﻿Thank you very much for giving me the precious opportunity
to make a presentation in this MIDD workshop.
Today, I'd like to talk about QSP application to our COVID 19 vaccine clinical development.
From late 2019, new coronavirus is still spreading all over the world. Rapid distribution of COVID 19
vaccines to the public at large is world urgent need. And many COVID vaccines are
being developed now. I'd like to explain the mechanism of vaccine efficacy briefly.
The efficacy of the vaccine is demonstrated by your immune system,
after the vaccination, memory T cells and memory B cells are stimulated by a (basic antigen phonetic)
which are presented by dendritic cells or a binded B cell receptor. Antibody against the basic
antigen is formed by plasma cells and we are protected from the infection. Thus,
immune response against the basic antigen is a desired immunogenicity by a complex immune system.
In the basic clinical development, it is essential to design the optimal dosage regiments, however
there are some challenges related to human immune response in dosage regiment selection
of a vaccine. First, traditional PKP approach haven't to be used in for dosage regimen selection
of a vaccine because relationship between dosage and immune response tended to be better shaped
curve and immune response after vaccination is complex for example, booster effect,
I will explain it later. Second, while the vaccines are
injected to various populations such as older adults, children, different ethnic groups,
immune response is considered to be different in each special population.
Crowd, there is a difficulty in translation of immune response between animals and humans.
Therefore, we think dynamics of immune response in humans should be considered
in dosage regimen selection of the vaccine to challenge these difficulties.
While there are some approaches for the dosage regiment direction,
we think M (inaudible) using QSP model, IG simulator is an open model approach.
This QSP model for immunogenicity shown here was created by Dr. Chen
(inaudible) and it is a starting point of IG simulator. The QSP model for immunogenicity
describes immune system related to vaccine immune response. In addition,
key factors involved in dosage regimens direction of a vaccine are also described in the QSP model.
The affinity of the T cell epitopes of the vaccine antigen to emit C class
2 (phonetic)(inaudible) two is important in immune response. Since the epitope and its affinity can
be predicted in silicon(inaudible) based on the amino acid sequence,
the affinity of the T cell epitopes to emit (inaudible)
of not only our vaccine, but the other COVID 19 vaccines can be input to the QSP model.
In addition, it is reported that there are ethnic differences in frequency of emit C
class two(inaudible) allele and haplotype. Since there is the database of the frequency,
the information collected from the database can be added to the QSP model and ethnic
differences in frequency of emit (inaudible) 2 allele and haplotype can be evaluated.
Immune memory is an essential factor in duration of efficacy, as a candidate's exposure to a
vaccine antigen triggers an immune response that is much more rapid and more vigorous than that
seen with the first exposure by memory (B cells phonetic) it is booster effect. In addition,
antibodies with higher affinity for antigen increase during the course of an immune response,
this affinity maturation and booster effect are also described in the QSP model.
Therefore, QSP model in our COVID 19 vaccine candidate will be developed based on addition
data, by adding information of our vaccine. After that, virtual clinical simulation was performed,
in the analysis, we evaluated IGG level as an efficacy value marker because it is being
reported that there is a positive relationship between binding IGG and the vaccine protection.
In addition, QSP model was kept updated using the clinical data of the other vaccines or
of similar modality to increase the model credibility for younger or older adults.
In the project of our COVID 19 vaccine,
there were several questions related to immunology for our COVID 19 vaccine clinical development.
For example. Is a phase one design appropriate? Will the efficacy in Japanese be observed?
Is the dose in younger adults sufficient for older adults? Will annual vaccination show
sufficient efficacy? Will our vaccine candidate be efficacious comparable with vaccines or with the
other companies? After injection of rounds of vaccine, should the dosing regimen be changed?
These questions relating to phase and study design were assessed using our vaccine QSP model.
I'd like to talk about the assessment for the questions on the short-term efficacy and
ethnicity. This is the initial phase one study design, the number of doses were three doses,
the number of dosing was two times, dosing interval (inaudible) was 28 days, target
race was Japanese, target population was young healthy subjects and old healthy subjects. The
condition of our phase one trial was input into the QSP model and the simulations were performed.
In this slide, IGG level against the antigen at low dose, and high dose are shown. IGG
levels in young healthy Japanese subjects were predicted to be sufficient and almost
similar to that in Caucasian. IGG levels increase in dose-dependent manner
in the dose range of the phase one study design, in addition, the duration of IGG level of the
target IGG level also prolonged in dose-dependent manner. Therefore, the three doses were considered
to be optimal in young Japanese subjects. The next question is about dosing interval.
Which timing of the second vaccination is appropriate for maximum efficacy? Here is
the dosing interval of the other vaccines: Corona Bug was tested 14 days and 28 days. Bnt162b1 or b2
were vaccines developed by Pfizer, was 21 days. Messenger Rna1273
was a vaccine developed by Moderna with 28 days. And our vaccine was also 28 days.
Shorter interval with high efficacy will be preferable from the viewpoint of
emergency use and the protection of the infection spread, on the other hand,
longer interval may be optimal for combatting rigid immune responses.
The influence of dosing interval on IGG level after second vaccination. Our booster hit
was evaluated using the QSP model. Maximum booster effect was predicted at the dosing interval of
21 days and 28 days. We could also evaluate the reason using the QSP model.
Right figure shows memory B cell production after a single dose. Parameter simulation, it was
suggested that number of memory B cells become the maximum around 28 days after first vaccination,
therefore 28 days were considered to be optimal.
We could also predict booster effect of longer-term dosing interval, for example:
84 days. Regarding the predictability of the longer-term dosing interval, we think it is
necessary to calibrate the QSP model using the actual data of the longer-term dosing interval.
Next question is about number of dosing. In general, two doses were applied in the
clinical study of COVID 19 vaccines. On the other hand, high immune response was observed
by three doses in the pre clinical study. If sufficient efficacy is conferred by two doses,
it is preferable from the viewpoint of supply of the vaccine, emergency use
and the prevention of infection spread. Is there any difference between two doses and three doses?
(inaudible) Graph is the simulation result of the IGG level after two doses and three doses.
IGG level and the duration of efficacy after three doses were higher and longer
than those of the two doses however the duration of efficacy of the two doses
was sufficiently long from the viewpoint of target efficacy. Therefore, when the
balance between duration of efficacy and the other factors such as vaccine supply,
emergency use and the prevention of infection spread, two doses were considered to be optimal.
Next question is about immune response in older adults. It has been reported that
immune responses in older adults drastically decrease compared with that in younger adults.
In the clinical development of the vaccine,
insufficient response in older adults has been also reported. Thus,
dosage regimen selection in older adults is essential in the vaccine clinical development.
Therefore, Characteristics of immune response in older adults were examined.
It was reported that T cell function is impaired age dependently though numbers
of B cells and T cells don't change. These data were incorporated into our QSP model.
The simulated IGG level gamma profiles in younger and older adults are shown in this slide.
Left figure is IGG level up to middle dose in younger adults, middle figure is IGG level of
high dose in younger adults and right figure is IGG level at high dose in older adults.
IGG level at high dose in older adults, right figure, was predicted to be
lower in younger adults than that at middle dose in younger adults left figure. Since IGG level at
middle dose in younger adults is target efficacy, much higher dose was considered to be needed
in older adults. We discussed this simulation result and it triggered to add
higher dosage than predicated in young adults highest to our phase one study protocol.
This is the summary of the assessment for our phase one design.
The initial number of dosing and the dosing interval was considered to be optimal,
and we didn't need to add the other dosing regimens. Regarding the ethnic difference,
it was predicted that efficacy will be observed in Japanese similar to the Caucasian.
The immune response in older adults was predicted to decrease compared with the younger adults.
And the highest dose was added to the dosage in the phase one study.
In vaccine clinical trials, a variety of the populations, type of vaccines,
formulations, and dosage regimens should be explored. Since there are many combinations,
it is impossible to implement all the arms in the clinical trial. However,
trials simulation for all the combinations can be performed using vaccine QSP model. And optimal
dosage regimens for the target populations can be predicted in mandates(inaudible) using
QSP model optimize study design for vaccine and accelerates their clinical development.
I would like to summarize today's presentation. QSP model for our COVID 19 model was created based
on IGG simulator to address our questions on the clinical development of the COVID 19 vaccine.
The credibility of the vaccine QSP model was increased by calibration and validation(inaudible)
using clinical data of the other vaccines.
The phase one study design for our COVID 19 vaccine was optimized by the vaccine QSP model.
Immune response in special populations such as different races, older adults,
could be predicted using the vaccine QSP model. We think that vaccine QSP model supports
acceleration of the vaccine clinical development by optimizing their clinical study design.
This work was performed by the cooperation with (inaudible) members
and is supported by these my colleagues. I'd like to thank my collaborators for this work,
thank you very much for your attention.