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OPTIMIZATION OF BLEND UNIFORMITY IN
THE PHARMACEUTICAL INDUSTRY
Akshatha Bhat
Gabrielle Schlakman
Eva Xu
NJ Governor’s School of Engineering and Technology 2012
1 Abstract
The pharmaceutical industry is a
dynamic and vast field that has
contributed significantly to the well-
being and betterment of the people.
Hence, there are stringent regulations
imposed on it regarding dosage form
safety. In order to assure that a product
is safe for human consumption, the FDA
states that the product has to meet
criteria such as hardness, friability,
disintegration, assay, dissolution time
and content uniformity, among others
[1]. To control these parameters a
process has to be in control, specifically
in terms of the unit operations that
govern the aforementioned attributes. It
has been repeatedly stated in literature
relating to the field that blending is the
critical unit operation that determines the
quality of a final drug product [2]. Blend
uniformity may be defined as the
homogeneous concentration of the
components of solid dosage drugs. This
is achieved through optimal blending
processes to result in the most
compatible drug delivery system. It is
important in terms of the chemical
distribution and physical properties of
the drug. For this reason, blend
uniformity is one of the most significant
limiting factors in the business due to the
challenges it presents in consistency and
lengthy testing duration. Considerable
amounts of time and resources have been
spent trying to optimize the mixing of
different powders. Near Infrared (NIR)
spectroscopy is one mechanism that is
utilized to monitor and control
uniformity because of its rapid non-
destructive analytical measurements. To
understand what components most affect
blend uniformity, various factors
including blender type, location within
blender, ingredient concentration,
rotation speed and time were tested in an
experimental fashion and then analyzed
with NIR spectroscopy to determine
which compositions were most
homogeneous.
2 Introduction
Blend uniformity is a critical aspect of
pharmaceutical blends because it
contributes significantly to the quality of
the products. There are two main
components of a drug: the active
pharmaceutical ingredient (API) and the
excipients. The API is the element in the
drug that acts as the therapeutic
component, while the remaining
constituents of the drug are its
excipients, which aid the drug in
reaching its target within the body and
digestion. In this specific experiment,
the API is acetaminophen and the
excipients are microcrystalline cellulose
(MCC) along with magnesium stearate
(MgSt). Blend uniformity is crucial in
the industry because if the API and
excipients are not homogeneously
mixed, the product can potentially be
toxic and thus be recalled by the US
Food and Drug Administration (FDA).
The manufacture of drugs with
dangerously high levels of API is one of
many plausible issues that could occur
due to the finicky nature of the powders
that comprise these blends. To test
homogeneity, the constituents of
Tylenol® were mixed with varying
factors to determine what contributes to
the optimal blend.
Currently, in the pharmaceutical
industry, achieving blend uniformity is
one of the biggest challenges that
companies face. The issue gained
precedence in 1999 when the US FDA
published draft guidance for analysis of
blend uniformity. The FDA stated that it
was necessary to have routine
consistency tests. Initially, it was only
necessary to test uniformity in the final
product, however that proved to be
unsafe, according to the FDA guidelines:
The US Department of Health and
Human Services Food and Drug
Administration Center for Drug
Evaluation and Research (CDER) states
in the Draft Guidance
X:/CDERGUID/2882DFT.WPD (AUG.
1999) that
“Under current Good Manufacturing
Practices (cGMP), an applicant is
required to perform a test or examination
on each commercial batch of all products
to monitor the output and validate the
performance of processes that could be
responsible for causing variability,
which includes adequacy of mixing to
ensure uniformity and homogeneity”.
[21 CFR 211.110 (a) (3)]
The FDA mandated that the United
States Pharmacopeia criteria for content
uniformity be 85-115%. But the industry
standard for the Blend uniformity is 90-
110%1, so fulfilling the FDA guidelines
is part of the challenge that
pharmaceutical companies face
concerning blend evenness [3].
Without blend uniformity, the
percentages of the various components
of drugs will not be even through the
mixture, so when batches of drugs are
produced, they will not have the correct
percentage of API, which could have
potentially harmful or even lethal effects
on consumers [4].
3 Background
3.1 Components of the Blend
Acetaminophen, magnesium stearate
(MgSt), and microcrystalline cellulose
(MCC) are the three main components of
Tylenol® that were blended.
Acetaminophen is the API in Tylenol®
and acts as the therapeutic element that
relieves fever, mild pains, headaches,
muscle aches, menstrual cramps, colds
and sore throats among other things.
Magnesium stearate is a safe-to-consume
lubricant, which is an inactive substance
that is pivotal in the manufacturing
process because it prevents the powder
mixture from adhering to the machinery.
Microcrystalline cellulose is the main
excipient of Tylenol®, and it aids in
dose measurements as well as processing
and distribution of the drug. MCC also
assists the body with absorption into the
bloodstream and digestion of the
medication.
3.2 Blenders
To test blend uniformity, two blenders
were utilized: the V blender and the
double-cone blender. Blenders try to
overcome segregation of powders which
results from particle separation due to
differences in their size, shape, or
density [5]. However, sometimes the
blender ends up enhancing these
properties. The V Blender is a self-
contained tumble blender intended for
laboratory applications. As it rotates, the
tumbling of the powders from the apex
of the “V” to its legs mixes the
ingredients thoroughly to create a
predictable blend. Due to the shape,
blending occurs from all sides, allowing
the r per minute (RPM) speed to be
medium.
Figure 1. The V-blender is widely used in
today’s industry as it serves to blend powders of
different physical and chemical composition into
an even assortment by rotation of the machine.
Figure 2. Another popular machine of the
pharmaceutical industry, the double cone blender
has the same purpose of the V-blender as it
blends the varying powders into a consistent
batch by spinning.
The double cone blender has a small,
cylindrical shell with two cone-shaped
frustums that allow straightforward
loading and unloading of the powders.
However because of the shape of the
blender many times the power will mix
vertically but not horizontally causing a
discrepancy in blend uniformity.
3.3 Near Infrared
Spectroscopy
The consistency of blends can be tested
with numerous methods such as high-
performance liquid chromatography,
ultraviolet spectroscopy, and the
increasingly popular method known as
near-infrared (NIR) diffuse reflectance
spectroscopy.
The NIR region is in the wavelength
range of 780–2500 nm, and in this area
absorption bands primarily correspond to
the main overtones and combinations of
fundamental vibrations that are
necessary to read the data [6]. This
technology has a variety of applications
that range from pharmaceutical blend
analysis to medical analysis, food and
agrochemical quality control,
neuroimaging, rehabilitation, etc. The
equipment’s ability to incorporate
various devices to fulfill different
applications makes the technology
extremely adaptable.
For pharmaceutical purposes, near-
infrared spectroscopy operates by taking
spectra of samples using infrared light
that is sent through the powder sample
and then reflected back into the NIR
machine. This technology has a variety
of applications that range from
pharmaceutical blend analysis to medical
analysis, food and agrochemical quality
control, neuroimaging, rehabilitation,
etc. This equipment’s ability to
incorporate various devices to fulfill
different applications makes the
technology extremely adaptable [7].
For pharmaceutical purposes, near-
infrared spectroscopy analyzes blend
samples by comparing them using
infrared light that is sent through the
powder sample and reflected back into
the NIR machine. The machine then
analyzes the data based on its chemical
and physical composition and graphs it
with respect to the x and y axes:
wavelength and absorbance [8]. Particle
size, homogeneity, and the
composition’s overall physical, chemical
and molecular properties all contribute
to the different wavelengths read by the
NIR [9].
This method does have certain
drawbacks that limit its ability to analyze
blends. When the percentage is less than
0.2%, the NIR’s sensitivity greatly
decreases, making sample readings less
accurate.
Figure 3. The near infrared spectrometer also
known as the NIR is an important tool in the
determination of the chemical and physical
properties inside an unknown sample.
3.4 The Unscrambler Software
An analytical software known as
“Unscrambler” was used to amass the
data collected by applying spectral pre-
treatments, specifically the first
derivative. Then, physical differences in
the baseline due to particle size were
eliminated and the chemical properties
of the spectra were enhanced. The
software is used in spectroscopy,
chromatography, and process
applications in research of non-
destructive quality control systems in
pharmaceutical manufacturing, sensory
analysis and the chemical industry. It
enables researchers to create a library of
their data and easily determine the
percent composition of their samples. By
using pretreatments, taking the second
derivative of the equation, and graphing
the line of regression, scientists are able
to determine the percent composition of
their samples and ultimately determine if
their blends are homogeneous.
4 Experimental Procedure
4.1 Optimizing Through
Selective Variation
Four different variables were
manipulated to best determine blend
homogeneity. Through the alteration of
percent composition, rotation speed, and
time in the two different blenders, blend
optimization was tested. First, percent
composition was varied in terms of
excipient and lubricant quantities in a V-
shaped and double cone blender and then
analyzed for its homogeneity. Once the
most uniform mixture with respect to
percent composition was detected,
rotations per minute (RPM) and the time
interval were varied in the two different
types of blenders. After each trial,
samples were consistently taken from
five different locations in each blender to
ensure that the blend uniformity was
consistent throughout the entire mixture.
4.2 Percent Composition
Trials
In experiment 1, 300 grams of the
powder mixtures were placed in a V
Blender and 300 grams were placed in a
double cone blender. In the five trials for
each blender, the percentage of
magnesium stearate in the formulations
was altered, ranging from .2%, .4%,
0.6% 0.8% and 1.0%. The percent of
acetaminophen was held constant at
5.0%, thus, MCC and MgSt varied
proportionally. The rotation speed and
time were fixed at 12.6 RPM and 8
minutes respectively.
4.3 Rotations per Minute
Trials
In the pharmaceutical industry, there is
no set standard for what mixture is the
most homogenous because each blend
has different particles and granules
whose compositions vary. Therefore,
each specific blend needs to be tested
with respect to different variables and
analyzed for its homogeneity. In this
experiment, the NIR readings indicated
that the most consistent mixture in both
the V and double cone blenders was:
5.0% (15g) acetaminophen, 0.6% (1.8g)
magnesium stearate, and 94.4% (283.2g)
microcrystalline cellulose. For the next
set of experiments, the aforementioned
values were set as the new constants and
instead, rotation speed (RPM) was
varied to determine its effects on
uniformity. The original trials already
tested the mixtures at 12.6 RPM so the
following trials only needed to vary the
blending speed to 25.2 RPM, the highest
speed dial of the blender, in order to test
the optimized sample percentages.
4.4 Time Trials
Once the most uniform composition with
respect to percent composition and RPM
was determined, the amount of mixing
time for the sample was varied. The
original trial blended the mixture for 8
minutes, so the subsequent time was
varied to 16 minutes to test if a longer
blending time would better mix the
ingredients of the drug.
4.5 Analysis Through Near
Infrared Spectroscopy
After each trial, samples from the
mixture were taken and read using NIR
spectroscopy in order to test the percent
composition and homogeneity of each
mixture. In order to analyze the data
properly, a calibration curve was first
made with the exact percentages of API,
excipient, and lubricant.
Figure 4: The spectra taken by the NIR of trial
1.3 that graphs wavelength on the x-axis versus
the absorbance on the y-axis. Similar spectra
were taken in every other sample made.
5 Data and Analysis
5.1 Calibration
In order to correctly analyze the
wavelengths and percentage correlations
initially taken by the NIR, a calibration
curve was created. The calibration
samples have fixed values of API, MCC
and MgSt. Thus, it provides a simple
database of known concentrations that
the samples can be compared to in order
to determine the exact percentage of
API. The calibration samples are put into
the Unscrambler program which easily
allows the unknown samples’
percentages to be read.
The calibrations constructed consisted of
3 sets with 6 samples of 7 grams in each
set. The first set included 1% MgSt, a
varying API at 0%, 2%, 4%, 6%, 8%,
and 10%, and MCC as a filler to make
each of the samples a total of 7 grams. In
the second set, there was no MgSt, but
the API continued to vary and the MCC
remained the filler. In the third set there
was 0.5% MgSt, the API at the varied
values, and MCC as the filler.
After the calibrations were made, three
readings were taken using the NIR. In
order to mix the samples, a Vortex
blender was used to the mix the powder
ingredients inside the glass vials so as to
provide a more uniform blend for the
NIR readings. The first two readings of
the NIR were set as the calibrations and
the third was made the validation. This
step was crucial to our experiments as it
was the set standard that the rest of the
analyses of the samples were based on.
5.2 Variable Analysis
After each experiment, samples were
taken from different locations and
analyzed with the NIR. The data was
then transferred to the Unscrambler
software, where it was compared to the
calibration curve and the ultimate
percentage of API after blending was
determined.
To obtain the most accurate reading,
three readings of each sample were taken
with the NIR. However, after the first
experiment, it was determined that the
samples were close enough in value that
there was no need to take more than one
reading of each sample.
Based on the structure of the flow chart
for the V blender, the progression of
how the samples were created and tested
is evident. With the V blender, the most
homogeneous blend consisted of 5%
acetaminophen, 0.6% MgSt, and 94.4%
MCC. These values were set as
constants in the next set of experiments,
which tested rotation speed in RPM. The
best formulation was that which was
blended at a lower setting, 12.5 RPM.
This result is interesting because
generally in the industry, increased
rotation speed increases blend
uniformity. For this reason, the blend
with higher RPM of 25.2 was actually
set as the constant for the next set of
experiments.
In the third set of experiments, time was
tested and the best blending process,
accounting for 2.93% of API, was that
which was run for a longer period of
time: 16 minutes. From the data, it is
evident that increased speed and time
along with 0.6% lubricant provide the
most homogeneous blend when using
the V blender. When RPM was varied
from 12.5 to 25.2, the percent of API
changed by -9%, which was at odds with
standard practice in the industry.
However, when varying the time from 8
minutes to 16 minutes, the percent of
API increased from 2.56% to 2.93%, a
14% increase, which is a significant
amount in the pharmaceutical industry.
This highlights the importance of time
when operating the V blender.
When using the double cone blender, as
indicated above, the most homogeneous
blend was composed of 5%
acetaminophen, 0.6% MgSt, and 94.4%
MCC. Once again, these values were set
as constants in the next set of
experiments which tested rotation speed
in RPM. The more homogeneous blend
was the one that was blended at 25.2
RPM, so this value was set as the new
constant in the next trial. In the third set
of experiments, time was varied and the
sample with the higher API percentage
was the sample that was rotated in the
blender for 8 minutes. When RPM was
varied from 12.5 to 25.2, the API
increased 9.5% of the original
percentage recorded. When time was
varied from 8 to 16 minutes, the API
decreased 0.6% of the original
percentage recorded. This value is so
small that it indicates that time is not a
significant factor when optimizing
blends with the double cone blender.
Therefore, RPM is the main element that
needs to be considered when creating
samples with this blender.
Overall, based on the above data, it can
be concluded that the double cone
blender creates the more homogeneous
blend. Nevertheless, when working with
larger, industry-level blends, companies
choose to use the V blender due to its
unique structure which proves to be
more efficient in the blending of larger
samples.
When blending, it is important to keep in
mind that although a constant may be
fixed, the random nature of the blending
process will cause numerous
inconsistencies due to human error, the
resistant nature of powders, segregation
of particles, and the overall process of
mixing the batch. When collecting data
in this experiment, samples were taken
from five different locations in each
blender: the upper right (UR), upper left
(UL), center (C), lower right (LR), and
the lower left (LL). No correlation was
found between specific locations and the
optimal blend. In some experiments,
there were higher concentrations of API
in the lower regions, while in others
there were higher concentrations in the
center and upper regions.
Although the applications of NIR
spectroscopy for monitoring and
controlling the pharmaceutical
processes offer a huge potential, the
technology still has its limitations. When
the infrared light is scattered and
reflected by the samples, the instrument
is not able to independently discriminate
between physical and chemical
differences. Therefore, the peaks in the
spectra are constantly varying, which
makes it challenging to determine what
factors are causing these variations.
Another limitation that the NIR has is
that it is less sensitive to samples with
components that are 0.2% or less due to
the minuscule nature of these values.
It has been recommended that the blend
samples be taken when they are in
motion and that the whole stream of
powder should be sampled for short time
intervals rather than parts of the stream
being sampled for the whole time [10].
Due to the fact that the NIR instrument
used was stationary and that the blenders
did not have an opening for fiber optic
placement, this recommendation could
not be heeded. However, perhaps doing
so with other would allow for better
sampling and results.
The best way to overcome all of these
obstacles is through continuous
manufacturing. This method has
minimal error due to the absence of
human error, constant analysis of the
samples rather than the onus of having to
sample and then test each respective
batch. This is the newest technology in
the pharmaceutical industry concerning
blending of drugs.
6 Conclusion
Factors that influence uniformity of
pharmaceutical blends--including
blender type, location within blender,
concentration, rotation speed and time--
were tested in this experiment. To test
this, a series of experiments was
designed and for each successive
experiment, the set of previous values
that produced the most optimal blend
was then held constant. To determine
which blends were ideal, the analytical
method known as Near Infrared
Spectroscopy was employed. By using
its fiber optical probe and
complementary second derivative
spectral library, the machine emits
infrared waves which can determine
homogeneity by using particle size and
the composition’s overall chemical
properties. When spectra were acquired
from the NIR and then analyzed, API
content was predicted via a multivariate
regression model. The physical
differences in the samples were actually
eliminated using a Savitzky-Golay
smoothing filter in the respective regions
[11]. This filter performs polynomial
regression to preserve features of curves
and distributions while giving the data a
semblance of smoothness.
Based on the data analysis, it is evident
that certain factors--blender type, time,
and rotation speed--have a stronger
impact on blend consistency than others.
However, it is important to note that
these results were optimized for a
laboratory and drug development-scale
blend. Therefore, when scaling up to
manufacturing and factory-level sizes,
other parameters such as hydrophobicity
and flowability must be taken into
account [12].
In addition, due to the blend
components’ differing densities and
sizes, segregation of particles is a
naturally occurring process that creates
the difficulty experienced when trying to
optimize blend uniformity [13]. For this
reason, there are blenders with many
different shapes, sizes, rotation speeds,
etc. that assist in overcoming this
obstacle.
7 Acknowledgements
We would like to thank the Governor’s
School of Engineering and Technology
and our counselors for their guidance,
support, and the time they dedicated to
us. Furthermore, we are grateful for the
assistance and knowledge of Sara
Koykov and Krizia M. Karry, the
mentors who instructed us in the
laboratory and assisted in our research
project. We would also like to give
special thanks to Josh Binder and Stoyan
Lazarov, our counselors who oversaw
this project and helped edit our paper.
Lastly, we are thankful for director Dean
Ilene Rosen, assistant director Jean
Patrick Antoine, as well as The
Governor's School Board of Overseers
and Rutgers University, The State of
New Jersey, Morgan Stanley, Lockheed
Martin, South Jersey Industries, Inc, and
PSE&G for providing us with their
resources that enabled us to study the
field of Pharmaceutical Engineering.
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9 Appendix Table No. 1 The table of data collected through
the experiments conducted using the V Blender.
EXPERIMENT LOCATION API %
1.1
5% API
0.2% MgSt
94.8% MCC
8 min
12.5 RPM
UR 2.25
UL 1.86
C 1.69
LR 1.56
LL 3.4
AVG 2.15
1.4
5% API
0.4% MgSt
94.6% MCC
8 min
12.5 RPM
UR 0.773
UL 1.42
C 2.03
LR 2.81
LL 2.47
AVG 1.9
1.3
5% API
0.6% MgSt
94.4% MCC
8 min
12.5 RPM
UR 2.92
UL 3.1
C 2.03
LR 2.82
LL 3.22
AVG 2.82
1.4
5% API
0.8% MgSt
94.2% MCC
8 min
12.5 RPM
UR 1.76
UL 2.02
C 2.78
LR 2.31
LL 1.91
AVG 2.16
1.5
5% API
1% MgSt
94.0% MCC
8 min
12.5 RPM
UR 2.55
UL 2.17
C 3.25
LR 1.25
LL 1.03
AVG 2.05
2.1
5% API
0.6% MgSt
94.4% MCC
8 min
12.5 RPM
UR 2.92
UL 3.1
C 2.03
LR 2.82
LL 3.22
AVG 2.82
2.2
5% API
0.6% MgSt
94.4% MCC
8 min
25.2 RPM
UR 2.03
UL 2.82
C 1.62
LR 3.48
LL 2.92
AVG 2.563
3.1
5% API
0.6% MgSt
94.4% MCC
8 min
25.2 RPM
UR 2.92
UL 3.1
C 2.03
LR 2.82
LL 3.22
AVG 2.82
3.2 UR 2.59
5% API
0.6% MgSt
94.4% MCC
16 min
25.2 RPM
UL 3.09
C 2.54
LR 3.54
LL 2.89
AVG 2.93
Table No. 2 The data collected through the
experiment conducted using the double cone
blender
EXPERIMENT LOCATION API %
4.1
5% API
0.2% MgSt
94.8% MCC
8 min
12.5 RPM
UR 2.05
UL 2.59
C 2.22
LR 3.28
LL 1.88
AVG 2.49
4.2
5% API
0.4% MgSt
94.6% MCC
8 min
12.5 RPM
UR 2.34
UL 3.02
C 2.01
LR 1.01
LL 2.91
AVG 2.26
4.3
5% API
0.6% MgSt
94.4% MCC
UR 1.46
UL 2.77
C 3.95
LR 3.35
8 min
12.5 RPM
LL 3.21
AVG 2.95
4.4
5% API
0.8% MgSt
94.2% MCC
8 min
12.5 RPM
UR 1.71
UL 2.45
C 2.61
LR 2.95
LL 1.5
AVG 2.24
4.5
5% API
1% MgSt
94.0% MCC
8 min
12.5 RPM
UR 2.21
UL 2.44
C 4.33
LR 3.5
LL 1.39
AVG 2.78
5.1
5% API
1% MgSt
94.0% MCC
8 min
12.5 RPM
UR 1.46
UL 2.77
C 3.95
LR 3.35
LL 3.21
AVG 2.95
5.2
5% API
0.6% MgSt
94.4% MCC
8 min
25.2 RPM
UR 2.86
UL 3.48
C 3.17
LR 4.28
LL 2.36
AVG 3.23
6.1
5% API
0.6% MgSt
94.4% MCC
8 min
25.2 RPM
UR 1.46
UL 2.77
C 3.95
LR 3.35
LL 3.21
AVG 2.95
6.2
5% API
0.6% MgSt
94.4% MCC
16 min
25.2 RPM
UR 2.6
UL 3.66
C 3.09
LR 4.87
LL 1.83
AVG 3.21
Figure 5: The layout of thieving (taking samples)
in the double cone blender.
Figure 6: The layout of thieving in the V blender.