Good morning, pioneers!

This is The Future of 3D Cell Culture. The #1 spot for 3D cell culture science and innovation.

The yearly pumpkin spice latte epidemic is sure to reach its climax any day now ☕.

Until then, here’s what we’ve got for you this month:

• Why “simpler” 3D models might be a better fit for drug discovery

• A unique consortium developing a 3D liver cell painting assay

• Novel research using organ-on-chip for in-vitro liver studies

Forwarded this email? Subscribe here.

Let’s get into it 🧬!

KEY OPINION LEADERS

Ultra-Miniaturized Primary Hepatocyte Spheroids for Early-Stage Drug Discovery  

This month, we sat down with Dr. Brinton Seashore-Ludlow. Dr. Seashore-Ludlow has a PhD in organic chemistry and synthetic methods from The Royal Institute of Technology (KTH) in Stockholm and did a postdoc at the Broad Institute of Harvard and MIT. 

Currently, she is a Senior Researcher at Karolinska Institutet and the Chemical Biology Consortium of Sweden focusing on precision cancer medicine. 

Dr. Seashore-Ludlow is an expert in 3D cell culture and is engaged in multiple research projects focused on implementing them at scale in drug development screening campaigns and in the clinic as precision diagnostic tools.  

3D Cell Models for Functional Precision Medicine & Drug Development

At what point did 3D models pop up on your radar? What research questions do you think they will help you answer? 

Dr. Seashore-Ludlow: We've been getting patient cells from ovarian cancer patients, and these can come with many different cell types. It's the cancer cells themselves, but then you get stromal cells, you get blood cells, you get lots of different cell types. 

And we really wanted to be able to assay these quickly to see which drugs they responded to. But we found that when we tried to do this in a 2D setting, that over just the course of a three-day assay, we would see extreme growth of the fibroblast, but not so much growth of the cancer cells. 

We thought that this was really biasing our measurements. There are a few companies working in the functional precision medicine area, like Exscientia. Then there are some groups that have shown that if you focus on cancer cell growth that you have a better predictive value, or better recapitulation of the in vivo context. So we thought, OK, we'll try this in 3D. 

And there we seem just to maintain growth of the cancer cells and minimize the growth of these other unwanted peripheral tumor microenvironments cells. So, that enabled us to streamline our assays. 

Outside of functional precision medicine in oncology, you're leading a Vinnova-backed project focused on scaling the use of 3D liver spheroids in preclinical drug development. Where are you in that project, and what are the main project goals? 

Dr. Seashore-Ludlow: The question behind that project was, ‘how do we take patient models that we know we can develop and better use them for drug discovery?’.  

Because we know there's a lot of heterogeneity between patients, and we know that understanding how effective a compound is across multiple patients is an aspect that we're missing prior to clinical studies. This idea stems from the functional precision medicine world. 

And we're trying to meet the drug discovery world. 

One of the key drivers for why 3D and patient-derived models aren’t used is that they're often deemed to be more heterogeneous and more expensive. The expense is driven by needing extremely expensive cultured media. 

It's also thought that you won’t have enough material to support the life cycle of a product that's run in drug discovery over multiple years. What would really help address both the challenges with cost and material availability is to miniaturize and get as much information out as possible. 

That's been the driving factor for the project. 

It would really help my own research as well, because if I can perform the same drug tests in 100 cells, I can test many more drugs than if I have to test in 3000 cells. 

Around that time, AstraZeneca was looking internally at what they should do to improve their own research. And Steve Rees came with this idea that he wanted to do 1000 measurements in 100 cells over 100 patients, which brought together all our expertise towards this project. 

Where is the project currently? 

Dr. Seashore-Ludlow: We've spent a lot of time looking at how to miniaturize. At first, we weren’t sure we were going to be focusing on 3D. But it really became clear that if we needed to work in precompetitive space, we needed to focus on certain types of toxicology or safety aspects. 

And it became clear that iPSC or hepatic models in 3D better recapitulate the in vivo biology they do in 2D. 2D hepatocytes lose a lot of their features that we think of as hepatocytes very quickly, within one to two days. 

That's where we are now. Working on miniaturizing these 3D models. 

We've set up a pipeline for qualifying these models. So, a range of different: 

• Assays 

• Compound treatments 

• Measurements  

that we think are required to say that a model is a good model. 

Once that's done, the plan is to conduct a screen on the miniaturized models. 

Challenges With Scaling 3D Culture in Drug Development

Depending on who you speak to in pharma, some people are very pro 3D models and others push back. What are the remaining questions that are causing some people to still be to doubt the value of 3D culture? 

Dr. Seashore-Ludlow: In general, there have not been many high-throughput assays adapted to the 3D setting. 

When you add primary cultures on top of the 3D setting, you're really making your life difficult. Meaning that you could potentially run into data that's so heterogeneous that you can't compare it between different runs. 

Showing that you're going to get the same readouts over multiple testing times is necessary to prove that your method is valuable. 

There's been a push in recent years to add high-content imaging to 3D methods. This has been challenging mostly because you get light diffraction, and with larger spheroids there's not much you can really do to avoid that while maintaining high throughput. 

It's a drawback of the method. And there as well is where miniaturization can help. To be able to use high-content screening and look all the way through a spheroid.  

I think it’s also taken time until we had microscopes that were compatible with this type of readout and well plates that were compatible in 3D settings. 

We need to have all the pieces in place to validate and show that these 3D methods are better. 

There is also a disconnect between academic standards and industry. I usually work with something that pharma would call low-throughput screening. 

But as an academic, I call it high-throughput screening. The numbers, they are just totally different. 

For me, it's OK to store 30 gigabytes of data per patient. But for their data sets, especially image-based, data storage becomes a huge challenge. I can understand why they might have some questions about whether it’s necessary.  

A final perspective in that area is that there are still a lot of questions about if we should do target-based screening or should we do phenotypic-based screening. I think the industry is still up in the air about that. 

There are a lot of reviews from around 2018 looking at what has been the most successful. I think one consensus is that new entities generally come from phenotypic-based screens. You're more likely to find a new mechanism of action or a new type of chemistry that way. 

When I talk to companies or academic groups who are looking to develop these models, 99% are all opting towards making very complex, heterogeneous models.  

I don't see many going for the smallest and simplest models that you are going for in the Vinnova project. Why is that?  

Dr. Seashore-Ludlow: The ultra miniaturizing is driven by both the cost of the assay and the amount of information you can get from the material. In the functional precision medicine area, where material comes from cancer patients, you want to get as much information as possible from the limited amount you have. 

What I see with the smaller models is that they're often accessible to many of the typical assay types that we're using. So, we can do imaging and typical readouts with a plate reader that are not always available to the larger models. 

With the larger models, we can’t do imaging through the depths to get an understanding of the single-cell architecture unless you go to things like light sheet microscopy, which is low throughput. 

There are some intersections between the miniaturized models we’re working on. If they do recapitulate an organ, then we could combine them with multiple organ types to assess toxicity together. I think this is an interesting question. How do we combine these miniaturized models together? 

I think this would answer some questions that I think that we're currently not answering today. 

I think a lot of the larger organoids that people are making have major drawbacks for screening purposes. Especially, if you want to go into functional precision medicine. A lot of them take time to establish.  

You're talking about three to five months to establish a line that you can culture continuously and use. And during that time, you might lose a lot of your primary cell phenotype that you had originally. There is also phenotypic drift that happens in the cultures. 

Read Dr. Seashore-Ludlow's full interview here.  

PROJECTS

Enabling Near-Patient Drug Development

The "Enabling Near-Patient Drug Discovery and Development" project, led by Dr. Seashore-Ludlow from Karolinska Institutet and funded by Vinnova, focuses on addressing the high failure rate in drug development due to the lack of accurate patient representation in early testing.

The project aims to develop advanced, miniaturized 3D disease models using cells derived directly from patients. These models will:

• Allow for more precise and affordable drug testing

• Address individual genetic and phenotypic variability

• Improve predictions of drug performance, and

• Reduce costly late-stage failures

A key component of this initiative is interdisciplinary collaboration among:

• Academia

• Healthcare

• Industry

The project integrates cutting-edge technologies such as microfluidics, AI, high-content imaging systems, and high-throughput screening with the goal to make these innovative models accessible, cost-effective, and expand their use across a wide range of diseases.

One of the major challenges the project addresses is the current "one-size-fits-all" approach to drug development.

By using patient-derived cells, the project aims to create personalized disease models that are more reflective of human biology, increasing the likelihood of clinical success.

RESEARCH

Exciting research that has advanced 3D cell culture recently:

Cool research in the field of 3D cell culture that you think would benefit the community? Reply to this email to discuss featuring it in our next issue.

EVENTS

Don’t miss out on these upcoming events in the 3D cell culture space:

That’s it for this month!

See you again in December.

P.S... Reply to this email and let us know:

1. What you thought of this month’s newsletter (1 - LOVED IT!, 2 - It was ok, 3 - Boring)

2. Which topics you’d like us to get into in next month’s issue