The Bioinformatics CRO Podcast
Episode 21 with Noor Siddiqui
Transcript of Episode 21: Noor Siddiqui
Grant: Welcome to The Bioinformatics CRO podcast. I’m Grant Belgard and joining me today is Matthew Holt. Matthew is a professor at VIB-KU Leuven in Belgium, in the Department of Neuroscience. He is also a big fan of the Liverpool football club. Welcome Matthew.
Matthew: Hi, Grant. How are you doing?
Grant: Doing well. It’s good to have you on.
Matthew: Good to be here.
Grant: So can you tell us about what you do? Yeah. So I, as you quite rightly said, am a scientist, but unlike the vast majority of neuroscientists who work on neurons in the brain, we’re very interested in the non-neuronal cell types, in particular the astrocytes. And we’re very interested in these cells because they’re the main support cells for neurons in the brain.
They influence how they work and during disease, they go wrong. And it seems increasingly likely that it’s alterations in these glial cells that actually initiate disease. So what we want to understand is the basic biology of the cells, so we can try and correct them when they go wrong. And hopefully we can keep people’s neurons alive in diseases, such as Alzheimer’s and Parkinson’s.
Grant: What have been some of the biggest discoveries in the glial biology space during your career?
Matthew: The big initial discovery for me was the discovery that astrocytes can actually sense activity in local neurons, but then can actually respond to it and respond to it by releasing neuroactive substances, which modulate neurotransmission. And then I think just recently a very, very interesting finding was the fact that astrocytes are responsible for the elimination of neurons, both during development and also in adults, which implies that they could also basically eat neurons during neurodegenerative disease. And I think that that’s a huge finding, which will alter the way people look at this type of condition.
Grant: How has the field responded to these discoveries? Are neuron-centric people still as strongly neuron-centric as they once were or has the field as a whole been moving in this direction?
Matthew: You know, I think there’s an increasing realization amongst neuroscientists that glia in general, so microglia and oligodendrocytes, are essential. I think one of the big issues of course though, is people are drawn to fields where they can actually do experiments. I think part of the problem with the glia field for years has actually been the lack of tools. And whenever you write a grant to work on glia, it can seem like it’s 20 years behind the people who are working on neurons.
And I think that that is a very, very difficult mindset to overcome. Having said that, I think with the type of technologies we’ve got now from single cell sequencing, viral vectors, genetically encoded calcium sensors, cAMP sensors, so on and so forth. It is becoming much easier to do those experiments. And I think as you see that, of course, it’s going to draw more people in.
Grant: So as our listeners can probably infer from your accent, you’re not Flemish. Can you tell us a bit about what it’s like to work as a scientist in a foreign system?
Matthew: Oh yeah. I’d be happy to, I mean, I think there are advantages and disadvantages of course. I think one of the advantages is personal growth. You get different perspectives on science. Of course different experiences of how organizations are run and you can sort of cherry pick the styles that suit you best. Of course, there are also disadvantages. I think being a foreign scientist coming into any system, you need to understand that system.
And I think the higher up you go, the more important that becomes. Especially. I think actually now the tendency is to get large consortium grants, and you need to have a preexisting network, you need to have established people who value your work, can vouch for you in a way and are willing to promote you.
And of course, that all takes time to develop. And I think one of the problems for early career stage scientists is very much that you have this five-year period where you are supposed to be hyper-productive. You’re supposed to pull in grants, publish songs and so forth. And if you’re having to fight your way into a system, it just makes things much harder. For me, I think I was quite lucky because I moved to Flanders with a very large European Research Council grant, which was one and a half million. So, for five years I could run quite a nice little group on that while we were finding our feet. But if you don’t have that type of resource, I can imagine it’s very, very difficult.
Grant: Other than getting a large grant, what steps can early career scientists take to mitigate that?
Matthew: I think the obvious one of course that I would say is network. Network as much as possible as early as possible. I mean, in general, I think most people usually end up going back to their country of origin because that’s where they have the biggest network available.
But that’s the first thing I would say, is get a very strong network. I would also say to most people looking at a postdoc, actually, that it’s very, very tempting to go to a big lab with lots of money, with star power almost. And assume it’s all about the science, but I think what you really, really want is a very good personal dynamic with your PI as well.
You want someone who’s going to pick up the phone in three or four years’ time and tell their friends. I have a really good postdoc. You should be picking this person up. And I’ve known one or two examples of that actually in my career. Erwin Neher, who was a director at Max Planck at the time, was like this. Let’s face it nowadays. There are lots of people with very good papers. It’s very attractive to assume it’s a metrics game, but I really do think personal connections count for an awful lot.
And I would actually even go so far as to say, if you don’t think that you’re going to get that level of support or develop that personal connection, you might want to get out as quickly as possible and find yourself a lab where you will have that support, if your ultimate goal is to stay in academia.
Do you think that’s also true at the level of where you do your PhD or does that really become a lot more important with where you do your postdoc? Is the PhD lab also critical in that respect because there’s often this decision people make: do I work with the young PI who can give me a lot more attention and who I’ll probably get more mentoring from, or the very senior PI who’s always on the road, but very well connected and well-resourced.
Matthew: You know, I would say that I think is probably a bit more important at the postdoc level. Or at least when I was doing my PhD, the people that I interviewed with, so long as you’re coming from a good Institute, were much more interested in trainable potential at that time than papers on the board.
Now, of course, whether that’s altered slightly now is open to debate. I mean, I certainly know that for the students coming through our institute now, it’s a much, much more competitive world. I have to say that I think that the example of the LMB, the Lab of Molecular Biology in Cambridge is awesome.
I mean, there’s a reason why it’s produced so many Nobel Laureates. And I think that this idea of small dynamic groups where students have a level of attention free to discuss with senior lab members, but also are given scope to try their own thing even when the PI is 90% sure it will fail.
I think it’s a wonderful model. Right. And it’s a very, very hard balance to get, of course. But in terms of young career developments, I think it is a fantastic model. For me personally, I think if I had been put in a lab of 50 people and I had been given a desk and told to sort it out, then I would have struggled. So I think it’s also person-dependent to some degree.
Grant: And let’s talk a bit about that because you had a pretty unusual path into science starting in law and so on. Can you maybe take us back, way back to what attracted you to law in the first place? And then why did you end up going into science?
Matthew: Why law? I think probably because of the background that I came from. There was always an assumption that if you went on to university, it was to end up with a professional career. So you would be a medic, a dentist, a lawyer. And I think there were two things that drove me to all law.
The first one was that my elder brother had already got into medical school and I had spent maybe the best part of over 11 years, constantly being compared to my brother. So going into law seemed not a bad idea. Then the other thing that sort of appealed to me was the almost romantic notion of defending the innocent party against the odds with these courtroom flourishes of oratory, maybe.
But I actually realized quite quickly that at least in the UK legal system, it’s all based on precedent. And if you actually have a science background, which is what I had all the way through school, it’s very, very difficult to accept that nowadays you should be bound by a ruling made 250 years ago.
Because up until recently, it was actually a central pillar of British law that basically, if I remember correctly, the judgments made in the House of Lords were absolutely binding from the time they were made onwards. And I find that whole concept actually quite absurd, with the way societies change, things that were regarded as perfectly acceptable for my parents’ generation and grandparents’, now, people would be appalled.
That was it. I found it very, very hard to accept that. I also found the work incredibly dull, if you like. I think because of course it’s very, very book and library based and I’d been used to being quite active in practical classes and taking a chunk of sodium and throwing it in the pond and watching the explosion experiments in chemistry.
So I was actually quite frustrated. And then one of the core legal courses that you have to take in the UK is actually at most universities is property law, and we have a lecture on intellectual property. It was a case about an American university, who had taken a tumor from a patient and this tumor produced large amounts of GM-CSF, which at the time was being touted as a therapy for HIV.
And the question was who actually would benefit from that? Would it be the patient or would it be the university who basically made the product? And it was quite funny actually because I was the only one in the class who had any idea of what GM-CSF was or what immortalized cells were. And as I was going out of the tutorial, the lecturer actually said to me, if you have a science background, you should seriously consider going back because over the next 10 years, patent law will explode and it will be scientists who drive this.
Grant: It’s incredible. How impactful those little comments can be
Matthew: Massively, massively. So I applied to go back and do biochemistry. And that’s why I ended up in Liverpool, hence the football connection. And the first two years were a really standard university experience. And in the third year, I actually did an industrial placement with Eli Lilly who at the time were arguably the world’s biggest pharma company in the neuroscience space. They had Prozac on the market at the time.
I was lucky enough to be there when they released Olanzapine, which was a huge hit for them as well. And this was really my first proper lab experience. I really enjoyed it. I got on fantastically well with the guy who was head of research at that time. He was coming from Bristol, David Lodge, who worked on excitatory amino acids. And David was just the most wonderful guy. And he used to take me to meetings, quite high-level meetings at Eli Lilly. Then he would take me off to meet collaborators at universities. Towards the end of my time at Lily, it was actually David, who said to me, I think you should do a PhD. Get back to me, if you want to do it.
And the funny thing was of course the arrogance of youth, it was almost like, Oh, I’ll find my own place to do PhD. And I applied to the LMB because I really wanted to go to the birthplace of DNA. I went for an interview with the guy that I eventually worked for. And the first thing he said to me was I see you know David lodge; he was my supervisor as an undergraduate. Which was my first first experience of how small science actually is.
And I had a great time as a PhD student. The LMB is a fantastic institution. There’s no doubt about that. I think at the time I’d probably have the best of it because there were still 5 Nobel laureates there. And it was fantastic. You could have coffee with these people, get to call them by their first names and they would know who you are and what you were doing. It was a fantastic environment. And then of course the question was, what do you do after you finish your PhD? Do you really want to put a suit on and go back to work in a bank or a law firm?
At the time I was actually renting a room from a family in Cambridge. One of the members of the couple, he was a senior academic at the university and his wife was always telling me, go and work for a bank, go and work for a bank or go back to law. And I remember one night he just pulled me in the room and he actually said, look, Matthew, just look at you. You’re just never going to do anything but science. I can’t actually imagine you wearing a suit type thing.
So I think that was it. And then that’s what got me on the postdoc train. But coming back to what you said actually about just to conversations or moments really having a profound influence. When I was finishing my PhD, I actually had offers from Shelley Halpain at the Scripps in San Diego.
Grant: You could have lived on a sailboat.
Matthew: Ah, yeah maybe. I also had an offer from Harvard Med and we actually went over, my supervisor and I to Erwin Neher’s 60th birthday party that was being held at the Max Planck Institute. And I just asked Erwin, can I go in and look around your lab please? Because I’m on the job market and it might be nice.
And I got in the lift and all the Max Plank directors have a floor to themselves, usually. And their name is on the lift button.
Grant: It sounds very German.
Matthew: Well organization is everything right? And I looked there and I actually saw the name, R. Jahn, and I’d seen Reinhard Jahn give a talk a year before at a FENS meeting. And it was a superb talk. And he had just been recruited back from Yale at the time. And I didn’t know this. And then you ended up with a single building with Reinhard Jahn, Herbert Jackle, who’s a fabulous developmental scientist, and Erwin Neher. I mean, it was just ridiculous. And that was it.
I wasn’t going to go to the US for a post-doc after that. And again, it’s just one of those really old moments. You walk into a lift and that’s it. It just changes the future in a way.
Grant: You’ve of course been in Belgium for several years now. But you spent a number of years in Germany. Can you talk about what your culture shock was like in both places? What maybe didn’t surprise you and what surprises did you find?
Matthew: The biggest shock was the expensive Belgium compared to Germany. I have to say Belgium, or at least where I am now in Leuven, is very expensive compared to Gerstungen, where I was based before. Although I should qualify this. If you have to live in Munich, you probably need to own a bank.
I mean, Munich’s very expensive, but the first thing was the expense. And I think generally Belgium’s much more expensive, but I have to say in general, I think that moving from the UK to Germany, from Germany to Belgium, there’s not huge changes. I think European culture is quite standard.
Maybe, I think actually your culture shock when you moved from the US to Oxford would have been quite larger. Right? I think that there is this fallacy that because we speak a common language with the US somehow the cultures and the expectations are quite close.
Grant: But what’s the expression? People divided by a common language?
Matthew: I think so. Yeah, definitely. I think people forget actually, the reason we ended up with a United Kingdom was because we wanted a protestant monarch from Hanover rather than the catholic monarch from Scotland. In terms of culture shock, that wasn’t so much. I do miss Jim and Brad, I have to say.
And I miss German beer, but luckily, luckily the European train network is fantastic and you can zoom around quite easily.
Grant: That’s a big thing to say for someone currently living in Belgium, right? It’s not like you’re drinking bud light
Matthew: Well sure, sure. I don’t know. Maybe it’s the orderly nature of Germany, the fact that they have laws, which govern everything. So you basically know what’s in the beer, whereas in Belgium, if you go to the shops in brews where all the tourists are: cherry beer. I mean, it does seem a little bit bizarre, some of the beers that they come up with.
Grant: Yeah, it’s good though as a one-off. So do you also have some unusual hobbies? Do you want to talk about road races?
Matthew: Yeah, so I was actually brought up in the Northwest of the UK and just off the coast is a little piece of rock known as the Isle of Man. And every year they close the roads on the Isle of Man and they just turn it into one huge race track. So it’s like 37 miles. It goes through Douglas, Ramsey goes over the mountain as well.
And it’s a big thing, actually, if you grow up there. It’s really quite spectacular, first of all, that there are still places that allow this and also that you actually have people that want to do it. I think it’s quite hard to explain, but basically you have people racing at speeds of 200 miles an hour, so 300 kilometers an hour. Basically on normal roads that on normal days have cars, tractors, buses, that type of thing. It’s in close proximity to stone walls, telegraph poles. And actually you’re so close that you could probably put your hand down and actually touch them as they go past.
And it really is the most spectacular thing to see and to hear. And also the people that tend to go really are hardcore motorbike friends. And it has to be said from a very young age, I have had this real interest in motorbikes, which I think comes from my dad. And we used to spend hours on a Saturday afternoon going around the local bank shops and so on and so forth.
So we basically try to get to the races where we can. And in Ireland, they also run smaller race meets at the weekends during the summer. And that’s also great because I love Ireland. I think Ireland is a fantastic place. I think the Irish are great people: very warm, welcoming. And Guinness is great as well.
Grant: And what’s this, I hear about Olympic boxing trials.
Matthew: Yeah. So it’s quite bizarre. My mom could never reconcile why a neuroscientist would like boxing? People have a preconception when you say boxing. I mean, obviously there’s the professional game, but the amateur level is a very, very different sport. but yeah, I do love amateur boxing because I think the guys are phenomenal athletes, to be honest with you.
They’re great athletes. They are incredibly dedicated and yeah, this time last year, actually I did have tickets to the British Olympic boxing trials. And then of course COVID hit. And they closed them down and then there was talk of starting them up, but the boxes would have to wear masks until they got to the ring and so on and so forth, which I thought was quite ironic. Given the fact that when they got to the ring, they would spend nine minutes hitting each other, but…
Grant: Socking each other in the face and breathing on each other.
Matthew: Yes, of course. You know, the aim of the sport actually is to amass points basically. And you score points by striking your opponent. For me, I find fighters that can actually slip punches, work the ring well, avoid taking punishment, and to do that actually for nine minutes at a high level, it’s the longest nine minutes you’ll probably ever have in your life.
It’s not easy. They are very, very fit, mentally very focused. The professional games are different, of course there’s money involved. I think that people are badly used in the professional game, but for the amateurs I do like it very much. You can say what you want, but American football, whoever had the idea of basically using the head as a weapon. I’m not sure there’s a lot of difference.
Grant: Yeah. Fair enough. So when you’re talking about the skills of boxers, my mind for some deranged reason went to science. So what do you think are kind of the scientific equivalents of that? What makes you a good scientific ring fighter?
Matthew: There’s a certain level of dedication. You have to love doing what you’re doing. I think there’s a certain amount of accepting pain. Whether that’s purely physical or just the sheer mind-numbing pain of things, not working. I think both are quite solitary events. I mean, basically your performance is dependent on yourself and I think there’s an awful lot of focus needed and many hours. To get to the top in either, I think you need good skills.
And those come through hours and hours of practice and repetition. I remember talking to you about this, so-called, 10 thousand hours of practice. There’s a reason why people get good at Western blotting. Right. And that’s just because they make a lot of gels and they do a lot of blotts.
And I think it’s sometimes really easy for people to overlook this because they regard it as a job or I’m at school. But rarely all those hours in the classroom and doing experiments, it’s all part of the 10 thousand hours. And that’s at least my view, if you like.
Grant: Following a bit on that cause I don’t know if this would be a contender, but what are the biggest misconceptions you see among young scientists these days? And what are the biggest mistakes you think early career scientists make?
Matthew: I think we all have this view of the heroic scientist, who’s on their own working all hours, day after day after day, trying to work through a problem. I think there are very, very few places in the world, unfortunately, where you are able to do that within the academic system. So I’m thinking of places like the LMB ,maybe Janelia Research Campus, but for most scientists, you are in a grant-based system and you are judged by productivity.
And I think one of the things that people need to keep in mind is actually knowing when to give up. This again was a conversation. I actually remember having it at the sink with a colleague of mine when we were at the Max Planck. I had just come from the LMB. There were no problems with money. There really wasn’t pressure to publish and my colleague, who’s a very good friend, he’d come from a normal university environment.
He actually said to me, this is not normal out in the real world. You are going to be asked at the end of three to five years, what have you got? And I think there’s a very, very fine line between persevering and giving something a really good shot, but also knowing basically when you just have to drop it or at least put it to one side for the time being. Because at the end of the day, you have to leave your PhD, you have to leave your post-doc with papers on the board.
Now of course you can argue whether that actually is a good use of public money. Maybe you could argue that public money would be better spent by letting people go away, have ideas, and give them funding for 10 or 15 years. But that’s not the system that we’re in.
To a certain degree. You have to play within the system you’re in, of course. And I think that for most people, it’s a fine line between pushing something that could give you your Nature Neuroscience, your Science paper, your Nature paper, or basically just putting your career into a grave after five years.
Grant: Well, what changes would you make to the current system? If Matthew Holt became the dictator of public science funding, what would we see?
Matthew: One of the big things that carries a lot of weight on grant committees tends to be track record. And this may be a little bit contentious to say because I can see it from both sides. Right. You could argue that the best predictor of future performance is past performance, right? I mean, you can argue that. And to a degree there is some truth, people who published in the past are probably gonna publish well in the future. But at the end of the day, a good idea is a good idea.
I can never remember the name of the guy, but the guy that won the Nobel Prize for the Higgs Boson.
Matthew: Well, yeah, Higgs, but I can’t remember his first.
Grant: Peter or something.
Matthew: Yeah. Peter Higgs. But you know, he had like seven papers over quite a large period of time and he openly admits that he would never have survived in the current academic climate. But all of those papers from what I understand, because this is physics that is well above my level, are hugely influential. So if I had control of a large budget, I think I would be far more likely to give money for ideas to a person without necessarily a huge track record.
I think in a way also this is why established large groups suck up a lot of grant money, and why it’s difficult for young group leaders to get on the ladder. Because funding agencies want to see something for their money. And I also think as well, that track record sort of feeds into this feeling that I can’t have a gap in my CV.
So you go from doing a PhD on LTP, and you do LTP as a postdoc, but it’s slightly different. And then you do it as our PI. But if you actually look at where big advances come, they usually come at the interface of subjects with people who have really broad expertise and can appreciate different techniques.
I think a fantastic example was Roderick MacKinnon, who started life as a channel physiologist. And he used the tools that he came to know about doing physiology to check that the channels he was purifying were still functional. And he was also smart enough to realize that bacterial channels were easier to purify and then to crystallize, but this was someone who cross-fertilized their research across disciplines.
I think that you want people like this. I think it was Seymour Benzer that actually said young people should be given a number of passes to do what they want when they’re a bit younger. Then when you run out of passes, you’ve got to decide what you want to do, but my concern is that people are being constrained far too early because of the comparative lack of funding, but also the number of people in the system as well.
Grant: Maybe one day, we’ll all have our own NFTs and go back to a Renaissance model of science funding. So for our closing question on what important question do you disagree most with your colleagues?
Matthew: Actually, I’d probably have to say the thing that I disagree most on is with my colleague Bassem Hassan about whether Liverpool or Bayern Munich are the better team
Grant: Important questions. Well, great. Thank you so much for coming on.
Matthew: Well, thanks for having me, Grant.
Grant: Welcome to The Bioinformatics CRO podcast. I’m Grant Belgard and joining me today is Lucia Huang and Jimmy Qian. Lucia graduated from Yale, worked in private equity, joined a biotech startup and got her MBA from Stanford before co-founding Osmind. Jimmy did his bachelor’s and master’s at UPenn and is on leave from the MD program at Stanford. He co-founded and led three nonprofits prior to co-founding Osmind. So Lucia and Jimmy, welcome to the show.
Lucia: Thanks, Grant, for having us.
Grant: What can you tell us about Osmind?
Lucia: Yeah, I’ll start. So Osmind is a data engine and digital infrastructure that we built for treatment-resistant mental illness. And so we’re really hoping to help move the needle for the patients that have suffered the longest in mental health.
And how we do that is we have software that serves practices all around the country. It’s an electronic health record and it helps clinicians that are using more intensive, innovative mental health modalities. And at the same time aggregates information that we can then use to help with things like drug development and better insurance coverage.
Grant: So Osmind is a public benefit corporation (PBC), can you tell us about that?
Lucia: Yeah. Jimmy, you should actually take this one, given you just published a blog in health affairs about why all healthcare companies should be PBCs.
Jimmy: Yeah. So our article mostly focused on hospitals and health care delivery companies, but Osmind is a PBC, public benefit corporation, and that is a for-profit entity that has specific legal frameworks around holding us accountable to following some sort of community benefit, which is pre-specified in our charter.
So we decided to become a PBC because we think in healthcare and life sciences, just companies in general should do this because we all care about patients. And that’s what really matters. And I think a lot of companies just in the healthcare space, sometimes don’t do a really good job of really maximizing patient outcomes and focusing on that.
So we decided to put that into our actual company charter, so that it’s as important legally as everything else is, such as maximizing stakeholder value, which is what every for-profit company has to do.
Grant: How do you anticipate being a PBC will impact your decision-making as you grow?
Lucia: Yeah, I mean, I think a lot of it comes down to our decisions around business model, for example, so as alluded to earlier, one of our core goals is to never charge patients ever for our product or services, even though they’re actively getting to use the software. And we plan on bringing a lot of other benefits to them. And part of the reason why we can do that is because we’re a PBC, because our stated benefit is to maximize patient access, innovative mental health treatment.
So, I think from a business model perspective, that’s just allowed us to think a little bit deeper about who we want to be, subsidizing the use for other stakeholders. And then on top of that, I think it’s also really helped us align with the types of people we want to build our team and to work with long term.
So, every single team member is really passionate about the cause as well, and has consciously chosen to work for a public benefit corporation. So there’s a lot of sort of social impact orientation and a mission in mind and then investors as well haven’t been an issue in any way in terms of attracting funding and it actually allows us to select for investors that are mission aligned with us as well.
Grant: So you mentioned not charging patients for anything. How does that square with insurance co-pay requirements?
Lucia: We don’t collect insurance from patients directly. We don’t provide the modality. We just provide the software that providers use with their patients. So we’re not actually collecting payments from patients, but for example, because they’re getting use of the app, we actually do help them submit insurance claims to insurance companies. And we don’t charge for that. That’s something that actually companies will charge a 10% fee for, and we don’t charge for it at all.
Grant: So who are your customers?
Lucia: Yeah, we have sort of a trifecta of customers: providers, so the doctors who are using the software. Electronic health record software has been around for about a decade, but it is the sole source of documentation for clinicians to track patients and how they’re doing is to write notes on them, to do things like prescribed and order labs and all of that. So that is the central hub for everything that they do.
And then there are pharma companies. With pharma companies there are a lot of different projects that we can work on, which is really exciting based on the infrastructure that we’ve built, everything from drug development, helping with designing the clinical trials and running them. For example, we still don’t know how depression works, right? Like we’ve got one general classification that classifies everyone with major depressive disorder, but there are many different subtypes that we just don’t have a good way of defining right now.
So with the data that we’re gathering, we’re able to better understand and segment patient populations and therefore guide pharmaceutical drug development to better optimize our clinical trials and targets and stratify specific patient populations for precision medicine.
So, that’s just one example of the many ways that we’re working with pharma, but another is recruiting patients too, because we’re getting to work with so many on our platform. We’ve got a better line of sight into which patients might actually qualify for these trials and reach out to them and give them that opportunity for help.
The last customer that we haven’t touched on is insurance companies. So I think insurance companies are also getting really excited about risk stratification, especially around treatment-resistant populations. The average treatment-resistant mental health patient costs twice as much as a “conventional” mental health patient.
So this is a huge cost for insurance companies to contain, and they need a better way of identifying these patients earlier on in their care journey. They need a better way of intervening using data. And that’s data and services that we’re able to provide to them as well.
Grant: How would the insurance company intervene in the patient’s care?
Jimmy: So these companies are really incentivized to decrease the cost of care for these patients, which involves both the actual direct costs of mental health and behavioral health, but also all the co-morbidities because unfortunately, a lot of these individuals often have other healthcare issues that are highly comorbid with their underlying mental health conditions.
So insurance companies have various different ways of dealing with this issue. Some of it is at the population care management level, where they try to optimize how to send people to the right types of care at the right times. And this could involve kind of preemptively sending them to certain types of providers and doing certain care programs outside of just traditional physician, patient relationships.
This also involves in-the-moment modalities, such as tracking side effects or medication adherence over time. So if someone is supposed to take a medication every day, the insurance company sometimes actually is involved in tracking that adherence because then that gets into longitudinal care management at the individual level.
Grant: How has Osmind evolved over time? Are you currently doing what you set out to do in your YC application?
Lucia: Oh man. Really good question. I feel like we’d both cringe if we went and read our YC application from almost a year ago. But no, I feel like we’ve stayed actually relatively on point from a mission standpoint. Like we set out and knew that we wanted to do something about treatment resistant mental health, because there’s a lot of mental health companies out there right now and that’s all really great because we need all the help that we can get. But there’s no one really focusing on this patient population that again is twice as expensive and actually a third of mental health patients are considered treatment resistant.
So we saw it as like a huge area of unmet need and help that was needed. So that’s always been the goal, but I think as we’ve evolved, we’ve really started to realize how much mental health and neuropsychiatry are at an inflection point. Like in the same way that oncology was transformed in the last decade with things like sequencing and chimeric antigen receptor T-cell therapies (CAR-T) and all of that mental health is really at that point right now.
And so we’re realizing that this huge unmet need finally is seeing hope with the new modalities that are coming online and the new understandings we have of phenotyping. So I think we’re able to yeah make a big difference here.
Grant: So your website talks a bit about ketamine and psychedelic assisted therapy and so on. Can you tell us about how you support that?
Jimmy: Yeah, so we work with a lot of clinics that provide ketamine therapy for people with mental health conditions and as psychedelic modalities become FDA approved, which we expect to happen within the next one to two years, we are also very interested in serving those providers and a lot of our current customer base are very plugged into the psychedelic medicine community.
So just kind of broadly were very excited by where the field of interventional psychiatry and psychedelic assisted therapy are going. And this includes a lot of substances, such as psychedelics, which can really induce new ways of thinking that can really help people get out of a funk when it’s coupled with psychotherapy.
But it also includes the psychoactive substances that also might have molecular mechanisms of action that just help people feel better even without psychotherapy. And this could involve non hallucinogenic analogs of psychedelics, and we are very interested in collecting data so that researchers can figure out what the best way to treat people is.
We don’t really know, as a field, whether hallucinations are actually necessary for people to get better. We don’t know if psychotherapy actually is as important or more important than the molecular mechanisms of action. And I think pharmaceutical companies, insurance companies, patients, physicians, everyone’s very interested in finding the answers because we have a really big problem with SSRIs just not really working for a lot of people. And we need to just stop pushing pills and find things that actually work.
Grant: Can you elaborate a bit on psychedelic medications being possibly FDA approved in the next year or two?
Jimmy: Yeah, this is a crazy idea to a lot of people, just because psychedelics have a really complicated history throughout the world and throughout the US. But ketamine has been FDA approved since 1974 anesthesia. And it is a substance that, although not a classical psychedelic because of the molecular mechanisms of action it has, it is still a psychoactive molecule that induces certain hallucinatory effects in people. So this is used for anesthesia, but in the past 20 years, it has become increasingly used for mental health.
And it could be used as more of a medical model where you just give someone ketamine and then they hopefully feel better or it could be coupled as a therapeutic dose together with psychotherapy. So someone would be actively undergoing some sort of psychoactive experience while talking to a therapist.
MDMA-assisted psychotherapy is in phase three trials right now, the first phase three trial by MAPS has already been completed. And the second one is underway and from everything we’ve heard, it seems to be going extremely well. And the field widely expects MDMA assisted psychotherapy to be FDA approved sometime pretty soon now.
It’s interesting because the FDA has been quite open-minded about these modalities, but they are very careful. For example, they’re ensuring that MDMA is being coupled with psychotherapy in a very controlled protocol, which involves a certain number of hours of therapy together with exactly how the medication would be administered.
Psilocybin assisted psychotherapy is in phase two B trials right now, and that will probably follow thereafter. And there are a number of other molecules that are in active development throughout phase one and phase two studies, which includes things like DMT and ibogaine and a lot of molecular analogs of all of these classical psychedelics.
Grant: Do you think Osmind might play a role in pharmacovigilance once these are approved?
Lucia: Yeah. I think that’s going to be a big part of it because we’ve got real-time monitoring built into the system. Patients are already reporting side effects, for example, of some of the treatments they’re receiving.
So there’s going to be a big need for collecting that sort of data and for the real time nature of it. So that’s built into the clinical workflow of the clinician, right. They want to know too, if patients aren’t feeling well or are feeling side effects based on these therapies. So it’s going to be a part of it.
Grant: Who are your competitors? What else is going on in this space?
Lucia: Yeah, it’s been pretty interesting. I mean, I think there are lots of mental health companies out there, which is all really wonderful, but not really anyone doing anything in treatment-resistant depression. So I think we’ve seen just different mental health models working with subclinical populations, either through self-insured employers or direct to patients by doing like an SSRI subscription or something, but again no one is really working in treatment resistant health whatsoever.
I think there are folks that are doing similar things that are more therapeutic-area agnostic. So, you know, the Flatirons and Tempus’s of the world, but there’s nobody doing it for mental health. So we’re hoping to sit at that intersection and provide something of value for both researchers and providers and patients.
Grant: What do you expect will be your biggest challenges?
Lucia: Oh man. Well, building a company is always tough, but I think overall like the whole field of mental health, while it’s got a lot of momentum behind it and moving in the direction that oncology was, there’s still the basic limitation of science, right?
Like we still don’t know what actually causes mental health conditions, whether it’s genetic or environmental or other factors. So there are the unknown risks, scientific risks that we have to deal with. But I think we’re seeing a lot of promise and potential, especially with new modalities, like psychedelic medicine and neuromodulation and whatnot that we are getting to a better understanding.
Grant: So what is the most valuable thing that Osmind does for patients?
Jimmy: We work with clinics and can really improve the patient journey. So this involves coordinating care with how they see their different providers, especially in this space, they often see multiple types of providers at the same time.
So we try to coordinate that as much as possible, helping lower barriers to treatment, which includes helping with insurance as Lucia had mentioned earlier. Helping the providers automate a lot of tracking of outcomes, which then helps providers give better care for the patient, but also empowers the patients and their family members and friends to also track progress along the way. So they can also take more ownership in navigating their own journeys. And I think that’s kind of the immediate impact. Longer term, definitely trying to just move the needle tangibly in terms of research.
Grant: How do you support researchers?
Jimmy: For researchers, we collect a lot of data that could be really useful to developing a better understanding of mental health conditions at a biological level. And this could include information that’s clinical, that’s patient-reported, that’s digital, and obviously biological as well. So that’s kind of the data engine that is useful for researchers. There’s also a number of tech-enabled services that would be really useful, such as enabling clinical trials to go much faster than it would otherwise, being able to have precision recruitment for clinical trials, and then site selection and managing the whole process.
So that we can actually get the right people into these studies. And I think one problem in mental health that has really been a big pain point is recruiting the right types of people into studies. My personal belief is a lot of assets have failed in phase three trials, not because they don’t work, but because they just fail to reach statistical significance due to the people that were put into the studies. That’s a kind of phenotyping and diagnosis problem that just the whole field has suffered from.
So, if we can now collect multi-modal data where we can really take an objective approach to enroll the right types of people that we already know are going to benefit the most, even without any underlying scientific innovation, that in itself could hopefully move the needle and get more medications approved for people.
Grant: Where did the idea for Osmind come from?
Lucia: Yeah, Jimmy and I met in a class at Stanford, a couple of years ago. It was one of the rare classes that are cross-listed between the business school and the med school. And we just became really good friends. And after that started talking about all the ways that healthcare and the healthcare system are messed up and really thought a lot about mental health, just given our own personal backgrounds.
We both grew up in households where mental health is extremely stigmatized and grew up in the Bay area where it’s very prevalent amongst adolescents, especially. And then also just realized that there’s still a whole population of people out there that haven’t been able to get the help that they need. That coupled with the excitement about where the whole field is going and had been following psychedelic medicine as well, made us realize that now was really the time to do something. And this is the opportunity to help treatment-resistant patients with everything new that’s going on.
Grant: So by the time you graduated, did you already basically have the basic business plan formulated?
Lucia: Yeah, the timing was that we started working on it in my final year and then by the time I graduated, we had started Y Combinator. I think we started it right before my graduation date and then Jimmy decided then to take a leave of absence from med school.
Grant: Very cool. And where did the name come from?
Lucia: Ah, yes, the name it’s kind of one of those cool combination names. For one, the person that coined the term psychedelics is named Humphry Osmond, so we really wanted to pay homage to him. And then also just because we’re really hoping to build the underlying infrastructure or operating system for mental health that is the OS and then mind, given that the mind is also the operating system of the body.
Grant: What have been your biggest surprises over the last year?
Jimmy: Yeah, I think a really big surprise for me, personally, I won’t speak for Lucia, is just the interest in the field that has popped up. And I think maybe initially I didn’t expect COVID to have this sort of massive impact on just everything in mental health and just scaling up mental health care to as many people as possible.
And public officials are finally realizing how big of an issue this is. So that’s been a very pleasant surprise. And I think seeing how that also has trickled down to the actual interest in the science. So the life sciences of neuropsychiatry and everything related to psychedelics and neuromodulation and neurochemistry or neuroimaging, like everything, I think that’s been a really pleasant surprise for me. And there’s just so much interest in the space right now.
Lucia: Yeah, definitely. I think maybe on the more challenging side is I have so much more respect for founders. I mean, I think Grant, you and I being at a neuroscience biotech startup together before, like I always respected our founder and our CEO, but now I realize how much she went through to keep the company going and to really serve the entire team.
And I think now I feel lucky that I get to do it with Jimmy and there’s the two of us tackling this, but it’s still really hard. It definitely tries you a lot. And it’s exciting to just, I don’t think I could imagine doing anything else now, but working on a company.
Grant: Cool. So what advice would you have for aspiring founders?
Lucia: Yeah, this is my go-to that Jimmy has heard many times, but definitely just learning to prioritize, which is something that I’m still learning too. It’s never a done deal, but I think there’s just an infinite number of things to do. And with founding a company, you’re already ambitious enough to found a company, so it’s never going to be like you didn’t do enough. That’s never going to be the problem. It’s going to be like, you did too much on the wrong things.
And so I think learning how to ruthlessly prioritize and figure out what’s actually going to move the needle because probably just 20% of what you can do in totality is going to move the needle at the 80%,like the Pareto principle. So I think just figuring out what those things are and ruthlessly prioritizing them are the biggest things I would focus on.
Jimmy: Yeah. I mean, I buy into your advice, that’s something I’m trying to improve on as well. I think something that’s stood out to me is to really selectively listen to advice. I think there’s a lot of advice flying around out there and it all cancels out to zero. And I think there are a lot of people who don’t know what they’re talking about. There are a lot of people who are very smart, but they only know what they’ve experienced in their own successes, which might be different for other people’s stories.
So I think it’s being able to kind of find the signal through the noise and just have an underlying self-belief that even if other people’s vice runs contrary to what you believe, as long as there are logical, rational reasons that you have your own mindset to then just follow that.
Grant: What’s the worst advice you’ve received on this path?
Lucia: My mentality towards investors and raising funding has sort of been just like whatever, it’s just commoditized. It’s a price game. But I think maybe a contrary to what Jimmy just said. We have met a lot of great people on our path that have been really supportive. And I think finding those long-term partners is actually worth something.
So as we’re considering our next round of funding, that’s definitely going to be a pretty big consideration, whereas in the past I’ve been told that it literally does not matter who your investors are. Just take the highest price. And I don’t think that’s necessarily true anymore. At least in my mind.
Jimmy: Some advice that stuck out to me as being pretty bad was for us to just try to raise a lot of money during YC, just so that we could say we had money. And just so we could say we knew investors, even though we didn’t need the money and that it would be extremely distracting as we were starting our company.
Grant: And so at Osmind, you have so many different types of people working for you. I don’t think either of you had ever managed software engineers before, please correct me if I’m mistaken though.
Lucia: No, you’re right.
Grant: How have you gone about that? You know, basically hiring people into roles that you may have never managed before and possibly never worked closely with in the past.
Lucia: Yeah, I think you’ve touched on a really interesting challenge of being founders: we are not great at much. Like, I think Jimmy and I each have a couple of functional areas of strength, Jimmy, more than me, but we gotta be dangerous enough in so many different categories and it’s all about breadth and that’s probably one of the most challenging things I find about being a founder is like I’ve never been a software engineer. Like my dad’s a software engineer. It’s probably the closest that I get to it.
Grant: Well, that’s much closer than most.
Lucia: Yeah, that’s true. There’s so many areas where that applies. So it’s about educating yourself and knowing enough so that you can at least be conversant in those topics and then finding people that you really trust to do the vetting for you. Because I think you’re never going to know enough to be like a hiring master in each of these areas, but knowing how to set your criteria in an objective way, and knowing how to find people who can help you with that assessment are really, really important. And then once you do make these decisions, like completely trusting in the people that you brought on because they are the domain experts and knowing what you don’t know with that.
Grant: What have you learned about each other?
Lucia: Oh, man. So much.
Jimmy: I’ll start just because it’s something I actually think about quite often, which is Lucia is just a machine in terms of getting things done and executing. And it’s something that I really try to learn from as much as I can. When something needs to get done, she’ll figure out a way to just do it even though, as we just alluded to, it might not be something she had previous experience in. And she will still figure out the right way to do it. And it’ll be great. It’s something I’m trying to learn more about.
Lucia: I feel like what’s been really great is that we challenge each other a lot and disagree with each other a lot. And there’s no pride in authorship and there’s no hurt egos or anything when we challenge each other. So that’s really great, but I feel like Jimmy’s really good at just throwing out crazy ideas, kind of seeing what sticks. And then we always walk back from it, but it’s really great because he just thinks really big.
And I think that’s really important because we are working in a field where thinking big is so critical and will get us to that next level. I like how he really pushes me and challenges me in that way. I think it’s super important. I think, as co-founders you learn everything about each other: what your sleeping schedules are, when we can expect a message back at 3:00 AM, things like that.
Grant: What are the major forces that shaped you to become who you are today? So going back to, you know, things from childhood things, from your education, key things you learned from previous jobs.
Lucia: Yeah. I mean, I think some of the industriousness that Jimmy alluded to is definitely from my earlier jobs in finance because those are just like real battlegrounds and training for the real world. Junior analysts are expected to work around the clock and to really be receptive and responsive to client requests. Things like that, that at the time were annoying and hard to deal with, but really shape you and give you a backbone. So I think that was really useful, but more than that, learning how to interact with different types of people, like working with you and the other folks, computational biologists on the team, like I had never met people so smart as you all.
I guess technically our COO had half a PhD, but I had zero PhD. So I had to figure out how to hold my own and build credibility in my own way. So I think the combination of learning what you don’t know again, and like the humility that comes with that, but also being able to have confidence in what you do know, and that it brings value to the table.
Jimmy: For me, part of the reason I really like thinking big is because I never really used to do that. I used to just kind of walk the road I was supposed to walk. So starting in high school, it was getting into a good college. And then when I was pre-med in college, I knew exactly what I was supposed to do to get into medical school.
And then there’s the whole, you do XYZ to do residency and you become a professor. And I think it’s similar to a lot of people who go to grad school, which is it’s very well laid out path. You publish or perish. And at some point, a lot of people just start asking, what’s the actual point of walking down this road? What do they actually want from it?
And so I think I’ve become much more open-minded about asking: what do I actually want to do with the time that I have? And what kind of questions do I want to answer most? What kind of issues do I want to solve the most? And I realized that I want to work on something in mental health. Partially because my high school and college were both very bad in terms of mental health. I saw lots of just really sad situations and it became something that was really important to me.
Grant: What do you think is the biggest mistake that smart, ambitious young people make?
Lucia: Oh man, I’ve got a really jaded answer: devoting themselves to causes that don’t move the needle. Kind of asking yourself the thought question of like, if I were not here today, would the world be any different or would what I’m working on actually make a difference? That is a good way to keep yourself honest. But I am pretty skeptical about a lot of the careers that people in our situations take.
Jimmy: I think I’m right on the same page. I think I would like to see a lot of people take action to work on the biggest problems in society because there’s so many: social justice, climate change, all aspects of healthcare, life sciences, poverty, so many things.
Grant: What’s the biggest issue on which you disagree with most of your peers?
Lucia: I think it’s similar to what we were just talking about. I’m not at all a fan of social media. And I think that overall it has really decreased the quality of living. I mean, I think there’s some baseline aspects of communication and connection that it fulfills.
But beyond that, I think it’s been largely detrimental to society. So I did my first Clubhouse event over the weekend. And it was like, Oh my gosh, I cannot believe someone convinced me to get on this. This is not my jam at all. And I’m sure people are finding a lot of benefit for the connection, especially during COVID. But I think if all those resources and brainpower were put into problems that Jimmy mentioned, our society would be much, much, much better off.
Grant: So there’s been a lot on social media. Speaking of social media, lately about problems people are having in the Bay area, this bit of a move to Miami. We actually had Delian on the podcast a while back and we spoke about this. At the time, he thought he would never move to Miami.
And I said, actually, it’s a pretty nice place. Never say never, never say never. And, and sure enough, now he’s bought a house there. So, what are your thoughts on this? Both the greater ability to work remotely that tech has brought, as well as some of the unique challenges people are having in the Bay area now. I know, Lucia, you’re from there, ight? So I’d love to get your perspective on that.
Lucia: Yeah. Speaking, Grant, of getting to know each other as founders. I feel like me and Jimmy aren’t exactly on the same page here, so I’ll let Jimmy give it a go. And I’ll probably disagree with what he says.
Jimmy: Well, I think as a company Osmind, we are going to be an in-person company. I’ll let Lucia talk about that more. But personally I do believe in the power of the Bay area. I think people have long predicted the decentralization of Silicon Valley, that it’s more than an idea than a particular location. It’s definitely an idea and a mindset for sure, but that’s not mutually exclusive with still having a lot of things in the Bay.
I think in general, we’re just going to see a lot of places throughout the world, not just Miami or Austin or Denver, like everywhere. I think as more people get interested in building cool solutions to big problems, we’re just going to get hubs all over the world of innovation. And I still think the Bay area will always be at the forefront of that, but it’s not made sure we exclusive with other places being successful.
Lucia: Yeah, I totally agree. Yeah. I thought you were going to be more “Raw raw!” about the bay, Jimmy, but I know that’s what you mean. And I’m a big, big fan of the Bay area. Both Jimmy and I grew up here, so I think it’s like the most magical place in the world from so many perspectives. Like from a professional perspective, like Jimmy said, it’s just somewhere where these big ideas aren’t weird, like we’d be laughed out of the room in so many other places and I think it’s really magical.
I think the diversity is magical too. I mean, it’s lacking in other types of diversity. Like sometimes I show economic diversity or diversity of thought, but like in many ways, it’s incredibly diverse and you know, my childhood grocery store was like half Asian, half Hispanic grocery store. Things like that, that I think I really took for granted growing up.
Yeah. I just think it’s a really wonderful place, and that is part of the reason why we’re going to be an in-person company after COVID, but we’re trying to be more flexible too. I think before COVID I would have scoffed at the idea of even working from home a day a week, but now we’re thinking, one or two days a week, folks can work from home and that’ll help them manage the commute throughout the Bay area.
So it’s definitely opened my mind a little bit. And I know,, m Grant, you’ve always been a big proponent of remote work, so I definitely believe that there is a way to do it effectively. We’ll just have to balance that with the in-person benefits too.
Grant: And what do you think would be the most effective things that local politicians in the Bay area could do to make more people feel the way you do?
Lucia: I think my quick thing, this is not meant to be another plug for Osmind, but I do think mental health services are a really big issue in San Francisco. In particular, a lot of the homelessness can be attributed to the lack of resources for that population, especially when it comes to substance use disorders.
A lot of people who are living on the streets have experienced some sort of psychosis in some way that prevents them from being able to hold down a job or a place. And so I think it kind of does tie back to mental health in a way that that situation has gone bad. And I think the homelessness is a big part of the other negative aspects of the city.So I would probably start there.
Jimmy: Yeah, I think in addition to what Lucia just mentioned, it’s a broader perspective around social determinants of health. And I mean, this is a really complicated question because I think there are just so many different perspectives and different groups of people that are all unhappy for different reasons.
And sometimes as Grant, you’ve alluded to, it seems like no one is happy in the Bay area, even though there’s so many awesome things about it because there’s some core issue that people can’t get around. And I think partially it’s having better dialogue around all of these different dynamics that are going on.
Very crudely, let’s say there’s one group of people who are in tech, who are unhappy for XYZ reasons. And there’s a group of people who are very unhappy with tech people for XYZ reasons. And I think sometimes there’s just no way to even get around that disagreement and figure out what the solutions are. And I think that is at least a starting point.
Grant: Changing topic a bit, how do you think COVID might play out this year? And how do you think that could affect Osmind and even your own plans? Are you planning to get vaccinated and go wild, start licking light posts and so on?
Jimmy: Never stopped that.
Lucia: Oh, man. I think it’s really fascinating. I don’t know if he saw the piece from Sequoia, the VC about when COVID first hit. They had this like Black Swan piece that was very foreboding, like the world’s going to end, like all these startups are screwed. And then a few weeks ago they had one that was the complete opposite. Like let’s grab the bull by the horns. This is going to be amazing. Just the tune completely changed. And I think the markets are pretty divorced from some of the realities that we face economically, but it does feel like we’re really on the upswing. And I think it’s kind of going to be like the roaring twenties after WWI, where we had the opportunity to spend a lot of money and be happy with society. And I feel like it’s going to be the same after this past year. People are just itching to get out of their shell and to start reviving the economy that is going to be a bit of a boom.
Jimmy: So that’s a really interesting analogy to the roaring twenties.
Lucia: Yeah. Well, hopefully it isn’t followed by a great depression.
Jimmy: We’ll see with the way the markets are growing.
Grant: Any thoughts on when that might start?
Lucia: I feel like it’s starting, it’s happening.
Grant: It’s starting now.
Lucia: Yeah, yeah. Yeah. The markets are crazy.
Grant: Great. Do you have any words of wisdom for our listeners?
Lucia: You’ve covered a lot of ground. I think I’m really grateful for the opportunity. Grant, I feel like we serendipitously worked together at a previous company, but that was kind of where I started to realize that being an operator and devoting ourselves to something that’s so important is neuroscience and now neuro-psych. It’s just been a total game changer. So definitely feel like along the lines of the questions you brought up that focusing our energy and intention on the big problems are what we would definitely recommend.
Jimmy: Yeah. I don’t know if this is very applicable to the audience of this podcast, but I think it’s just my scientist. I think this is the age of a life sciences revolution. And I firmly believe that this is what’s going to solve a lot of the big issues that we have for the next decades and so on. And I think just a plug back to CNS, I think this is a major frontier in biomedicine and it’s very, very exciting together with things like genomic medicine or immunology. And I think just not being intimidated by previous failures or difficulties within the space and just hopefully people will come into neuroscience and just help on all aspects of it, because it really is so important.
Grant: It’s definitely the century of biology. Well, great. Lucia, Jimmy, thank you so much for joining us.
Jimmy: Thanks a lot.
Lucia: Thanks Grant.
Grace: Welcome to The Bioinformatics CRO podcast. I’m Grace Ratley, editor of the podcast. And this week we’re doing something a little bit different. Today I’m going to be talking with our usual host Grant Belgard. Grant, can you introduce yourself please?
Grant: Hi, I’m Grant. I’m a computational biologist and founder of The Bioinformatics CRO. I started the company almost three years ago now.
Grace: Yeah. I also know you work a little with bit.bio. Is that something we can talk about?
Grace: Cool. So what’s your role at bit.bio? What does bit.bio do?
Grant: Sure. So a bit.bio differentiates cells from pluripotent STEM cells. So I’m head of bioinformatics there and on an interim basis, a head of data management and IT.
Grace: So what kind of cells do you guys differentiate? Have you had any success? I haven’t really followed much of it.
Grant: Yeah. So we have some, some products on the market. If you go to bit.bio, you can check those out. There are also a number of products in beta.
Grace: Very cool. Yeah. So moving on to The Bioinformatics CRO, which is the namesake of this podcast, can you tell us a little bit about what The Bioinformatics CRO does?
Grant: The Bioinformatics CRO provides computational biology services for small to midsize biotechs, academics, and also big pharma companies. Basically, we allow clients to work with bioinformaticians with specialized expertise that they may not have in house, and also to tap into a broader pool of computational biologists when they simply don’t have the resources in-house to successfully tackle projects, or to complete those projects in the timelines needed.
Grace: So The Bioinformatics CRO is an all remote company. We started out all remote, but COVID has kind of shifted things a little bit. Have you noticed any sort of change in the workflow as a result of COVID or has your workflow mostly stayed the same?
Grant: So things haven’t really changed for us that much. But we have seen changes in the perception from clients. And in some cases, when we started the company, remote work was still somewhat of an aberration in the industry. Some clients, frankly, didn’t understand it. And now everyone does.
I mean, I had been working remotely most of my career, well before I founded the company, and so I knew that it worked very well for bioinformatics. But I had to kind of convince the rest of the world of that. And COVID, although it’s been net extremely negative overall, has accelerated some positive trends, and I do think remote work is one of those.
Fully distributed companies have gotten easier to run in recent years: Slack is wonderful, Zoom is wonderful. There are platforms now such as Deel, remote.com, and Oyster HR that facilitate working with people, regardless of geography. There are also programs such as Estonia E-residency that allow people to form companies and operate companies, regardless of where they live. And I think in the long run, we’re going to see very large impacts in the industry.
Most people in biomedical research don’t work where they grew up. About a third of biotech activity in the US is in greater Boston, about a third of that is in the San Francisco Bay area, and about a third is elsewhere in the US and you see similar patterns of extreme concentration elsewhere in the world.
And that extreme concentration is largely driven by network effects. Network effects are very strong in biotech. It can be challenging to find seasoned executives, so it’s easier to start and grow a company in an area where there’s a lot more activity in the industry. Geographic proximity promotes serendipity, but the circumstances for serendipitous encounters can be engineered even in an all remote world, right?
So there are programs such as lunch club and growth club and many of the local and state biotech trade associations that used to have in-person meetings are now having Zoom meetings. The networking interactions that happen are obviously different from those that happened in person, but really, I’m not sure that they’re less efficient.
Overall in terms of the time that people put into less spontaneous interactions, I think we’re moving slowly as an industry to a world where a much greater proportion of people not only work from home, but work some distance from where their company headquartered and their company may well be headquartered in a room in someone’s house.
I think it won’t happen immediately, in part because when people have children who are in middle school or high school, and they really put down roots in say greater Boston or something like this, of course, many of those people are not going to want to move. And certainly there are many advantages to living in those hubs, but I do think the greatest hub of the future will be the internet and there will be people who will essentially see their professional lives lived out online.
And this may sound weird for something like biotech, where you may think you need people at the bench. And certainly you do need a lot of people at the bench to bring a product to market, but they don’t necessarily have to be people working for the company to bring the product to market.
I think another trend that will continue to accelerate in coming years is networks of contract research organizations working with small, nimble virtual biotech companies. And many people will not work for just one small virtual biotech company. They may work for a few. And I think networks will be more important than ever before.
I think increasingly networks are going to supplant big companies in the space of innovation. Big companies have advantages: they reduce transaction costs. So, if you are at a big pharma company and you want to access expertise in very different areas, you can do that. And you can do that without having to negotiate and sign contracts and so on.
And that’s great, but there are a lot of inefficiencies you have with large companies that are corrected to some extent with networks of virtual companies and CROs and so on. So you’ll either have people paid to do marginal work in order to have them available or on standby for more important urgent work or you’ll under-hire and bottleneck progress.
Either of those is a problem. And more importantly, there’s been a lot of work done on how innovation is often stifled in larger companies. Disruptive innovation often requires a lot of work to create a product that is superior to whatever the current state of the art is. And it can be difficult within the context of larger organizations to have that sustained investment that’s required to really bring forward new technologies.
Looser networks also have an advantage in that it can be difficult for outsiders to know who’s competent and who’s a blowhard. These repeated interactions where someone does great work–you work with them at one company, you work with them at a second company, you work with them at a third company–can really move things along.
You certainly don’t want to have Google be your port of call for finding all the expertise that’s required to develop a drug. And so I think these distributed networks with distributed companies will become an increasingly important component of the global biotech landscape and that you’ll see a lot more innovation coming out of these types of companies. And none of this is new. I mean, all these organizations exist and a lot’s been said and written about them, but I do think we’ll see a lot more of that.
Grace: Yeah. I think those are all really great insights. I think that with the networks, there’s a lot more buy-in when you’re working in a small company, everybody feels like they play a bigger role in driving the company forward.
Grant: And everyone does play an incrementally much larger role. If it’s a team of five you certainly have a much bigger impact on the company than a company of 50,000.
Grace: Yeah. I know working for The Bioinformatics CRO, I have done all kinds of crazy tasks that I never would have imagined doing. I know one concern about working with all of these remote companies is the difficulty with finding that interpersonal connection because you’re not going into the office every day and you’re not seeing your coworkers every day. But for me, at least I feel more connected to the company because I am one of five because there are fewer people. And we do communicate a lot because I have a lot of roles to play in the company. And so there’s more of a commitment to the company for me, at least. And I think that’s something that a lot of people don’t think about when they’re thinking about working remotely.
Grant: Right. And I think as well, large companies are by nature a bit sociopathic. There’s no human agency, maybe I’m painting with too broad a brush. But with very small closely held companies, there’s I think much more of a human element, that frankly, Fortune 500 companies can never have.
Grace: Yeah, definitely. So as we move into this virtual world, as a biotech industry, remote technologies like Zoom have really come to the forefront and they’ve evolved a lot. Like now I can go to all my meetings as a cat with the cat filter on. What do you think are some really important tools or technology that’ll make this transition easier for everyone?
Grant: I think these days, everyone is certainly familiar with Zoom and most people have probably tried their hand at something like Slack. One thing I think is really critical for all remote companies is to create opportunities for people to interact informally and sometimes to interact around topics that are completely unrelated to their work, right?
The water cooler type conversations that ordinarily happen in an office. And that’s because everyone’s a person and relationships are at the core of everything we do as humans. And if you have a poor relationship or non-existent relationship with someone, or maybe an entirely transactional relationship, firstly, it’s just a lot more unpleasant as a person. Secondly, when things get difficult–and there are always periods in any company and in any project where sometimes things are hard–it makes it that much harder if you don’t have real relationships with the other people who are working through that with you.
So there are tools such as icebreakers.video and so on that give people those opportunities. And even things like the random channel on Slack, can give people an opportunity to interact informally, to share things that are funny, just to be people, because I think it is easy by default for all remote teams to become more transactional than they should be.
So you have a meeting on a specific topic, you discuss that topic, the meeting’s over. You discuss projects in emails and things like this. Having all your interactions be like that is not healthy and not normal. So I think that kind of thing is often not explicitly addressed in traditional in-person companies, to the extent that it needs to be in fully distributed companies where you can’t have these spontaneous interactions occurring in the lunchroom, because the lunchroom is your own dining room at home.
So one thing we’re doing at bit.bio that I think is pretty neat is a month where people are running or walking and basically recording that. And as a team seeing how far everyone can get. We’re most of the way from Cambridge, UK to Spain now, if you add up everyone’s distance. You know, it’s both a way to promote fitness and it’s also kind of a neat thing for people to be able to do together at a time when people generally can’t as easily get together.
Grace: Yeah, I think that kind of stuff is really cool. Social technology is probably what’s needed most. Social technology that brings smaller groups of people together, that’s what I would like to see. Yeah. So I know you’ve had a really interesting path to where you are right now. Let’s start from the beginning then. So when you were a kid, what got you into science?
Grant: So when I was in seventh grade, I started reading books by Richard Feinman and reading a lot of books about nanotechnology and things like this, and got really drawn in by quantum mechanics and nanotech. They just both seemed incredible and non-intuitive. Then I realized, well, if I want to understand more physics, I need to understand more math. So I started getting pretty far ahead in that. And really, I would say from seventh grade on I was really quite focused on science.
I wasn’t exclusively focused on science. I mean, I was also very interested in history, very interested in economics. I was a very active and fairly good cellist, but science was what I was most interested in. So for the last two years of high school, I went to a public statewide boarding school in the state of Louisiana, called the Louisiana School for Math, Science and the Arts. (LSMSA)
And that was fantastic because they had a lot of college level courses and even where they didn’t have courses, you could do independent studies. So, I was able to do a partial differential equations course as an independent study in high school, which was fantastic and a ton of programming courses. It’s funny. I actually learned the vast majority of what I know about programming in high school, not later. That was a really great experience.
This is a shameless plug. So there’s a foundation that supports the school, so would encourage any, any listeners who are kind of moved by what the school can maybe do to check out the LSMSA foundation. So the great thing about LSMSA is it provides opportunities for students from across the state, that very few of them could possibly have had at their home schools. It really opens doors at a critical time in people’s lives. Right.
So a lot of these kids were 15 years old, now the school accepts sophomores. But it can be especially transformative, right? Louisiana is not typically known as a state with strong educational opportunities, but actually when it comes to requirements for gifted education and then opportunities like this school, it actually is pretty exceptional in terms of services that are mandated.
So I worked pretty hard while I was there. I think senior year, one semester I took something like 13 classes and the other semester 14 and basically graduated with a ton of college credit. I got accepted to a lot of really good places, but I wasn’t really thinking too far ahead about the financial aid part of it.
So I ended up going to Rice as a good kind of compromise. Where it was a good school I could go to and I could graduate without taking on any student loans. And Rice punched and continues to punch above its weight in the nanotech space, which was what I was most interested in at the time.
So actually, somehow I managed to snag the email@example.com email addresses as my personal email when I started. I majored in chemistry, physics, chemical physics, and biochemistry and cell biology, not just because I wanted to stay for four years and see if I could do it, but also to get a broad base of education. You know, for something like nanoscience, it’s very, very interdisciplinary.
And so it’s important to understand the physics of what you’re doing, the chemistry of what you’re doing, maybe the biological system in which you’re applying it, and so on. And senior year at Rice, I read The Selfish Gene by Richard Dawkins, which was a phenomenal book, and it really helped me realize that biology isn’t just stamp collecting, and is increasingly becoming a field that we can understand through the lens of information and data, and that I didn’t need to spend my PhD pipetting, which I had grown to dislike through my four years at Rice.
I mean, I loved the kind of problems I was working on in the lab, but I just did not want to pipette anymore. And I loved computers and always have loved computers, but also while at Rice, I was very busy working a number of jobs on top of school. So that, by the end of it, I was on the verge of being burnt out. I figured on a lark I would apply for the Rhodes scholarship and the Marshall scholarship, and if it worked out, then that would be great. And if not, I was thinking of maybe joining the Houston fire department or something for a year just to do something totally different.
Grace: Wait. The fire department?
Grant: Yeah, that or I was looking at going to work on a fishing boat.
Grace: Wow. I mean, those are very interesting gap year choices. Did you have any EMS or fire fighting experience beforehand?
Grant: No, no.
Grace: You just wanted to try it out? That’s so interesting.
Grant: Yeah, just something pretty different than what I had been doing for the last few years. Yeah, I think my parents were not thrilled with those choices, but fortunately for them, the Marshall scholarship worked out. And then there was also this NIH-Oxford Cambridge program that was still at the time relatively new, and they had a partnership with the Marshall scholarship. So, you I kind of had to put in an independent application and all this, but I went ahead and did that because I figured it would make for a more interesting PhD splitting between labs.
So basically in the program, you split your time between at least one lab at Oxford and at least one lab at the NIH and a number of people had multiple supervisors. So at Oxford, I worked with Chris Ponting, who had been a contributor to the human and mouse genome projects, and some other big genome projects and had been doing a lot of work looking at functionality in long non-coding RNAs.
There is a project that they had already discussed doing, but didn’t really have anyone to take it forward. It was a collaboration between Chris Ponting’s lab and Zoltan Molnar’s lab, who is an anatomy professor at St. John’s College at Oxford, and Elliott Margulies at the NIH, who worked in the genome technology branch and was doing a lot of work on the tech dev side with this new set of so-called next-generation sequencers. They were originally made by Solexa. Solexa was bought out by Illumina. When I was there, everyone was still calling them the Solexa machines because it was still in the very early days.
And now there are tools that are fairly standard to do all this stuff for you, but at the time you were having to write your own tools to do most of the steps of what’s basically automated now, which was nice because you really got to learn the ins and the outs of the technology. I spent a couple of years primarily at Oxford going back to NIH periodically, and then a year at NIH, going back to Oxford, periodically.
When I was at NIH, I met Angel, who was to become my wife as you know, Grace, because you have babysat for our first born before. Yes. So Angel was in medical school at the Uniformed Services University for Health Sciences, so it’s basically the military medical school. And she was in the first half of med school at the time.
So she was based out of Bethesda, Maryland literally across the street from NIH. If you’ve lived in the DC area, you’ll know that proximity matters because traffic is pretty bad. So my NIH supervisor Elliot left the NIH to take a job as director of scientific research at Illumina in Cambridge.
So that was kind of my first experience, I guess, working remotely. When Elliot was gone, people in the lab started trickling elsewhere, and at that point there was really no reason to go in anymore. You might as well work from home or from wherever else you are. I mean there were certainly very nice people at the NIH, but you could also get in a bit of trouble.
I had my internet access cutoff for a while once because I had been speaking with my mom on Skype and I forgot to close it before I went into the office one day. And so they detected that Skype was on, trying to access some port and I guess it was banned by NIH policy. So I had my internet cut off and I had no idea why. I was trying to get by without it for a few days and then finally one of the IT people from main campus found me, and they explained what was going on. But it was just easier not to be working from the NIH campus.
Yeah. So I was basically remote for a while there, and because Angel was moving around every month–at that point, she was doing her clinical rotations and at USU your clinical rotations are at different military hospitals across the US–I was able to spend some time with her. We got to spend some time in Honolulu while she was doing a rotation at Tripler Army Medical Center where I was writing up my dissertation. So that was nice.
Basically during the week she would work and I’d work on my dissertation and on the weekends, we’d be able to enjoy Hawaii. Basically, I was living out of a suitcase for six months at that point. I did a short course in Okinawa and then returned to the UK to finish writing up some papers. Basically to get onto the next thing.
So after finishing up the PhD, I flew back to my parents’ house where we’d kind of been storing everything. And my parents had been watching Angel’s cat. We got the cat, Lena, who is no longer with us now, but at the time she was already starting to show her age. I basically packed my vehicle floor to ceiling and had Lena in there. Lena realized that she could open and close the automatic windows, which was not great because at one point I had things flying out on the Texas interstate and had to go and collect them.
So I made it to LA. I was originally going to live on a sailboat and then some people down at the Marina were concerned about what the California authorities might think of having the cat there, which as it turns out would have been perfectly fine. I mean, the weather is very nice. And so for that reason, I got a very small, moldy apartment close to UCLA campus to live in during the week and did a number of projects that were pretty interesting.
So I inherited a project looking at gene expression in the brain in autism, which built upon a previous paper they had published in Nature. Then our paper was in Nature and I think actually is pretty impactful because it was showing these common changes in gene expression basically in most people with autism, which didn’t have to be the case for a psychiatric disorder with, at the time, no known neuropathology.
There is a bit more about that now. And certainly there was a lot known about the neuropathology of certain syndromic forms and so on. I did some work in autism genetics and did some work in comparative neuro-transcriptomics, following up on the comparative neuro-transcriptomics that I had done in my PhD. But here we’re looking more across primates.
So I was physically at UCLA for a year. And then Angel and I got married halfway through that year. And she was stationed at Wright-Patterson Air Force Base outside Dayton, Ohio, the following summer. So basically I walked into Dan Geschwind’s office and told him, Hey, I’m moving to Ohio so I can keep working remotely or I can find a new job.
So, I kept working remotely. Did that for a year. And then heard again from Chris at Oxford about a really interesting project that he was getting involved with, where we would be sequencing transcriptomes in single cells of the brain. And this was very new technology at the time.
I mean, now everyone’s doing it. We do single cell projects constantly at the CRO, but there were still a lot to figure out at the time. I thought that was really cool because I kind of saw single cell sequencing as the future, which was true. So I returned to Oxford, but this time remotely, and as part of a large multinational consortium. So that was pretty interesting.
We worked with a lot of people in very different disciplines. They were trying to use these data to model things like protein levels and different cells and so on. It was really interesting working with them to try and figure out how to do that.
That was also when I ran out of pages in my passport and had to get more sewn in.
Grace: Wow. What a dream to fill a passport. I feel like that’s probably on most people’s bucket list.
Grant: Yeah there was actually a customs officer in Chicago who got to know me because I kept going through his little booth. And there was once where I entered the country twice within 10 days or something. And he remembered the conversation we’d had on the previous visit.
Grace: That’s a way to get to know someone.
Grant: I also learned don’t connect in Chicago in winter, unless you want to have a pretty reasonable chance of spending the night in Chicago.
Grace: Duly noted.
Grant: So a couple of years into that, I heard from an old friend at UCLA who had started a company focused on genomics-driven target identification with someone else in the lab at UCLA, using a lot of the techniques that I’d used in my PhD and postdoc. It sounded pretty, pretty intriguing. At first, they were basically seeing if I could move out to the San Francisco Bay area and the answer to that was definitely not.
We were still in Ohio at the time. So then they said, well maybe you can start as a consultant. And so I did that. And before long, I got so heavily involved in things that it made more sense for them to bring me on as an employee. So, I worked remotely initially in Ohio and then in Florida. and that was very interesting.
I definitely learned a ton about the biotech industry, and learned a lot about AWS. I had never really done any cloud computing before. And of course now with the CRO and at bit.bio we use AWS all the time. But ever since I was a kid, I got really interested in science and I was thinking about what I wanted to do when I grew up.
I wasn’t really thinking that I wanted to be a professor, but I wanted to run a science company and I didn’t really know what that meant, but I was thinking, well, you know, a company that does science. And it turns out that’s actually exactly what a CRO is, right? I mean, you’re a company that does science. People hire you to do science. Like how cool is that? And you don’t even have to go and write grants and things. They come to you with here’s the problem, and here’s the budget. Go figure it out.
Grace: That sounds like the best part to me: not having to write grants. Because I’ve written a 2 already and it’s not my favorite thing. Here it’s like the money comes to you.
Grant: I mean, yeah. There are obviously hassles and things like that, as there are with any job, but I was also thinking, almost my entire scientific career had been remote. And certainly at the time it was looking like that was going to be the case indefinitely. So, I wanted to get something in place that would be more robust where I wouldn’t be worried about, Oh, what happens if this company goes out of business or whatever. So I basically took some money we had saved up to put into this company, so that we wouldn’t need any external investors.
That was stressful for the first six to eight months until I didn’t have to write checks to the company anymore. So that was a good milestone. And yeah, The Bioinformatics CRO has definitely grown a lot since then, as you know well. We’ve worked with several dozen clients worldwide. We work with over a dozen scientists and we’ve kind of figured out basically how to run things, how to juggle across all these projects and make sure that people are doing the things that they know how to do well.
I found a big part of that is the opposite of what you may sometimes have happening in very big companies where you really have to know the strengths and weaknesses of every person and kind of bring assembled teams together for projects in a way that reflects that.
Grace: Yeah, thanks so much for talking with me today, Grant. Thanks for allowing me to interview you. I’ve learned a lot about you.
Grant: Well, thank you for doing this. Certainly you’ve spent more time kind of working on the podcast than anyone. Now you’re getting a title role.
Grace: Exactly. Exactly. So if you guys liked this episode, please rate us wherever you listen to your podcasts and please give us feedback. We’d love to hear from you at firstname.lastname@example.org
Grant: Welcome to The Bioinformatics CRO podcast. I’m Grant Belgard and joining me today is Ambika Bumb. Ambika Bumb is the Health Science and Technology Advisor for the Department of State’s crisis management and strategy within the Office of the Secretary. She is currently a board member for the International Biomedical Research Alliance and has formerly been Strategic Advisor to the energy sciences area of Berkeley lab and CEO of the biotech Bikanta. She graduated from Georgia Tech and obtained her doctorate from the NIH-Oxford program, while also on the Marshall scholarship. She followed that up with two postdocs at the National Cancer Institute and the National Heart Lung and Blood Institute. Thank you for joining us today.
Ambika: Thank you for having me.
Grant: Great. So tell us about what you’re doing now.
Ambika: So I am in the office of the Secretary of State and I work primarily in the office of Crisis Management and Strategy. And so that’s literally what it sounds like: any crisis that is occurring, the management of what that response should be, how to better plan for future crises, and get protocols and SOPs of different kinds, strategic relationships, make sure that they’re maintained to be able to coordinate those responses.
All of that happens, from this office and I’m the health science and technology advisor to the office. So what that means for me, particularly, I started this position in December of 2019. And you can imagine then that I’ve been primarily working on global health security and particularly the COVID response.
Grant: That must’ve been nuts.
Ambika: Oh, it’s a bit insane. It has been such an experience. I’m a fan of the TV show Madam Secretary. I don’t know if you’ve heard of it or seen it before, but it’s all about a Secretary of State and handling a bunch of different issues. And so I semi took this position because I’m such a fan of the show, but I felt like I was in the TV show the first couple of weeks.
Like in the third week is when the rocket attacks happened on American citizens in Iraq and the embassy was attacked and that led to then the US counter attacking and the assassination of Qasem Soleimani. So that all happened in my third week and as that was going on COVID was starting to sneak up.
And so it just really felt like: man so much is going on at the same time. And it was a very eye opening and somewhat insane experience at the beginning.
Grant: Yeah, lurching from crisis to crisis. It’s interesting because for most people, most of the time, things that happen like this in the news don’t impact their daily lives. Obviously COVID has been a grand exception to that, but for most people, they don’t really need to stay on top of the news. This is obviously quite different from you. How do you deal with the volume of information?
Ambika: Ah, yeah, a lot that comes through. Being in this particular office, we are tracking information that’s coming from media sources, from social media as well, but more critically from what is coming from post. So post means embassies or consulates that are out in the different nations. So they’re having their conversation with diplomats of other sorts within the country looking at what’s happening on the local level.
And so we coordinate with posts, but we also coordinate with offices within the DC side of the Department of State, we’re getting information from places like HHS and CDC. Well, that’s where I focus a lot of my attention because I’m looking a lot at global health security things.
You get very good at reading emails really quickly and triaging things, deleting stuff that’s not so critical and learning to prioritize what is immediate, what needs to maybe be addressed later, short one-liner kinds of passing of information and it’s kind of a skill you develop. It’s hard for me to explain. At the beginning, it was incredibly overwhelming to be like, do I have to read every sentence of each of these thousands of emails,
But internal and Department of State information is passed through cables; and they have a clear format on how they are conveying information. So you can learn to pick it up just like in scientific papers. You can get the take-home points from the abstract and the conclusions. And so you find ways of being efficient at reading, I guess.
Grant: What surprised you the most about what you found in this position? You had spent many years in academia, time in the biotech startup space, and then moving to government. So as a bit of a trifecta, what surprised you?
Ambika: I guess retroactively, it shouldn’t be so massively surprising, but things have a different pace of how they move in different scenarios. Right before I came to the Department of State, I was working within the DOE at the Lawrence Berkeley national labs. As you mentioned, I was a strategy advisor to the energy sciences area. And my project there was primarily on developing the vision for a new campus that would house a bunch of facilities related to energy sciences, nanotech, quantum tech, and to get anything moving.
There’s so many steps, so many processes, and everything takes time. You have to get a lot of buy-in from a bunch of people. And here I came in and in week four, essentially, I was sitting in on national security council meetings where decisions like we’re going to ban travel from certain areas and countries of the world completely, which had never been done before. And those decisions were made in a matter of 10 minutes. To see something like that, where a decision was made and enacted within like 24 hours or 72 hours, that is insanity. Just how quickly things can happen when you’re at that level of crises or at that level of resources. I thought that it would take a lot longer for anything to get done, but when it needs to get done stuff can get done very quickly. And I was just surprised by how that can be so quick.
Grant: Do you find it varies by the decision?
Ambika: Yeah, it definitely does. There are other things that take them way too long, but when something is of great concern to national security, that stuff moves very quickly. So how you define that national security priority becomes the issue.
Grant: Tell us about COVID because you had a very central seat to the US government response to this.
Ambika: In my initial days of being at CMS, we started getting reporting from posts about this new virus that had occurred. And I started digging into things from my own contacts in the virology world to try to get a better perspective on it. And as things took off, it was very interesting.
So I came to the Department of State because what I wanted was essentially like a government MBA experience. I wanted to understand how leadership functions, how they get stuff done, how they move mountains when needed, and who are the key players to actually make policy function? Definitely learned that within the first month or two of being here because I was sitting in on like national security council meetings.
Those are inter-agency meetings that are led by the White House, which brings in all the different departments or agencies jointly who have their different expertise to talk about the issues. And so when we were sitting in a national security council meeting where it was just about to start, and I was walking in with my supervisor, we got a notice that Wuhan had just been quarantined like an entire city being quarantined was, seemed insane at that point.
Now it’s very different, but at that point, that was very shocking news and at the meeting, which was ongoing at that point, everyone had to stop and say, we’re going to take a break. Everybody needs to go back to their own agencies and figure out how we are going to manage this. And from there all sorts of firsts happened. A travel advisory for the entire world. Don’t travel anywhere. That had never historically been done. Decisions like these were being discussed.
But what was interesting from a Department of State perspective and from a crisis management perspective was that in the 44 years that the crisis management & strategy has existed, there had never been a scenario where the same threat was occurring simultaneously overseas and in the US. Because as COVID started coming here, things had to change.
And so it was a lot of management of what’s happening externally, but also what’s going on internally and how we are going to still function. CMS is the lead on setting up task forces that span departments to coordinate the response. You bring in all the expertise from the relevant bureaus and offices, and then you all sit around a table–like what you imagine in movies–a control mission room with a bunch of people around a table with a lot of computers and big screens and maps everywhere.
That’s the traditional model, but we had to very quickly turn that into something that was virtual and the Department of State has a long way to go with technology. It’s not the same as my experience from Silicon Valley and the biotech industry. So getting everybody used to new tools immediately was one challenge, but we ended up coordinating a virtual task force of more than 400 officers that were serving from all over the place: 30 different offices and bureaus from different parts of the world.
It was one of the most high profile and complex operational responses that the state has executed to date. What we were managing was not so much the foreign aid policy component of things, we were focused on evacuating people, getting people out from all parts of the world. So the task force was called the repatriation task force.
The Department of State doesn’t have its own airport or its own fleet or anything. Each flight had to be individually chartered or organized. And there were 1100 flights that had to be organized. We brought back more than a hundred thousand people from different parts of the world. It was very complex, operationally to coordinate. And everybody’s story is so individual; there are people who are like missing their medication and people whose parents are stuck in some remote location. There were literally times when we had to send boats to collect people and bring them back and transport them from one town to another, to get them on a flight.
It was really eye-opening and personal. At the same time, from a management perspective, it was very complicated operationally. There’s a lot of data that had to be managed. You’re also wanting to understand if people are COVID positive and how to manage that. And how do you pay for all of these flights? How do you get approval to pay for all the flights? How do you get a flight crew that can actually do this because there are limits to how long a crew can fly? But working with foreign governments on this, getting flights into countries that don’t typically have them, and landing permissions into those countries.
Then there are cruise lines. And why were people going on cruise ships during a pandemic? But those are very complicated and because there’s so many health complications with this. There was an added level of work that had to be done. It was literally like playing diplomatic gymnastics, trying to coordinate all of these things. Eventually it became very clear that there were going to be a number of policy issues that had to be managed related to aid and getting vaccines and other things as they were developing.
So we set up what was called a coordination unit that then recruited diplomats from different offices. And that became the CGRC, coronavirus, global response coordination unit–alphabet soup from every agency I’ve worked in. It’s hard to remember which one is what. But now that is managing a lot of internal responses. And so it’s been very eye-opening how things have been managed globally versus domestically. My role has had less impact on the domestic side of things, but I’ve definitely been involved in a lot of those conversations.
Currently, I spend a lot of time thinking about how we get people to actually take the vaccines when they are available and engaging with people about that and whatever. It’s a lot about science communication. So as the only science person really in this office has been to interpret all the science that’s coming out of this and make bullets that are short and get the point across.
As scientists, we love to elaborate. Science is evolving. You learn more as you get more data. And so no one ever wants to be so presumptuous as to say that you know everything about everything. But in these kinds of scenarios, being very assertive about things that you do know is very critical. And so my role has been a lot about science communication for sure.
Grant: That’s really interesting. So I guess you kind of have to parse things very carefully, too, to be emphatic about what you know to be true and hope that others are doing the same and not just being blowhards, right?
Ambika: Yeah, exactly. You got the point I was trying to make. It was also very interesting how different worlds of mine that I’ve professionally gone across thus far have collided at different points during the past year. My first day as a grad student, I was in the National Institute of Health and Oxford-Cambridge program, as you were. On the very first day, they sat our class down–I think there were like 15 students–with Tony Faucci. And I remembered that conversation a lot throughout my graduate years.
Because I found his background in science and the way he has been leading NIAID very interesting. And then circle around to the first weeks of my new job. I was sitting in meetings with him as the pandemic was emerging and it was so interesting to see what he valued and what comments he made and how he managed the room at times.
It was interesting on that front. When I was sitting in the task force, I remember we had the news on and CNN bullets would come out about the vaccines in Oxford that are being developed. And because I’m on the International Biomedical Research Alliance board, which helps to work with students who are in this Oxford program who were working on this vaccine.
It was really like, man, I know the people who did that and like Adrian Hill who was a professor when I was there. And I did some work with him. He was up on the news there, and it’s just strange to be seeing people who you’ve interacted with coming around on the TV screen as you’re working on stuff.
And a lot of my role also became connecting with startups on the ground, providing insights on what kinds of things to work on. It was a nice merging of all the experiences that I’ve had in the past as well.
Grant: So let’s talk a bit about those experiences and maybe it might make logical sense to go chronologically. As a kid, what did you think you wanted to be when you grew up?
Ambika: I don’t think I knew exactly what I wanted to be. So what I can say is that my father is an engineer. My mom is in sciences as well, and I definitely had a personal interest in science subjects. I also felt like I did well in them, so that helps you. For me at least, it made me want to do more because it felt like I was good at it. So I’m Indian and Indian family culture really plays a lot into how your life develops and molds. And I feel like there’s this respect for knowledge and education that I think has just been passed down in my genes.
My father was the first PhD in his family. My maternal grandfather went back to school in his 40s to become a veterinarian. And he also made sure his two daughters had opportunities to broaden their horizons, and they were the first women from their town to go to college in STEM. So my aunt is a physician. My mom majored in chemistry.
When my parents came to the US, they encouraged and were very supportive of exploring any education, but they encouraged our STEM work. And I liked engineering, medicine, economics, and law. I liked all of those. That’s what I liked in high school. So I tried to blend as many of them as I could when I went into college, and I chose to study like biomed engineering and econ, because it covered as many of those that I could fit into education as an undergrad.
I’ve always been drawn to finding solutions to things and building stuff. And I think my parents might be a bit like that as well. And some of the kinds of activities that we did as a kid: I would build furniture with my father. I would do lots of art projects. And so it was like creating a vision about something and completing it. I don’t know if that was my parents’ way of keeping us occupied. I don’t know that it was very intentional, but that’s how things were.
I’m like one of those people who when I have a to-do list, I check off things. It makes me happy. Feeling like I’ve accomplished something or getting to a solution on something, I think that that’s probably what was instilled in me from those earlier days. I mean that leads you to engineering in some ways. Maybe that’s where that came from.
Grant: So tell us about grad school.
Ambika: For grad school, I was very fortunate, like you, to be able to get into this one program that allowed you to have a research PhD graduate experience that’s international. So my PhD work was done both at the National Institutes of Health here in DC, as well as abroad at Oxford. And I cobbled together a project. I didn’t go in with this vision of I’m going to do this particular thing and that’s going to be what I like. Again, I kind of just went in with this attitude of, I like solving problems, but I don’t know what problem I want to solve. A lot of people go to grad school, and they know exactly what they want to research and dive into.
All I knew is that I like applying engineering skills and that’s like such a naive way to approach anything, but that’s what I did do. And what I ended up doing is I interviewed with like 14 labs or 16 labs at Oxford and similar numbers at the NIH. And of the projects that I was discussing and brainstorming at all those interviews, the one that caught my attention most was: so I went to this one lab, Lars Fugger, he’s an immunologist and he primarily works on multiple sclerosis models. And while I was sitting there, he saw in my CV that I had done some nanoparticle or nanotech work for undergrad research.
And he on the spot brainstormed an idea of being able to better follow T-cells in some of the animal models he has using nanoparticles, if I could figure out how to do that. He had no experience with it, he had no idea of how to go about doing it. He was like: if you can figure that out, that would be a project you like to do.
And so I was just attracted to that idea. So in the end, I ended up bringing together four different PIs. I had an advisor: Lars Fugger, who was an immunologist; at Oxford, I found someone in the engineering department whose name is Peter Dobson, with some material science background as well; and at the NIH, I worked with Martin Brechbiel in the radio-immune oncology branch; and with Peter Choyke, who is the head of the molecular imaging clinic. And so basically the fields that were being brought together for this were: chemistry, imaging, material science and cancer and autoimmune diseases.
So the project I worked on was developing this trimodal imaging particle, so what I mean by that is you can image it in magnetic resonance imaging, optical imaging, as well as SPECT nuclear imaging. Using the same particle, you can get different kinds of information from these imaging techniques. And to make it a platform technology that could be applied in a variety of kinds of pathologies and diseases, we tested it in autoimmune diseases, as well as in cancer.
So it was my first foray into nanomedicine and I loved it. I don’t know what your experience was in this program, but for me it was very eye opening to me. How interdisciplinary science is amazing. You can bring resources from different disciplines together, but also different institutes and cultures.
I think in Oxford, I learned how to think wisely about the smartest experiment to do because resources are a little bit more limited in that university setting. And then at the NIH where resources were plenty, I learned about how to do science by just trying 10 experiments and see the results that you get.
And to be able to have a graduate experience with both of those kinds of ways of thinking, I think really benefited me. But also the other thing that came out of this was: the manager of this project was not any one PI it was me. So you learned a lot about how to manage a project and how to manage people and cultures and all sorts of things. And I definitely think that those skillsets carried forward in my next endeavors, for sure.
Grant: Yeah. So tell us about what you did in your post-docs.
Ambika: Yes. So afterwards, I came back to the National Cancer Institute and I worked on a project specifically trying to image glioblastomas or brain tumors.They have particular challenges because you’re trying to cross the blood-brain barrier, which is like this extra shield for your brain. And getting anything across it to better image is more complicated. And so we were using basically this scorpion toxin, that you can attach to the side of the particle to try to break through the blood-brain barrier into these tumors.
And I followed that on with another postdoc that was kind of a different perspective. So most of my work was about how do you translate things into humans and into clinics? And this was more going the other direction of understanding basic molecular science stuff. And I moved into a biophysics lab that was doing single molecule imaging.
And that’s where we developed some technology around a different kind of nanoparticle called nanodiamonds. They have some really unique imaging properties that then launched a huge part of my career after that. But it started in that lab and working on making nanodiamonds usable by putting coatings on them. That actually makes them able to be used in biomed applications.
Grant: So after that, as you alluded to, your career took a very different turn. Can you tell us about that?
Ambika: It starts with understanding what the technology was. So the nanodiamonds are particles that are essentially microscopic diamond dust. They’re on the order of 10,000 times smaller than a strand of hair.
And they have these fluorescent properties that are infinite. They don’t ever blink or bleach. Bleaching is when a fluorescent signal starts going away. And that makes them very unique for imaging applications and single molecules. You can track molecules individually a lot better, but I was interested in this other property where you can control their signal because their electron spin states are such that they have a triplet spin state, which basically means you can control their signal with a magnet.
So, if you’re flipping a magnetic field on and off, you can actually make the signal go from bright to dim, bright to dim. And that is interesting because if you had them in tissue or if you had them in a patient, and you’re controlling the signal from bright to dim, bright to dim, while everything else is constant, you can cancel out noise and background a lot.
That’s the challenge with fluorescent imaging, optical imaging in tissue and in humans is you don’t get a lot of depth penetration because you have so much background from other signals that naturally exist from blood and water and other molecules in your skin.
And so we developed an instrument that would allow you to enhance the signal coming from the diamond. And diamond material is also very biocompatible. It doesn’t seem to have any toxicity issues. And so through this, we developed a method and an instrument, both on the particle side, so the contrast agent that you’re injecting, as well as the imaging instrumentation side that would improve signal a hundred fold over conventional imaging techniques.
And so what that can open up as far as opportunities in the clinic is very wide and very exciting. Because a lot of the robotic surgery directions that things are going into would want fluorescent optical signals to follow not magnetic resonance and nuclear kinds of imaging. Because those have other more complicated situations of how you have to image. Light imaging is the least complicated.
So if you have a good imaging agent, it opens up all sorts of doors. So that led to me starting Bikanta. I launched the company in 2013 and we had a lot of great success. So we went through the whole journey of running a startup and developing a product from scratch and finding customers, working with partners and legal teams. You know, every aspect of what running a startup comes with.
Grant: What propelled you to take that leap?
Ambika: Yeah, so when I was in post-doc mode, I was definitely applying for academic positions. And I was thinking about the more–I don’t know if you’d say traditional, but–traditional paths of having my own lab. And I was interviewing at universities. And I was very interested in this work, not just being at the bench, but translating it into actual patients.
And for that, you have to commercialize. The primary route of doing that is through a company. Initially I was debating whether to try to launch a company while being a PI at a university as well, but we were recognized with an award by the NIH for technical achievement, one of two awards that year, for developing a platform technology that can apply to so many different diseases.
And that started actually getting people approaching me about starting a company. Then it just kind of felt like the timing was better for us to just launch this. I put all my effort into getting it off the ground. In my head, I was thinking, I’m going to get this company started and then I’m going to be on the scientific board and then go back to doing academia and doing research. That was my plan. But as I started doing the startup. I was able to raise the funds very quickly. And once you have money, man, can you go fast? And you can really build a team and get stuff done.
And it was just moving so fast and eating my bigger picture goals of wanting to actually translate this. I’ve realized looking back now that a lot of my career decisions have been driven by where I feel I’m making an impact. Each step has been about broadening how I can personally have an impact on a bigger picture. And commercializing the technology definitely was going to help with getting it out faster. And so it wasn’t this preconceived notion that I definitely have to do this startup. It wasn’t my plan when I was a postdoc, but I took the leap because it felt like the right thing to do with that moment. And I thought that I could re-engage on faculty position discussions later if I needed to. But I enjoyed it. And I stayed with the company on that path.
Grant: So what did you do?
Ambika: Man, there’s so many different categories of things that you do as a startup. Initially, it’s bringing on a team, raising the funds. Learning how to talk to investors is a very different skill set than learning how to talk to scientific audiences. Learning who the key players are and all that stuff. Even some of the basics, like how you set up a company payroll, and all the things that seem minor. But you start with all of that and you build out your team. Once you have a team you’re moving really fast on the science side.
I was working a lot with Lawrence Berkeley labs at that point, because we were using their facilities. I found resources that we could use. As a startup you’re mean, and you’re lean. You don’t have a lot of resources and you are hungry to get stuff done quickly because it is important that you deliver for your investors, so you can get more, keep moving forward. There’s like this time clock pressure also because you’re paying people, As a leader of the company you have to make sure you can keep having funds to pay people.
So there’s this extra level of responsibility, but there is an efficiency with which you’ve moved and that’s also protecting your IP, getting IP in place, getting collaborators in place. So we were working a lot with customers and universities and we were using the facilities that we could access for free.
And we ended up doing a lot of work with Lawrence Berkeley national labs. It’s like developing an understanding of your market, developing the plans for it, communicating about things, knowing how to hire and get a good team, creating good culture, all sorts of different aspects. It’s it was a very fulfilling thing to work on so many different ranges of things that are all driving towards the end goal of getting this technology out.
Grant: And what did you guys get out?
Ambika: So here’s the interesting thing. Being in a diagnostic space is not as easy as being in therapeutics. And so we were very fortunate at the beginning when Bikanta was first being launched. There were a lot of things that sort of timed out well to get a lot of interest in our company.
The Nobel prizes that year included a bunch of people who were doing nanodiamond work. Google X decided to launch into areas related to nanomedicine and nanotech, and there were all these articles comparing Bikanta and Google X. The timing of things was such that Y Combinator–you know, the Harvard of accelerators if you want to call it that–decided to go into biotech companies and Bikanta was one of the first biotech companies that they invested in that had clinical directions.
And so the timing was really optimal. So many tech investors were turning to invest in biotech and we presented ourselves well, got a lot of funding and things went really well. Our technology development went very fast and we got to the stage of wanting to launch this into clinical trials. And then around that time is when Theranos happened and they exploded.
Grant: Timing is everything right? Good or bad.
Ambika: Timing is everything! In general, investment in diagnostics is a lot smaller than therapeutics. It’s something that I’m sure you’re very familiar with. And when Theranos blew up, it was a diagnostic company. Investors got spooked and then raising money in a diagnostic space became very, very challenging. And I tried to frame the companies more in imaging and different things, but we were only able to raise a partial amount of money and not enough to close out the full round. So in the end we ended up transferring the technology, the IP back to the NIH. And I had to decide to dissolve the company because of the timing. Launching a clinical trial requires a large amount of capital.
I learned a lot in that process as well, about how to make decisions that are tough like that. Because the technology clearly has a lot of value and there is interest in where it can move and develop. But unfortunately we weren’t able to make that happen after five and a half years, Bikanta was not the avenue to make it happen.
Grant: Do you think nanodiamonds will one day be used for diagnostics in humans?
Ambika: I do. Yeah. It’s already being done in a very academic setting and there are about six different companies that are doing this kind of work as well.
There are clinical trials that have already started and have completed actually in other kinds of applications, not so much in cancer yet. That’s where Bikanta was focused. We were starting with melanoma as the first target. But there’ve been uses in dental applications and stem cell related work.
And so there is definitely a lot that is going on. Nanodiamonds in general are used in other fields in insane amounts. Nanodiamond work originally began in physics, in quantum tech kinds of applications: cubits and quantum computing. There are a lot of directions there. So nanodiamonds are going to be around for a really long time, but translating them into medicine…
I think COVID actually has changed a lot: you know, testing and diagnosing COVID. How much money has been thrown at diagnostics in the past year? Everything about biotech is going to be very different after the pandemic experience.
So I do have a lot of hope that this is going to re-kick up again. In general, molecular imaging as a field is successful. It’s doing very well and it’s only going to get better because that’s the direction the medicine is heading.
Grant: It’s really interesting, speaking with a number of entrepreneurs who started their first companies at different times how this theme of timing is very, very often related to external circumstances. So, you know, the .com boom, and then the .com bust and all this is a very consistent theme.
Ambika: Yeah. I don’t know what your experience has been, but I have found that the more and more I’m progressing through different stages of my career. It is so much about factors other than just pure science or the technology itself. That is what drove all of this to begin with for me. I was so interested in technology development. It plays with your imagination. It’s so motivating and inspiring to work on that stuff. And that continues to be there, but to actually make something and have impact with it, to drive it into the market, to get it out to people,and then once it’s out in the market for it to be wide enough reaching to everybody, those factors are a lot of other kinds of skills.
It’s about being good at developing partnerships. It’s about communication. It is about timing. It’s a lot about who you know, actually. That’s what it’s really about. And it’s a lot of these other kinds of soft skills. They’re not the science skills that take the technology forward. It’s all these other factors that really end up leading to it being the most impactful.
Grant: So then you took a turn into the world of nano and science policy. Tell us about that.
Ambika: Yeah. So while I was running Bikanta, I was from time to time writing about nano policy issues as well. Even while I was in grad school and postdoc. So I was on the Marshall scholarship during grad school, and part of the mission and bigger picture of the Marshall Program is creating relationships between and maintaining the US and UK relationship. That definitely played out while I was in grad school and post-doc for me on a nanoscience level because one of my advisors from Oxford, Peter Dobson, was also the advisor to medical research councils in the UK on nanotechnology.
He did a lot of policy and government advising kinds of work there. And when I was at the NIH, I got involved in the national nanotech initiative and I invited him and did a lot of connecting back and forth with people who were working on nanoscience policy in the UK, as well as here in the US.
And so I just always had on the side this interest of how we’re thinking about the bigger picture and where funding goes, because funding leads to like resources that allow you to actually do the science. It’s not just science. Where’s the money coming from? Where are the connections and whatnot?
So even though I was running Bikanta, I continued to be engaged in those kinds of things. And I would write about nano policy for things like tech crunch or different avenues. And because we were accessing user facilities at a national lab, they would often bring in policymakers, Congressman, whatever, to tour the facilities. And I would often be someone who they would talk to about how this is benefiting the entrepreneurial community and startups, and like how it’s creating jobs, et cetera.
Through that, I ended up being recruited to take a position at Lawrence Berkeley labs in one of the offices and leadership, thinking about the bigger picture of things. And so. That’s where that step took. And I mentioned that I feel like I’ve been progressing toward making a bigger and bigger impact. And definitely the resources that the government has was something that I was intrigued about. How to develop multiple different kinds of technology simultaneously and move things forward. And how do you do that from a lab perspective? So that’s what drew me to that position.
Grant: So you’ve worked across all these different areas. What do you see as the major differences among academia, biotech startups, and government?
Ambika: I love talking about this topic. I can talk about this for hours if you want to. Okay. So the way I would think about it is academics are very creative. If you look at the academic side in universities and whatnot, there’s a lot of creativity. There’s a lot of motivation for just understanding knowledge. That’s kind of where everything is driven from. And so you get some of the smartest people, the most innovative people in academia.
On the entrepreneurial side it’s very much about speed and execution and they’re driven by the market by what can be translational and what can also bring financial return. So that’s entrepreneurial as well as a larger industry, I would say. And then from the government side, the government has all these massive, massive resources, insane amounts of resources that you can then direct to different things.
And what they’re driven by is social impact and public need, as I’m thinking through things that I’ve experienced and what I’ve seen. I’m really trying to see how we can maximize the intersection of these three things. How can we better form bridges between them because, in some ways, they’ve been very siloed.
Through this past year with the pandemic, clearly the collaboration between industry and government made massive impacts. That’s what drove all the diagnostic testing technologies, Operation Warp Speed, Radox. All of these programs were these massive collaborations or partnerships you could say between both entrepreneurial and larger industry, the industry as a whole and government. Government through a lot of money that allowed things to get commercialized and move quickly and the vaccines as well. So this past year has been a prime example about where the benefit of crossing all three of these bridges really is important and that’s kind of the space that I want to explore more as I move forward in my own career.
Grant: Cool. So what areas of biotech are you most excited about?
Ambika: I’m going to say the kinds of things I was most excited about, like before I came into my current role and that would definitely be: personalized treatments of rare diseases. Sequencing your genome has never been cheaper. The cost has gone down from $95 million to $950 in just 10 years.
And so that efficiency of being able to understand individuals and then tailor treatments. That’s just a wide area of things that excites me. I also think a lot about precision treatment, which correlates with that. So real-time imaging and diagnostics and sensing in general, those technologies combined with machine learning, so you have this auto feedback loop about stuff as you’re developing your therapy or doing your therapy, if it’s surgical.
And in that similar vein, Theragnostics, which is a combination of therapy and diagnostics, which is where a lot of nano work happens, but other stuff as well. I think those areas have always been, in my mind, great areas for us to be moving forward in.
And that has a lot to do with materials development and big data related information, AI machine learning kinds of techniques, all combined. And I also find it very interesting, a lot of the virtual and augmented reality work that’s happening in these kinds of spaces. Although I still don’t know where it’s going to go yet, I’m intrigued by some of the stuff that’s going on with it.
But now in the past year, in the role that I’ve been in, my framework of thinking has changed a little bit, almost like putting on a public servant hat and looking at the whole of society today. I think the things that maybe are less sexy, but are really important to be working on are the evolution of clinical trials, how quickly and how still reliably we get technologies out, but also learning from this pandemic, the resilience of a lot of our systems.
And I know this isn’t as biotech and sexy, but it’s about how we manufacture or supply systems or some of the materials that you need to actually develop these things and get them out quickly, the supply chains of those. And the partnerships of industry and government. So again, it’s not like a particular technology, but the bigger, broader picture of this.
And part of it has to do with democratizing our innovations across the country. So if you think about GPS, it was a technology that was democratized and how it impacts so many different things in life now. Can you imagine where the benefits of GPS have gone by doing something like that? And we have all these national labs that are just treasures of our country that produce so much information and knowledge, but linking them to actually turning into commercial technologies quicker, the policy side…
I’m really excited by particular technologies for sure, but a lot of my brain work has gone towards what will set our country forward for the future. Like it’s been a lot of discussion about how China is going to beat us in terms of innovation within seven years.
And how are we going to continue to stay competitive? In some ways it’s basic things like having good broadband everywhere. The big part of this, if you’re thinking about the bigger picture, for sure, is education. How we treat STEM education from the early years on. Who gets access to it? Underprivileged and underserved communities as well.
And then because of the kinds of academic journeys I have been on, I think a lot about how are you training as a PhD, as an MD, as a scientist, what kinds of skills are you garnering along that process? How you might have interdisciplinary ideas and what kinds of these other softer skills that I kind of mentioned in terms of how to manage projects are you developing. Because the reality is that some kinds of supply chains are going to be cheaper in other countries.
So what are the skillsets that Americans can offer? It has a lot to do with risk-taking, being innovative and being good at managing things and communicating out. I mean like why is quantum tech a big talked about thing? A lot of it has to do because there’s interest from certain parties about it and that has to do with people being good about communicating the benefits of it. And so it’s like these skills of good communication and other things that need to be incorporated into your science training.
It didn’t really happen. At least I didn’t experience it. It happened on the side, but not as a primary thing. I’ve mentioned things that I’m interested in, but also I think some of the other bigger problems to solve are not as sexy, but important things to keep our country innovative and competitive moving forward.
Grant: I think the systematic approach is really interesting. I wonder how many things are kind of lurking beneath the surface waiting to be fixed and we don’t even realize it. I think a lot of myths have exploded over the last year with how remarkably fast we were able to get almost miraculously effective vaccines out for COVID and it really makes you think what could we have been doing differently all along and what can we do differently going forward?
Like I hope we learned from this and maybe make changes around how we do things because certainly COVID is especially urgent, but Alzheimer’s is urgent, cancer research, etc. There are a lot of things that are really important and affect a huge number of people where maybe it would make sense to kind of review how we’re doing things and see if we can do it faster.
Yeah. Do you have any parting thoughts for our listeners, maybe advice for younger scientists.
Ambika: So advice that I would give looking back at what my experiences have been: follow your interests and your passions, but as you’re moving forward, whether it’s in your particular science field or as you’re broadening into other aspects that impact how your science translates and gets out to folks, keep in mind some of these other things that are also important. It’s about learning what others are doing and being open to how that might shift, how you’re thinking on things. Because keeping your mind open about these things brings on these other opportunities that can sometimes be so much more like exponentially effective at getting you to where you want to.
It sounds like I’m saying something that’s very vague, but if I hadn’t been open to some of these ideas, I don’t think I would have gone down the path of doing a startup and that experience completely changed everything that I’ve done since. And when I initially took this job in crisis management strategy, I wasn’t expecting there to be any direct application of all the years of training in all my specialty into nano medicine and experience of doing a startup and working at a national lab.
I didn’t have any clear vision of how this was going to translate into something in this office. And I was motivated to come here because of just wanting to gain other kinds of skill sets and perspectives and take those back into the work I do later. But also I just thought it would be more impactful for me to come in during this timeframe and be a bridge into what I hoped was going to be the next administration and be able to bridge some of those science communication gaps.
If I hadn’t chosen to just be open to that, I wouldn’t have had the opportunity to also be in this position to see how the pandemic played out, not that I want there to be a pandemic or anything like that. But the experience that I’ve gained through this, I don’t even think I can fully communicate it yet, or understand the perspective that it has given me on how I think about the problems I want to tackle and how I want to tackle them.
Combining the different experiences I have, all of that starts off with being open. So be fun, I guess, is the take home message. Just as a scientist, you are always seeking data and being humble enough to know that you don’t know everything, but to actually more actively seek out the information, I think it can benefit you in how you approach things in the bigger picture as you progress further.
Grant: I think that’s really good advice. Thank you so much for joining us. It’s been a lot of fun.
Ambika: Yeah. Same here.
Grant: Welcome to The Bioinformatics CRO podcast. I’m Grant Belgard and joining me today is Lucas Steuber. Lucas, can you introduce yourself, please?
Lucas: Sure. First of all, thanks so much for having me, Grant. I appreciate it.
Grant: Thanks for coming on.
Lucas: This is a cool topic for a podcast, right? Bioinformatics is one of those niches within a niche. It’s really cool to, I think, have opportunities to learn more about and to share what we’re working on.
So I think like a lot of folks, I’ve had a bit of a twisty turny journey to where I’ve ended up professionally. I was originally a computer science and business major at the University of Oregon years ago. And had a lot of expectations from my family in terms of like, you’re going to be a businessman and this is what you’re going to do.
And I already sort of had snuck in the computer science element. To my great chagrin, the university forced me to take some classes outside of my major one year. So sort of like, okay, you need to go experience some other things. And I was kind of like, why am I doing this?
And so I signed up for some courses in linguistics and I literally think that it was because I could sleep in that I chose those. It was like Oh, good. On Tuesday-Thursday, I can get to campus at 10.
And just really immediately fell in love. I had always been really into computer science and math from a structural basis. And I really liked the sort of elegance of taking something really complex and watching it sort itself out into a simple solution or making noise from the chaos so to speak.
Language I found has a lot of that same element. It really is math in a fundamental sense. There’s structure and syntax and all these different things.
So I ended up getting a bachelor’s and a master’s in applied linguistics, studying a language structure when it’s disordered. Right. So looking at schizophrenia specifically, and some other situations where language starts to break down and then decided I didn’t have enough student loan debt. And so I went back and became a speech language pathologist.
So it’s funny when I’m on airplanes and people ask me what I do. The two job titles are basic. I am a speech language pathologist and a brain-computer interface product manager. And I don’t want to explain either of those two things.
So I just ended up wanting to make something up, like, I’m a real estate agent and I don’t know, go to sleep. So in any event, I worked clinically as a speech language pathologist with a specialty in low incidence populations. So we would call these orphan disorders or rare disorders of folks that really require the use of assistive technology and specifically in our case, augmentative and alternative communication.
Most people will tend to think of Stephen Hawking as the Cardinal example of somebody who used assistive technology to speak. So I’ve been in the industry, and now I’ve been designing and building those systems for about the last nine years. And I sort of head up clinical and marketing for a company called Cognixion, which is a brain-computer interface startup out of the Santa Barbara area although we also have offices in Toronto.
There’s a lot of deep learning and analysis that goes into this not from a genetics standpoint, but certainly harnessing the signal out of the noise from a lot of biological data. And so that’s what drew me to this podcast and I’m excited to be here and talk more about it.
Grant: Fantastic. Thanks for coming on. Yeah, we’re really happy to have you here. So, and just for our listeners, that’s a Cognixion spelled with an X. So we’ll have a transcript on the website where you can link out to the website. So tell us about your product.
Lucas: Yeah. So this has been sort of a stealth thing for the past couple of years. What it is is a coupled brain-computer interface with augmented reality displays. So, we call it a mixed reality. It is sort of becoming the new term. It’s not as complicated as it sounds from a use perspective.
So let me put that out ahead of time, just to frame the conversation a little bit. There are a ton of people in the world and specifically in the United States that can benefit from augmentative communication. But it’s largely an invisible population, right? I call this the silent minority. According to the best of my math, only something like 7% of the people who could benefit from augmented communication are even aware that it exists.
Right? So there’s a lot of people with say cerebral palsy or that have had a stroke, or maybe who’ve been paralyzed in an accident. These are all people that we don’t really see out in our daily lives. A lot of them are in care facilities or in their homes. And very often without the ability to speak.
The industry of augmentative communication has largely been focused on tablet based, touch solutions in the last couple of years. So certainly for children with autism, where there isn’t any sort of motor disability and they’re still able to touch and interact with an iPad. The Cardinal company in that, for which I was the director of products for some time for Tobii, which is well-known in the sort of eye-gaze world, which is really also sort of a bioinformatics platform, just in a more structural reflectometry way, measuring gaze using IRL light.
But that still doesn’t meet the needs of this other population of folks that maybe either have too much movement to adequately track gaze. So the example would be like spastic CP. If you have a lot of chorea, which is the involuntary movement, and you’re all over the place. Or then the opposite, if you have late stage ALS or folks that are totally locked in after a stroke, things like that, that currently really don’t have a means to communicate.
So. What our product does is it presents a language and sort of home control interface in the augmented reality environment and then measures using electrodes on the occipital lobe on the rear of the head. It measures evoked potentials, and specifically, visually evoked potentials or SSVEPs. To define evoked potential, it’s like being pinched, right? If somebody pinched your arm that would cause a spike in the specific sort of part of your brain that’s measuring pain or tactile experience in that part of your body.
What we’re doing is the same thing from a visual standpoint. So the different interface elements in the AR environment basically fluctuate or have different frequencies to them, say 5 Hertz, 8 Hertz, 12 Hertz. And depending on what somebody is fixated on, we can measure the evoked potential of the spikes in their occipital lobe to associate that then with intent and make a selection.
So it’s been really cool. There’s a lot of signal to noise questions around it, which I can go into. But that’s the rough path.
Grant: It’s really interesting. So are the people essentially being shown different letters, for example, at different frequencies and you’re effectively measuring attention?
Lucas: Yep, absolutely. I liked that you said attention, like there is a distinction between gaze and fixation in this sense. For example, if somebody is unable to move their eyes, we can still detect what they’re attending to within their peripheral vision. So it is very much attention instead of explicitly looking at something in an eye gaze modality.
And yes, it has to do with the frequency of the elements. There is sort of a threshold at which the noise becomes too much. Right? So one of the things we’re working on is trying to have as many interactive elements as possible within that, with the ultimate goal of getting to a full QWERTY keyboard, where somebody can type out.
We’re working towards that. We’re not quite there yet right now. We have for example, something called a SpeakPros keyboard, which is based on the frequency of letters within a sort of subset of the alphabet and then comes back up. And, we do a lot of things in terms of natural language processing and prediction and context awareness to try to predict the right stuff.
And then we also have a pre-built phrase inventory that’s relevant to certain contexts— medical needs, social needs, stuff like that— as kind of a starting point, but then really we anticipate everyone’s going to customize that and add their own phrases and stuff. Right. You can’t predict what everybody wants to say.
I think one of the reasons why I love this field is that it sort of combines a lot of different disciplines. I mean, we obviously have this sort of clinical healthcare component, but then we also have a really deep language and linguistic research component and the BCI and all these other interesting engineering questions. So there’s never a dull moment.
Grant: So out of all these challenges, what do you think is the most difficult?
Lucas: It’s tough to answer. I think that I’m going to go with two of them. Well, three of them, can I give you three? I’ll give you three equal challenges. So one of them is the design of the language system, right?
So one of the issues with these historically has been that what we do is provide people with an inventory of what we want them to say, rather than giving them the flexibility to say what they really want to say, which in Chomsky’s terms would be like the infinite general ability of language.
The fact that you can say anything in English, you can make a sentence of infinite length with all these modifiers and different things. That’s hard to capture when you’re working with a UX where you maybe are constrained to something like 8 interactive elements, right? So we don’t want to make people have to dive 30 pages deep or paginate for an hour in order to find these pre-programmed things.
What we want to do is increase their rate of production as much as possible using artificial intelligence based on their context or prediction and this other stuff. And so it’s been a challenge because language prediction and language generally is not very well understood even among the neuro-typical populations.
But then you think about the specific needs of someone with ALS or CP or Rett syndrome. And it’s been a really interesting journey, working with the community and having actual users of augmentative communication, vet these out for us and give us feedback.
We have about a hundred users that work with us on the development of the language model. And that’s one of our core principles is: nothing for us without us. Right. So keeping all those folks involved.
The second one I would say is the sort of signal to noise question. And the analogy I guess I would have is the hearing aid industry, which is really big by the way. There’s like billion dollar companies that are making hearing aids.
And from a consumer standpoint, they’re under a lot of pressure to increase battery life and decrease the size of the devices, but they also are under a lot of pressure to improve their far-field sound detection and differentiation between people sitting close to you. Like my grandfather had hearing aids 20 years ago.
And it just amplified everything. So if you’re in a crowded room, you just basically can’t hear anything because you hear everything all at once. And so there’s just a ton of algorithm work that has gone into trying to be like, who are we actually listening to here? Let’s tune out all the rest while also dealing with battery constraint and firmware and everything else.
And that’s really similar, right? Because we get all this electric data from the brain and we’re really looking for this one little needle in that haystack. And everything else can go away. And so meanwhile, we need to make something that’s wearable and portable and durable and has adequate battery life to work all day. That’s, I think, been the balance and the trade-off, why we’ve been working for several years. If this were an invasive solution, like Elon Musk, for example, with Neuralink, a lot of those questions would become a lot easier. But we really didn’t want something invasive. We wanted something that you could just sort of put on and take off.
And then the third piece of challenge, which is yet to be sort of fully vetted out, is just explaining it. When you think of these folks that maybe have ALS, this might be your 70 year old grandfather who isn’t particularly computer proficient, right.
And suddenly we’re asking him to be a cyborg with this brain-computer interface. So if you’re a 20 year old with a CP or a 40 year old with ALS. I think those folks are a lot more caught up with modern technology and are willing to experiment with the environment.
One of my jobs over the next few months is to be preparing all these materials and webinars and everything else to try to show everyone why this is valuable for them at any age.
Grant: That’s really interesting. So how far do you think non-invasive methods can be pushed?
Lucas: There is sort of a theoretical threshold that’s been established in research. Like if we’re looking specifically at the keyboard access use case, there was an article published recently arguing, basically, that 30 words per minute is about what we’re going to get in terms of being able to sort out the intentionality from specifically the acceptable use case.
Grant: 30 words per minute is not bad.
Lucas: Yeah, it’s alright. And that’s about the rate that most people text. It’s a little bit slower than typical conversational rapport, which is really what we sort of want to get to. And so we scaffold that a bit with things like the pre-built phrases, but that’s our goal is to get to that point.
I think that it’s probably premature to say that we have a hard and fast threshold, right? Like that’s just famous last words because somebody is going to come up with a better electrode or somebody is going to come up with a better algorithm. And if we didn’t have the constraints on battery and firmware, like if we could hook somebody up to a mainframe with these, we could easily exceed that.
But we’re trying to build something that’s compact and portable. I am confident that things will evolve and I’m confident they’ll evolve in such a way that at some point the distinction between invasive and noninvasive for this sort of measurement is probably going to be moot. We’re probably still 10 years away from that.
Grant: And what does the learning curve look for people as they start to use this? When it’s their first day versus a month versus a year, how much difference do you see in speed?
Lucas: Yeah. So that’s a big subject, obviously the clinical trials and user testing and human factors that we’ve been doing now for quite some time and will continue to do. And we still have three more iterations of that testing before the first version of the product goes to launch, which will be in early summer. This is kind of a cop out answer, but I would have to say it depends.
I think that folks that are used to, for example, even just the QWERTY keyboard interface, right. Those folks, that maybe have just had a cell phone that had been texting or computer literate to begin with, they’re going to move pretty quickly through being able to do this. Like, I can almost hit that 30 words per minute threshold right now. And we found that to be consistently true for folks with ALS that are familiar with computers and folks with CP, MS, supranuclear palsy. I mean, all these different things.
Basically, if you are a literate adult who then goes through a transition into needing to use this, then the pick up period is pretty quick. There are two exceptions to that. One of them is the sort of older adult who maybe doesn’t have a lot of experience with typing in an electronic environment, so there’s a little bit extra there in terms of what we would call operational competence, knowing how the thing might work or how you want it to work and that it needs to be charged and all these basics.
And then at the other end would be, this population like Rett syndrome is a really good example. So Rett syndrome is a rare disease that only affects women. It tends to cause paralysis from the waist up. And it has an onset of between three to five years old. And so there’s a lot of girls out there with Rett syndrome that I’ve worked with personally that maybe haven’t had a communication system, their entire life or have been doing something like, somebody holds up a piece of plexiglass with words written on it, and they try to guess what the person’s looking at, that sort of thing.
And when we catch them at 16 or 18 years old, there can be a little bit of a learning curve there too, because they haven’t even necessarily been exposed to written language in a sort of authorship sort of capacity.
So, that’s one of the things we think about too, is how to scaffold people up to literacy. Maybe they never were exposed because of their disability.
Grant: It’s interesting. Long-term do you see any nonmedical applications for this kind of technology?
Lucas: Yeah, absolutely. And in fact, I think that’s where most of the market is focused. Especially when it comes to augmented reality. I mean, a lot of people are looking at industrial applications, but also entertainment stuff. I know one of the companies we work with just licensed their lens technology also to the Super Mario Land sort of thing in Disneyworld.
And so there’s all kinds of cool stuff going on with it. So within medicine there’s stuff that I haven’t even addressed. So there’s the concept of therapeutics for Alzheimer’s using this. There’s the concept of diagnostics. All of that is outside of our use case, but is really, really interesting.
And then beyond that, one of the things that we’ve done—and Amazon has been really, really gracious with us. We use a lot of their sort of backend for our computing and for privacy reasons and things like that. They’ve enabled us to embed Alexa as a virtual assistant, which is cool because you don’t actually need to own an Alexa. It actually is an Alexa hub itself.
And there’ve been times when, for example, this assistant is in our kitchen and my wife was cooking a few weeks ago and it couldn’t hear me, like there was too much other noise. And I was like, man, I wish I just had the wearable and I could just tell it with my brain to turn off the lights. So, I mean, I absolutely think that stuff is coming.
Grant: Where do you think the first applications will be?
Lucas: It’s interesting to look at who’s most interested versus what gets actualized first. For example, we have a ton of interest and a lot of inquiries from gaming companies, who are interested, not just as a control modality. But also, and this is something that I worked with at Tobii, a fair amount of training and measurement for like professional video game players like, what were you looking at when you did this play? And what were you thinking about? Those sorts of things.
However, that’s really not where we see the first investment happening. I think the first investment is much more industrial and medical in another sense. So remote surgical tools, things like that. If you think of it as almost a separate access modality, like there might be somebody who’s who is interacting with, let’s say a remote surgical tool and they have a mouse and they’ve got a right click and a left click, and you’ve got a keyboard in front of them. But now they also have this totally other modality where they can interact in sort of a third way.
You can just zoom in on a piece of what they’re looking at or whatever, using the brain-computer interface component. I see that stuff coming and I also see it for a neuro-typical audience, not even being that expensive. Just the pure brain computer interface stuff is probably going to become pretty popular.
I mean, you look at stuff like a Muse now, these companies that are offering meditation awareness. That’s all fundamentally the same sort of technology just applied to a different scale. So it’s going to be really cool to watch. I think that we’re going to see a real revolution in terms of what these things can offer us over the next 5 to 10 years.
Grant: Going back to the medical applications, you talked about therapeutics, how could something like this have a therapeutic use? Can you discuss that?
Lucas: Yeah. Sure. So there’s been some evidence for example, that in Alzheimer’s specifically, as well as in certain visual impairments, that specific frequencies of the steady state visual elements might actually in one case breakup plaques, in another case serve as a training and attention mechanism for people that maybe have cognitive impairments or attention impairments in terms of like attending to their entire field of view. We have a lot of folks that are interested in using our hardware for that.
And that’s one thing I’ll say is that, I’m a speech language pathologist. My role has been to build this for our initial use case, which is augmentative communication. But we also do see this as a platform for other folks. If people want to develop on top of it, the reason why frankly, we’re not jumping into diagnostics ourselves right off the bat is just as a result of the FDA and requirements that would be involved with that.
We are a startup. We’re honing in on something that we know we can do really, really well.
Grant: Start with the tractable use case.
Lucas: Yeah for sure.
Grant: And can you maybe discuss diagnostics a bit?
Lucas: Sure. Yeah. I mean, there’s all kinds of different things that have been shown to be diagnostic from specifically an EEG use case.
So one of them that I’ll just throw out there as a metric that we measure is fatigue. If we have somebody who’s been using the device for a long period of time, we are able to tell that it’s been wearing them down. And one thing that we could do for example, was to simplify the user interface or bring the interactive elements closer together.
So there’s not as much range of motion or attention involved. We’re looking at all those things. There’s ethics that come into play there too. It’s like, I don’t want it to be like, you’re tired now. You can’t use language.
We need to sort of balance what we’re doing. Fatigue is sort of like an in-the-moment thing that’s relatively easy. More complex things would be actually tracking the rate of neurodegenerative diseases and prognosis in that regard of which there is compelling evidence for ALS, Parkinson’s, and Alzheimer’s that that all could be possible, but it’s still pretty exploratory right now.
There isn’t a clear blueprint of X plus Y equals Z at this point, but we’re certainly willing to throw this tool into the mix in terms of something else that people can experiment with.
Grant: So, if you were to speculate way out, 50 years, what do you think will be the long-term implications of this kind of technology? You’re writing a scifi story involving a noninvasive brain-computer interface technology with AR.
Lucas: It’s funny you asked that question. So I’ve been listening to Ready Player Two, the audio book by Ernest Cline. He wrote Ready Player One and they made a movie out of it, directed by Steven Spielberg.
But I was listening to Ready Player Two. And literally the premise of the book is about the first noninvasive BCI AR and, to the point where I was walking to work and I’m looking around, wondering if somebody was listening to me, like I thought I signed an NDA about all this. How do they know?
So other people are thinking about it, absolutely, in the science fiction context. And, his take is a little bit dystopian and it’s that people sort of begin to enjoy this BCI mixed reality environment more than life itself. Right. So they sort of dive into it wholesale. For better or for worse, I can assure you we’re not there yet.
There’s all kinds of other evoked potentials that are explored in the novel: taste, sound, haptics. All of that is there, but we’re really still looking at visuals. But I would say that there’s probably two futures. I see one of them in terms of the assistive technology use cases. I really feel like we’re moving towards a future where accessibility is going to become synonymous with personalization, which is very much something that I want.
There’s a quote that I love that is: for some people, technology makes things easier, but for others, technology makes things possible. I really want to just sort of raise the bar with assistive tech and establish this as the new standard moving forward. We should be looking at all of these things, not just BCI, but also just the context of life and use in whatever form we can get it.
And then for society generally, that is going to be really interesting to see. I see all these sort of hypothetical’s come up. Obviously there is an immense military application, right, which is a whole other conversation in and of itself, but there’s also application as we look towards autonomous vehicles.
If you would have asked me five years ago, I never would have guessed that this meditation use case would be as popular as it is, but people really like the self monitoring of their emotional state and getting that feedback. And so it would not surprise me at all if a BCI wearable in some form became a pretty common piece of technology for people to have in 10 years.
Grant: Interesting. And what do you think are the military use cases?
Lucas: Yeah. Coming from the eye gaze tracking field, there is a lot of military interest in that realm as well. So I can speak to it pretty comprehensively from that perspective. We have not worked for the military directly, although we have had conversations with various space agencies, but I think it sort of comes back to the idea of having that other access modality.
Right. So if your hands are tied up flying that jet with 600 other things, that you have to do. And, the jet is able to infer what you are attending to, whether that’s a threat or simply something within the cockpit interface, that adds another layer of really interesting data. Not only in terms of what it can do in the moment for predicting what needs to be done by the jet, but also in post-flight analysis.
If something went really right or something went really wrong, how can we harness the intentionality of the pilot to either repeat or prevent that from happening again? And I think a lot of that translates into the space use case as well. So I’ll be really interested to see what people come up with. Frankly.
Grant: It seems a bit like the possibilities are limitless.
Lucas: Yeah. Right. Well, it’s this whole other sort of measurement that we haven’t had access to at a consumer scale before. One of the sayings in linguistics that I was sort of, raised with in all my college training was that the only measurement of cognition is language and behavior. Right.
We can’t look into someone’s head. We can only see what they say and what they do in order to measure what’s happening up there. And that’s kind of changing, right? I mean, I wouldn’t say that we’re reading words directly, but we can definitely get a really clear sense of at least what people are looking at and paying attention to at any given moment.
And I think that’s pretty telling in terms of their behavior. I would love to hear from anybody listening. I mean, it is so cool to have podcasts like this that focus on something really specific like bioinformatics because I’m sure there’s a lot that I have even missed in terms of talking about this and I’d love to hear ideas.
So again, it’s cognixion.com. And there’s a whole long story I won’t go into in terms of why that’s the spelling, but it means things, and my name’s Lucas Steuber. I’d love to hear from you at Lucas@cognixion.com with any sort of thoughts or interests people might have. I guess my final bit is that I think we’re the first that has blended the BCI and AR modality or at least the first to market.
It wouldn’t surprise me at all if other people were working on it, but we won’t be the last, I think that the disability use case is one that’s really compelling for me. And it’s one that’s going to really benefit a lot of people around the world, but it’s also sort of a harbinger of what’s to come. I think that everyone is going to be looking at and talking about devices like this a lot more in the next 20 years.
Grant: Thanks so much for joining us, Lucas.
Lucas: It’s been a lot of fun.
Grant: Welcome to The Bioinformatics CRO Podcast. I’m your host Grant Belgard and joining me today is Trevor Martin. Trevor, can you introduce yourself, please?
Trevor: Yeah, thanks for having me. My name is Trevor. I’m one of the co-founders of a company called Mammoth Biosciences and the CEO.
Grant: Great. Tell us about Mammoth.
Trevor: Yeah, it was founded about three years ago. We were spun out from Jennifer Doudna’s lab at the University of California, Berkeley. And the other co-founders of the company include Jennifer Doudna herself and two former graduate students from her lab, Janice Chen and Lucas Harrington.
And the catalyst for the formation of the company was a couple of things. So first and foremost, the invention in Jennifer’s lab of this new field of CRISPR based diagnostics, that’s very exciting. It’s really come into its own over the course of the pandemic. And also this recognition that the way we were able to invent that field and the way that we’re driving forward a lot of things across CRISPR is through the development and commercialization of new proteins that go beyond the kind of standard CRISPR systems that most people think about, like Cas9, for example, which is maybe the most famous.
So we actually work with CRISPR proteins that are coming from just totally different families, like Cas14 and things like CasΦ. And these have really exciting properties fundamentally, that enable new types of products that wouldn’t be possible working with the original Cas systems.
Grant: What kinds of properties?
Trevor: Yeah. So taking CRISPR based diagnostics. As an example, when you’re doing CRISPR editing, the main thing you’re concerned about is what you could call cis cleavage. So that’s the cutting of whatever target sequence you’ve programmed the CRISPR protein to bind to. And for those that aren’t familiar with CRISPR proteins, they’re kind of these programmable molecular machines, where by giving it what’s called a guide RNA, you can tell it to go to a certain region of a genome and to bind there. And then things like Cas9 come with a pair of scissors that can then cut that DNA. And through the process of repairing that cut, it’s a classic way of introducing some sort of edit that you would like to have.
When you’re thinking about CRISPR based diagnostics, it’s not terribly useful to edit the sequence that you’ve targeted. So what you’d rather do is program the guide RNA. To be specific to some sort of disease, like for example, COVID-19, that you’re trying to detect. And then instead of editing that disease, you’d rather somehow have the CRISPR protein read out a signal or amplify signal that it successfully found its target.
A property that many of our new proteins have that enables that is what’s called trans collateral cleavage or you can kind of think of it as just a molecular shredder. Where if, and only if it successfully binds its target, not only does it cut the target cis, so cutting the thing it’s bound to, but it also will then cut all sorts of other molecules in the solution. So you go from binding a single molecule to cutting many more molecules, kind of in this molecular shredder functionality. And that means that you can read out an amplified signal. So for example, if you’re doing some sort of RNA diagnostics, then it binds to an RNA that’s maybe specific to an infectious disease, and then you have other RNA marker signal molecules in the solution that are then cut and release some sort of color floressence. And that can be a way of amplifying a molecular diagnostic signal in a way that’s not possible with Cas9.
Grant: Can you tell us about things like the length of the guide RNA, how much specificity you might have to something like SARS-CoV2, and what concerns you might have about new variants arising and how easy is it to adapt to those?
Trevor: Yeah. One of the advantages of the CRISPR diagnostic system is that it’s very adaptable. So at the start of the pandemic, one of our really great scientific advisors, Dr. Charles Chiu saw this going on and we were able to put together some really exciting work very quickly within a matter of weeks, showing that CRISPR based diagnostics can actually detect COVID-19.
And we published that as a white paper and then eventually into a pretty seminal paper, CRISPR-based Diagnostics in Nature Biotechnology, where we showed it in real patient samples. And we were one of the first groups to do that. And I think in terms of sensitivity and specificity, starting with the specificity, that’s really quite exquisite for these CRISPR based diagnostics systems. Even a single base pair, you can design it to very easily distinguish between different alleles at that spot.
And in terms of the sensitivity, you can get really high sensitivity, both from the CRISPR amplification itself, and then combining that even with other isothermal techniques and the same or different reactions to get sensitivity beyond what even PCR can achieve.
And in terms of additional variants, one really exciting thing about CRISPR based diagnostics is due to the simplicity of how you can target different things, you can actually also multiplexer very easily. So you can either choose to target regions that maybe are less prone to variants and are very constant, or you could actually target varying areas specifically.
So you could differentiate between them and actually identify what variant is being detected. Or you can do a combination of the two by actually having multiple guides for different variants in the same reaction so that any of the variants could actually activate the detection. It really gets you a lot of optionality basically, on which of those approaches you want to take.
Grant: How do you see this fitting into the menagerie of other molecular diagnostics?
Trevor: So right now there’s kind of this choice in diagnostics about, do you want like a super accurate result? Something like PCR, like molecular style, or do you want something that is very accessible and very easy to use? Simpler, but maybe it would lower sensitivity and specificity?
And it’s kind of this trade-off between the two. I think one of the promises of CRISPR based diagnostics is removing that trade-off to a large extent. So being able to have something that is a molecular-style result, but it in a much simpler reaction, a very multiplexable reaction, something that’s very accessible and easy to use and kind of getting rid of that dichotomy that diagnostics has existed in for many decades.
Grant: And what do you think is the biggest challenge to getting this out in use?
Trevor: With the pandemic it’s been really exciting to see that already Mammoth has gotten emergency use authorizations for the technology for detecting COVID-19. So before maybe I would have said that one of the big hurdles is that it’s a brand new molecular technique and those take time to come to market.
Helping out with the pandemic has been a great thing, both for CRISPR diagnostics itself, but also obviously for just helping play a role in combating the pandemic. I think in general, the main things we think about right now are scaling all the different formats that it can go into. There’s just so many different opportunities. So which ones to prioritize.
In general, I think it’s kind of cool just looking back. Obviously the first things people thought about with CRISPR were things like CRISPR based therapeutics and editing, and there’s a lot of exciting things going on there including at Mammoth, but it’s pretty exciting to see that actually the first commercial uses where people are actually interacting with CRISPR were on the CRISPR diagnostic side. Even though that’s a much more recent invention.
Grant: What do you see as the most exciting opportunities?
Trevor: There’s a couple. So I think what’s interesting about CRISPR based diagnostics is that it is a new way of doing molecular detection and one of the first new ways in many decades. Essentially, there haven’t been many of these new techniques that have come out over time. So I think that ‘s cool.
There are a ton of different formats that it can go into, and it can have a role all the way from like central labs. So increasing the throughput of central labs and the testing that can be done in there. For example, like we have our boost product launching, and I think that there can play a big role in like reducing the wait times and like really increasing the accessibility of testing by having higher throughput and really enabling all the labs around the country, not just the large labs to really do serious amounts of molecular COVID testing.
And then on the other end of the spectrum, you have the radical decentralization of testing. And I think that’s a trend that existed before, but COVID has now accelerated it by many years. Cause it’s really shown how critical that can be. So thinking long-term I think it’s really exciting to imagine that the next time there’s COVID-2023 or something–hopefully not, but unfortunately it’s probably a matter of when not if– instead of having that become a global pandemic, what if you had millions of tests in warehouse that could be very quickly reprogrammed with a new CRISPR guide RNA to then go after this disease and actually test and trace and contain at an earlier point. So thinking about things like that is pretty exciting.
Grant: I think the importance of that is underlined by actually the last podcast we recorded just before yours was with a few COVID experts. It emphasizes the importance of rapid and scalable and precise diagnostics.
Trevor: Yeah, definitely reliability is a key as well because if you’re testing a lot of people, but you’re not confident in the result, then it can be tricky to understand what to do with that information.
Grant: Right. So can you tell us a bit about the details of the origin? How did the discussions around Mammoth begin and you were, I think finishing up your PhD, right?
Grant: Yeah. That’s really incredible. Can you tell us about how that came about?
Trevor: Sure. My PhD is a bit more on the computational biology side, actually rewinding the clock a little bit more. I originally got interested in computational biology at Princeton when I took a program that had been started by David Botstein, basically trick mathematicians and physicists and computer scientists to become hardcore biologists.
And I started the program on a whim, but I really fell in love with the concept and biology in general, and definitely a different way of thinking about biology than I’d been exposed to previously where it can often be thought of as more memorization or a little bit squishier. That’s how I got introduced to computational biology.
And in general, I think what attracted me to that is thinking about life as something that’s kind of programmable in some sense, and has rules. And it’s something that it’s like very tractable and very difficult, obviously, to understand fully, but it’s not magic. And there are fundamental principles and algorithms that can be applied.
Towards the end of my PhD. I think one thing that’s really cool is that computational biology has infiltrated all areas of biology. And I think now it’s almost silly to call something computational biology differentially because everything is computational, which is great. I think that’s a sign of the success of the field.
That it’s just a fundamental tool in your toolbox now for doing any sort of biology. A consequence of that as well, though, as I started thinking about what are other fields that are about to undergo that type of transformation in biology, where taking something that’s like kind of a new concept, but it’s going to become fundamental to everything that’s going on.
And I immediately started thinking about synthetic biology and like all these new tools, like CRISPR that are coming out in terms of really pushing the envelope of how we think about–in this case a little bit more literally–programming biology, synthetically.
Had some initial ideas around different cool tools for this and in general, I’m a big fan also of this idea of there being power in the intersection of fields and especially like fields that maybe have seen less innovation: diagnostics, for example. I was thinking a lot about the intersection of synthetic biology and diagnostics and had some initial ideas for stuff in that space, but not exactly a trained synthetic biologist through my PhD directly.
And around the time these thoughts were swirling around in my head, Jennifer’s lab was pioneering this kind of new field of CRISPR based diagnostics and Janice and Lucas were at the forefront of this. And I saw the papers being published and pretty much thought, okay, wow, this is way better than any of the things I’ve been thinking about and is exactly a fit for this thesis around just really the transformative power of synthetic biology and these fields that are maybe a bit underappreciated.
So I reached out to Jennifer and the team. And I’d like to say that we just immediately started the next day, but of course spent some time getting to know each other and found that we really shared this thesis around where the field was headed and how transformational this could really be in diagnostics, but also in thinking about next generation therapeutics and just next generation biology overall. And then decided that we had a really exciting opportunity to start Mammoth together and build this platform for what’s next and CRISPR and synthetic biology broadly.
Grant: And what were the early months like?
Trevor: One thing that I really appreciated about the Bay area ecosystem–and I don’t think I understood fully going into it, coming just from a pure academic career–is that there are a lot of people and institutions that are extremely helpful. And there’s just like an ecosystem that is really well-designed for thinking about big ideas like this and getting them off the ground. So on the one hand, there’s lots of great advisors that we connected with early on, who just shared advice for free and were really helpful in terms of thinking through how we’re setting things up, thinking about the future of the company.
Then also just more practically being able to start doing experiments really quickly by getting like a single lab bench in a building that has single-lab benches for tons and tons of companies. I think those are really exciting times for sure at the beginning of the company because there’s just all these things to figure out, but it’s all something that you can tackle and there’s no one telling you to do it one way or the other, which of course can be scary as well, but I think it’s also very liberating and exciting.
On top of that, I think the excitement more broadly for these new approaches to biology is something that truly is at the start of an inflection point. And that’s always an exciting place to be. There’s been a ton of progress. And now we have emergency use authorizations for CRISPR based diagnostics and things like that. But I still think we’re just at the very first part of the infection, even with all that progress that’s been made. So that’s just something that makes it a really interesting area to work in.
It’s always exciting to be somewhere with so much potential in terms of just the field. I think the other thing that happens in the first few months is that there are some things that your PhD training really does prepare you for. And I think one of those that’s maybe under-appreciated is dealing with uncertainty.
Like when you’re doing a PhD, there’s no right answer. Obviously there’s no textbook. That’s the whole reason you’re getting the diploma. You’ve hopefully expanded the knowledge of the field. And startups actually have a lot of parallels to that in terms of there’s no right answer, right.
Otherwise someone else would have just done it or there wouldn’t be any reason to form a startup around it. So I think dealing with that uncertainty over long timescales is a super great asset and is under-appreciated. On the other hand, I think somewhere that maybe you’re a bit woefully under-prepared both for academia and startups is hiring people, managing people. That’s definitely somewhere that you have to have a lot of growth very quickly especially in the early days, but also every single day at a startup, even after you have thousands of people.
Every single hire is going to define what your startup is and where it’s going. So it’s one of the most critical decisions that you make day to day. So I think that’s somewhere that has a super steep learning curve in those first few months
Grant: Interesting. So what areas of biotech other than CRISPR based diagnostics are you most excited about?
Trevor: Yeah, there’s a lot. Some of the ones that are talked about more that I think are interesting are things like the data storage side. That’s a little bit directly related to synthetic biology because being able to read and write is greatly influenced by those tools.
I think more generally something that’s a little bit, maybe less visible that I’ve seen a lot of and is pretty exciting is non-drug or molecular approaches to therapeutics. Whether that’s sound-based or diet based or other things, I think that there’s a lot of really interesting work going on there. And I think that it is going to require even more exquisite on some level understanding of what’s going on to really understand mechanisms and have better hypotheses about what works and doesn’t work there. But I think as we develop that really great foundational molecular understanding, it does allow a lot of opportunity to take it to the next level.
The simple answer is I think even fields like CRISPR that obviously have been in the public eye for several years now are just at the start of the inflection points. It seems like something that has already made so much progress because it has, but I think it’s still not even at the 20th percentile of its potential in terms of where it’s headed over the next 10 years.
Grant: So, what would you say are the most important things you’ve learned since you started the Mammoth journey?
Trevor: I think the first one would definitely be going back to managing people. You do things like collaborations in academia, and obviously you work closely with other people, but I think something that’s underappreciated is how much a startup can align people around a common goal.
And that can be a really exciting thing. I think there’s a lot of stuff to be said around having that shared vision and actually bringing something into the world as well. It’s something that I appreciate a lot. So, whereas in academia you might publish a paper and hopefully it gets cited many times and drives the field forward. That’s definitely rewarding.
But for me personally, there’s something an order of magnitude more rewarding about having a product that actually then goes and helps someone directly rather than always being a few steps away from that. And I think that that’s something pretty enriching about startups in particular that maybe I didn’t fully appreciate at the beginning.
But yeah, I think in general all the learning about working with people and recruiting people and managing people, that’s been some of the most rewarding stuff and interesting stuff that has been a part of the Mammoth journey so far. You’re exposed to a lot of different ways of thinking. And it’s also just critical to the business, so you’re kind of forced to learn a lot about it.
Grant: What advice would you have for first time CEOs?
Trevor: The main advice I would have is to not be too wrapped up in knowing everything. And it goes back to the people as well, but like trust is the critical element. Because, in my opinion, the only way you’ll scale into a successful company or as an individual is by relying on others that you trust. Because yeah, you can read every book you want and might try and become an expert in all these different areas. But you’ll never be able to do that fast enough to cover all the things you need to learn as quickly as you need to learn them, even if you’re just a perfect genius.
So the only way to scale yourself is to scale yourself with additional hires and people and the team. And the only way you’ll leverage them effectively so that they can actually help you scale is if you hire people that you trust and really empower them to do the things that you hired them to do, right?
And worse is to hire someone that’s really awesome and then make all the decisions for them anyway. So I think that would be the number one piece of advice, because it can be a little bit counterintuitive because on the other hand, you also don’t want to seem like you know nothing and you want to convey some aura of authority. More fundamentally, a lot of that trust from the team in the other direction comes from the trust towards them. And I think that’s something that can get lost, especially for early stage founders. And that’s one of the most important transitions that happens as the team grows.
Grant: What is something on which you disagree with most of your peers, and why are you right?
Trevor: Yeah, that’s a classic Peter Thiel interview question. One thing that I’m a big believer in right now is definitely a more controversial topic in the Bay area: whether Silicon Valley will remain the place that the next generation of startups are going to be born in and grow, or if now maybe accelerated by COVID-19 everything’s going to be decentralized and sort of all becomes more of a mindset than necessarily a place. In biology, it’s kind of funny because in many ways you can replace Silicon Valley with Boston actually for that comparison. But I think there’s some trivial reasons why that’s not true for biology, just in terms of like you need lab space and people to come into the lab.
And maybe those are the less interesting reasons why I think that the San Francisco Bay area will remain the center of innovation. But the less obvious reasons would be around what I mentioned at the beginning around the ecosystem and how supportive it is. Like, man, these first few months, I can only imagine if I was not in the Bay area–or Boston for that matter–just how many orders of magnitude more difficult it would have been to get started. Like it’s just way harder. And so one result of that is maybe the deep tech startups stay in these certain hubs and maybe software truly can just be built anywhere.
I don’t know, like I am not familiar enough, but my gut intuition is that I do hope it does democratize access to building a startup more. Cause I think that’s important. I think the ideal would be that you could anywhere to start a company just as easily. That’s definitely the ideal in my mind, but I do believe that the reality is that places like the Bay area in Boston will continue to be where you kind of have to come to really build startups that are really ambitious and have a lot of capital behind them to really tackle audacious goals.
Not just because that’s where the capital is–although that’s part of it as well–I think the barriers to that are lessening, but more because of the support networks around. Even from the small stuff, like the lab space, and the bigger stuff like where you’re going to recruit people. I think that that’s something that is just way more sticky than even a year of forced remote working can remove.
And I think there could be things that disrupt that longterm. But this would be on like a 10, 20 year timescale and have nothing to do with the power of remote tools, but more to do with do other areas start to encourage innovation more than the Bay area? Or things like that. So, yeah, that’s my kind of thoughts on is Miami going to supplant San Francisco? I hope that Miami grows into an awesome tech hub, but I don’t think that’s necessarily at the expense of the Bay Area.
Grant: So what can cities like Miami do to increase their biotech competitiveness? Why are we starting from pretty far behind? Maybe we can talk about Raleigh-Durham or something.
Trevor: Well, I think that’s a great example actually, because you are starting to see a lot more startup activity there. I think it’s a bit of a chicken and the egg problem. So one way to get around that is to build a pretty massive public investment and incubator space and lab space and increase access to capital and things like that.
I think that’s like table stakes. I think the harder part is the mindset, because I think something that’s really important and you see this even in little microcosms of the startup world is an example of success. And I think this is one thing that I hope Mammoth can contribute back long-term is an example of where scientific founders built a successful company coming out of PhDs and contributed back to the world in a really impactful way.
Because right now, the classic biotech model is that the scientists have some idea that gets passed off to maybe like a venture firm. And then they hire a bunch of people and go off and do it and deliver something of value to the world. But I think there’s this new model that Mammoth and other companies are pioneering, where you don’t have to pass it off to someone else and you can really kind of drive that vision forward yourselves.
And I think that can have huge advantages long-term for the vision of the company and where it’s headed and the innovation that happens after the technology has been transferred, that long-term beats the pants off transferring the technology to people that aren’t the ones that necessarily invented it.
But I think Silicon Valley and the Bay area and other places have a huge advantage that people have seen others found companies that go on to be super successful. And it opens your imagination. Right?
Like I grew up in Georgia and knew nothing about startups. I just wasn’t exposed to it all. I just had no understanding of it at all. I couldn’t even think about that as something that would be interesting to me. It wasn’t even on my radar, but then when you come to somewhere like Stanford, everyone is doing it for better or worse. It’s just like an accepted part of the paths you can take.
And that means you have more people trying it. And that means you’re going to see more examples of it and that’s just a self-reinforcing loop. So I think the trickiest part is getting places that are not the Bay area or Boston or the usual suspects. I think they need a few big wins and a few companies to really pioneer it.
So anything that can be done to support those companies and get them across the finish line. Cause then you’ve opened everyone’s imagination. They’re like, Oh yeah, those people from Duke. They founded that company that has a bunch of products that we use all the time. They were in my shoes 10 years ago. Why can’t I do that? And I think that’s the trickiest part and that’s the big advantage that the Bay area has over anywhere else is just that mindset somehow.
Grant: So you touched on something that I think is pretty important and that we try to explore a bit on this podcast and that is inspiration. And what inspires people to do what they do. At what age did you know you wanted to be a scientist?
Trevor: It’s an interesting question because I think early on in my life, I wanted to be a historian. I was obsessed with history and read anything I could get my hands on. It wasn’t until maybe late in high school, when I started to really understand and was introduced to the idea that you can build up a model of the world. Actually, in some ways, similar to history. The part of history I was most interested in was what can we learn from our mistakes? And how can we avoid repeating history. And science is like an ultimate form of that. By doing experiments, you build up a corpus of knowledge. You can actually predict the result of the next experiment you’re going to do and use that to build things that are really helpful to people.
So I think that’s kind of when I first started to get interested in science. But especially in high school, you can fall into the trap of thinking that physics is really the place to do that only because it’s just how these subjects are classically taught.
Grant: That is a very recurring theme. I was the same way. I wasn’t that into biology until actually later in college.
Trevor: Yeah, exactly. Because I think once you’re in college and you’re finally exposed to the immense amount of interesting research that’s going on across all fields, you can kind of realize that every field is like this and does experiments and can learn from them and predict what’s going to happen next. And use that to advance our knowledge and build cool things that help people. So then I think it was in college in the program that David set up that really solidified my interest in science and kind of how exciting it can be to push the limits of human knowledge in ways that are reproducible. And also that can be communicated and replicated and yeah that’s a pretty exciting concept in general.
Grant: Do you have any thoughts on how we should change curriculum? How science is taught?
Trevor: In general, it’s a bit of a double-edged sword these days. Cause I think one of the coolest things you could do would be to introduce earlier that science doesn’t know everything and that there’s so much room for innovation. Instead of thinking about math and science as learning these rules, which obviously you have to do–to break the rules, you have to learn the rules–and I’m a big believer in you’ll be the best at breaking a rule once you fully understand it. And like every assumption that’s in it, et cetera.
So I think introducing that a bit earlier in people’s science education, like middle school. It’s not just like, Oh, there’s hypotheses and learn all this stuff that’s all fixed. Giving a bit more of a story around how we figured out that genetic information was coded in DNA and things like that. And maybe even letting people do that experiment, or if we wanted to take it further, what were the things that we thought we knew that were wrong? Like experiments that prove one thing, but maybe ended up being disproved later and actually doing those experiments as well. I think that would really open people’s minds to the possibility of like Oh, I can contribute to this on the other end.
There’s so much skepticism and conspiracies about science that it’s tricky. The strength of science is that it can withstand that stuff, but then are you just going to lead people down this weird path of like no trust in science. And I think that really comes down to how well that process is taught.
So I think if we want to do that and we want to have a really great science education that emphasizes that it’s a continuous process and it’s valuable and that there’s things that are wrong even today. I think that relies on investing way more in education than we do at the moment, or at least better allocation of resources. So it’s a tricky answer.
Grant: So speaking of things we thought we knew, but we were wrong. What jumps out to you today as a candidate for that? So if you look at statistical genetics, for example, you can certainly look at a lot of the work that was done on candidate genes studies some years ago that mostly turned out to be BS.
Trevor: Probably a million things would fall into that bucket 10 years from now, but I think maybe one of the biggest ones is. I think there’s still an under-appreciation for non-novel results. Just like replication of results. Towards that point because right now I definitely think the publishing standards are a little bit skewed towards new results, whether those are confirmed later or not.
So it’s just, all the incentives are aligned towards publishing a new candidate gene rather than replicating a previous candidate gene. And there’s reasons why that is helpful. Right. You want to be driving forward new knowledge, not just resting on the laurels. I mean often in this crisis of replication–that term is thrown around a lot. I don’t necessarily know if I’d use the word crisis–but I think something that would help uncover what we should be building on and what things maybe we have a few false assumptions on that could be fixed and then built on, would be if we change the incentive structures around replication and the rewards for validating results and things like that.
I hope that in 10 years we look back and we say, wow, what we’re even thinking? Like in terms of how we reward replication studies and things like that. Clearly that was the worst possible thing we could do.
Grant: Do you have any, any final thoughts for our listeners?
Trevor: Yeah, I think the main thing would be to emphasize to anyone that’s doing a PhD that there’s a lot of different options you can do after your PhD. Like going into academia, I have friends that have really rewarding careers down that path and really enjoy it. I know people have gone down that path and hated it as well. Same for any path. But in addition to industry, I think that there is this new wave and openness towards having people at the forefront of science also be at the forefront of business and is definitely not something that people should be shy about or think that they can’t do.
Grant: That’s a good positive note to end on. Well, thanks for coming on Trevor. It was great.
Trevor: Yeah. Thanks for having me.
Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining us today is Dan Geschwind. Dan, can you introduce yourself please?
Dan: Sure. Thanks for having me here Grant. It’s really great to be here. I’m a professor of neurology, psychiatry and human genetics at the David Geffen School of Medicine at UCLA, and also in my role as associate vice chancellor and senior associate dean, I direct the relatively newly formed institute for precision health at UCLA.
Grant: Can you tell us more about that?
Dan: Sure. Basically the idea is that there’s a whole new wave of genomic information and a revolution on the horizon in medicine that even the most advanced academic medical centers have not really prepared for. Although again, like UCLA, we are beginning to prepare for it.
It kind of falls under this umbrella of what many call precision medicine. We call it precision health because it involves prevention and health. We’d like to be precise before people get sick. And the notion, I think Obama stated in the most succinctly and clearly of course–one of his speech writers did–every person gets the right treatment at the right time.
And so what that really means is that we take into account individual differences as we are treating patients. And that involves crunching a lot of data to understand the individual’s genetic background, as well as in the future, potentially a lot of other environmental and sociodemographic factors. There’s a huge amount of social determinants of health as well that can be put into these equations.
Grant: For what types of indications do you think we’ll see the earliest advances in this space?
Dan: Well right now precision medicine is being done in about 6% of cancers. A mutation is identified in the tumor that requires a very specific targeted therapy. And of course, 10 years ago, that was less than 1% of cancers. So this is a rapidly changing field in the area of Mendelian disorders or rare genetic disorders, which individually are rare. These are disorders that are fewer than one in a thousand, but if you add up the percent of people who will have a rare disorder, or who have a common disorder, but a rare cause of it, it’s probably somewhere between 7 and 10% of people.
And again, in major medical centers that might even be higher at tertiary care centers. And so those people get diagnosed. Often. They go through what’s called a diagnostic Odyssey where they go to multiple experts around the country. Nobody can figure out what it is and it’s not until they get a clinical sequencing study done that it becomes obvious, Oh, they have that disease because they have a mutation in that gene.
And so we believe in taking a sequence first approach to people with rare diseases because we– being UCLA and my colleague, Stan Nelson and others– have shown that the yield of the test is somewhere close to 25-30%, depending on the indication. Again, congenital heart disease, rare neurodevelopmental developmental disorders, immunologic disorders, et cetera. And there’s really no single clinical test that has a yield as high as that. And as the sequencing costs get cheaper, it becomes the least expensive and most efficacious thing to do.
Of course, it’s not just an expense issue. It’s an issue of the poor patient’s family going through years of uncertainty and stress going to multiple doctors, et cetera. So those are the main areas that it’s being done. It’s in undiagnosed diseases, developmental disorders, of course the typical pediatric genetic syndromes, more and more in unusual clinical conditions and then in cancer.
And most of that is finding rare mutations that cause things, but there’s also common genetic contributions to diseases. And as more and more of that is understood we’ll be able to apply what are called polygenic scores, that is look at the common additive risk that people share in common that might predispose them to various common conditions.
And we can use that in screening tests. So for example, you could imagine that right now the way women get screened for breast cancer is by age and there’s some consideration of family history. In the future, you could have polygenic scores and genetic testing to identify everybody with a rare mutation and increased surveillance at early ages in those patients. As well as try other preventative approaches that might come along.
And then with those that are in the top fifth percentile, or depending on what percentile of the risk you’re in and your age, there would be a formula for when it’s optimal to screen you and what kind of screening you should have.
But you could imagine if you’re in the top 10% of risk for many conditions, you might have a risk that’s similar magnitude to some of those who have a rare mutation and one should probably be screened and treated that way clinically. And so as sequencing costs come down, it’s going to make sense for health systems and hospitals to actually do this as just part of their normal routine care. Hopefully insurance will reimburse it, but even if it’s not directly reimbursed, it’s going to have to become part of care one way or another.
Grant: How do you think the rollout will happen? With this work being spearheaded at major academic medical centers, when will community health centers in Mississippi see this?
Dan: Well, that’s a great question. Well, number one, you know one of the pioneers in this area is the Geisinger Health System, which is a private non-academic system very much like Kaiser. I think those types of systems where they’re both the insurer and the provider really makes a lot of sense because it’s a very, very cost-effective approach.
So I actually see that it might happen there even more quickly than in some of our more disjointed systems where we’re actually not the insurer, just the provider. And I think smart insurance will make agreements with providers to actually provide these things because it will reduce cost and morbidity and the burden of illness later on.
But right now I think we’re in an area where there’s enormous promise. There’s some areas where certainly genomic medicine is used in clinical practice and should be used a lot more like I just described, but there’s a ton of research that needs to be done using the clinical records and genomics to identify those people most likely to benefit and to develop the algorithms.
So it’s a whole new area of research and medicine. One of my colleagues has likened it to thinking of the healthcare system as a learning healthcare system, where the patient records and other research data that we collect, like genetics, gets integrated. And we learn from that as we follow patient trajectories and then apply that information back into clinical practice.
And that’s our view of this at UCLA. And in fact, we’ve initiated a major, what we call population health initiative called Atlas, where we’re collecting 150,000, at least of our 45 million patients and genotyping them. And we have a collaboration with Regeneron genetic center to sequence those patients and get the data back.
Part of our plan is to return results that are clinically actionable. Like some of those that I mentioned, let’s say, if somebody has a familial cancer predisposition gene, or a rare mutation that increases their risk for heart disease in the lipid pathways, that those will get flagged and treated as long as the patient’s consent that they want that type of data back.
Grant: What if it’s not actionable?
Dan: Then it’s not actionable. Then we don’t give it back because we’re not necessarily helping anybody. It might be of interest to people, but because we’re not really a commercial entity, we’re not trying to do things that entertain people.
For example, we’re really trying to do things that are serious and that help our health. We’re focused on things that are clinically actionable. And that our colleagues in medical genetics and the American College of Medical Genetics would largely agree are things that should get returned to patients because there’s actually something that you could do to help them.
Sometimes it’s just knowing that they have something that predisposes to certain things. And so they get an annual scan to check that out. And other times there’s actually an action or a medicine that should be taken to prevent things. So those are the types of clinical actions that should be taken.
Geisinger, who has kind of pioneered this in their sample, which is not an ethnically diverse sample, like our sample at UCLA, but still is a very informative sample and a very important set of observations have been published there. The prevalence of such clinically actionable findings could be as high as 3% and is going to increase as drugs get made. In fact, there are multiple drugs that have come out for certain diseases in the last two or three years that wouldn’t have been on that list five years ago. And so as that changes, we’ll be reviewing that and adding to that as well.
Grant: What are your thoughts on the data management side of this? Do you think HMO’s will be storing a patient’s genome or exome sequences?
Dan: Yeah. I mean, our data management is all on the cloud. Like we’re currently using the Microsoft Azure cloud, which our health system has been using and is very involved in. Microsoft’s been very collaborative on that, but I think most of it is going to be in the cloud.
Some will be on premises as well. It is a cost, right? And so for the smaller systems that don’t have academic laboratories and academic people to come and help with this, there may be commercial solutions to that, or local governmental solutions just depending on regulations and laws and things. So one example is that at UCLA, we’re building this enormous infrastructure and this involves the health system informatics people, who are experts in that.
But they’re also working hand in hand with people in engineering and computer science who were experts in databases and genetics and genomics–which the health system isn’t–to streamline these efforts and to make them work. And so that takes the merging of two very different cultures sometimes and working together.
It’s been remarkable to watch it happen. It didn’t happen overnight. But it’s working really, really well in our system. We’re really fortunate to have collaborative, excellent people on both sides. So that’s really critical, but I could see how many of the smaller hospitals, or even large hospitals that don’t have resources like this and faculty who are willing without being paid to come and work on this thing.
Because number one, it’s so interesting. And number two, it’s so important. It’s such an incredible opportunity to have an impact. There are number of geneticists who are computer scientists, who have changed their direction of research because of this and are focusing almost a hundred percent on these biobanks and medical records now because they realize it’s a source of big data, but they can also have an impact with their research that was not possible before. But a lot of what they’re doing in the initial phases of this is quite voluntary. It’s really great for me to watch that and just see all the goodwill that comes with this new area.
Grant: What do you think are the greatest roadblocks?
Dan: I think it’s knowledge. There’s a lot we don’t know. In cancer, there’s 6% that are actionable. Even some of those aren’t cures. Right. We want to get to a point where we’re over three quarters or 90% or everybody. Right. But of course not all cancers are genetic, but even those with an environmental cause it’s of course probably driving something in the somatic tissue that one can identify.
And so the genetic and genomic technologies I think are there. And as the costs come down, they’re applicable. And I think culture has to adapt. I think it’s much easier to implement these things in countries like England, where they actually are now sequencing every child, who’s admitted to the hospital and certainly all of those in ICU. It makes total sense.
It should be done. It’s not being done here because it’s not an organized single payer system. And so it’s going to depend upon research philanthropy and a health system, deciding they want to waste some money on it, right? Because at some level it takes away from their bottom line.
And a lot of health systems are profit driven. That’s by the very nature of our system. They have to remain financially solvent or they cannot operate. It’s a very different kind of system than most other developing and developed countries out there that have more organized public support for the health systems. And of course, with COVID, we’ve seen the remarkable failure of our lack of that.
So I’m hoping that we move towards a more organized, centralized system, so decisions are made rationally. In fact, COVID is an interesting example potentially of precision health. Where we, with a bunch of our other California health systems, have organized a consortium where we’re sequencing and genotyping our patients with COVID to identify mutations that make people particularly susceptible to very bad outcomes.
Because as you know, most people get it and even if they get sick, it’s like a bad flu. But then there are those that have catastrophic complications, either neurologic, clotting, or pulmonary or immune. And we think that that’s likely due to rare variants in the immune system. That it’s due to host factors.
There are probably two things going on. One is the dose that you get, the amount of COVID you’re exposed to and that gets into your airways and invades your body. And the second is how you respond to it. The host response, and that’s largely driven, not a hundred percent, but has a big genetic component and that can be measured. And there already are a lot of hints in published work that there are genomic features related to interferon and immune response that drive that.
And so down the road we could potentially, before anybody gets COVID know who’s at highest risk for these things and really that would be quite easy to prevent it, right. You just really keep those people away, make sure they have N95 masks and face shields and all that when they go out and really treat them carefully.
Then there are elderly people with a lot of comorbidities who should do really poorly, who just do great and are asymptomatic. And those people may have protectable alleles. And that could be super helpful in helping us develop drugs to treat it. So all of that is just a current example on many ends of how our system can approach this, but also how it’s failed in some ways, unfortunately,
Grant: On the topic of COVID are you willing to go out on a limb and speculate about how we might see it play out this year?
Dan: Well, I can tell you what my concerns are. And they’ve been there actually since the beginning. It’s viral evolution.
That’s why we don’t have vaccines to common colds and why we get flu vaccines and they’re marginally effective. So the more that something gets in human populations, the more it has a chance to mutate and also there’s natural selection going on. It’s a natural selection experiment.
It’s not just the mutations, it’s the selection pressures. I think it’s a strong possibility that this is not going away. And COVID is just an example of this. But these types of illnesses will become more and more prevalent and there’ll be times where they’re low level. And then when evolution occurs, they could explode and we have to be ready for them. But my sense is that even though I’m getting my second dose of vaccine tomorrow, I would not be surprised if in the fall or winter, I have to get another shot, not just to boost my immunity to what’s out there now, but what’s new.
The good news is that technology could bring us ahead of this. Right. It’s really extraordinary how the RNA packaging with the lipid coated nanoparticles and things that have been developed by Moderna for years for delivering gene therapy or vaccines. It’s really shown its amazing utility. So I’m obviously very, very bullish on technology in the future, but I think just like HIV and hepatitis have really changed biology, it just shows us how important infectious disease is and will remain. We’re never going to conquer infectious disease, although we think we have/. Because it’s commingling with us. It’s part of who we are. It’s part of life on Earth.
Grant: So let’s talk about your lab. What would you say have been the highlights of your scientific career?
Dan: Well, that’s a tough one. I’m hoping that they’re still ahead of me. My goal when I started all of this was to use the basic science–genetics, genomics, and neuroscience–to understand disease and to develop therapies in the disorders that I work on. We haven’t gotten there yet. So I’m hoping that the big advances are still ahead of us.
And we have some glimmer of that. I think the methods and approaches that we and others are taking with genomics really does provide new opportunities to develop better, more targeted therapies that are more mechanistically driven, safer and more efficacious. So I’m positive about that.
It’s an interesting thing. I don’t reflect very much, but I had about 30 seconds of reflection this morning as I was looking through my calendar and seeing that I have a great chance to talk with you, Grant, which I’ve really been looking forward to. It’s always wonderful to talk with you. And I was thinking, you know, if I’m so interested in disease, why don’t I work at a biotech or pharmaceutical company?
And that has crossed my mind over time. I bet you were going to ask me that. And I think in some ways there’s some lost opportunities there, right? Because that is the focus of biotech and big pharma. It is to develop these medicines and to relieve suffering. But there are two parts of it. One is in the earlier parts of my career, I really got an enormous pleasure and always have, and always will from seeing patients, just having that experience. And even patients with rare genetic diseases that we can’t treat, discussing with them and talking about the research that we’re doing to give them and their families hope. Telling them that somebody cared about them and was working on this. So that was a piece of it. Another piece was the training. There’s nothing I enjoy more than walking through the lab at 5:00 PM and seeing what people are doing and just sitting down and talking with them about what they’re doing.
Unfortunately, you know, being so busy and now with COVID, I don’t get to do it at all, but I recognized at one point that with the notion of enjoying yourself while you’re trying to have a significant life and making a difference in the world and having impact, I realized that one of the impacts that gives me the most pleasure is watching students and postdocs grow in the lab. Interacting with them, arguing, learning from them because you know I’m still in school all the time.
And especially from the more mathematically inclined people like you, that I was always asking a lot of questions about. It was really, really great for me to explore these areas that aren’t my areas of expertise with some of the people in my lab, but also then help students and other people find their way. The training part of it has been really, really satisfying to me just personally. I enjoy it. It’s fun. Kind of just like the way I’ve really enjoyed having a family, having kids and having some dogs, even though sometimes I regret that latter decision when the dogs are chewing up the furniture.
Grant: So if you were a college student today, in what direction do you think you would go?
Dan: Well, I’d be tempted to work in some startup-y kind of thing. Cause that seems to be what all kinds of smart, driven people are doing. I’m sure I would. In fact, as you may know, when I was in college, I wasn’t sure what I wanted to do. I was a chemistry major–it was called chemistry modified with psychology. So it was like a chemistry major with a minor in psychology. And I was interested in science and was thinking about medicine and had a lot of family role models in those areas.
But I had grown up in the sixties and seventies, when I was a teenager. And I was rebellious enough against any kind of authority–even though my parents were certainly not authority figures by any stretch, they were very gentle and kind to people and really helped me a lot. Still by nature, I was a–I don’t want to use any bad words on the podcast, but just fill in the blank–a rebellious ______.
Grant: It might’ve been some chemistry modified by psychology as well as psychology modified by chemistry.
Dan: There you go. I think I’ve always been a little bit like that. So I kind of started to think about business and all my family were physicists or chemists or doctors. So business was interesting to me because of the entrepreneurial spirit and all of that.
And so I went to work at Boston Consulting Group actually, rather than go directly into science. What really attracted me to the Boston Consulting Group was that it was just full of incredibly smart intellectuals of all different sizes, really a huge variety of smart driven people. Some with PhDs in economics and with law degrees, women, men, you know a really interesting place. That’s I think what drew me to it.
If I were in college today, I’d be looking for a place of opportunity, a place where I felt like I would have mentors. Even if they weren’t doing what I wanted to do, people who I really respected and was going to learn from and who would challenge me.
And that certainly was the case. So I think I would look for something like that. And I think in some of the startups, there’s quite a bit of that, but I’d probably end up in graduate school one way or another in the end. Just because I think graduate school is one of the places where you learn the most about yourself.
Grant: So why did you go to grad school? Can you maybe talk a bit about why you left BCG?
Dan: BCG was filled with impressive people, many of whom have gone on to do really impressive things. I was reflecting on my life. And again, part of this was family. My father had sent me some articles written by Freeman Dyson, the physicist who did his great work while he was 19 and 20.I had had this thought that, well, you know, I’ll go and make some money. And when I’m like 30 to 35, I’ll kind of be a gentleman scientist down the road. But I’ll make some money first.
And I kind of realized that maybe those first two decades of life after college might be my most productive years. Of course, in biomedical science that’s not the case. It’s not like physics or math and I didn’t have the brain for those fields or the drive.
But I’d done some chemistry research. And realized that I wanted to do something that was connected to people. And that really fit with the medical kind of thing. Medicine just seemed like the right thing. To me, it seemed like the perfect blend of science and helping people. If you like science and biology and you’re curious about people and want to help do something good, it just all fit together.
And talking with a family friend, really a couple things helped me. One: I had this friend named Craig Bradley. He had gotten one of these honorific masters scholarships, and he was in Edinburgh doing a master’s in English. He came back and we were having beers in Boston at, I think it was the Cheers pub. And the whole time I was talking about brain science for like an hour and a half. He goes, Dan, geez, we’ve been here for an hour and a half. You haven’t told me anything about your work at BCG. What are you talking about? All this other stuff, and it just kind of dawned on me. Huh. You know, maybe I have other interests.
So it was that. And some of this realization that life is short and this real drive that I still have to really have an impact and to have a significant life. You know, you can have a significant life in many ways, of course. And with your family, it’s always going to be significant, but to have an impact beyond my first degree relatives, to really have some influence to help humanity in that way. I realized I had this drive and I think that’s really important.
Grant: Can you maybe elaborate a bit? I totally agree with you that in the biomedical field, for the most part, your greatest impact comes later in your career. Something I found pretty impressive about you is you’re able to kind of pick up enough about so many different things and then tie them together in really nice ways. Can you maybe elaborate a bit on that? What do you think are the most important compounding returns that makes a biomedical scientist so productive later in their career?
We actually recently did some work, looking at citation metrics of computational biologists. And what you see as people get later in their career, is there’s this huge divergence in different measures of productivity, starting from a smaller divergence. I mean, it really does look like compounding returns.
Dan: Yeah, it’s interesting. So, some of that may have to do with the sociology of science and human endeavors, right. Successful people read your work and then that helps you get grants and other things. So success does breed success like it does in other fields. If you started a few companies and they’ve been successful, it’s going to be easier for you to raise money.
Same thing with grants. So there’s that kind of practical piece of it. It’s very interesting because my early view of this was that stuff that I’m doing now is a combination of a vague vision that I had when I was 37.
I had several beliefs. One of them is that we didn’t know very much. Number two was going to take methods that were kind of unbiased. I really believed heavily in screening and discovery. We needed to spend a lot more time on that rather than testing hypotheses. And I guess part of that came from the notion, which I think most of my colleagues would agree with—there may be some who disagree with me—but me and most of my colleagues know which hypothesis is the best to test.
And therefore, if there are potentially thousands of competing hypotheses, why are you testing that one? You need to have a reason for it. And omics techniques and other systematic ways of looking at things that was the kind of thing that I started with. And, that was initially not a very popular way to go.
People would call those fishing expeditions. It’s not hypothesis driven, especially in neuroscience. So I think that’s part of it. It took a long time for that kind of stuff to catch on. I went for five or 10 years without being recognized at all, getting a lot of heavy criticism. But you know, my feeling in the long run is that what works out, works out, what doesn’t work out will go away eventually.
And that’s one of the problems with biology and biomedical research in general is that unlike physics and mathematics and to a lesser extent, things like chemistry—cause there’s all kinds of laws in chemistry—where there are fundamental theorems that drive things, the theoretical basis of the field and in biology has to do with the central dogma and also evolution, which drives everything. But that’s such an overarching explanatory theory that, you know, where do you go from there? So we’re at a very descriptive stage in all of biomedical research. And so my belief has been in collecting large data sets and trying to interrogate them and let the data hopefully bring us somewhere.
But sometimes we’re helped in looking at those data sets by having some knowledge of biology and some biological insights. And I think that’s where the kind of compounding occurs is by having some knowledge and also by reading a lot and having a decent memory. I can remember what I read and I go, Oh, I just read about that. And that is related to this.
And so it helps you connect things. And so having a broad fund of knowledge in this area is helpful, even when analyzing big data, because sometimes it’s not obvious what the next steps are.
Grant: I would say, especially when analyzing big data. Right. It’s great to be able to look at a slide and recognize some old friends among the genes that pop out with these unbiased approaches.
Dan: Right. And you want to be careful about that, right? You know, about that old friends approach, the dartboard kind of approach. I know that approach. So you have to be sanguine about that as well. And sometimes what happens is it can really help you by having read something and understanding how these nine genes are part of a pathway that I just read about two weeks ago, or I heard a talk about them. That can really take you somewhere.
But you have to have some relatively rigorous way of prioritizing hypotheses. Let’s put it that way. And so using these genomic methods and more systems methods, it’s really helpful in that regard. That’s been my belief throughout my career. And I think finally people have started to work on it, you know, and use these technologies.
It’s interesting in neuroscience, people really weren’t interested in any omics technique until the single cell analysis came out. Which again is quite interesting to me and yeah reflects a certain type of bias in the way people are looking at it. But of course the single cell technologies are wonderful and extraordinarily powerful.
Grant: It’s interesting. It seems to be almost universal for omics technologies that they’re pioneered first and cancer and they reach neuroscience almost last.
Dan: Well, I’ll tell you why. I mean, this is my view of this since graduate school, when I wrote my qualifier on the notion of oncogenes and neuroscience, what they were and why.
Number one, we are a decade behind cancer, but the reason for that is that cancer is a practical field. Cancer is based on combating a terrible array of illnesses that afflict humans. It’s all about discovering what the problem is and making a drug or a treatment to stop it.
Right. And the problem with neuroscience is that I study cerebellum. That’s the most important organ in the brain. I studied this. That’s the most important organ. I’ve studied that. The ground truth ends up being what very charismatic opinion leaders decide is going to be the most important thing.
And that’s how awards for neuroscience and other things are given. And I really do believe that that’s pervasive. You know, it’s almost like the high school prom voting. That’s not to denigrate those who have made extraordinary advances. I think there’s some things that if you got a hundred people in a room, 90 of them would agree on things like optogenetics, single cell approaches, receptor trafficking, and synaptic function, vesicle release, G-protein coupled receptors.
Nobody’s going to argue about how seminal those things are, but there’s a whole bunch of stuff around that. Again, it’s just in camps or almost tribal. What’s so interesting. If you look at the Society for Neuroscience, the organization of the meeting has not changed since 1985.
It’s not changed since then. And maybe since the sixties, when it started with this idea of motor systems, sensory systems, development, then there’s neurobiology of diseases as a separate little section. None of that organization has changed despite what’s been learned. And there’s also been chronically this gulf between kind of systems neuroscience and molecular neuroscience.
Right. You study how a receptor works using physics and math at the same level that you can study functional brain networks using similar methods. And I think as technology advances, those things will begin to merge. And my hope was that things like transcriptomics and these molecular techniques would provide intermediate quantitative merging phenotypes that would allow us to bridge molecular and systems neuroscience. And I still believe that although it’s of course a very, very tough path.
Grant: Yeah, what do you disagree with most of your colleagues and why are you right?
Dan: I don’t argue with anybody. It just doesn’t get you anywhere. I’ve learned that at home, after 35 years of successful and wonderful marriage. Have you ever read the book, which I read recently, called The Righteous Mind by Jonathan Haidt?
Grant: It’s on my list.
Dan: Yeah. I think you should read that because it really gets to fundamental issues about people’s beliefs in general and how people argue. But I guess a couple things over the years that I’ve noticed that have been frustrating to me. Let’s put it that way.
Number one, when we started doing microarrays initially in neuroscience, everything was like, Oh, it’s a fishing expedition. It’s just a useless fishing expedition. Yeah. Well, how did you figure out that hypothesis that you’re testing? How do you rank that hypothesis? Quantitatively, show me the equation that you use to discern that hypothesis A is better than hypothesis B, you know? And so we would have those types of conversations where I would say what I’m trying to do is use these data to help me rank hypotheses using data, rather than some emotional attachment to something, which is how most of this has been done.
Not to denigrate the power of those emotional attachments. Those can be really important motivators and drivers, and that led to great discoveries. Don’t get me wrong. So I disagreed on that, obviously that was pervasive for about a decade. And again, it has to do with the fact that neuroscience is not necessarily focused on disease.
Neuroscience has a lot of different avenues. And in fact, many of us started studying the brain because it’s so interesting. Understanding the brain is a fundamental human endeavor. There’s so much of that, and it’s so difficult and it’s so challenging from so many different angles that all of that basic work is essential.
But I guess my belief has always been that even though I can’t know what’s important, like you might in mathematics, the notion that I’m working on disease makes me at least think or believe that what I’m working on is important. At least to some people whose diseases I’m studying. So that’s one thing. That’s an older thing now, cause that’s kind of water under the bridge.
More recently what I’ve noticed among a certain ilk of extraordinarily brilliant people, geneticists, especially—it’s not everybody—some people have not understood or have very, very strong opinions, who heavily criticized transcriptomics or these omics techniques. They say they don’t have any place in genetics. What are they really doing? There’s no causality. It’s just a phenotype, blah, blah, blah statements.
Like none of these network methods work. Like what do you mean by that? Like what do you mean by work? And I think some of it has to do with the fact that the methods aren’t understood. People come from a math background and don’t understand how to connect it to biology. Sometimes it’s a lack of imagination. Even among the extraordinarily brilliant, imaginative people in one area like math or whatever, who don’t have imagination outside of that, or there might be actual, real criticisms that are extraordinarily valid, but I just haven’t heard them or had a chance to discuss them.
So there’s some of that going on, right? Again, this territoriality around that. As I get older and older and move along more, that becomes less important and we focus more and more on the output. In other words, we are moving things forward to a point where we might have therapeutic targets that then we can work with.
One area is dementia, which I think is helped by the fact that we’re looking at a phenotype that we can screen in a dish, which is a depth of a neuron. And in some instances in neurorepair, I think we’re actually making progress using these techniques. And we have proofs of principle where we can really show that these networks identify real targets that we can predict and then verify.
And that’s where it becomes really exciting when there’s experimental verification. So we have some projects in our lab that started over 15 years ago that are finally coming to fruition and that with other collaborators, we’re developing drugs based on these targets. And it would be a dream, you know, for these drugs to eventually go into people and have some impact.
Grant: Right. What lessons do you wish you had learned earlier in your career?
Dan: Well, one of the things that I’ve learned, because there’s a lot of frustration in this work is I think the better you can accept criticism, the easier it is. I’ve always had a hard skin and belief in what I’m doing, but also accepting and not only accepting, but integrating the criticism has always made our work better.
And early on, that can be tough, but I think it’s really, really important. And I wish there were more criticism in a collegiate way. Another is there’s a lot of frustrations in academia that happen. A lot of people have written about this. There’s so much politics because there’s so little at stake.
I haven’t run into a lot of that, but I think when I do run into things that I think are wrong, either at the institutional level or otherwise are happening in the field. I think understanding how to reframe things and how to be positive and how to come up with solutions rather than criticism is another thing that has helped me adapt better than just identifying the problem and complaining with my colleagues about it, which a lot of academics do. It’s like, okay, that’s a problem. How do we solve that? So I would say those are two things.
Grant: Do you have any parting words, words of wisdom, words of Dan.
Dan: Buy low, sell high?
What else? You know I took a circuitous route as you know, Grant, and we decided not to talk about that. I think we talked about things that are more substantive actually in some ways. And I appreciate that.
Grant: We really wanted to talk about your path, but ran out of time.
I mean all I could say is that I think it’s important when you’re young to explore your interests and your curiosities and not to funnel too much into what either your parents or society or other people are telling you to do. And I think some of my early wanderings had to do with that. Like for example, taking a year off college, and going skiing and making ski movies, going to the Boston consulting group all of those things were extraordinary growth experiences for me as a person.
And that sat with me for my whole life. I do believe in experiences and I believe in following your curiosity, but I also recognize that I came from an extraordinarily privileged background, not from a let’s say financial standpoint, but from a familial support and background standpoint.
And that I had parents who were incredibly supportive and academic and intellectual. So I was exposed to a wide variety of things and there was no reason why I couldn’t do any of them. Right. I was expected to think about all of them. And in our society, that’s not the case for many people.
And I think even more so now because of the expense of education and how hard these things are to reach. And that’s been one of my real joys of being at UCLA, because it is a public institution. And although it’s an elite public institution in terms of academics, it’s not elite from a financial or other family background standpoint.
And there are a lot of first-generation Americans that I have the privilege to work with. And I have to say that that is some of the most satisfying work that I’ve had at UCLA. I think as a country, we really have to face that because so much of our greatness comes from people with drive, who come from outside as well as inside.
So, I do realize that I had a head start in many ways. I had many pathways that I could take. I’m grateful for that, but I also think that, no matter what your opportunity set is, exploring and following them one step at a time. I don’t know if you remember Grant, but one of my favorite statements in the laboratory, especially for graduate students, not as much to postdocs, was this great French proverb that I just love: Little by little, the bird makes his or her nest.
But the notion that it’s a step-wise thing. And I really do view my career in that way as well. It was little by little. It didn’t happen overnight and it’s been constant work and high level of motivation, which I think is the most important thing in life: to work hard.
Grant: Yeah. It’s interesting. You’re actually our third podcast guest that essentially their message was you have to go and create your own path. The first was a Thiel fellow, who’s now going to be manufacturing organs in space.
Dan: I think it’s really essential. There are amazing opportunities for people and careers and stuff that we couldn’t even imagine.
Grant: Well, thank you so much for coming on. It was really enjoyable.
Dan: Yeah. It’s always great to talk with you, Grant, and I hope we can talk even outside this podcast sometime. Thanks for doing this really, really enjoyable.
Grant: Thank you.
Deepti is a clinical epidemiologist and senior lecturer in machine learning at Queen Mary University of London. During the COVID-19 pandemic, she uses machine learning to understand prominent clusters of patients’ symptoms and how people are likely to progress over time.
Christina is director of the Clinical Operational Research Unit and a Professor of Operational Research at University College London. During the COVID-19 pandemic, she works closely with clinicians and public health professionals to communicate research to the public.
Nisreen is an Associate Professor of Public Health at the University of Southampton, researching maternal and child health. During the COVID-19 pandemic, Alwan uses social media to communicate public health messages and to call for long COVID to be counted and measured.
Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today are three heroes of COVID from the UK. Nisreen, can you please introduce yourself?
Nisreen: Yeah, sure. Thanks, Grant. So I’m Nisreen Alwan. I’m an associate professor in public health at the University of Southampton and an honorary consultant of public health at University Hospital, South Hampton.
So my research area, before the pandemic, was maternal and child health. So for a few years now, I’ve researched particularly around pregnancy, before pregnancy, between two pregnancies what modifiable factors mothers have or are exposed to, including environmental factors. And how they influence their health and the health of their children, both in the short and the long run.
Now in terms of COVID. So obviously I’m in public health and when the pandemic started in the UK back in February and March, I got very much involved with a group of public health academics and epidemiologists to try and provide some independent public health input to what was going on. So we produced a few outputs and letters looking at the government response and putting some expert input into what was going on in the spring. While I was doing that, I also got symptoms of COVID-19 in March, which didn’t completely go away. I kept getting relapsing symptoms, so basically what we now know as long COVID.
So I got involved in the advocacy around long COVID because people weren’t really talking about illness from COVID-19 at all. It was very much black and white, death or nothing happens to you. So I started talking about how we need to measure illness and what long COVID looks like. That’s been my heaviest involvement I would say in the COVID-19 pandemic so far, but I also continue to input on things which are very much relevant to public health, particularly in my area of interest, which is around children and families.
Grant: Thank you so much and Deepti?
Deepti: Hi, I’m Deepti Gurdasani. I’m a clinical epidemiologist and senior lecturer in machine learning at Queen Mary University of London. A lot of my work over the last decade has focused on studying the genetic and clinical determinants of disease in a global context. So particularly in more ethnically diverse populations, more recently my work has focused on trying to understand the impact of different interventions on COVID pandemic trajectories in a global context using machine learning methods. As well as studying long COVID alongside Nisreen to understand what the prominent clusters of patients’ symptoms are and how people are likely to progress over time.
And a lot of my work is essentially in developing new methods to help us understand what puts people more at risk and what influences how a pandemic starts and continues in different parts of the world.
Grant: Thank you so much. And last but not least, Christina.
Christina: Hi, I’m Christina Pagel. I’m a professor of operational research at University College, London. And operational research is like a branch of very applied mathematics. I think in the States it’s called operations research or management science, or systems engineering. It has all these different terms, depending on which department you’re in.
But it’s basically motivated by using whatever kind of quantitative methods you can to help solve real problems. And that’s kind of what I do. And part of that has been national policy, so we have worked quite closely with the department of health around pandemic planning, but back around swine flu time, say 2009/2010.
And since then, a lot of my work has been working with clinicians and people living with congenital heart disease and kind of trying to use national data sets to understand outcomes, communicate outcomes, make sure we’re researching what matters to patients, what matters to doctors and how do you explain that really complex interaction with the health system over time?
So that’s kind of what I’ve been doing. And then when the pandemic hit. My projects came to a big hole because we work so closely with doctors and they obviously all pulled off in that effort. And then we worked quite closely with local hospitals, just trying to help them understand what was happening. Helped them think about how to organize their care and what made a difference just because no one really knew what was happening in the first few months. And then in May, I was asked to join Independent Sage, which is like a group of 12-13 scientists from different backgrounds.
And that kind of was meant to be just one, two hour meeting in May. And it’s ended up taking over my life eight months later. And we’ve kind of ended up serving in both a policy advocacy and public communications role. And that’s what I’ve been doing. Just trying to take all the amazing science that’s happening from people like Nisreen and Deepti and just trying to explain to the public what it means, what’s happening, are things bad, are they good, what do we have to watch out for, where is it going. And just trying to do that in a way that is accessible and not sensationalist, I suppose, is what I’m trying to do.
Grant: Thank you so much. That is a great segue to our first topic: where things are going. I would love to hear all of your thoughts on the new variants. And maybe for the sake of our listeners: there’s a lot of noise out there. If you can kind of distill down what we know and what remains unresolved.
Christina: Well I can start on that. I’m just a mathematician by background and I work mostly in the more operational side of healthcare. And I don’t have a background in viral and infectious diseases, so I hadn’t really been thinking about variants to be honest.
So I knew that obviously there’s potential for mutation and people talked about it, but over the summer I was hearing, “Oh, don’t worry. It’s mutating slowly.” So I just hadn’t really been thinking about it. And then in November or very early in December, when they first mentioned that there might be a new more transmissible variant in the UK, I was just like, “Oh”, and then I remember very vividly watching the press conference where Matt Hancock said it might be up to 50%, 70% more infectious.
And I thought, “this is really, really bad, really bad.” Just from the basic maths of it. Once you have something that’s that much more infectious, then everything we’re trying to do to keep things down is that much harder. I also, to be honest, felt not relief, but at least it suddenly made sense why things were going wrong in the Southeast of England. Because I just could not understand why cases were going up with the kind of restrictions that we had.
And so that made sense. I was like, “Oh, okay, well that kind of explains it.” But then I just thought, “then we’re in trouble. We’re in real trouble. Unless we do something straight away.” And that’s kind of proven to be the case. We had a terrible, terrible January in the UK and other countries that have had the UK variant had real problems: that’s Ireland, Israel, Portugal.
And then of course there’s the South African variant and then the Brazilian Variant. And you realize now that we have so many millions of people who’ve had COVID. And it has had so many opportunities to mutate. And if it comes to a situation where it can infect people more easily or it can infect people who’ve already had it or who’ve had the vaccine, that gives it a big advantage.
And I think that is the biggest danger now because we’ve got millions of people who’ve been vaccinated and millions of people with COVID and you’re really giving it a lot of incentive to evolve in a way that’s quite bad for us. And so we’ve ended up in a bit of an arms race, and I’m not particularly happy about it.
Deepti: So while there’s a lot of talk about new variants now with the media, the truth is that the virus has been evolving over a long period of time. So in February itself, we know that there was a variant called D614G that was identified and was circulating. This is a variant that was subsequently found to be about 20 to 30% more transmissible than the previous variant and became dominant globally by June. And since then, there’s several variants that have risen on the spike protein, which is the part of the virus that we know binds to human cells and is necessary for infection that have potentially allowed the virus to escape immune responses directed at previous variants, at least in the laboratory.
And subsequently in September, we heard about many new variants arising from infection to and back from mink farms. So mink were a reservoir for infection. And a large number of mutations were accumulating that were being transferred back to humans. And once again, there were concerns about how these mutations could potentially escape immune responses, directives as previous variants.
So in many ways, this was expected, I guess, given the high levels of transmission that we’re continuing in many countries, but I guess the degree of increase in transmission was quite a shock when that happened. So we heard about the UK variant very close to when we heard about the so-called South Africa variant with both of those potentially increasing transmission by about 50%. And subsequently it also became clear that there were other variants in Manaus as well.
And there were many shared mutations between these variants. So they were particular mutations such as the 501Y mutation, which was potentially associated with increased transmissibility, increased affinity to the ACE2 receptor, which is a receptor required for binding and infecting human cells. And there were also variants such as the mutation such as the E484K, which has been associated with escape against immune responses, directed at previous variants.
Now we have become aware that unfortunately, these laboratory findings are translating into reduced vaccine effectiveness. We know from the Novavax and Johnson & Johnson trials, and more recently a report from preliminary data from the AstraZeneca-Oxford vaccine, that effectiveness to prevent symptomatic disease at least is likely to be lower against this particular variant for vaccines that were manufactured against previous variants, and this is something that’s really concerning at the moment.
But what’s really interesting is the way these variants have emerged in different parts of the world, again and again, independently, but converged onto the same mutations, which suggest that these are actual virus adaptations that are favorable to the virus in certain environments.
And there’s no reason to think that adaptation will stop here and not continue given that we’ve seen the virus evolving pretty much since February 2020. So we really need to tackle this in a different way than we have so far. We’re also hearing about new adaptations on top of variants.
So for example, the UK variant now seems to be evolving in the direction of the South African-Manaus variant, developing the same mutations that have been associated with escape and reduced vaccine effectiveness. So unless we really do something to stop this, this is likely to continue.
Grant: Nisreen, would you like to give input on that? Or should we move on?
Nisreen: There’s not much more. I can add really to that explanation. I suppose the main, simple truth around the variants to me from a public health point of view, is that it’s obvious that if you give the virus a lot of room to spread you will get these mutations and variants. You could get more of them.
And so I think it’s about how much room we’re giving the virus to spread. And the other thing from a public health point of view is even though there is a difference in the transmissibility of these variants, actually the public health measures that we use are the same and they would work on all the variants.
So that’s good news in a way, in that we don’t have to learn this all over again in terms of what we can do to suppress the virus other than the vaccination, obviously, which will have to adapt to the variants. But in terms of the non-pharmacological interventions, they’re the same.
Christina: And we’re seeing that in the UK now, like the quite strict lockdown is working to reduce cases, even though the English Variant is now dominant across the country, but we are still having reduction in cases and reduction in hospital admissions.
Grant: And so following on from that, what do you think is a reasonable spectrum of possibilities for where this might go in the future. Do you think it’s in the realm of possibility that we’ll be back to normal life in the foreseeable future or that we’ll have some kind of a COVID-22 that has 30% fatality or something? Is it just a complete unknown?
Christina: Define foreseeable. What’s the foreseeable future? Is it a year, five years, 10 years?
Grant: Can you give us some hope? Like what, what would be a time range?
Christina: I don’t think we’ll be back to normal this year. That’s why I think, I don’t think we will this year. If we can get as many people vaccinated everywhere, not just in rich countries, it has to be everywhere. Keep some measure of quarantine at borders. Keep really good surveillance in place. Keep cases down until you get kind of local elimination and you’re looking at sporadic outbreaks. Then I can see a return to normal, but doing that is going to take well over a year.
And if we don’t, if we just say we’ll vaccinate our own country, then you will get new variants rising and you will get variants that evade the vaccines. Not in the least because if they can evade antibodies from people, who’ve had it before, that same evolution might help them if they get the vaccine. So we just can’t risk it and we’ll just end up doing it again. So that is my fear. That we will end up in some kind of COVID Groundhog day, which would be just really, really miserable. But we know there’s a way out. It’s just, will we do it?
Nisreen: I think one of the things, if I may say, which might have hindered the pandemic response is this desperate urge to go back to normal. I think if we had adapted a bit and said, this might be with us for a while, so we have to change our behaviors. We have to change how we do things in a certain way, which is tolerable to us as a society, to our mental health which doesn’t result in severe social isolation.
For example, if we had adapted a bit from the start and looked at it as a bit of a more of a long-term adaptation, then maybe we would be in a better place now. And also this is not too late to think about. So I think this pandemic has been played and I just see it and it pains me. Everything is black and white. Everything is really bad or we want to go back to completely normal and the same with the mortality. “This virus doesn’t touch you at all, so go and get it.” And every single aspect of the pandemic has been plagued by this black and white picture. And we need a bit of gray in here. It’s a difficult time for us, but it doesn’t have to be black and white. It could be gray if we get the balance right.
Christina: Yeah. I think that’s a really good point about this desire to get back to normal, almost making things worse as people kind of keep trying to push it. And it just slaps you back in the face.
Deepti: To me, the question about when we can get back to normal is completely dependent on government strategy and political wealth. So there are two end points to this. One end point is complete elimination so we don’t have cases anymore, and life can return to a new normal. And the other end point is achieving herd immunity so that we don’t see at least large outbreaks of infections anymore.
The former probably is faster to achieve because many countries have done this within a period of about six months or so, but requires very strong political will, fixing a lot of systems within the UK that are broken and actually persevering with lockdown, supporting the public through the spirit until the cases reach near zero before lifting restrictions and then having good surveillance systems in place to actually pick up cases and support people, and isolating where needed.
The second strategy, which is pursuing herd immunity primarily through vaccination. It is much harder to predict what the end point of that will be. And the reason for that is that it’s really unclear whether herd immunity can even be reached through vaccination. We’re dealing with a variant that’s more transmissible, which means the herd immediate threshold is likely to be higher.
The vaccine is still not going to be available in many groups, particularly children who do transmit the virus. And we don’t know what the vaccine uptake is going to be across the population. And of course, we don’t know what the vaccine effectiveness is in preventing infection. All of those unknowns make it very hard to understand whether herd immunity can be reached at all. And if it can, when it will be reached.
If we add new variants into the mix, it’s even more complicated because as new variants emerge, they may be able to escape vaccine acquired immunity, to the previous variants, which would make trying to achieve a herd immunity threshold almost impossible and outbreaks could potentially continue for many, many years.
So I definitely favor the former strategy because I think it’s more clear cut. There’s less uncertainty around it, and it can be achieved in a shorter period of time, but it’s all down to political will and strategy. And it’s very clear that many countries are still pursuing the latter, even though that’s a lot less certain and the potential public health costs and the economic cost is much higher.
Grant: Anything else to add on that before we go on to long COVID.
Christina: I would just add that to me, like the arguments for an elimination strategy, like Deepti was saying, have just got stronger and stronger over the last six or seven months. And I think, especially with the new variants and the new transmissibility. I actually think we probably won’t ever be able to reach the herd immunity threshold.
And if we did, we still don’t know how long immunity lasts, so that’s the other issue. So an elimination strategy just seems like the one thing that we know we can do because lots of countries have done it. The problem is that if only some countries did it and some countries never do it, then you’re looking at having quite strict border controls for years and years and years. And that has consequences. So I kind of felt like we have to work together as a whole globe. And I’m not sure we have a great track record of doing it.
Deepti: Thanks for pointing that out, Christine. Those are very, very important points.
Nisreen: I agree with both Christina and Deepti, so there’s no sensational disagreement on this one. I think an elimination strategy is the way forward.
Christine: It just seems like a no-brainer. And I have to be honest, like whenever I see pictures of people going to concerts and New Zealand or just going out, I’m just so jealous. I’m like, I want that. Or people in a busy market in Thailand or Taiwan and going out to restaurants: why can’t I have that in my life?
Nisreen: And people over there have accepted certain sacrifices to do that in terms of lots of things, but including border control. It’s tough not being able to leave, and they’ve accepted that. And I think what was key to that is a very clear public communication right from the very start.
Basically the policymakers saying, this is the goal. It’s clear. You know what we’re aiming for. These are the sacrifices that need to be made, but this is what we’ll get if we make these sacrifices and that’s what happened. And we didn’t have that clear strategy and I’m afraid we still don’t have it yet, a year in.
Christine: It’s definitely happened in the UK, that we try to “tech” our way out of it. It was kind of like: we’ve got this great tech technological solution, whatever it happens to be. And it’s kind of been a series of them, none of which have worked. Instead of actually just saying we’re in a global pandemic and there’s nothing about that that’s going to be very fun.
I mean, sometimes people are like, I don’t want to do this. And I’m like, well, it sucks. Right. It sucks to be in this situation. And we’re kind of the first, really bad pandemic in the modern era when you have a globally connected civilization. So it is really, really difficult, but that honesty of messaging and of saying, if we do these things, we’ll get these payoffs. We just didn’t really get it here.
Deepti: And yeah, I completely agree with that. And also the fact that we’ve learned a lot about SARS-CoV-2 over the past year, but the fact is we didn’t need any of that information to know how to manage it because the countries who managed it successfully right at the beginning, treated it as it was: a highly infectious respiratory virus.
And it was just public health 101 that they followed and that worked. And we know that’s what works even now. But as Christina said, we tried to find these new technologies, which we’ve not even added to our response, but rather use them to replace basic public health responses. And it’s no surprise that it’s completely failed.
Grant: Speaking of those technologies, where are we on vaccines? And do you think with all the new variants going around that the mRNA-based vaccines will be able to be updated quickly enough? Do you think there is a technological path out of this?
Christina: Well, I can’t really talk about vaccines without sounding like a complete and utter idiot. Like I barely know what mRNA is. Seriously, go for it.
Deepti: Okay. I mean, I’m happy to talk about that. So vaccines can be updated. And while technically it might be an easy thing to do, they still will require a huge amount of testing and the laboratory in people and validation, which will take several months.
And I think the big question is: what are going to be the variants in place by the time that vaccine comes out. So we need to actually take preemptive action to prevent variants from emerging right now. By really curbing transmission and pushing towards elimination. Because unless we do that, we’re constantly going to be behind vaccine manufacturing.
The point is that we can’t wait three to four months to actually vaccinate people because our emergency services were overwhelmed until a few weeks ago. And even now many places are very close to capacity. So the idea that we can maintain huge amounts of transmission alongside vaccination and have a good vaccine response isn’t really grounded in reality.
We need to remember that we have these variants emerging right now, even without huge amounts of immune selection pressure within the population. As we start vaccinating more and more people, there’s going to be much more immune related selection pressure. Potentially speeding up the sort of adaptation we’ve been seeing, and we may see even more escape mutations. So we need to actually keep ahead of this by following elimination and preventing emergence of new variants, rather than constantly having to adapt our vaccines too late to these variants.
Nisreen: Yeah. I mean, again, I agree. I think it’s that we can see now and everybody, the public is probably seeing there’s a race between the vaccine and the virus adapting and changing.
And I think all the technology in the world will not win the race if the virus has a lot of room to spread and infect more and more people and change. So it’s very clear now that the vaccine can not be our only strategy out of this. Although I am increasingly surprised by the many people who are still reinforcing this method.
Christina: So the fact that we have so many good vaccines within a year, I still think is pretty miraculous. And it does put us in a much better position than we would otherwise have been. Even with all of the caveats and the concerns that Deepti and Nisreen have spoken about. That is pretty amazing to me.
Nisreen: And I agree.
Deepti: I agree completely with what Christina said. We have a large number of vaccines that are effective against not just the previous variants of the virus, but still highly effective, at least in preventing severe disease for most vaccines, even against current variants of the virus. And if people are offered the vaccine, they should absolutely take them. But I’m more talking about adapting policy to kind of ensure that these resources are protected for our future.
Grant: Great. Thank you so much for that. And in our last 10 minutes, I think it’d be great to talk about long COVID given that obviously a very large number of people have been infected and realistically it sounds like a very large number will continue to be infected in the foreseeable future. So can you talk about long COVID and what we do know, what we don’t know, what maybe your biggest concerns are long-term for people’s health?
Nisreen: So surprisingly, we still really don’t know a lot about long COVID. What we do know is it is to have long-term illness. Obviously, when I say long-term, that is the age of the pandemic, which is not very long now. And we know that some people who get the infection do not recover quickly.
And that includes two things. People who are severely ill and hospitalized because of the infection. They might get discharged from hospital and still feel unwell for a while. They might suffer complications like heart or lung clotting complications, neurological complications. But also people who were not hospitalized at the start and had a so-called mild illness, might not recover.
So the estimate at the moment is about one in 10 people have not recovered even from a mild illness at about three months from onset. So that’s quite a sizable proportion depending on how many of the population are getting infected. So it’s basically a collection of symptoms that people experience and it’s multi-system, so it could be heart, lung, neurological, skin, general symptoms.
It’s rare that people will have just one system affected. And the severity of it is really variable. We still don’t know the mechanism, and if there’s more than one underlying mechanism of long COVID. Because of the variable clinical picture as well. And the proposed mechanisms include overdrive of the immune system that happens after getting COVID-19 that causes an autoimmune process, or it could also be a persistent virus that flares up from time to time. Or a general inflammatory process. So there are multiple mechanisms proposed.
My main concern about long COVID is relevant to what we’ve been talking about, which is basically the strategy. The strategy is focused mainly on vaccinations. So if you say that that is the most important thing and our target is to prevent severe disease, which basically means hospitalization and death. If that’s achieved, that’s fine. We’re out of the pandemic. We don’t need to do much about it.
Then that could actually allow for a lot of long COVID where people don’t get to that threshold of needing to go to the hospital in their initial illness, but then they don’t really recover. And that means huge implications for society in terms of people not being able to work and the sick pay not being able to care for people.
So. I don’t really see this factored in. I don’t see long COVID factored in in all of these pandemic policy decisions that are being taken at the moment. And that’s a big concern.
Grant: Is there data on the effect of vaccines on the incidence of long COVID?
Christina: No, we just don’t know if it’s effective.
Nisreen: There is no data. There are anecdotal stories of some people improving after getting the vaccine, some people getting worse symptoms. So really there’s no systematic data on it. And that is probably also because we don’t have a lot of long COVID patients who are already vaccinated or given the priorities for vaccination. Because lots of people with long COVID are in younger age groups and healthcare people who don’t have any underlying medical conditions before getting it.
Deepti: I think that’s a very important point that when we look at severe disease, which is defined as hospitalizations and deaths, the sort of demographics of the age groups we look at are quite different from the ones that are affected by long COVID. And targeting just one group is not going to necessarily impact the other. For example, in the UK, easing of lockdown has been tied in right from the beginning from vaccine targets.
And that doesn’t really account for any of this at all because the vaccine essentially will prevent severe disease in those who are being vaccinated, but we want to vaccinate enough people to come close to reaching herd immunity. So transmission will very likely continue amongst the younger population who are the majority of cases anyway, if we start easing lock down.
And that will mean many, many more cases of long COVID. So we will probably end up with a pandemic of chronic disease in a few months time or even a few years time. It’s something that we don’t fully understand, but will not take seriously, unfortunately until it’s too late.
Christina: I mean, it has just been so irresponsible. So at the moment in the UK, we’re trying to vaccinate everybody over the age of 70 plus health care workers by the middle of February, and we’re on track to do that. And that’s amazing. And that will have a massive impact on deaths and the reasonable impacts on hospitalizations. But 90% of cases are under 70 year olds.
They’re not the drivers of transmission and just the idea that somehow that these schools open up and it’s fine for younger people to get it.. It’s just crazy because it’s not fine. Even something like one in seven kids have symptoms after seven weeks from the Office of National Statistics.
It’s just the idea that we would just expose people to potentially long-term problems. Well, we know there are long-term problems, but also we don’t know what could happen in five years time. We don’t know what the long-term impact is at all. How can we, it’s only a year old, right? I mean, after the Spanish Flu in 1918, there was a kind of mini epidemic of Parkinson’s 15 years later.
Because it ended up damaging the brain. We don’t know any of that. So the idea that you would just encourage people to get infected because you can’t be bothered to have an effective public health strategy just drives me crazy as you might be able to tell.
Nisreen: I think that’s exactly what it is. The uncertainty of it all. And right from the start we saw that. I certainly did. It’s a new virus. You don’t know what it’s going to do. And there was this certainty around saying, No, it’s going to go away and it won’t touch you. And when people were talking about herd immunity and getting young people infected, it was really a horror movie happening in front of my eyes. Because I was thinking Do they know something that I don’t know? Because how do they know this virus won’t give you long-term effects?
And even now you say long COVID, it’s just post viral syndrome. So the two things. First of all, first of all, post-viral syndrome is not “just”. It disables people severely, potentially for a very long time. So if you have loads of people with it, that’s a major problem. But also you don’t know if it’s post-viral so you don’t know if it’s going to go away and you don’t know who’s likely to get worse.
There’s so much we don’t know. And the way we’ve abandoned the precautionary principle and embraced that uncertainty, I find it very astonishing. And we continue to do so. We really need to revisit that in terms of how we communicate that uncertainty and act on it.
Grant: Great Deepti, Christina, thank you so much for joining us.
Christina: Thank you.
Deepti: Thanks for having us.
Nisreen: Thank you so much.