Manufacturing Challenges for Biosimilars: Complex Production Explained

When you take a generic pill for high blood pressure, you know exactly what’s inside - the same chemical formula, made the same way, every time. But when it comes to biosimilars, that simplicity vanishes. These aren’t copies in the traditional sense. They’re living products, grown in cells, shaped by temperature, pH, and nutrient levels. And getting them right? It’s like trying to recreate a Michelin-star dish without knowing the chef’s recipe, the oven settings, or even what ingredients were used.

Why Biosimilars Aren’t Like Generics

Generics are small molecules. They’re made in labs using chemical reactions that produce identical structures every time. If you mix the same chemicals under the same conditions, you get the same molecule. Simple. Predictable. Repeatable.

Biosimilars are different. They’re large, complex proteins - sometimes made up of thousands of atoms arranged in intricate 3D shapes. These proteins are produced inside living cells - usually Chinese hamster ovary (CHO) cells - that act like tiny biological factories. The cells don’t just assemble the protein; they modify it. They add sugar chains called glycans, fold the protein into the right shape, and attach other molecules that affect how it behaves in the body.

This is where the core rule kicks in: the process defines the product. Change the temperature by half a degree. Swap out a nutrient in the growth medium. Adjust the oxygen level in the bioreactor. And suddenly, the protein’s structure shifts. Not drastically - but enough to change how the body responds. That’s why biosimilars can’t be exact copies. They have to be highly similar - and proving that similarity is the biggest hurdle in manufacturing.

The Glycosylation Problem

One of the most stubborn challenges is glycosylation. These sugar attachments aren’t just decoration. They control how long the drug stays in your bloodstream, how strongly it binds to its target, and whether your immune system reacts to it. A single missing sugar molecule can make the drug clear from your body faster. Too many sugars? It might trigger an unwanted immune response.

The problem? Glycosylation is controlled by the cell’s internal machinery - and that machinery is sensitive. It reacts to everything: the type of cell line used, the pH of the culture, the feeding schedule, even the agitation speed in the bioreactor. The originator company spent years optimizing these conditions. The biosimilar maker has to reverse-engineer it - without access to any of those details.

To match the reference product, manufacturers run hundreds of small-scale experiments. They tweak one variable at a time, then use advanced mass spectrometry to map the exact glycan profile. It’s like trying to match a fingerprint using only a blurry photo. And if the glycosylation pattern drifts even slightly during scale-up? The whole batch might be rejected.

Scaling Up Without Breaking the Product

Getting a biosimilar to work in a 10-liter lab bioreactor is one thing. Getting it to work in a 2,000-liter commercial tank is another. At larger scales, mixing becomes uneven. Oxygen doesn’t distribute the same way. Heat builds up in spots. Cells in one corner of the tank experience different conditions than those in another.

These differences might seem tiny - a 0.1% change in dissolved oxygen, a 2-second delay in nutrient feed - but they can alter protein folding, increase unwanted variants, or reduce yield. That’s why scale-up isn’t just about bigger tanks. It’s about re-engineering the entire process to account for physics that don’t matter at small scale.

Many manufacturers struggle with this. Smaller companies often lack the capital to invest in multiple pilot-scale bioreactors for testing. Without that, they’re flying blind. One wrong move in scale-up can mean months of lost time and millions in wasted materials.

The Cold Chain Nightmare

Biosimilars don’t just need careful production - they need careful handling after. These proteins are fragile. If they get too warm, too cold, or are shaken too hard during transport, they can denature - losing their shape and function.

That means every step after production - from filling vials to shipping to hospital storage - must happen in a tightly controlled cold chain. Temperature sensors, insulated containers, real-time tracking: all non-negotiable. One broken refrigerated truck can wipe out an entire batch. And because biosimilars are often used for chronic conditions like rheumatoid arthritis or cancer, a supply disruption doesn’t just cost money - it can delay life-saving treatment.

Regulatory Hurdles and the Data Burden

Regulators don’t accept “it looks similar.” They demand proof. That means manufacturers must run exhaustive analytical tests - comparing over 100 critical quality attributes (CQAs) between their biosimilar and the originator product. These include protein sequence, charge variants, aggregation levels, glycosylation profiles, and biological activity.

Each test requires expensive equipment and highly trained scientists. Not every lab can do this. Many biosimilar developers partner with specialized CROs (contract research organizations) just to run the required analytics.

And the rules aren’t the same everywhere. The FDA, EMA, and Health Canada all have slightly different requirements. What passes in Europe might need more data in the U.S. That means manufacturers can’t just make one version and sell it globally. They often need multiple manufacturing lines or regulatory submissions - adding cost and complexity.

How Manufacturers Are Fighting Back

To cut through this complexity, smart manufacturers are turning to technology.

Single-use bioreactors are replacing stainless steel tanks. Why? No cleaning validation. No cross-contamination risk. Faster changeovers between products. They’ve become the new standard, especially for mid-sized producers.

Process analytical technology (PAT) lets companies monitor key parameters in real time - pH, dissolved oxygen, cell density - and adjust automatically. If a parameter drifts, the system corrects it before the product quality is affected.

Automation reduces human error. Closed, robotic systems handle filling, labeling, and packaging. Fewer people touching the product means fewer contamination risks and less variability.

And now, AI and machine learning are stepping in. Companies are feeding years of manufacturing data into algorithms that predict which process changes will cause quality issues before they happen. It’s like having a crystal ball for bioprocessing.

The Market Is Growing - But Only for Those Who Can Survive

The global biosimilars market is expected to hit $58 billion by 2030. That’s huge. But only a fraction of companies will make it.

Why? Because the cost to develop and manufacture a single biosimilar can exceed $100 million. The technical barriers are high. The regulatory path is long. And the margin for error is razor-thin.

Smaller players are getting squeezed out. Big pharma companies with decades of biologics experience, deep pockets, and global supply chains are dominating. The ones that survive are those who treat manufacturing not as a cost center, but as their core competitive advantage.

What’s Next?

The future belongs to continuous manufacturing - where the product flows through the system in real time, instead of in batches. This reduces variability, cuts production time, and improves consistency.

Complex biosimilars - like antibody-drug conjugates or bispecific antibodies - will be the next frontier. These aren’t just single proteins. They’re engineered hybrids. Each one adds another layer of complexity: extra purification steps, refolding challenges, unstable linkers.

But the biggest challenge won’t be technical. It’ll be staying ahead. The reference biologics they’re copying are aging. Newer ones are coming. And as patents expire, the race to launch will get fiercer. The companies that win won’t be the ones with the cheapest production. They’ll be the ones who understand that in biosimilars, every step matters.