Hi everyone.
First of all I want to thank the
Discovery Summit committee
for letting me the possibility
to present to you the work
we perform in my team on the
implementation
of a split-plot design platform
to study the purification of our
innovative excipients.
First of all, ADOCIA is a biotechnology
company founded in 2005
by Gérard Soula and his two son,
and we are located in Lyon, France.
Our mission is to develop innovative
formulation of approved
hormones for the treatment
of diabetes and obesity.
The business model it is license product
after the approved concept.
And currently in our pipeline we have
three patented technology platforms.
And one product that is approved
to enter phase three in China.
Five products with clinical proof
of concept,
and six projects that
are the preclinical stage.
We are about 115 people in ADOCIA
and 80 percent are dedicated to the R&D.
Speaking about the technology platforms.
Today
we will speak about the historical
one, which is the BioChaperone platform.
The BioChaperone is a
pharmaceutical excipient
a synthetic organic one,
and it will form a complex with a protein
such as insulin or amylin or glucagon.
This complex inspired by nature
will improve the solubility or stability,
accelerate the absorption
of the peptide
or protect it against enzymatic
degradation.
BioChaperone platform potentializes
the performance of insulins
and other hormones.
And today five of proprietary products
based on BioChaperone
are in clinical development.
The development BioChaperone chemistry
I think it's the same in many
pharmaceutical company.
We will go from a early stage process
that will deliver batches of few grams
that will enter preclinical studies
such as toxicology efficacy.
And once BioChaperone is designed
as a lead
it will come in my department,
in my team,
to develop a final process
to deliver phase three batches.
And at the end commercial batches
ranges at few hundred kilos per year.
The changes that we will face are
imposed by large scale feasibility
and we will be driven
by cost and performance
and many changes will be suffered.
So we need to understood
them and document them.
The goal of our work is to have
a complete understanding
of the relationship between
parameter of variation
and their impact on product quality.
Indeed, as we will perform at large scale,
we know that temperature
cannot be targeted
at 10.0 degree every time,
it can be 11, it can be nine.
This is suffered due to the large scale.
We will speak about robustness,
that the process need to absorb this
inherent variability in a defined range.
And we need to know its
impact on the product quality.
At this stage we will go
from reproducibility at early stage,
to robustness at large scale
and final process stage.
To do that we have tools.
Two of them are the risk analysis
that will help us to prioritize,
to rank the work.
And DoE are very useful because
we know that in chemistry
we have a lot of interactions
between parameters,
so this is very useful for us.
Today, we will speak about
the purification of the excipients.
This purification is done
by diafiltration.
A quick overview of the process is
that raw material
enters a chemical transformation,
that will give a crude excipient
in solution.
This crude excipient in solution will be
purified by diafiltration
to give the pure excipient in solution.
What is diafiltration?
We have first of all the classical
filtrations
that is called the dead-end
filtration in which we have a solid
or
that in excipient in a
solution and a membrane.
the solution will come up to bottom
with the pressure to recover the solid
on the upper face of the membrane.
A cross-flow filtration we have
the retentate which is brought
in parallel to the membrane using a pump.
And we will apply a pressure using valves
that will push a part of the flux to go
through the membrane,
that process defined pores and let only
small molecules to go
through the membrane.
And we have the big molecules
that will stay in the retentate.
And we use this technology
because we know that our excipient
are oligomers, which means
that they are not small molecules.
They are not polymers,
they are between the two sizes,
but they are quite big and they
will stay in the retentate.
A quick overview of the unit.
We have the retentate.
A vessel with the retentate which will be
brought through the membrane
using a feed pump and a back pressure
valve will allow us to
have a pressure in the membrane and push
through a part of the flux in the permeate
that will eliminate the small
molecule impurities.
On the right you have the 50 liter scale
diafiltration pilot that we use at ADOCIA
on the back, the 50 liter vessel.
On the up left we have the housing
with the membrane and all the pipes used
with valves and instruments
to monitor the work process.
For our study we have only
one bulk that we can use.
One bulk of crude excipient solution.
The idea was to have a flow circulation
of the flux meaning that the permeate is
brought back to retantate every time
like that we will have a retentate
which is representative of the process.
At the beginning of every event
of the DoE.
For a dry filtration
we have factors that are determining
very early in the process,
which is the membrane reference,
meaning that the cutoff size of the pore
and the material of constrictions.
Once it's set, it's set
and we will not change it.
And the loading, meaning the
kilograms of excipient per surface
of membrane, membrane are defined surface
and the kilogram of excipient
is defined by the process.
When it's set, it's set.
We will not change it.
What we can tune as factor RDSA.
The concentration of the BioChaperone
in the solutions that we need to purify,
the feed flow, which is the flux
imposed by the feed pump.
The transmembranar pressure, which is a
way to control the pressure that will
push the permeate through the membrane
and the temperature of the solution.
The responses we can look at which are
the losses through the membrane that will
impact the yield of the process,
the impurities that goes through
the membrane that are in the permeate
that will impact the quality
of the product, which is the most
important part of this work.
And permeate flow rate
that will impact time.
It will give insight on the whole
process time at large scale.
The objective of the study was to define
design space which is a multivariate space
that guarantees the conformity
of the responses,
Here, it's the quality of the product.
And we will go for a design type
which is a response surface model.
The first attempt we ran.
We ran the Box-Behnken design.
To run a Box-Behnken design you go
on DoE classical response surface design.
I will load the response.
Run one
response and I will load
the factors.
One factors.
Okay.
Yes.
Here we have the three responses.
I speak about earlier losses,
the elimination,
the impurity elimination
and the permeate flux.
And we have the four factors
which are temperature,
pressure, concentration or assay
and feed flow.
Box-Behnken is the first
proposal in this box.
We will continue to make the table.
And we have the standard Box-Behnken
table with randomized run as you can see.
We started to run this DoE and after one
trial it was clear that we will not be
able to run this DOE
in a randomized order because we cannot
concentrate or dilute
the bulk between each one.
If we concentrate the bulk
through the membrane
it's perfectly feasible but we will
lose some impurities.
Our bulk is not anymore representative
of the upstream process.
It is not a solution.
We can distillate the bulk but it will
take very long time because it's water.
And the second parameters that will not be
easy to change between each factor is
the temperature due to the recirculation
is quite longer to stabilize
the temperature between each
run when we have to change it.
We've done something that will make
some people scream in the audience.
But we ordered the run by temperature.
And by assays.
Let me just
add some color on it to have
a better view.
Value color.
This is what we've done.
We have assay which are in block with 90
then the 60 block and the 30 block
and in each assay block
we have temperature
which has ordered 40, 30, 20 etc.
We've run this DoE like that.
Here are the data we obtain.
We can
analyze it using the fit
model platform with the four
factors and we will
look at losses and run.
We have quite a good
model for the losses.
It's okay, we have a good PValue, we have
parameters.
It's okay.
But the thing is
we were quite disappointed
by the statistical approach because we
know that the first
rule is to randomize run to have
a good estimation of the error.
It was not satisfactory.
We came back to our studies I would say
and just as a reminder
the state of play was that we cannot use
a fresh batch between each one.
Too much bulk will be required
and we don't have it.
The bulk assay cannot be changed between
each one due to representativeness
of the bulk and temperature cannot be
changed easily because it will
be really highly time consuming.
We look at books and at the end of many
books we found a solution
which are the split plot design.
Split plot design were introduced
by the agriculture field because they have
typically have to change
factor such as farming fields.
Let's say you want to study
the different treatments
on different cereal or crops
and you don't have any room on one field.
We will have many fields
and these fields are different.
But this is not the thing
you want to study.
The idea behind split plot design is
to have a whole plot which are filled
with that will be analyzed
as random blocks.
And then on each whole plot you will apply
treatment or culture one, two, three, four
and you will study it inside the whole
plot and they are called subplots.
How it is done on JMP.
I will close this.
This.
This.
And this.
To run a split plot design you have
to go on DoE platform custom design.
I will load the responses
for the run two.
Open.
And the factors.
What we see here.
Sorry.
We see our four factors
assay temperature,
CFF is the feed flow and the pressure.
And we have an additional column
which is name changes.
And you can tune the fact if it's hard,
very hard to change or easy to change.
Very hard to change
is the concentration or the assay.
The hard to change factor
is temperature because we can change it
but we don't inside assay block.
And the easy factors
are the two other factors that can
be randomized between runs.
We want to go for surface response model.
You click on RSM here we put
six whole plots and 12 whole plots.
We have 36 runs and we make the design.
It will take a few seconds
to make the design.
Just to remind you
that for the Box-Behnken DoE.
It was 27 runs.
We have much more runs on this DoE.
I will make the table and as for
the Box-Behnken I will add some colors.
Column.
What did I do.
Sorry, I redo the table.
Yes.
Okay, sorry.
Yes.
Okay.
You see that we have assay which are
arranging blocks and it corresponds
to the whole blocks one, two, three, four
etc and in each
assay block you have temperature blocks
which are the subplots
one, two, three, four etc.
And CFF and TMP are
randomized inside those two blocks.
We perform this DOE
data and you can go for the model.
Here we see the differences between
the analysis between standard
Box-Behnken DoE and split plot design.
We have the whole plots that are added
in the effects and they are treated as
random blocks and we have all the other
parameters and effects.
And here we have the method
which is the REML analysis method.
We run
the DoE.
If we focus on the losses answer
we see that we have
a very good model with 96 percent
of the variation explained by this model.
We see that we have
significant effects.
And
the additional box we have to look
at with this analysis is the REML variance
components estimates that will give us
an insight on the behalf introduced
by the blocks, the whole plots and we see
that the PValue is not significant.
We can go further.
There are no issues
with the blocks so such
like other DOE
performing JMP we can go
and have a profile to optimize to define
ranges and design space.
Here we see that
the assay is the most impacted factor
on every responses and losses is
the response that is impacted by all
the four parameters of factors
and we can see
that with the parameters we use as target
we are pretty good with the optimization.
What could be interesting is to look at
this model, this DoE using a standard
analysis.
I remove everything.
I will take the four
factor.
All this.
For standard analysis.
I run it.
Here.
I come back under the losses and response.
Okay.
Here I will look at
the losses.
What we can see
is that we don't have the exact same order
for the parameters, effect or estimate.
In the standard analysis we can say
that assay is the most impacting factor
while in the spectral design is the flux,
assay's the fifth one
and it's quite normal
because when we use blocks and we do
the standard analysis we will give more
strength to these blocks and we will make
errors on it
and we will define it as
impacting but it's not.
This is perfectly normal.
This is why you need to do
an analysis using REML and blocks to have
the right order of impacting factor.
In conclusion of this study.
In this case study
split plot design allowed
to carry on regardless of non
randomization of some parameters.
We were able to run
in a shorter time frame the whole DoE.
Even if it required 36 runs versus 27.
But as we don't need to concentrate
to stabilize the plotter between
each run it was much more shorter.
And we will be able to justify properly
the design space with strong
statistical evidence.
It is worth noting
that the split plot design
platform is now implemented to quickly
develop and optimize our proprietary
excipient purifications
and as take away messages.
Just be careful on factor randomization.
We know it's the first rule to run a DOE
but it's very important to have a proper
design that will
allow you to have a statistical
knowledge on your process and a proper
design could allow to save time
even if more runs are required.
Thank you for your attention.
