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: All right, welcome everybody.
Back to part B of I Spy two.
I hope you got the episode, the previous episode where we are visiting here with
Don Berry and we are revisiting the I SPY two trial and we left off, uh, where
he's using MRI markers, auxiliary markers to speed up the adaptive algorithm to
model the rate of PCR six month PCR.
And he was now talking about one particular application where
the time machine is so valuable.
So how, what is a time machine in a clinical trial?
Don Berry: So let me tell you, in the context of ice I two, um,
we're, uh, 2014,
Scott Berry: 14.
Don Berry: uh, we've got, uh, several arms in the trial.
Um, one of the arms.
Uh, used, uh, Pertuzumab, which was an anti her two therapy.
It was used in addition to Trastuzumab, uh, also called Herceptin, um, in
her two, uh, positive, uh, women, uh, that is with her two positive tumors.
And, um, we had other therapies as well.
The standard arm in her two positive disease included Herceptin and Paclitaxel
and, uh, doxorubicin, um, in this six month period that we talked about
it, six months of the, of therapy.
Um,
and it's, uh, it, it so happened that, um, gen, uh, Genentech.
Uh, had been investigating Pertuzumab, uh, in her two positive disease.
Um, and there was a, another trial, another neoadjuvant trial that was
comparing it plus Herceptin, uh, to just Herceptin plus the, the chemotherapies
of, uh, uh, paclitaxel and, uh, doxorubicin and Cyclophosphamide.
Um, so,
uh, the, uh, FDA gave accelerated approval to Genentech's Pertuzumab.
Scott Berry: to ext
Don Berry: And that meant that, uh, Genentech could sell the drug.
Um, and they had obligations about coming back with, you know, the
longer term endpoint, but it was based on an an, an improvement PCR rate.
Um, and our physicians and our patients, our patient advocates, uh, and our data
safety monitoring board said You have to drop the standard arm for those patients.
You can't give the standard arm because it doesn't include pertuzumab.
Um, and I said, okay, we'll do that.
Scott Berry: that.
Don Berry: Um, we did it
by zeroing out the randomization to the standard arm.
Uh, we luckily had pertuzumab in the trial already as one of the experimental
arms, and so we were able to, uh, sort of compare to Pertuzumab, but
there were only a few patients that were in, and so it wasn't a big deal.
So I went to Scott and I said,
Scott Berry: said,
Don Berry: what am I gonna do?
I, I'd like to be able to use the controls that we already have in
that, in the her two positives that did not get pertuzumab, that some
of which still don't have surgery.
Um, and I'd like to use them.
For control.
Um, but I don't want to keep using them, you know, for, for five years from now
when, uh, other things will have happened and the disease may have moved on.
And so maybe I should discount them somehow for, uh, the, um, for the amount
of time that they've had in follow up.
And, you know, when they entered the trial, what do I do?
And Scott said,
Scott Berry: said,
Don Berry: you know, I know how to do this.
I did it.
I did it in baseball and in golf and in hockey, uh, and Scott,
uh, to you, the time machine.
Scott Berry: Yeah, so we in in sports, there's the age old question of how do
you compare players of different eras?
How do you compare a Mark McGwire to a Babe Ruth?
How do you compare Tiger Woods to Jack Nicklaus They never played together.
The beautiful thing about sport is they didn't play together, but they
played with players that played with players that played with
players that did play with them.
So, babe Ruth May have played with Jimmie Foxx They overlapped in their careers.
Jimmie Foxx overlapped with Ted Williams.
Ted Williams overlaps with Mickey Mantle and Reggie Jackson.
And Hank Aaron overlapped a lot of people.
There's a bridging.
Over time of players at different eras.
And if you can estimate the effect of time on the players, sports is almost
a little bit more complicated 'cause players age themselves, drugs don't age,
but that if you could estimate the effect of time, you can isolate the individual
effects of different players and compare them even if they never played together.
Well here in a brand new kind of trial, a platform trial, this exact problem
comes out where arms are in the trial at different times and now you want to
compare an arm to patients that were enrolled at a control at a different time.
It's exactly the same scenario where it's only time that's different
in the scenario to make this.
Don Berry: So we, we put it in the trial.
Uh, we're going to use the, uh, all of the controls.
Uh, and there is this, uh, time machine that's Scott, uh, talked about.
And in the example I gave in the previous and part A of Pertuzumab, uh.
Pertuzumab the control arm At the time that we did, uh, the analysis
and graduated, uh, the control arm had about a 20% in triple negative breast
cancer had about a 20% path CR rate.
And the, in, on the basis of the one patient that we had from, uh, Pertuzumab
who had the results of surgery.
The result of surgery was A-A-P-C-R and these other controls, uh, these
other, uh, patients, um, that we use the MRI to predict, predict and to
conclude that the result was going to be, you know, 60% for, uh, Pertuzumab.
Um, we very little information.
About the experimental arm, of course, but that information was so compelling
that it was to the right of 0.2,
uh, with high probability.
And so it met the graduation threshold, which was a predictive
probability calculation.
So, and, but we didn't stop there.
We did it for, for all of I spy two.
From that point forward, all of I spy two was based on the time machine and
this, uh, the FDA calls them contemporary controls, controls that were in the
trial, but not at the same time.
Um.
And they've agreed to use that approach.
It's really
amazing, uh, the time machine approach in registration trials.
So the two trials that I mentioned, one in, uh, glioblastoma, the other
one in pancreatic cancer, uh, they use the, the time machine as a
fundamental thing from the get go.
It isn't just when you had to stop the, the experimental, the, uh,
control arm that this applies, but it applies for everything.
And
as
Scott mentioned, you know, uh, uh, Ted Williams in his list was
way back in the, in the middle.
Um, we.
Uh, we want to use, um, uh, Ted Williams during the that period.
Uh, and,
uh, we want to use an experimental arm.
We want to compare Ted Williams.
We get, we get a notion of what is the control rate during, uh, Jimmy Fox's
period during, uh, Hank Aaron's period.
Uh, and so we in the algorithm explicitly compare Ted Williams to Jimmy Fox.
We explicitly compare pertuzumab to other therapies.
It's just we don't tell.
Merck as the, uh, the drug's, uh, uh, sponsor owner.
Uh, we don't tell Merck what the Eli Lilly drug was doing.
We don't tell Eli Lilly what the Merck Drug is doing, but we're comparing
everything to everything in order to, uh, uh, assess what is the control rate.
Scott Berry: control.
So, uh, behind the scenes there's one model that's estimating the individual
interventions in the trial and provides that estimate to a standardized control.
And it's using all of the data from all the arms.
'cause it enables us to estimate the effect of subtypes, the effect
of time, all of these variables in providing that estimate.
Then you can hand a particular sponsor the estimate for
their arm relative to control.
And the machine is used, all of the data.
Now you, you mentioned the, um, graduation.
Uh, which by the way I think is a new word that came out of ICE by
two as sort of some level of success that wasn't statistical significance
traditionally, but the success in ICE by Two was innovative in and of itself.
What was graduation or success from a phase two trial?
Don Berry: So the way we defined graduation was it's going to do well.
Um.
And we did predictive probabilities, which
is kind of essential in this business.
That you, you, and integral to the kinds of things that Barry does, um, we look
at the data, well, we, I say we look at the data, the algorithm looks at the
data programmed to look at the data.
It's kinda like, uh, artificial intelligence, um,
um, and, and learns, uh, what
data.
Uh, what, what are the
Scott Berry: the
Don Berry: current probabilities of benefit for the various therapies?
And we do prediction.
We say, well, is this, does the data at hand suggest that this is a drug?
That this is a drug that can be marketed, that's gonna be successful
in the context of ISI two?
Is it gonna be successful in a phase three trial?
And in a phase three trial predicting the results of a phase three trial?
Uh, the Bayesian approach, of course, is critical in this.
Uh, the,
uh, PCR rate in the experimental drug has a probability distribution called
a current probability distribution or posterior probability distribution.
Uh, but what really matters is the future.
So there's data, there's two kinds of uncertainty.
One is in what is the PCR rate, that parameter, uh, and the other
is what are the data that come forward, the inevitable uncertainty
associated with the future results.
So these two uncertainties combine to allow for calculating a prediction.
What is the probability that a future trial will be successful?
If it has these characteristics, and that drives everything in.
I spy too.
It drives the adaptive randomization, it drives the graduation, it drives the
possibility of stopping for futility.
There's a stopping for futility.
Um, that's across the board that we calculate based on the predictive
probability, uh, distribution.
But there's also, as Scott suggested, the, the possibility of stopping in a subtype.
Um, because the randomization probability is so small that it, the
patient, you know, no patients are gonna be assigned to this subtype.
That actually happened.
Neratinib was one of the early drugs in the trial.
We published them in New England Journal of Medicine, the results of the Neratinib,
uh, trial and indicated that there was.
Some subtypes where it got zeroed out,
Scott Berry: out,
Don Berry: but meanwhile, in other subtypes, it was doing very well.
So it stopped randomizing in the subtypes where it was not doing
well and increased the probability.
And the other subtypes graduated since, uh, has been approved by the FDA for
the, you know, marketing, uh, approval.
Um, and nobody, as Scott indicated, nobody knew that this happened.
I mean, if this happened in a two arm trial and you, you said, uh, the DSMB
tells you, you gotta stop the control,
Scott Berry: control,
Don Berry: that would be a big deal.
How could you stop the control?
Could you then go in and, uh, get another control or, or wait a while
until you figure out what to do?
We never announced that Neratinib was no longer being assigned to those subtypes
of, of women, um, until after the trial.
Of course, the, the, uh, DSMB was involved, uh, and the DSMB
was enthusiastic about doing it.
In fact, before they realized that it was going, that it wasn't assigning
probabilities, uh, wasn't assigning, uh, neratinib to any of these subtypes.
Uh, one of the DSMB members said, we should stop Neratinib in that subtype.
I said, you don't have to.
algorithm has already stopped it.
Scott Berry: Yeah.
Yep.
Okay.
So a little bit of the numbers of I Spy.
So what are we talking about?
I SPY two started rolling in 2010 and it ran through 2022, and I
believe in that time, 23 different investigational therapies went through
Don Berry: Correct,
Scott Berry: and nine of those were successfully graduated.
So you talked about pancreatic cancer, you talked about GBM where.
Very little works.
And it's the, the huge challenge of these platform trials is
to, to try as much as you can.
And breast cancer therapies worked and some of them in subsets of women,
the right subsets of women, but nine of 23 successfully graduated.
And, uh, I, I think you mentioned that four of the nine have
received marketing approval.
Don Berry: Yeah, something like that.
But there's still, the others are still
Scott Berry: To be
Don Berry: Yeah, no.
Scott Berry: Yeah.
Yeah.
So from the perspective of the impact on breast cancer,
the I-SPY 2 trial was amazing.
Before I sort of turn to the, the, the, the larger impact of I Spy two and other
things, I, I think there's a few people that you wanted to make sure that due
credit, one of them is Janet Woodcock.
Don Berry: Yes.
So this is really a story about three dynamic women.
Uh, one of course is Laura Esserman.
Um, uh, the other I mentioned is Anna Barker, uh, who was, uh, very helpful in
the, the funding aspect and then give and giving advice about how, how to do this.
Uh, and the other is Janet Woodcock, who was at the time the, um, head of
CDER uh, at the FDA and, um, has been.
Uh, pushing adaptive designs and, for example, this thing about adaptive
randomization, she is, um, publicly announced adaptive randomization
is adequate and well controlled.
Now, if you know
regulatory stuff, you know that that's, that's the
criterion for a phase 3 trial.
For a pivotal trial, for a trial that, uh, a registrational trial,
adequate and well controlled.
So she has been enormously helpful, um, in I-SPY 2 and in setting
up, uh, GBM Agile and uh, uh, the pancreatic cancer, uh, trial.
Scott Berry: so, uh, Anna Barker, Laura Esserman.
Janet Woodcock, a huge role and a couple people, perhaps a
little bit behind the scenes.
So Meredith Buxton was there for quite a while, had a huge impact on ice by two.
Don Berry: And the current circumstance with Meredith Buxton is.
Scott Berry: Well, she is now, she's graduated, you will.
Um, uh, she successfully, so she now runs, uh, uh, she's now the CEO of GCAR,
global Coalition for Adaptive Research, and they run multiple platform trials.
Uh, so she, she did, uh, incredible work on a lot of the logistics and making,
making this go and now does this for GCAR.
And, uh, a little bit on a personal side from Barry Consultant's side is.
Berry Consultants built the code to run Ice Spy Two, and Ashish Sunil
at Berry was intricately involved in making sure that code ran, working
with Meredith running for years.
Uh, Ashish, Sunil had a huge impact on making that trial run.
He has sort of, uh, uh, very, very sadly, uh, came down with a LS,
uh, and, and uh, in that, but he had a huge impact on ice by two.
Don Berry: Uh, so I should mention, um, Kyle Waltham.
Kyle was at, uh, MD Anderson with me when we set this up, and he wrote the original
code, um, and it, it worked great.
Um, and then when we had to vary from the code for, you know,
we're adaptive, um, we went back to MD Anderson and, uh, they, we.
I had lost the code.
So I went to Scott and I said,
what can I do?
Um, and he said, well, I'll, I'll rewrite it now.
You have to understand the original code was really long and, and
developing and, and writing and deciding what we wanted to do and all this
adaptive randomization and et cetera.
Um, and it took and including with some funding from Anna
Barker, um, for Kyle's salary.
I never, uh, had any kind of a of a.
Uh, salary or anything.
It was all voluntary and all of Barry consultants' work was, was
voluntary, uh, in ice py too.
Um, so Scott took, I think three days instead of, instead of, uh, two years,
it took Scott three days, but of course he knew what we, what the goal was.
Scott Berry: Yep,
Don Berry: Uh, and that's, that's what we've used since and
model modified it, uh, since I.
Scott Berry: Yep.
So I-SPY 2 in many ways was the, uh, a forefather a a a foremother
for, for platform trials, one of the, the early platform trials.
Phenomenal success, success in the disease, but is really in
many ways changed the industry.
GBM Agile doesn't exist.
Pancreatic Cancer Action Networks trial PanCan, that, that, that trial
Precision Promise doesn't exist and COVID hits and, um, operation
Warp speed, the ACTIV trials.
Are largely I-SPY 2 for treatment of therapeutic COVID and Janet
Woodcock's leading the ACTIV network and it's all platform trials.
So the impact of I-SPY 2 and this idea that you said, you, you bet the farm at
the time, and you and Laura kind of went on this strange, uh, risky path that
cooperative groups might not embrace.
And you get, you know, you get innovative funding, you're getting Safeway
grocery stores to help fund this.
Um, and it's, it changed the world literally.
Don Berry: Thank you, and I agree.
I
Scott Berry: Yep.
Alright.
So they're sort of b before I spy two and after I spy two.
So, uh, phenomenal trial, uh, phenomenal effort and, and an amazing story.
So Don, I appreciate you walking us through that story.
Don Berry: thank you for
leading the walk.
Scott Berry: walk.
Yeah, right.
Well, thank you everybody for, for doing two parts of I Spy two and in
the next time we are in the interim.
Thank you.
Don Berry: Thank you.
Scott Berry: Thank you.