Are you optimizing your process – or your analytical noise? | CHO-based mAb process development

Analytical continuity in CHO-based mAb development

The analytical decisions made early in CHO-based monoclonal antibody (mAb) development can influence process understanding, manufacturing readiness, and product quality throughout the development lifecycle. Rather than adding analytical methods as new questions arise, building a connected analytical strategy or analytical continuity, from production through purification helps generate comparable data, support more informed process decisions, and create a stronger foundation for scalable manufacturing.

In this blog, you’ll learn three practical questions that can help strengthen analytical continuity throughout CHO-based mAb process development.

Are you optimizing your process – or your analytical noise?

Three questions every CHO-based mAb development team should ask

If there’s one thing I’ve learned from working with teams developing CHO-based monoclonal antibodies (mAbs), it’s this:

Analytical strategies rarely start out as workflow strategies.

More often, they grow one assay at a time, and validate as they require regulatory compliance.
A titer method is introduced to support upstream development. Later, another assay is added to answer a purification question. Then another method is introduced as the program matures or new characterization needs emerge.

None of those decisions are necessarily wrong.

The challenge is that they aren’t always connected. Without analytical continuity, small changes in methods or sample preparation can make it difficult to compare data across production, harvest, and purification.

Months later, when comparing Design of Experiments (DoE) results or evaluating process changes, you may discover you’re working with data generated using different analytical methods—or even different sample preparation approaches.

At that point, answering what should be a simple question – ‘Did the process actually improve?’ – becomes much harder.

Sometimes the process isn’t the problem – the data you’re trusting is.

When analytical methods change throughout development, teams risk optimizing analytical variability rather than true process performance. Building analytical continuity early isn’t about performing more tests—it’s about generating comparable data that supports confident process decisions as programs scale. One way to avoid these challenges is to ask the right analytical questions at each stage of process development.

One workflow. Three important questions.

Each stage of process development poses a different question.

During production, you’re asking whether culture conditions are improving antibody expression or quietly making changes to your molecule you didn’t anticipate. At harvest, you’re asking whether you’ve recovered the antibody you produced. During purification, you’re asking whether process-related impurities have been removed to the levels you need.

They’re different questions—but they’re all connected by one thing: The quality of the analytical data you’re using to answer them.

Here’s how I like to think about it. Each stage builds on the one before it, making consistency in analytical methods essential for confident process decisions.

Are you comparing your process – or your analytical methods?

Most CHO-based mAb process development decisions begin with one critical measurement: antibody titer. Whether you’re evaluating media composition, feed strategies, dissolved oxygen, pH, or other process variables, Design of Experiments (DoE) studies rely on accurate and reproducible antibody titer measurements. Yet one of the most common challenges isn’t measuring titer—it’s measuring it differently as development progresses.

Sometimes it’s a new analytical method.
Sometimes it’s a change in sample preparation.
Sometimes different teams adopt different workflows as the program evolves.

Individually, those changes may seem minor.

Together, they can make it much harder to distinguish genuine process improvements from analytical variability. Changes in sample preparation, input volumes, or analytical methods can introduce misleading trends, causing teams to optimize analytical noise rather than true process signals.

One analogy I often use is measuring with different rulers. If you switch rulers halfway through a project, it becomes difficult to know whether something actually changed—or whether you’re simply measuring it differently.

The same principle applies to antibody titer monitoring.

Maintaining a consistent analytical approach throughout upstream development helps support confidence that differences in your results reflect changes in the process, rather than changes in how the samples were analyzed. That consistency becomes especially valuable as DoE studies become more complex, and process decisions carry greater downstream impact.

Consistent titer monitoring throughout process development

Affinity chromatography provides a practical approach for reproducible antibody titer measurement throughout process development. Thermo Scientific POROS Protein A analytical columns support affinity-based antibody quantitation using common HPLC systems, helping laboratories generate consistent titer data across iterative DoE studies and supporting more meaningful comparisons throughout upstream development.

Does your harvest data tell the whole story?

Harvest often feels like the handoff between upstream and downstream operations. Analytically, it’s also an important checkpoint in CHO-based mAb process development because it reveals how efficiently your process recovered the antibody you produced.  

This is where you begin answering a critical question: Did we recover the antibody we expected?

Comparing post-harvest titer with upstream measurements can provide valuable insight into recovery while also helping identify potential aggregation, fragmentation, or other process-related changes. Just as importantly, harvest provides an opportunity to confirm that the trends observed during upstream development continue to hold true once the product leaves the bioreactor.

One thing I encourage customers to think about is data comparability.

It’s perfectly reasonable to adjust sample preparation for different matrices. Clarified harvest material isn’t the same as cell culture supernatant, and your preparation steps may need to reflect that. What becomes much more difficult is changing the analytical method itself. If upstream and harvest data aren’t directly comparable, it becomes harder to determine whether changes in antibody recovery reflect the process—or the analytical workflow used to measure it. Maintaining analytical continuity across these stages helps build confidence that the trends you’re seeing reflect the process itself rather than differences introduced by the analytical workflow.

Consistent titer monitoring throughout process development

Maintaining the same affinity chromatography workflow for production and harvest titer measurements helps generate comparable recovery data while supporting more reliable process optimization and scale-up decisions.

See how Thermo Scientific POROS Protein A affinity HPLC columns support consistent antibody titer monitoring from production through harvest.

Related resource: Application note – Antibody titer determination for upstream and downstream process monitoring using Protein A affinity HPLC

Are your impurity assays keeping pace with your process?

By the time purification begins, the analytical questions have changed.

You’re no longer focused primarily on how much antibody you’ve produced.

You’re asking how effectively you’ve removed process-related impurities while maintaining product quality. Residual host cell proteins, endotoxins, and residual host cell DNA (residual DNA) all play important roles in downstream analytical characterization. Of these, residual DNA testing can be particularly challenging because concentrations become increasingly low as purification progresses, requiring analytical methods with sufficient sensitivity to quantify them reliably.

I’ve found it’s helpful to think about residual DNA testing the same way you think about titer monitoring—not as a standalone assay, but as another critical component of a connected analytical strategy.

When analytical measurements build on one another throughout development, it becomes easier to understand not only where your process stands today, but how confidently you can move it toward technology transfer, validation, and manufacturing.

Sensitive residual DNA analysis during purification

Residual host cell DNA concentrations decrease as purification progresses, making analytical sensitivity increasingly important. The Applied Biosystems resDNASEQ Quantitative CHO DNA Kit provides a standardized PCR-based workflow for quantitative measurement of residual CHO DNA, supporting impurity monitoring throughout downstream process development.

Sensitive residual DNA quantitation is particularly important at later purification stages, when residual DNA may be present at very low levels. Using an analytical method with appropriate sensitivity helps teams monitor residual DNA clearance and generate the data needed to assess whether downstream processes are achieving established residual DNA limits and regulatory expectations.

Related resource: Article – Reframing residual DNA testing: what it is – and what it isn’t

Why analytical continuity builds confidence in process development

It’s easy to think about production, harvest, and purification as separate stages of CHO-based mAb process development. In reality, they’re connected by the analytical data that links them together. When analytical methods evolve independently, answering simple process questions can become surprisingly complicated. A connected analytical strategy—or analytical continuity—helps support data comparability across development, contributing to more reliable process optimization, technology transfer, and manufacturing decisions. Data becomes easier to compare. Process decisions become easier to defend. And as your program matures, you’re building an analytical foundation that can support the next stage of development.

Each development program is different, and there isn’t a single analytical workflow that’s right for every molecule. But one principle remains useful across development programs:

“The earlier you build analytical continuity into your workflow, the more confidence you’ll have in the decisions you make later.”

One final thought

If I could offer one piece of advice to teams early in CHO-based mAb process development, it would be this: Don’t wait until analytical questions become analytical problems.

The analytical choices you make early in development shape the quality of the data you’ll rely on throughout your program – from process optimization and technology transfer to validation and manufacturing.

Building a connected analytical strategy from the start won’t answer every question you’ll encounter—but it can help ensure you’re asking those questions using data you trust.

Continue the conversation

This article introduces the principles behind building a connected analytical strategy for CHO-based mAb process development.

If you’re taking a fresh look at your own workflow, the companion resource, Building scalable analytical control strategies for CHO-based mAb development, explores these topics in greater depth. You’ll find practical guidance on selecting analytical approaches across production, harvest, and purification, along with recommendations for consistent titer monitoring, sensitive residual DNA analysis, and analytical continuity with scalability and technology transfer in mind.

Explore the knowledge hub

Discover practical resources in the Pharmaceutical Analytics Knowledge Hub to support your analytical workflow from antibody titer to residual DNA testing.