As appeared in October 2019 Tablets & Capsules

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www.tabletscapsules.com

quality by design
Using multivariate analysis
of batch-to-batch excipient
variation to reduce risk

B. H. J. Dickhoff, T. Schigt,
H. L. Blaauw, and A. W. van Gessel
DFE Pharma

This article describes the batch-to-batch variation of a multiyear excipient production data set and the subsequent impactof the variation observed on the excipient’s processability and/or functionality. This type of consistency data can help drug
product manufacturers assess the risks associated with using
specific grades of excipients and/or vendors for their products.

n the pharmaceutical industry, a detailed understanding of the impact of raw material variation on a drug
product’s performance and manufacturing is key [1]. Drugproducts consist of an active pharmaceutical ingredient(API) as well as many functional excipients. The functional excipients enable the API to be processed into a
final dosage form and may also enhance stability, bioavailability, or patient acceptance [2]. While the impactof API variability (such as particle size, morphology, orICopyright CSC Publishingpurity) on a drug product is evident, studies are increasingly showing the importance of considering the variationin functional excipients used in formulations as well [2-6].
Commonly, excipient variation has been studied ona batch-to-batch and vendor-to-vendor basis [2-5]. The
impact of batch-to-batch excipient variation depends on
the application [7, 8]. The degree of batch-to-batch variation has been shown to differ by excipient vendor [2]and/or type—such as lactose [3], microcrystalline cellulose [9], and HPMC [6].
Few researchers have evaluated the use of large data
sets to assess the impact of excipient variation on drug
product performance. Kushner [3], showed that a greater
understanding of excipient variation can reduce the number of experimental studies required during drug productdevelopment. Better understanding of excipient variability is also key to improving the consistency and qualityof manufacturing processes in line with the FDA’s Quality by Design (QbD) initiative.
QbD has driven the use of multivariate analysis(MVA), particularly principal component analysis (PCA)
and partial least squares (PLS) regression [10, 11]. Multivariate analysis is a set of statistical techniques that allows
the simultaneous analysis of many variables. The technique is ideally suited to studying relationships withinlarge, complex data sets. PCA is ideally suited for investigating batch-to-batch excipient (and/or API) variation, as
it can be used to investigate patterns or clusters in complex data sets. Using PCA on large excipient data setsallows researchers to identify the major sources of variation as well as noise variables that have no effect.
Lactose and microcrystalline cellulose are the mostcommonly used pharmaceutical excipients for manufacturing solid oral dosage forms. Lactose is a soluble
binder/diluent that produces robust tablets with good
disintegration properties [2]. Lactose is widely available
and can be obtained in four solid forms (granulated,anhydrous, spray-dried, or milled and sieved) as wellas in many grades (or types). Lactose is considered asafe, cost-effective, functional excipient that is tasteless(or slightly sweet), water soluble, and stable, with a low
hygroscopicity and good compatibility with APIs andother excipients [12].
This article describes a study conducted to evaluate
the multivariate quality of granulated lactose monohydrate and show the effect of the variation on functional-related properties as tested by a pharmaceuticallactose manufacturer.

Materials and methods
The study evaluated more than 300 production
batches of the granulated lactose monohydrate functional excipient SuperTab 30GR (Figure 1). The material
was produced by DFE Pharma at the company’s site in
Nörten-Hardenberg, Germany, over the period from January 2011 to December 2017. The researchers used certificate of analysis (CoA) data as well as bulk and tapped
density data to perform the MVA.

Umetrics Simca-P 16 software was used to generate
a PCA model (or score plot) and a loading plot for thequantitative data. A Sympatec Helos dry powder laser
diffraction analyzer was used to analyze the particle size
distribution. Each sample was tested five times using an
R5 lens. The materials’ bulk and tapped density were
determined in triplicate using an Engelsmann instrument
according to USP <616>, and its specific surface area
(SSA) was determined using the multipoint SSA method.
Differential scanning calorimetry (DSC) was performed using a Mettler Toledo DSC 1 instrument. The
researchers accurately weighed 5 to 10 milligrams ofmaterial into a 40-microliter standard aluminum pan andthen crimped on a pierced aluminum lid. Analysis was conducted from 20° to 250°C at 10°C per minute in triplicate.
Thermogravimetric analysis (TGA) was performed
using a Mettler Toledo instrument. The researchers
accurately weighed 5 to 10 milligrams of material into
a 40-microliter standard aluminum open pan. Analysis
was conducted from 20° to 250°C at 10°C per minutein duplicate.
The lactose excipient was mixed with 2 percent w/w
superdisintegrant (Primojel, DFE Pharma) for 8 minutes
and with 0.5 percent w/w magnesium stearate (Sigma
Aldrich) a further 2 minutes using a Turbula mixer at 90rpm. Placebo tablets were compressed at 10 kilonewtons
using RoTab tableting equipment with a gravimetric feed
and 9-millimeter flat beveled tooling. The target tablet
weight was 250 milligrams (±0.5 milligrams). After 24hours, 20 tablets were tested for hardness (F), diameter(d), and thickness (t) using a Sotax HT100 tablet hardness tester. Tablet tensile strength (TTS) was calculatedusing the following equation [13]:TTS = 2F/πdt
Tablet disintegration was tested in an Erweka DT dissolution tester. Disintegration times for six tablets were

Figure 1
Scanning electron micrograph of granulated
lactose monohydrate (SuperTab 30GR)

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Figure 2
PCA model (score plot) for 300+ batches of SuperTab 30GR taken over a 7-year period
2011
2012
2013
2014
2015
2016
2017

Hotelling’s T2 95%
confidence limit
4
3
2

t[2]

1
0
-1
-2
-3
-4
-5

-4

-3

-2

-1

0
t[1]
R2x[1] = 0.158
R2x[2] = 0.14

1

2

3

4

Figure 3
Loading plot for 300+ batches of SuperTab 30GR taken over a 7-year period

0.6

Density
Impurity
Moisture
Particle size
Specification

PSD %<355 micron

0.5

PSD %<150 micron

0.4
0.3

p[2]

0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.3

PSD %<75 micron

Sulphated ash (%w/w)
KF (%w/w)
Calculated dry matter
Specific rot. (o)Acidity

LOD (%w/w)
Hausner ratio

Bulk density (kg/m3)
Tapped density (kg/m3)

Color, 400nm
UV 210-220nm
UV 270-300nm
-0.2

-0.1

0

0.1

0.2
p[1]
R2x[1] = 0.158
R2x[2] = 0.14

0.3

0.4

0.5

0.6

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The particle size, bulk and tapped density, and specific surface area values for these batches are shown in
Table 1. The particle size data for the eight selectedbatches indicates a high consistency for the productmanufactured during the years studied. This is in alignment with the R 2(x) values of PC 1 and PC 2 indicatingthat only 29.8 percent of the variation can be explainedby the data (Figure 2). The particle size data are inagreement with Hagrasy, et al. [14], and Moolchandani,
et al. [15], which reported x50 values between 142 and149 microns and an x90 value of 287 microns for thisproduct. The bulk and tapped density range shows little
variation, and the tapped densities are consistent with theliterature [15]. The calculated Hausner ratios from thetested batches range between 1.20 and 1.25, indicating afairly flowable powder. The specific surface area results
did not show any significant differences between thebatches tested.
The thermal data for the eight knowledge-spacebatches are shown in Table 2. These results are consistent
with literature values, as the DSC traces did not show
an exothermic event around 167° to 174°C associatedwith amorphous lactose [16, 17]. The DSC data show anendothermic transition event associated with the dehydration of the incorporated crystalline water between
140° and 145°C for all samples [18], with subsequentmelting of the alpha-anhydrous form (between 200° and
220°C) and decomposition of lactose [19]. The TGA
shows a weight loss by dehydration of 4.5 to 4.6 percent,which is in alignment with literature values [16].

recorded in seconds, and the average disintegration time
was used.

Results and discussion
The PCA score plot is shown in Figure 2. As the figure shows, the R 2(x) of the first two components was
0.298, meaning that there was little trend in the data, as
29.8 percent of the variation is explained by PC 1 and PC
2. The overlap between colors representing the different
production years indicates that there was a high degreeof similarity between batches from the years tested.
Additionally, no clusters or shifts in the year-to-year data
were observed.
Figure 3 shows the loading plot of the same dataset. The figure shows that the highlighted densities andparticle-size parameters are the key contributors to thevariation depicted in Figure 2. Other parameters, such as
the specific rotation, moisture (by Karl Fischer titration),and the acidity of the lactose, did not vary significantly
within the production data set.

Selection of batches for further evaluation
Specific batches were selected from the dataset toperform functional testing, as shown in Figure 4. To
understand the variation observed, six batches wereselected near or outside the Hotelling’s T2 95 percent
confidence limit (represented by the blue ellipse in the
figure). These batches are well suited for understanding
the maximum variability delivered to users/customers
and should be representative of the largest batch-tobatch variability. Two batches were taken from the midpoint of the spread in data (or knowledge space). Samplesof these eight batches were taken from retained storage
and further analyzed.

Compaction and disintegration
The six knowledge-space batches that were near oroutside the Hotelling’s T2 95 percent confidence interval

Table 1
Additional physical parameters tested on eight knowledge-space batches of SuperTab 30GR
Particle size*(microns)

Density**(kg/m3)

Product batch

x10

x50

x90

Bulk

TappedSpecificsurface area**(m2/g)

130.4±0.4

132±2

298±5

565

667

0.33

2

32.0±1.0

143±5

312±7

561

681

0.30

3

32.1±0.6132±3294±15

540

660

0.32

4

36.3±0.7140±4306±16

541

676

0.28

5

34.1±0.6

135±5

321±9

549

659

0.30

6

33.0±0.7129±3

288±16

548

665

0.29

7

32.4±0.5140±4

331±11

580

710

0.29

8

31.9±0.6142±5322±12

584

715

0.31

* measured five times (±x.x is 2 times the standard deviation)** measured in duplicate

Copyright CSC Publishingshow a low variability and, therefore, good consistency interms of tablet compaction, reflected in the TTS values
shown in Figure 5a. The TTS for all eight batches averaged around 1.56 to 1.69 megapascals, indicating a highconsistency in tableting behavior. The disintegration time,
as shown in Figure 5b, was around 60 seconds for all eightplacebo batches, indicating good and constant disintegra-

tion properties. This implies that the batch-to-batch variation of this lactose type is unlikely to cause any variation
in formulation development and production.

Conclusion
To help increase patient safety in a QbD context, it isof utmost important to understand the factors that may

Table 2
Thermal properties of eight knowledge-space batches of SuperTab 30GR (n = 3)
DSC—Loss of hydration event

DSC—Melting event

Product batch

Onset
(°C)

Peak
(°C)

Enthalpy(J/g)

Onset
(°C)

Peak
(°C)

TGA
(% w/w)

1

139

144

135±5

213

220

4.6

2

140

144

135±4

212

220

4.5

3

139

144

138±4

213

220

4.5

4

140

144

135±4

212

220

4.5

5

140

144

139±3

213

220

4.5

6

140

143

143±1

213

220

4.5

7

140

144

145±1

212

220

4.5

8

140

143

140±1

213

220

4.5

Figure 4
Score plot showing eight batches selected for further investigation
No ID
ID

Hotelling’s T2 95%
confidence limit

4
3
2

t[2]

1
0
-1
-2
-3
-4
-5

-4

-3

-2

-1

0
t[1]
R2x[1] = 0.158
R2x[2] = 0.14

1

2

3

4

Copyright CSC Publishinginfluence the functionality-related characteristics or critical material attributes of the final formulation. You could
do this by testing all possible variables, but the design ofexperiments (DoE) for this would become unfathomable.
Understanding which contributing variables pose thehighest risk and which pose no risk can help to focus a
DoE on the most important parameters. Excipients havebeen shown to introduce variability into a formulation,
but this variability differs between excipients and evenbetween suppliers of similar excipients. To make a goodrisk assessment, you must understand the variability ofeach individual excipient grade, and MVA is the preferable method for arriving at this understanding.
This study used MVA to evaluate more than 300 production batches of SuperTab 30GR granulated lactose
monohydrate produced during the period from January
2011 to December 2017. Batches selected from the edgesof the MVA plot—those having the maximum variation
during the selected time frame—did not demonstrate
any visible effect on the critical quality attributes ofresulting lactose placebo tablets.
Due to the large number of degrees of freedom associated with a typical solid dosage form, the consistencyof the ingredients alone may not be predictive of thefinished product quality. However, pharmaceutical companies can evaluate the variability of high-consistency
excipients as part of the control strategy, rather thanDoE, to identify where there is a low probability of
impact. Using well-controlled excipients designed for
purpose can reduce life-cycle risk in pharmaceuticaldevelopment and production. Excipients with low variation in physical/chemical size, density, and specific surface area, as in this study, may be classified as low risk,
and companies may then use this classification to significantly reduce their DoE strategy when using such products in pharmaceutical development.
T&C

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Figure 5
Properties of placebo tablets made from six batches near extremes of MVA plot
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3.0

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B. H. J. Dickhoff is development manager, oral solid dose; T.
Schigt is research specialist; H. L. Blaauw is category manager, marketing; and A. W. van Gessel is regional sales andmarketing manager, Japan, at DFE Pharma (+492823 9288
770, www.dfepharma.com).