Dr. Bas Trietsch

♪ [music] ♪ Hello, my name is Bas Trietsch. I'm the co-founder and CTO at Mimetas.

So, there's been some really exciting progress in the field of disease modeling over the last years, where we've really seen a trend of taking very simple 2D tissue models and replacing them with way more comprehensive models, where we include different cell types, where we build them into a complex architecture to really represent tissues as they are, as we can see them in the body, that also then thus recapitulates the complex disease processes that are at the source of these tricky-to-solve diseases. And my passion there, really, has been to try and take those models and build them in a way that can actually be leveraged to do true science. For example, in a recent screen that we did, we generated organoids from tumors that were excised from different patients.

We co-cultured them with endothelial cells, so, vascularized these tumor organoids. We had immune cells in there, we had stromal tissue in there, and cultured them in our plates, several hundreds of them, that allowed us to do a screen on dozens of donors, all treated with dozens of different therapeutic options, and we are currently really looking into that data to see how these different aspects of the tumor, of the context of the tumor, how they are affected by different treatments.

We are looking at all of those tissues from all different angles. We are doing different types of analysis. We're doing high-content imaging. But there are also a lot of examples where we are actually wanting to look at much more dynamic processes so, we've, for example, been applying the SPARC SITO, where we could have this very high time resolution measurements, seeing, okay, is the perfusability of these vessels, is it changing? Do we see that if we just measure in a general overall level, how media is flowing through that, how many cells are flowing through these tissues at a very high time resolution? Can we see changes going on in there?

At the same time, being able to combine that, at different points in time, having imaging data as well, and seeing how we can correlate that high spatial resolution with that high time resolution in a single experiment.

It's giving us a lot of avenues to fully investigate everything that's going on in these very complex tissues. And that helps us gain more understanding of the mechanisms at play, which gives us new hints and new pointers of where we can find the differences between patients, how we can understand why certain treatments work for some patients and they don't for others.

It is my firm belief that to cure these complex diseases, we will have to bring together the best biology we can muster, comprising all of the different aspects of these tissues, so, not just the tumor tissue, but the stromal tissue surrounding it, the immune system, to really try and recapitulate the entirety of the mechanisms at action in a tumor, and combine that with the advances in technology for imaging, for running different assays on these tissues, on data sciences, on using AI and smart technologies to make sense of this huge amount of data that these models can generate, and to actively work with pharma partners to take all of that knowledge and make it into new therapies.

So I think this is something that no one in this field can do by themselves. I think working together, combining all these different types of technologies, organoid technology, biology, microphysiological system, organomachip technology, the readout equipment, the data sciences, we need to bring all of these together to really push through to new therapies and to help patients that are in dire need of better treatments. ♪ [music] ♪