Last time, we covered the biology of how raffinose works and the experimental journey that led to a 2.8-fold increase in high mannose glycans. Today, we're getting practical. I'm going to walk you through when raffinose works, when it doesn't, and the exact three-experiment protocol you can run in 8 weeks to validate it for your process.

Let's dive in.

This concept is discussed in greater detail in the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.

When Raffinose Works—and When It Doesn't

First, let's talk about scope. Because raffinose is not a universal solution, and I don't want you spending time and resources on something that won't work for your program.

✅ Use raffinose when you need to increase high mannose for biosimilar matching. Specifically, when your cell line's baseline high mannose is 1 to 3 percent and you need to get to 5 to 8 percent. That's the window where raffinose shines. You have room to move, and the effect size is large enough to hit your target.

✅ Use raffinose when you have analytical bandwidth to track Man5, Man6, Man7, and Man8 individually. If you're only measuring total high mannose, you're flying blind. You need to see the distribution because raffinose shifts the profile toward Man5. If your reference product is heavy in Man8 or Man9, raffinose won't get you there.

✅ Use raffinose when you're in process development—before you've locked your process for regulatory filing. Media optimization is expected at this stage. Regulators understand it. It's low risk.

Now, when should you not use raffinose?

❌ Don't use it if you need to decrease high mannose. If your baseline is already 10 or 12 percent and you need to bring it down, raffinose will make it worse. In that case, look at feed strategies or temperature shifts to drive glycan elaboration.

❌ Don't use it if your baseline high mannose is already above 10 percent. At that point, you have a cell line issue, not a media issue. Media tweaks won't fix a cell line that's fundamentally not processing glycans correctly. You need to go back and select a better clone.

❌ Don't use it if you need Man8 or Man9 specifically. Raffinose gives you predominantly Man5. If your reference product has a different high mannose distribution, you need a different tool. Kifunensine might be your answer, despite the cost and complexity.

❌ And don't use it if your titer is already marginal—below a few grams per liter. In that case, prioritize productivity first. Get your titer up, then worry about glycan matching. You can't afford to take a 20 percent titer hit when you're barely viable.

🔑 The key thing to understand is this: raffinose is tunable. The sweet spot for most processes is 15 to 50 millimolar. At concentrations above 65 millimolar—even with constant osmolality—you start seeing growth inhibition and titer hits. So you have a working range, and you need to find your optimal point within that range.

That's what the three-experiment protocol is designed to do.

Your Three-Experiment Implementation Plan

Here's the roadmap. Three experiments. Eight weeks total. Clear go/no-go decision points at each stage.

1️⃣ Experiment 1: Dose-response screen in 96-well plates.

Test four concentrations: 0, 10, 30, and 50 millimolar raffinose. Do this in your current basal medium. Maintain constant osmolality by adjusting sodium chloride. This is critical—if you don't control osmolality, you're back to confounding variables.

Track three things: viable cell density, titer, and glycan profile at harvest. You need all three data points to make an informed decision.

Go/no-go decision: If you see at least a 2-fold increase in high mannose at 30 millimolar with less than 20 percent titer loss, proceed to Experiment 2. If you don't hit that threshold, stop here. Raffinose won't solve your problem. You'll need to revisit your cell line or explore other glycan control strategies like temperature shifts.

2️⃣ Experiment 2: Spin tube confirmation.

Take your top two concentrations from Experiment 1 and run them in spin tubes. Spin tubes give you better metabolic profiling than 96-well plates. You can sample every two days and track glycan evolution over the entire culture duration.

This is where you see if the high mannose increase is transient or stable. Some media additives give you a Day 5 effect that disappears by Day 10. You need to know if raffinose holds through to harvest.

Optional but insightful: measure intracellular UDP-galactose and UDP-GlcNAc if you have the analytical capability. This tells you whether raffinose is affecting nucleotide sugar pools, which would explain part of the mechanism. But if you don't have this capability, don't let it block you. It's not required for the go/no-go decision.

Go/no-go decision: If the high mannose increase is consistent across the time course and titer recovers by day 10 to 12, proceed to Experiment 3. If you see a glycan reversion after day 7 or if titer stays suppressed, you have a problem. Either adjust your concentration downward or reconsider the approach.

3️⃣ Experiment 3: Scale-up in bench-top bioreactors.

This is where you validate robustness. Take your lead concentration and run it in controlled pH and dissolved oxygen conditions—the environment your manufacturing process will actually see.

And here's a tip: challenge your process with stressed conditions. Run one batch at pH 6.9 instead of 7.0. Run another at 35 percent dissolved oxygen instead of 40 percent. Spike glucose on day 12 to see if metabolic stress affects the glycan profile. You want to know your boundaries before you commit to manufacturing.

Go/no-go decision: If all three batches hit your high mannose target and you don't see unexpected issues—aggregation, charge variant shifts, titer collapse—you have a robust process. Document it. Lock it in. Move to your next process development milestone.

What I'd Do Differently Now

Let me share three mistakes we made during this work—and how you can avoid them.

❌ Mistake 1: Waited too long to involve analytical.

We optimized media formulations in 96-well plates for weeks before getting our first glycan data back. We were measuring titer and viability, but we were blind to the quality attribute we were trying to control.

The fix? Get analytical buy-in on Day 1. You need rapid turnaround—ideally 48 hours or less—from sample harvest to glycan data. If your analytical team can't support that, this project will drag on for months. Build that partnership early. Make it a priority.

❌ Mistake 2: Didn't map the design space early.

Remember earlier when I said we tested raffinose at fixed pH? We never explored pH-by-raffinose interactions. We never tested temperature-by-raffinose interactions. We simply didn't check whether the raffinose effect would hold across different pH or temperature conditions.

The fix? Once you have a lead concentration from Experiment 1, do a mini design-of-experiments: raffinose by pH by temperature. Understa nd your boundaries. Know where the effect is strong and where it's weak. That knowledge will save you when you hit an unexpected process deviation at scale.

❌ Mistake 3: Didn't check feed interference.

We optimized raffinose in basal medium and assumed the effect would carry over when we added our standard bolus feed on day 7. We didn't test whether feed components might interfere with the raffinose mechanism.

Given what we learned about osmolality—that it can completely mask or confound the raffinose effect—feed interference could be equally substantial. Feed compositions vary widely and often contain components like manganese, galactose, or other supplements that could promote or inhibit glycan processing.

The fix? Test raffinose in your actual feed schedule from the start, and test higher and lower feed additions. Feed composition matters. Don't optimize basal in isolation and assume it will carry over.

These mistakes cost time. They cost materials. They cost credibility with your manufacturing partners. You can avoid them by planning more carefully upfront.

The Bigger Lesson

Here's what this research taught me, and it goes beyond raffinose.

Glycosylation isn't downstream of the process. It's not something you fix at the end after you've optimized titer and viability. Glycosylation is designed into the media from Day 1.

Most scientists optimize for titer first. They pick a cell line. They tune the feeds. They hit 3 or 4 grams per liter. Then analytical comes back with glycan data, and it's out of spec. Now they're scrambling. Temperature shifts, feed adjustments, maybe a late-stage media tweak. It's reactive.

The teams that win? They co-optimize titer and glycosylation from the first design-of-experiments study. They set up their 96-deepwell screens with glycan profiling built in. They track high mannose, galactosylation, sialylation, and fucosylation alongside titer and viability. They see the trade-offs in real time. And they make informed decisions about where to land on the productivity-quality curve.

Raffinose is one lever. There are others—we'll explore them in future episodes. Manganese, galactose, feed timing, temperature profiles. But the principle holds: your media is your glycoengineering platform.

In short, media optimization can be a powerful way—faster, cheaper, and less risky than cell line reengineering—to optimize the quality attributes of your recombinant protein.

If you lock that mindset in early, you'll avoid the late-stage scrambles. You'll hit your regulatory milestones on time. And you'll save your team months of rework.

Closing

If you want more details, you can access the full peer-reviewed paper in the Journal of Biotechnology, 2017, volume 252, pages 32 to 42. DOI: 10.1016/j.jbiotec.2017.04.026.

If you found this episode valuable, I'd love your feedback. The best way to share it is by leaving a review. It helps other scientists discover these insights and lets me know what's resonating with you.

Thank you for taking this journey with me into media-based glycosylation control for biologics manufacturing.

Until then—smarten up your biotech.

Your Next Step

Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call

David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.

He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.  

Hear It From The Horse’s Mouth

Want to listen to the full interview? Go to Smart Biotech Scientist Podcast. 

Want to hear more? Do visit the podcast page and check out other episodes. 
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