularly from its move toward point-of-care testing in infectious disease. Technologies like the LumiraDx platform demonstrate how automated, closed, plug-and-play instruments can deliver lab-comparable accuracy with rapid turnaround times, high throughput and usability designed for non-specialist users. Adapting such engineering approaches to CGT could streamline release testing and reduce delays significantly.

LumiraDx Point of Care (PoC) instrument, developed by Team Consulting

Condensed or streamlined QC analytics

“We just need to look at QC as a whole for each product and see—can we start to streamline this and make it cheaper and less complex to meet the demands of scale-up? Condensing methodologies into formats like digital PCR or next generation sequencing NGS could reduce sample volume, cut costs, and speed up release times.” – Sharon Barkatullah, Head of Analytical Development at Cell and Gene Therapy Catapult

Another emerging approach to streamlining quality control is the use of condensed analytics. Leveraging single technologies to capture multidimensional data and enabling multiple release tests within one platform, this approach reduces sample volume, turnaround time and cost, while enhancing data richness and traceability.

For instance, next-generation sequencing (NGS) is being explored not only for identity testing but also to detect adventitious agents, vector integration sites, residual host DNA and replication-competent virus, consolidating several assays into one workflow.

Multiplex flow cytometry also shows promise for cellular therapy release testing by measuring multiple critical quality attributes in parallel. These attributes include identity, viability, transduction efficiency, activation and potency. To advance this approach, key steps involve: automating on-board sample prep and staining to cut labour, developing standardised panels and optimising workflows and data analysis to improve usability and consistency. Further validation of surrogate functional assays for high-throughput release testing will help realise its full potential.

Reflections

The development and manufacturing of cell and gene therapies present unprecedented challenges due to the complexity and variability of biological materials and processes. This article highlights the critical role that advanced analytics play across the entire CGT lifecycle, spanning input material characterisation to process development and release quality control. Upstream testing is particularly important to build robust commercial systems and fast-forward the implementation of necessary controls. Direct applications include the control and monitoring of particulate contaminants or patient cell filtering and count.

To accelerate and de-risk cell and gene therapy manufacturing, purpose-built analytical tools that are rapid, low-volume, automated and easily integrated into closed systems, are essential. These tools enable detailed measurement, real-time insights and predictive control, which are vital for managing variability and ensuring consistent CGT product quality.

Significant progress has been made in integrating PAT to drive process understanding and facilitate continuous manufacturing, and a step forward towards real time batch release. However, the lack of standardised testing and technological gaps remain – particularly in real-time, non-invasive cell characterisation methods suited for CGT’s unique demands. Advances in surrogate markers and enabling technologies show promise in overcoming these gaps.

Accelerating release quality control with rapid, automated and multiplexed testing is critical to reducing delays and ensuring timely patient access. There is scope for transferring existing technologies from the diagnostic sector to support biomarker discovery as well as facilitate point of processing testing.

Continuously advancing capabilities in AI are integral to the future of CGT manufacturing, by producing actionable data from multimodal or multidimensional measurements. This could eventually enable the building of predictive models to optimise therapy efficacy. Models do necessitate input from large data sets, which can be challenging to produce, especially for example, regarding rare diseases, hence the importance of building historical data libraries that are accessible with equity. Importantly, analytics are supporting the development of different manufacturing models, centralised or decentralised.

Ultimately, the continued convergence of technological innovation, collaborative data sharing and strategic analytics implementation will be key to unlocking the full potential of CGTs, improving manufacturing efficiency and delivering transformative therapies to patients faster and more reliably.

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