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In this episode of "In the Interim…", Dr. Scott Berry delivers a metaphoric critique of single-question trial infrastructure through the sports arena analogy, illustrating the cost, patient burden, and data inefficiency of conventional clinical trials. He provides a methodical comparison of traditional trial models and the platform trial approach, clarifying distinctions between platform, basket, and master protocol structures. Through examples from HEALEY ALS, I-SPY 2, PALM (Ebola), REMAP-CAP, RECOVERY, EPAD, GBM AGILE, and Precision Promise, Scott outlines the measurable efficiencies of platform trials: shared control arms, flexible arm addition and removal, reduced placebo exposure, accelerated timelines, and improved statistical inferences. The episode further examines platform trial performance during the COVID-19 pandemic, highlighting trial adaptability, and the rapid generation of actionable evidence. Scott also addresses failure scenarios, focusing on EPAD Alzheimer’s as a cautionary case in platform sustainability, cost allocation, and initial funding barriers. Listeners will gain a perspective on the operational and statistical design choices governing today’s most innovative clinical studies.
Key Highlights
Key Highlights
- Arena analogy applied to delineate clinical research inefficiency.
- Operational, statistical, and patient-focused efficiencies in platform versus single-question trials.
- Precision in terminology: platform, basket, and master protocol definitions.
- Effects of platform trials on speed and scientific rigor.
- Factors underlying both platform trial successes and failures.
For more, visit us at https://www.berryconsultants.com/
Creators and Guests
What is In the Interim...?
A podcast on statistical science and clinical trials.
Explore the intricacies of Bayesian statistics and adaptive clinical trials. Uncover methods that push beyond conventional paradigms, ushering in data-driven insights that enhance trial outcomes while ensuring safety and efficacy. Join us as we dive into complex medical challenges and regulatory landscapes, offering innovative solutions tailored for pharma pioneers. Featuring expertise from industry leaders, each episode is crafted to provide clarity, foster debate, and challenge mainstream perspectives, ensuring you remain at the forefront of clinical trial excellence.
Judith: Welcome to Berry's In the
Interim podcast, where we explore the
cutting edge of innovative clinical
trial design for the pharmaceutical and
medical industries, and so much more.
Let's dive in.
Scott Berry: Ah, welcome back everybody.
Welcome back to, in the Interim,
I'm your host, Scott Berry.
I have a, uh, a really interesting news
release that I want to share with you
from the world of sports in this setting
and, and as you'll tell, um, being a bit.
A bit overdramatic here.
Uh, and for those that turn into a
few of these episodes, you'll, you'll
understand that much of what I, what I
think in, uh, clinical trial statistic
science can be an analog to sports.
So the NFL has decided to make
sweeping changes the National
Football League, where for each
game that's gonna be played.
They're going to build a new arena
when they're planning for the
Vikings to play the Packers.
They're going to plan for the arena.
They're gonna get the approval of the the
components to the arena, and they're gonna
build it much like we do at the Olympics.
Now, we build that
arena to host that game.
We're going to set up the
rules specific for that arena.
We're gonna train umpires.
We're gonna train the film crew
security, the food prep, preparations
all the components to it.
We're gonna build it for that
one game that's going on with the
Vikings against the Packers, say.
Uh, we're gonna put out
bids to do all of this.
We're, we're gonna create teams that are
really, really good at creating arenas.
And then for every arena, uh,
for, for every game that's played,
we're gonna create this arena.
We're gonna do this, we're
gonna put all the vendors
through a bid defense for this.
We're gonna get really
low prices for this.
And yes, it takes about two
years to build the arena.
And then the game's gonna be
played and we're gonna tear down
the arena and then the next game,
we're gonna do that all over again.
And in their sweeping announcement,
they have announced that ticket
prices are likely to go up.
Yeah, it's gonna be much more
expensive to attend that.
And we're gonna do pay-per-view
to pay for the cost of the arena.
And the components of that.
And oh by the way, we might not actually
let other teams watch this or it, it'll
be sort of secret the what comes out of
this now that that part's of changing
of this, now that this is absurd.
That the NFL would do that.
Uh, it's absurd that when it comes time
to ask this question of who wins between
the Vikings and Packers, that we build a
whole separate arena for that question.
It's so absurd.
But you, you can imagine
where I'm going with this.
This is what we do in clinical trials.
When it comes time to address, does
treatment a improve upon a control?
We build a whole clinical
trial around that.
And I, it may have sound absurd to say,
it takes two years to build the arena.
In many cases, it might take two years
from the start of the design of the
trial, the inclusion exclusion criteria,
to get our CRO to go to regulators,
to get the approval of it, to get the
approval of our arena and the rules
we're gonna use what the scoring system
is at the end of the trial, all that.
It's two years before we
actually play the game.
Between that treatment and the control.
Now, the cost of that is similar to
if the NFL would rebuild the arena
every time it comes time to play.
You can imagine the cost of these tickets
are incredibly high because the cost to
build an arena to use it for one question,
a clinical trial answers one question.
And then we shut it down.
And the learnings from that, the
contracts from that, the vendors who,
who carried it out, it all goes away.
And then if somebody else in the
same disease wants to ask a question,
to play a game, to do a clinical
trial, they do the same thing.
Now there's an aspect even that is
almost worse to this, that every
time we build one of these clinical
trials, that's not entirely true.
So many clinical trials, it's
comparing two active treatments.
It's standard of care plus a therapy.
But it's not uncommon in many
diseases that every time we play
a game, we play against a placebo.
And so the five clinical trials that
are built in this area, they're all
enrolling 50% on a placebo, and we've
got these multiple games going on, can
imagine the incredible waste, uh, similar
to building an entire arena to play a
single game and then taking it down.
Sounds absurd for the National Football
League to do that, but we do that in
clinical trials and we keep doing that.
By the way.
We get groups that are really,
really good at building the arena.
They're so efficient and they're,
they're good at making sure the CRO bids
low amounts and the data and all that.
You know, that they can make
this a year and a half to build
the arena, and we're efficient.
We lower the cost to it.
But again, then that same group might
build a whole nother trial in the same
disease and they build the whole thing
over again and the the costs come to that.
The first time this, this sort
of occurred to me is when I
started designing clinical trials.
This was over 25 years ago.
We were doing with bringing
the Bayesian approach.
We were doing a number of device trials.
The, at the time CDRH, the Center for
Devices was doing a lot of Bayesian
things, Bayesian statisticians.
We were designing a number of trials.
For spi uh, spinal implants.
So this is degenerative disc disease,
and a great deal of research was going
into, uh, devices that could be put
in, uh, uh, artificial discs that would
prevent pain and, uh, the issues that
go with degenerative disc disease.
And we were simultaneously
designing three trials.
That were very, very similar for three
different devices and we were working
on them, and it just seemed so odd
that this trial was enrolling 150.
Patients on the standard of care,
spinal fusion, which is the control.
This trial was enrolling
150 on spinal fusion.
This trial was enrolling
150 on spinal fusion.
It was like, geez, maybe if
you could get these three
companies to talk to each other.
Enroll one group, 150 patients
on spinal fusion, and we prevent
enrolling 300 in that case.
One arena, share the resources
of this, and then maybe a fourth
one comes on board and you build
an arena to play multiple games.
Think of the savings that
could be done in that scenario.
Now, this is a reality.
And we, we, we now understand this and
so I'm gonna walk through a little bit
this idea of a platform or an arena to
carry out questions of clinical trials.
We refer to these as platform
trials when multiple, uh,
therapies will be investigated
in the same trials, perhaps even.
Over time, staggered arms coming in
a perpetual trial to investigate a
particular disease where, like the
NFL we play half of our games in a,
in a home stadium, and we reuse that.
So that's a platform trial, and
I'm gonna walk through today
and talk about platform trials.
There's another form of trial that
does a similar thing where it's
an arena to investigate multiple
questions, which is a basket trial.
So a platform trial investigates
multiple therapies in one particular
trial arena, and it's the multiple
therapies that is the multiple of
a platform trial in a basket trial.
It's the multiple patient types.
If the first sets of basket trials
were typically in oncology where you
had a therapy that might be beneficial
in multiple histologies, head and
neck cancer, lung cancer, breast
cancer, uh, that you would go in and
maybe there are mutation positive
of a particular type, but maybe it's
effective in one and not in the other.
It might be in other diseases, the
severity of disease, the genotype
or the phenotype of the disease,
where what we're investigating
is the multiple patient types.
That's typically referred to as a basket
trial, a bucket trial, where it's really
investigating the, the heterogeneity of
effect across different patient types.
Again, multiple questions
investigated in the same trial.
It's a bit of a kind of platform.
We don't call that a platform trial.
It's just terminology semantics,
where a platform trial is multiple.
Treatments where a basket trial,
and we can argue about the name,
but it's the, this sort of thing.
The path well worn here is, this
is what, what it's being called.
A basket trial is multiple patient types.
Yes, there are trials that are doing both.
It's multiple treatments
and multiple patient types.
The other thing both of these
are called is a master protocol.
So what is a master protocol?
A master protocol is just the
document, the protocol of an arena.
We have protocols if we're
investigating a single treatment
treatment, a against placebo.
We create a protocol which describes
the treatments, the patients, the
visits, the endpoints, the rules
of the trial are in this protocol.
A master protocol is one that is going
to incorporate the multiples, and I'm
gonna talk mostly about platform trials.
I'll, I'll, I'll touch in on basket
trials and it's, it's hard to talk
about one with the other one, but
let's think about a platform trial.
The master protocol is the scaffolding
that allows multiple treatments
to come and go in the trial.
The master protocol describes
the patient experience, the
endpoints we're gonna collect the
inclusion exclusion of the trial.
It also sets out the rules for
how the different arms behave
when they're in the trial.
If a patient comes in and meets
the inclusion exclusion that are
in the master protocol, how do
they get assigned to a treatment?
And it, it lays that out
and it lays out the rules.
What it doesn't have in the master
protocol is anything about a treatment.
It, you don't put the
information about a treatment.
Those come in in modular appendices.
So the master protocol is this
document that is the scaffolding
of the arena, and then it says in
this document that it will have.
Modules, appendices, substudies, lots
of terms to these that these modules
will come in and that will describe
everything about the particular
treatment, but it sits in this
master protocol that describes what
happens when a treatment comes in.
The treatment can leave the trial.
And you pull it out and the master
protocol hasn't changed at all.
It's built to have these modules come in
and go during the course of the trial,
and you set the rules up for this.
So a master protocol, a lot of
times somebody will say, oh,
we're doing a master protocol.
It's not very descriptive.
It could be a basket trial.
So a master protocol could have
a single treatment, and you could
put the treatment name in there.
But the modules are the,
the, the different subtypes.
The different histologies.
Those might come and go.
You might stop enrolling
histology three, but the other
histologies are still enrolling.
A master protocol can handle that.
So the master protocol is a
scaffolding to handleable handle,
handle multiple questions now.
Well, a lot of times it's just
sort of vaguely thrown out as
we're doing a master protocol.
Again, it's not very descriptive.
You have to find out, is
this a platform trial?
Is this a basket trial?
Is it both within the setting?
So you'll hear lots of terms.
This is a new science.
Platform trials are new, and
I'll walk through a little
bit about platform trials.
I'll walk through a little
bit about the history of this.
The role of the pandemic had
a really, really interesting
role in the, in the life cycle.
The, the, the science of platform
trials and, and the roles of them.
So, uh, I'll give you an
example of an incredible.
Platform trial.
And we have a podcast, uh, with Dr.
Merit Sekovich, who is
the, the PI of this trial.
This is the Healy, a LS platform trial.
This is a trial, by the way, the name
Healy and I, I, I'll talk more about this.
The name Healy is Sean Healy
was a patient with a LS.
And he was involved in the
development of this platform trial
and he put funding towards that.
And hence his name is attached to this,
uh, I think at, at uh, mass General.
There's a Healy Center, and he
was incredibly excited about the
development of a platform trial for the
treatment of a LS, this platform trial.
Allows treatments to come and
go, and they share controls.
Remember the example that I gave you of
three companies all enrolling control arms
in, in uh, uh, degenerative disc disease.
Now in a LS, they're sharing placebos.
There have been, I believe it's now eight,
you can go back to the podcast and and
hear this eight different arms that have
come and gone in this perpetual platform
trial and more arms are coming in and
are investigated in the Heal a LS trial.
Okay, now the heli a LS trial,
I'll describe a little bit of it.
It's a great example.
There are multiple treatments that
have, that have left the trial.
They have read out, the results of the
arm, have read out, and you can see
how this is published and what this
looks like in this platform trial now.
The, the Helia a LS trial is set up where
there's, there's large inclusion exclusion
criteria at the master protocol level.
It is possible that.
In one of the arms, they don't include
everybody from the master protocol.
They can have more specific, you're
trying to get these arms to have
similar inclusion, exclusion criteria,
similar patient types within that,
but every arm specifically might have
specific things, specific allergy
to only that arm where somebody's
not, not allergic to the thing in B.
So they may be able to get
B, but they can't get a.
You don't want these to be
mutually exclusive in that setting.
Otherwise you're kind of running,
just disconnected trials in that
setting and there's no synergy to it.
It.
The more overlap it is, the
more synergy you get to this.
Imagine they're completely the same
patient population, uh, in that setting.
You can enroll them now.
You can enroll them, and all
of the placebos for one are
gonna count for the other one.
So what does that mean?
A patient comes in and they
meet the inclusion exclusion
for the individual arms.
And they get randomized to the
cohort of A, the appendix or the
cohort of B or the cohort of C.
If there's three arms right now that
are enrolling patients A, B, and C,
they are not blinded to
whether they're an A, B, or C.
They're told you're gonna go to
A, you're either gonna get a.
Or a placebo, but you'll
be blinded to that.
But it's very rare in these that a
patient is blinded across A, B, and C.
It is incredibly burdensome to
a patient to have to take an
infusion and oral to blind them.
In some ways, it can be almost
impossible because C might stop rolling.
Uh, at, at a particular time.
And it can be awkward as to somebody
continuing to take a placebo for that
when they may know they're not on that.
So a patient is not blinded to A, B, or
C, but they're blinded to active or not.
So another randomization in the
Healy trial, it's three to one.
So 75% of the patients get
the active, 25% get placebo.
Meanwhile, a patient might be enrolled to
B and and could get the placebo and they
were eligible for a, all patients that got
a placebo for any of the particular drugs
that were eligible for a will be used.
When a analyzes, is it having a positive
clinical effect, they undergo the same.
Uh, visit schedule the same endpoints.
They were originally eligible and could
have been randomized to a, we know that
about them during the course of that.
And so that's what this
heal a LS trial looks like.
So it does blind them to active or not,
but not across the different, the, the,
the different, uh, investigational agents.
You can go and see the publication of
this and in the the consort diagram,
it will show how many patients were
randomized to a placebo for a and other
placebos, but were eligible for a.
And that goes into the primary analysis.
It shows a secondary analysis against only
that arm's placebo, which is three to one.
But if there were three arms enrolling at
the same time, now the borrowing of those
placebos, uh, not the borrowing, the use
of those placebos directly in the primary
analysis might be 80 patients on active.
In in that setting.
It might have been, let's do 75.
75 on active, 25 on the placebo, but
they got 50 other patients that were
given a placebo, so it's 75 against
75, meanwhile, B enrolled 75 and 25.
It's 75 against 75.
And so each one of these are getting
the power of 75 versus seventy
five, a hundred fifty patients,
but they're enrolling a hundred.
So there's huge inferential benefit.
And imagine, imagine five arms coming in
and they all have, uh, uh, in this case,
you want to enroll 80 patients on a.
And if they all act
independently, it's 80 against 80.
You enroll with these five arms,
you enroll 800 patients, each
enrolls 160 patients, 800 total.
If they go together in a platform
trial, you enroll now 80 placebo
and 80 on each active arm.
That's 480.
You've saved 40, 40%
reduction in patients.
That's 320 patients less.
Each one's gonna be faster in that
setting, and each one is gonna have the
same power because it's 80 against 80.
Now we're enrolling as a community,
less patients on placebo Overall.
17% of patients get placebo
within this HEALEY ALS trial.
Uh, in the case of five arms enrolling
at any time, uh, if, if, and done in this
platform, 17 per get percent get placebo.
If we act all separately and each
company builds their own arena and
they do one-to-one 50% of global
patients are getting a placebo.
For not better inferences,
the same inferences.
You can see there's this massive
improvement in the ecosystem If we have
one standing arena, not too dissimilar
from the NFL in that and the absurdity
of building separate arenas to play
different games in that setting.
Okay.
Now, um, in that setting.
It so that that's a platform.
There are basket trials where
you're investigating multiple
subtypes, and I, I, we will do a
different podcast on basket trials.
There's really interesting parts to it.
So today I'm gonna walk through a little
bit of history of these past platform
trials, a little bit of the controversies
or the new scientific things to deal
with in these trials that are different.
You, you run into the same questions
about it, but what's different about
these trials, uh, uh, in this setting?
Because there's a potential for
a massive improvement of this.
I wanna go back to the I Spy two trial
as another example of a platform trial.
The I SPY two trial is a
trial that is still running.
It's, it's moved on.
It's, it's evolved into a
platform of I spy trials.
So I'm gonna specifically
talk about I SPY two.
I was involved in i I two.
Barry was involved in the modeling of
i I two, so I know more about that.
The i I two trial was run
by Quantum Leap Healthcare.
It's a nonprofit, and Laura Esserman is
the PI of this trial, and she's amazing.
And she was a force in
the creation of this.
My father, Don Berry, was a, a
co-investigator in this and did this
work through his work at MD Anderson
as this was originally funded from
multiple efforts, the foundation
for the NIH also donations, uh, for
example, Safeway Grocery Stores donated
to get this, this trial started.
The Icey two platform
is also investigating.
It's a basket trial and a platform trial.
Multiple arms.
The I SPY two trial started
enrolling patients in 2010.
It's for neoadjuvant
treatment of breast cancer.
So the, uh, a patient, a woman,
has breast cancer, and in this,
there'll be multiple arms.
It's a phase two trial.
Trying to demonstrate proof
of concept on the trial.
And the trial enrolled more than 25 arms
over the course of the first 11 years.
You can go to the website and see all of
the different arms that came through with
common controls all during that time.
Now, breast cancer is understood
not to be a single disease.
And so in the trial, specifically in
the protocol and in the inference of
this, the breast cancer is broken up
into eight subtypes of breast cancer.
A two by two by two factorial
of hormone receptor status, plus
minus her two status, plus minus,
and MammaPrint status, plus minus.
So every woman that comes in
that qualifies in the trial.
Meets the condition and they fit into
exactly one of these buckets called
subtypes in the trial that defines the
eight different subtypes of breast cancer.
Each treatment that comes in the
efficacy of the treatment relative
to control is analyzed separately
by each of the eight subtypes.
Randomization to patients
is done separately within
each of the eight subtypes.
The trial employed response,
adaptive randomization, so a woman
that comes in that is hormone
receptor positive, HER two positive.
I'll, I'll, for, for sake of this,
I'll ignore MammaPrint and sort of
think of the, those two, uh, her, her
two status hormone receptor status
would be randomized differently
than somebody that was her two
negative hormone receptor positive.
And those were updated during
the course of the trial to favor
the arm that was doing better for
the woman of the same subtype.
So here's this platform that is a
single arena that ran for over 10
years with more than 25 arms coming in.
Common control over this time,
and the individual arm can look
specifically and address efficacy.
Four different subtypes of breast cancer
and the women were randomized more likely
to the arms that were working better
for the women with the same subtype.
So early on, two of, uh, one of the
first arms that graduated and was
published in the New England Journal of
Medicine in July, 2016 was Neratinib.
Was an arm that graduated from the
platform positively and neratinib.
It, it goes through in the title of
the New England Journal of Medicine
article is Adaptive Randomization
of Neratinib in Early Breast Cancer.
The the Neratinib arm was enrolling
during the course of the trial and it was
performing better in hor, in in, sorry.
It was performing better.
In her two positive hormone receptor
positive patients, it was doing
worse in her two negative patients.
The randomization was then favoring the
groups that it was doing better in, and
even at a time, some subtypes would come
in and they'd have zero randomization.
For Neratinib, but in the subtypes
that Neratinib was doing well,
it was getting high proportions.
Meanwhile, other arms might be
doing well in the groups that that
Neratinib is doing less well in, and
the randomization increases in that.
So for a particular arm that
comes in a trial, it gets
more patients in the groups.
It's doing better in trying to identify.
The groups that it's doing better
in where to go in phase three.
Meanwhile, patients coming in are
more likely to be getting arms that
are, are performing better, but
it's a common arena of more than 25
arms per perpetually in this trial
for the treatment of breast cancer.
If these 25 arms would've
all been done separately.
Massive increase in patience, time costs.
It's, it's incredible the amount
of efficiency that the Icey two
trial created in the treatment.
I think it also made it that
taking a shot on goal saying, let's
investigate this drug in B in breast
cancer, was a much lower hurdle.
The time was shorter.
The cost was smaller, that we took
more shots on goal in that setting.
If I have to go build my own arena and
my own cost, the cost is much higher.
The time to information is much higher.
I never take that shot on goal.
But if there's a re an arena sitting
out there that the drug can go in, the
cost is smaller, the time is shorter.
We take shots on goal.
That's good.
This is good.
More cheaper shots on goal
is good in the setting.
In the trial.
If you build your own trial and you
go through all the parts of this, the
contracts, the protocol development,
everything to this, it might be two years
before you enroll your first patient.
In I spy two from the time
they said, yep, I want to go.
In the trial, it was more like three
months because the trial already
exists, the time to that first patient,
so much shorter, so much easier.
The cost is less.
We're sharing resources,
we're sharing patients.
Everything about this is better.
Now, maybe the negative of
this is we have to fit within.
The master protocol, we have
to fit within those parts.
You can customize a certain amount
of this and so for example, some arms
never expected that they were gonna
benefit her two negative patients.
They directly were expecting HER
two positive, so they only enrolled
within the HER two positive groups.
And they, there were always zero
randomization to her two negative, so
that's half of the subtypes within that.
All of the analyses were done by subtypes.
What does the arm do in the different sub?
We could incorporate that very naturally
in the model of an arm that it just got
zero randomization in the other group.
So in this basket and platform trial,
it could very easily incorporate that.
Now you can also go to the podcast
with Don Berry talking about all
the innovations of ice bite two.
But I want to hit on this
for some of the amazing.
Efficiencies that these
platforms can have within that.
So Harken back to 2015 and the
Ebola pandemic, the outbreak
of Ebola in West Africa.
Uh, there's an outbreak and a, a large
number of patients are getting Ebola.
For those that went through
this, it was pretty scary how
this is gonna go very deadly.
Uh, the upward of 50% mortality
for people that get Ebola.
Huge concern about the spread.
There were a number of clinical
trials that once the outbreak hit,
and this was kind of fall of 2014.
Huge rates of cases going on,
and the clinical trials are
now being developed for this.
By the time they get approved and
by the time they're ready to enroll
patients, they have potential treatments
for it, the disease has waned.
We learned almost nothing from that
initial outbreak of many hundreds of
patients in how to treat Ebola now.
Enormous success in how to control
the spread and prevent this
from being a global outbreak.
But the clinical trial infrastructure
wasn't set up very well, where everybody's
out trying to run their separate
trials, individual trials within that.
Okay.
We had a trial sponsored by the
Gates Foundation that we developed.
It was a platform when this outbreak
hit incredibly quick development.
We were very aggressive, got
it approved by multiple IRBs.
It was ready to go.
It never enrolled a patient.
You can read, there's a clinical
trials, uh, a response, adaptive
randomization platform trial for the
vis efficient evaluation of Ebola.
And, uh, I'm the first author on it.
You can go see that in clinical
trials if you wanna see that.
This was published in 2016.
It never enrolled a patient.
There was concern at the time.
Of course, we're not ready for a pandemic.
2016 in the setting.
And one of the realizations
is that the clinical trial
infrastructure isn't ready for this.
We can't wait for a pandemic
and develop a trial.
Now interestingly, the
NIH ran another trial.
There were other outbreaks of Ebola.
So they got, they, they set up a
platform and they were ready for
this, and they enrolled what's
called the Palm Platform trial.
Funded by the NIH and they
investigated three different
treatments, uh, with a control.
The control was rem Desi severe,
so it was an active control.
And then three investigational
treatments all in one platform,
sharing this common control.
An incredible trial, incredible trial
that found two of the three treatments
were effective, and they lowered
mortality from about 50% to 30%.
I, I, I urge you to go look at that.
Published in the New England Journal of
Medicine, uh, in 2019, incredible trial.
Meanwhile, other, uh, other groups
understood that we're not ready
for a, a pandemic in the eu.
There was a, uh, trial called
the Prepare Trial, and it was a
prepare network that the EU funded
to be ready for the eventual.
Pandemic that is gonna come.
We just don't know what
it is, where it will be.
Part of the P Prepare solution was
to create a platform trial that's
there before the pandemic hits
was understood that almost surely
this pandemic would would hit,
intensive care units would hit ICUs.
So the Solu prepare solution was to create
a platform trial that's enrolling patients
with community acquired pneumonia.
And, uh, uh, investigate.
Community acquired pneumonia,
which is a, is a burden, a patient
burden before the pandemic comes.
But design the platform.
Trial says we're an arena and if
a pandemic hits, we're immediately
gonna enroll that pandemic and
we're gonna bring therapies.
Some of those therapies we're already
investigating in community car pneumonia.
And that became the REMAP CAP trial.
So this became a global trial that
was ready for a potential pandemic.
So it started enrolling patients, I think
in 2017 in community acquired pneumonia.
Enrollment was somewhat slow, and of
course, when the COVID-19 pandemic
hit that trial immediately said, we're
enrolling patients with COVID-19 coming
through the intensive care units.
Enrolling globally to allow
wherever the surge is.
It's enrolling patients.
It immediately flips steroids,
um, uh, into that which were
already being investigated in.
Community acquired pneumonia.
It was investigating immune modulation
in that it immediately, some of
those therapies were brought over.
Uh, and immediately was enrolling, but
it also brought in antivirals a number
of different therapies within that trial.
And COD, it ended up investigating 66
different therapies in one arena for it.
Platform trials were a hero.
In the COVID-19 pandemic for the
treatment for therapies to treat it.
Now, the vaccine had a phenomenal impact,
uh, on COVID and saved millions of lives.
Um, and, and we, we certainly
saw that in the REMAP CAP trial,
but trials like the remap.
CAP trial.
The recovery trial was an amazing
platform trial that ran for very, uh,
small resources, but they were the
first ones to produce steroids as a
life-saving treatment for, for COVID.
Um, the recovery, the recovery trial,
the remap Cap trial, the active platform
in the NIH, the operation warp speed.
They created 11 different platforms and
investigated 37 different therapies.
So think of this, we have this pandemic.
There's urgent need to see
what affects, uh, COVID.
What, what, what works.
Even two weeks earlier means
many lives saved globally.
So two weeks matters.
There's this urgent need.
You can't go build a clinical trial,
just like in the original Ebola
outbreak to answer a question.
By the time you build the trial and you're
ready to go, there are no patients left.
These trials investigated a huge
number of therapies, created the IL-6
receptor antagonist, demonstrated a
number of therapies were, were bad.
Actually, a number of antivirals for,
uh, a hospitalized patients were,
were, were negative, uh, in that.
And a number of therapies had
huge positive impact on the
therapeutic treatment of that.
The only way to address this
pandemic was platform trials.
They were an incredible success in
platform trials and even now for the
next pandemic, a number of these platform
trials, REMAP-CAP still running recovery,
still running that this is seen as as
a way to go about it and you can see
why in the incredible things now other
disease areas are getting into this.
Precision Promise was a trial in
pancreatic cancer funded by the
PanCan Network, which is a patient
organization, and largely what they
saw is companies weren't bringing
drugs into pancreatic cancer because
it was so hard and things didn't work.
If they could build an arena
that allowed easier shots on
goal for the treatment of this.
More companies are gonna bring
their potential therapies in.
This was a thing they did for patients.
It was some of these trials
were being created by federal
governments in Alzheimer's.
The EAD trial, they created a platform
trial for the treatment of Alzheimer's
looking for disease modifying therapies.
The incredible burden of Alzheimer's
is gonna bankrupt many of these federal
governments that pay for healthcare.
And so we're gonna help invest and create
an arena to make this easier and make
this more efficient to find therapies.
Again, this urgency in the
cost to building this arena.
Glioblastoma is another effort.
Partially patients, partially nonprofit
organizations creating GBM Agile as
a platform trial to investigate that.
The Healy trial is exactly that,
a combination of academic and, and
patients creating a trial in a very,
very hard disease to find effective
therapies to make this easier.
The NIH is now investing in a, a, a,
the step platform for stroke therapy.
We, we have some really effective
therapies for acute stroke.
We don't know who they all
work in, but let's learn.
Let's create this platform
to learn and investigate many
therapies within this disease area.
So disease focused platform trials are
being created in many rare diseases.
In many hard to treat diseases
in potential pandemics, and
again, where there's urgency,
there's need to do more efficient.
These are being created now.
They're also being created by individual
pharmaceutical companies who might have
three or four treatments in a disease.
Should they build three separate arenas?
No.
They should build one platform to bring
the multiple therapies together to make
this more efficient for their development.
There are lots of groups that are now
incredibly interested in the development
of these platform trials because of
the incredible efficiency of them.
Now I will provide a
cautionary tale of this.
EPA is a cautionary story.
More than 50 million Euro was
spent to develop this platform
trial to bring multiple therapies
in, in a phase two platform for
Alzheimer's between 2015 and 2020.
An incredible effort that included,
uh, registries to, to, uh, to look at
patients at risk, to get, um, a periodic
biomarkers and samples and scanning.
And get a trial ready cohort of patients
that could go into the clinical trial.
Avoid large rates of screen
failures, incredible effort that
never had an arm join the trial.
And so that's a cautionary tale,
and you can go back to the podcast
with Craig Ritchie that we do.
You can go back to the, the,
the group and we talk a little
bit about why this didn't work.
The opposite of that is the Helia
a LS trial and partly the Helia
a LS trial was able to fund the
first few arms in the trial.
In all of these efforts that we've
worked on, once you get one arm and
you demonstrate that you can enroll
patients, that you can collect
data, that this works, the, the,
the risk or the potential risk of
bringing an arm and gets much, much.
You can Now, the cost of this,
you can't ask the first arm.
To pay for the initial cost to build
this trial and to be able to fund
the first few arms in that trial was
enormously beneficial to get it running.
And then once it running, once
it's running, these things can
can generate, the costs are lower.
It's a no brainer for pharma
to enter these trials.
So it's a cautionary tale in
the development of these trials.
It is.
When would you maybe not want to do this?
Or what?
Or what's, what's harder?
It is harder to develop a platform than
to develop a protocol for a single drug.
You're building scaffolding.
You're building it for what ifs.
You're trying to build this
larger master protocol.
It takes longer and it's harder
to build the initial effort.
You're building a database for
multiple arms as opposed to one,
so it, it is more challenging.
You're creating A-D-S-M-B that's
gonna oversee multiple arms.
This is harder to build than one,
and that's why a lot of times you
just, oh, let's just build one
and then let's just build one.
And you never actually build the arena.
You do what the NFL is gonna
go backwards on and just build
a new arena for every game.
So you've gotta figure out how to
build this and then it's so easy.
But when do you build it?
How do you cost it?
And the extra time to build it, to
get it started is the thing that
generally is more challenging.
So can a patient organization build it?
Can a federal government, can
academic, uh, uh, group build it, you
know, in, in the setting And now it
makes a huge impact on the disease.
Huge inferential benefits,
huge infrastructure benefits,
lower cost, faster times.
Just makes a ton of sense.
Okay, so.
I hope you can see a little
bit about the promise of this.
There were smattering of
platform trials before COVID.
The success of the platform
trials during the COVID pandemic
journals published these papers.
They can see what a a, a
consort diagram looks like.
All the barriers were gone.
Regulators accepted it.
They approved treatments based on it.
All of this now accelerated the
science of the platform trial years.
Faster than it would've been if it
was just organic growth of these
various sporadic disease areas.
So now we've seen this incredible
acceleration of platform trials.
And now, uh, with, with
the pandemic o over.
Many of these disease areas have the
same urgency that we have in pandemic.
Patients have urgency, costs
are higher, doing it other ways.
So there's huge potential benefits.
So I hope you can see a little bit
the neat of, of the science, the,
the incredible synergy that it
provided in, in the COVID pandemic.
There's a lot of new science
that goes with this with with
platform trials for statisticians.
New papers published on this.
So it's a whole burgeoning, uh, a
science that's developed and you can go
back to some of our podcasts that look
at using non concurrent controls and
other issues, and we'll keep coming out
with more of them that dive into this
new science of, of platform trials.
So I hope you enjoyed this
little, uh, uh, navigation through
platform trials through the arena.
And thank you for joining me, and until
next time, we'll be here in the interim.