Crowdsourcing Hope for ALS Research w/ Tris Dyson

Every 90 minutes, someone in the U.S. is diagnosed with ALS — a devastating disease that has long resisted conventional research and treatment efforts.

In this episode of Caretalk, Tris Dyson, Founder and Managing Director of Challenge Works, joins host John Driscoll to discuss how prize-based innovation and crowdsourcing could unlock new breakthroughs. Dyson shares his personal journey, why shifting incentives matters, and how bold new models can spark hope for ALS patients and families worldwide.

🎙️⚕️ABOUT TRIS DYSON
Tris has led Challenge Works from its origins as a Nesta experiment to being a global hub for expertise and insight on challenge prizes. His aim is to deliver challenge prizes that inspire and enable the development of high impact innovations 

Before joining Nesta, Tris co-founded and led the social enterprise Tempo and was included in the first cohort of the Observer’s New Radicals. Tris has also run a Welsh university think tank, founded a social sector consultancy and was a Teach First Geography teacher complete with corduroy jacket.

In 2012 Tris was included on the Independent on Sunday’s ‘Happy List’ the alternative to the Sunday Times Rich List. Tris would rather be on the other list.

🎙️⚕️ABOUT CARETALK
CareTalk is a weekly podcast that provides an incisive, no B.S. view of the US healthcare industry. Join co-hosts John Driscoll (President U.S. Healthcare and EVP, Walgreens Boots Alliance) and David Williams (President, Health Business Group) as they debate the latest in US healthcare news, business and policy.

GET IN TOUCH
Follow CareTalk on LinkedIn
Become a CareTalk sponsor
Guest appearance requests
Visit us on the web
Subscribe to the CareTalk Newsletter

Transcript

John: 0:12

Welcome to Care Talk. I'm America's home for incisive debate on healthcare, business and politics, and this morning we have a guest who's a gift from across the pond. Tris Isen, who is the founder and CEO of Challenge works. A recently diagnosed a LS patient who has come up with an innovative way to crowdsource solutions for this devastating disease. We're excited to have you Tris.

Tris: 0:37

Thanks. Well, it's nice to be introduced as a gift as well.

John: 0:40

Well, we rarely get guests from across the pond. And you're recently back from Japan, so I, I feel like it's, it's a particularly, uh, uh, lucky time. Can you maybe just set the stage for, um, the, the, our listeners for kind of what a LS is and, and, and kind of what the consequences are if we don't come up with solutions?

Tris: 1:05

Yeah. Um, so a LS is amyotrophic lateral cirrhosis. Um, in the US It's often known as Lou Gehrig's disease after the baseball player, uh, who ha who came down with it in the twenties. Um, and basically what happens is, is your motor neurons stop working. Uh, and this means that your body shuts down. So you stop being able to. Walk, um, stop being able to use your arms, limbs, um, stop being able to talk, feed yourself and, um, ultimately you stop being able to breathe. Um, and I think people will be familiar with Stephen Hawking. Um, so he's perhaps the most famous person who had this disease, Joe. Uh, he was in some ways quite fortunate in that he lived a long time. Many decades. Um, but for most people the life expectancy after diagnosis is about three to five years. Um, it kind of varies a little bit, but it doesn't really matter where you are in the world. It's, that's the kind of the, the, the, the outcome. Um, but it's highly varied. Um, so some people like Stephen Hawking can, can live a lot longer than that, and for some people it's, it's the other way around. Um, it affects more people than you might think. Um, so because people don't live very long, there aren't that many patients. Um, but uh, it affects about one in 300. So, wow. You know, in the average cinema, that's markable might be one or two people. Yeah. Yeah. Over the course of a lifetime. So it's, it's more common than you'd think. Um, yeah. And,

John: 3:00

and, and up until now, what is, how effective have the treatments been and what kind of treatments are available?

Tris: 3:08

Yeah, I mean, there's basically no treatments. Um, I mean, that's a slightly glib answer. I mean, there's, there, there's a drug called Rizo, which is available in most countries. Uh, in the us Uh, there's a couple of other. Drugs, but, but they don't really do very much. Um, they might extend life by a couple of months, that kind of thing. Um, so there's not really any effective treatment. Um, and it's not really actually a regular way of diagnosing it either. Like it's, it's through, um, a mission. They, they work out what you don't have, um, and then they, that's how they come up with the diagnosis. So nothing's really changed. You know, in 150 years, quite honestly, uh, it doesn't matter what country you're in, what healthcare system. It's, it's the, the, yeah, that's it really. And,

John: 3:58

and Nesta, you know, you, you, your organization Challenge Works is partnered with Nesta. Can you just describe Nesta, then we'll get into Challenge Works and kinda how you're, I think trying to hack this from an innovation perspective.

Tris: 4:11

Yeah, so Nest, the UK's Innovation Foundation. So it's an organization in London, uh, with some offices in various other places as well. And it has an endowment and its job is to understand, invest in, and support innovation for good. Basically, there isn't a kind of. Equivalent

John: 4:31

in the us But is it the NHS funded or is it privately funded through the government or not?

Tris: 4:36

It's an, it's an independent organization. It was initially set up by the UK government, but it was given its freedom, its independence. So it might be more, it, it might be like a, a bit like a Bloomberg philanthropies. Or a Rockefeller, but with not quite that level of resource. Um, um, that, but it's that kind of thing.

John: 4:59

Um,

Tris: 5:00

yeah.

John: 5:01

And the does it, does it, does it, does Nesta focus on drug and healthcare innovations specifically, or just innovations for good sort of broadly cast

Tris: 5:13

nester? No, it doesn't. Nester's had very many different lives. Um, uh, and so it, it cha it changes in what it focuses on at the moment. It's very UK centric. In the past it's been very international. Um, it's been very technology focused, whereas now it's much more around data science, behavioral insights, these sorts of things. So it's not really, um. No, it's not particularly relevant for this, for what we did. So like drugs

John: 5:46

and drug development. So, yeah. So talk to, tell us a little bit about the origin story around Challenge Works for your organization.

Tris: 5:55

Yeah. Um, so Challenge Works was set up about 12 years ago by Nesta and also by the UK government. And the idea was really we were looking across to you guys in the US. Um, and looking at the challenge prizes that were being particularly used by people like nasa, darpa, and also XPRIZE on the west coast, um, really at the turn of the century that had sort of created a number of breakthroughs. I mean, things like driverless cars basically originate from darpa, from challenges. Um, the private space flight, uh, industry was at. Initiated through, through challenge prizes. Um,

John: 6:37

well just contextually here, tr this was where 150 years ago, 200 years ago, you know, uh, wealthy families and sometimes governments would lay out a prize. Yeah, around technologies as, as, as things that we take for come as like the, the perfect clock or particular voyages. And so the, yeah, the, the prize thing sort of went out of fashion for about a hundred years as far as I can tell. Yeah. And then to your point with uh, Peter Diamandas and the xprize, I think he was probably the first person to catalyze it. And there were others who laid out. Yeah. Uh, dollar prizes that, that caught the attention of innovators in the US And, uh, it, it sounds like that, that a, a little bit of that, um, buzz caught

Tris: 7:28

you. Yeah, I mean, we were kind of looking at all that and thinking, well, why aren't we doing this in Europe? And particularly as the, you know, historically there were lots of challenge prices in Europe, particularly in the uk. Uh, yeah. Um. Uh, flights and, you know, uh, uh, horology and all sorts of things. Um, and so the organization was set up to, to become a kind of European version of xprize, I guess. Um, and over the years we've got bigger and bigger and bigger and bigger. And, um, and now we, we work internationally on all sorts of, we look for areas where there's a, where there's a, there's an, there's an unmet need. Where if you crowd in new thinking, new technologies, new innovation, you can crack a problem. And then we create this sort of series of incentives, some of it financial, some of it other things to, um, in order to come up with solutions basically.

John: 8:25

But, but do you look at every hard problem that way? Or do you, do you sort of pick, pick some and eliminate others?

Tris: 8:32

I mean, not all pri not all areas, not all problem areas are, are appropriate for a kind of crowd model. Um, I mean, in, in, in many cases you might want to, I mean, if you took COVID for example, um, you know, the approach was give money to the big universities and pharmaceutical companies and they'll come up with solutions and that was, that was probably the right, right approach. You wouldn't use a challenge prize model there. Um. Maybe the market's quite nascent. The incentives aren't quite there yet, but they will be. Um, where approaches are. Uh, you know, haven't changed very much for a long, for a number of, you know, the people are still trying the same approaches. Well, perhaps

John: 9:22

novelty is unwelcome.

Tris: 9:25

Yeah, exactly. Yeah. Um, and in those cases, prices are quite good. Um, and investors like them as well because, um, you, what you're doing is you are, you know, they might be interested in a market or a new area, but they're not sure where to put their money 'cause it's high risk. And so what we do is just, we filter it. Basically. We, you know, we know we, we, we, we award on the basis of results. Um, and then the investors, uh, like that, 'cause then they can, it's a, it's a sound of bet. It's a

John: 9:55

validation. Yeah. I, I think it's fascinating because I think prizes often work really well with a big hard problem where people have stopped investing.'cause it's a big hard problem. But it, it, it, it seems conceptually hackable or where. It's a reasonably large size problem and people refuse to do anything to your point earlier than what they've done in the past. Those are particularly fertile areas where I've seen people, and these are relatively small dollars relative to this human scale of the failures here of a LS being a great example. So, but how did you, how'd you raise the money for, for this tris? I, you got $10 million. That's a lot of cabbage.

Tris: 10:39

Yeah. Well, I mean, I started with talking to governments and didn't get very far. Um, um, our government was a, was going through a change, uh, as was yours. Um, so it, I mean, it comes from, it comes from organizations all around the world, including in the United States, um, who've raised money through patient advocacy. Um, and through, you know, things like marathons and stuff like that, basically, that's basically where money comes from. That's fantastic.

John: 11:14

Yeah. And, and, and you've got $10 million and what's, what, what, what is the path to win the prize?

Tris: 11:23

Well, I mean, so I say the first thing is our hypothesis is, well, we know this, right? There was a, there was a revolution in drug discovery. It's already happening, but it's only gonna get much, much bigger over the next decade, which is through the use of ai. Uh, it's, it's one area where everybody agrees. It's all good, it's all positive. Um, and the, the role of AI in shortening, um, the drug discovery process, but also improving accuracy, understanding patterns. Working out what new drugs might work and, and that kind of thing. It, it, it's a data problem basically. Um, so what we are interested in is accelerating that. Um, and so we've pulled together the largest, um, data set of its kind is a huge international data set, omics, genomic clinical data, um, of tens of thousands of patients. And we put it all into one environment. Um, and that's the big kind of attraction really, I think. Um, it includes lots of data and just,

John: 12:44

and just to be clear, Triss, I mean, people have been using advanced compute and automated algorithms and neural networks. They've been hacking at this for multiple diseases in big pharma for years. But I think the two things that are different this time are the power of the models and how competitive they are, you know, which is a really both dangerous and, and, and super interesting, you know, with more people using the tools like OpenAI and Anthropic faster than any other product that's ever been introduced to the world. So you've got massive number of consumer and business uses. You, you, the utility of artificial intelligence of advanced compute using deep neural, convoluted neural networks really depends on standardized, accurate, tagged data so that you have data to train the models. The models can't learn unless the data is, is very consistently clean, high quality, standardized, and, and effectively. For, for just. For a simple word rather than to get into details Readable in one. Yeah. In one or, or, or an associated language, so that then the models can work with those. So I think that massive database that you put together is, is probably pretty crucial to making the data readable for, for researchers. Is that the right way to think about it?

Tris: 14:11

Yeah, I think that's, that's right. I mean, I mean, I, I mean, I should say what we've done is, is that we've. Together. People have already done the hard work in terms of creating those data sets, and we've pulled them into one environment. So, you know, in the US it includes data from the New York Genome Center, um, a LS Compute, uh, which is based out Boston. Um, answer a LS, uh, which is down in. Uh, also on the East Coast. Um, and, um, uh, so, so, uh, but the, you know, and then lots of European data and, and we sort of brought it all together, but it's a, it's, it's a, it's a, it's a highly, um, well, uh, harmonized, um, and very rich dataset. Um, and I think, I mean, I think the thing is with, with a LS. Well, if you think, think we like this. Like I, I mean, I think like when, when politicians talk about AI in drug discovery, the thing they'll always say is cancer. They'll say, cancer, cancer, cancer. And they'll say that, 'cause everybody, everybody has an experience with cancer. And, um, and it's obviously what everybody, so it's, it's relatable, that kind of thing. Um, but cancer is already in many, there's lots of different cancers, but cancer is already kind of, um. You know, a huge amount of progress has been made in, in cancers, uh, and the level of understanding and knowledge of, of, of, of cancers is, is, is fantastic. A LS though is that there are no, there, there are no, as we were saying earlier, there basically no treatments.

John: 15:51

Reasons why, to give, to give people context here, you know, with all the investments in cancer, which will affect, I know roughly one outta three women and one out four men. Um, and, and we'll, and this, and the pre, the, the incidence is going up because the people live longer. Um, the five year survival rate for cancer in your, uh, in the seventies was around 48 years old across all cancers. And now it's closing on 70, it's 67, 68 years old. So we've made. Massive, massive success Yeah. In Cancer to Trade, while it's still, it's deeply complex and we're still trying to create it. And there are a lot of, you know, you know, quite, quite terminal cancers and it's killing people all over the place. Yeah. The, the general progress towards, uh, a portfolio of solutions been really remarkable. And, and I don't, and I think effectively what you're saying, TRS is for a LS over the same time period.

Tris: 16:49

Yeah, basically nothing. Um, I mean, I and I, and I think this is why it's potentially quite a big opportunity, um, because, um, what has happened over the last 10, 15 years is the level of basic understanding of, of the biology has improved massively. Um, and so the, so we're not, we're, we're no longer looking at a black bot or a void. There's a kind of rich understanding. Um, there are also, I mean, there is now a promising treatment for a small subset of patients. A drug developed by a, a company in Boston, Biogen, uh, called Ferin. This is for a, this is for a small group of patients who have this particular gene. Um. Which is a complete transformation for those patients, and it shows what's possible. Um, so you've got, you've got this sort of, the, the tide is turning and what we're doing is then pulling together this huge data set and we're saying to teams around the world, uh, it will give you access to this data and money, uh, and other things, access to investors and various other things. Um, and your job is to identify targets. And targets, uh, are not the drugs, obviously, but it's the thing that you need to, to aim a drug at. Uh, and that's what the prize is focused on. Um, and so yeah, there'll be 20 teams. Um, I can't, I, I'm gonna get the numbers slightly wrong in terms of conversion to, to US dollars, but I think it's maybe about, that's okay.$130,000, something like that. And then there'll be 10 team and they'll get access to data and they'll get nine months. And that's, and

John: 18:39

that's enough tri to get them going.

Tris: 18:41

Yeah, I think so. I mean, like, if you are, if you, if you are developing models that are looking at identifying targets, um, this is quite exciting prospects of things.'cause you get this data, you get some money. Um, and if you can, if you can come up with solutions that you, you, you're gonna create a huge. Um, impact. Um, and there's a big market as well. Um, at the end of the day,

John: 19:09

I mean, just to contextualize the market in the us, 30,000 Americans today are living with this disease one out of every 300 people over. And that's a, that's a lot of people with a, you know, a, a de a disease. I don't have to remind you. Is, is devastating. Yeah. You've got this, I'm very excited about this. Prospect for catalyzing, uh, research and solutions. Do you think this will also accelerate the timeline to its gato to innovation? Because you've, you've created a new market effectively of, you know, of, of work at a time when the tools are getting better to actually identify those targets?

Tris: 19:49

Yeah. No, I think so. I, I and I, I, I should just, I should just. Talk through what, what, what happens? So you get, you get 20 teams. They have to identify targets, prioritize them. They get $130,000, and then we'll go down to 10 teams. They'll get something like $260,000. Um, they'll need to then start to validate those potential targets. Also in s but also in the. Five teams, they'll get about$750,000, something like that. Um, and if they need it, they'll get lab support and all that stuff, uh, to do the lab work.'cause then it moves into the lab and then there'll be a winner with a, with a 1.3 million, something like that dollars, um, for the, for the highest potential target. But by that point, they'll, they'll be getting lots of investment and interest and everything else. Um, do you think you

John: 20:47

could, have you set a timeline for this? It's really exciting.

Tris: 20:50

Yeah, it's five years. Um, from end to end, um, we'll publish all the stuff that doesn't work. Um, you know, obviously the teams hang onto their ip, but everything else will put out there as well. Um. I mean, it, it's a bit, it's a bit difficult to judge. I mean, if you talk to the universities, they want longer. Uh, and if you talk to, they always want longer.

John: 21:17

They want more money and more time. Yeah. If it talk, the

Tris: 21:19

AI companies, they say they can do it in a couple of weeks, you know? So, um, we're somewhere in the middle. Um, and, and I think we, we were expecting teams to be both private sector and academia. Um, um. So it should, I I think it's, um, uh, I think it's an exciting prospect.

John: 21:41

Uh, it, it's, it's, it sounds really exciting at two levels. One to, to, to, to make a dent on the devastating, um. In dignity of a, of a disease that takes away your autonomy and independence and for which conventional medicine with a, and it's not like it hasn't been researched, it has not cracked the code. Yeah. But the application of new tools and crowdsourcing honestly tris. It also, I think when you change incentives and take them out of conventional ways of doing business. It worked. It's worked in, in, in, for American, for American in American innovation. It's worked uk. Yeah. That won dude prize of 1714. And did, and it's worked in Silicon Valley. Um, I'm really. Quite excited and hopeful that that, that we'll have the same kind of yield here. What, what is most exciting about doing this interview about, uh, such a hard subject, um, is you've got a kind of a novel, optimistic, and sort of human-centered innovation set of solutions out here that'll be competing. It's, it's, um, I'm, I'm really excited for, for, for, for you and for everybody who's suffering from this disease.

Tris: 22:50

Yeah, I agree. I you, by the way, you mentioned the longest you prize of 1714, so I should say, uh, and you know this, 'cause I think that's why you mentioned it, the, the, the, the new, this is called the long you prize, uh, on a LS 'cause it's based on that heritage. Um, um, but yeah, and I, and I think you also mentioned the market. I mean, we mentioned this drug to rson, this developed by this Boston company, remarkable drug. If you took that price point that they're u that they're using at the moment. It's not cheap, but it's not ridiculously expensive either. And you applied that to, uh, everyone in the us uh, your, just your US market would be 4.8 billion on a, on an analyzed basis. Um, so yeah. And obviously internationally it'd be much, much more so, so yeah, I mean, I think it's, um, all the incentives and all the opportunity is there and, and the, and the kind of. We can see because of things like. The opportunity to break this is, is definitely there. Yeah.

John: 23:56

That's exciting. Well, thank, thanks for joining us, uh, for Care Talk and for our listeners, if you liked what you heard or you didn't, we'd love you to subscribe on your favorite service. And tr thank you so much for what you're doing and for giving us, uh, inspiring us, uh, for an optimistic approach to what's been a really, really hard disease. Thanks to.