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.
