21 - Hit-to-Lead and Lead Optimization (S2E6)

All right, ready to dive deep today. Always ready.
Into the world of drug discovery. Sounds good.
We're tackling hit to lead and lead optimization.
I know it sounds a bit, well, technical, but
believe me, it's fascinating stuff. Yeah, it
really is. Kind of like, you know, taking this
rough gemstone. Right. And carefully cutting
it, polishing it until you have this dazzling,
valuable jewel. I like that analogy. We're talking
about taking a molecule. That shows a glimmer
of potential against disease. That's our hit.
Exactly. Our starting point. And transforming
it into a powerful contender. That's the lead.
Our lead. You've given me a ton of info on this.
Oh, yeah. Our mission is to sift through it all
and extract the most important insights and help
you understand what really matters. Sounds like
a plan. So it's not just about finding a molecule
that works against a disease, right? Right. It's
got to function as a medicine inside the human
body. Got it. What's this balancing act all about?
What are researchers trying to optimize when
refining these molecules? So they're really focusing
on three main things. Potency, safety, and what
we call drug -like properties. Potency, safety,
drug -like properties. Let's break those down
one by one, starting with potency. Potency is
all about how well the molecule interacts with
its target. Think of it as the drug's strength.
OK, the drug's strength. How effectively it can
do its job. Makes sense. And safety. Safety,
of course, is about minimizing any potential
side effects. We don't want that cure to be worse
than the disease. Yeah, definitely not. So that's
a major consideration throughout the whole process.
Right. And then there's Drug -like properties.
That one's a little less obvious. What exactly
does that mean? So, drug -like properties are
all about making sure the molecule can actually
be absorbed by the body. distributed to the right
places, metabolized effectively, and then eventually
eliminated safely. So it's like making sure the
drug can navigate the body and get to where it
needs to go. It's like making sure the drug can
navigate the intricate highway system of the
human body and get to its destination without
causing any traffic jams or accidents along the
way. I love that analogy. So it's not just about
hitting the target. It's about making sure the
drug can travel there safely and efficiently.
Exactly. And to achieve this balance, researchers
use a variety of techniques. One of the most
fundamental concepts here is SAR, which stands
for Structure Activity Relationships. SAR. OK.
So how does tinkering with the structure of a
molecule actually change how it behaves? It's
a lot like, you know, you're an architect designing
a building. Even a small change to the blueprints,
the position of a wall, the size of a window
can have a huge impact on the final structure.
Right. It's the same with molecules, even tiny
adjustments. Adding or removing a single atom
can drastically alter how the molecule interacts
with its target, how it's absorbed, metabolized,
and even its potential. side effects. So chemists
are like molecular architects. Exactly. They're
making these tiny adjustments and observing how
they change the molecules behavior. That sounds
incredibly intricate. It really is. It's a delicate
dance and sometimes even a seemingly minor tweak
can make a huge difference. I can imagine. So
we're talking about a lot of trial and error
here. To some extent, yes, but it's not completely
random. There's a lot of strategy involved. One
study we looked at focused on metabolic stability
during lead optimization. They found that a lot
of promising candidates were being dropped because
they were metabolized too quickly in the body,
meaning they wouldn't stick around long enough
to be effective. Oh, I see. So a drug might be
potent in a test tube, but if it gets chewed
up by the body's enzymes before it can reach
its target, it's essentially useless. Exactly.
And what's interesting is that this screening
process often only focused on the original parent
compound. So they weren't looking at what happened
to the molecule after it was broken down by the
body. Right. They were missing a crucial part
of the picture because sometimes those metabolites,
the molecules it breaks down into, can actually
be pharmacologically active themselves. They
might even have better drug -like properties
than the original compound. So a molecule that
seemed like a dud because it was rapidly metabolized
could actually be a hidden gem. That's a fantastic
example of how important it is to consider the
entire journey of a drug within the body, not
just its initial form. Absolutely. And this complexity
is amplified when we consider the different approaches
to lead optimization. On one hand, you have predictive
modeling, which uses computer simulations to
try and predict which molecules will be most
effective. So it's like a virtual laboratory
trying to speed up the discovery process. Exactly.
It's a way to explore many different possibilities
without having to synthesize and test each one
in the real world. But then you also have empirical
optimization, which involves actually synthesizing
and testing those molecules in the lab. good
old fashioned trial and error. So it's like weighing
the pros and cons of a virtual scouting mission
versus actually venturing out into the field.
That's a great analogy. And the choice of approach
really depends on the specific target, the available
resources, and the desired timeline. So there's
no one -size -fits -all approach. Not really.
Sometimes it's a combination of both. You might
start with predictive modeling to narrow down
the possibilities and then use empirical optimization
to fine -tune the most promising candidates.
So you use the virtual world to guide your exploration
and then the real world to confirm and refine
your findings. Exactly. It's a powerful combination.
Speaking of real -world applications, you mentioned
COVID -19 antivirals earlier. I'd love to dive
into a specific case study where lead optimization
played a crucial role in developing a treatment,
or maybe even where it presented significant
challenges. Do you have any examples that come
to mind? Absolutely. The development of SARS
-CoV -2 antivirals is a perfect example. It was
a high -stakes race against time, and lead optimization
played a critical role in both the successes
and the setbacks. Okay, so let's dive into that.
What were some of the key hurdles researchers
faced when trying to optimize antiviral leads
for COVID -19? One of the biggest challenges,
as we touched on earlier, was balancing potency
against safety. You want a drug that can effectively
stop the virus from replicating, but you also
don't want it to cause harmful side effects.
And finding that sweet spot can be incredibly
difficult, especially when you're dealing with
a novel virus. Right, it's a delicate tightrope
walk. Yeah. Were there any other factors that
complicated the optimization process? Another
crucial factor was ensuring the drug could actually
reach its intended target. Remember, we're not
just dealing with a - molecule in a test tube
here. We're talking about a complex biological
system, the human body. And getting a drug to
the right place, in the right concentration,
and at the right time is no easy feat. So it's
not just about designing a molecule that can
bind to the virus. It's about understanding how
it will behave within the intricate environment
of the human body. Precisely. And to make things
even more challenging, researchers also had to
think about potential drug resistance. Viruses
are notorious for their ability to mutate and
evolve, and there was a real concern that the
virus could develop resistance to any new antiviral
drugs. So it's like playing a game of chess against
an opponent who can change the rules at any moment.
That's a great way to put it. Researchers had
to anticipate how the virus might evolve and
try to design drugs that would be effective even
against potential mutations. So they had to be
one step ahead of the virus, constantly adapting
their strategies. Exactly. It was a monumental
task, and it involved a deep understanding of
virology, medicinal chemistry, and even evolutionary
biology. This is all starting to paint a picture
of just how complex and multi -layered this process
is. It is, and I think it's a testament to human
ingenuity that researchers were able to develop
effective antiviral treatments for COVID -19
in such a short amount of time. It's a reminder
that even in the face of seemingly insurmountable
challenges, science can prevail. Absolutely.
And it all starts with those early stages of
hit -to -lead and lead optimization. Those initial
steps are crucial for laying the foundation for
a successful drug discovery journey. All right.
So we've explored this intricate dance of optimizing
a molecule's potency, safety, and drug -like
properties. We've talked about the challenges
of navigating the human body and outsmarting
a constantly evolving virus. But before we move
on, I'd love to dig a bit deeper into this idea
of using computer simulations to predict which
molecules might be most promising. Ah, yes, the
world of predictive modeling. It's a fascinating
field that's rapidly transforming drug discovery.
So how exactly does this virtual exploration
work? What kind of tools are researchers using
to predict the behavior of these molecules? They're
using a variety of computational models, each
with its own strengths and limitations. Some
models are incredibly detailed, focusing on simulating
the interactions between a drug and its target
at the atomic level. So it's like having a high
-powered microscope that can zoom in on the tiniest
details of that interaction. Exactly. They can
visualize how the drug molecule fits into its
binding site, how it interacts with specific
amino acids, and even how those interactions
might change the shape or function of the target.
That level of detail is incredible. But I imagine
there are also models that take a more zoomed
out perspective, looking at how the drug behaves
within the larger context of the body. Right.
There are models that focus on predicting a drug's
pharmacokinetic properties, things like its absorption,
distribution, metabolism, and excretion. They're
essentially trying to simulate how the drug will
travel through the body, where it will accumulate,
and how long it will remain active. So it's like
having a virtual map of the drug's journey through
the body. That's a great way to put it. And these
models are becoming increasingly sophisticated,
taking into account factors like blood flow,
organ function, and even individual genetic variations.
They can help researchers identify potential
roadblocks early on, like a drug that's poorly
absorbed or rapidly metabolized. So it's a way
to identify potential problems before they derail
the entire drug development process. Exactly.
And then there are models specifically designed
to assess a drug's potential toxicity. They can
flag potential red flags like interactions with
other drugs or off -target effects that could
lead to adverse reactions. So it's like having
a virtual safety net that can catch potential
problems before they harm a patient. Precisely.
And these safety assessments are crucial for
ensuring that only the most promising and safest
candidates move forward in the development process.
This all sounds incredibly promising, but I imagine
these computational models aren't perfect. There
must be limitations to what they can predict.
You're right. No model is perfect. They're based
on our current understanding of biology and chemistry,
which is constantly evolving. And they rely on
experimental data, which can be incomplete or
even contradictory at times. So it's important
to remember that these models are tools, not
crystal balls. They can provide valuable insights,
but they can't predict the future with 100 %
certainty. Exactly. And that's why it's so important
to use these models in conjunction with empirical
data, the results of actual experiments. The
two approaches complement each other, providing
a more complete and reliable picture. So it's
a constant back and forth between the virtual
world of simulations and the real world of laboratory
testing. Precisely. And as our understanding
of biology and chemistry advances, and as computational
power continues to increase, these models will
become even more accurate and powerful. This
all leads to another question that's been on
my mind. We've talked about these computer simulations
as a way to predict the behavior of existing
molecules, but could they also be used to design
entirely new drugs from scratch? That's a great
question, and the answer is a resounding yes.
In fact, that's one of the most exciting frontiers
in drug discovery, the use of artificial intelligence,
AI, to design completely novel drug candidates.
AI designing drugs, it sounds like something
out of a science fiction movie. It does, doesn't
it? But it's becoming a reality. AI algorithms
can analyze vast data sets of chemical structures
and biological activity, identify patterns, and
even generate new molecules that have the desired
properties. So it's like having a virtual chemist
who can sift through billions of possibilities
and come up with new chemical structures that
we humans might never have thought of. Exactly.
And these AI -designed molecules can then be
synthesized and tested in the lab just like any
other drug candidate. This is blowing my mind.
So AI is not just speeding up the drug discovery
process. It's actually expanding the possibilities
of what's possible. Precisely. And this is just
the beginning. As AI technology continues to
advance, I think we'll see even more groundbreaking
applications in drug discovery. This is all incredibly
exciting. It feels like we're on the cusp of
a new era in drug development. But before we
get carried away with the possibilities of AI,
I'd like to circle back to a topic we touched
on earlier, the challenge of drug resistance.
Ah, yes, the constant battle between pathogens
and the drugs we develop to fight them. It's
a reminder that even as we develop new and innovative
therapies, the pathogens are constantly evolving,
always one step ahead. It's a constant arms race
and it's one that we can't afford to lose. So
how can we stay ahead of these evolving pathogens?
Is there a role for AI in combating drug resistance?
Absolutely. In fact, AI is proving to be a valuable
tool in this fight. AI algorithms can analyze
the genetic sequences of viruses and bacteria,
identify mutations that are associated with drug
resistance, and even predict how these pathogens
might evolve in the future. It's like having
a virtual evolutionary biologists who can predict
the next move of the enemy. That's a great analogy.
And this information can then be used to design
drugs that are less likely to be affected by
resistance. For example, AI can be used to identify
multiple targets within a pathogen, increasing
the chances of developing a drug that will remain
effective even if the pathogen mutates. So it's
like a multi -pronged attack, hitting the pathogen
from multiple angles, making it harder for it
to develop resistance. Exactly. And this approach
is becoming increasingly important as drug resistance
becomes a growing threat to global health. This
has been a truly fascinating discussion. We've
covered so much ground, from the intricate dance
of optimizing a molecule to the mind -boggling
possibilities of AI -driven drug design. It's
clear that drug discovery is a complex and ever
-evolving field. It is, but it's also incredibly
exciting. We're constantly learning new things,
developing new tools, and pushing the boundaries
of what's possible. And it's all driven by a
common goal to improve human health and alleviate
suffering. Absolutely. That's the ultimate motivation
for all the hard work and dedication that goes
into drug discovery. I think this is a great
place to pause for now. We've explored the fundamentals
of hit -to -lead and lead optimization. delved
into the challenges of drug resistance, and even
glimpsed the future of AI -driven drug design.
Sounds good. Welcome back to our deep dive into
drug discovery. Yeah, we've covered a lot of
ground already. We have, exploring how to turn
a promising hit into a potent lead. even took
a peek into AI -driven drug design. It's really
amazing stuff. It is. But now, let's get out
of the theoretical and into the real world. Sounds
good. You know, real world examples. You mentioned
SARS -CoV -2 antivirals before. Yes. Can you
walk me through a success story or even a case
where optimization hit a snag? Of course. The
race to develop COVID -19 treatments, it was
a real roller coaster. Yeah. Full of both triumphs
and setbacks. One example that shows how complex
lead optimization can be is molnuperevir. Molnuperevir,
I think I remember hearing about that. Yeah.
Wasn't that one of the first oral antivirals?
Yes, one of the first oral antiviral pills for
COVID -19. I see. It was initially seen as a
potential game changer. Wow. A small molecule
that stops the virus from replicating. Okay.
And early studies, they showed some promising
results. So it had real potential. It did. But
there were some hurdles. Of course. One of the
biggest challenges was its safety profile. Oh.
Early studies suggested it might be mutagenic,
meaning... it could potentially cause changes
to DNA. That's a huge concern, especially for
widespread use. Absolutely. Researchers had to
carefully weigh the benefits against the risks.
Right. They conducted tons of studies to assess
its mutagenic potential and to figure out the
right dosage and treatment length to minimize
risk. So a real balancing act, trying to use
the drug's power. while making sure it's safe.
Exactly. And this shows just how crucial lead
optimization is. It's not just about finding
a molecule that works. It's about refining it
to make it as safe and effective as possible.
I see. So in this case, were they able to mitigate
those safety concerns? To an extent, yes. Further
studies showed the mutagenic risk was lower than
initially thought, especially with a short treatment
duration and the recommended dosage. Regulatory
agencies ended up authorizing it for emergency
use. but with precautions. So it became a useful
tool, especially early in the pandemic when we
had fewer options. Yes. But it also reminds us
that drug development is rarely straightforward.
There are always trade -offs. Yeah. And even
promising drugs can face unexpected challenges
during optimization. So even when something seems
like a sure thing, it's not over until it's over.
Right. It shows how important rigorous testing
and careful evaluation are throughout the entire
process. That makes sense. But not all drugs
are successful, right? Right. Not every promising
lead makes it to the finish line. What are some
reasons why drug candidates might fail during
optimization? Well, one common reason, as we
saw with molnupiravir, is toxicity. Right. Sometimes
a drug that works well against its target might
also have bad side effects. It's that balance
between effectiveness and safety again. It's
like trying to tame a wild beast. You want its
power, but you don't want it to turn on you.
I like that. That's a good analogy. Another reason
is poor pharmacokinetic properties. Remember,
a drug can't just be potent in the lab. It needs
to work inside the human body. be absorbed, reach
the right tissues, be metabolized properly, and
then be eliminated without causing harm. So even
if it hits the target, it might get lost, break
down too quickly, or build up in the body. Exactly.
And these pharmacokinetic hurdles can be super
hard to overcome. Sometimes, no matter how much
you tweak the molecule, it just won't behave.
So we're up against the complexity of the human
body. We are. It's hard to predict how a drug
will act with all its systems. Precisely. And
then there's drug resistance, which we talked
about earlier. Some are diseases, especially
infectious diseases. Those pathogens, they can
evolve. They can outsmart even the most potent
drugs. Like a constant game of cat and mouse.
They adapt and find ways to survive. It is. And
that's where understanding evolutionary biology
is key. Researchers need to think ahead. predict
how a pathogen might mutate and become resistant.
They need to design drugs that can stay one step
ahead. So it's not just about hitting a target,
it's about predicting the target's next move.
Yeah, like playing chess against a grandmaster.
It sounds like a real detective story. Gathering
clues, analyzing evidence. It is, and it shows
why collaboration is so important in drug discovery.
It's not a one -person job. It takes a whole
team, each with their own expertise. Chemists
making the molecules. Yes. Biologists studying
how they interact. Pharmacologists seeing how
they behave in the body. And even computer modelers.
Exactly, all working together to solve this complex
puzzle. This brings up another question. We've
talked about potency, safety, drug -like properties.
How do researchers actually decide which is most
important? Is there a set order, or does it depend
on the drug and the disease? That's a great question,
and there's no simple answer. It really depends
on the situation. OK. For example, a life -threatening
disease with no current treatments, you might
be willing to accept higher risks for a more
potent drug. So the potential benefit outweighs
the risks. Right. But for a chronic condition
where there are already treatments, safety might
come first. You wouldn't want to introduce a
new drug with serious side effects if there are
already safer options. That makes sense. It's
nuanced. And it involves ethics, too. Oh, of
course. Researchers have to think about the benefits
for patients versus the risks, and their decisions
must align with ethical guidelines. Drug development
isn't just science. It's about making responsible
choices for patients. It absolutely is. The human
element is always there, at the heart of it all.
I'd like to understand more about how they actually
optimize a lead molecule. What do they use to
make those improvements? Well, it uses both experiments
and computer modeling. One basic tool is structure
activity relationship studies, SAR, which we
mentioned before. Right, SAR. It's all about
how changes to a molecule's structure affect
its activity. Like those molecular architects.
Exactly. Chemists can make small modifications
to the molecule, adding or removing atoms. changing
the arrangement of functional groups, even adding
entirely new parts. They're basically testing
different versions, trying to find the best one.
Right. And they use a lot of different techniques
for these SAR studies. One approach is combinatorial
chemistry, where you make a huge library of molecules,
all with slight variations. Then you screen them
all for the desired activity. Testing hundreds
or even thousands of molecules at once. Exactly.
And that can be efficient, finding promising
candidates quickly. But it can also be pricey
and take a lot of time. Speed versus cost. Right.
Another approach is structure -based drug design.
This uses the 3D structure of the target molecule
to design new drugs. So they have a blueprint
of the enemy and they design a weapon to target
its weak spots. Exactly. Researchers can see
how the molecule interacts with its target at
the atomic level. Then they make changes to improve
things like binding affinity and selectivity.
So it's much more precise. compared to combinatorial
chemistry. It is, and it can be very powerful,
especially when we know the target structure
and how it works. It sounds like lead optimization
is a back and forth between experiments and computer
models. It is, and it shows human ingenuity and
perseverance. Despite the challenges, we've come
a long way in developing treatments, and I'm
excited to see what the future holds. Me too.
It's a field of possibilities, driven by wanting
to improve human health. That's what makes it
so compelling. This has been really enlightening,
this journey through lead optimization. We've
seen the challenges, the wins, the science, and
the ingenuity. And we've seen how, even though
it's complex and demanding, it's all about helping
people, improving lives. It is. I think this
is a good place to pause. OK. In the last part
of our deep dive, we'll talk about some broader
things in drug discovery. The ethical. societal
and economic factors at play. I look forward
to it. You too. I'm sure our listener does as
well. Welcome back to The Deep Dive. It's been
quite a journey so far, right? It has, from those
early hits to the whole process of lead optimization.
We've uncovered so much. the science, the challenges,
the victories, you know, everything that goes
into developing new medicines. It's been fascinating.
I think we both have a much better grasp now
of how complex it is to turn a promising molecule
into a safe and effective therapy. We've talked
about a lot of the technical stuff, the molecular
changes, the simulations. But as we wrap up,
I'd like to step back a bit. Look at the bigger
picture. Because it's not just about molecules
in a lab, right? Absolutely not. Drug discovery
is really about people. It's about easing their
suffering, improving their lives, helping them
live longer. And that means thinking about how
our decisions affect people through the whole
process. I like that. So shifting from the lab
to the real world, what are some of the big things
that shape drug discovery? Well, money plays
a huge role. Drug development is incredibly expensive
and risky. Yeah, I can imagine. It often takes
years, sometimes decades, to get a new drug out
there. Wow. So it's not just about scientific
discoveries. It's about finances, too. The research,
the clinical trials, getting approval, marketing,
it can cost billions. Billions? And there's no
guarantee it'll work out. Oh, wow. Many drugs
fail along the way, and companies lose huge amounts
of money. So it's a big gamble. It is. So companies
have to balance the need for innovation with
the financial reality. I bet that's tough. trying
to make life -saving treatments accessible and
keep the company afloat. It is. It's a tough
situation, and there's no easy solution. But
we need to talk about it. We have to find ways
to encourage innovation while also making sure
essential medicines are affordable for everyone.
It's a reminder that drug discovery isn't isolated.
It's not. It's connected to economics, markets,
even politics. You're exactly right. And as we
think about these bigger issues, we can't forget
about regulatory agencies. They're crucial. They
make sure new drugs are safe and effective before
anyone can use them. So they're like gatekeepers,
protecting public health, making sure only the
good drugs get through. Exactly. They set high
standards for trials, study the results, and
decide if a drug's benefits are worth the risks.
It's a big responsibility. I'm glad they do it.
But that process must slow things down. Right.
And make it more expensive. It can. And that
can be frustrating for patients waiting for new
treatments. But it's a balance we need. We have
to make sure new drugs are thoroughly checked
before they're available. It's not just about
being fast. It's about being careful and putting
patient safety first. Absolutely. And in all
this, we can't forget the patients themselves.
Of course not. They're the ones who matter most.
They're the ones who benefit or are harmed by
the drugs. We need to hear what they have to
say. Patients are at the heart of it all. So
it's important to involve them. Right. They have
valuable insights about their experiences, their
needs, what they think about the risks and benefits.
It's a two -way street. Researchers need to listen
to patients, and patients need to speak up and
be part of the decisions. Exactly. And as we
finish up, I think it's amazing how far we've
come in drug discovery. It is? We've created
life -saving treatments for so many diseases,
help people live longer, healthier lives. It
shows what we can do with ingenuity, persistence,
and collaboration. It does. But we can't stop
here. Right. There's always more to do. There
are still diseases without effective treatments,
and new challenges are always appearing. So the
search continues. We'll keep looking for new
and better medicines to ease suffering and improve
health. And I think, as long as we Keep asking
questions, keep learning, and keep patience at
the forefront. We'll keep making progress. That's
a great message to end on. Thank you so much
for sharing your knowledge with us. It's been
an incredible journey. It's been my pleasure.
And to our listener, thanks for joining us on
this deep dive. I hope you've learned a lot about
this important field. Keep exploring, keep asking
questions, and keep believing in the power of
science. Until next time on the deep dive.