How advanced analytics are reshaping gene therapy development

Advanced gene and cell therapies hold transformative potential, but realizing that promise comes with unique and often underestimated challenges. Beyond the scientific complexity of designing effective modalities, the path from early development to commercial supply is shaped by decisions made long before large-scale manufacturing begins. Each choice — from platform adoption to process design and analytical strategy — can reverberate across the program, influencing efficiency, cost, and scalability.

In a sector where manufacturing intricacies define the pace and economics of success, understanding the interplay between process, analytics, and product knowledge is essential. In a recent conversation with Hena Divanovic, an Application Scientist at Refeyn, DDN explored how emerging analytical tools, adaptive development strategies, and a deeper integration of manufacturing insight can help developers navigate the high-stakes landscape of advanced therapies, mitigating risk while accelerating progress toward patient access.

Advanced gene therapies still carry very high entry and manufacturing costs. Which cost drivers are most underestimated early in development?

One of the most underestimated cost drivers in advanced gene therapy development is not a single technical challenge, but the downstream consequences of early strategic decisions made purely for speed. Platform processes and rapid phase-appropriate manufacturing can create early momentum, yet when they are applied without sufficient product and process understanding, they often shift risk, rather than reducing it. The real costs appear later through process variability, inefficient tech transfer, repeated comparability work, underperforming batches, and delayed clinical timelines.

Suboptimal outcomes rarely originate from one gap alone; they reflect fragmented chemistry, manufacturing, and controls (CMC) development, where analytics, manufacturability, and scalability evolve separately instead of as a unified strategy. Platform approaches remain powerful enablers, but they require scientific judgement, not blind adoption. In my view, sustainable acceleration comes from integrating product knowledge, process design, and analytical strategy early, enabling data-driven decisions that minimize re-development and protect long-term program value. In a field where manufacturing defines success, early development discipline is ultimately one of the strongest levers for cost control.

Many manufacturing technologies used today were inherited from earlier biologic modalities. Where do these legacy approaches fall short when applied to complex biomolecules such as viral vectors or gene-editing systems?

Unlike traditional biologics, these products carry inherent heterogeneity, instability, and a level of biological variability that does not scale in a linear or fully predictable way. Applying the same scale-up logic, simply increasing vessel size or equipment capacity, often fails to address the real bottlenecks, which are productivity limits, process sensitivity, and product fragility.

Analytics is another area where legacy thinking persists. While we often measure familiar parameters, methods remain insufficiently standardized and may not fully capture critical quality attributes unique to these modalities. Emerging analytical technologies exist, but industry adoption can be slow due to regulatory conservatism and operational complexity.

At the same time, viral vectors and gene-editing systems are far more susceptible to environmental conditions, meaning small process deviations can translate into significant yield loss or quality risk, something traditional biologics platforms were not designed to manage. Ultimately, the gap is not just technological but conceptual. Legacy processes assume control through scale and standardization, whereas these newer modalities require deeper process understanding, adaptive manufacturing strategies, and willingness to integrate new analytical and production technologies earlier. Without that shift, programs risk high capital investment without proportional gains in robustness, scalability, or cost efficiency.

Scaling gene therapy remains particularly challenging. How much of that challenge is fundamentally a manufacturing problem, and how much is actually an analytical data problem?

How well do we understand how the process shapes the product? With the right level of insight, individual unit operations can be leveraged strategically rather than simply scaled mechanically. Analytics is a critical asset, but only when it delivers reliable data that truly reflects what is happening during production. The challenge is not deciding whether scaling is a manufacturing or an analytical problem, it is recognizing where variability originates and applying the right risk-mitigation strategies.

For example, persistent variability in a downstream step should trigger structured root cause analysis to determine whether the driver is process conditions, equipment performance, raw material variability, or the assay itself. Strong controls and integrated development allow teams to resolve both aspects together, ensuring optimization is guided by real process behavior rather than uncertain signals.

In this field, scalability is less about bigger equipment and more about better decisions, and those only come when manufacturing insight and analytical confidence move together.

What does “fit-for-purpose analytics” really mean in the context of advanced therapies, and how does it differ from traditional quality control approaches?

Fit-for-purpose analytics in advanced therapies means that analytical strategies evolve as the product matures. Early in development, the goal is often to generate directional insight rather than fully validated datasets, which has significant implications for how assays are designed and implemented across the product lifecycle. Instead of applying legacy quality control (QC) frameworks too early, methods are built around the specific questions being asked, particularly for highly heterogeneous modalities where commercial standards or reference materials may not yet exist.

At early stages, analytics should prioritize understanding potency, product composition, and how process changes influence quality, rather than enforcing strict release-style testing. As product and process knowledge increases, those same methods can mature into more robust QC assays. This differs from traditional biologics, where assays are often standardized and validated early to confirm consistency against well-defined attributes. In advanced therapies, analytics begin more exploratory and orthogonal, enabling scientists to build true product understanding before locking processes into rigid specifications.

What is mass photometry, and what fundamentally distinguishes it from the analytical assays that advanced therapy developers have traditionally relied on?

Mass photometry is a label-free, single-particle analytical technology that measures the mass of individual biomolecules and viral particles in solution by using light scattering. Unlike many traditional assays, it requires minimal sample preparation, does not rely on labelling, and requires only 10µL of sample, in contrast to analytical ultracentrifugation (AUC), which can require up to 1mL of highly concentrated material, enabling rapid, high-resolution measurements within minutes.

What fundamentally distinguishes mass photometry from analytical approaches traditionally used in advanced therapies is the level of direct insight it provides. Conventional assays often rely on bulk measurements or indirect readouts, whereas mass photometry allows developers to observe heterogeneity at the single-particle level, for example, distinguishing empty, partially filled, full, or overfilled viral capsids. This high-resolution, rapid assessment enables faster process understanding and decision-making, making it particularly powerful for complex and heterogeneous modalities where traditional methods may lack resolution or speed.

Do you see mass photometry primarily as a replacement for existing assays, or as an enabler that allows companies to ask new questions about their products?

Advanced therapy development rarely progresses by simply replacing one assay with another. Instead, new technologies expand the scope of questions we can ask about complex and heterogeneous products. Mass photometry should therefore be seen primarily as an enabler rather than a substitute for existing analytics.

By providing high-resolution mass distribution data within minutes, it shifts how early development and process understanding evolve, allowing teams to explore aspects of product heterogeneity that were previously difficult to observe. For example, during upstream optimization, it can help developers understand how media composition or transfection conditions influence the formation of partially packaged capsids in near real time, moving sophisticated biophysical insight closer to routine bench-top decision-making rather than a late-stage quality check.

This early insight can also strengthen process robustness and comparability strategies, supporting smoother translation into good manufacturing practices (GMP). Its growing recognition within GMP frameworks, including acknowledgement by the United States Pharmacopeia, British Pharmacopoeia, and National Institute of Food and Drug Control in China, further supports its integration into regulated environments and control strategies.

Moreover, its rapid turnaround time, typically less than five minutes per sample, and minimal sample requirements create opportunities for deployment across GMP workflows, potentially accelerating release testing and enabling faster, data-driven decision-making. Compared to many conventional techniques, mass photometry offers significantly faster analysis without compromising accuracy, demonstrating strong agreement with established orthogonal methods such as AUC and charge detection mass spectrometry, which has contributed to its rapid adoption across industry and increasing interest from regulatory agencies.

In that sense, mass photometry doesn’t replace the analytical toolbox, it expands it, enabling developers to ask questions they wouldn't before.

It’s often said that better analytics can ultimately help bring down the cost of advanced therapies themselves. How direct is the link between analytical innovation and patient access?

The link between analytical innovation and patient access is more direct than it sometimes appears, because analytics fundamentally shape how quickly and efficiently a therapy can be developed, scaled, and transferred into GMP manufacturing. Imagine a viral vector program that has progressed for several years using only legacy assays; each batch is costly, tech transfer is slow, and traditional methods require extensive development time while still struggling to resolve key critical quality attributes. Those delays, additional experiments, and manufacturing inefficiencies ultimately accumulate into the final cost of the therapy and extend timelines to the clinic.

Now consider a scenario where a new analytical approach provides rapid, high-resolution insight into product composition or process behavior early on. Optimization becomes more targeted, tech transfer smoother, and fewer resources are spent troubleshooting late-stage issues. Faster, more reliable data doesn’t just improve science; it accelerates robust process design, reduces development risk, and shortens the path to both clinical and commercial supply. In that sense, analytical innovation isn’t an abstract improvement, it directly influences the cost of goods, development timelines, and ultimately how quickly patients gain access to advanced therapies.