Better models could reveal toxicities and speed rare-disease trials

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 32:10

   So you can see atomic resolution, kind of the binding interface is correct. I know a lot of your work is really focused on basic research and kind of building out the fundamentals. If I look at actual translation into drugs or drug development, often a clinical trial will be 15 years, it'll cost $1.5 billion. About $50 million of that often is the molecule in preclinical work, and it's a few years of work.

2. 32:30

   And the other 1.45 billion in decade plus is actually the drug development side of it. A lot of that seems to be gated on some regulatory issues, some of it's recruitment, it's a variety of things, but a lot of it also has to do with the failure of drugs and trials around things like absorption or toxicity or things like that. Have you considered at all tackling that other chain of sort of molecular design and thinking, or is the primary focus more on the basic biology and sort of the initial sort of molecules?

3. 32:55

   I mean, at least my hope in building this comprehensive model of how cells work is actually also being able to predict off-target effects. I think you can do some of that actually with biological models. Because right now, some of the off-target effects are we just didn't know your kidney cell also expressed this receptor. And then when we test it in human, we see it happening and we see renal toxicity.

4. 33:22

   And if you have a single cell atlas that looks at all the different cell types, some of which actually were not predicted before we modeled them, you can start looking at which cells actually do have receptors for the target you thought you were exclusively targeting and be able to predict some of these downstream effects before we get into the human trials. And I think that that's actually one of the more exciting applications of like a transcriptomic model.

5. 33:52

   to understand actually how the different cells will react when you intervene and do something. And, you know, but I think when you think about delivery mechanisms and patient care, that's where you start having to be creative about when you ask, like, what disease do you want to cure first? There are certain diseases that will be easier to, like, deliver a therapeutic to or the risk-reward makes more sense.

6. 34:22

   And, you know, I think we were all inspired by baby KJ, I think last year now, when the team at CHOP was able to deliver a CRISPR therapeutic to edit a mutation that he had that would have inevitably led him to significant neurotoxicity and altered his life. But we were able to, that disease was very carefully chosen because we needed to target his liver cells. And if we could easily deliver a product that would work in his liver.

7. 34:58

   And I think that's. the creativity, the wherewithal to choose the right applications can help us unlock the first applications. Maybe something just to add to that also, you know, because, I mean, kind of you described the conventional, you know, drug development process, right? And I think, you know, these kind of tools have the potential to have a lot of impact on that process. But, you know, what's interesting is to really start to think about kind of the new paradigms that can open up And, you know, what does it mean if, if you can, you know, the barrier to develop a drug, to design a molecule, you know, to kind of get through all of those stages is so much lower. And so you have programmable biology and you can, you know, really start to, you know, create a medicine for every individual patient. I think that has enormous implications for how we, you know, how we do drug development and what the future of medicine looks like.

8. 35:52

   It'll be an exciting day when the FDA accepts like a virtual clinical trial for the phase one or something or, you know, that's based on some personal view of that person. Yeah. Or even short of that, like thinking about the specific like mechanisms where you see this acceleration, like I imagine if people feel like they can predict impact in kidney cells or have a stronger perspective on TOX because they have this broader understanding, they'll be willing to try many more programs. Right.

9. 36:20

   Yeah, the recruitment could also change. And we have this program, Rare is One. And the basic idea is that a lot of people focus on the most common diseases, but there's this long tail. And the economics don't quite work out for companies to focus on those diseases. But if you can make it so that the groups of patients can kind of come together and organize and say, hey, we would take an experimental drug on this, then it actually, because of the cost that you're talking about and how that's a huge amount of the overall cost, if you can flip that, then it actually makes it so that the economics make a lot more sense to then if you can generate something more easily and you can pair it with a group of people.

10. 36:59

    I think one of the interesting things from science and engineering is that often you can hit your head against the wall on the common problems and in this case diseases, but a lot of times you learn a lot more about a system from finding some kind of rare or weird side thing that's happening in the edge case. So I don't know. I think that that's always been kind of an interesting part of this that actually connects pretty well to this, because now you're going to be able to enable a long tail of new kind of.

11. 37:30

    ideas to get tried and enable them to potentially get tested more easily. Yeah, that's a really good point on rare. In our rare disease cohorts, first of all, they're incredibly inspiring and powerful. But patient groups are self-organizing patient registries, natural history registries, biobanks. They're organizing their own clinical trials. There's gene therapy that one disease group has moved forward over the course of like, I want to say like three to five years rather than decades.

12. 38:01

    And the speed is so fast because the patients themselves have organized the resources that a scientist or a clinician might need. And it's incredible. But I think to some degree you're going to need something like this because there are going to be many more new things that can get created. But that doesn't mean that for like the general population that you're not going to want the same level of vetting that we've had historically.

13. 38:26

    But making it so that people who want to be on more of the frontier have the ability to do that is, I think, also going to be pretty helpful. Yeah, letting people opt in to be part of trials, I think, is one of the big shifts that is starting to happen but could really help accelerate biology in general.