44 - Risk Management in Preclinical Phase (S3E14)

All right, welcome back everyone. Today we're
going deep on something super important in drug
development. It's risk management, but specifically
in the pre -clinical phase. And we've got some
great research papers and regulatory guidelines
to help us unpack it all. We're gonna figure
out how to identify those risks and how to assess
them and how to mitigate them. You know, all
that good stuff. Yeah, it's a phase where things
can go wrong really easily. And the thing is,
even small problems can become huge problems
later on. Imagine you find a toxicity problem
in an animal model. That's way better than finding
it in human trials. Oh, for sure. Huge difference.
So I think our listeners already know the basics
of the preclinical phase. So let's just jump
right into the good stuff. What makes risk management
in preclinicals so different from, let's say,
a later clinical trial? Well, the stakes are
just way higher at this early stage. You have
so many unknowns about the drug. You're figuring
out how it works, how the body breaks it down,
how toxic it could be. It's all very uncertain.
And that means high risk. And of course, we're
not testing on humans yet. So the ethical considerations
are different, too. Absolutely. Using animal
models has its own ethical concerns and scientific
limitations. So in preclinical, you're managing
those complexities, too. Right. Let's get specific.
Where do the risks usually pop up? Our listener
is really interested in the scientific and technical
risks. Okay, yeah. One of the toughest parts
is moving from those in vitro studies to in vivo
models. What works great in a dish might not
work the same way in a living thing. And I bet
the animal models themselves can cause even more
risk, right? Oh, definitely. Different species
can react so differently to the same drug. A
classic example is drug metabolism. The pathways
can be completely different across species. What's
safe in a mouse might not be safe in a human.
Okay, so how do researchers actually deal with
all these scientific and technical risks? Our
sources talk a lot about risk matrices. Is that
where they begin? Well, risk matrices are a good
place to start. They help visualize and rank
the risks, you know, based on how likely they
are and how bad they could be. But, and this
is important, they have limitations. Oh, really?
I thought they were like a magic solution. What
kind of limitations? Well, you have to put good
data into a risk matrix to get good results out.
If you're wrong about how likely or impactful
a risk is, the whole thing is messed up. And
sometimes they don't show you how different risks
can actually interact with each other. So it's
a tool, not the whole solution. What else helps
researchers with this? Our notes mention decision
frameworks. Right. Those are more structured.
They help you analyze the risks and then decide
on the best way to reduce them. They make you
think about different situations, what could
happen, and then weigh your options very carefully.
That sounds really useful, especially when there's
so much uncertainty in preclinical. How would
that actually work? Can you give us an example?
Sure. Let's say you're developing a new drug
for a neurological condition, and your preclinical
studies show there's a chance it could be toxic
to the heart. A decision framework would guide
you through all the factors. Like, how severe
is that toxicity? How beneficial is the drug?
Are there other treatments available? So it's
not just saying, oh, there's a risk, stop everything.
It's more about making a smart decision after
looking at everything. Exactly. A good framework
makes you consider all the ways you could reduce
the risk. Could you tweak the drug's formula
to make it less toxic? Could you change the dose?
Are there certain patients who would benefit
more than they'd be at risk? So it's really helpful
for those tough ethical situations. It is. And
it helps different teams be consistent with their
decisions across all their projects. So we've
got these tools. these matrices and frameworks.
But I'm curious, how do they actually work in
the real world? Looking through these research
papers we have, are there any examples that really
show this stuff in action? Oh, yeah. There are
some really interesting cases. There's one study
that looked at the problems of making a new delivery
system for a chemo drug. They were trying to
make it target the tumor directly so there would
be fewer side effects. Sounds promising. What
kind of risks did they run into? A big one was
making sure the drug was released from the system
at just the right speed. If it was too fast,
it could become toxic, too slow, and the drug
wouldn't work. Yeah, I see. That's a tough balance.
So how do they use risk management to deal with
that? Well, they used computer models and in
vitro testing. That helped them predict the release
rate in different situations. Then they did a
bunch of preclinical studies in animals to see
if the predictions were right. So they were trying
to find the problems and test solutions before
even going to human trials. Exactly. And that's
a big lesson for our listener. If you manage
risk early on, it can save you a lot of trouble
later. Definitely. There was another example
I saw, a drug that looked amazing in early studies.
But then they had problems when they started
studying how it gets absorbed. Oh, right. That
shows how important it is to understand the drug's
ADME profile right from the start. Some of our
listeners might not know what that means, though.
Can you quickly explain what ADME is? Sure. ADME
stands for absorption, distribution, metabolism,
and excretion. Basically, it tells you how the
drug moves through the body and how it eventually
leaves. Thanks. So this drug had issues with
absorption. What happened there? Well, they first
use an IV, which skips the absorption step. But
when they tried making it a pill, the bioavailability
dropped way down. Meaning it wasn't getting into
the blood very well. Right. And no one had really
thought about that as a risk. They were focused
on if it worked and if it was safe. They didn't
see the absorption problem coming. So what did
they do? Was the project over? Not necessarily.
They had to rethink their whole formulation strategy.
In the end, they used a different excipient,
which is an inactive ingredient, that helped
the drug dissolve better and get absorbed better.
So something small like that can really make
or break a drug's success, huh? Oh, absolutely.
That's why you need a holistic risk management
approach, one that looks at everything in drug
development, not just the obvious stuff. We've
talked a lot about the science side of risk management,
but what about the regulations? I know our listeners
want to know how the FDA and ICH are involved.
Regulations are huge in preclinical risk management.
The FDA and ICH have strict guidelines for everything,
from how you design your study to how you collect
data and write your reports. So researchers need
to know these guidelines from the beginning.
For sure. If you don't follow the rules, your
clinical trials could get delayed. or your applications
could be rejected. Sometimes that could even
be legal trouble. Wow. Okay. Are there any specific
regulations that are really important for risk
management in preclinical? There are a few. Good
laboratory practices or GLP are really key. They
make sure the data from preclinical studies is
high quality. So it's not just about the results
you get, but about doing things the right way
to get those results. Exactly. And there are
also regulations about using animals ethically
in research. Researchers have to explain why
they're using animals, they have to use as few
as possible, and they have to make sure the animals
are treated well throughout the study. Yeah,
those ethical concerns are really important.
Now, I'm wondering how these guidelines actually
affect risk assessment. Do they change how researchers
identify or prioritize risks? They definitely
do. For example, if the FDA has concerns about
a certain type of drug, maybe because of safety
problems in the past, researchers will be extra
careful when assessing risks for a new drug of
that same type. So you need to know the general
regulations. But you also need to be aware of
any specific concerns that the FDA might have.
Absolutely. Things are always changing. Researchers
need to keep up with the latest regulatory news.
It sounds like... Navigating those regulations
can be just as tough as dealing with the scientific
risks. Oh yeah, it adds a whole other level of
difficulty. But it's crucial for making sure
new drugs are safe and effective. Which is the
whole point, right? Protecting patients and making
sure new treatments are brought to market responsibly.
Before we move on, I wanted to go back to something
you mentioned earlier about the limitations of
animal models. I know this is something people
talk a lot about in science, and I'm sure our
listener has their own thoughts on it. Yeah,
it's a complicated issue. Animal models are really
important for preclinical research, but they
don't always predict how a drug will work in
humans. So how do researchers take that into
account when they're assessing risks? Like, if
a drug seems toxic in an animal, does that mean
it's automatically a bad drug? Not always. It
depends on a few things. How toxic is it? How
similar is the animal to a human? And how much
could the drug potentially help people? So they
have to weigh all the evidence carefully and
make a judgment call. Exactly. This is where
those decision frameworks we talked about are
super helpful. They give you a way to evaluate
risks and benefits systematically, even when
the data isn't perfect. It's like walking through
a minefield. You need a map and a compass. And
you need to be very careful. That's a great analogy.
Experience and expertise are so important in
preclinical risk management. It's not just about
checking boxes. You need to understand the science,
the regulations, and what could happen if things
go wrong. So it's all about pushing the boundaries
to make new treatments, but doing it safely for
the patients who will use them. Exactly. That's
what makes preclinical risk management such an
important and interesting field. Yeah, it's a
really crucial field. I think our listeners are
getting a good sense of how much work goes into
it. Before we wrap up, though, I wanted to touch
on something you mentioned before, the role of
technology. There's so much talk about AI and
big data changing drug development. What kind
of impact are those advancements having on preclinical
risk management? Well, that's an area that's
changing really fast. AI algorithms can analyze
huge amounts of preclinical data. They can find
patterns and connections that we might miss.
So it's like having a super smart research assistant
who can go through all that information and find
the potential risks early on. Yeah, that's a
good way to think about it. For example, AI is
being used to predict if a drug might be toxic.
They look at its chemical structure and how it
interacts with different biological pathways.
That's pretty amazing. You can save so much time
and money if you can flag the risky drugs right
away. Exactly. And it can even help design better
preclinical studies so they're more efficient
and focused. So AI isn't replacing the researchers.
It's more like giving them a boost. Yeah. That's
the key. It's a tool that can make human expertise
even better. And as these technologies keep getting
better, we'll see even more ways to use them
in preclinical risk management. I'm excited to
see what happens. But like with any new technology,
there are ethical things to think about, like
data privacy and the possibility of bias in the
algorithms. Those are definitely important concerns.
We need to address those head on. As we use more
AI in drug development, we have to make sure
it's being used responsibly and ethically. It's
not just about the technology itself. It's about
using it the right way with good values and principles.
Absolutely. We can't forget about the potential
consequences. We have to use AI to benefit patients
and help science move forward, but it has to
be done in a way that aligns with our values.
Well said. I think that's a perfect place to
wrap things up. This has been a really insightful
look at preclinical risk management. I know we
just scratched the surface, but we turned it
a lot. It's been great sharing my thoughts with
you and our listeners. Now, before we go, I always
like to leave our listeners with a question to
think about, something to keep those minds working.
So here's one for you. As technology changes
the way we develop drugs, what skills and knowledge
will be most important for the next generation
of preclinical researchers? That's a great question.
I think understanding both the science and the
ethics of drug development will be more crucial
than ever. Researchers will need to be comfortable
with new technologies, but also stay true to
those ethical principles. It's a fascinating
challenge, and I'm sure our listeners are already
thinking about it. I hope so. The future of drug
development relies on new researchers who are
not only brilliant scientists, but also ethical
and compassionate people. I couldn't agree more.
And on that note, we'll wrap up this episode
of The Deep Dive. A big thank you to our experts
for all their insights and to all our listeners
for joining us on this journey into pre -clinical
risk management. Keep those brains buzzing, and
we'll see you next time for another deep dive
into the world of knowledge.