﻿ I want to thank the organizers for selecting this work to present at the
MIDD Immunogenicity Workshop today. I will be going through a case example
for a nivolumab plus ipilimumab combination in first line melanoma that illustrates our
approach to evaluate the immunogenicity risk and impact across our Nivo and Ipi portfolio.
When ADA presents, we know it's important to assess the impact on PK, efficacy,
and safety endpoints. Nivolumab is approved as monotherapy and in combination with ipilimumab
across many oncology indications. A deeper dive into the impact of nivolumab immunogenicity
was prompted when it was observed that nivolumab ADA increased in the presence of ipilimumab. This
slide is a reminder that there was a larger team effort to assess the impact of immunogenicity
across nivolumab monotherapy and in combination with ipilimumab and across indications. In the
next few slides I'll present a case example in advanced Melanoma that exemplifies this work.
CheckMate 067 is a randomized phase three trial evaluating Nivo
plus Ipi combination in a one to one to one randomization with Nivo and Ipi monotherapy
to assess the efficacy of the combination and Nivo monotherapy versus ipilimumab.
The trial was stratified by BRAF mutation status, American Joint
Committee on Cancer M stage melanoma, and tumor PD L1 status. Primary endpoints were
PFS and OS versus the Ipi monotherapy and the secondary endpoint was tumor response rate.
As you can see in the top right Kaplan Meier plot an OS benefit has been maintained now out
to five years with the Nivo Ipi combo in green followed by Nivo Mono and then ipilimumab. At
the bottom right of the tumor objective response rates highlighted showing that over half of the
subjects had a tumor response in the combination compared with lower rates for monotherapy.
Because we had a randomized trial with the combination therapy and the monotherapies it made
for a complete data set to allow us to examine immunogenicity in more detail. This is a summary
of the ADA incidents for Nivo and Ipi monotherapy and in combination from CheckMate 067.
Treatment emergent ADAs are highlighted with the pink box with Nivo Mono on the left,
Nivo Ipi combo in the middle columns, and Ipi Mono on right. Treatment emergent ADAs are defined
as at least one positive ADA sample post treatment. And for a baseline positive
subject the titer had to increase by fourfold to be considered post treatment positive.
Treatment emergent ADAs for Nivo in combo with Ipi were 3.5 times higher at
44 percent incidence compared to 12.3 percent incidence for the Nivo monotherapy arm.
Persistence positive here is defined as two or more consecutive ADA positive samples at least
16 weeks apart and neutralizing ADAs as measured by a cellular based potency assay. Persistent ADA
was low overall, but, again, somewhat increased for the combination. Interestingly, there was
no significant difference in Ipi ADA incidences for the combination compared to the monotherapy.
This is a time course of the ADA titers versus time for CheckMate 067 after treatment with
Nivo Ipi combo on the left and Nivo Mono in the right. Nivo Ipi combination is given every three
weeks for four cycles in the induction phase, and this is followed by a Nivo maintenance therapy.
As you can see, ADA titers are higher during the Nivo Ipi induction dosing phase on the left
compared to Nivo monotherapy on the right. And the decline in titers during the Nivo monotherapy
maintenance phase suggests a direct association of ipilimumab dosing and nivolumab ADA.
We next wanted to evaluate the clinical relevance of the ADA immune response. This
table shows the rate of acute infusion related or hypersensitivity reactions for Nivo Mono
on the left, Nivo Ipi combo arm in the middle, and Ipi Mono arm on the right.
Highlighted with the pink box is the total subjects with an event related to the preferred
terms listed, such as infusion related and hypersensitivity reactions. This data showed
similar rates across ADA positive and negative subjects in each treatment arm and this indicates
a lack of clinically meaningful association of ADA incidence in these specific events.
We next looked at the Nivo ADA impact on PK in tumor objective response rates for
the combination arm in CheckMate 067. To the left is a box plot showing the Nivo Cmins at
steady state versus ADA status, stratified by titer magnitude using a titer cut of 32.
Cmin steady states are trending approximately 20 percent lower for ADA positive subjects.
On the top right is a breakdown of the tumor objective response rate by the same ADA
titer cut and it shows no impact on the overall objective response rate. So this was reassuring.
However, we also wanted to evaluate the data at the individual level, so we conducted
multivariable logistic regression analysis where ADA titer impact on ORR could assessed
after accounting for the impact of other baseline disease factors. The data from this analysis is in
the bottom right table. The ADA titers are highlighted with the pink box and showing
that there was no significant impact of ADA titer on the odds of achieving a tumor
objective response rate with 95 percent confidence intervals of the odds ratio that included one.
It's also noted that Nivo Cmin after the first dose indicated as Cmin1 in the table or as an
exposure prior to ADA effects was also not significant in the logistic regression model
and explains why despite a lower Cmin, objective response rate was not impacted. So it was good to
see the lack of an ADA association with proximal effects, like hypersensitivity and fusion related
reactions in tumor response. However, we also wanted to assess the longer term impact
using the overall survival endpoint. The Kaplan Meier OS curves on the left show the
combination regimen from CheckMate 067 stratified by ADA status, again using the same titer 32 cuts.
The red and blue curves represent ADA positive subjects at high and low titers, and OSes we
ran a log rank test on the OS curves and the p value here indicated lack of significance.
You know, however, we recognize that baseline disease factors could alter our interpretation,
so to account for the differences in disease factors we performed propensity score matching
using seven baseline disease factors included in the bottom of the slide. And the right plot shows
the ATA stratification in this disease matched data set with more closely aligned OS curves,
but you can see that the log rank p value was even higher, again indicating not significant.
You know, one challenge with using propensity score matching is that it shrinks the data set
with the potential to reduce the power to detect the difference. So to look at the unmatched data
set in a more comprehensive way, Multivariable Cox Proportional hazard analysis was conducted.
This slide shows the Forest (phonetic) plot from the exposure response OS analysis with the ADA
titers highlighted with the pink box. For each titer cut the 95 percent confidence intervals
included unity, indicating lack of significance after adjusting for baseline disease risk factors.
In this Forest plot for categorical covariates the reference denominators indicated to the right
of the colon under the covariant name on the left and for continuous covariance the median
and the 5th and 95th percentile are indicated. Continuous covariates have two hazard
ratios defined at the 5th and 95th percentile relative to the median
in a box kind of wrapping these effects, with the blue color indicating that the continuous value
is greater than the reference. Nivo and Ipi exposures were not significant predictors of
OS and not unexpected the end stage poor performance score, higher Nivo clearance,
had increased risk of death whereas higher baseline body weight had a reduced risk of death.
Nivo clearance was previously shown to be a strong predictor of OS in melanoma and other tumor types
independent of the dose and exposure. This is believed to be associated with a higher catabolic
state in cancer patients that present with cachexia that also then metabolize the therapeutic
antibody faster. I've listed several references on this topic at the bottom of the slide.
So, the totally of the quantitative assessment using multivariate
propensity score matching models indicated no ADA impact on efficacy
endpoints for the Nivo Ipi combination in melanoma despite the higher Nivo ADA.
You know, however, the lingering question is, why is Nivo ADA higher when Ipi is on board? Both are
checkpoint inhibitors involved in increasing the immune system response. Understanding the answer
to this question will be important for developing other checkpoint inhibitor combinations,
including the next generation anti CTLA4s. As this slide is depicting, mechanistically Ipi
acts primarily in the lymph nodes to improve antigen presentation of tumor antigens,
but inevitably non tumor associated antigens are presented, i.e., Nivo antigens.
Ipi has also been shown to deplete and activate Tregs, which are required to maintain peripheral
tolerance to self antigens. Nivo acts primarily in the tumor microenvironment
to relieve T cell exhaustion. This led us to two primary hypotheses as to why Nivo ADA is
higher with ipilimumab. Ipilimumab could lower the threshold for dendritic cell and T cell priming,
allowing low frequency germline sequences from nivolumab to be recognized by T cells,
and/or ipilimumab could reduce T regulatory cell activity, reducing immune system
surveillance of self antigens with Tregs being important for maintaining tolerance.
So, to address the mechanistic questions we turn to a mechanistic model. Now, Dr. Chen gave a nice
overview of the first published mechanistic model to predict biotherapeutic immunogenicity and this
is referred to as the Chen Hickling Vicini model, or CHV model. Her group published the MATLAB code
several years ago and we were able to use that model to make predictions on a phase one FC
fusion protein. This was a non oncology asset we were developing. The CHV model predicted ADA for
this phase one asset after single dose quite well, but overpredicted ADA after multiple dosing. So,
we reasoned that T regulatory cells are an important component of tolerance mechanisms.
And after multi dosing of a biotherapeutic, T reg induced tolerance may be important.
As indicated on the left, we added T regulatory cells to the model,
and they were included downstream with antigen presentation and were induced in parallel to
T effector cells. This work was published in 2019. Inclusion of the Treg cells in the model
dampened the humoral immune response, resulting in a predicted ADA incidence after both single
and multi doses that were comparable to the observed data. As summarized in the manuscript,
we then went on to predict impact of ADA on PK and excuse me, PK and PD.
The other advantage of having the mechanistic model is to predict ADA under different trial
conditions. The phase two trial was being assessed with coadministration
of corticosteroids as maintenance therapy, so we updated the model further to include the known
in vitro effects of corticosteroids and T&B cell proliferation using the corticosteroid
dose being administered, and we then predicted a decrease in ADA with corticosteroid use.
So, using this backdrop of the QSP model that now contains T regulatory cells allowed us
to use the model to predict what would happen to nivolumab ADA if Treg cells were gradually
decreased to mimic the action of ipilimumab. So, what is the evidence of CTLA4 involvement
in Treg function? Well, we know CTLA4 is constitutively expressed on Tregs and it's
important for maintaining peripheral tolerance. CTLA4 blockade is known to suppress T regulatory
cell activity. On the plot to the right, our model simulation showing Nivo ADA incidence over time at
different percent Treg reductions. The bottom blue curve represents Treg reductions with the
predicted ADA incidence similar to that seen in CheckMate 067 for Nivo monotherapy indicated by
the dotted line. The subsequent curves are increasing percent Treg reductions with the
yellow and red curves on the top representing 75 in 100 percent Treg reduction, respectively,
with similar predicted ADA incidences to that compared to the observed Nivo ADA
from the Nivo Ipi combination in CheckMate 067. So, the model could replicate the hypothesis.
So Ipi at a single dose level appears to increase Nivo ADA incidence compared to Nivo monotherapy,
but is there an Ipi dose dependence to this effect? This table summarizes
the ADA incidences for Nivo monotherapy pooled across multiple tumor types on the left column.
The low dose Ipi combination regimen in RCC and CRC in the middle column
and the high dose Ipi combination regimen in melanoma on the right,
and as you can see there's a gradual increase in Nivo ADA incidence from 11 percent to 26
percent to 38 percent with ipilimumab dose. Now, it's true that the tumor type has changed
in this comparison and the Nivo dose is changing, but Nivo ADA incidence is fairly consistent across
tumor types as monotherapy, so unlikely those ADA differences is due to tumor type differences.
And for the melanoma high dose Ipi regimen, higher Nivo ADA is unlikely
to be due to the lower Nivo dose since a lower Nivo dose would favor lower ADA. So, evidence is
accumulating on these ipilimumab mechanistic affects on T cells that this can increase
Nivo ADA. However, can we get to the source of the issue, which is identified and nivolumab antigenic
sequences that get displayed by antigen presenting cells that then trigger the T cell response?
In the upper left box, bioinformatics tools can provide a first insight into potential antigenic
sequences that bind (phonetic) MHC class II with residues that faced the T cell receptor.
However, these are often followed up using in vitro experiments depicted here on the right top
box where proteins of interest are pulsed with monocyte derived dendritic cells to allow any
peptide antigens to be displayed and the cells lyse to isolate the MHC antigen complexes.
The antigens are then identified by LC MS. In the bottom box are the
results of this analysis with nivolumab. A low germline epitope identified in the
variable heavy chain between CDR 2 and 3 was identified. This potential Nivo
antigenic sequence had no protein homolog, no human protein homolog. However, the gene
sequence was found in a low germline sequence found in a specific gene that codes for mRNA.
Armed with a potential antigenic sequence that was detected in vitro to bind to MHC class II,
our next question was, is this Nivo sequence antigenic in vivo?
So this is ongoing work. To address this pre and post treatment, PBMCs from patients dose Nivo or
Nivo Ipi in CheckMate 067 will be evaluated to determine if memory T cells are present
that proliferate in the presence of suspected nivolumab antigenic sequences.
I don't have the results to share today, but I wanted to present this because it feeds
into the overall strategy for developing mechanistic models of immunogenicity.
QSP models, like all models, require Learn & Confirm iterative cycles as illustrated here.
For the immunogenicity model in the upper left here, in silico tools initially make predictions
on amino acid sequence antigenicity and use a generic MHC class II population distribution.
However, as additional experimental data become available, for example,
from MAPPS assays or ex vivo T cell recall assays, like we're going to conduct with CheckMate 067,
this allows you to refine the antigenic sequence. In addition, if you can genotype the patients
for their MHC class II type, you can then use the actual trial distribution of MHC
Class II genotypes, and that combined with the observed PK and PD and ADA data from the trial.
This information can all be fed back into the model to further optimize the prediction.
Let me summarize the results here. Nivolumab ADA incidents was increased approximately 3.5 fold
when dosed in combination with ipilimumab in the first line melanoma from CheckMate 067 and
this is compared to Nivo monotherapy. However, there was no change in ipilimumab ADA incidence.
Nivo ADA was not associated with clinically meaningful increases in hypersensitivity or
infusion related reactions. However, it was associated with a 20 percent lower exposure,
but this reduction did not result in an impact on efficacy as assessed by multivariable
analysis and propensity score matching. Mechanistic understanding was enabled using a QSP
immunogenicity model. The QSP model combined with in vitro and in silico bioinformatic sequence
tools allowed for hypothesis testing on the mechanism of ipilimumab induced
nivolumab ADA increases really for the design of the next generation biotherapeutics.
The QSP model predicted nivolumab ADA will increase with T regulatory cell reduction,
which was a surrogate for ipilimumab effects. And the future direction here is to
assess the T cell recall assays, which are ongoing in CheckMate 067,
to see if we can identify memory T cells exposed to predicted nivolumab antigenic sequences
to continue to inform this Learn & Confirm cycle of QSP immunogenicity model development.
I'd like to acknowledge the patients and investigators from CheckMate 067
that enabled the data analysis and an interpretation of this work. Also,
my fellow colleagues in clinical pharmacology at Bristol Myers Squibb, who are a part of a larger
effort to assess the impact of immunogenicity across our ipilimumab and nivolumab portfolio.
I'd like to thank you for dialing into this session today
and I look forward to catching up with you in the Q&A session.