﻿Hi, everyone. This is Xiaoying. First, I would like to thank the organizing committee
for the opportunity to speak today. So, during my presentation, I'm going
to share with you a systems pharmacology model my previous Pfizer colleagues and myself developed
to help us tackle the challenges from immunogenicity prediction.
Predicting immunogenicity could be challenging because of the complex underlying biology.
We understand that there could be multiple risk factors for immunogenicity development, such as
drug related, treatment related, patient related, or assay related (phonetic) factors. So those
factors could all impact the immunogenicity outcome and once an unwanted (phonetic) or new
response is developed in patient, it could be associated with significant clinical consequences.
For example, the developed ADA or (inaudible) could impact the PK/PD
and the efficacy in patients. In addition, there could be safety implications. For example,
adverse immunological reactions could be developed in the patients against a therapeutic protein.
In addition, in cases where there's an endogenous counterpart
with the therapeutic protein cross reactivity could be developed against an endogenous compound.
Because of those potential implications,
we want to develop a predictive tool to help us simulate the potential clinical consequences
for these therapeutic drugs of interest. In an ideal situation, we would like to have a tool
that has the following features. First of all, we would like to have
the tool to be integrative so that it can integrate those information
about the various immunogenicity risk factors. We also would like the tool to be quantitative
so that this tool can make quantitative simulations about the magnitude of the new
response and the magnitude of the impact. Lastly, we also would like to have the tool that can
simulate clinically relevant endpoints so that it can inform important drug development decisions.
I'd like to give an overview of the potential impact of immunogenicity using bococizumab
as an example. It is an anti PCSK9 monoclonal antibody for reducing LDL cholesterol. The figure
shows the cumulative increase in ADA incidents during this 12 month phase three trial under
the impact of ADA on the pharmacodynamic marker, LDL cholesterol. Notably, only 15 percent of the
subjects had a significant loss of PD at 12 months, as highlighted in the red box,
from an immunogenicity prediction perspective. It is notable that after the lengths of a phase
one or phase two A trial no significant impact of ADA were observed. This illustrates the challenge
for us to predict the long term impact of immunogenicity based on early clinical results.
Our approach is to develop a systems model of immunogenicity in a bottom up fashion.
The model recapitulates biological mechanisms involved in immunogenicity from gene protein level
to individual patients and ultimately to a patient population.
The model integrates risk factors, including drug and system related factors.
Drug related risk factors can be obtained from in silico or in vitro experiments.
System related factors such as genetic background, co medications could be included into
model inputs as well. The model is set up to provide simulations that informs
clinical consequences such as ADA incidents, change of PK/PD, as well as loss of efficacy.
There are multiple immune system components involved in immunogenicity development.
Immune cells, such as dendritic cells, T cells, and B cells, are critical players.
The dendritic cells uptake, process, and present the peptides derived from therapeutic proteins
to the antigen specific T cells.
Once a T cell gets activated, they subsequently activate antigen specific B cells,
triggering the differentiation of B cells into memory B cells as well as plasma cells.
The plasma cells are responsible for secreting ADA. Other components of the immune system.
such as T regs and cytokines, are also contributing to the immune response development.
The systems model was built using a bottom up approach by recapitulating the underlying biology.
Critical immune cells, such as dendritic cells, T cells, and B cells are included in the model.
The model captures the activation, proliferation, and the differentiation of the immune cells.
Due to the importance of the antigen presentation process,
a subcellular antigen presentation model was also included to capture the uptake, process,
and presentation of the therapeutic protein within the dendritic cells. Once the ADAs are secreted
from the plasma cells the model also captures the binding between the ADA and the therapeutic
protein so that the ADA development can alter the clearance rate of the therapeutic protein.
The model was built for two species, mouse and human. The mouse version of the model was
utilized for model collaboration, taking advantage of the rich experimental data.
The mouse version of the model was calibrated using in vivo mouse experiment with a model
antigen called ovalbumin. As shown in the figures, the model simulated profiles for CD4 positive
T cells and the plasma cells were reasonably consistent with the experimental measurements.
Here are the model simulations for a new response in a mouse vaccine study.
With repeated antigen challenge, the model simulates the boosted immune response
against that antigen. In addition, the model can simulate antibody affinity maturation process
consistent with the knowledge for ADA development during an immune response.
After model collaboration in mouse, the human version of the model was developed.
This is a mechanistic model that allows for integrating information about various risk
factors. Drug specific factors, such as the number and binding affinity of T or B cell
epitopes on the therapeutic proteins, could be obtained from in silico or in vitro experiments.
Patient specific factors, such as the MHC II allele genotype, naive T and B cell numbers,
could be obtained from the database or from In vitro experiments.
The model was built in a way that it can integrate various risk factors as model inputs.
And the model can provide simulations that are clinically relevant,
such as ADA incidents, change of PK/PD, and the loss of efficacy.
Here are the model simulations for immune response in an ADA positive patient who received repeated
dosing of a monoclonal antibody for a treatment period of a year and a half.
As shown in the figures, the model simulates immune cell kinetics, including the activation,
proliferation, and a differentiation of dendritic cells, T cells, and B cells. The model also
simulates PK and ADA profiles. With repeated dosing, ADA concentrations increase accompanied
by the decreasing in drug concentration due to accelerated drug clearance induced by ADA.
The model also simulates antigen presenting process.
Besides simulating individual patient, the model can incorporate inter-subject variabilities,
such as their variabilities in their MHC genetic background, drug clearance rate, and so on,
to simulate immune response in a heterogeneous population. As illustrated in the top figure,
ADA concentrations at the end of a virtual clinical trial in 1,000 subjects were simulated.
You can see the ADA concentrations could vary significantly. When the patients are stratified
by the number of T cell epitope MHC binding pairs, the patients who can form binding pairs, meaning
that their dendritic cells can effectively bind and present the T cell epitopes, developed higher
concentrations of ADA. The model simulation also suggests ADA development can impact drug exposure
as patients who had higher ADA concentrations exhibited lower drug concentrations.
Another clinically relevant endpoint for immunogenicity is the ADA incidence. First
the model can simulate the ADA concentration time course. By incorporating ADA assay specific
information, including sensitivity and drug tolerance, the model is able to predict the ADA
status of the ADA samples based on the specific assay. By summarizing the ADA status for all
patient samples the model is able to predict the immunogenicity incidence in a population.
Once the mechanistic model is established it can be applied to form various drug
development questions. One example is the application to biosimilar drug development
where PK equivalence could be affected by the potential variability in immunogenicity.
In order to demonstrate PK equivalence, it is preferable to have comparable immunogenicity
incidence in both the biosimilar as well as the originator arms.
However, the intrinsic variability in a patient's MHC genetic background could cause differences
in their immune responses against the same therapeutic protein. To help inform the optimal
sample size for a phase one trial for biosimilar, we simulated three different sample sizes:
N equals 250, 100, and 200. And for each trial size we simulated 100 random clinical trials.
The patients in the clinical trials have MHC genetic background that are randomly assigned
based on a predefined distribution. As shown here, as a sample size of the trial increases
it can help decrease a potential variability in patients' MHC genetic background. And as a result,
the increase in a sample size helped decrease variability in ADA incidence as illustrated by
the smaller range for the ADA positive incidence as the trial size increased.
So this information was provided to the project team to help inform the trial design.
In summary, a mechanistic, multiscale systems model was developed
as a tool to help inform immunogenicity risk. The model is set up in a way that it can integrate
various information from preclinical and clinical settings and from various experimental platforms.
We hope this model can serve as a helpful tool to work with the latest immunogenicity prediction
tool suites, integrate their information, and provide quantitative simulations.
The model continues to be developed as you will be hearing from Laura and Andre
in their presentations shortly. We hope we see continued collaborations between scientists,
this QSP approach could provide value and enable more efficient decisions for drug development
of biologics regarding immunogenicity. This wraps up my presentation. I would
like to thank my previous Pfizer colleagues for their support. Particularly I'd like to thank
Paolo and the team for their guidance during the model development. Thanks for your attention.