RNAnalytics’ Viktoria Enkmann Ponders Nanoparticle Characterization Advances at CPHI Frankfurt: Part One

Viktoria Enkmann, co-founder and CEO of RNAnalytics, spoke to Pharmaceutical Technology® Group as part of its coverage of CPHI Frankfurt 2025. Enkmann delivered a lightning talk at the conference, “Overcoming Bottlenecks in Drug Development: The Future of Nanoparticle Characterization,” on Tuesday, Oct. 28, 2025.

In the conference presentation, and in her discussion with PharmTech Group, Enkmann talked about the emergence of personalized medicine that is driving continuous innovation in nanoparticle quality control (QC), and how it has impacted method development.

“Think of a person with 40 years of relevant experience in a pharmaceutical lab,” Enkmann says in the interview. “They might know a lot about their analytes, they might know about certain quality attributes just from looking at the raw data, but they might struggle with a very complex software with user interfaces, with all the automation that's happening right now. And then on the other hand, we might have younger and less experienced people in a lab who are having an easy time implementing new software, but they don't have decades of experience. And we kind of want to bring these two together.”

Check back shortly for part two of Enkmann’s conversation with PharmTech Group. In that portion, Enkmann addresses the decision made earlier in 2025 by United States health officials to pivot away from supporting research for RNA-based vaccines.

Click here for all of our CPHI Frankfurt coverage.

Transcript

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

I'm Viktoria Enkmann. I'm the CEO and one of two co-founders of RNAnalytics, and our mission is to make the quality control of nanoparticles easier. I come from the interface between chemistry and biopharma. I've had several roles that were in analytical development, and then I pivoted to more customer-facing roles in pharma and biopharma, and I think in these years, I got great insights into what challenges people in their daily work.

The problem of current quality control for personalized medicines is that it is built for a drug development process where we screen a lot of candidates until we single out what works well, we go through the clinics, we go to the market with an approved drug, and then we do batches of batches that thousands or millions of people receive, where we have certain quality checks that we run. But this whole concept doesn't work for personalized medicine, and therefore we really need to rethink the way, how on the one hand during drug development, but also during manufacturing and commercializing personalized medicines, we check if they are actually safe and effective.

One example in the world of RNA therapeutics, encapsulated in lipid nanoparticles, is the particle size and the encapsulation efficiency. Those are both very important quality attributes, but right now we need to run different assays on different instruments. Both of them consume quite some sample. The sample is very precious, especially if you have tailored it specifically for one patient, so you don't have milliliters that you can use. Therefore, it's really important to have a chance to bring these medicines to the patient as fast as possible, to work with miniaturized testing and to have a small amount of analytical equipment needed to cover a broad range of quality attributes. And this is exactly where we come in by having a single platform on which we run the assays for particle size and encapsulation efficiency.

I think when you're doing analytical work for pharmaceuticals, you have two very different approaches. The one is, when you are in method development, you want to be able to tweak a lot of parameters to really make the optimal method, and then also in data processing, to set your boundaries, to have a lot of operator individualization potential to make the best method and then validate it. If you are in release testing and classical QC, you want quite the opposite of that. You want as few variables as possible and a mainly automated workflow that just ticks the boxes, and all the samples should be the same. All should adhere to the quality thresholds that you have designed before that during the validation runs, and then you can have an automated workflow that just checks the boxes.

But what we want to do is to bring the simplicity of a very automated workflow to the part where a lot of modalities are screened, but not every run looks the same, and we want to support the users by guiding them through the method development. So think of a person with 40 years of relevant experience in a pharmaceutical lab. They might know a lot about their analytes, they might know about certain quality attributes just from looking at the raw data, but they might struggle with a very complex software with user interfaces, with all the automation that's happening right now.

And then on the other hand, we might have younger and less experienced people in a lab who are having an easy time implementing new software, but they don't have decades of experience. And we kind of want to bring these two together by implementing AI [artificial intelligence] features in our software. We want to guide people through method development as if there was a very experienced person sitting next to them, guiding them through all the steps. But everything needs to be traceable so we know what was actually processed, what was actually modified as in a QC workflow, so we also meet the compliance regulations.

This, together, helps us define the quality parameters that we are tracking, the boundaries of these quality parameters. And then, of course, it also allows for a much simplified routine QC.