27 - Early Safety Screening (ADME-Tox) (S2E12)

Have you ever heard of a drug that shows incredible
promise, just sails through those early trials,
only to get yanked at the last minute? Because
it turns out it causes some kind of serious heart
problem or something. It would be like investing
years of research and millions of dollars. just
to hit a brick wall at the very end. Right, it's
a huge problem. That's the kind of nightmare
scenario that early safety screening is designed
to prevent. Exactly, and it's not just about
dodging the obvious poisons. Right. We really
need to get a good understanding of all the subtle
ways that a drug can interact with the body.
OK. Because sometimes those interactions, they
can have some really unexpected consequences.
Yeah, even dangerous ones, right? Definitely.
So today, we're going deep on those crucial early
tests. Yeah. The ones that can help weed out
those problematic drug candidates before they
go too far. We'll be looking at how those tests
work, why they matter so much, and what makes
drug metabolism such a fascinating and sometimes
tricky area. He really is fascinating. You've
given us a really interesting mix of materials
for this deep dive. Well, I tried to find some
good stuff. Yeah, we have excerpts from textbooks
on medicinal chemistry and drug development,
some really intriguing research papers, and even
sections on the role of AI in all of this. Well,
AI is becoming a bigger and bigger part of the
field. That's right. So it's important to understand
how it's being used. Absolutely. So our goal
today is to get a handle on this concept of fail
fast, fail cheap in drug development. Yeah, that's
a key principle. It seems like it's all about
identifying and eliminating those risky candidates
early on before they cause problems down the
line. Exactly. It's all about making smart decisions
from the get -go. It's kind of like how software
developers will use rapid prototyping to find
flaws before they go and build out this whole
program. Yeah, that's a good analogy. It's like
you catch it early before you put in all that
time and effort. Exactly. You got to find those
bugs early. OK, so one of the key tools in this
early screening process is the H -E -R -G inhibition
assay. Right. That's one of the big ones. OK,
break that down for me. Sure. What exactly is
the H -E -R -G channel and why is blocking it
such a big deal? Well, the H -E -R -G channel,
it plays a really critical role in the electrical
signals that control your heartbeat. Okay. And
if a drug blocks this channel, it can really
disrupt that delicate balance. Yeah. And that
can lead to an irregular heartbeat. Oh, wow.
And specifically, a very dangerous rhythm problem
called torsades de pointes. I remember reading
about the case of... Turfenidine or seldine being
pulled from the market because it was linked
to that exact heart problem. Yeah, that was a
big one. That must have been a major setback.
It was, and turfenidine is a classic example
of what we call a prodrug. Okay. It's actually
inactive in its original form, and it has to
be metabolized by your body into active compounds.
Okay. But unfortunately, one of those metabolites
turned out to be a very potent HERG inhibitor.
So the drug itself didn't actually cause the
problem, but its byproduct did. Right, exactly.
That's a really sneaky twist. It really is, and
it highlights why understanding a drug's metabolic
pathway is absolutely crucial. We need to know
how the drug itself behaves, but also how it
breaks down in the body and what those resulting
compounds might do. That's where fexofenadine
or Allegra came in, right? Yeah, that's a good
example. I was like scientists realizing that
they could just use the active metabolite of
drifanidine. Right. Which provides the antihistamine
benefit without that HERG blocking effect. Exactly.
So they basically created a safer version of
the drug. That's a pretty clever solution. So
it seems like identifying these potential problems
early on, like with the HERG test, can actually
lead to the development of safer alternatives.
Absolutely. It's all about finding ways to mitigate
risk. Another drug that we have some information
on is Celecoxib. a COX2 inhibitor used for pain
and inflammation. Right. And the research you
provided really dives into its metabolism and
particularly the role of a specific liver enzyme
called CYP2C9. It's a really important enzyme.
Okay, why is that enzyme so important in this
case? Well, it gets into the concept of polymorphism.
Okay. Which basically means that there are variations
in how each of us metabolizes drugs. Okay. Based
on our genetic makeup. Gotcha. Some people might
break down a drug very quickly, others more slowly.
Interesting. And those differences can really
have a big impact on a drug's effectiveness and
its safety. So understanding how CYP2C9 interacts
with coxib. help predict potential drug -drug
interactions. Yeah, that's a big part of it.
And even identify individuals who might be more
sensitive to side effects. Exactly. It's all
about moving towards personalized medicine. Interesting.
Tailoring treatments to individuals for maximum
benefit and minimal risk. It makes sense, but
even with our best efforts. There are always
going to be cases where toxicity is only discovered
after a drug has been on the market. That's true.
Like troglitazone for liver toxicity and rofecoxib
for cardiovascular risks. Those come to mind.
Yeah, those are unfortunate examples. It's a
sobering reminder that we can never be too careful
when it comes to drug safety. Definitely. It's
an ongoing process. It's this balance of getting
potentially life -saving medications to people
quickly. Right. But also making sure that they're
truly safe. It's a tough balance to strike. Which
brings us to this growing role of artificial
intelligence in drug discovery. AI is changing
everything. And you included some really interesting
material on AI -driven toxicity prediction models.
It's a rapidly developing field. Yeah, it sounds
like these AI models are trained on huge amounts
of data about chemical structures and their known
toxicities. Right, they're learning to spot those
red flags. It's like they can learn to spot those
red flags before we even start testing in the
lab. It's pretty amazing. And one of the most
promising AI platforms out there is called AtomNet.
Yeah, AtomNet's doing some great work. It uses
deep learning to analyze molecular structures
and predict how they'll interact with biological
targets. Including potential toxicity issues.
So instead of testing millions of compounds,
we can use AI to narrow down the search and focus
on the most promising candidates. And hopefully
the safest. And hopefully the safest. But AI
is still just a tool, right? Of course. It's
not replacing scientists anytime soon. Right.
We still need that human expertise. Absolutely.
Humans are still in charge. To interpret the
data, make the decisions, and consider all the
factors involved. It's a collaboration. AI can
provide those incredible insights. Right. But
ultimately, it's up to us to use that information
wisely. That's the key. Yeah. It's a partnership,
you know? Right. AI can process information and
spot patterns in ways that humans just can't.
Yeah. But we bring the critical thinking, the
ethical considerations, and understanding of
the big picture. Right. It makes you wonder,
like, how AI might change the whole landscape
of drug development in the years to come. Do
you think we'll ever see a day when AI can predict
and prevent drug toxicity with, like, 100 % accuracy.
Well, that's the million dollar question, right?
Right. AI is making incredible strides. Yeah.
But I think it's really important to remember
that biology is incredibly complex. There will
always be nuances and unexpected interactions
and new challenges that we haven't even thought
of yet. Right, so it's not about replacing human
scientists. It's about giving them a powerful
new tool to work with. Exactly. Think of it like
this. Okay. AI can help us to narrow down the
possibilities. Okay. Identify the most likely
risks and speed up the testing process. But it's
still up to the human experts to make that final
call, considering all the scientific and ethical
factors involved. Speaking of narrowing down
possibilities, let's circle back to that fail
fast, fail cheap concept. It seems like AI could
really supercharge that approach. Absolutely.
The earlier we can identify potential problems,
the better. And AI can help us to do that on
a much larger scale, screening thousands, even
millions of compounds very quickly and efficiently.
So instead of spending years and huge sums of
money developing a drug that ultimately fails
in those later trials, we can use AI to weed
out those problematic candidates early on. Exactly.
It's all about making the whole drug development
process smarter, faster, and ultimately more
successful. And that means getting those safe
and effective medications to the people who need
them sooner. Right. Exactly. Which is a win for
everybody. But we've been focusing a lot on toxicity.
What about those other parts of ADME tox, like
the absorption, distribution, and excretion?
How do those factor into that early safety screening
process? Well, they're all interconnected. Think
of it like a journey that the drug takes through
your body. First, it needs to be absorbed into
the bloodstream, then it's distributed to the
target tissues, metabolized into active or inactive
compounds, and finally excreted from the body.
So it's not just about whether a drug is inherently
toxic. It's also about how it moves through the
body and how that might influence its safety
and its effectiveness. Exactly. So for example,
a drug that's poorly absorbed, it might never
reach the levels needed to actually be effective.
On the other hand, a drug that's eliminated very
slowly could build up in the body and increase
the risk of side effects. So early screening
needs to look at the whole picture. the entire
journey of that drug within the body. A whole
life cycle. And I bet AI can play a role here,
too. Right? Absolutely. AI can help us model
those complex interactions, predict how a drug
will be absorbed, distributed, metabolized, and
excreted, and flag potential problems that might
not be obvious just from looking at its chemical
structure. It's amazing to think about how much
data is involved in all of this. It's a lot of
data. And one area you mentioned that I found
particularly intriguing was the world of... reactive
metabolites. Ah, yes. Those are the troublemakers.
Yeah, they can be real troublemakers. They're
often the culprits behind some pretty dramatic
and unexpected drug reactions. Yeah, the materials
you gave me talked about them being like these
sneaky molecules that can cause all sorts of
trouble. That's a good way to put it. And what
makes them so fascinating and challenging is
that they often arise from the breakdown of a
drug in the body. So the drug itself might seem
perfectly safe. Yeah. But then one of its metabolites
turns out to be highly reactive. Oh, wow. And
it can bind to proteins or even damage DNA. That
sounds pretty scary. It can be. And sometimes
these effects aren't discovered until a drug's
been on the market for a while. Right. Which
is why that post -marketing surveillance is so
important. It's an ongoing process. We're always
learning and refining our understanding. So how
does scientists even begin to identify these
reactive metabolites early on? Right. It seems
like finding a needle in a haystack. It is a
bit like that. It's a combination of approaches.
Oh, OK. Scientists will use experimental techniques
to study how drugs are metabolized. They'll develop
computational models to predict potential reactive
metabolites. And they even rely on their own
experience and intuition to identify potential
red flags. So it sounds like a bit of art. and
a lot of science all rolled into one. You got
it. And this is another area where AI is starting
to make a really significant impact. I bet. AI
models are being trained to analyze massive data
sets of drug structures and their metabolic profiles.
OK. To identify patterns that might predict the
formation of those reactive metabolites. It's
like having a super -powered detective that can
sift through all those clues and pinpoint the
suspects before they even commit the crime. That's
a great way to think about it. So cool. So we're
constantly developing new tools and techniques
to make drug development safer and more efficient.
That's the goal. But with all these advances,
I imagine there are still... plenty of challenges
and unknowns ahead. Oh, absolutely. Biology is
incredibly complex. Yeah. And we're always learning
new things. Right. But that's what makes this
work so fascinating. We're constantly pushing
the boundaries of knowledge and working to develop
better, safer medications for everybody. It is
incredible to think how far we've come in understanding
how drugs interact with the human body. It's
amazing. But as you said, there's always more
to learn. Always more to learn. There really
is always more to learn. It feels like we've
only just scratched the surface of this, like,
incredibly complex field. Yeah, it is. Like so
much goes on behind the scenes to just bring
a new drug to market. It's really a monumental
effort. Yeah. It involves years of research,
countless experiments, and the dedication of
so many talented people. Right. It's not a one
-person job. Definitely not, and as we've seen,
there are so many factors to consider, from a
drug's chemical structure to how it interacts
with your body's own unique biochemistry. Yeah,
it's amazing to think about that journey a drug
takes, from the initial discovery phase to those
really rigorous pre -clinical tests, then through
the clinical trials, and finally, if it's successful,
to your medicine cabinet. Yeah, it's a long road.
A really long road. And even then, the journey's
not over. No. Post -marketing surveillance is
really crucial. Oh yeah, you always hear about
that. Yeah, it's all about identifying any long
-term effects or rare side effects that might
not have shown up in that earlier testing. You
really can't be too careful. Nope. Safety is
paramount. It makes you appreciate all the work
that goes into ensuring that the medications
that we all take are both effective and safe.
It really does. It's a huge responsibility. So
what does all of this mean for, like, the listener,
the person who might one day benefit from these
new medications. Well, it means though when you
pick up a prescription, you can have confidence
that it's been through a really rigorous process.
Right. to evaluate both its safety and its efficacy.
Scientists are constantly working to improve
our understanding of how drugs interact with
the body and to develop new tools and techniques
to predict and prevent potential problems. It's
reassuring to know that so much effort is being
put into ensuring that our medications are as
safe as possible. Yeah, we're all working towards
the same goal. But even with all of these advances,
do you think there will always be an element
of risk involved in taking any medication? That's
a great question. And while we strive to minimize
risk, you know, every drug does have the potential
for side effects. Of course. Biology is incredibly
complex, and there will always be individual
variations in how people respond to medications.
So it's about finding that balance between the
potential benefit and the potential risk. Absolutely.
And that's why open communication between you
and your health care provider is so important.
It really is. By discussing your medical history,
any other medications you might be taking, and
any potential concerns you might have. You can
work together to make those really informed decisions
about your treatment. It's a reminder that we're
all part of this process, both as individuals
and as a society. We benefit from these scientific
advancements, but we also have a responsibility
to be informed and engaged in our own health
care. Well said. And I think that's a perfect
note to end on. Yeah. You know, as we wrap up
this deep dive into the world of early safety
screening, I hope you come away with a greater
appreciation for the incredible amount of work
that goes into developing safe and effective
medications. I know I do. It's been a fascinating
journey. It has. And I have to say I'm feeling
a lot more informed. That's great to hear. And
maybe even a little bit empowered, knowing all
that goes on behind the scenes to keep us healthy.
That's what we hope for. So as we sign off, I'd
like to leave our listeners with just this one
thought. OK. We've talked about these incredible
advances in AI. Right. And how they're really
transforming drug development. But imagine a
future where AI can not only predict toxicity,
but also design entirely new drugs. Oh, wow.
That are tailored to your unique. Genetic makeup
that would be incredible like drugs with maximum
effectiveness and minimal side effects of a holy
grail Yeah, it's a future that's closer than
you might think it really is and it's definitely
a future worth getting excited about