GMP analytical testing for biologics, in practice

A conversation with Amber Raines, Senior Director, Client Project Management, Coriolis Pharma 

Clients tend to picture GMP analytical testing as a button and a printout: load the sample, run the instrument, get a number. Ask someone who has spent years on both sides of that process, first running the lab and now managing the client relationship built on top of it, and the picture gets considerably more interesting. 

We sat down with Amber Raines, Senior Director and Client Project Manager in the Analytics track at Coriolis, to talk about what actually decides whether a project needs a fast-track analytical method or a custom one, what clients consistently get wrong about speed and standardization, and the case behind one of the more memorable particle investigations of her career. 

You ran a particle characterization core facility for years before moving into client project management. What made you make that shift, and how does it shape how you work with clients now? 

It came down to what I found most rewarding. I enjoyed leading scientific teams and had a real passion for particle characterization work, but I was increasingly drawn to the client side where the harder, more interesting problem usually sits: not running the assay itself, but figuring out what a client actually needs, sometimes before they’ve fully articulated it themselves, and translating complex science into a decision they could act on. 

The scientific background hasn’t stopped being useful. It lets me engage credibly with technical teams, understand the nuances of an analytical challenge, and explain that same challenge to a client in terms that make sense to them. Combining that with project leadership means I can sit between the science and the client in a way that leads to stronger partnerships and better outcomes, not just faster ones. 

What’s usually behind a client’s first call? 

It’s rarely just one thing. Sometimes it’s a regulatory deadline bearing down. Sometimes it’s a result nobody expected and can’t yet explain. Sometimes it’s a molecule so new, or so unusually engineered, that there isn’t an established way to characterize it yet. The common thread across all of those is that something is blocking a decision, and there’s almost always real time pressure sitting behind it. 

We’re solving an analytical problem, but the reason it matters is always the same: it’s helping move a therapy forward. Sometimes that means supporting a filing. Sometimes it means finding the source of a particle fast enough that a clinical program doesn’t stall. Even when we’re one small part of a product’s development, that contribution can help get a treatment to patients faster. 

Coriolis offers both fast-track, ready-to-run, standardized methods and fully tailored, custom method development. How do you and a client figure out which one a project needs? 

It starts with understanding where the client is in the development lifecycle and what decision they’re actually trying to make. Early-stage programs usually need speed above almost everything else, and that’s exactly what Coriolis’s fast-track methods (COR-Competences®) are built for: proven, already validated approaches that get an answer quickly without a setup cycle. Later-stage programs more often need something purpose-built instead, to address a specific product attribute, satisfy a particular regulatory expectation, or close a gap that a standard panel won’t reach. 

In a lot of cases the right answer is both: we’ll run a fast-track method to get an immediate read on where things stand, while developing a more tailored approach in parallel for whatever that standard method can’t cover.  Having led a fast-track group myself, I know the pull toward reaching for complexity, because it can look more rigorous on paper. It usually isn’t the point. It’s to give the client the right level of support for their stage of development, balancing scientific rigor, speed, cost, and the data they need to move forward with confidence. 

Can you describe a project where a “standard” method wasn’t enough, and you had to build or adapt something for the molecule in front of you? 

The projects that stick with me most involve what I’ve started calling “Frankenstein molecules”: highly engineered therapeutics that combine multiple modalities into one product. As the industry has moved past classic monoclonal antibodies, ADCs and AOCs, we’re seeing constructs that don’t fit neatly into any existing analytical platform. 

We typically start with established methods to see what we can learn quickly. It usually becomes clear pretty fast whether that’s enough. When it isn’t, we look at what makes the molecule unique and either optimize a method’s parameters or combine several orthogonal techniques into a characterization strategy that’s actually fit for purpose. Those projects push both the science and the technology, and they’re exactly where a customized approach earns its cost. 

What’s a common misconception clients have about how fast, or how standardized, analytical work can really be? 

That biologics analytical services is the same everywhere. Clients who’ve mainly worked with large CDMOs are used to an environment where testing supports manufacturing, so priorities are set by production schedules and batch release. What often surprises them is how agile a specialized CRDO can be, because analytical science is our core focus rather than a function sitting behind a manufacturing line. 

That said, not every scientific question can be answered overnight, especially once method development or a real investigation is involved. The key is setting expectations upfront: some questions have a fast, standardized path to an answer, others genuinely need deeper investigation, and our job is to help the client see that difference clearly and build the most efficient path through it. 

Is there a project that still sticks with you, where analytical data cracked a mystery nobody could explain? 

A client was intermittently failing USP <788> lot release testing due to elevated particle counts. The testing was being run at another GMP lab, batch release was at risk, and nobody understood why. We started with an independent investigation using orthogonal particle characterization, including Micro-Flow Imaging (MFI) alongside light obscuration. Flow imaging mattered here because it gives you particle morphology, not just a count, which gives you clues about where something came from. 

The strange part was that we couldn’t reproduce the failure. Every sample we tested passed, even using the same light obscuration method that had produced the out-of-spec results elsewhere. That told us the problem probably wasn’t the product itself, but something in how the samples were being handled. 

So, we didn’t stop at the data. We worked directly with the client and the analyst at the external lab and walked through the entire testing process step by step. That’s when we found it: a siliconized syringe was being used to withdraw and pool drug product from vials before analysis, and it was shedding fine silicone oil droplets that the instrument was counting as particles. Once that syringe was swapped for an appropriate pooling technique, the failures disappeared completely, and stayed gone. 

What I like about that case is that the answer didn’t come from running another assay. It came from combining analytical science with critical thinking and a willingness to keep digging even when our own data looked clean. That’s what ultimately helped the client identify a root cause that protected not only that batch release but potentially future programs as well. 

Is there a question a client has asked you that surprised you, or changed how you think about explaining your own work? 

One has stayed with me for years: a client essentially asked, “if we already know the conclusion we want, can you make the data fit?” It caught me off guard, because it showed how differently some organizations think about what analytical science is for. My job has always been to uncover the truth about a product, whether the result supports the hypothesis or challenges it. 

I explained that our role isn’t to produce the desired outcome, it’s to produce reliable, defensible data the client can actually make a decision on. Sometimes the data confirm what they hoped for. Sometimes they surface a risk that needs addressing. Either way, the data leads to the conclusion, not the other way around. Clients aren’t paying for a result, they’re relying on us for objective scientific judgment, and that’s what protects patients and holds up under regulatory scrutiny later. 

What do you think sets Coriolis’s analytical approach apart from a typical CRO or CDMO lab? 

Most labs run the assays a client asks for and report the numbers. Coriolis treats analytical characterization as a scientific discipline, which means digging into the why behind the data: root-causing instability, a degradation pathway, an aggregation mechanism, rather than just flagging that a spec was passed or failed. 

That shows up in how the teams work. They’re often early adopters, sometimes co-developers, of new analytical technology rather than just validating what’s already standard, and they build orthogonal method strategies specifically to catch what a single technique would miss. Coriolis’s nearly 200 peer-reviewed publications aren’t a marketing statistic to me. They reflect a culture where scientists are expected to generate real insight and publish it, which is rare in a contract lab where the commercial pressure usually runs the other way. It’s the difference between a lab that tells you your product passed or failed, and one that tells you why, and what to do next. 

Now that you sit on the project management side, what’s the biggest disconnect you see between what clients think analytical work involves and what it actually takes to get a result? 

Clients often picture analytical work as pushing a button: turn on the instrument, get a number, get a report. What they don’t see is everything that happens around that measurement to make it trustworthy. Before a sample is ever tested, there’s instrument scheduling, procuring the right reagents and reference standards, and sample preparation, which for biologics can be highly customized. Before anyone trusts the resulting data, there’s system suitability testing, confirming the instrument and method are genuinely performing as intended, not just switched on. 

After the run itself, there’s independent data review, frequently by a second qualified reviewer working separately, and then a full report constructed to survive scrutiny that might come years later, long after the original project has closed out. What looks like one data point to a client can represent a week of coordinated work across scheduling, operations and quality. That’s the piece I spend the most time translating now: helping clients see that timelines and pricing aren’t padding built in for our benefit, they’re precisely what makes the results defensible. 

Why choose Coriolis 

 

A lot of the fast-track analytics versus custom method development traces back to one structural fact: Coriolis doesn’t run a manufacturing line underneath the analytics. There is no fill schedule to keep busy, so the method a client gets is the one the molecule needs, not the one that happens to fit the production calendar.  

A lab with a manufacturing relationship to protect has a reason to stop looking once a sample passes the test. The syringe case Amber highlighted is what it looks like when a lab does not stop there.  

Across fast-track and fully tailored work, the data has to hold up, whether that’s to a client’s own development decision, a regulator, or a reviewer reading the file years later. If you have a molecule that doesn’t fit the standard menu, or a result you can’t explain, that’s usually where a conversation with Coriolis’ Analytical Services team is worth having.  

The fast-track methods behind that conversation, offered as the COR-Competences® platform of ready-to-run, GRP-compliant analytical methods, are covered in full, with turnaround options and pricing, on Coriolis’s Fast-Track Analytics page.