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Proteomics in Proximity discusses 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. Co-hosted by Olink's Dale Yuzuki, Cindy Lawley and Sarantis Chlamydas.
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
and they've found us through different
social media avenues.
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.
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.
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Thank you for listening to the Proteomics
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