CU Anschutz Harnesses Technology and Innovation to Speed Drug Discovery

Chris Casey:

Today we welcome Dr. Dan LaBarbera, who is a professor of pharmaceutical sciences at the Skaggs School of Pharmacy and Pharmaceutical Sciences, and the founding director of the CU Anschutz Center for Drug Discovery. Dr. LaBarbera runs an independent research program focused on cancer drug discovery and development that has been continually funded by the NIH, the DOD, the state of Colorado and others. He also directs the drug discovery and development shared resource, also known as the D3SR, which is the state-of-the-art Drug Discovery and Development Research facility. The D3SR is part of the Center for Drug Discovery established through a grant from the ASLAM Foundation and through institutional support. The D3SR is also supported in part by the University of Colorado Cancer Center. Hello, my name is Chris Casey. I'm the director of digital storytelling in the Office of Communications here at CU Anschutz Medical Campus. It's great to have you with us here today, Dan. Welcome.

Dan LaBarbera:

Thank you. I'm very happy to be here.

Tom Flaig:

I'm Tom Flaig, I'm the vice chancellor of research here in the CU Anschutz campus. And Dan, I tell you, we've been looking forward to this conversation, I think it's a really exciting area. Should be, I think, a lot of interest in this. So before we dive into this, maybe you could just talk to us a little bit about the big pictures, pull up, talk about drug discovery. What's the landscape for that now in academia? Maybe touch a little bit on industry, and how do your efforts fit into that?

Dan LaBarbera:

Yeah, that's a great question. So what I would say is over the past 20 years, academic drug discovery has made a significant advancement. And part of this is due to technological advances. Academia has always played an impactful role in innovation, and this is primarily driven by academic freedom of research. So on one hand, you have a high level of innovation through academia, and also because of its close ties with hospitals and clinics. On the other hand, you have industry.

Now, industry also has innovation, and they have significant resources. But these are very much focused on drug product development, and it's a very expensive process. And as a result, it's resistant to high-risk innovation. And so what that does is it creates a gap between translating academic innovation to the commercialization, and as a result, limits translating innovation to the clinic. And so really, the Center for Drug Discovery, a main goal of it is to provide infrastructure and know-how to bridge the so-called valley of death, so where most academic innovation goes to die.

Tom Flaig:

Do you want to define the valley of death? It comes up in conversation, doesn't it?

Dan LaBarbera:

Yeah. So, the valley of death is that stage of, I would say, technological development where it requires significant resources to bridge to the next level, which is clinical translation. And so typically, it requires significant investment by angel investors, capital investment, or large pharmaceutical companies. And as a result of that, there's a high bar for the development of a drug product to a certain stage. And if you don't meet that bar, oftentimes the technology is lost into the valley of death. So this is right at the stage where I would say a drug is in between the validation stage of the lead drug, and the stage where it's going to be interrogated at the pre-clinical regulatory stage, what we call investigational new drug (IND) enabling studies.

Tom Flaig:

My sense is that with the technological changes that academia has the potential to really have even a bigger role in some of this high-risk innovative research. And I agree, I've worked in drug development in oncology, I've seen a lot of clinical trial involvement. But you're right, there's a different availability in the academic world to say, "I've got an idea, I can pursue it and get that first read to see if there's something there and go with it." I think that's what you're saying there, there's a role for both academia, industry, and then working together to get to the next level. And I think your center plays a huge role in helping those academic ideas move forward.

Dan LaBarbera:

Yeah, I would agree with that. And to be clear, we're not trying to compete with big pharma or biotech companies. No, really what the center's goal is is to provide infrastructure, know-how, and the ability to more rapidly translate academic innovation to a stage that's more attractive to the pharmaceutical industry and/or capital investors. And the reason for that is, if we can bring a therapeutic, whether it's a small molecule drug or a biologic, to the stage where it is considered de-risked for investors or pharmaceutical companies, then it becomes more attractive for them to get behind the later stage development.

Chris Casey:

And Dan, you've devoted much of your research toward cancer drug development. Could you give an example of where you went through the process, and maybe how you partnered with resources here on campus to help expedite the development of the drug, move it through the valley of death as you've described? Could you just describe how there are resources you've tapped into here that have given you a little bit of a boost in the whole lengthy process of a drug?

Dan LaBarbera:

Sure. So I think several years back I was, I think very fortunate, I received a competitive grant through the SPARK | REACH Program through CU Innovations. And this is a really great program. It provides seed funding initially, focused on developing a drug or a diagnostic product. But I think more than that, what it provides is entrepreneurial training in what it takes to move an intellectual property or innovation from academia to the commercialization phase, and then beyond that towards clinical translation. So it’s very, quite comprehensive training, in terms of meeting with people from industry, getting workshops on different phases of the drug development process. So I think it was a very valuable experience. And what it taught me was it gave me the confidence that I think if an academic has an entrepreneurial spirit, they shouldn't be afraid to move forward into that stage of a commercial startup company.

Chris Casey:

So you're a big proponent of academic entrepreneurship, and your Center for Drug Discovery is right in the center of that. Could you talk about some recent advances at the Center for Drug Discovery, and how that's enhancing the whole academic entrepreneurship ecosystem?

Dan LaBarbera:

Absolutely. So first of all, the Center for Drug Discovery houses the Drug Discovery and Development shared resource. And what we're doing is we're building four major core facilities underneath the D3SR umbrella. The first being high-throughput, high-content screen robotic discovery core. The other is a medicinal chemistry core. Then we have in-vitro pharmacology and in-vivo pharmacology. So for the regular person, what does that mean? Well, high-throughput sciences is really driven by robotic automation. So what we're able to do is we can harness academic innovation. Most academic researchers on campus are dealing with really innovative complex models that are low-throughput.

So the first stage is we would meet with investigators. We then take their low-throughput assay, we miniaturize it into a format suitable for robotic high-throughput screening. Typically, this would require a 384 well plate. And then what we do is we validate that assay, and we can screen it against tens of thousands or hundreds of thousands of potential lead drug compounds or biologics. And really what you want that initial phase is you want this assay to replicate some aspect of the human disease that you're interested in targeting. And then by doing so, we can validate potential active compounds, which we call hits. And the next phase is to validate those hits as what we call lead drug therapeutics.

Tom Flaig:

Yeah, I've seen the robot in action. It's pretty wild. And what struck me by watching it in action is how quickly it can do a large number of things in a very specific and precise way. And I suppose that's the advantage, right?

Dan LaBarbera:

Absolutely it's the advantage. And I'm really proud of the fact that we have a state-of-the-art automation platform that is really unique to CU Anschutz. And with regard to the high-throughput discovery sciences core, we just obtained a state-of-the-art 3D cell and tissue printer. And what's unique about this, people are pretty familiar with 3D printers, in terms of printing plastic objects or even metal objects. But this allows us to print different aspects of more complex organotypic models. For example, an organoid model which mimics some aspect of organ function, or more importantly, we can mimic aspects of human disease.

But with this instrument, what's unique about it is we're working with two companies, Revvity, formerly known as PerkinElmer, and also CELLINK. So CELLINK is the company that has built the BIO CELLX, this 3D printer that we have. But what's unique about us is we have one of the first ones that's compatible with robotic high-throughput screening. So we're working with both of these companies to bring it onto our automation platform, and that's going to put the Center for Drug Discovery here at CU Anschutz on the cutting edge of high-throughput drug discovery.

Tom Flaig:

Can you give an example of how you might use that printing capacity then in a real-world example?

Dan LaBarbera:

So for example, a patient in the University of Colorado Cancer Clinic may present with colon cancer and the cancer is biopsied, or the tumor is resected. If we can get a piece of that tumor tissue and culture it ex-vivo, we can then build what are known as patient-derived organoids, and we can print those really precisely on the bottom of these very small high-throughput screening plates.

And not only can we print single organoids well, but we can use very complex combinations of the patient tumor tissue, aspects of the tumor microenvironment, also other microenvironment cells such as immune cells. And you could use this model to test really innovative therapies being developed on campus. For example, through the Gates Center, or the Gates Institute, they're working with CAR T-cell therapies. And using our image-based analysis, we can image CAR T-cells, and how they interact with tumors to kill them. That's just an example.

Tom Flaig:

That's a great example. And I think a lot of the traditional cell-based models we've used have been around for a very long time, they're used over and over again. This is an example of using a much more unique, almost a personalized approach to a specific modeling.

Dan LaBarbera:

Absolutely. But I do want to say, beyond high-throughput discovery sciences, the other cores that we've built or that we're building are critical for de-risking academic lead drugs. And that is the medicinal chemistry aspect. Discovering a lead compound is an important step, but it's very unlikely that that a compound's going to be ready for humans right from the discovery phase. So we have to do quite a bit of optimization of the drug pharmacophore, as we call it, the parts of the molecule or the biologic that interact with human disease targets. So medicinal chemistry is absolutely critical.

On top of that, our pharmacology is really focused on improving the drug-like properties, what makes the properties of a drug work better in animals first and then in humans. And so we were really focused on pharmacokinetics, but also looking at aspects of drug metabolism and drug formulation. Really drug formulation, and for those who are not really familiar with the term formulation, an example is a capsule or a pill. At our stage, we're typically utilizing liquid formulations, but excipients and drug formulation play a critical role in improving the drug's oral bioavailability.

Tom Flaig:

So this gets into the nuts and bolts of it, right? The pharmacokinetics, the distribution, how quickly it's metabolized, how it's metabolized, and it's a really important concept. So you might have a target, might have a drug that hits that target, but you have to optimize that drug so that you can actually translate it to clinic. And I think that's some of the capacity you're talking about.

Dan LaBarbera:

Yes, and I'll give you an example. So Chris had asked me about my experience in the entrepreneurial aspect in developing lead drugs and translating those through the startup company. And my company that we started in 2022 is Onconaut Therapeutics Incorporated, and we're trying to develop small molecule drugs, first-in-class drugs, targeting a contemporary oncogene known as CHD1L. This is a unique target, it's a chromatin-remodeling enzyme that functions really at the interface of promoting tumor progression and tumor cell survival. So what we found with our lead drugs during this process was that we could improve the in-vivo pharmacokinetics just by altering a few functional groups on the drug molecule. So for example, our first lead compound had a three-hour half-life in the blood plasma of mice. And just by putting a simple new functional group onto one of the positions of the drug, we improved the half-life from three hours to eight hours.

Tom Flaig:

So that could mean in terms of dosing a drug, you could give it once or twice a day versus four or five times a day or something, right?

Dan LaBarbera:

Absolutely. So a three-hour half-life would require you to dose twice a day.

Tom Flaig:

Twice a day.

Dan LaBarbera:

Yeah, at a minimum.

Tom Flaig:

Yeah. Versus a once-a-day dosing. And for those that have to take occasional things, that's a lot easier to do than multiple times a day.

Chris Casey:

Given the complexities of getting a drug commercialized, translated to the clinic, is there any rough approximation of how long that process usually takes, Dan, in terms of just maybe a therapeutic for cancer? And how maybe with the emerging technologies on the horizon, including the 3D cell tissue printer and others, is there any idea of how much that process could be compressed or accelerated? I know that's asking probably very general, but is there a standard length of time currently it takes? Could it be years to get a drug to clinical translation?

Dan LaBarbera:

At a minimum, if you're at the lead drug stage where you show good pharmacokinetic profile in animals and a reasonable initial toxicology profile and efficacy, it would take about a two-year process. And that includes the pre-clinical IND enabling studies, which are required by the FDA, but also the drug manufacturing process. But stepping back a little bit, I would say at the turn of the century, around the year 2000, a typical timeline for a drug to be developed was about 20 years. And it's an expensive process from the discovery phase all the way to FDA approval. It could be anywhere between $800 million to one and a half billion dollars.

Chris Casey:

Wow.

Dan LaBarbera:

And typically about 20 years. But that ceiling was shattered with the development of Imatinib. That was one of the first targeted kinase inhibitor therapies. And that process took, it took about 10 years to develop Imatinib once the molecular target, which was known as BCR-ABL, was discovered. And so that really changed the landscape of the time it would take to develop a drug. And so I think we're currently in a stage where an academic drug could be developed at the pre-clinical level within five or six years. And then the pre-clinical IND enabling studies at another two years. So I think anywhere between six and eight years before first in human trials.

Tom Flaig:

Interesting. I wonder if you want to shift over and talk a little bit about artificial intelligence and some of the things that are going on there.

Dan LaBarbera:

Sure.

Tom Flaig:

I hear a lot of excitement, interest about artificial intelligence, AI, machine learning, quantum. And then I also hear a lot of people say, "Well, what does that mean exactly, and how's it going to translate?" I would say here in Colorado, there's a lot of excitement around our capacities statewide in quantum research, certainly our many colleagues at CU Boulder. But also on the commercial side, there's a lot of industry here developing quantum. And there's a lot of excitement about the recent federal announcement that Colorado has been named a federal tech hub, one of a select few, nationally, and specifically the Elevate Quantum Colorado. So Dan, do you want to talk a bit about AI and how it influences you? And maybe start by talking about this Elevate Quantum Colorado effort, any involvement you may have in that?

Dan LaBarbera:

So first of all, I want to commend the researchers at Boulder for leading this effort. It's really a great thing for Colorado to be designated a quantum computing hub. I think quantum computing and AI are really going to impact drug discovery and development, and potentially revolutionize how drugs are discovered. And at the Center for Drug Discovery, we're very interested to be part of this. In the next five and 10 years, there's going to be huge advances. Because really right now, I think we're still at the very early stages of quantum computing and even AI as applied to drug discovery. And so really what quantum computing is going to do is increase the throughput of the computing power, and that's through hardware.

One of the challenges is going to be developing the software to be compatible with the quantum computing hardware, and then combining that with specific software related to the drug discovery process. Once that's in place, it can then be compatible with a lot more artificial intelligence algorithms. So currently at the Center for Drug Discovery, we're working with computer scientists at CU Denver and CU Boulder to develop AI as an infrastructure for the Center for Drug Discovery. So we're developing projects to utilize AI applied to structure-based drug design, using crystal structures of molecular targets, and then using AI to predict drug pharmacophores that would likely be effective.

But we're also combining that with actual biological studies to validate the AI algorithms. So far, we're at the beginning stages, but we're already making great advances in terms of developing the AI algorithms. And this has been a very well-supported endeavor on our campus, which I think is fabulous. The chancellor is very supportive of AI. We have the new Center for Health AI led by Casey Greene, and also the new Department of Biomedical Informatics. So all of these together, along with CU Innovations, who's been very supportive of AI technology, have really provided a landscape for innovation in AI here at Anschutz.

Chris Casey:

One of the things with quantum, I've read a bit about it and it always kind of makes my head spin when I do read about it... But they talk about quantum, there's different parts of it, quantum computing, quantum sensing. The quantum sensing side, or maybe it's the quantum imaging side, either of those perhaps gives, from a drug development and discovery side, will give amazing new insights into cells, perhaps, allow scientists to quantify or see things that they weren't able to see inside a cell before. Am I on the right track there, like layers or textures of cells?

Dan LaBarbera:

Yes, absolutely. So one area that we utilize in high-throughput drug discovery is known as high-throughput imaging. So we have really sophisticated instrumentation that allows us to image cell models. They can be simple cell models cultured in monolayer, or really complex 3D organotypic culture models. So we can image both in 2D and three dimensions. And really, the technology stems from... For example, facial-recognition technology, the kind of technology that's utilized in imaging human faces and being able to quickly use computing power to determine different features of the human face.

Very similarly, we can do that with cells, and more complex cell models. So for example, by imaging and using computing power through quantum computing and AI, we can advance our ability to determine effects of drugs on disease state cellular morphology. The kind of effects that the human eye can't even comprehend, where we could measure thousands of different features of a cell that the human eye just can't comprehend. And we can do that very rapidly. That's an area known as machine learning technology.

Chris Casey:

And how far off would you say is it, Dan, from this being applied in a practical way, say within the CDD, the quantum computing and quantum imaging?

Dan LaBarbera:

Well, I think quantum imaging, as I said, one of the bottlenecks will be developing the software to be compatible not only with the current instrumentation, but future improvements of the instrumentation. So I think it's going to require a bit of collaboration with computer science experts developing the hardware and the software, along with industry people, such as companies developing this instrumentation. So we're not quite there, but what's really exciting is we're already beginning to play with these technologies and beginning to investigate them and understand them. And so I'm really excited about the next couple of years working with researchers on our campus, CU Boulder and throughout Colorado, to really develop this quantum computing, but also AI-driven drug discovery to advance as much as we can the drug discovery and development process. I don't know exactly how long it's going to take to really become an advanced technology, but I think we're already moving forward towards it, which is really exciting.

Chris Casey:

I'm curious in your practice, Tom, this must be very exciting, these types of developments, in terms of speeding possible bench-to-bedside treatments for cancer patients.

Tom Flaig:

The field of the AI machine learning and how quantum fits in it, there's so many broad, immensely powerful ideas behind that. It's oftentimes hard to even imagine, bring it down to what it could actually do, even going beyond drug discovery. But tying into that, you can think of images. So as an oncologist, we rely on histologic, pathologic diagnoses. How can machine learning and AI change those sort of things? We look at scans, radiographic scans, how can you scan different images to have a different layer of artificial intelligence, machine learning, that analyzes that and finds things that the human eye might miss or augments, or has a human eye go back and look because they found something different? You could imagine some of those same techniques of image recognition. And Dan, we visited about this a little bit. Even looking at cell culture-based things, or some of your 2D or 3D models and so forth, there's so much you could do with this. I give you a couple examples in the clinic, but again, in drug discovery, I can imagine all different sorts of ways that this could be used to analyze drugs.

Dan LaBarbera:

It can be used to analyze cell-based models, perhaps structural biology models, like using cryo-EM to analyze the images taken from that, all sorts of applications.

Tom Flaig:

And I think seeing Colorado in this position, with this federal hub designation, as Dan's pointed out, with the great work being done with our colleagues across the CU system in Boulder... And then figuring out what's the healthcare angle, what's the biomedical angle to that, utilizing that, building on that. I'm really excited about the potential for us to have an impact in healthcare in this area in the years to come.

Chris Casey:

Yeah, I would say we would look forward to having follow-up conversations with you on a regular basis, Dan, because this is going to be fascinating to see how this evolves. I think it's just one of those monumental breakthroughs, it sounds like, on the quantum side. As well as some of these other technologies you're talking about.

Tom Flaig:

If I could ask you this, Dan, if the three of us sat down at five years, we're sitting here talking about these topics, what would we be seeing differently? What do you predict would be the things we'd be discussing, or at least you'd hope we'd be discussing then?

Dan LaBarbera:

What I hope to be discussing in five years is that the Center for Drug Discovery has done what it's set out to do, in that we've advanced even a single drug therapy to the clinic that's impacted millions of patients. And what I mean by that is, if we can just translate one therapeutic that impacts millions of patients in Colorado and throughout the world, this will have a tremendous benefit for CU Anschutz. For one, it demonstrates that we have the capability of doing that as an academic institution. But more importantly, I think the investment in that drug development will come back to be fruitful for the campus to develop other drug technologies … other technologies on our campus. So that's my hope and vision, is to really be a conduit for our faculty and scientists at CU Anschutz, CU Boulder, Colorado State University, but also startup companies. We've been working with many of them to help them advance their therapeutic technologies towards the clinic.

Tom Flaig:

I'll ask one other thing here, Dan. What are you most proud of the center? What makes you most proud of being the director of the Center for Drug Discovery?

Dan LaBarbera:

I think an aspect of pride is one, developing innovation that's unique to CU Anschutz and putting us on the map in terms of drug discovery at the academic level. That's a point of pride. But I think more than that, whenever you're in a position like I am, and I look at it as a position of service, it's a very rewarding process because I often work outside of my own comfort, in terms of knowledge of the disease model. But because of the infrastructure that we've built, because of our know-how in drug discovery and development, we can work really closely and collaboratively with a lot of teams. And so it's been really rewarding being part of teams that we're not always familiar with, and helping them develop their really innovative research and apply it towards drug discovery. So I think being a point of service for faculty on our campus and throughout Colorado has been a very rewarding process. I'm very proud of that.

Chris Casey:

Well, and that very hopeful and exciting note closing out the year, I think that's a wonderful way to close out our podcast. And again, thank you, Dan, for joining us this morning.

Dan LaBarbera:

Thank you.