The Changing Biotech Business and Big Data Ecosystem - Dr. Jeffrey Reid, Regeneron
Talking Biotech 390 with Dr. Kevin Folta
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[00:00:00] Kevin Folta: Hi everybody, and welcome to this week's Talking Biotech podcast by Collabora. Now, every week we get together and we talk about innovation, the solutions to problems that are based on the fundamental chemistry of biological processes. We explore new technologies with experts from around the world, and we discuss the nuances of new technology that serves people in a.

Now this is exciting stuff and, and honestly, something I look forward to every single week. It's kind of the highlight of my week and it's going into its ninth year soon. And I'm especially grateful to Collabora, and this is a company that felt that this enterprise was worth sponsoring in assisting with issues in scheduling and production.

It really helps and I'm extremely grateful for that, especially since my university said, this is something we want nothing to do with. I'm glad someone else finds value in this.

The added bonus is that I have other folks with different perspectives that come from more of a business side that can help illuminate biotechnology beyond the test tube, and that's where today's presentation comes from. It's the first in the series on the entrepreneurial and business ecosystem where modern biotech operates.

And so what are the transformative organizational changes that allow companies to thrive And. When technology blows forward at breakneck speed, what are the opportunities? What are the barriers? And how do those listening today in early career learn about the successes and mistakes, or possibly plan for a future in integrating into business of biotechnology and its associated products?

So today we're speaking with Jeffrey Reed. He's the Chief Data Officer of Regeneron Genetic Center, a critical modern arm of Regener. So welcome to the podcast, Jeffrey. 

[00:02:00] Jeffery Reid: Thank you so much for having me. 

[00:02:02] Kevin Folta: Yeah. This is a little bit different than what we normally do. It's, they were really focusing more on organizational behavior and how organizations change the challenges and change with the times and, and how that really affects a company that is focused around medicine and the application of biotechnology.

So let's start out at the beginning. Can you give us an idea of who Regeneron is and what are the major areas of 

[00:02:25] Jeffery Reid: focus? Sure. So Regeneron is quite unique in the field as a relatively large now pharma company that is still run by the founders. And um, you know, we like to say we were an overnight success that took like 25 years.

There was a very, very long period where Regeneron was working really, really hard to try to understand biology and, and bring drugs to market and. You know, it was a very slow process. But out of that, uh, as an institution, I think Regeneron learned a lot about how to utilize technologies and how to inter-operate technologies to, to solve important problems.

And, and as, uh, Regeneron grew both in its success and in the size of the company, there was a realization that there was a really truly unique opportunity to bring human genetics as a tool into drug discovery. Um, Regeneron being a company that before the rgc, before the Regeneron Genetics Center was launched to really do this large scale human genetics, uh, Regeneron as a company had really, uh, positioned itself as a leader in the mouse genetics field.

And so we felt like we had a really good mechanism and model for taking genetic insights and understanding how to bring them to patients as therapeutics. Uh, the rate of discovery of those genetic insights was a little too slow for our taste, and so we went ahead and, and built a, a very large scale genetics effort within the context of the company and, uh, it seems like it's been pretty successful.

So 

[00:04:00] Kevin Folta: that's the Regeneron Genetics Center. So how is that different from just the core Regeneron? So, 

[00:04:06] Jeffery Reid: so the R D C is is a separate entity and it's set up to focus on collaboration. So at the R G C. The data that we make and the data that we use is not our data. It's data that we're sharing with people who are collaborating with us around generating the genetics data into cohorts where we can make these kinds of discoveries.

So, for example, UK Biobank is this Biobank effort, uh, in. Britain that had over years and years collected, uh, samples as well as, uh, health information from 500,000 people. And so we partnered with them and some other pharma to generate the sequence data for that project. That's data that now is in use by researchers all over the world as well as ourselves.

And so, um, the R G C. Is entirely focused on building these collaborations where we can bring human genetics data to large cohorts and then share that data with our collaborators and, uh, do research that can drive both discovery and drug development. 

[00:05:11] Kevin Folta: Yeah, so I think most companies that are going through growth and adjustment, uh, over time where technology is moving in different directions that the, seems like the trend is well buy other companies just buy a smaller ones that have those areas of expertise, whereas the model here, Form a cohort of scientists who really, rather than, uh, have, rather than buying out other companies or other technologies, hiring a cohort of scientists who have the expertise to identify good collaborators and just do go forward into new areas as a collaborative 

[00:05:45] Jeffery Reid: effort.

Yeah. And I, I think if you look at, at Regeneron s success, just in our, in our primary business in drug development, right? I, I think, and Ella can correct me if I'm wrong, all of our therapeutics on the market, were homegrown. These are all things that were fundamental discoveries that were made within the walls of Regeneron.

And we think it's important as a science-driven effort to make sure that we really, truly understand the science that is. To the therapeutics that we make. And, and so you're absolutely right. The model at Regeneron is wherever possible, try to build the expertise internally because you, you know, you just don't really understand something until you really sort of live with it and you kind of think about it every day and work on it every day.

Um, and, and we feel like having that. To really, truly understanding all aspects of the science, um, makes us better at making therapeutics at work. 

[00:06:45] Kevin Folta: Well, let's talk for a second about when you joined the company. What was the computational ecosystem like? Um, r and d of, uh, informatics, that kind of thing when you joined Regeneron?

[00:06:56] Jeffery Reid: Yeah, so it, so it's a great question because. When the RGC was envisioned around 2013, and we, we launched in, uh, 2014 and I came on board at the end of 13. Um, to, to help set this up, Regeneron as a company didn't have a really truly large, stale kind of computational science perspective. Obviously, you know, as a, as a working company.

Drugs in the market and, and doing so much great research over many years. Of course there was some local hardware and you know, people were doing a lot of stuff, but you know, the scale of computing for something like sequencing a hundred thousand exomes a year, 500,000 or a million exomes a year, and then pulling in medical record data and then trying to do analytics across that.

That was something where we knew we were either going to have to like basically build a new data center from the ground up because. You know, we would've easily consumed all of the computing capacity of the whole of the company, uh, and still not had enough. Um, but luckily, or maybe not luckily, maybe, uh, you know, part of the plan, um, Cloud computing was really coming online at the same time.

And so, and, and I had in my previous life as an academic, already done some work on doing large scale, uh, genomics, compute in the cloud. And so this was sort of the perfect moment to, to make a strong commitment to cloud computing. And so in advance of the R G C, You know, I would say that's in some ways maybe the biggest transition that the field has made.

Uh, moving really from a perspective where people were primarily using local hardware first to something that's more cloud first. And that was fundamentally important in the success of the rgc because we would've been slowed extremely by a need to actually physically install servers. We would've ha, you know, spent way more money and had to have a much bigger team.

And given that as soon as we started, we felt compelled to scale up rapidly because it, things were working so well. Uh, we would never have been able to do the scale up. We did as rapidly as we did if we had had, you know, to buy a bunch of boxes and, and put 'em in, in racks in some building somewhere. Uh, and, and so really before the RGC Regeneron, um, was not.

Committed to the cloud in any meaningful way. And now it's, I, I would say our, our primary mode of operation, both in the RGC and in the wider Regeneron, uh, r and d, uh, it community. Well, what 

[00:09:30] Kevin Folta: were some of the other complications that were present at the beginning, uh, when you're trying to get the RGC established?

[00:09:38] Jeffery Reid: Yeah, so I think, you know, one of the things is just like building, you know, building a team, like starting with a very small number of people and trying to pull people in and pull the pieces in, uh, that would make everything work. Of course, you know, we have this sort of, Um, support and relationship with wider Regeneron.

So it wasn't like, you know, we were a, a startup that's out in the middle of nowhere with no resources. But yeah, like, like finding the people who were interested in doing this kind of oddball, although I think it's much less oddball now, kind of academic human genetics, but in the context of industry as part of a drug development effort.

Where we're partnering with these academic partners, finding the team that really, uh, connected with that vision, that that was difficult. And then, you know, finding partners, finding people who were willing to work together with us to sequence samples and, you know, that that was and continues to be a large effort to try to try to find, uh, people who are interested in doing the kinds of science.

Yeah, I always 

[00:10:45] Kevin Folta: joke about it that, uh, they say geological time being slow, uh, genomics time is really fast. Oh yeah. So when you talk about, when you talk about 2013, this, you know, seems like, you know, a decade ago, right? Um, um, yeah. 

[00:10:59] Jeffery Reid: But it's, it's, yeah. It's like two epics ago, right? 

[00:11:02] Kevin Folta: Right. Exactly. I mean, the amount of information that we've obtained in that decade is insane.

And so how have those challenges changed from 2000. 

[00:11:12] Jeffery Reid: Uh, I mean, it's, it's a very good question and I, and I'll say I think we don't actually even really understand how it's changed yet. And of course, we now have these new technologies, like large language models, you know, GPTs that are, that are looking like they're gonna be further disrupting, kind of the way we think about handling and accessing data and information.

But I, I would say that just having. Stakeholders as we grew and as our data set grew, the, you know, there was an exploding number of external collaborators, and as we got a better understanding of really, truly the power of human genetics in driving our drug discovery, then, you know, we had more and more asks from wider Regeneron to, to try to help address the problems that the scientists were seeing.

And so it, it did get just a. Overwhelming. Um, but I, I would say the, the, the, the kind of direction that it's going is, is really towards a place where we're going to be inter-operating with data in. Platforms as opposed to having people sort of directly addressing or, or directly inter-operating with data.

So, so what I mean by that is if you look at sort of like pre 2015, let's say most large scale genetics projects, Would be accessed by, you know, somebody going to DB Gap or something, and downloading the data. The idea that you would try to access the data in someone else's computer, or you would not have the right or the ability to bring the data into your systems.

That was not what people, you know, in the research community wanted to. Um, and, and now we've moved to a place where, you know, if you look at UK Biobank as an example, they've stood up a research analysis platform. We're seeing more and more projects like, uh, the fin gen project in Genomics, England, where there are these trusted research environments that are kind of controlled.

And so as the data's gotten bigger, it's gotten much less mobile. There's been a lot more stakeholders. And so now we're moving to these solutions where people. Sort of bringing themselves to the data instead of bringing the data to them. Uh, but we don't, I, I don't think we have really fully digested this evolution.

And of course, the sequencing technology keeps changing and cloud computing technology is changing. And, you know, with, uh, AI and ML models like really ramping up things are, things are very much in flux right now. As a, as an astrophysicist, I would say we're in the sort of, You know, the, the messy part of the phase transition, we have not yet reached wherever we're going.

[00:13:58] Kevin Folta: Yeah, and, and it's really interesting seeing the change happen. And to me, as somebody who really got all of his training in the eighties and nineties, I'm almost, I mean, really overwhelmed by trying to understand how do I incorporate into this, how do I integrate into this? Because I think about drug discovery in questions like that in an old school kind of a way.

So maybe for me in some of the other listeners, how do mountains of genomic data and maybe even. Uh, personal data from things like biobank. How do those things collide to give us drug discovery, uh, information. 

[00:14:31] Jeffery Reid: Yeah, so perfect example is, uh, the, the case of PCSK nine, right? This, this predates CRG C, but the idea of, uh, finding a particular genetic variant or finding a particular class of genetic variants in a gene where you can see that the person who is the carrier of that variant is protected from some disease state that immediately.

Creates kind of a, a clear path where you could envision, okay, well if the person has this genetic variant that is, that is knocking down, for example, the function of this gene, so this protein is not present in this person, we can reiterate that. Not easily, but in a relatively straightforward way with an antibody.

And so it immediately provides at least sort of a starter hypo hypothesis of, okay, well let's see what happens if we knock down the function of this gene in people who have a working copy. Because what we're seeing in people who don't have a working copy is they're protected from the disease state that we would like to protect people from.

And so there's been a variety of insights like this. These sort of like large protective effect variants. HSD 17, B 13 is is a great example. Um, And, and there's really countless others. Now, of course, that's not, that's not the only piece, right? That's one, uh, direction, one way of, of deriving insights. But, um, but that's a, that's sort of in some ways the, the most direct path you could think of to go from a kind of basic genetics insight.

To, uh, a therapeutic hypothesis and, and something that you could really tangibly start to work on in terms of functional modeling and, you know, mice and cell lines. And thinking about, um, how this is gonna o operate in the biology 

[00:16:19] Kevin Folta: and what is happening now to improve data collection and maybe just computational capacity or organizational.

I, 

[00:16:29] Jeffery Reid: yeah, I think one of the, one of the things that's been a bit of a sea change in the last 10 years, or maybe 15, um, the recognition that there are these other modalities of gathering health data that are also quite valuable. So the medical record is great as a billing tool. It. Not great, but can be made usable as a research tool.

But you know, the medical record doesn't capture all sorts of really relevant things. It doesn't capture in any consistent way, for example, what your mother died of, right? It doesn't capture. In really in any way, usually maybe in the physician notes that, you know, your, your, um, grandfather died of Alzheimer's or something, and so there's a lot of information about how people live.

There's a lot of information that's really super relevant to their genetics and their health. That is, Historically not been very accessible through the methods of data capture in the healthcare system. And so I think in some ways the most important kind of evolution, uh, is this recognition that there is actually truly unique value in sort of participants directly answering questionnaires like in UK Biobank, for example.

But there are, but there are many other projects that are doing this. And of course the consumer genetics testing companies are also, like the 23andme and ancestries of the world have also proven out that, uh, this is a useful modality of data, which before I don't think, uh, people were necessarily taken very seriously.

[00:18:04] Kevin Folta: Yeah, let's pick up on that. On the backside of the break. We're speaking with Dr. Jeffrey Reed. He's the RG Seed Chief Data Officer at Regeneron, and this is the Talking Biotech Podcast by Col Collabora. And we'll be back in just a moment. And now we're back on the Talking Biotech Podcast by Collabora, and we're speaking with Dr.

Jeffrey Reid. He's the Regeneron Genetic Center, the R G C Chief Data Officer. And we're speaking about Regeneron and how their organization has changed through time to meet the modern challenges of modern biology and modern medicine. And what, before the break, we were speaking about how large data collection has really changed.

Over the years and change the ability to begin to predict how drugs can be made, and it really starts to bleed more into the. Uh, from one side, you know, we need more data to, to design the drugs with the other side where we're starting to see more emphasis on personalized medicine and people identifying genetic variants that may change diagnostics as well as therapeutic applications for patients.

And so are we really at the point, and you mentioned the utility of the UK Biobank, are we really at the point whereas if everybody could be genotype, And would report their diseases or whatever family histories that maybe we could really accelerate progress in the area of drug discovery and human therapeutic.

[00:19:32] Jeffery Reid: I, yeah, I, I mean, I, I personally believe that we have really just scratched the surface in what human genetics can provide, and this is one of the reasons why at the rgc we're still pushing and trying to like move to even bigger scale, get more samples, get more partners, and really build much larger data sets.

And, and I think worldwide, this is pretty well recognized now. So, you know, in the US the, uh, there's the All of Us Project, which is attempting to sort of bring together this kind of large cohort in, uh, the uk. There's now this project, our Future Health, which is, uh, nonprofit partnering with the National Health Service there to endeavoring to try to sort of gather information from roughly 5 million people, uh, and, and sort of follow them.

You know, through the course of their healthcare journey to like pull that data together. So I, I think there is a recognition that we just need a lot more data. And in particular, you know, because we're talking about genetics, we have to recognize that the genetics of one person. Is, you know, sometimes quite different from the genetics of another person.

And so for example, if we're creating tools based on data sets that are made primarily on European ancestry individuals, we need to be very careful in applying them to non-European ancestry individuals because there could be some effects based on what the sort of bulk genetics of that ancestry is. Uh, and so, You know, building a large number of very diverse cohorts with lots of data is I think, generally accepted by the field as where we need to go.

But of course, you know, that's, that's a daunting challenge to like, Build multiple, you know, multimillion size cohorts and, and gather all this data. So I, so this is in some ways I, I would say the big challenge of bioscience in the, in this era is, is really how do we build these data sets? How do we make them, you know, useful and useful in an equitable way, a across the spectrum of, of ancestry and socioeconomic.

[00:21:43] Kevin Folta: Yeah, we think about that, you know, equity and access and, and that kind of thing to the therapeutic side of this. But on the data side, you know, you, you mentioned the, you know, diversity of folks that are out there. We find that nature or evolution or, um, you know, just, uh, different ethnic groups have, ID have developed specific solutions to problems already.

And so, like for instance, you know, malaria. Right. Folks who get sickle cell don't. That genetic variant leads to, uh, a resistance to malaria. So does, how important is it in terms of screening substantially larger populations? Just that a solution may already exist. We just haven't found it. 

[00:22:27] Jeffery Reid: Yeah. No, and, and, and.

And you know, one of the things that we get really excited about is like very rare genetic variants that have a large effect on the phenotype or the biology. The only way we're gonna really truly understand the full spectrum of rare variation is to really, truly understand the full spectrum of human variation.

And so we know that there are very many discoveries just waiting to be made, probably primarily in. Ancestors that have been under ascertained, which is really frankly, everything that's non-European because most of these studies have been funded and run, uh, you know, in, in the West. And so this is the sort of, um, locality bias, right?

We're like sequencing and analyzing the data on the people who are around us who, who look like us. Um, but we are missing so much because we have not really thoroughly understood or, or even attempted to, to like thoroughly investigate the entirety of the sort of sequence of African ancestry, which, um, we know, you know, is a much older ancestry and has a crude, uh, uh, a a varied, you know, wide spectrum of variation compared to, uh, some of the others.

So, Yeah, this is, this is, I think in some, in some ways it's exciting, right? Because it's like we see so clearly this opportunity. In some ways, it's frustrating cuz it feels like we should have already made data sets that are, that are more diverse than what we have. 

[00:23:57] Kevin Folta: Yeah, I can understand that. How, how is your work today different than it's been, say when you joined the company?

[00:24:05] Jeffery Reid: I, I, it's funny, I've been thinking a lot about this and, uh, you know, I, I don't know that I even truly remember, but it was like, you know what I mean? It's like things have changed so much. Um, you know, Regeneron as a company has hired, I think over 10,000 people in the roughly 10 years I've been here. Um, and so, you know, really everything is different.

And of course the pandemic, you know, that really, really kind of scrambled. Um, a lot of things. Um, I would say the, the biggest difference that I notice kind of on a day-to-day basis is, you know, I'm just in, I'm, I'm in a lot more meetings than I was originally. Like in the very early days, there was a lot more doing.

Than talking. But you know, as the organization grows and as I was saying, as the appetite for the inclusion of human genetic evidence into really everything that we do grew, you know, now we have like a lot of really enthusiastic stakeholders, uh, which is great, but it can sometimes be a little bit overwhelming.

So we get a lot of asks, you know, there's, um, there's an enormous amount of work to do. There's always. You know, something that we want to get done that doesn't quite make the priority list. Um, so it's sort of, uh, you know, it's, it's harder to. Like coordinate with the people we need to coordinate with.

It's harder to find time to, you know, sit and quiet and think, but at the same time, you know, we're much bigger, our data sets are much bigger and we're finding that our data is having a lot more impact. And so that's sort of what keeps the motivation up, right? It's like, yeah, it can be harder to coordinate with so many people and, and to juggle so many projects, but you know, These discoveries are literally giving us the opportunity to save people's lives.

And that, uh, you know, that is extremely motivating. 

[00:25:57] Kevin Folta: And when you say data sets are bigger, those kinds of changes, what was the one most transformative change that's occurred? Or maybe have to boil it down to two or three, but what were the biggest changes that have, uh, made what you do today possible that weren't there when you joined?

[00:26:15] Jeffery Reid: Yeah, so I'm gonna go actually back and say number one to me is really UK Biobank in. In some ways I see that as a true transition point in the field. There we went from, Not really having a truly large scale data set that was accessible to researchers, right? Like, you know, there were, there were a variety of projects that were, you know, funded by various governments here and there, but like these data sets tended to be somewhat scattered and quite restricted.

But the, the genius of Biobank was to say whatever investment is being made has to then be. Available back to the research community. So really our working with them and other pharma to bring large scale sequencing so that now the entire world of researchers can access, you know, a half million exomes with, you know, matched phenotype data and, you know, really, really broad phenotype data, which is important.

Um, that makes a huge difference because now things that before. You know, difficult to source like an answer like, Hey, is there an association or a meaningful association between pick your favorite gene, a b, CD one, and you know, lung disease or something. Um, now you can just kind of look that up. Right now, there are resources out there where you can like source that answer without having to go build a cohort.

And so not only does it give the opportunity for a broader community of researchers to use that data in their science, it also. Actually creates this beautiful kind of instant validation. If you have your own data set and you're finding something and it looks interesting, but you're not sure if it's real, if you also see it in Biobank, or if you also see it in the TOPMed dataset, or if you also see it in the thousand genomes data, right?

Then you start to get a lot more confidence in the insights you're gaining, and so just. Having like that data set of record is really kind of like night and day and it's, it's, I think, a true testament to the sort of vision and the, and the value of UK Biobank. 

[00:28:27] Kevin Folta: Yeah, I didn't think about that. I, I always thought that Biobank would be a re would be a useful resource for discovery, but I never thought about it as validation.

Yeah. Instant 

[00:28:37] Jeffery Reid: validation. 

[00:28:39] Kevin Folta: Right. It's really like a, a good resource that you can test your hypothesis in your population and compare it to this other one, and whether it holds or not, may be a function of the diversity of the cohort. Well, those kinds of things there. 

[00:28:51] Jeffery Reid: Yeah. Just because it doesn't replicate doesn't mean it's wrong, but at least you have an opportunity to look.

[00:28:58] Kevin Folta: That's right. And, and understand the nuances of that kind of relationship. That's, yeah, that's really cool stuff. So, if you had to recommend something to other scientific teams that were pursuing, uh, interest in greater digitization or data augmentation, what would that be? 

[00:29:13] Jeffery Reid: I would say the, the one thing that frustrates me most is, People should really recognize that the value of data is not in the tool or in the method, or in the idea.

The value of the data is the effort that you put into making the data valuable. So, you know, one of the things that we learned in, in trying to, you know, do some like really enthusiastic and aggressive, like, you know, large scale kind of, um, multimodal analytics is like, You spend most of your time just trying to pull the data together, trying to get the data in the format that you need it to be in and, and get it where it goes.

So when people get really excited about, oh, you know, g p T four or, uh, there's some new company that has some, you know, new whiz paying analysis black box that I wanna run, it's like that. Excite me. That doesn't, I don't find that particularly interesting. Talk to me about what is the data asset? Tell me about like what you've done to make this data.

Speak to the question that, that, that you have. It's like really, it, it sounds maybe weird, but we don't always actually, from the perspective of people who are thinking about trying to make a transformation with data, we don't always actually focus on the data. We often get drawn into focusing. On, you know, what are we gonna do with the data or, or what is the, the analysis tool that, that, you know, we want to apply?

Um, and that, you know, the values in the data, it's not in the tools. And so that's, that's sort of the one thing that I, that I kind of, it's a bit of a hobby horse for me, but it's one thing that I sort of always point to in these discussions around data. It's like, don't underestimate the amount of time and the value.

Doing what is really often difficult and not so rewarding directly work, right? Like just cleaning data sets isn't, isn't always fun. Um, but it's often the most valuable thing you can do. 

[00:31:17] Kevin Folta: No, that's really, that's really good that I understand that concept. So, if you were to give a prescription to folks who are planning on, uh, playing in this area going forward, you know, undergraduate, maybe graduate students who are considering roles in human genetics and big data, what kind of advice would you give them?

Because this is moving so fast that if they're going to be hitting the market in 20 28, 20 29, What needs to be in their toolbox? 

[00:31:45] Jeffery Reid: Uh, uh, no, it it, it's a great question, and as you sort of frame it, I, I think it is, it is self answering in the sense that I don't know that we actually know what people will need to know then.

Uh, but I, I definitely, you know, can give my take on, on, you know, Sort of what I would advise people, uh, you know, first and foremost, I, I tell people to try to envision, you know, what they want. I think if, if you're thinking about getting into something, it's worth thinking about why you want to get into it and like what you want to do with it.

So, you know, people should really think about what they're doing and why they're doing it, but, Beyond that, it's like just start doing. It's really, really easy now to get access to, you know, modest datasets to get access to like synthetic datasets. Certainly, you know, researchers can get access to UK Biobank and other sort of more formally managed, um, resources like that.

But, you know, I'm always really impressed when a young person comes and. I'm passionate about this and this is why I'm passionate about this, and here's the five things that I did, you know. We're in the service of that passion. If, if, if they have that passion and that passion comes through, and you know, it's that idea of I don't want to go to bed yet cuz I, I want to finish this analysis to see what the answer is kind of feeling like if you connect with the.

Data and the work like that, it doesn't matter where the field is going because you will just naturally be drawn along because you'll be excited to be using the new tools and to be answering new questions in new ways. Um, so I think kind of most important to just like have kind of a vision for yourself and, and do it, right?

Like don't, don't just read a book, find a sample data set and try to do some analysis. Try. Answer a question that you have and one, see how you like it Cuz right, if you don't, if you don't like the, the exercise of actually doing the work, you're probably not gonna want to be in that field. Um, But yeah, also that you know that if that doesn't feel like it's feeding your passion, then you should probably do something else.

[00:33:59] Kevin Folta: That's really good advice on a couple of levels. Cuz I can teach people the, the stuff in the textbook, but I can't teach 'em Enthusiasm. Yeah, exactly. And uh, and you know, the, I used to always ask, how do you, how do you pack a bag when you don't know where you're. And I guess the answer is you put in, you pack it full of the most comfortable and durable underwear you have.

Yeah. Cause as long as you take care of the foundation Yeah, yeah. You, you can build on it. Right. If, 

[00:34:27] Jeffery Reid: if, if, if you're excited to be doing what you're doing, then I would worry a lot less about if you have like the specific skills. And, and, and the other thing is you can avoid the, there's, there's a certain sort of pattern I see where as, I like to say people want to be a rockstar but not a musician.

Right. Because like, being a musician is not always that exciting. You're like, you know, when I was playing clarinet in high school, right? It was sitting in my room playing scales for hours on end. Um, If you don't love that actually doing part of it, if you only love the vision of what you think it will get you, then you will probably end up being m.

[00:35:07] Kevin Folta: That's good advice. I, I guess maybe the last question I would have for you is, if you got out your crystal ball and just from the trends that you've seen at Regeneron and, uh, trends you've seen across, uh, data and how it's finding associations with human disease, do you have any bold predictions for, say, the next decade with potential therapies, which could be highly likely for 

[00:35:28] Jeffery Reid: various diseases?

So, I, yeah, I mean, it's, it's a very good question. I, I would say the, The one thing that I would put forward as a bold prediction, although I don't, I don't really love armchair futurism. Um, and maybe my bold prediction will reveal that I, I don't think the world will be destroyed by AI in the next 10 years.

Right? Like, there, there, there seems to be this sense that, um, you know, AI technologies are going to like, you know, completely change humankind. I do think that they're gonna be disruptive across a lot of industries, including ours. I think actually they're gonna be really, really useful in healthcare in many, many ways.

But, you know, I, I don't think we're gonna have to fight off the, the sky net anytime soon. 

[00:36:21] Kevin Folta: Yeah. I, I, I fight, I fight AI with, I. Right, exactly. And they've been telling me that I've been disruptive ever since kindergarten. So, you know, that's, I've so it, it, it's like it's, you, you've met your match ai, right?

Well, 

[00:36:34] Jeffery Reid: I, I've been accused of being an AI personally, so, no, but I'm, I'm curious to know from you, what do you see is, I mean, you have a unique perch. What do you see as what transformation genetics will bring, you know, just to the world in the next 10? 

[00:36:51] Kevin Folta: You know, it's, it's really hard to say from my perspective because I work mostly in agriculture and watch that.

But what I do think is that, uh, from the interviews I've done as a podcast host, is that you have so many good people and so many companies, so many academic institution dealing with some of our most insidious problems that I think you're going to see resolution for. You know, you will never see something like cures cancer, right?

But you will find a very effective therapeutic that will help us manage. Something like breast cancer or prostate cancer where you'll take a pill every day rather than undergo chemotherapy or radiation. Um, I think the management of long-term neurodegenerative disease and these other types of disorders will be something that'll be very realistic.

Um, so, uh, I just, I, I just, that's my crystal bond. I, I think that's pretty good. 

[00:37:44] Jeffery Reid: Yeah. No, and, and I, I totally agree on, uh, you know, I think one of the things about, um, About AI that's very interesting is if you think about the enablement to someone who maybe is motion impaired, you know, if they can just say what they need, have that natural language processed, and then have some tools sort of get them what they need.

Um, you know, this ability of computers to more effectively understand natural language and sort of human communication in the way that humans communicate, I think it's actually gonna be really enabling for, for people who have, you know, mobility issues or cognitive function issues. 

[00:38:26] Kevin Folta: The thing that bothers me about it is the question of access and who would be able to, you know, will the price point of this thing keep most people who need?

Out of the market, but, you know, flat screen TV was $30,000. Once it's true, and as we scale up and these things become more commonplace, uh, maybe we will be able to help more folks with, uh, opportunities like that. 

[00:38:48] Jeffery Reid: So I, I, I think you're, you're absolutely right. Um, but yeah, we're, we are, we are gonna have to have at least a bit of a reckoning parti.

In the healthcare space around equity of access, and that's not, that's not a problem that is solved easily, and I, I don't see it going away anytime so. 

[00:39:06] Kevin Folta: Well, maybe we can tackle that one in 2029. Yeah. Or 

[00:39:10] Jeffery Reid: leave it for somebody else. Let's keep the world from being destroyed by an AI first. 

[00:39:14] Kevin Folta: Yeah. We'll, we'll cross that bridge when we get to it, I guess.

Well, Jeffrey, thank you very much for joining me today on the podcast. I was very helpful in understanding how organizations adjust and go with the flow and the changes that occur inside, uh, big data and how it's being used to solve problems. So thank you for joining me. 

[00:39:31] Jeffery Reid: Thanks so much for having me. 

[00:39:32] Kevin Folta: And for the listeners, thank you very much for joining on another week of The Talking Biotech podcast by Collabora.

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