Welcome to my speech. I am Paolo.
I work in a research and development laboratory
in a pharmaceutical industry, Menarini from Italy.
And now, I show you some of our work
in [inaudible 00:00:16] with JMP.
Nowadays, almost every process equipment is able to collect a lot of data,
making it available to exporting and for future analysis.
And especially in a R&D lab,
equipment replicate in a small scale of the manufacturing process.
So the analysis of data may be helpful to do a process understanding.
But each equipment run its property software.
Sometimes data analysis can result uncomfortable
if done on the onboard system for so little screen
or some touch screen that not properly easy to use.
Export all data on a JMP data table
make analysis easier and more comfortable.
And also, it can help to link results
from different process steps
and driving us, hopefully,
to discover an unexpected relationship between variables.
We start to use JMP with the release number seven.
So we have a lot of trials to show you.
We start with the ancestor.
Bulk and Tapped Density.
The bulk density of the powder is the ratio of the mass to its volume,
including the contribution of interparticulate void volume.
Then, the sample density is increased by mechanically tapping.
Because the interparticulate interaction
influences the bulking properties of the powder,
but also, the interaction that interfere with the powder flow.
So a comparison of bulk and tapped density
can give a measure of the flow properties.
As comparison is often use an index
that speak of the ability of the powder to flow,
this index is the Carr index or compressibility index,
calculated with the tapped and bulk density.
And here, we have a ranking of flowability
related to the Carr index.
We started taking volume measure by end, after the 5, 10, 15 taps and so on,
and recording it on a data table, on the JMP data table.
After, we try to use a sensor, a light sensor, like these,
that can measure the distance of the powder from the top of cylinder
and record and then store the results in a data table,
a CSV file, a comma-separated value file.
The results are quite the same.
Data, whether if they come from an automated or a manual data entry
fit well with the appropriate Kawakita equation.
This is the Kawakita equation
plotted with the nonlinear equation platform of JMP.
This equation explain how the powder settled during the tapping.
And it has three parameter.
The first one is the bulk density.
The second one is the Carr index.
And here, we can see 22 value
that is not a good property flow of this powder.
And this one speak about the speed of the settling of the powder.
I think this is all for this first data acquiring.
Again, we have another instrument.
Now, we go to speak about topic form as cream or gel.
Geological properties are important for topic dosage form
because the viscosity influence the production,
but also the packaging or the usage of a topic product.
You can think to spreadability on the skin.
So a proper flow characterization is a fundamental importance
during the development phases of a topic form.
Nowadays, flow and viscosity [inaudible 00:05:17] increasing or decreasing shear rate
are simply obtained using automated equipment.
Here, we have the picture of a 30-years-old rheometer
that we had in our lab.
And we had a dedicated computer system.
But data can also set manually on a logarithmic paper.
Or simply, we can do a data table, a JMP data table.
And with a graph builder, we can have the same output
of flow curve
or the linear regression using data transformation
and access transformation in a logarithmic.
But very important, it's this
because using the bivariate platform
and using the spline fit,
we were able to predict
to get an estimate of the shear stress
when the shear rate go near to zero.
This is the yield stress or yield point.
It's very important because it's...
How to say? It's very...
The maximum stress below which no flow occurred in the system.
So the maximum stress below which the cream or the gel don't move.
This is an important information
when you plan a volumetric filling of a fluid material.
And that was not available when we had all the instruments.
So JMP was very useful
to understand the behavior of our topic form.
Come back to the solid oral dosage form,
looking at single-station bench top tablet press.
This tablet press is ideal for R&D development,
for research and development because very often,
only small samples of active ingredients
are available for the first testing.
With these, we can set,
we can control independently compression force and weight
to meet the tablet requirement and specification.
It works with the same tools that are used in the manufacturing scale press.
And we can plot tableting and formulation characteristic
in order to eliminate or mitigate some potential tableting deficiencies.
On this model, there is no automated data collection,
so we simply enter data in a file,
preferably in a JMP file.
There is the data table.
And we can get some important plot
that are the compressibility, the compactibility
and the tabletability plot.
They speak about the behavior of the formulation under compression.
And in this data table, we have some equation
that relate the compaction pressure that we applied to the formulation
and to varied characteristic, to the tablet's characteristics.
With a study that cover from 50 to 300 megapascal,
we cover theoretically all the compaction pressure
that can be applied on tablet, on pharmaceutical tablet
of every formal sites.
Now, we close some one of these.
And then we go...
... to the moisture analyzer.
The moisture analyzer is a balance.
It's a balance that heating the sample
by an halogen lamp or infrared lamp.
It can measure its moisture.
It's also known as loss on drying or LOD
because heating the sample, the sample lost its moisture,
and we can record measuring the weight,
the change of weight.
It's important to know residual moisture of samples
whether they are granules, powders or of other.
But could be useful also to see
the rate of loss as a function of time or a function of temperature.
The analyzer is equipped with an algorithm software
that collect data in XLS file.
Import in JMP, it's easy.
It's very easy.
We need only to set the number where data start,
the number of column where data start and the row,
and click on Next.
And after, on Import.
Here, we have the data table with time and loss on drying.
We have to adjust something about column name and so on.
But here, we have the same data cleaned.
And we repeated the measure three times.
So we have three replicate, but we stuck it on the same column.
And with Fit Y by X platform,
we have the function that relate loss on drying with time.
And I don't save it.
You see there is a dedicated hardware and a dedicated software,
but I also try to do a script.
Do a script to capture the data.
But it's partially work, but I never fix it
because the simpler and more effective way to collect data is import the XLS file.
But more or less, we can do the same:
opening a new data table,
define the column that we want in the data table,
and put the JMP to wait data from the Port com3.
That's all.
The oral route of drug administration is the most convenient for patient.
So tablet is the most popular solid oral dosage form.
And so I speak a lot about tablet press.
In the manufacturing, we saw a single punch press,
but in a manufacturing environment, we use rotary tablet press.
And also, we have in the lab, a rotary tablet press
that has a large number of punches.
Our press is equipped with strain gauges
to measure compaction force, ejection force,
and also the force needed to detach tablet from the punch surface.
All these data are displayed and recorded by a software.
They are very useful to monitor, to study the tableting process.
Normally, software display data,
but use also them to do a real-time weight adjustment of the process.
But our version, the lab version give also the way to analyze
the single event, get statistic, print report, export report, and so on.
Here, we see some screenshot from our software.
Raw data come as a text file, a txt file.
The reason to open with JMP
Here, I have the txt file.
I try to open it using JMP.
I select this All files.
This is the text.
It's better remove this one and choose Data (Using Preview),
and go to Open.
Here, we have the data that come from software.
Data start in one, two, three, the fourth line.
So we correct it.
And click on Next
And this is as character.
No, we need the numeric data.
And this is the same.
Column three is the same of column one, so I exclude.
This is the compression.
Sorry because the software is in Italian,
but it's compression force.
This is, again, the time.
So I deselect.
This is the scraper
that measure the first to the detach the tablet from [inaudible 00:16:35] surface.
This is, again, time, and I deselect.
And this is ejection.
Click Import.
And I get my data table.
I have to correct this again,
but nothing of difficult.
Every data become numeric data.
I have elaborated the JMP file, already JMP file.
Here, using graph builder, I can show the process.
This is the compression for one tablet,
this is the ejection force,
this is the scraper force.
In the original software, I have on two different page.
Here, I have in a three,
the same page, the three variables but it's not changed.
Now, I select one tablet.
But if I enlarge the X axis,
I think 30 seconds, not more to record.
Here, I have the whole process that I recorded.
I have also found the software another report that
record the peak values of each variable.
And this is can be useful for these I import on JMP.
And here, I have for every punch, for every station, for every rotation,
the maximum force of compression, scraper and ejection.
For example, here, we changed the lubricant of the formulation
that decrease the ejection force.
And we can see the effect of this change on the three variables.
This is the ejection, and there is effect on the change.
And now, I close it.
And we go to NIR process monitoring.
Here, in the lab, we have NIR spectrophotometer,
but there's a small form factor.
You can see it can stand in the hand,
and a Wi-Fi connection.
The main characteristic is flexibility in installation of various equipment.
The most common application is blend monitoring.
Generally, tumbling mix for powder consists in a container,
rotating on X axis, like this.
And the most common are cubic-shaped container,
and they are called bin.
The NIR is mounted on the bin by a three-clamp flange.
And during the mixing operation, the instrument collect a spectrum
of the powder at each rotation.
As the mixing will go on
and the system become more and more homogeneous,
the spectra become more and more similar to the previous.
To get the most from the NIR data, it's mandatory to apply chemometry.
And, of course, we do it with NIR software.
But it's also possible to export data in XLS file and go in JMP.
Importing data from XLS file is very easy.
It's simply to open it.
Here, we have the file.
We can give a look, the raw data file.
We have for every wavelength of NIR spectrum,
we have the value of lab solvents at every rotation.
The whole processes was 80 rotation of the bin.
We can give a quick look to the graph, to a spectra,
selecting the wavelength,
putting in the X axis.
Select,
and Parallel Merged.
Here, we have the spectra of each rotation.
I said that normally, we have to apply chemometry
to NIR data to get the most information, the more information as possible.
A pretreatment normally used in this type of analysis is
standard normal variate pretreatment
that is a normalization of a spectra,
subtracting each spectrum its own mean and dividing by standard deviation.
Here, I do a script
to do with this pretreatment.
This is the file of raw data.
We can run the script.
Select the wavelength,
and get the new data after predicting.
With the graph builder, we do the same.
Parallel Merged.
Here, we have the difference of spectra,
of raw spectra and...
... sorry, if I found it...
... Raw spectra.
This one.
And predicted spectra
So here, we have the difference from raw data and a pretreated data.
But we can see better in this file
where the spectra are colored by rotation, from red to green.
And you can see that
the very first spectra is the red one here.
And the last spectra are the green ones.
And they are more and more similar one to each other
respect to the red and yellow spectra.
We can see these also with the principal component analysis.
This one is the first rotation, and so on.
And we can see that
spectra become more and more similar
and principal component analysis are more and more the same
when we get the end of the process.
Another way to see the end of a process is...
the moving block standard deviation.
Here, we see a plot from our NIR software.
I will show you something in another windows about it.
The high shear mixer.
The granulation process is of real importance,
really important step in pharmaceutical manufacturing.
Granulation improve physical characteristics
of mix of a powder
as flow properties and content uniformity.
Granules can be used as is
in a delivery form like sachet or a stick pack.
But they can also be pressed into tablets.
High shear mixer are a key point able to do a wet granulation,
in granulating powder by the binding solution and the shear force
due to a rotating impeller.
After, the wet granules will be dried in another step.
In our high shear mix,
we can control by software every process parameter:
the speed of impeller, the speed of chopper,
the rate of addition of a binding solution.
And moreover, the software correlates some variables
as part of temperature or power consumption.
And data are stored in a CSV file,
so it's very easy peasy to import in JMP.
Here, but is data imported in a JMP data table.
I have some column that I colored in yellow
that come from some calculation about water amount added and so on.
But it's important to see that with graph builder,
we can see the whole process, the parameter of whole process.
For example, we can see the torque measured by the software
during the wet granulation and during the massing time;
you can see how it changed.
Here, in this picture, you can see that our NIR instrument
was also fitted in this step, in this process
to monitor the granulation process.
Here, we have some results,
always come from the software of NIR.
We give a color to identify each phase of granulation.
With the principal component analysis,
we can see the start of the granulation.
And when we added water, there is this change
on the physical properties, on the physical aspect of the granules
that become more and more wet, more and more agglomerated
until the end of the process.
Here, we have the same data in the transpose matrix
to get a better visualization on graph builder.
And we can see the spectra of a different phase.
Here, we have our very first time of granulation
when we are mixing the powder without adding water.
Here, we have the adding of the water
and the final massing time.
And we can see that the peaks are changing.
Here, we have the maximum absorption of the water in NIR spectrum.
Here, we have the start, the medium point,
and the end point of granulation.
All this data can be resumed in a journal, like this,
where we had highlighted a variation of a peak,
independence of a single step of the process.
Here, we have another two equipment,
the fluid bed and the tablet coater suite.
These are important, very important,
keep maintain pharmaceutical manufacturing.
We have a particular suite that is made from three units.
The main control unit that is the same of the two process,
and the other two units that are interchangeable
for the other purpose of the process.
We start with the fluid bed.
The fluid bed is another way to get the wet granulation
because wet granulation is not done only using a high-shear mixer,
can be done also using a fluid bed.
The fluid bed technology
mean that the powder to be granulated are suspended
in keeping motion by an upward flow of heated air.
A binding solution is sprayed on the suspended powder,
and the flow of area remove result during the whole process.
The onboard software give us a total control of a process parameter,
and every relevant variable is collected.
Reports are stored for future analysis, and it's possible to export as PDF file.
Here, we have a PDF file,
reduced PDF file for the purpose of this presentation.
I try to open in JMP.
And here, I have this one.
I know that in the first page, there isn't a table that I'm interested to import.
So I click Ignore tables on this page.
In the next one, this little one is not of interest,
so I ignore it.
And I ignore it also very last one.
Here, there is graph of data, Ignore table on this page.
Here, I have a preview of the table that I'm interested in.
I click OK.
And I have my data in a JMP data table.
Something to fix as numeric instead of continuous, but it's not...
... of chart, but it's not a problem.
Now, we see to go coater system.
The coater system is to do a tablet coating.
The tablet can be coated for several reason.
The coating can have a specific function,
for example, delay the drug release
or simply it's need to reduce the dust
during the packaging operation
or have some cosmetic need
as masking some bad taste of the tablets, and so on.
Whenever the coating is done,
spraying a coating suspension on tablet rotating in a drum.
A flow of heated air remove solvent during the process.
As the software is the same with the fluid bed,
they have the same control unit,
data and report are the same that we can get from the fluid bed.
So here, we have a JMP data table obtained
from the PDF file that we see before.
Always using the graph builder,
we can give an overview, a look to the process.
Here, we see the spray rate or the product temperature and so on.
Also, here, we try to do some application of NIR measure.
We simply put the instrument inside the pan during the coating
and taking a measure of weight gain of the tablet
at various time interval.
Here, we have the spectra,
how change the spectra during the process.
And we can see here that data are pretreated.
That is a first derivative treatment on the raw data
that are able to increase, to highlight the peak spectra variation on...
... on the report.
Here, we do a relation between
spectra sample and weight gain measure during the process.
And so we have a relationship
between spectra and weight gain.
And for the next batches,
we will able to predict the weight gain of the tablet
simply take spectra during the process.
Now, we close it.
And it's enough.
At last, we come back to the topic dosage form.
The laboratory reactor
that we have is useful to optimize process
as mixing, homogenizing, dispersing in a lab scale.
The system can be adapted quickly and easily
to wide range of application.
The main use for us is to do topic form as gel or also cream.
It has integrated scale, pH and temperature sensor.
The onboard system allow process control, display the process graph,
but it's also able to store every processor relevant data
in a PC as an XLS file.
So it's simply for us to import in JMP and always using the graph builder.
It's easy to see the whole process.
For example, we can see the speed of dispersing system
but also the torque
or the viscosity trend of a system of a gel during each phase.
Again, we try to use
the NIR spectrophotometer to monitor the process.
For this application, we add to the reactor a recirculation stream.
And the NIR was placed on it by an appropriate flange.
Data collected and elaborated with the NIR software can be imported in JMP.
So here, we have the spectra.
For example, spectra collected during the whole process
or the principal component analysis.
And we give a name to each phase,
so we see by this local data filter.
The first step when we do an aqueous solution of the base,
when we add the active ingredient,
when we had the ethanol or the gelling agent,
and when have the gelification of a system,
and when we have the finished product.
We see here 19 spectra
that are all in the same point of the principal component [school] plot.
I spoke before about moving block standard deviation.
A moving block standard deviation is simply
a standard deviation of a block of spectra.
And comparing the standard deviation of a current block with the previous,
we can see how the variation of a system changed.
So when the system become more and more homogeneous,
the moving block standard deviation become more and more similar.
Here, we have a plot.
And we see the same that we have seen
with the principal component analysis
that are the five phase of aqueous solution,
where are the second phase where the active ingredient is added,
and also where the ethanol, the ethyl alcohol is added,
and the gelling agent, the gelification,
and finally, the finished product.
And the moving block standard deviation become very, very similar
and very, very quite to zero.
Well, I think we have seen enough;
we have seen a lot of process and a lot of equipment.
They, of course, have software specially designed
to control, to collect and to analysis process data.
These software are not replaceable from another.
They are important to control and to drive the equipment.
But every system is standalone.
So sometimes we can't use the equipment to do that analysis
because it's busy with another project.
Or sometimes we need to merge data from different step
to have more global overview of the product.
So we can do easily using JMP.
Just import file.
I thank you and goodbye.