Welcome to the Proteomics and Proximity Podcast.
Where your co-hosts, Cindy Lawley and Sarantis Chlamydas from Olink Proteomics,
talk about the intersection of proteomics with genomics for drug target
discovery, the application of proteomics to reveal disease biomarkers,
and current trends in using proteomics to unlock biological mechanisms.
Here we have your hosts, Cindy and Sarantis.
Hey, everyone.
Hello and welcome back to Proteomics in Proximity.
Thanks to our 11 listeners at Sam Ray, Carolina, others.
We are grateful for your attention
and your feedback, and
our listeners have given us some valuable feedback over time
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But to make that easier, we're announcing that
we've got actually an email address now, so we'll put this into the show notes.
But it's just P-I-P for Proteomics in Proximity at Olink.com. [pip@olink.com]
And and we'd be happy to hear from you around suggestions
you have or any interview recommendations you might have.
And with that, today, we are talking to Evan Mills.
Evan, I'll let him introduce himself, but he is
an illustrious pharma executive, here
actually at Olink, and we're excited to talk to him
about how pharma are finding proteomics
super relevant on many different levels.
So with that, let's get on with it.
Hey, Sarantis, how are you?
Hello. I'm fine. Thank you, Cindy. Welcome, Evan.
I'm looking forward to
hear from you all the great news.
Likewise. Good afternoon, Sarantis.
Good early morning to you, Cindy, on the West coast.
It's a little dark over here. A little dark
No, that's all right. I'm really honored to be here.
And I've been wanting to talk about how proteomics
and the pharmaceutical industry are aligning for really exciting things.
So very happy to be here.
Can you give us just a little background on your history in this area?
You've been in this for a while.
I have.
So I was a bench scientist really, you know, passionate about oncology
and neuroscience research.
I did some work at Yale University for awhile
and then I got into the sales commercial side of this world,
started actually in the pharmaceutical sales industry, which
was exciting because of the opportunity to help patients,
right?
But my real passion was in the science and about a decade ago,
there was a very innovative proteomics company
that caught my attention and that's where I started this journey,
where I've now been at Olink for over five years.
And yeah, supporting the most innovative,
ambitious researchers in this multi-omics space has just been a phenomenal journey.
So my background is: I love science, I want to help people,
I want to have some sort of translational impact with the work I do. And
right now at this moment, there's never been more momentum in that direction.
It's really, really exciting. Yes, very exciting.
We've just had here at Olink
three pretty exciting nature papers
come out in
I think it's the online [version on] October 4th.
But the print journal [on] October 11 with a beautiful frog on the cover.
It's an exciting time with those three papers.
So those include a lot of applications
around why pharma would invest in proteomics.
So I'd love to get your thoughts on why.
Why did 13 pharma come together, invest in proteomics?
What's the outcome?
What's the result that they see out of that?
Yeah, I mean that's been a real career
highlight, is being able to be involved in that project from its inception.
And you know, Cindy, with your background in genetics,
there was a previously formed consortium around whole exome sequencing
in the UK Biobank and then eventually the whole genome sequencing.
But there was this idea, and I was having lunch
outside of the Harvard symposium with Dr.
Chris Whelan, very smart geneticist who was at Biogen at the time.
He's now at Janssen.
And he just asked the question.
He's like, "Hey, we're thinking about what makes sense to do next.
We have all this richness in the genomic data,
but we want to do something closer to phenotype.
Would it even be conceivable for Olink to run 50,000 samples?"
And this is before,
you know, some of the innovation that would have made that possible.
And we said, "Yeah, I think we can do that.
I think we can get there, I think we can do that."
So it was just born out of curiosity
and the desire to get closer to phenotype.
So the
goal really of this ambitious project was:
can we both better understand
drug targets that have causal links to disease
and can we simultaneously find biomarkers to help the drug development process?
Because obviously with proteomics you can do both, right?
It can act as a bit of a filter to tell you which of these
genomic disease associations have a plausible biological story
and which ones should be pursued, and which ones should perhaps be killed,
but simultaneously you can develop a suite of tools
to determine risk based on proteomics, to determine disease
progression based on proteomics, and to discover biomarkers,
which are obviously always desired
to aid clinical development. So,
I mean, we're just starting to see all the publications.
We're starting to understand all the utility that's going
to come from this data set.
And it was just such an ambitious,
smart idea by Chris and then eventually Melissa and Linden and Brad,
that countless others who contributed to the project.
Evan, going back to this journey, this amazing journey,
how easy or difficult was it to convince the genomics community
because you mentioned it was like this heavy genomics community, right?
Change their mindset in a way to measure proteins,
how easy or difficult was this process?
It's really hard.
I think it is.
I think it was really hard because if you just think about the tools
one would need to develop to measure, right?
Our DNA is very nicely organized into a helical structure.
There's four bases to measure, and Illumina and others
now have developed amazing tools that can measure that at scale.
Think about the proteome, right?
There's 20 amino acids.
They combine in a myriad of different ways.
I mean, it's just such a formidable challenge that geneticists would say,
"I'm not so sure.
I'm not so sure the tools exist.
And oh, by the way, yeah, we can measure everything with genomics.
We can measure everything.
And you're approaching us with something that measures 1500 at the time?"
Right.
And then 3000 of what people assume would be
maybe 20,000 proteins that you could try to capture
in plasma / serum, there's a big debate about that.
So it is challenging,
BUT the obvious central dogma of being closer to disease
and things
that are reflective of real-time biology versus your blueprint for your biology
was compelling enough for them to give it a shot, but it was not easy.
So you must have focused on what is the near-term
return on investment for pharma, for running a proteomics project.
And I would consider this UK Biobank sort of pQTL developing therapeutic targets.
All of the -
all of those things you've already mentioned is more mid- or long-term goals.
How did you - I think it's a great question Sarantis asked -
how do you talk to them about what you believe is the value?
And I will
also say we - Gary and I - looked ... Gary is our illustrious person
who manages our database of over 1400 peer-reviewed publications
and he has seen over 84 of those are pharma relevant publication.
So there's a significant number of publications that have been
that have been put out there that document
some value to pharma, but that's pretty recent.
How did you approach them when you first came to Olink?
Yeah, no, it's a good question.
So we can take a bit of a sidebar from the UK Biobank discussion because
really, fundamentally, drug developers
are trying to bring effective therapeutics to market faster
and they also invest enormous resources into each program.
And it takes what, 10 to 15 years on average, you know, to get something approved.
And how many millions of dollars, right?
And patience.
And then what,
90% of clinical trials fail I think, or somewhere around there.
Yeah.
I recently had a discussion with an executive vice
president of research at a major company who said he would be the world's
best drug developer if he failed 80% of the time.
Isn't that wild? If he could go 80% after failing 90%, he would be the best.
And it's - right?
It's just such a high attrition game.
But those are, Cindy, the way that
a lot of people in the industry are starting to look at
this is: with population-scale proteomics or high-throughput proteomics,
you can learn a lot about things you've already invested in.
So let's say that you have a drug that's approved such as -
I can never say that correctly.
Jardiance, let's go with Jardiance [empaglioflozin].
Yeah.
You know, a very, very effective SLT2
inhibitor used for the treatment of
diabetic control.
They've also noticed, after having it in enough humans in the wild,
that there's significant benefits to heart failure.
So if you can access - and this is one of the publications that you referenced -
if you can access samples from completed clinical trials
and most companies are sitting on these, they're just in their freezers
waiting to be analyzed if they have the exploratory consents.
If you take a
look at proteomics at scale from
lots of humans treated in the clinical setting, you can learn
a tremendous amount about why certain people respond and certain people don't.
Right.
That's the Holy Grail, essentially is: can you proactively know
which patients could go, should go, which therapy.
We often call that "stratifying
patients," just to use the term that we've used before.
Yep. Yes, absolutely.
And you know, understanding the mechanism of these drugs, right?
Because, you know, you have a target, you have a hypothesis, you tested it in cell-
based models, animal-based models, but you don't really have a chance
to look at scale in a human population to see how it impacts the human body.
So then with that data, you can A) better understand why there's
this benefit in an indication for which the drug was not initially approved.
You can understand - Excuse me,
what other pathways are being
impacted by your therapeutic. Are there repurposing opportunities?
Is there a way to very rapidly
take this thing you've invested in, this asset,
and figure out that there's more places that you could help people,
there's more indications where this drug would actually be a really good fit.
So that's a very short win, short-term win.
And we've noticed multiple clients building this as a strategy
to take Olink Proteomics in this case to better
understand already approved drugs, which, in some ways, is counterintuitive.
Right?
I mean, ideally you think from the beginning
you would want to know everything you can about the drug,
but there's this reverse translation
movement that seems to be bearing quite a bit of fruit for the industry.
That was actually my next-
I'm sorry,
that's certainly my intriguing invite.
This is my next question, Evan, do you see now this trend of a strategy
in the pharma
because you talk with
the executives, right? And you would know the strategy and you discuss
about this. Do you see this coming?
Do you see that using large-scale proteomics, a big number of data to reposition
a drug, for example, to identify mechanisms of action even in the late stage?
How is your feeling? And why would they ever publish this?
Right.
We think of pharma as needing to hold these things tight.
So yeah, great questions, Sarantis.
That's a good question.
So the answer is yes, in pockets.
I think it's just becoming
a strategy for the more innovative companies.
Right.
There's always some concern, right, for ongoing trials.
Do we really want to know that much at a phase three?
Right.
If we have a candidate compound, do we want to do exploratory research?
Maybe we find something we can't explain.
Maybe we find some safety signals.
So what I'm describing is drugs approved.
Let's extract as much value from that asset as we can.
And there's definitely companies that are taking that on as a strategy.
And to your point, I mean, having gotten to know
folks in pharma really well for the last decade, I mean,
they're great scientists. I think there's this -
I think, not to insult
any of my academic colleagues or people
I've worked with or people that, you know, I've supported over the last 20 years.
I think there's incredibly talented
scientists that see the opportunity
to have a fast path to impact.
And they do want to share. They want to publish.
I mean, look at this consortium.
It was 13 companies that are competitors coming together
I was complimenting one of the pharma researchers on a hire,
a new hire from academia.
And she was saying they came to me because they're a physician,
an M.D., Ph.D., and they said,
I can help one patient at a time in my practice.
But if I come here and do more
broad-based research, I can affect millions.
And I was like, Wow, that's that's an interesting perspective.
I like that. And it lines up with what you're saying.
I think a great example of reverse translation that you've talked about.
I think one of the
examples you've talked about in the past of
of this, you know, taking samples that are sitting in the freezer
where a massive investment has been made is the one from Simina Ticau.
And
Paul Nioi. Paul is, of course,
also on the UK Biobank
flagship paper that came out last week / this week,
whichever online footprint you want to reference.
Can you tell us about that example?
Yeah, this is a really, really interesting story
and that this originated about five years ago and was published in 2019.
So it's a bit dated, but I think the point is incredibly powerful.
So, you know, hereditary transthyretin-mediated amyloidosis is
a genetically defined disease
that really has -
And can I just say that you can pronounce that,
but empaglioflozin is pretty
darn easier to say than -
I'm sorry. It just seems funny.
[Empaglioflozin is] Jardiance, but anyway, you know, back to back to hATTR.
No,
you know,
I've probably told that story more times than -
Yeah,
my gosh, it's hard.
So no, but, hATTR is a really,
really debilitating disease with a variable rate of onset.
So if it's the hereditary form, it runs in your family.
Right.
You can be screened to know if you're carrier
and know if you're at risk for developing the disease.
Alnylam developed a drug, patisiran,
that is an siRNA - excuse me,
RNAi-based therapeutic where they are
very effective at slowing the symptoms and helping these patients.
However, even with this genetically defined population,
it was hard to know when the disease was becoming active,
when these patients were a good candidate for treatment.
So they ran a retrospective study.
This is before Olink had an NGS readout, so it only measured
like 1100 proteins and they discovered neurofilament light [NFL],
which is a very ubiquitous biomarker for neuronal damage.
But they found that this biomarker, this neurofilament light, was
A) indicative of disease progression, was also a biomarker of efficacy.
so after patients were treated with patisiran, it dropped significantly
and it was a disease biomarker, it was 4-fold elevated
in the patients versus healthy controls that they measured in the study.
And so now what's really interesting is there's a protein-based assay
that could give treatment decision information, right?
So it's being validated
and it's only a single biomarker and it's a ubiquitous biomarker.
But in this subset, you know, proteomics is giving you some actionable insights
in a genetically defined population where they're now
developing cutoffs to try to see, hey, if you come to your clinician
and NFL is measured, and once you hit a certain cutoff,
that might actually indicate, even though you don't have symptoms,
the disease process has started and you are a candidate for treatment.
So it's great for the patient.
It's obviously great for Alnylam.
So they can, you know, justify patients getting on their therapy.
And it was where a proteomic screen, right?
they didn't know what to look for.
They didn't have this hypothesis.
They just wanted to see what what's changing
in these patients after treatment, what's changing over time.
And I think that's a powerful way that unbiased proteomics
can point us in the direction of actionable
biomarkers to help patients and clinical development.
So yeah, that was that was a really interesting story.
Great.
Actually, I would like to go way back
because you mentioned about pharma and academia and then we know
at the beginning it was really difficult to communicate, right?
The two little worlds, they were like separated:
academic research versus pharma research. Do you see this changing?
And do you see a benefit of this change?
Yeah, absolutely.
So we just got off the phone.
Cindy and I were just on a call with a really, really impressive
academic researcher who mentioned that she's on the board
for two very large important studies that are being run by pharma companies.
Right?
She's an expert in her field and she's advising on how they should
spend, you know, their research dollars to best move,
you know, very important therapies through the clinic.
I see it happening all the time.
I mean, so,
our team focuses on primarily pharma and large population cohorts.
Right. And there's
incredible connections between the two.
Right?
Because if you think about it, if I'm a pharma company
and I'm interested in atopic dermatitis, for example,
it would behoove me to really profile with all these new omics technologies
as many patients from the best cohorts in the world that have atopic dermatitis.
You could do that through a population cohort
and you know there's going to be some subset.
What's probably more efficient is to work with, you know, KOLs in the field.
Yeah. And then they've collected the samples.
Yeah. You provide the resources
and then with that right from the protein side
you could discover, yeah, are there disease progression biomarkers, are there
endo types,
are there sub phenotypes where there's slightly different molecular drivers
that we could then approach with different molecular entities
that we either have or that we could develop
to have a higher rate of success in the clinic. Precision medicine.
So absolutely.
No. Yeah, Yeah.
And that's been a term that's been really kind of reserved for oncology.
Right? Primarily.
And, you know, I think that
that's because the tools have existed
at the genetic level and obviously cancer is a very genetically driven disease.
But if you look at, you know, some of the more
multi-system diseases that, you know, in the cardiometabolic space,
in the autoimmune space, you know, proteins
I think will be the next big thing in terms of finding
signals that can differentiate subtypes of patients
and then give them better, better treatment options in the future.
You talk about cardiometabolic. Would you consider
like a blockbuster kind of disease, do you see
pharma investing more on these or expanding on this research?
Because for me, seeing pharma, they are moving far away
without of course leaving behind the traditional type, if we can say
a disease like cancer, I see that now pharma is going to rare disease,
they're going to cardiometabolic disease, they're going to PCT disease.
What is your feeling? What
do you see in the upcoming years with pharma?
I mean,
without getting too philosophical about why,
you know, the GLP1, GIP1 that
you know, the other Lilly and Novo competition and others,
you know, there's Pfizer and a lot of other companies are getting involved.
Right.
There's just a huge societal issue with obesity
and there's enormous amounts of investment happening in that field.
I do think that there's a bit
of a gold rush right now, but scientifically,
what's really interesting is, you know, it's not just about obesity.
I've been fortunate enough to talk to some of the leadership at these companies
who are really trying to develop the next,
you know, Mounjaro, the next Semaglutide,
and what they're noticing is there's so many knock on benefits
and there's so many benefits to multimorbidity
that they want to both understand at the molecular level
what's driving that, but also understand, you know, are there patients
who have a more aggressive form of obesity for lack of a better term?
Right.
Is there a subtype of patients that really need
30% weight loss or 40% weight loss?
So it's a fascinating effort and I mean, given
the reality that it's a very environmentally driven condition,
proteomics, I think, will will be an indispensable tool.
I mean, again, the other day, talking to, you know, a KOL in this space saying
these companies and
society generally says, well, let's do genomics first, right?
Like, we have all these samples.
We're going to just do whole genome sequencing
and see if there's some sort of signal in the genetics
that's going to help us answer these questions.
And they're starting to say, hey, wait a second, there's these
proteomic tools now that don't you think it makes more sense in obesity
to look at the proteins and they're dynamic and you can look
at multiple timepoints and see what's changing post-treatment, etc., etc.
So it's just an interesting side note that
in this field I think proteomics is going to be particularly valid.
And I just want to define a couple of terms. Okay.
KOL as key opinion leader.
We use that a lot at Olink around here.
People that are driving and influencing
decisions that are happening out in the field particularly or,
you know, we are thinking in terms of genetics and proteomics
and then the the semaglutide and these GLP1 agonists
that Evan mentioned are not only relevant in obesity,
but they're actually being almost prescribed
where people pay out of pocket in some offerings.
So I've met people that are really keen to be on them
or are on them and who have had a lot of success in reducing their,
maybe not in the, you know, obese category,
but an overweight category where again, you can expect
based on what we've seen, health benefits there as well.
So I just wanted to throw that in. Really interesting space, right?
And yeah, maybe also on that mode, a lot of these drugs
and lot of these inhibitors, as you mentioned, Evan, there are influencing
more than one disease, right? There are targeting
more than one.
And I think that's sort of where some of them left off.
You have a drug for more than one disease
is like my feeling or have you seen this happening from your perspective?
Yeah, for sure.
I mean, it's
then that's where the deeper understanding of the mechanism of these drugs.
Right. Which, you know, Yes.
There are great model systems that if you using a cyno [cynomolgus macaques]
model, monkey model, you know, eventually mouse models.
Rat models. There's all kinds of models.
And you can get a good sense of how your drug's behaving.
But you know, often with these phase one or phase two studies,
the amount of patients is fairly small.
You can get an idea, but that's why I do,
I believe that, you know, companies are investing significant
resources to look at the bigger studies.
Right.
Because you can just see, you just get more statistical power.
You have a better chance of really understanding how
my drug's impacting multiple pathways, multiple organ systems.
And then once you have that knowledge, it just makes you so much better
informed for new therapeutic ideas,
even just repurposing the existing therapeutics.
So, yes, Sarantis,
the more indications, the better,
I mean, just from a simple pragmatic business perspective.
But having the molecular, you know, justification
I think is what, as a society, we should all ask for.
I mean, just seems to be what they -
Obviously when we're consenting
and all of us where you know participants in these sorts of trials.
I think another promise here and we're going into ASHG soon [ASHG = American Society of Human Genetics]
and we've got 25 different posters of folks leveraging it, leveraging
some proteomics from Olink, which is really exciting to see.
This is a genetics conference and clearly there's this value of layering
the genetics onto - or, the proteomics onto the genetics.
There's also seven talks
that doesn't include the talks that we're sponsoring.
So I think
in this in this environment,
I guess I'm wondering:
what are you most excited about, Evan?
Sorry.
No, that's fine.
I mean, it's a hard question.
Let's let's just talk about this environment being
the fact that there are three publications in Nature, right?
Three publications that just dropped
about the promise of population proteomics.
Right.
So, I mean,
I just think it's the beginning, right?
So 50,000 samples from a largely northern European cohort has led to a treasure
trove of insights, 14,000 associations, 80%-plus of which were novel.
People can dig into that for a
long time. And reference back to it with their own studies to
- Yeah, that's the way forward -
corroborate the signals that they're seeing.
I think we've talked about that before.
Yeah. Go ahead, Evan.
And that's super exciting right, because that will provide a bit
of a backbone to understand causality
and give us insights into drug targets and biomarkers.
That's great.
You know, but it's just a small
subset of the world's available resources from a cohort perspective.
So there's enormous benefit to going bigger
as the AstraZeneca rare variant paper shows.
Right.
To capture these rare variants.
And this is what the Regeneron Genetics Center has done for years, right?
They're doing genomics on all of these very large populations, these founder
populations, to find these signals that really come out when you go big.
That will happen at the protein level as well.
I think going to different parts of the world,
there's just going to be enormous richness as we go from that. Diversity
Without question, everyone wants to do that.
But if you say the thing I'm most excited about, to be honest,
is proteomic risk scores and the potential
for a whole suite of tools to help
perhaps, you know, consumers one day, certainly drug developers,
perhaps health insurance companies, who knows where this all goes.
But, you know, speaking to Ben Sun and some of the head analysts from the UK
Biobank project, they, with just 50,000 samples and machine learning,
and I'd say algorithms, are able to pick up on these patterns
right out of sometimes a small number of proteins.
I believe Claudia Langenberg and Robert Scott had a paper where
it was between like five and 20 proteins
could distinguish your risk of a large number of common diseases.
I think
once those are validated and those are refined,
that is a game changer because then I'm a drug developer,
I can apply these algorithms to all my clinical trials
and better understand, "Hey, are we on the right track
and what other impacts are we having on a wide range of diseases?"
I mean, to me that's incredibly exciting.
And it's not without its challenges, right?
I mean, you have to validate these things and sufficiently
have statistically powered studies,
but one could imagine that there could be
a suite of tools in the future based on, you know,
a manageable number of measurements that could be used clinically.
And that's where I think the next big evolution will be
is taking this data that's been generated by either
academic funding, pharma funding, government funding to really
look at a lot of diseases at the protein level, at scale,
using these new proteomic technologies
and then whittling it down to things that are clinically actionable
that you would have never found if you didn't take a broader view.
Right. I think that's the difference.
And just to double click on those authors, so there's Ryan Dhindsa
on this rare variant paper.
He's the first author.
He's at Baylor working also with AstraZeneca, where Slavé
Petrovski is the the PI on that paper.
There's Ben Sun who you mentioned and Chris Whelan paper.
That's our [UK Biobank] flagship paper.
We consider it sort of
the broadest group from the UK Biobank Pharma Proteomics Project
and then, of course, the I want to also just touch on Grimur
Eldjarn and Kari Stefansson's
paper.
Well if only to to highlight
something Kari said about proteomics in general
and that was along the lines of what you're describing, that proteomics,
that an algorithm they've been able to develop with proteomics, can predict
all-cause mortality in any individual.
So how many years does one have left to live? Right.
So if I go into a clinical trial
and I've got a prediction of 30 years left to live,
and then I go onto this drug and part way through that trial, or maybe three
quarters of the way through that trial, you look at my proteomics score
on my prediction, on how long do I have to live.
This is a way to have very short clinical trials
that actually are representative of a longer period.
I mean, imagine a depression trial.
I remember there was there was one trial on depression.
It was something like six weeks. Right.
If you're talking about major depressive disorder, a six-week
window is a hard one to draw conclusions from.
And we do the best we can.
But having something like this that is a reflection in the future
of what this is doing to your proteins, I think is very exciting.
I mean,
yeah, I'm thinking about data that's new.
Let's say there's an era of proteins, versus big data generation
for biomarker discovery, then what is coming next?
The in vitro diagnostics era is booming, for example? Then some of these biomarkers
be like customized and used for clinical diagnosis.
So how do you see this road map? I know this is difficult to predict, but
what do you see this coming actually from your perspective?
I think so. I think so.
And Cindy's point, I think is really - so to sort of touch on that real quick
and then and I'll touch on that, Sarantis, because they're certainly connected.
But they're slightly different in my view.
So this idea of having a risk score to help, you know, shorten a trial, right.
Give you some sort of a surrogate end point or some sort of early read.
I mean, I remember, you know, Kari [Stefansson] in a
presentation he gave mentioning that, you know,
if you could apply this, you know, risk score,
you could
cut the time of the cardiovascular outcomes trial, you know, significantly,
I think by more than half and save hundreds of millions of dollars.
Right.
And I think broadly that would help everybody
because the
the companies developing therapeutics would not have to spend so much money,
it would be less expensive and the right patients would get,
you know, the right drug
because they're at higher risk if you use an enrichment strategy.
So I think that's absolutely coming.
There's no doubt about it. But then,
you know the
real end game, I think, Sarantis, was
what you've referred to in terms of this in vitro diagnostics piece.
You know, so I was recently visiting Roche Diagnostics, you know, in Basel.
And they're, you know, world leaders in diagnostic tests
and by and large, today it's a single-plex assay.
Yeah.
You measure one thing. and there's a lot of reasons for that
you know it's challenging to have multiplexed assays validated to the level
that today we're used to being required from the FDA and others.
But biologically and just
you know, if you just think about
the complexity of disease, single market is probably not the best thing to do.
So I do think that's coming and I hope in the rest of my career
I have a role to play in that
because if we can have very predictive
multi marker tests to be used in the diagnostics space,
that to me will be the biggest societal benefit
that can come from all of the amazing work that's happening right now.
I think that that's where this all goes.
And you can just imagine a future where there's much more resolution
to your personal risk for disease, your personal response
to therapies that we just don't see today.
So yeah, I think that's where it goes.
It's a hard road.
Well, but we're already seeing multi-gene testing in cancer
and stratifying and diagnosing to help better serve cancer patients.
So I think there's still a lot to be done there and I think you know that
pan-cancer study that
came out of Mathias Uhlén's team, which we've talked about on the podcast
before, is a great place where proteomics is making inroads.
So, yeah, fantastic.
Also,
to add onto that, as we said, one biomarker is not enough.
We have a lot of examples in papers
where you see the
additive value of having more than one biomarker
that are really great. Erik Michaëlsson, Mathias Uhlén,
and you know, there are plenty of papers and so there is a divide.
And I think that if only the community start realizing that
having more than one biomarker will increase the value of their work
Yeah yeah.
And it really just depends on
who you're talking to in terms of what they think the next big thing is.
Right? You asked for my opinion, I gave you my opinion.
You know, someone else could say, "Hey, I just want to measure
ten, you know, million samples and then we're going to get much
richer insights into the next best drug targets.
And then that's going to create more efficient pipelines and a better,
you know, drug development universe in the next 50 years."
And yes, I think that'll happen, too.
But but yeah, there's just on all ends of the drug development spectrum,
these innovations, you know, that Olink and others have made
I think are really, really going to be transformative.
And they already are.
They already are. But it's so early.
I'm sorry, not to go on a tangent, but it really is early.
Yeah.
You know, and part of with whole genome sequencing,
the cost dropping has really enabled things.
And that's an important point. To be honest,
it really is.
You know, if we're just going to be frank and honest about,
you know, the opportunities to help as many people as possible,
if a tool is
prohibitively expensive, it's never going to have broad adoption.
And when I joined Olink
things cost a certain amount of money and now things cost less.
There's certainly -
We get more from it. Yeah, yeah, yeah.
Yeah, exactly. Yeah.
There's more data coming out for a lower all over cost.
Right.
And that's what the market has expected.
That's what people are demanding and again
that's very hard. Innovation, it takes a lot of innovation.
But, you know, that's I believe I'm excited to be here
because I know that the mission is the democratization of proteomics
to just get it out there, get it in the hands of the best and
brightest analysts out there. Right.
All of the great big data
folks who have developed such great tools in that genetic space.
And I'll also say, you know, when you were talking to Chris Whelan
well before this whole UK-PPP project
came to fruition, there was
no guarantee that Olink was going to be the chosen technology.
It's such an honor that the tools
and the priorities that we
thought were important - specificity, all that -
that those were also important and continue
to be very important to pharma and and then I'm going to also
just point out that we're now at just, as of this year, at about 5400 proteins
and a really increased
streamlined workflow, increased throughput capability,
which is very exciting to see, too. Any
last comments? Yeah.
Please go ahead, Evan.
No, no, I will.
And I hope this I hope I can say this because, you know, I'm an Olink employee
and this is a Proteomics in Proximity podcast, right?
So I do think that there's going to be
multiple tools eventually that are going to answer these questions, right?
I mean, I'm not so myopic as to think that Olink is the only tool out there,
I think we
have some really compelling attributes
for the large scale projects and for these large clinical analyzes.
But I get excited about continued innovation across,
you know, the earlier side of the research spectrum where there could be tools
that can rapidly tell you about all these different proteoforms
and phosphorylation states.
And yeah, it's a community, right, that's coming together.
And I think that,
there's just so much has happened in the last
decade that I've really been focused in the space
and it's going to continue to evolve. And
I'm grateful that we've gotten
13 companies together to do something really big.
We continue to be integrally
involved in the strategy of drug development
from a large number of the world's best companies.
And I just think that it's all leading to a more efficient process.
I mean, I have X number of years on this planet.
I want my time to be spent making a difference
for my kids and their kids.
And I truly believe that this kind of work is going to enable that.
So thank you for having me on.
Yeah, it was great. Sarantis,
any last words from you?
I mean, it was great. It was great to hear
your perspective and I agree with you.
I think that proteomics is the major research
from now on. And you're going to see
a lot of papers. And it's only the beginning.
And we're looking forward to the upcoming projects. Fantastic.
Well that's it for us today.
Again, thank you, Evan, for joining us.
Thank you very much. Thank you.
I think there are a couple of authors that we may not have said clearly,
and that was Faiez Zannad who was integral in this.
And Milton Packer, I don't think we mentioned
Milton, who both were integral in really understanding and repurposing,
identifying, repurposing opportunities and their
empagliflozin [Jardiance].
And I think there's an "a" in there.
Empagliflozin.
Yeah, so I just wanted to click on this and we'll put those into the show notes
as well.
Thanks as always to my co-host, Sarantis.
Thank you, of course. If you enjoyed listening
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And remember, you can reach out to us at Proteomics in Proximity
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who should we interview?
We would be grateful for the suggestions and the feedback.
And with that, we'll close.
Thanks, everyone. Thank you.
Thank you for listening to the Proteomics in Proximity podcast
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