Elizabeth Iorns: Advice for Biotech Founders
Adora Cheung: Alright guys, we're going to get started. Sorry for being late. So I have up here, Elizabeth Iorns, is it Dr. Elizabeth Iorns or Professor Elizabeth Iorns? So Elizabeth is a cancer biologist by training. You got your PhD in Cancer Biology from University of London, and you did your postdoc, and then became an assistant professor, which you still are, at University of Miami, School of Medicine. Got this correct?
Elizabeth Iorns: Yes.
Adora Cheung: Cool. So in 2011, she started Science Exchange, then called The Bench, which was an online marketplace for outsourcing science experiments
And she was part of the Y Combinator Summer 2011 batch, and think you might have been the very first biotech-type company in YC, right? That's where we started with you
And she's also the chairman of Reformer Therapeutics, which just went through YC as well, last batch. She's also a part time partner at YC, which means in her free time, she helps all our biotech companies which, this points like hundreds
I don't know what the exact number is but hundreds of biotech companies. So today we want to spend time talking about two things in particular, one is running a marketplace company, since that's what Science Exchange is, and two is getting your perspective as someone who's now advised hundreds of biotech companies, what you think about, what's going on the space, I mean particularly because I think there's increase in slope of scientists coming into the field, so that'd be cool to talk about. So to get started, like I said you're a cancer biologist by training. You were, I believe, doing breast cancer research right after college, I guess. How did you even ... Going down the academic route, how did you come upon startups? Where did that idea even come from?
Elizabeth Iorns: Yeah
It was random, especially to start a technology company like Science Exchange rather than a biotech. So I was actually, I guess taking Y Combinator's advice without knowing about Y Combinator's advice to solve your own problem. So I was looking at the way that scientific research was evolving
It's becoming much more specialized, much more multi-disciplinary, had to form these collaborations with other labs in order to access all of the latest technologies. And I experienced that myself and realized that it was incredibly inefficient
And so that process of how you find somebody to work with, how you evaluate the quality of their ability to do that specialized work, and then even just the infrastructure to actually work with them. So in biotech and in scientific research, the actual ownership of the scientific results is very important
And so just figuring out who is actually the owner of those results? Who has the intellectual property, publication rights? All of those things
It's very difficult
And so I was talking about this with my co-founder and we looked at other examples of industries that had solved the similar problem. So we were looking at oDesk and Elance at the time, which has now become Upwork. But that was really an example of, you could solve this with a marketplace of experts, and that was the basis of Science Exchange and it's never really evolved much more beyond that in terms of the fundamental goal of what the company wants to achieve.
Adora Cheung: Can you dive a little bit deeper there? So before, if I wanted to do some research and run an experiment, basically what it sounds like, is this big, disorganized process of, I need 10 different things, it's super-fragmented where the stuff is. Can you give it, I guess ... Let me give you an example related to the research you were doing, of how it's so disorganized and then how Science Exchange now collapses all that into one place.
Elizabeth Iorns: Yeah, definitely super disorganized, lots of fragmentation, both in terms of who you work with, but also how you find the information and in some cases, the information is actually not really available. So at the time, when I was working as a breast cancer researcher, I was running multiple different types of experiments
And one example was microarray analysis, which is differently showing my time away from the lab. Because that's a very old school technique now, but at the time, it was what we use to analyze gene expression profiling, and we just didn't have the types of arrays that I wanted to use
And so I was trying to use Google and asking my friends and asking my old lab, "Can we use your infrastructure?" And it was just incredibly inefficient. Also actually the inefficiency drives a lot of inefficiency and pricing in the market. So that was something that was a big learning point for me. At that time point was you could actually create a company that had margin and that space because the market was so inefficient, due to the lack of information. So for example, I was seeing literally 10 times price differences for an equivalent experiment, because people really just didn't have access to what should it actually cost
And we see that still on Science Exchange today, is that when people go and look for multiple options, and they actually don't have the barriers of being able to work with them, they see very frequently price differences that are quite extraordinary for the same results that could be generated.
Adora Cheung: Got it. Just to take one step back, when you were an academic, I don't know if that's a bad word.
Elizabeth Iorns: No.
Adora Cheung: But when you were an academic-
Elizabeth Iorns: Maybe to some people.
Adora Cheung: Was that a tough choice, to just make that switch? Because I mean, you spent so many years studying for this one thing and then you just are starting essentially what is a tech company, that is not solving that one thing that you spent, up until that point your entire life going after?
Elizabeth Iorns: Yeah, and actually at the time I didn't really have a lot of role models to look to either, so I was in Miami, which was not a very startup place
And now it's actually interesting to see the evolution not just in the biotech world, but just generally in the startup ecosystem, and all of the infrastructure that's been created
I think University of Miami even has now an incubator associated with the School of medicine that helps people create companies
I remember at the time having this idea for a company, and I actually asked the university because I was a little bit worried if I have this idea, while I'm still working there, will they try to get some ownership stake from the idea? Because actually, when you work in academia, the results you generate don't belong to you. So I think there's a common misperception that universities actually own your work. So it's often a challenge to spin those results out of the university. So I worried, "Well, if I have this idea, will they own it?" And so we went and talked to the Tech Transfer office and they were just, "Whatever, go do whatever you want." And it was interesting, and they didn't really have any guidance on how to do that. But coincidentally, I read about Y Combinator in Wired Magazine. So there was actually a Wired magazine interview all those years ago about Y Combinator
And that was where I first learned about it, and I was like, "Wow, that sounds super cool. Why don't we just apply for it?" And we were total outsiders, we didn't know anybody at Y Combinator. We didn't know anybody that had gone through Y Combinator. We just applied with this idea and were fortunate enough to get in.
Adora Cheung: So you talked a little bit about tech transfer
I mean, I do think that a lot of people in academia who are thinking about starting a start up, do think that is a huge issue
And in some sense they are, from some universities we've had to work with, with some of our startups, it is a huge issue. Do you have any advice for them on how to navigate that process? Should that be the barrier in your mind?
Elizabeth Iorns: Yeah
I do think it is an issue, particularly for Scientific startups, because if you're trying to spin out actual research that was done already
And I think people often ask me, "What's a good time to join Y Combinator if you're creating a science startup?" And actually, I do think a good time to join is once you've already generated a lot of the R&D from grant-funded work, because you don't want to spend years, and all of this money trying to pursue a research hypothesis, you really want to be in the development phase
And so I think that gets very difficult with a lot of the universities
I think some of them are trying to be more founder-friendly and create ways to more readily license the results that were generated, but it's still a significant obstacle.
Adora Cheung: Do you see that academics, is a trend they're moving more into startups? Or what do you see or maybe we just get the bubble view here in San Francisco. But I mean, that's the only thing I see anecdotally. But do you think that's a growing trend overall?
Elizabeth Iorns: Yeah, I think people are more willing to just create companies, I think generally
I think there's a macro-trend, way beyond sciences, really just entrepreneurship has become this viable career path
And people are, "Oh yeah, I could create a company, and it's just become a lot easier." The infrastructure that's being created through Y Combinator, but also through things like stripe, and things like AWS has made it so much easier for people to create companies, and I think in the science space we're starting to see that, so obviously Science Exchange is one way to do that, but there's some really interesting other evolutions like LabCentral, and other lab space [inaudible] that provide lab space by The Bench that's very cost effective, and you can just rent one bench per month and be able to get started really cost effectively. So those things are lowering the barriers to entry
And so I think academics, what I would like to see more of is how can we allow, the true, I would say the analogy to what we've seen in the engineering world, the software engineering world is that the actual developers, the software developers, become the entrepreneurs and they maintain that and really build this company around them. I think that's still very difficult for scientists, particularly PhD and postdoc scientists. So they actually make the discoveries in these labs, so they're the ones doing the work, but a lot of time for them to actually be the ones to be the founder of the company is pretty challenging
And usually what you'll see is a well established famous PI, so a principal investigator, being a co-founder of that company, just to give it legitimacy and then that usually allows people to bring in funding. But, I think that's also sad, why do we have to have these figureheads who own large stakes of these companies, in order for them to get off the ground and get started? And I want to see that change.
Adora Cheung: Yeah, that's something we as you know, talk a lot about to people, when they apply, and then we see that structure, and then you try to go help them fix it, essentially.
Elizabeth Iorns: Yeah.
Adora Cheung: Don't want to lose too much of your company too soon. All right, so Science Exchange. So you already described what problem you're trying to solve
And then you've decided at this point in 2011, start the startup, how did you get your first users? And you have two sides the marketplace, so it's doubly hard for you. How did you approach both sides?
Elizabeth Iorns: Yeah, I think marketplaces are really challenging because you don't control really the success of the business
I mean, you do, but you don't have ... You're not just building something and selling it. You have to then make sure you have the things to sell on the other side. So it is really challenging
I think when we first started, we were very MVP like in our approach. So I think the first version of Science Exchange, which as you mentioned was actually called the bench which was renamed during Y Combinator, thanks to Paul Graham's very good advice about our poor naming choice. So yeah, we threw up this website and it was literally like, "Put what you need on this website and then other people will see it and that will give you options." Which is even at the time as a scientist, I was like, "I want to use this." It seemed like very quick to get something up and see who would test it out, but it was obvious right from the start that we'd actually have to create a true marketplace and have supply that was visible, and actually even more so with Sciences Exchange. What we had to do was create a curated marketplace that operates in the B2B sector. So what we had to do is build QAs or quality assurance, we had to actually qualify and contract every provider that's available through the Marketplace, and then surface all of the results, so that when somebody comes to the marketplace, they can literally find what they need, and they can give them the information that's required for them to be able to quote accurately because they already know there's confidentiality agreements in place
And then they actually also had to manage those projects. Because these aren't just put it in your cart and checkout, you have to actually manage a project with somebody who works in another part of the world from you
And so all this had to be built into the software. So that was many iterations of learning about where really is the value that comes from what we are providing
And I think marketplaces have evolved a lot from being predominantly consumer-focus, there's a lot of interesting B2B examples that are emerging, and Science Exchange obviously, sits very firmly in that category.
Adora Cheung: What was more difficult, supply or demand?
Elizabeth Iorns: Definitely demand. So supply for us, is and was pretty straightforward in the sense that providers are looking for work, providers of these services, it's a very fast growing industry. So also highly fragmented, and they actually also suffer the same challenges that the demand side has. So for them, if they want to sell one of their projects, they actually have to go on contract with each client they work with, which can take several months and they have to go through quality assurance and get set up
And so for them, if they are on Science Exchange, now, all of a sudden, every one of our clients can work with them instantly. So that's a huge value proposition. It took us a lot longer to convince the large players in the market, so the large CROs, that this would be something that they should be part of. But actually, we have been able to do that as well. So I think one of the important lessons that we learned quickly was, for this to be a really effective solution, it had to be the platform that was used for everything at the companies that were using it. So not just one off come and shop on the marketplace, but actually use it as a system for managing all of the projects that are going on with the external partners
And in order to do that, you truly do have to have all of the major players that they want to work with on the platform
And I've seen other companies who have B2B marketplaces struggle a little bit where they haven't had the large established players on the supply side
And without that, I think it's very difficult to have an enterprise partnership that uses the platform.
Adora Cheung: Do you remember on the demand side, the very first customer? What were they? What did they do?
Elizabeth Iorns: Yeah, totally
I remember actually. So our first customers were totally my friends who were scientists, who I would tell, "If you have experiments you're running, you have to try out Science Exchange." And actually it was great user feedback because for them, it did turn out to be this really high touch, very concierge service. So they found great options that were really cost effective. So it wasn't they just used it because we made them. But I think they they taught us enormous amount. But I also remember the first customer that ever was just not related to us, just came onto the platform and used it
And that was pretty exciting. We were actually in Italy in my brother's wedding
And I was like, "Oh, my God, somebody used our website, yay."
Adora Cheung: Stop the wedding
I need to check this out.
Elizabeth Iorns: Totally.
Adora Cheung: What was the experiment they were trying to run? Do you remember?
Elizabeth Iorns: It was microarray as well. This was popular at that time.
Adora Cheung: So we talked a lot about at Startup School, product market fit. For you what was that like? When did you know you had that? And how did you define it? And what metrics were you looking at?
Elizabeth Iorns: Yeah, for us, I think we defined product market fit when we had our first large pharmaceutical client actually use the platform for whole sectors of their business. So for us, it was Amgen and they used they launched Science Exchange for all of their discovery research, which was a really big deal for us at the time
And we realized, "Oh, we actually have something that ... They do a lot of steps manually at the moment, and this product actually automates providing all the information about who they should work with, all of these new options for new types of technologies that they might be just exploring and then even actually their business and business intelligence around, looking at spend and supplier performance." So those things took us a while to figure out where the core value props were.
Adora Cheung: You know you're onto something when a big company who spent many years and money trying to build that problem, and solve that problem internally has all of a sudden just shifted to your product
And it's starting to pay you for that. That's a big thing.
Elizabeth Iorns: Yeah.
Adora Cheung: And not just in biotech, but in general for-
Elizabeth Iorns: Yeah
I mean, much moreso I think in software
It's really interesting, because people always ask me today, "Who are your main competitors? How do you think about competitive differentiation?" And all that, and still to me, our main competitor is status quo, so that people still set up these really crazy SharePoint processes to manage their external vendors
And I always go in there, I'm like, "This is horrible, why are you doing this?" And big companies, they just love SharePoint, they're like, "Oh, yeah, my SharePoint site, I have created all of these really Byzantine processes, which only apply to five vendors I work with
And everyone else has to go through some other crazy process, and I'm like, "Okay, but you can just transition all of that onto this platform." And it's always this interesting battle of who set up that process? Because trying to convince him that the software automates all that hard work is often actually one of our biggest challenges.
Adora Cheung: So one of the things I really love that you're doing, and announced a long time ago, was the Reproducibility Initiative. So the idea that it seems obvious to do, which is, if you run an experiment and you publish results, someone else should be able to reproduce it, to validate that it's being done. So can you talk a little bit about why you're doing that? What you're doing, and why has this never been done? Has it just been a money problem or is it something that only Science Exchange actually can do, because you have the process in place?
Elizabeth Iorns: Yeah, the Reproducibility Initiative, I still think it's a really cool project
It's still very controversial even years later.
Adora Cheung: Really?
Elizabeth Iorns: Oh, yeah.
Adora Cheung: Why is it controversial?
Elizabeth Iorns: Oh, my God, still so controversial
It's controversial because of a lot of reasons
I think the main reason is that people are not sure of the value of it, which I think is legitimate
I think there's still a question of, how efficient is it to replicate experiments? And that's, that was actually ... When you say, "Is it Science Exchange, was the only way we could do it?" Our hypothesis was that, "Yeah, having a network, where people already had all of the right essays, or the right infrastructure, or the right animal model, would be the only way that was actually practically possible to do it." Previously, obviously, people didn't have that, they had that same challenge of, "Oh, I have to go find somebody who has that same animal model or has that same instrument, and then I have to convince them and contact them." And all that stuff. So yeah, the Reproducibility Initiative actually is Science Exchange's, part of our mission to improve the quality and efficiency of scientific research
And that that project is quite broad. So it covers both things like antibody validation, which we've done a lot of. Reagent validation, things like re-analysis of epidemiology results. So we worked with the Gates Foundation to do that
And then the projects that we're most well known for, which is actually replicating published results
And we do that both for the pharmaceutical industry, which is way easier and way less controversial, because they just come and say, "Hey, I'm interested in this result." And then we quickly find a facility that can replicate the key results and provide these back to them. However, the Cancer Biology Reproducibility Project and the Prostate Cancer Foundation project, are the two ones that people have followed the most, which is public replications, where we've actually published the replication studies
And that's controversial because people don't generally do this. So I think it goes against the cultural norm of really not publishing replication studies
I also think people are really afraid, and they mix up failed replication with things like fraud. So people are really afraid that if the result is found to be non-reproducible, that that will have a negative impact on their career, which I think is probably true, but it shouldn't be. So the reality is that the results that have been generated show that most published results are not reproducible. So if that's true, then we should try to understand that and study it, which is my opinion as a scientist, we should study the science behind, why is this the case? Rather than try to go after individual people and say, "Oh, your science is horrible." Because clearly, that's not the case. Clearly, it's that most things are not reproducible when they publish, because of a variety of complex factors, of which we've started to impact. But I think there's still a lot of work to do.
Adora Cheung: What are the two top two complex factors?
Elizabeth Iorns: So I think the main reason that I've seen things are not reproducible is the quality of essay validation. So it's really interesting when you go and talk to pharmaceutical companies, because they also transfer experiments. So they replicate experiments in the sense that they'll see something up at their facility, and then they'll outsource to a CRO to run their essay for them, over and over again
And they call it tech transfer or essay transfer
And that process is incredibly efficient, they can do this very easily, and it's got a high probability of working
And so when you look at that, I don't think it's much different than if you take a published result, and you try and rerun that essay in another lab. But the main difference is that when you work in a pharmaceutical company you have SOPs, you have everything documented, you look at essay variability, you look at positive and negative controls. There's just a lot more actual validation of the type, the actual experimental essay that's used before it's transferred
And that's very different than in academia. So in academia, you tend to take, "Oh, yeah, I've got this animal model in my lab." Or, "I've got this cell line in my lab, I'm just going to run this experiment." And hopefully, you include your positive and negative control, but maybe not
And you also don't look at things like variability of the essay, reproducibility of the essay
And I think a lot of what we see in published results is likely to be noise, rather than true experimental effects
And so until we understand that more, we will have an issue with reproducing those results.
Adora Cheung: That's sort of scary. But you're saving the world. So save us from this problem. So going back to Science Exchange, what is the biggest challenge as a CEO of a marketplace company that you have today? And how has that challenge morphed over time? What was the biggest challenge when you just started to what is the biggest challenge today?
Elizabeth Iorns: Oh, yeah, I'm sure. So many challenges along the way. When we first starter Science Exchange, the biggest challenge was, "Can we get anybody to use it?" I think that was the first challenge
And then, "Can we build something where the very conservative pharmaceutical industry will use it?" And that was important for us, because when you look at spinned breakdown, the market is predominantly for outsourced research, focused in the pharmaceutical industry, so that they control over 100 billion dollars of spinned per year
And so if we get them to use the platform, we would have a lot of issue with really creating a truly large company. Today, I think our biggest issues are more around scaling. So we're still only 85 people, we've designed a couple of extremely large partnerships
And so we're really trying to execute against some pretty tight deadlines with staff that are focused on just one of these large integrations
And so trying to make sure that we don't take on too much while also realizing there's enormous opportunity. So we don't want to overlook that. So I think just staying focused, executing on the right things and being really disciplined about that, which is always really hard when you have multiple opportunities that you can go after
I think that discipline is incredibly important.
Adora Cheung: Yeah, focus is really hard, especially as you get more employees, and you get more funding and stuff like that. So I want to switch gears a little bit and talk about biotech and startups. As we talked about earlier, there has been this implosion of biotech startups recently, especially in Silicon Valley, what do you think that is? What are the causes for this?
Elizabeth Iorns: Yeah, the biotech industry is so interesting right now
And it's actually amazing, I love ..
And it's not just here in Silicon Valley, it's everywhere. So the UK, Cambridge, San Diego, everywhere, there's lots and lots of biotech startups
And I think it's a really cool time for that sector of the industry
It's I think driven by a couple of things, one capital, so access to capital never is unprecedented
And the amount of capital that's going into biotech is definitely unprecedented. There's also an interesting evolution and in terms of where biology is, in terms of the actual therapeutic modalities that are available. So I'm not sure that people who are outside the industry may not recognize this as a real turning point for biotech. So up until very recently, there was really only small molecule inhibitors, and then after that, there was Genentech and Amgen, so there was actual biologics. So antibodies and proteins. But just in the last year, we had gene therapies approved, we had cell-based therapies approved, we had RNAi approved, it's actual marketable products for the first time. So all of a sudden, you have this huge window opened up for you and the different types of approaches you can take that are viable to create a commercial product. So I think that science has caught up to a point where there's just some phenomenal opportunities to tackle diseases in a way that was not possible previously.
Adora Cheung: Interesting, sort of Moore's law in biotech.
Elizabeth Iorns: Yes, really
It's a really exciting time
And there's also I think, a convergence on ... Really a critical mass of people looking at, "Okay, I have this experience in the pharmaceutical industry, I'm going to go into the biotech industry." So again, just so many people with really great scientific experience, with true experience of bringing, not from academia, actually an industry bringing drugs to market and now going into biotech, and they're taking that risk and founding companies and being early employees and these five-person companies
And I never had seen that previously
I think that's a new phenomenon as well.
Adora Cheung: So you sit in this cool intersection of, you've built a software company, a marketplace software company and that intersects with biotech
And then you have the perspective of advising lots of more true I guess, biotech companies. What's the difference between biotech and software?
Elizabeth Iorns: So different.
Adora Cheung: In terms of running the startup itself?
Elizabeth Iorns: Yes, really different
I think in some ways-
Adora Cheung: Or let me rephrase the question.
Elizabeth Iorns: Okay.
Adora Cheung: How is it the same? Are there any similarities?
Elizabeth Iorns: I think it's the same in terms of people, focus, funding, all of those things are the same. So trying to find the right people and retain them, building a good culture, all of this thing, actually, I think YC is really being able to do a great job of having companies across different sectors, because of their focus on really learning from successful entrepreneurs. So big lessons about creating a company, and not so much about the very minute tactical details. Biotechs are different because you can't really change the outcome of the science, the science is the science, so either it works, or it doesn't work
And that's very different than software where you can actually pivot around, and keep developing your product and keep getting product market fit. With science it's about key milestones that demonstrate real inflection points in terms of mitigating the risk of the drug working. So actually having different stages along the development path, where you've eventually got to a point where you demonstrate in a clinical trial that your drug is effective at treating disease that you're trying to treat.
Adora Cheung: What does an MVP mean in biotech?
Elizabeth Iorns: Yeah, I think MVP in biotech is ..
I don't know if there is an MVP. So for a biotech company that's in the therapeutic space, really, you're not going to have any commercial revenue until you have an approved product that is sold on the market, which actually most biotechs never have. So most biotechs go through process of developing new therapies, and they do partnerships with large companies that fund the expensive clinical development of those products
And so that's really where a lot of them end up exiting. So they either acquire it or they sell or partner their product in early clinical development. We're starting to see, which I think is really cool, some biotech companies actually commercialize their own products now, and that was something that didn't happen for quite a long time. So just recently, there's several companies that are actually selling their product themselves. So building their own sales force, and distribution channels
And those challenges are really interesting. Mostly those companies are going after rare diseases where you can go after key opinion leaders and have a really efficient sales channel. But it's still really exciting to see them do that.
Adora Cheung: That's great. Well, let me actually double click on that point you made. So essentially what you're saying is that I can bring my product to market without getting acquired, which used to be the case. At that specific juncture, what has changed that has allowed that to happen?
Elizabeth Iorns: Yeah, that's a good question
I think capital. So access to capital, really important. That the companies can actually access enough capital to get all the way through to approval. I also think the FDA has done some really interesting work trying to come up with reasonable clinical development strategies for particularly rare diseases, where companies can actually get registration with a fairly small trial. So that makes it actually feasible for a small company to be able to do that
And then in terms of distribution, I do think that strategy of just building really strong patient advocacy networks, working with the key opinion leaders in the community, through the hospitals that these patients are treated at, provides a way to distribute that
In the past, I think if it's a blockbuster indication, it becomes very challenging, you have to build a huge sales force, you have to go and sell this drug with old school pharmaceutical sales reps, it's not easy to do that.
Adora Cheung: I also see this proliferation of startups, maybe, actually, since the beginning of Science Exchange, where you're helping try to speed up experiments, you're helping speed up trolls, you're helping speed up getting through the FDA process
And so that may be helping a little bit as well.
Elizabeth Iorns: Yeah, definitely. We've funded some really interesting companies in this space, that put in place, regulatory infrastructure for the FDA. So the expertise is out there, and people are starting to productize that, in a way that maybe wasn't available previously. So people would have to go and hire regulatory consultant, that would be very expensive, literally like more than $100,000 to come up with your FDA strategy
And now there are companies that you can actually go and work with them to provide the productize vision of that process. So Enzyme is the company that Y Combinator funded, which I think is super interesting. And all of these are just enabling technologies that will hopefully provide the infrastructure similar to what we've seen in this office space.
Adora Cheung: So you've now like we said before advised and you've mentored hundreds of biotech founders at this point, what are some of the common mistakes you've seen biotech founders make, and what are the ones that you just need? What do you avoid at all costs because it's just so detrimental?
Elizabeth Iorns: Yeah
I mean, I think our biotech founders first of all are really amazing, because they are often those people who have taken that risk and have stepped out of academia or other very established careers and said, "I'm going to go do this startup and there isn't really that many role models for me to follow or success stories for me to follow." So I think they themselves are incredibly impressive
And often I do office hours with them, and I'm just learning from them
I say, "She really cool." But I think, one mistake I have seen people make is just ..
I think all startup founders do this, it's not doing the killer experiment, not actually just [inaudible] to the chase and saying, "Okay, these are just the minimal things I need to do to truly answer the question." And you almost don't want to do it. Because if it doesn't work, the company's did, and I allow the time push our companies a little bit, I'm like, "But why haven't you done that experiment? That experiment would tell you today if this is going to work or not." And I think it's hard when it's your own company to do that, but the sooner you do it, the sooner you have enough money to work on something else. Because like we talked about, with science if you can't get the actual science to work, you really are in a difficult position.
Adora Cheung: So say you're a scientist or you're an academic who's looking to get into startups, I think a lot of people still think maybe you need some business co-founder, tell me do x, y & z. What do you think about that and should that be a goal?
Elizabeth Iorns: No, I don't think that should be a goal, I think ..
I actually do have a business co-founder, so he will probably be mad if I say that, and he's great
And I literally couldn't have built Science Exchange without him. But not because of his business background, right? Because he's a great co-founder. Because he hassles and figures stuff out, and we work well together
I think business, so much of it is just common sense
I used to get so concerned that I didn't have this finance background
I didn't know how to read all of the income statements and all that. And then I realized after a while, "Okay, I'm just going to sit down with my business co-founder, and he's going to teach me." And so he taught me and then I was, "Oh, it's so obvious
It's definitely not rocket science." So I think the business side, you really as a CEO, when you grow the company, one of the key skills is actually recognizing the areas that you're good at, and bad at, and where you should be investing and bringing in top talent
And so for us, we recently actually hired a CFO. So a year ago, we hired a CFO, and that's been, I think good for the company, but also good from a perception perspective. So as the company reaches a growth stage, actually having that legitimate CFO person does help you, but from I mean, starting out, you should just find people who really want to solve the same problem as you, and really care about it, and also who you really like working with, because that's by far the most important thing.
Adora Cheung: On the flip side, if you're not a scientist, and let's say you're a programmer, but you're interested in getting into biotech, what should you do? Should I go back to school, get my PhD? What's the potential path for me?
Elizabeth Iorns: That's a good question. Part of me thinks you should go back to school, like, I think there is this, there's a lot of interest from Silicon Valley in biotech, people are super interested in even just hacking themselves, this whole movement around really personalized, understanding all of your own biology
I think it's really cool
And I By the way, I do think that the future of biotech and where I always think about for reformer and the work that we're doing is I believe that the future will definitely involve a strong component of user pays. So I think the products that are developed will have to be in indications that the patients are actually willing to pay for
And there's a lot of research at the moment, that's in areas which I think, are potentially problematic, because they are diseases where people are not really that sick, or they don't really feel sick
And so getting them to adhere to those medications, very, very challenging
In contrast like things like migraine drugs, actually Amgen's migraine drug has outperformed, it's predictions in the market by 10 times
And I think that's because people genuinely go to the doctor because they are debilitated by migraines and they will pay for those drugs. So I think trying to think about ways that you can focus on users is good. So anyway, to go back to the question, because I got sidetracked. About computer programmers, what should they do? I think for biology and actual scientific research, there is this element of just getting in the lab and truly understanding how experiments are designed and how to interpret them, which I don't know that you can just learn from not doing. But then there is a lot of use for for example, bioinformatics and other analysis tools and platforms where people can get involved without having lab experience
And we do have some successful companies in Y Combinator that are founded by non-scientific founders that are in the biotech space. So Notable Labs is one that I think is incredibly impressive. The funders have basically self taught themselves everything about the sector that they're in, and they're super smart and hungry
And straightaway when I interviewed them, I was, "Yeah, they know just as much as PhDs who work in the space."
Adora Cheung: Cool. So two more questions. One is, just going back to Science Exchange for a minute, looking back, of all decisions you've made, in the early days, aside from just starting the startup itself, that's obviously very critical, but what's a decision you made where you're looking back, you're, "That was a game changer. That was an inflection point in my business."
Elizabeth Iorns: Wow, that is a good question. Game changer. Actually, I think the decision to do the Reproducibility Initiative was a game changer, and it was non-obvious. So in some ways, the Reproducibility Initiative goes against focus
It was a distraction, "Okay, we're going to do this project. But it's not directly related to just going the marketplace." Although the marketplace was used to run the project. But it was so timely and so high profile that it did change the branding and the opportunities for Science Exchange in a way that we never expected
And it did open all of the doors that eventually led to our pharmaceutical partnerships to really like a lot of the success of Science Exchange. So that was probably one example.
Adora Cheung: Interesting
I did not know. When I saw it, I was like, "Oh, wow, Science Exchange." I knew it could be big, but it could be this much bigger, because it just showed what you could do with it.
Elizabeth Iorns: Yeah.
Adora Cheung: Okay, last question is, always my favorite question
In 100 years from now, I mean, you've been around for seven, eight years now. But in hundred years from now, what do you think Science Exchange will be?
Elizabeth Iorns: Yeah, that is such an interesting question. Because I think if you just think about what the world will be like in 100 years from now, I'm not sure any of us have a good answer. But I do think that 100 years ago, the scientific method existed, and people were doing scientific research, and I think scientific research will exist 100 years from now. So Science Exchange has always been extremely purpose driven. So the company's purpose is to enable scientific breakthroughs through connections
And so I think, whatever the world looks like at that time, that's what Science Exchange will be doing
And hopefully, we're just providing that infrastructure that enables people to instantly work with whoever they need to collaborate with in order to make these scientific breakthroughs happen.
Adora Cheung: It's crazy to think that scientific method was only discovered, invented, or do you want to call it, just not long ago, which create this explosion of science in general. Okay, cool. Well, that's all the questions, I have any questions from the audience? Back there. Speaker 3: Given that you've touched on reproducibility several times now, and I believe one of the problems is that, you use statistical methods, you show correlation [inaudible] . But how open do you think the industry is in terms of taking new approaches that might tend towards [inaudible] as a research methodology?
Elizabeth Iorns: So the question is about whether we should look at new approaches too, instead of just looking at correlations to look for causality, right? Speaker 3: Especially in a space with unknown unknowns.
Elizabeth Iorns: Especially in a space with unknown unknowns. So, I think the way that ... So I'm a biologist by training. So I tend to think that we're not just looking at correlations, we try to design experiments that allow us to change something in the system that is a controlled system, and then read an output from that, and determine whether that fits of our hypothesis, that we change something, and has therefore had this downstream impact
I think there's really interesting work that's been done on correlations, particularly with real world data. Trying to look for ways that we can actually use humans in the wild to examine new theories that we have, and then apply those back into the lab. But at a basic level, when you're in the lab, you are using model systems to try to reduce the unknown unknowns. So that you can test specific theories. Speaker 3: Thank you. Speaker 4: You talked about how you basically, went from an idea to what was basically a minimal viable product, in very short space of time, what were they few steps for you to do that?
Elizabeth Iorns: Yeah, so the question is about going from an idea to an MVP in a short space of time, and the steps that required to do that
And for us, actually, that was one of the lessons that I really took when I started Science Exchange, was tried to do something really quickly and get it off the ground. Because I see a lot of people start companies, and they have a lot of enthusiasm at the start, and then they're also working full time jobs, and trying to do this on the side
And the progress you make obviously is limited, because you just don't have the time to put into it. So where possible I think, it's really great if you can just take time out to say, "Okay, I'm going to do Y Combinator or something for three months, and really launch the company." So for us, we literally had the idea, and then I think it was February 2011, and we were talking about different ideas, and then we thought, "This is really a good idea." And so then we applied to Y Combinator. So we made this video
And it was just me and my co-founder, we had nothing, and then actually Y Combinator, Alexis Ohanian , he Skype me and he said, "You're not going to get in." And I was, "Oh, no, why?" And he was, "Because you don't have a technical co-founder, and you really need a technical co-founder." And so we then spoke to all of our friends
And we found a technical co-founder
And this was organized in two weeks. And then we built actually a really hacked together version of the MVP
And we came to our interview, and we had already got something, was very basic. But we had something, and then we got in, and we moved out here in May
And it was three months of just, "Okay, now let's get that launch." And we were actually doing transactions off platform. So we actually were talking with all of our scientists, and I was traveling a lot talking to the people who I could get to use the product. So we actually did hundreds of thousands of dollars of transactions during that time, just to prove that we understood the demand and the supply side, and started to build what would become the product. Speaker 5: Sou mentioned the case of getting a PhD or something like that, that going back to school would not be a main thing that you might need to do, but if you're going into something like biotech, and I guess this is [inaudible] , how seriously, you take an application, especially in biotech, where there's no doctor of there's nobody from an established Medical University with an established degree? And then the second part is, when you go and talk to other scientists, the scientific community, how seriously would they take you if you do not have those credentials on paper?
Elizabeth Iorns: Yeah, so the question is about the importance of credentials in the biotech space
And I think credentials are very important. So if you can have credentials in the space, it's going to give you obviously a huge advantage. But in saying that, I don't think it's ... We have examples where people have been successful without that, and how they were successful is by being incredibly credible themselves when you actually interview them. So in the example of Notable Labs when I interviewed Matt, and Pete, they just had researched everything about the space. They'd read every scientific paper, they knew in-depth about cancer stem cells, about the limitations, about the essays that they wanted to use
And I met with them much longer than I would have, if I had a surrogate, which would be the credential of them having a PhD from a top university. But by talking to them, it was clear that they did understand the space, and that they were incredibly motivated, due to a family connection to really try to put something in place that could help solve this issue, which was, in the case of them, they were looking for new therapeutics for glioblastoma
And so I think if you are not a scientist, having a personal driver of why you're doing this actually can serve as a surrogate to get you in the door. So it can get you meetings with top scientists, it can get you meetings with patient advocacy groups that can help you get the company started. Speaker 5: So if [inaudible] .
Elizabeth Iorns: I think it can
I think if you're building a biotech company, you obviously have to build a scientific team, and then you'll end up with PhDs in your team, but you can be a co-founder without a PhD. Speaker 5: Thank you. Speaker 6: This process of this idea to [inaudible] , when did you decide to leave the university?
Elizabeth Iorns: So the question's about when did I decide to leave the university? So I was so fortunate when I started Science Exchange
And I think a lot of people don't realize how difficult, and I totally don't take this for granted, when people ask me about our journey of starting Science Exchange
I think we had enormous luck in the sense that my boss was the Dean of Medicine, at the University of Miami, and he was incredibly supportive of Science Exchange. So he thought it was a great idea. He thought that if I didn't do it, then somebody else would do it
And so he actually let me take three months off to go and do this
And he looked after my lab for me while I was gone
And then once we're out here, it was clear that the idea was going to be successful
And we raised funding straight out of YC
And so I decided, I'm not going to go back
And I was actually nervous about telling him, I wasn't going to go back. But he was so amazing about it. He was just like, "Yeah, I know it's doing great
I knew it would be a great success." And so having that mentor who gave me that opportunity, I think not many people get that, especially in academia. That's actually, again, where I sometimes have this frustration of, I hear the opposite of PhD students and postdocs, they tell me, "Oh my boss just really didn't want me to leave. Really didn't want me to start a company." Actively worked against me, rather than helped me and I think about my experience, and how different it would have been if I didn't have that support.
Adora Cheung: Alright, last question. Right here. Speaker 7: How exactly did you match the expectations from both sides, what the demand side expected out of the supply, and the supply, and started to control the quality perception for both sides?
Elizabeth Iorns: Yeah, so the question is about quality control, and a two sided marketplace. So for us quality control was incredibly important, and actually is one of the core value propositions of Science Exchange. So we qualify all suppliers before they're available through the marketplace
And then we also have a continuous monitoring process where we actually look at performance of every single transaction. So we have more data on performance than anybody else
And we can actually say with certainty, well at least more certainty than other people, "This provider will likely do a very good job on this type of experiment." We also put in place the way our actual platform is structured. There's clear outline of the deliverables that are generated, the expectations are set upfront
And I think a really interesting stat that we tracked closely is Science Exchange's Net Promoter Score is 78, and our suppliers, Net Promoter Score is 67, and the industry average is zero. So we think that's amazing, because it's the same suppliers, but when used through the platform, they perform much better
And I think the reason is because it's structured and it's clearly outlined, what's going to be delivered
And then there's an expectation that if you don't perform the information will actually be available to everyone else, when they're making a decision. So it becomes a very strong incentive for people to perform and make sure that they're delivering what they agreed upon.
Adora Cheung: All right. Thank you so much, Elizabeth.
Elizabeth Iorns: Sure. Thank you.
Adora Cheung: Thank you everyone.