Dr Krystyn Van Vliet on Tackling Scalability, Cost When Using 3D Platforms for MS Drug Discovery

Krystyn Van Vliet, PhD, of Cornell University, discusses the scalability of 3D platforms in multiple sclerosis (MS) drug discovery and compares their costs with those of other drug discovery methods.

Krystyn Van Vliet, PhD, vice president for research and innovation at Cornell University's Meinig School of Biomedical Engineering, discusses the scalability of 3D platforms in multiple sclerosis (MS) drug discovery and compares their costs with those of traditional drug discovery methods.

She expanded upon the topic on March 1 at the Americas Committee for Treatment and Research in MS (ACTRIMS) Forum 2024 during the session, "Barriers to Neural Repair"; her presentation was titled, "Engineering 3D Platforms to Overcome Barriers of Drug Discovery for MS."


In terms of scalability, what challenges and solutions exist in translating findings from small-scale 3D platforms to larger, more clinically relevant models for MS drug discovery?

When we and others started doing this, we were focused on getting the details right, but the throughput of the approach was very low. In fact, in my lab, Anna Jagielska and Daniela Espinosa-Hoyos were the 2 members of my lab who were developing this process. We were printing those artificial axons, those 3D constructs, one coverslip at a time, and each coverslip would take about a day. That's fine for academic lab work, but that's in no way appropriate for drug screening in any context that is going to help the patient.

That was over 10 years ago that we started that work. Today, we're able to print through work by those individuals, as well as additional students and research scientists in the group, multiple multi-well plates in an hour.

So, what we're able to do now in terms of scalability is take what works well in the lab and make it amenable to high throughput drug screening platforms that are used to using multi-well plates for lots of replica observations.

So, that's one key to that scalability. The other key is that we're now able to use human cells to ask those questions, and that can make the results more translatable and, therefore, more scalable patient by patient or, at least, cohorts of patients.

In terms of cost-effectiveness, how do 3D platforms compare to traditional methods in MS drug discovery? what should be considered when transitioning to these advanced models?

I think that the approaches that are commonly used on the market are consumable plastics, like a multi-well plate, to put cells in combination with drugs and observe the response.

There's other approaches, too, that are more biologically complex, using tissue slices or cell co-cultures. In all of those cases, they're so similar to other platforms that we use to ask other questions about cells outside of the context of MS that they're commodity cost, very, very low cost, cents per plate; you don't even think about them as being a key part of the drug discovery pipeline, they're just a consumable.

The 3D-printed artificial axon constructs, while they're still in multiple wells, there's a lot of development time and effort and extra hours of processing to actually make that plate. So, right now, the cost of those plates in a commercial context would be more expensive.

We've actually spun off a company completely separate from the university called Artificial Axon Labs. Artificial Axon Labs works on that commercialization to bring the cost of the plates down to be competitive and then be able to ask those questions in partnership with drug companies. So, that's one thing is the cost of using something that you think of as just a minor component of the drug discovery process with these 3D constructs becomes much more useful but also a bit more expensive.

The second is that you have to think about how you're going to visualize and quantify the results. We and others have worked a lot on that, the data visualization, the post-processing, the machine learning. It's not just enough to have the plate, you have to be able to analyze the results in ways that are predictive of drug response and, eventually, of human response.

There's a lot of innovation there, including using artificial intelligence, but also working closely with biologists and clinicians for what features are best to quantify.

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