Industry Insights with Bas Trietsch and STEMCELL Technologies

This episode of Industry Insights features Drs. Bas Trietsch from MIMETAS and Jenna Moccia from STEMCELL Technologies as they explore the use of stem cell-based models in drug discovery. They discuss how regulatory milestones underscore the growing acceptance of these models, prompting scientists to seek out clinically predictive tools such as organoids and organ-on-a-chip systems.

This interview has been edited for clarity and conciseness. Click here to watch the full interview.

LISTEN TO THE PODCAST:

Thank you for having me. Really eager to talk a little bit about the things that we are doing currently at MIMETAS.
We’re a biotech company in the Netherlands, and we offer organ-on-a-chip solutions to pharma. We work with pharma to help them kickstart and improve their drug discovery processes by offering best-in-class human disease models.

I’m one of the cofounders of MIMETAS, we’ve been around for about a decade, and I’ve always been pushing our capabilities, pushing the technology, and that’s microfluidic products as well as biological products, automation and screening capabilities, data sciences, basically everything we need to do to enable us to achieve our joint goals with our customers and partners faster.

Yeah, I think this really gets us to the heart of the matter very, very, very quickly, right? And that is that most of the easy diseases, we’ve got solutions for them now, we’ve got therapies for them now, but there’s still a host of afflictions out there that we simply cannot cure. That is, if you use the wrong model to test your new drugs, if you use the wrong systems to do your early research or your late state safety and efficacy assessments, you’re going to get the wrong answers. It’s much more like a maze. If at the start you take the wrong avenues down that maze, it’s very difficult to course-correct.

We’ve found treatments for diseases in mice already multiple times over. We’ve cured cancer in a vacuum already multiple times over. But, to actually address pathophysiological mechanisms in the full human system requires human-relevant models. This is where people come to us. If they’ve been struggling already for a long time to understand complex diseases better, to really have a way that allows pharma to modulate biological processes or to test different compounds in a way that actually tells you how this will affect the human body and the mechanisms at play in real, diseased patients.

That’s where they just need better models. And better models, in our mind, that means 3D systems, always human-based, multiple different cell types together in an architecture and in a context and in a cell-cell interaction environment that really recapitulates the physiological situation to the very best of our abilities. That’s often involving human stem cells, using organoids as a base material, but taking these organoids, putting them in the right context, vascularizing them, perfusing them not only with compounds but also with different cell types. We really try to take all of the pieces that make up this very complex interplay between different mechanisms that make up a full pathobiological process. We want to have them in our own hands, have them in an assay that you can modulate, that you can study, and actually do all of those things without sacrificing throughput.

Because that’s basically the other pillar of what we always want to have in our models. We want to have best-in-class biology with the right interaction between cell types, but at reasonable throughput.

Yeah, I think there is a place for different models, right? And I think there is a place in the world for immortalized cell lines that have a huge legacy, an immense body of literature already available, which gives extra value to any observations you do with your new compound in a model system like Caco-2’s, which has its value. It’s very well defined, it’s very well known, you have a great context. But it is also an inherently limiting type of cell to work with. It has its uses, but what it lacks is all of the interplay and the complexities between the different aspects of human biology that you were not aware of upfront are relevant for the models that you’re looking at.

To get as close as possible to an in vivo situation, you want to have multiple cell types that can interact in the way they can interact in the body. You want to have these cells as representative of real-life human biology as possible, because then you actually get the full picture. That’s where advanced in vitro biology, I think, can make a big difference versus more simple molecule-to-molecule assays, but also immortalized cell lines that lack certain functionalities, or maybe even more dangerously, have the wrong expression levels of different types of enzymes and proteins that can send you on a wild goose chase.

So the early adopters, the really innovative folks that were always looking for the best, newest, and most modern ways of approaching drug discovery, they were already doing this before the FDA Modernization Act came about.
But what this made happen is it really woke up, I think, also the other parts of pharma that maybe were biding their time a little bit more and were saying, “Well, we’ll see when this new fun stuff comes our way.” I think those are the people that got shook up a little bit more and really thought, “Okay, this is not a fad. This is something that we should really look into and really get on board with in our processes because this is the future.” And that’s what we’ve seen primarily.

It’s been helpful in basically making best-in-class biology the talk of most C-suites out there, and also it got even the little bit more conservative researchers out there thinking about, “Okay, this is something that’s coming our way. Let’s see how we can make that of value to our research questions.” And, yes, so we’ve seen that it’s a good conversation starter, and it makes everybody realize a lot of the possibilities that maybe were not forbidden before, but are now much more in front of them and perceived as a little bit more mainstream already.

I think there’s two sides to that which we usually see. One is we basically give a model for a process that was just completely untouchable before. If you want to indeed look at interplay, if you want to look at MASH and you accept that this is a complex interplay between all the different cell types that are there, where you want to have a vascularized liver tissue with hepatocytes, with stromal cells, with resident and circulating immune cells, have all of them interacting with each other together in 3D, there’s just no other way to look at that complete picture.

That’s something that’s just one example of assays that are just not possible any other way. Using this type of technology, you’re just for the first time able to even approach or attack a certain type of biological process. On the other side, it’s also really about taking an assay that’s already there, but basically making it more translatable, making it more sensitive, and making it more reliable.

If we’re doing IBD-type research where we look at not only the interplay between fibroblasts and the epithelial cells themselves and immune cells, but also how these get damaged, recover after an insult, but maybe maintain sensitivity to the new inflammatory cues.

So I think the most impactful gains are really having reliable data at scale, but it also being meaningful data. And in the end, that’s what will get you to the right answers to your research questions a bit faster.

Yeah, I think the collaboration with the pharma and with the regulators is absolutely crucial. I mean, in the end, that’s where all roads lead, right? That’s where we are, where all of the research needs to funnel towards to, in the end, really have a drug reach a patient. So having that to be fully aligned is something that is really, really important.

Because to be fair, indeed, there’s a lot out there, and I’m not jealous of a pharma out there that has to come in and needs to look around these hundreds of different offerings for which it’s pretty, sometimes it’s pretty difficult to distinguish between a new idea of a bright PhD student and an established technology that has been proven and that’s really directly deployable at scale.

If I have to pick probably the biggest challenge for the field of organ-on-a-chip at the moment, it’s confused customers who have so many options to pick from that they know where to start. We’ve been in this field for a long while. I think we’ve built our brand on being a reliable partner that delivers good data for really advanced questions.

I think the challenge for the whole field is going to be, how do we take all of these bright minds that are now all doing new different approaches, actually, indeed, on that front, bundle our strengths to be able to offer to pharma the right solution for their ask at that moment without basically having to do an evaluation of 200 different technologies on offer before they can even start.