All right, so today we're diving into drug discovery.
And more specifically, how scientists actually
validate a target. Yeah, it's like, you know,
before you build a house, you got to make sure
you have like a solid foundation, right? Exactly.
You don't want to spend years developing a drug
that's aimed at the wrong thing. And you've brought
in some really interesting research on this.
Oh, yeah. From like classic lab techniques to
AI. To AI, exactly. And all of it burrows down
to this one key question. How do scientists confirm
that a specific molecule is actually involved
in a disease? Right. Because it's not enough
to just kind of have a hunch. No, not at all.
You need evidence. Hard evidence. Hard evidence
to avoid wasting time and resources. So let's
start with the traditional methods. Okay, I'm
seeing kind of like two main approaches here
genetic and chemical Yeah, those are the two
big ones So genetic methods so genetic methods
involve manipulating genes to see what happens
like actually like tweaking the blueprint of
a cell Yeah, think of it like you're an engineer
and you're tinkering with the design So one of
the classic techniques is the knockout study
the knockout study where scientists essentially
silence a gene. So they just like turn it off.
They turn it off and then they see what happens.
And from that, they could tell that that gene
was doing. Exactly. It's like removing a cog
from a machine. You take out a specific gene
and you observe what stops working. OK, so it
helps you pinpoint what goes wrong in a disease
when that gene isn't functioning properly. Right.
Our sources talk about PEP2 knockout mouse models.
PEP2. Yeah, they've been really helpful in understanding
how the PEP2 transporter affects drug absorption.
Interesting, so like how well a drug gets into
the body? Precisely. OK. But I imagine there
are limitations to these genetic techniques.
Oh, absolutely. Even with powerful tools like
CRISPR, there's always a chance of what we call
off -target effects. Off -target. Off -target
effects. It's like you're trying to edit one
specific gene. OK. But you accidentally end up
editing another gene. Oh, so you get like misleading
results. Exactly. So you always have to be careful
with the interpretation. It's like needing a
double check system just to make sure you didn't
accidentally delete something important. Exactly.
Now let's move on to chemical meth. Chemical
methods. These use something called tool compounds,
which are basically small molecules designed
to interact with the target of interest. So each
tool compound is designed for a specific target.
Yeah, like a key fitting into a lock. Key and
a lock, OK. So by observing the effects of blocking
or activating the target with these keys, researchers
can kind of tease out its role in the disease.
It's like seeing what happens when you throw
a wrench into the machinery. Precisely. And,
you know, tool compounds are also used in lead
optimization. Lead optimization. So that's where
researchers are actually fine -tuning promising
drug candidates. Right. They're assessing things
like metabolic stability. Metabolic stability.
How quickly a compound gets broken down in the
body. OK. So does it hang around long enough
to do its job or? Exactly. Or does it disappear?
Right. If it disappears too quickly, there's
no point in developing it further. Makes sense.
So these tool compounds, do they always behave
perfectly? Sadly, no. No. Sometimes it can be
a little bit tricky. Tricky how so? Well, sometimes
they interact with other molecules besides the
intended target. OK. And that can lead to false
positives. So it's like the key fitting into
the wrong lock. Exactly. You might think you've
found the right target, but you're actually looking
at something else entirely. So how do researchers
avoid getting tricked by these false positives?
It all comes back to rigorous experimentation.
You know, using multiple methods to really confirm
a target's role. OK, so it's a gathering evidence
from different sources. Exactly. You want to
build a really strong case. Yeah, you don't want
to jump to conclusions. No, definitely not. And
that's actually a perfect segue into the world
of AI -driven target validation. Ooh, now we're
talking. I've heard so much about how AI is revolutionizing
drug discovery. It's pretty amazing. Imagine
having this super -powered research assistant
that can analyze massive data sets. Like a supercomputer.
Way faster than any human could. And what kind
of data are we talking about here? Oh, everything,
you know, genomic information, protein structures,
scientific literature, all of it. Wow. And so
AI can sift through all of that and find potential
targets. Exactly. And it can even predict how
drugs might interact with those targets. So could
it actually help us avoid those accidental delete
moments we talked about earlier? In a way, yes.
AI can flag potential issues early on. That's
incredible. It must save researchers so much
time and effort. Oh, absolutely. Our sources
mention AtomWise. AtomWise. Yeah, and their AtomNet
platform. AtomNet. It's a really great example
of how AI is changing the game. So how does it
work? Well, AtomNet uses deep learning to sift
through enormous chemical libraries. OK. And
it looks for molecules that could bind to species.
specific targets. So it's like a matchmaking
service for drugs and targets. Exactly. And they've
even used it to identify potential drug candidates
for neglected tropical diseases. Oh, wow. So
AI is giving these diseases a fighting chance.
It really is. It's a really exciting development.
But even with all of his promise, AI and drug
discovery... is still in its early stages, right?
It's right. So we still need those traditional
methods for validation. Absolutely. It's about
using the best of both worlds. I like the best
of both worlds. AI can provide really powerful
insights and accelerate the process. but we still
need that rigorous experimental validation. Exactly,
to confirm those findings and make sure we're
on the right track. This is all so fascinating.
It really highlights the complexity and the ingenuity
involved in drug development. Absolutely. And
we've only just scratched the surface. Oh, I
bet there's so much more to uncover. Oh, yeah.
Maybe we should pause here, let our listeners
absorb all this. That's a good idea. Take a break,
and we'll be right back. Okay, so we're back
and we were just talking about how AI is really
like shaking things up in the world of target
validation Yeah, it's really exciting to see
how fast things are moving. Yeah, it is. But
um, you know, even AI can't solve every problem,
right? That's right. It can help us find those
targets, but Doesn't mean drug development is
suddenly easy Right, like there are still challenges.
Big challenges, and one of the biggest is what
happens during the metabolism process. Oh yeah,
you're talking about those reactive metabolites.
Exactly, even when a drug binds perfectly to
its target, things can still go wrong. And these
metabolites, they can cause some serious problems,
right? Oh yeah, from drug reactions to liver
damage, even DNA damage. That's scary. So it's
not just about finding the right target. No,
it's also about what happens to the drug once
it's in the body. And like how it breaks down.
Exactly. Researchers need to figure out if a
drug is going to be metabolized into something
harmful. That sounds like a super complicated
puzzle. It is. It's like trying to predict all
the possible moves in a chess game. So how do
scientists even begin to predict and mitigate
these risks? Well, there are a few different
strategies. One approach is to design drugs that
are less likely to form those reactive metabolites.
So it's like designing drugs that are more body
-friendly. Exactly. And AI can actually play
a role here, too. Really? Yeah. Some AI algorithms
can actually predict the metabolic fate of a
drug candidate. Wow. So it's like having a sneak
peek into the future. That's a great way to put
it. It helps researchers identify potential problems
early on. But even with AI... I'm guessing scientists
still need to do a lot of real world testing.
Oh, absolutely. Prediction is just the first
step. They use a combination of in vitro and
in vivo models. OK, what does that mean? So in
vitro means they're testing the drug outside
of a living organism, like in a test tube. OK.
And in vivo means they're testing it in a living
organism, like an animal model. Got it. So they're
basically testing it at different levels of complexity.
Exactly. It's like a dress rehearsal before the
main performance. Yeah, you want to make sure
everything works properly before you test it
on humans. Exactly. And speaking of complexity,
we haven't even talked about protein interactions.
Oh, yeah. Those are super important, too, right?
Absolutely. Remember, proteins rarely act alone.
They're constantly interacting with each other.
They're like little social butterflies. Yeah,
exactly. They form these intricate networks that
influence cellular behavior. So even if a drug
hits its intent, target perfectly, it could still
trigger unintended consequences elsewhere in
the network. Exactly. It's like throwing a pebble
into a pond. Oh yeah, the ripple spread out.
Exactly. Affecting the whole ecosystem. So understanding
those protein interactions is really crucial.
So how do scientists go about mapping out these
intricate protein networks? Well, they use a
combination of experimental techniques and computational
approaches. One common method is called yeast
-to -hybrid screening. Yeast -to -hybrid screening.
I remember reading about that. Yeah, it helps
scientists figure out which proteins interact
with each other. So it's like solving a cellular
puzzle. Exactly. And there are other techniques,
too, like co -immuno precipitation and mass spectrometry.
They all sound so complicated. They can be, but
they all help identify the different protein
partners that interact with a target. But I bet
all of that data can be really overwhelming.
Oh, it definitely is. That's where computational
biology comes in. OK, so how does that help?
Well, scientists use sophisticated algorithms
to analyze these huge data sets and create detailed
maps of the molecular networks. So it's like
having a Google Maps for the cellular world.
Exactly. And those maps can show how a drug might
affect not just its target, but also its target's
friends and their friends and so on. It's like
a chain reaction. Exactly. And it helps researchers
understand the bigger picture and anticipate
potential ripple effects. It's pretty amazing
to realize that cells are such complex ecosystems.
It is. And it highlights why drug development
is such a challenging endeavor. But as we learn
more about these cellular processes, we get closer
to developing safer and more effective therapies.
Absolutely. So where do we go from here? What
does the future hold for target validation? Yeah.
What's next? I think we're going to see even
more integration of AI and computational biology
into every stage of drug development. So those
technologies are going to become even more powerful.
Exactly. And that means scientists can predict
and prioritize targets with even greater accuracy.
So it's all about working smarter, not harder.
Precisely. And we're also seeing the rise of
new approaches like phenotypic screening. Phenotypic
screening that rings a bell. Yeah. It's a different
way of looking at target validation. So remind
me, how does that work again? So instead of starting
with a specific target in mind, you start by
observing the effects of a drug on cells or organisms.
So you're kind of working backwards. Exactly.
You see a desired effect and then you try to
figure out how it happened. It's like being a
detective searching for clues. That's a great
analogy. And you know what? Phenotypic screening
has actually led to the discovery of drugs that
are on the market today. Really? So it's a proven
approach. It is. It's been really valuable, especially
when those traditional target -based approaches
have hit a wall. It's really encouraging to know
that scientists are always exploring new ways
to solve these complex puzzles. It is. And I
think we're just scratching the surface of what's
possible. Yeah. As our understanding of cellular
processes grows, so too will our ability to develop.
even more effective in targeted therapies? Absolutely.
All right, so we're back. And we've been on this
deep dive into target validation. And it's definitely
been ajourner, right? It has. It's clear that
this is like a really critical step in drug discovery.
Yeah, you could say it's the foundation of the
whole process. Yeah, like you were saying before,
if you're building a skyscraper, you got to make
sure that foundation is solid. Exactly. You don't
want to cut corners when it comes to target validation.
And we've seen how researchers are using all
these different tools to do it. from traditional
lab techniques to AI to, you know, a deep understanding
of how cells work. It's really amazing to see
how it all comes together. It's a really cool
blend of science and technology. But I think
throughout our conversation there's been this
one theme that keeps coming up. Oh yeah, what's
that? The importance of context. Context, okay.
It's not enough to just find a target and hit
it with a drug. Right, it's like you need to
see the bigger picture. Exactly, like what's
the target's role in the cell? How does it interact
with other molecules? And even how might different
patients respond differently? Right, because
everyone's different. Yeah, exactly. So it's
about appreciating the complexity of biology
and understanding that there's rarely a simple
solution. Right, like those protein networks
we talked about. Oh, yeah. Those are a great
example. They're so intricate and interconnected.
Yeah, disrupting those connections can have a
domino effect. Even if a drug is doing what it's
supposed to be doing. Exactly. And that's why
researchers are really starting to focus on systems
biology. Systems biology. Yeah, it's this holistic
approach that considers the whole system. Okay,
so instead of looking at just one protein, you're
looking at the entire network. Exactly. It's
like the difference between looking at a single
tree and then stepping back and seeing the whole
forest. That's a great analogy. So systems biology
helps us see how a drug might affect not just
its target, but also like everything around it.
Right. It allows us to anticipate potential side
effects and develop therapies that are more targeted.
And speaking of targeted therapies, we also talked
about personalized medicine. Oh, yeah, that's
super exciting. It's like the idea that treatments
can be tailored to each individual patient. Exactly.
Based on their genetic makeup. So we can predict
how they might respond to a drug or even identify
potential risks. It's like moving away from this
one size fits all approach to medicine. Yeah,
and creating treatments that are truly customized.
Right. It's about finding the right drug for
the right patient. This has been a really eye
-opening conversation. We've covered so much
ground. We have. It's been great. But I think
as we wrap up our deep dive into target validation,
there's one question I keep coming back to. OK,
what's that? What does all of this mean for,
like, the average person? Right, why should they
care about this really complex science? Exactly.
Well, the answer is actually pretty simple. Target
validation impacts all of our lives. How so?
Think about all the medications we take every
day. For everything. For everything. Headaches,
heart disease, infections, you name it. So those
scientists working in labs, they're the ones
who make those medications possible. Exactly.
They're the unsung heroes of our health. It's
a powerful reminder that science isn't just about
knowledge. No, it's about making a difference
in the world. and target validation is a huge
part of that. It really is. It's about developing
drugs that are more effective, safer, and more
personalized. It's about taking those scientific
discoveries and turning them into real benefits
for people. Absolutely. This has been an absolutely
fascinating deep dive. I've learned so much.
Me too. It's been a pleasure. And for our listeners
who want to learn even more, We've got links
to some of the research articles we've discussed
in the show notes. Yeah, definitely check those
out if you're interested in learning more about
this really important topic. And with that, we'll
wrap up this episode of the Deep Dive. Thanks
for joining us. And until next time, stay curious.