59 – Real-World Examples (S4E14)

All right, so today we're getting into the nitty
gritty of how some really cool early clinical
trial designs are being used. Like, we're not
just talking theory here. We're going deep into
real world examples, the stuff that's actually
happening in studies and pulling out those practical
learnings. Yeah, absolutely. You've gathered
some really interesting scientific literature
for us to dig into. Oh, yeah. And our mission
today is to break down some specific cases of
these adaptive designs in action. Right. We want
to see how that flexibility, how being able to
adapt the trial as it goes actually leads to
smarter drug development and better, more useful
results. OK. So for anyone who's tuning in and
maybe hasn't heard the term adaptive designs
before, what's the basic idea? Sure. So it's
not just about changing things mid study, right?
The core concept is that these changes are planned
out in advance. There are possibilities that
are built into the trial design and they're triggered
by the data that's being collected as the trial
progresses. So it's like having a roadmap with
detours. Exactly. It's like having a roadmap
with... with detours, and these detours are already
marked, right? Right. So as you gather information
along the way, you know, about how well the drug
is working, or if there are any unexpected side
effects, you can then decide to take the most
informative detour, whether that means adjusting
the dose of the drug, refining the group of patients
that you're studying, or even adding new treatment
arms to the trial. I see. So it's a way to constantly
optimize the trial as it's happening, right?
Yeah. sticking to this super rigid plan that
might end up being inefficient or even causing
you to miss crucial information. Precisely. You
don't want to be stuck with a plan that's not
working, right? Right. And one of the earliest
ways adaptive designs were used, and something
that researchers are still refining, is in figuring
out the right dose of a drug, especially in those
early phase I trials. So traditional methods,
they often involve giving escalating doses of
a drug to different groups of participants. But
adaptive designs can make this whole process
much smoother. You're using the safety and polarability
data that you're gathering in real time to guide
you to which doses to test next. So you could
potentially zero in on that optimal dose much
faster. That makes total sense, especially in
terms of making the trial more efficient. Right.
You also mentioned refining the patient group.
Yes. How does that work in practice? So this
often falls under the umbrella of what are called
enrichment designs. And actually, the clinical
trials and neurology source, it really highlights
how much interest there is in these approaches,
especially for chronic conditions. The basic
idea is that as you're collecting data, you might
start to see that a certain subgroup of patients,
maybe those with a specific biomarker, are responding.
way better to the treatment. So what you can
do is adapt the trial to enroll more participants
from that specific subgroup. Got it. So then
you can get a much clearer signal of whether
the drug is actually effective. Right. So instead
of maybe diluting the results. Exactly. With
people who aren't going to respond to the drug
anyway, you're focusing on the folks who are
actually showing a benefit. You got it. It's
about making the most of the data and really
understanding who the drug works for. And I imagine
this is super important when you're dealing with
a limited number of patients. Oh, absolutely.
And that's something that the small clinical
trial source really emphasizes. Right. When you
only have a small group of participants. Yeah.
Using these optimized designs becomes even more
crucial to making sure you can draw meaningful
conclusions. So let's dive into some concrete
examples here. OK. Oncology is one area where
we've seen a ton of innovation in trial. design
and the anti -cancer drug development guide.
gives us some really interesting historical perspective,
right? It does. It talks about this study from
1974 by Mortel and his colleagues. They were
looking at phase two chemotherapy trials for
gastrointestinal cancer. OK. And their findings,
well, they were a real eye opener. What did they
find? They showed how having these broadly defined
patient populations in a trial could completely
mask the potential benefits of a drug in a smaller
group of patients who might actually respond
really well to it. It wasn't just about being
careful about who you include in a trial. It
was a much bigger realization. It was about recognizing
that traditional rigid trial designs just couldn't
handle the complexity of how different people
respond to drugs. And this was really a turning
point, a direct path towards the more adaptive
approaches that we're talking about now. So those
earlier trials, in a way, they showed us what
not to do. Yeah. And sort of paved the way for
these more nuanced approaches. is that adaptive
designs can offer. Exactly. I mean, think about
it. A drug might show no benefit overall in a
mixed group of patients, but it could be incredibly
effective for a smaller subset of those patients.
That's a really important point. And how has
that thinking evolved in oncology trials today?
Well, now we see. these biomarker -driven approaches
being incorporated more and more. So let's say
a drug is designed to target a specific mutation.
An adaptive design might start by enrolling a
wider range of cancer patients. But then as data
starts coming in about how those with and without
the mutation are responding, the trial can shift
gears and focus on enrolling more patients who
have that biomarker. I see. And that lets researchers
get a much more efficient assessment of how well
the drug actually works in the people who are
most likely to benefit from it. That makes a
lot of sense, especially with these targeted
therapies that are becoming so prevalent. Right.
Now let's switch gears a bit and talk about biologics.
OK. a whole different class of drugs. And the
pharmacokinetics and pharmacodynamics of biotech
drugs source really highlights some unique challenges
with biologics, particularly around something
called immunogenicity. That's a big one. So can
you break down what immunogenicity is and why
it's such a big deal for biologics? Sure. So
one of the main differences between biologics
and those smaller molecule drugs is that biologics
have the potential to trigger an immune response
in the body. They're larger, they're more complex,
and so they can sometimes cause the immune system
to react in ways that those small molecules don't.
Interesting. And what kind of impact? can these
immune responses have? Well, they can actually
be quite varied. And the pharmacokinetics and
pharmacodynamics of biotech drugs source actually
goes into some detail about this. They have a
table, table 8 .2, that shows all the different
types of immunogenicity that can occur. OK. But
basically, these responses can range from mild
to really severe. And they can affect both the
safety and the effectiveness of the drug. So
a traditional trial design might not be able
to fully capture these longer -term effects.
Exactly. Traditional trials are often designed
around the idea of acute exposure to a drug,
right? You give the drug, you watch for a short
period of time, and you see what happens. But
with biologics, those immune responses can develop
over much longer periods, and they can be really
unpredictable. So you might miss something important
if you're not looking for it. You could. You
might see that the drug seems effective initially.
But then, months or even years down the line,
the patient starts to develop antibodies that
neutralize the drug, and then suddenly it's not
working anymore. So how can adaptive designs
help with this? They offer the flexibility to
build in longer follow -up periods, for one thing.
So you can monitor those patients for a longer
time and watch for any delayed immune responses.
Right, and if you start to see those responses.
Yeah, if you see that a significant number of
patients are developing antibodies, for example,
then you can adapt the trial protocol. You might
investigate different dosing strategies to see
if that makes a difference or even explore ways
to kind of dampen the immune response in future.
Wow, so you're almost learning and adapting on
the fly, but in a very controlled and scientific
way. Precisely, you're building that learning
process right into the trial design. That's pretty
amazing. Do you have another real world example?
Maybe something in the area of like gastrointestinal
inflammation. Sure. Actually the same source,
the pharmacokinetics and pharmacodynamics of
biotech drugs. Okay. Talks about some really
interesting research that was done on antisense
oligonucleotides or ASOs. This was at Ionis Pharmaceuticals
and they were looking at using ASOs to treat
local inflammation in the colon. Right, they
were looking at local delivery. Okay. And what's
really cool is that they found that when they
administered these ASOs directly to the colon
using retention enemas. Retention enemas, okay.
They were able to achieve much, much higher concentrations
of the drug in the colon tissue. Okay. Compared
to when they gave the drug intravenously. Interesting.
And we're not talking about a small difference
here. Table 10 .1 in that source shows that the
difference was huge. Huge, like how huge? a couple
of hours, the concentration of the ASO in the
colon tissue was something like 100 times higher.
Wow. 100 times higher with the local delivery.
Yeah. It's pretty remarkable, right? It is. And
you're saying that this approach could be really
useful for treating conditions like inflammatory
bowel disease. Exactly. And this is where adaptive
trial designs come in. Imagine a trial that's
evaluating this local ASO delivery. OK. They
might start the trial with a certain dose and
frequency of the enemas, but then based on early
data, like how much the inflammation is being
reduced, which they could measure through things
like endoscopy or biomarkers. They could adapt
the dosing regimen as they go. So they might
find that some patients need a higher dose or
that others who respond really quickly can actually
do well on a less frequent schedule. So it's
about personalizing the treatment based on how
each individual is responding. Exactly. And that's
a level of fine -tuning that you just can't get
with traditional trials where everyone gets the
same dose. Right. And especially when you're
talking about systemic treatments, it's much
harder to control where the drug is going and
how much is reaching the target tissue. Right.
So these examples really show how versatile these
adaptive designs can be. They do. But these innovative
approaches don't just exist in a vacuum. Of course
not. What role do regulatory bodies like the
FDA and the ICH play in all of this? Oh, they're
crucial. Their main focus is on making sure that
any new medicine that comes to market is safe
and effective. Right, of course. And so they
provide guidelines and frameworks that influence
how all clinical trials are designed and conducted,
including these adaptive trials. And while they're
definitely supportive innovation that can make
drug development more efficient, they also also
emphasize the need for rigorous scientific methods
and ethical considerations. So it's not just
a free for all? No, not at all. The small clinical
trial source actually talks about this. What
does it say? It stresses the importance of having
well -defined objectives for the trial, clear
rules about when and how you can make adaptations,
and the right statistical methods for analyzing
the data. So even with an adaptive design, you
still need to be really... rigorous about how
you select your participants. Absolutely. And
sources like New Drug Development Regulatory
Paradigms really highlight this. You need to
make sure you're studying the right group of
people and that the results you get are actually
meaningful. So it's not just about being flexible,
it's about being flexible within a really strong
scientific framework. Exactly. The flexibility
needs to be built in from the beginning. Okay.
Not just thrown in as an afterthought. So everything
needs to be planned out in advance. As much as
possible, yes. Including how you're going to
analyze the data. Right. You have to specify
all of that in the study protocol beforehand.
Got it. That way you can ensure that the trial
is scientifically sound. Yeah. And that the regulatory
agencies will have confidence in the results.
So... Thinking about all of these examples we've
discussed, what are the key takeaways here? What
have we learned from seeing how these adaptive
designs are being used in the real world? Well,
I think the biggest takeaway is that adaptive
designs are not just theoretical ideas. They're
actually being implemented successfully in all
sorts of different therapeutic areas and for
different types of drugs. We've seen that. We've
seen how they can help refine patient selection
in oncology trials so that you're focusing on
the patients who are most likely to benefit from
the treatment. And with biologics, they provide
that flexibility to monitor for and potentially
even counteract those complex immune responses
that can happen over time. And we saw how they
can even be used to optimize local drug delivery
in things like inflammatory bowel disease. By
adjusting the dosing based on how the patient
is responding. It's like they allow for a much
more tailored and efficient approach to evaluating
new treatments. Precisely. And they have the
potential to give us more informative data, sometimes
even with fewer participants. And they allow
us to optimize those dosing and treatment strategies
in a way that just isn't possible with those
traditional fixed trial designs. So the learning
process is baked right into the trial itself.
It is. It's like a constant feedback loop. You're
getting information, you're adapting, you're
learning more, and then you're adapting again.
And it's not just about designing individual
trials better. It's about changing how we think
about drug development as a whole, right? Absolutely.
And one of the things that's really crucial for
all of this is understanding how drugs work in
the body. Right. There are more probacinetics
and pharmacodynamics. Yeah. We need to integrate
all of that knowledge into how we design these
adaptive trials. OK. And how we interpret the
results. So to sum up our deep dive into these
real world examples. Yeah. What's the main message
you want our listeners to take away? The main
message is that these innovative early trial
designs, especially these adaptive designs, are
really transforming how we evaluate new medicines.
They're not just some niche thing anymore. No,
not at all. They're becoming more and more common
across a wide range of diseases and drug types.
We've seen that. From cancer to biologics to
those localized treatments for inflammatory conditions.
And they're making the whole process more efficient
and giving us better information. Exactly. And
ultimately, that means we can get closer to developing
more personalized and more effective treatments
for patients. And I think it also highlights
how much clinical trial design is evolving. It
is. It's a dynamic field. Always adapting to
the complexity of biology and the need to get
those new treatments to patients as quickly and
as safely as possible. Absolutely. These examples
really give us a glimpse into the future of drug
development. Trials are becoming more responsive,
more tailored to each individual patient, and
to the specific characteristics of the drug being
tested. So here's something for everyone listening
to think about. Given how sophisticated and widespread
these innovative trial designs are becoming,
how do you think they're going to change the
landscape of pharmaceutical research and development
in the years to come? That's a great question.
Think about the impact on how quickly new drugs
get to market. the cost of drug development,
and ultimately the treatment options that are
available to patients. It's a really fascinating
area with so much potential to change how we
approach treating diseases. Thanks for joining
us for this deep dive. We'll see you next time.
See you then.