43 - Integrating Preclinical Data for IND (S3E13)

Hey everyone and welcome back for another deep
dive. Today we're going to be looking at how
all that data from before human testing like
toxicology, pharmacokinetics, and how well a
drug works all get pulled together for that big
step of applying for an IND application. Yeah,
it's kind of like putting together a puzzle,
making sure all the pieces fit to tell the FDA
the complete story of the drug. Exactly. And
to really understand this, we're going to start
by doing a deep dive into a textbook chapter
on, get this, basic pharmacokinetics. You might
be wondering, why are we going back to the basics?
But understanding these core principles will
honestly make the whole IND process much clearer.
Trust me on this one. Plus we'll uncover some
cool facts along the way. Did you know that a
drug can actually seem to spread out in a space
larger than your body? It's true. It's called
the apparent volume of distribution. We'll explain
how that's even possible a little bit later.
What I think is so cool is that all this basic
knowledge is what helps us understand how a drug
will behave in a living organism, which is exactly
what the FDA needs to know before they approve
an IND. OK, let's back up for a second. First
things first. What exactly is an IND? An IND
is basically a request to the FDA asking for
permission to test a new drug in humans. It's
the gatekeeper to doing clinical trials. And
without it, well, you can't test on humans. So
it's like a permission slip, but with a ton of
emphasis on safety and showing that the potential
benefits are greater than the risks. That's a
great way to put it. So then what kind of information
goes into this super important permission slip?
Well, a ton of data goes into it. It's not just
a collection of random facts and figures either.
The IND has to tell a really clear and compelling
story about what this drug does in a living being.
OK, so let's start with toxicology. Before we
even think about giving a drug to a person, we
need to know for sure it's not going to hurt
them, right? Exactly. That's where animal studies
are super important. They let us figure out how
toxic a drug is. So we're talking about like
those lab rats, those classic experiments. Yeah.
Yep, pretty much. But there are really important
ethical considerations. Scientists always try
to gain as much information as possible from
the smallest number of animals. That makes sense.
So you're not just trying to find out if a drug
is flat out dangerous, but also if it causes
any side effects, right? Yes. We give the animals
different doses of the drug, sometimes for different
lengths of time, to see how they respond. The
goal is to figure out how safe a drug is so we
can give people the right dose in a clinical
trial. Okay, so toxicology helps us understand
the risks. But what about how well the drug works?
Does that matter at this stage? Oh, yeah, definitely.
The IND needs to show that the drug can actually
treat whatever condition it's designed for. That's
where efficacy studies come in. So you're saying
it's not enough to just show the drug doesn't
harm the animals. You also have to show it actually
does something. Exactly. The FDA is not going
to approve human trials unless there's a good
reason to think the drug might actually help
people. OK, I got it. Now let's get into pharmacokinetics.
I think this is where things start to get kind
of complicated. Oh, it's not that bad. Imagine
pouring some dye into a container that has all
these different compartments representing the
different organs in the body. So pharmacokinetics
is basically the study of how that dye spreads,
how fast it goes into each compartment, how long
it stays there, and then eventually how it gets
eliminated. OK, so basically it's all about how
the drug moves through the body. You got it.
And PK data is really important for the IND because
it helps us to determine the right dose of the
drug to give to people and also how often they
need to take it. Makes sense. Giving too much
of a drug or not enough or maybe at the wrong
time could be ineffective or even dangerous.
That's right. We need to know how much of the
drug makes it into the blood, how long it stays
there, and how it eventually gets broken down
and leaves the body. And scientists figure this
all out by measuring how much drug is in the
animal's blood after they give it to them. Yeah,
they take measurements at different points in
time to get a picture of the drug's journey through
the body. How well it's absorbed, distributed,
metabolized, and eliminated. Okay, this is starting
to become more clear. But remember that thing
we talked about before? The apparent volume of
distribution. The one that's sometimes bigger
than the body. Can we explain that a little more?
Sure. It may sound weird, but the apparent volume
of distribution, or VEDA, isn't actually a real
volume. It's a calculated value that tells us
how much of the drug is in the tissues compared
to the blood. So going back to that dye analogy,
it's like some of the dye is sticking to the
sides of the container, making it seem like there's
less dye in the liquid. You got it. That's a
perfect analogy. If a drug likes to bind to tissues,
it will look like it's disappearing from the
blood. And that gives it a larger VEDA. So large
VDI doesn't mean the drug is literally taking
up more space than the body. It just means it's
more spread out in the tissues. Exactly. And
here's where things get really interesting. You
know, our source material gives some specific
examples. Drugs that stay mainly in the blood,
like heparin, have a V that's about the same
as the blood volume. But drugs that prefer fatty
tissues, like amphetamine, can have a V of up
to 200 liters. Wow, 200 liters. That's a lot.
But why should we care about the VEED? Does this
really matter in the real world? Yeah, for sure.
VEED helps us to predict how much drug actually
reaches its target in the body. So a drug with
a large VEED might need a higher dose to have
the same effect as a drug with a small VEEDed.
So it's all about getting the right amount of
drug to the right place in the body. And that's
why PK data is so important for figuring out
safe and effective doses. Exactly. VEED is just
one piece of the puzzle though. We also have
to think about things like elimination half -life,
which is how long it takes for the body to get
rid of half of the drug. Right, that makes sense.
If you know how long a drug stays in the body,
you can figure out how often someone needs to
take it. Exactly. Elimination half -life can
be really different depending on the drug, and
it can also be affected by things like how well
someone's liver or kidneys are working. So if
a drug doesn't stay in the body very long, someone
might need to take it multiple times a day to
keep enough of it in their system, right? But
if it sticks around for a while, they might only
need to take it once a day or even less often.
You're getting it. Understanding a drug's half
-life is really important for coming up with
a dosing schedule that makes sure it works the
way it should without causing any problems. Okay,
I think we've covered a lot of ground here. We've
talked about how important the IND is, the different
kinds of studies that go into it, and even the
basics of pharmacokinetics, including that crazy
concept of apparent volume of distribution. We've
done a lot, but there's still so much more to
talk about. There is. So stay tuned for part
two of our deep dive, where we'll get even deeper
into the details of IND applications. Welcome
back for part two of our deep dive into IND applications.
Last time, we left off talking about elimination
half -life and how important it is for figuring
out dosing schedules. Right, right. But this
time, let's focus on another big piece of the
IND, the efficacy data. OK. It's great if a drug
doesn't hurt the animals in preclinical testing,
but does it actually work? That's the big question,
right? You're right. We need to know for sure
that this new drug can treat the condition it's
designed for. And that's where efficacy studies
come in. So these studies, they basically involve
giving the drug to animals that have the disease
you're targeting, right? Like if you were developing
a drug for high blood pressure, you'd give it
to animals with hypertension and see if it lowers
their blood pressure. Exactly. That's the basic
idea. And there are a bunch of different ways
to measure efficacy, depending on what kind of
drug you're studying. Like, if it's a new antibiotic,
you might infect animals with a specific bacteria
and see if the drug kills the bacteria or at
least stops it from growing. Gotcha. So efficacy
data is all about showing that the drug can actually
do what it's supposed to do. It's not just about
safety. Right, and this data is a really important
part of the IND application. It shows the FDA
that there's a good chance the drug will work
in humans, not just that it won't cause any immediate
harm. OK, that makes sense. So now we have toxicology
data to figure out the risks, efficacy data to
show the potential benefits, and PK to help us
figure out dosing. Seems like that's a lot of
information to put together for the FDA, though.
Oh, it definitely is. And that's where the challenge
of the IND comes in. You're basically putting
together all this preclinical data to make a
convincing argument for why this drug should
be tested in people. So it's not just like throwing
a bunch of data at the FDA and hoping for the
best. No, not at all. The IND has to tell a story,
and that story needs to make sense to the people
who are reviewing it. Okay, so it's got to be
organized, right? Yes, exactly. For starters,
it has to be organized in a very specific way.
It needs clear labeling, indexing, you know,
all that good stuff. The FDA reviewers need to
be able to find the information they need quickly
and easily. They have tons of these applications
to review, so being clear and organized is super
important. Yeah, that makes sense. But it's more
than just organization, right? I mean, there
are rules about what kinds of studies need to
be done, the statistics that need to be used,
and how the results should be presented. Right.
You're exactly right. There are these very specific
guidelines that come from both the FDA and international
organizations, like the ICH, the International
Council for Harmonization. And these guidelines,
they set the standards for all of this. The ICH.
What is that exactly? Some kind of international
drug development police force? Well, not exactly
a police force, but they are pretty important.
The ICH is a group of regulatory authorities
and pharma industry experts from all over the
world. And they get together to develop these
harmonized guidelines for drug development. So
they're kind of like the United Nations of drug
development. Yeah, something like that. They're
trying to make sure that new drugs are developed
in a way that meets high standards for quality,
safety and efficacy everywhere. OK, so the data
is organized and is following all the rules and
guidelines. What else needs to happen before
you can hit that submit button? Well, remember
how we talked about the IND telling a story?
That story needs to be, well, a good one. It
needs to answer all the questions that the FDA
reviewers will have. Like is this drug even worth
testing in humans? Exactly. The IND has to make
a strong argument based on solid scientific evidence
that the drug has the potential to benefit people.
And I'm guessing it also needs to address any
potential safety concerns. Of course. It has
to outline a plan for managing and monitoring
any risks that might come up during clinical
trials. It has to be very clear that patient
safety is the top priority. So being honest about
what you don't know and having a plan to deal
with it if something unexpected happens. Yeah,
pretty much. And all of this information, it
goes into a document that can be, honestly, hundreds
of pages long. Hundreds of pages. Wow. Yeah.
It really shows how much work goes into developing
a new drug. It's a huge undertaking, but it's
an important one. It's all about making sure
that new drugs are tested thoroughly before they're
given to people. OK. So we've talked about how
the IND is structured, the guidelines it has
to follow, and the fact that it needs to tell
a convincing story. What else do we need to know
about putting it all together? Well, one really
important thing is how all the different types
of data fit together. Remember, we have data
from toxicology studies, efficacy studies, and
PK studies. All of these pieces need to work
together to tell the complete story of the drug.
So it's like putting together a puzzle. Exactly.
A really complicated puzzle. And this is where
the people who put together the IND really need
to be experts. They need to be able to see the
big picture, spot any problems with the data,
and then draw conclusions from all of this information.
Sounds like it takes a lot of skill. It definitely
does. It's not just about summarizing what each
study found. It's about figuring out what it
all means together. So each data point is kind
of like a character in a story. And the IND team
has to figure out how to weave all those individual
stories into one big narrative. That's a great
way to think about it. And that narrative has
to be clear, easy to understand, and convincing.
The FDA reviewers need to be able to follow the
logic, agree with the conclusions, and ultimately
believe that this drug has the potential to be
both safe and effective for people. So no pressure.
Right. But seriously, it is a big responsibility.
Okay. So can we look at some real -world examples
to see how all of this data integration works
in practice? Sure. Let's say we're developing
a new cancer drug. In the toxicology studies,
we see that the drug can cause some liver damage,
but only at really high doses. But then in the
efficacy studies, we see that the drug is super
effective at shrinking tumors. So now you have
a tough decision to make. Do you risk the potential
liver damage to get the benefits of shrinking
tumors? Right. It's a classic risk -benefit situation.
And that's where data integration is so important.
We can look at the PK data to see if we can get
the same tumor shrinking effects at a lower dose,
a dose that's less likely to cause liver damage.
So you're trying to find the sweet spot, the
dose that gives you the most benefit with the
least amount of risk. Exactly. Now let's imagine
that the PK data shows that the drug is eliminated
from the body slowly. That could mean that patients
wouldn't need to take it as often, which might
also help reduce the risk of side effects. I'm
starting to see how all these pieces fit together.
But what about those regulatory guidelines we
were talking about before? How do they affect
the way you integrate the data? Well, remember
that the FDA and the ICH have all these rules
about how data should be analyzed and presented.
So when we're putting together the toxicology,
efficacy, and PK data, we need to make sure we're
following those rules very carefully. So you're
not just drawing your own conclusions. You're
also making sure you present those conclusions
in a way that meets the FDA's expectations. Right.
If the data isn't presented the right way, the
FDA could reject the whole application. Wow,
that's pretty intense. Things like developing
a new drug is not only about doing good science,
but also about being really good at communicating
that science. Yeah, that's a really good point.
You have to be able to speak the FDA's language,
basically. So in a way, it's like a partnership,
right? It is. The IND team and the FDA are working
together to make sure that new drugs are safe
and effective. Yes, that's exactly what it is,
a partnership. And we're back for the final part
of our deep dive into IND applications. We've
come a long way. From the very beginning, talking
about preclinical research with all that data
from animal studies to where we are now, ready
to talk about clinical trials with real people.
Exactly. and all the things we talked about before,
toxicology, efficacy studies, all that PK stuff.
It's all led up to this moment. This is the chance
to see if this new drug really can help people.
Yeah, it's exciting. But there are some challenges
too. I mean, animals aren't humans, right? Of
course not. Yeah. But how does that difference
actually affect how we use preclinical data to
design those first human trials? Well, animal
models give us really useful information, but
we have to remember that a drug might act differently
in different species. A drug that works great
in a mouse, it might not work exactly the same
way in a human. So how do we make that leap?
How do we take what we've learned from animals
and use it to design safe and effective trials
for people? Well, for starters, the IND has to
be upfront about those differences. It needs
to be really transparent about the limits of
the preclinical data and show exactly how it's
going to address those uncertainties when it
moves into clinical trials. So it's like you're
admitting you don't know everything and you have
a plan for what to do if things don't go as expected.
Yeah, exactly. One of the tools we use to go
from animal data to human dosing is alimetric
scaling. Alimetric scaling? Yeah. It sounds complicated,
but it's basically using math to predict how
a drug's behavior will change based on body size.
OK, so if we know how a drug acts in a tiny mouse,
we can use that info to figure out how it might
act in a much bigger human. That's the idea.
It's not perfect, but it gives us a good starting
point for those early trials. It helps us to
figure out a safe starting dose for people. That
makes sense. You don't want to just guess when
it comes to giving people new drugs. Definitely
not. And when we're talking about these early
trials, we have to consider who those first participants
will be. We can't just give the drug to anyone.
Choosing the right people for the trial is super
important. Oh yeah, you need people who represent
the group the drug is for and who don't have
any other health conditions that could mess up
the results, right? Exactly. The IND has to lay
out very specific criteria for who can and can't
be in the trial. It's a balancing act, you know?
You want the participants to reflect the real
world, but you also need to make sure you get
clear data from the trial. Of course, keeping
people safe is the most important thing during
the whole process. Absolutely. We need to be
ready for anything that might happen. So, like,
having backup plans in place, just in case. Yeah,
exactly. The IND has to include a detailed plan
for how to monitor and manage any side effects,
including what to do if anything unexpected happens.
Okay, got it. And this leads us to one of the
most interesting parts of the IND, how all that
preclinical data helps us design the clinical
trials. Right, because all that work we talked
about before, it's not just for the animals.
No, definitely not. All that preclinical data
is meant to help us design those human trials.
It tells us how much drug to give, who should
get the drug, and what to look out for in terms
of safety, right? Yes, and more. The IND is basically
a bridge between the animal studies and the human
trials. So you're not just showing the data,
you're also explaining why it matters and why
it makes sense to move forward with testing the
drug in people. Precisely. And that argument
has to be really, really strong. The FDA is going
to look at every detail trying to find any holes
in your reasoning. It's almost like you're presenting
a case in court. You have to anticipate what
the other side will say. and make sure you have
all the answers. That's a great way to think
about it. And this back and forth between the
IND team and the FDA is really important. It
makes sure that the trials are designed in a
way that makes sense scientifically and is ethical,
too. So it's a real collaboration. It is. Both
sides are working towards the same goal, which
is getting safe and effective new treatments
to people. Exactly. Well, we've talked about
a lot in this deep dive into IND applications.
We started with preclinical research, then got
into all the complexities of data integration,
and finally made it to the transition to clinical
trials. It's been quite a journey. I feel like
I have a much better understanding now of how
new drugs are developed. And it's just the beginning.
The IND is a critical step, and it holds the
potential to help a lot of people. It's been
so interesting to see how scientific evidence
is used to create real solutions for patients.
We hope this deep dive has helped you to understand
the dedication, the expertise, and all the hard
work that goes into making new medications. Thanks
for joining us on the deep dive. And keep being
curious about the world of science and medicine.