FACULTY OF PHARMACEUTICAL SCIENCES
Department of Pharmaceutical Analysis
Laboratory of Pharmaceutical Chemistry and Drug Analysis
Academic Year 2008-2009
SOLID STATE PROPERTIES OF FREEZE-DRIED PROTEIN
FORMULATIONS
VALIDATION OF WATER CONTENT DETERMINATION IN FREEZE-DRIED PROTEIN FORMULATIONS BY NIR SPECTROSCOPY
AND
INVESTIGATION OF FREEZE-DRIED PROTEIN FORMULATIONS BY XRPD
Delphine GILDEMYN
First Master of Pharmaceutical Care
Promoters
Prof. Dr. W. Baeyens
Ass. Prof. Dr. H. Grohganz
Jury
Prof. Dr. C. Vervaet
Prof. Dr. K. Braeckmans
FACULTY OF PHARMACEUTICAL SCIENCES
Department of Pharmaceutical Analysis
Laboratory of Pharmaceutical Chemistry and Drug Analysis
Academic Year 2008-2009
SOLID STATE PROPERTIES OF FREEZE-DRIED PROTEIN
FORMULATIONS
VALIDATION OF WATER CONTENT DETERMINATION IN FREEZE-DRIED PROTEIN FORMULATIONS BY NIR SPECTROSCOPY
AND
INVESTIGATION OF FREEZE-DRIED PROTEIN FORMULATIONS BY XRPD
Delphine GILDEMYN
First Master of Pharmaceutical Care
Promoters
Prof. Dr. W. Baeyens
Ass. Prof. Dr. H. Grohganz
Jury
Prof. Dr. C. Vervaet
Prof. Dr. K. Braeckmans
“ The author and the promoter give their permission to make this work available for
consultation and for sharing or copying parts of it for personal use. Any other use is subject
to the limitations of copyright, particularly with regard to the obligation to specify the source
when quoting results from this work.”
May 29th, 2009
“ De auteur en de promotor geven de toelating deze masterproef voor consultatie
beschikbaar te stellen en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik
valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de
verplichting uitdrukkelijk de bron te vermelden bij het aanhalen van de resultaten uit deze
masterproef.”
29 mei 2009
Prof. Dr. W. R. G. Baeyens Delphine Gildemyn
My stay in Copenhagen has been an
unforgettable experience.
I wish to thank Holger Grohganz, my
supervisor at the University of
Copenhagen, Professor Jukka Rantanen,
head of the research group and Fang Tian
for her help with the Raman
measurements.
Furthermore, I would like to thank James
Flink and Erik Skibsted, my supervisors at
Novo Nordisk, and Erik Fredriksen, Mark
Dawson and Freddy Silva, also from Novo
Nordisk, for their help with the practical
work.
TABLE OF CONTENTS
1. INTRODUCTION 1
1.1 FREEZE-DRYING 1
1.1.1 Freezing 2
1.1.2 Primary drying 4
1.1.3 Secondary drying 5
1.1.4 Excipients 6
1.2 NEAR-INFRARED SPECTROSCOPY 7
1.2.1 Basic principles of Near Infrared Spectroscopy 7
1.2.2 Pharmaceutical applications of Near Infrared Spectroscopy 8
1.2.2.1 Qualitative analysis 8
1.2.2.2 Quantitative analysis 9
1.2.2.3 Use of NIR spectroscopy as a PAT tool 9
1.3 MULTIVARIATE ANALYSIS 10
1.4 X-RAY POWDER DIFFRACTOMETRY 11
1.5 STATE OF ART 12
2. PURPOSE OF THE PROJECT 15
3. MATERIALS AND METHODS 16
3.1 MATERIALS 16
3.2 PREPARATION OF THE SAMPLES 16
3.2.1 Sample composition and distribution 16
3.2.2 Labelling system 18
3.3 PREPARATION OF THE SATURATED SALT SOLUTIONS 18
3.4 METHODS 19
3.4.1 Freeze-drying 19
3.4.2 Near Infrared Spectroscopy 19
3.4.3 Data analysis 20
3.4.4 Karl Fischer titration 20
3.4.5 XRPD 20
3.4.6 Raman spectroscopy 20
4. RESULTS AND DISCUSSION 22
4.1 PCA OF NIR SPECTRA 22
4.1.1 Principal Component Analysis of untreated data 22
4.1.2 Principal Component Analysis of SNV corrected data 24
4.1.2.1 Comparison of the first and second principal component 25
4.1.2.2 Comparison of other principal components 27
4.2 DEVELOPMENT AND VALIDATION OF A NIR SPECTROSCOPIC METHOD FOR THE
QUANTIFICATION OF WATER 29
4.2.1 First look at the Observed versus Predicted plot 29
4.2.2 Model for the quantification of water 30
4.2.2.1 Development of the model 30
4.2.2.2 Validation of the model 32
4.2.3 Applicability of the model when varying the sample composition 35
4.3 XRPD MEASUREMENTS 36
4.4 RAMAN MEASUREMENTS 40
5. CONCLUSION 44
6. REFERENCES 45
LIST OF USED ABBREVIATIONS
ASTM American Society of Testing and Materials
DTA Differential Thermal Analysis
FDA Food and Drug Administration
GMP Good Manufacturing Practice
hGH Human Growth Hormone
ICH International Conference on Harmonisation
LoD Limit of Detection
LoQ Limit of Quantification
NIR(S) Near Infrared (Spectroscopy)
PAT Process Analytical Technology
Pc Chamber Pressure
PC Principal Component
PCA Principal Component Analysis
Ph Eur European Pharmacopeia
PLS Partial Least Square Projection to Latent Structures
RMSEE Root Mean Square Error of Estimation
RMSEP Root Mean Square Error of Prediction
σ Standard Deviation
S Slope of regression line
SEP Standard Error of Prediction
SNV Standard Normal Variate
Tc Collapse Temperature
Teut Temperature at the Eutectic Point
Tg’ Glass Transition Temperature
TGA Thermogravimetric Analysis
Tp Target Product Temperature
XRPD X-ray Powder Diffractometry
1
1. INTRODUCTION
Advances in biotechnology and in the area of drug formulation and development of
innovative drug delivery systems during the past decade now allow to produce therapeutic
proteins on a commercial scale. Many proteins are already on the market and many others
are currently in clinical trials. However, some challenges concerning the formulation of these
proteins still exist. The stability of the protein during manufacturing, packaging and storage
has to be guaranteed. Especially stability of proteins during storage is a major challenge for
pharmaceutical industry (Carpenter and Chang, 1996).
An aqueous liquid formulation is the most easy and economical to handle during
manufacturing and is the most convenient for patients to be used. Unfortunately, proteins
are prone to degradation in liquid formulations: water provides an environment which
favours degradation by facilitating molecular movement and by being a possible reactant.
(Carpenter et al., 1997; Carpenter and Chang, 1996). The most commonly used method to
achieve stable protein formulation is freeze-drying of the formulation (Carpenter et al.,
1997).
In this project, the properties of freeze-dried products have been investigated using
near infrared spectroscopy (NIR). This tool is particularly suited for the investigation of the
water content in freeze-dried samples because of the strong absorption band of water in this
spectral region.
XRPD (X-ray powder diffractometry) is a widely used technique to determine crystal
structures and can be used to determine the polymorphic forms of excipients present in a
freeze-dried formulation.
1.1 FREEZE-DRYING
Freeze-drying consists of three main stages: freezing, primary drying and secondary
drying. Specific excipients added to the formulation can influence the cake structure.
2
1.1.1 Freezing
The freezing step is in an important desiccation step. During this step the
temperature is reduced below the freezing point leading to the formation of ice and the
separation of the solvent from the solute.
During freezing different events occur. First, the solution normally supercools to a
temperature 10 to 20°C below the equilibrium freezing point. During supercooling water
remains in the liquid state. Since crystallization is an exothermic process, once the
crystallization occurs, the temperature rises rapidly to near the equilibrium freezing point.
Then, the temperature will decrease slowly until the shelf temperature is reached.
During the progression of crystallization, the solutes become more concentrated. This
process is known as freeze-concentration and represents an important stress for proteins.
The increase of the protein concentration enhances the protein-protein interaction, possibly
leading to aggregation. During freeze-concentration important pH shifts can occur due to the
preferential crystallization of buffer components. For example , a decrease in pH of 4 units is
observed due to the fact that the basic buffer component Na2HPO4 crystallizes more readily
than NaH2PO4, because of the lower solubility of the disodium form than the monosodium
form (Larsen, 1973). This change in pH represents a great stress for proteins. If a protein is
sensitive to pH shifts, crystallization of the buffer must be avoided. The best solution is to
choose a formulation in which the weight ratio of buffer to solutes is very low (Pikal, 2004).
During freezing proteins may absorb to the aqueous-ice interface, which may result in a
perturbation of the conformation. As the rate of cooling increases, the number of ice
crystals increases and the area of the aqueous-ice interfaces increases. Thus, the formation
of ice itself represents an important stress during freezing.
During freezing an additional annealing step can be added. During annealing samples
are held at a temperature between the ice melt temperature and the glass transition
temperature of the freeze concentrate, Tg’, for a period of time (Tang and Pikal, 2004; Wang
2000). This step is introduced to allow the efficient crystallization of the bulking agents.
Crystal growth is possible at a temperature above T’g , but won’t occur at a temperature
3
below T’g since the system is in a glassy state (Figure 1.1). Sufficient annealing time is
required to allow complete crystallization.
FIG. 1.1: A THEORETICAL PHASE DIAGRAM SHOWING ICE FORMATION, CRYSTALLIZATION,
EUTECTIC POINT (Teut) AND FORMATION OF THE GLASSY STATE (Tg’) DURING FREEZING. (Wang et al., 2000)
Crystallization of the bulking agent during freezing is required to prevent
crystallization of the bulking agent during primary drying which could possibly lead to vial
breakage (Milton et al., 2007). On the other hand crystallizing of the bulking agent has the
advantage of providing structural support to the cake. The impact of annealing on the cake
structure is described by Lu and Pikal (2004). They experienced that a cake that was not
annealed was shrunken and partially collapsed. The cake that was annealed at -23°C was
partially collapsed and slightly shrunken. The cake annealed at -23°C and -33°C didn’t lose its
structure. The appearance of the different cakes is visualized in Figure 1.2.
4
FIG. 1.2: THE EFFECT OF ANNEALING ON THE VISUAL APPEARANCE OF FREEZE-DRIED CAKES
(Lu and Pikal, 2004)
Another reason for introducing an annealing step during freezing is that annealing
allows the growth of bigger ice crystals. This yields the formation of pores with a bigger size
which allows water to evaporate more easily. Fastening of the water evaporation means that
primary drying time will be reduced (Tang and Pikal, 2004).
1.1.2 Primary drying
The second step is the longest stage of the freeze-drying process. During primary
drying removal of surface water is performed by sublimation of ice crystals. To achieve this
the chamber pressure is decreased below the vapor pressure of ice and the shelf
temperature is raised. Sublimation is an endothermic process and an increase of the shelf
temperature is necessary to provide the energy needed for sublimation. Water that
sublimates condenses on the ice-condenser, which is kept at a temperature below the
temperature which reigns in the freeze-dryer (Tang and Pikal, 2004).
Because primary drying represents the longest stage of the freeze-drying process,
optimisation of this step is very important. To achieve this an optimum target product
temperature (Tp) is chosen. The aim is to rapidly bring the product to the target temperature
and hold this temperature constant during primary drying. Tp should always be several
degrees below the collapse temperature Tc. This allows to obtain a dry product with an
acceptable appearance. Tp should be as close as possible to Tc because a high temperature
5
yields a faster drying process. On the other hand, if the temperature is too close to Tc,
collapse will occur. Therefore, a security margin should be introduced: 2°C if freeze-drying
time is long (e.g. more than 2 days) or 5°C if freeze-drying time is short (< 10h) (Tang and
Pikal, 2004).
Another factor affecting the outcome of the primary drying process is the chamber
pressure Pc. To allow a high sublimation rate Pc has to be below the vapor pressure of ice at
the target product temperature. At a given temperature, the smallest chamber pressure
gives the highest sublimation rate. Usually, a chamber pressure of 0.065 to 0.265 mbar is
applied (Tang and Pikal, 2004).
1.1.3 Secondary drying
During secondary drying adsorbed water is removed by desorption. The aim of this
last stage of the freeze-drying process is to obtain the desired level of stability by reducing
the residual moisture to a targeted level (Chang and Patro, 2005).
To avoid collapse of amorphous products the shelf temperature should be increased
slowly. A rate of 0.1 to 0.15°C/min is considered safe. Temperature can be increased more
rapidly (0.3 to 0.4°C/min) for crystalline products because these products have no potential
for collapse during secondary drying (Tang and Pikal, 2004).
High temperatures are necessary to allow water desorption. The shelf temperature
needed to reach the desired moisture level depends on the solute concentration and on the
physical state of the product. Water doesn’t absorb in the same manner to crystalline or
amorphous products. The binding of water in crystalline products is more easily reversed by
raising the temperature and lowering the pressure (Zografi et al., 1988). As a consequence,
crystalline products are rather dry after primary drying. Amorphous products are more
difficult to dry, therefore higher temperatures and a longer drying time are requested to dry
these products. A higher temperature is also needed during secondary drying of a sample
with higher solute concentration (>10% solids in solution). The dry product has a smaller
specific area and it is more difficult to remove the water (Tang and Pikal, 2004).
6
1.1.4 Excipients
During both freezing and drying different stresses including low temperature, solute
concentration, pH changes and dehydration can occur which can affect protein’s stability.
Different stabilizers can be added to the formulation to protect the protein from
denaturation.
During freezing, the protein is in an aqueous environment most of the time. A
cryoprotectant can be added as a stabilizer. In the presence of this excipient, the protein will
rather interact with water, and the excipient will be preferentially excluded from the surface
of the protein (Pikal, 2004).
A second type of stabilizing additives are the lyoprotectants which stabilize during
drying. One hypothesis explaining the mechanism of protein stabilization is the ‘water
replacement hypothesis’. Hydrogen bonds are formed between the excipient and the
protein. By acting as a water substitute, the lyoprotectant prevents the drying-induced
denaturation of the protein (Wang, 2000). The second hypothesis explaining the mechanism
of stabilization is a ‘kinetic theory’ suggesting that the protein is mechanically immobilized in
the glassy state during dehydration. The unfolding of the protein is inhibited by restriction of
the translational and relaxation motions. On the other hand, proteins are separated in space
and therefore cannot aggregate (Carpenter et al., 2004). A very important point to ensure
stabilization of proteins is that the excipient remains in the amorphous state. Under certain
conditions an excipient can crystallize out. The most studied example is the crystallization of
mannitol during freeze-drying. It results in the loss of its direct molecular interactions with
proteins, thereby losing its stabilizing capacity (Izutsu and Kojima, 2002).
A wide variety of stabilizers have already been studied. Disaccharides have been
shown to stabilize most of the proteins during freeze-drying. Both reducing and non-
reducing disaccharides can be used as stabilizers, but reducing sugars should be avoided
because of the high risk of degradation of the proteins via the Maillard reaction. Therefore
non-reducing sugars, and essentially sucrose and threhalose, are being used.
The ability of polymers to stabilize proteins has also been studied. One of the most
used polymers is serum albumin. The stabilization mechanism of polymers is based on
7
important properties of these molecules: preferential exclusion, surface activity, steric
hindrance of protein-protein interactions and the limitation of protein mobility due to the
increased viscosity (Wang, 2000).
1.2 NEAR-INFRARED SPECTROSCOPY
The existence of the NIR region was first described by Herschel in 1800. He
discovered that there is radiation beyond the visible red light. This region of the
electromagnetic spectrum is now called the near-infrared region. Due to the fact that near
infrared bands are severely overlapping and difficult to interpret, this region wasn’t
considered useful for spectroscopy in the early 20th century. Nowadays this technique has
gained importance for both qualitative and quantitative analysis in different domains such as
food, agriculture and not at least in pharmaceutical industry, where near infrared
spectroscopy is used for analysis of raw materials, product quality control and process
monitoring (Reich, 2005). Above all, NIR is particularly suited to be implemented as a Process
Analytical Technology (PAT) thanks to its unique properties being real-time monitoring, non-
destructive nature of the analysis and speed of the measurement (Luypaert et al., 2007).
NIR analysis of samples has many advantages compared to other analysis methods.
NIR is often preferred because of its low cost and its high speed. Besides that, no sample
preparation is required and the samples are not destroyed during analysis, allowing
potential re-use after the measurements. The major disadvantage of NIR is the complexity of
the spectra. In a complex spectra many transformations are possible, resulting in a broad
range of possibly overlapping bands. Furthermore, trace analysis is not possible using NIR
due to the high detection limit of the technique.
1.2.1 Basic principles of Near Infrared Spectroscopy
The NIR region is situated between the red band of visible light and the mid-infrared
region. This region of the electromagnetic spectrum is defined by The American Society of
Testing and Materials (ASTM) as the wavelength range between 780 and 2526 nm. This
corresponds to the wavenumber range 12820-3959 cm-1. The NIR spectrum is a consequence
8
of the absorbance of light due to overtones and combinations of fundamental vibrations of
C-H, O-H and N-H bonds.
The main components of a NIR spectrometer are a light source, a monochromator, a
sample holder and a detector. The light source is usually a tungsten halogen lamp. The main
detector types used are silicon, lead sulfide (PbS) and indium gallium arsenide (InGaAs)
detectors. These detectors have different properties. A silicon detector is fast, small and
highly sensitive from the visible region to 1100 nm. A PbS detector is slower, is sensitive
from 1100 to 2500 nm and provides good signal-to-noise properties. An InGaAs detector
combines the properties of the two first detectors: it is fast and small and is sensitive from
1100 to 2500 nm (Reich, 2005).
1.2.2 Pharmaceutical applications of Near Infrared Spectroscopy
1.2.2.1 Qualitative analysis
One of the possible applications of NIR is the use as a tool to perform qualitative
analysis: identification of raw materials and detection of polymorphic forms.
Raw materials intended for pharmaceutical use must meet the requirements as
prescribed by Good Manufacturing Practice (GMP), Guidelines for Medicinal Products and
pharmacopoeial monographs. The analysis of incoming materials is nowadays performed
using NIR because of the minimal sample preparation. The identity of the material is
confirmed by comparing the spectrum to the spectra of a library (Reich, 2005).
Polymorphism is the existence of different crystalline forms of a molecule. Different
crystalline forms yield different solid state properties and different solubilities and therefore
the crystalline form needs to be investigated. The possible application of NIR to determine
the crystalline form of an active pharmaceutical ingredient was confirmed in recent studies.
For example, Vora et al. (2004) studied the use of NIR to investigate the crystalline form of
theophylline. NIR spectra could also provide information enabling to characterize
azithromycin (Blanco et al., 2004).
9
1.2.2.2 Quantitative analysis
One of the most important applications of NIR in quantitative analysis is the
determination of moisture content in samples. Water is a critical factor affecting stability of
the product and therefore measurement of the water content should be performed.
Determination of water content in freeze-dried products is important. The aim of
lyophilisation is to provide a good shelf stability to the final product and the presence of
water can affect this negatively. Traditionally, determination of the water content is
performed using Karl-Fischer titration. Advantages of NIR compared to Karl Fischer are the
high speed and lack of sample preparation. Vials don’t have to be opened to perform the
analysis because measurement through the glass vial is possible (Kamat et al., 1989). In this
way, contamination of the sample with atmospheric moisture is avoided.
Water absorbs very strongly in the NIR region, making the technique very suitable for
the determination of water content in samples. The two most strong absorption regions
which are observed are the one between 1400 and 1450 nm, which is caused by the first
overtone of the O-H stretching band, and another in the region between 1900 and 1940 nm,
which is due to the combination of O-H stretching and O-H bending (Luypaert et al., 2007).
1.2.2.3 Use of NIR spectroscopy as a PAT tool
The manufacturing of a pharmaceutical product consists of multiple stages. To
control and improve the quality of the final product, analyses are performed on samples
collected at the different stages of the manufacturing process. This approach has lead to the
production of pharmaceuticals with high quality, but has one major disadvantage namely
that it is a time-consuming process.
PAT (Process Analytical Technology) is an innovative approach that can be used to
improve development, manufacturing and quality control of pharmaceuticals. PAT is
considered by the FDA (Food and Drug Administration) to be ‘a system for designing,
analyzing, and controlling manufacturing through timely measurements (i.e., during
processing) of critical quality and performance attributes of raw and in-process materials
and processes, with the goal of ensuring final product quality’ (FDA, 2004). In PAT three
different types of measurements can be distinguished. Samples are measured at-line when
10
they are removed from the process stream and analyzed next to the process stream. On-line
measurements are performed by deviating the product from the process stream, analyze it,
and return it to the manufacturing process. When the product is analyzed directly during
manufacturing, without removing it from the process stream, we call it an in-line
measurement. Although NIR has usually been used as an off-line tool, some applications of
NIR for in-line and on-line measurements have already been mentioned. NIR can be used for
powder blend analysis (De Maesschalk et al., 1998), to carry out in-line determination and
differentiation between surface and bound water (Zhou et al., 2003) or to analyze the
uniformity of a tablet coating (Kirsch and Drennen, 1996). These studies confirm that NIR is a
method well-suited for the use as a PAT tool.
Other spectroscopic methods have also been shown to be useful as a PAT tool. The
implementation of PAT in a freeze-drying process using Raman spectroscopy was
investigated by De Beer et al. (2007).
1.3 MULTIVARIATE ANALYSIS
Spectroscopic data are often multivariate: there are multiple variables, measured on
multiple samples. To be able to make a conclusion from the obtained data, a multivariate
analysis should be performed to represent the data in a comprehensible way. A multivariate
analysis in SIMCA, the program used during this project to analyze the obtained NIR spectra,
is performed using the projection methods Principal Component Analysis, PCA, and
Projection to Latent Structures, PLS. The observations are represented as a swarm of points
in a K-dimensional space (K = number of variables) and are then projected on a lower-
dimensional plane.
The starting point for PCA is a matrix of data with N rows (observations) and K
columns (variables). PCA then calculates lines, planes and hyperplanes in the K dimensional
space that approximate the data as well as possible in the least square sense. Before
performing PCA, data are often pre-treated. When using spectroscopic data scaling is
performed, usually mean-centering. After scaling, the first principal component (PC1) can be
calculated. PC1 is the line in the K-dimensional space that best approximates the data in the
11
least square sense. Each point can be projected onto this line in order to get a coordinate
value along this line, called a score. More principal components can be calculated to
represent the data matrix. The second principal component (PC2) is a line in the K-
dimensional space, orthogonal to PC1. Together, PC1 and PC2 define a plane. When
projecting all the observations on the plane, a score plot is obtained, showing the
relationships among the observations.
The second method used in multivariate data analysis is PLS: partial least square
projections to latent structures. This method is used to connect the information in two
blocks of variables X and Y to each other. The goal of PLS regression is to predict Y (a set of
dependent variables) from X (a set of independent variables). PLS finds a multidimensional
direction in the X space that describes the multidimensional variance direction in the Y space
the best (Eriksson et al., 2006).
1.4 X-RAY POWDER DIFFRACTOMETRY
XRPD (X-ray powder diffractometry) is a powerful technique for pharmaceutical
analysis. The technique provides information on the crystallographic structure of materials.
The identification of species from their X-ray powder diffraction patterns is based on the
position of the peaks in terms of 2θ and their different intensities (Skoog et al., 2005).
Different pharmaceutical applications are currently used: determination of crystal
structure, determination of the percentage of crystallinity and monitoring of crystallinity of
pharmaceutical ingredients or excipients. The method is particularly suited for the detection
of polymorphic forms of a drug substance. Different physicochemical properties can be
attributed to the different crystal forms and therefore differentiation between the
polymorphs is of particular importance. XRPD is also used in the detection of different
polymorphic forms of excipients since these can have different biological activity.
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1.5 STATE OF ART
A commonly used bulking agent in freeze-dried pharmaceutical products is mannitol.
Mannitol can exist in three anhydrous forms (α, β, δ). The feasibility of the use of FT-Raman
spectroscopy to quantify mannitol polymorphs was studied by Campbell Roberts et al.
(2002a).
The existence of a hemihydrate form of mannitol was reported by Yu et al. (1999).
The authors provide thermal and crystallographic evidence for the formation of mannitol
hemihydrate during freeze-drying. In the XRPD pattern peaks corresponding to δ and β were
observed as well as additional peaks. These additional peaks disappeared after heating the
samples at 70°C for 30 min. Thermal data (TGA and DTA) were also analyzed. The authors
observed a steplike weight loss in the TGA curve and a well-defined endotherm in the DTA
that coincides with the weight loss. These data suggest the loss of structural water from a
crystalline hydrate. The existence of a mannitol hydrate form has various practical
implications for freeze-drying processes. Hydrate water is strongly bound in the crystal
lattice. As a consequence the sublimation rate can be reduced. Secondly the hydrate water
that is not completely removed during freeze-drying, can be released during storage thereby
affecting product stability.
Because of the low stability of mannitol hemihydrate in freeze-dried formulations,
determination of the crystal structure of this form was of particular importance. A unit cell
of mannitol hemihydrate consists of two independent mannitol molecules and one water
molecule. The structure of mannitol hemihydrate consists of alternating layers of this unit
cell, where the consecutive layers are linked by hydrogen bonds (Nunes et al., 2004).
Zhou et al. (1998) investigated the use of NIR-spectroscopy to determine moisture in
hygroscopic drugs. Because of the strong water absorption bands in this spectral region,
enough sensitivity for an accurate determination of moisture is provided. Significant changes
in the spectra of the samples with various moisture levels were observed. PLS regression was
used to build a calibration model. The conclusion of this research is that moisture levels can
be measured accurately by NIR spectroscopy (SEP=0.11% (w/w) in the range 0.5-11.4%
(w/w)).
13
Because of the existence of a mannitol hemihydrate form and its influence on the
stability of freeze-dried products a method allowing for the differentiation between surface
and bound water was necessary. Research on the feasibility of NIR to determine and
differentiate between surface and bound water in dried drug substances was performed by
Zhou et al. (2003). The authors concluded that reliable NIRS calibration models can be
constructed by PLS regression in the spectral region of the combination bands of water.
Determination of water in lyophilised samples is more difficult because these
samples usually contain lower levels of moisture and therefore produce signals with lower
intensity. Cao et al. (2006) studied the use of NIR to determine and quantify surface and
hydrate water in freeze-dried samples. For the purpose of the study lyophilised samples
were stored at ambient temperature and NIR spectra were taken every 30 minutes. In these
spectra two water absorption regions were visible. One region corresponds to the first
overtone of O-H stretching (1410-1480 nm); the other region is the combination of O-H
stretching and bending (1880-1980 nm). The peak around 1428 nm in the first overtone
region was attributed to surface water, the peak around 1465 nm to hydrate water. In the
combination region the peak around 1905 nm was ascribed to surface water, the peak
around 1947 nm to hydrate water. Mannitol hemihydrate is an unstable form and when
stored at room temperature it gradually dehydrates. This was shown in the spectra: peaks at
1465 nm and 1947 nm decreased with time. Quantitative methods for mannitol hemihydrate
and surface water were developed using PLS.
XRPD is a widely used method to determine the crystal form of a material and to
differentiate between polymorphic forms. The XRPD patterns of the various polymorphic
forms of mannitol can be found in literature. Burger et al. (2000) provide the reference
patterns for α and β mannitol, while the XRPD pattern of the δ-polymorph was published by
Botez et al. (2003). Characterization of the crystal structure of mannitol hemihydrate was
performed by Nunes et al. (2004) and they provide the reference pattern for this form.
Campbell-Roberts et al. (2002b) investigated the effects of preferential orientation of
the crystals on the quantification of mannitol polymorphs when using XRPD and examined
whether or not this preferential orientation effect could be minimized by rotating the
sample during measuring or by reducing the particle size. Rotation of the sample was shown
14
to lead to results more representative for the sample mixture. Grinding the sample improved
accuracy, but can lead to polymorphic transitions, thus possibly affecting stability.
15
2. PURPOSE OF THE PROJECT
Therapeutic proteins often present significant stability problems, but pharmaceutical
products must have adequate stability during storage over a longer period. A widely used
technique to improve stability of proteins is freeze-drying. The main aim of this project was
the validation of the water quantification in lyophilised products using NIR. The method has
gained interest in the past decades due to important advantages compared to other
analytical methods and the potential use as a PAT tool.
162 samples with a specific distribution were prepared. The samples consisted of
mannitol-sucrose mixtures with different weight ratios. Difference in mannitol-sucrose
concentration was also investigated. Other samples were prepared by adding either NaCl,
insulin or human growth hormone to the various mannitol-sucrose ratios. After freeze-
drying, the samples were stored at different relative humidities, leading to samples with
different moisture contents.
The samples were measured with NIR after 7 days of storage at the different relative
humidities and Karl Fischer titration was used as a reference method to determine the water
content of the samples. Multivariate analysis (PCA/PLS) was used for the evaluation of the
results. New analytical methods must be validated prior to use in pharmaceutical industry.
The proposed NIR method for the quantification of water was validated in accordance with
the ICH guideline by assessing precision, accuracy, linearity, range, limit of detection, limit of
quantification and robustness.
The project was carried out together with Novo Nordisk. This pharmaceutical
company is a world leader in diabetes care. Besides this, the company is specialised in
growth hormone therapy, hormone replacement therapy and treatment of haemophilia
with the recombinant factor VIIa.
The second part of the project consisted of the investigation of freeze-dried protein
formulations by XRPD. 54 samples were measured with XRPD in order to gain insight into the
methods’ suitability to determine solid state forms in freeze-dried mixtures. Additionally,
Raman spectroscopy was used to study the polymorphic forms of mannitol in the different
samples.
16
3. MATERIALS AND METHODS
3.1 MATERIALS
Mannitol (Ph. Eur. grade) was purchased from Unikem (Copenhagen, Denmark).
XRPD study showed that the purchased mannitol consisted of the β-polymorph. Mannitol
acts as a bulking agent. Its crystallization results in a good cake structure.
Sucrose was used as a lyoprotectant and was obtained from BDH Analar (VDW
International Ltd., Poole, United Kingdom).
NaCl was obtained from Merck (Darmstadt, Germany). NaCl was added to study its
effect on the crystallization of mannitol. It was shown in previous studies that sodium
chloride effectively inhibits the mannitol crystallization process (Telang et al., 2003).
Two different therapeutic proteins, insulin (Novo Nordisk A/S, Gentofte, Denmark)
and human growth hormone (hGH) (Novo Nordisk A/S, Bagsværd, Denmark), were included
in this project to investigate whether the model for the quantification of water could be
used if proteins are present in the samples and to determine whether or not the type of
protein influences the results.
NaOH and HCl (both Merck, Darmstadt, Germany) were used to adjust the pH of the
solutions containing proteins.
3.2 PREPARATION OF SAMPLES
3.2.1 Sample composition and distribution
One reference set containing only mannitol and sucrose with a ratio of 9:1, 7:3, 5:5
(w/w) and with a concentration of 50 mg/ml was prepared. Besides this, four test sets were
prepared. One of them was prepared by adding NaCl (2.92 mg/ml) to the various mannitol-
sucrose ratios. The influence of density was studied by preparing a test set consisting of
mannitol and sucrose with a ratio of 7:3 and a lower concentration of 30 mg/ml. Two other
test sets were prepared by adding either insulin (10 mg/ml) or human growth hormone (10
mg/ml) to a 50 mg/ml mannitol-sucrose solution (7:3). In order to achieve a pH suitable for
17
protein stability NaOH and HCl were added to the solutions containing proteins. This led to a
NaCl concentration of 0.30 and 0.030 mg/ml in the insulin and hGH solutions respectively.
All samples were prepared by pouring 2ml of the solution into a glass vial.
After freeze-drying the vials containing the different mixtures were stored with open
stoppers for a period of 7 days at three different relative humidities, around 5%, 11% and
35%, in desiccators kept at room temperature. Six parallels were made. All six samples were
analyzed by NIR after 7 days of storage in the desiccators. Three of the six parallels were
additionally analyzed by Karl Fischer titration, two were analyzed by XRPD and one with
Raman spectroscopy. This distribution resulted in a total of 162 vials. An overview of the
sample distribution is given in Table 3.1.
TABLE 3.1: SAMPLE DISTRIBUTION
Mannitol-
sucrose
concentration
(mg/ml)
Mannitol-
sucrose
ratio
Storage
condition
(% relative
humidity)
NaCl
(mg/ml)
Insulin
(mg/ml)
hGH
(mg/ml)
Reference
set 50
5:5, 7:3, 9:1
5, 11, 35 / / /
Test set
NaCl 50
5:5, 7:3, 9:1
5, 11, 35 2.92 / /
Test set 30
mg/ml 30 7:3 5, 11, 35 / / /
Test set
insulin 50 7:3 5, 11, 35 0.30 10 /
Test set
hGH 50 7:3 5, 11, 35 0.030 / 10
The precise molar ratios and relative humidities were chosen because it has been
shown in literature that a high sucrose content and storage at high relative humidity leads to
the collapse during storage thus making the data from Karl-Fischer titration and NIR
spectroscopy unreliable (Grohganz et al., 2009).
18
3.2.2 Labelling system
Because of the sample variations and the high number of samples a labelling system
was developed. Each vial was marked according to the system described in Table 3.2.
TABLE 3.2: LABELLING SYSTEM
The mixture B Binary mixture mannitol-sucrose with concentration of 50mg/ml mgmgmg/ml L Binary mixture mannitol-sucrose with low concentration of 30 mg/ml
N Tertiary mixture mannitol-sucrose-NaCl
I Tertiary mixture mannitol-sucrose-insulin
G Tertiary mixture mannitol-sucrose-hGH
The weight ratio 50 Contains 50% mannitol
70 Contains 70% mannitol
90 Contains 90% mannitol
Used technique X Sample analyzed with NIR and XRPD or Raman spectroscopy
K Sample analyzed with NIR and Karl-Fischer titration
Relative humidity 5 Sample stored at 5% relative humidity
11 Sample stored at 11% relative humidity
35 Sample stored at 35% relative humidity
Sample a, b, c, d, e, f because 6 parallels were made
For example, the vial marked with I70X11b was filled with the tertiary mixture of
mannitol-sucrose-insulin, with a mannitol-sucrose ratio of 7:3. The sample was analyzed with
NIR and XRPD after having being stored at a relative humidity of 11%. The letter b indicates
that the sample is the second parallel.
3.3 PREPARATION OF THE SATURATED SALT SOLUTIONS
During this project the vials were stored in desiccators at three different relative
humidities: 5%, 11% and 35%. Drierite (98% CaSO4, 2% CoCl2, Sigma-Aldrich, Steinheim,
Germany) was poured in a desiccators, leading to a relative humidity of around 5%. A
relative humidity of around 11% was obtained by producing a solution saturated with LiCl
(VWR, Leuven, Belgium). A solution saturated with MgCl2 (Applichem, Darmstadt, Germany)
was prepared, which led to a relative humidity of around 35%.
19
3.4 METHODS
3.4.1 Freeze-drying
The freeze-drier used during this project was the CD8W model from HETO (Heto Lab
Equipment A/S, Allerød, Denmark). This freeze-drier has a single-chamber system, is
equipped with three shelves and a sealing device. The freeze-drying cycle used is described
in Table 3.3.
TABLE 3.3: FREEZE-DRYING CYCLE
Time Total time Temperature (°C) Pressure (hPa)
Freezing
1h 1h 5 � -45
3h 4h -45
0h30min 4h30min -45 � -10
2h 6h30min -10
0h30min 7h -10 � -45
3h 10h -45
Primary drying 1h 11h -45 �-28 0.05
60h 71h -28 0.05
Secondary drying 8h 79h -28 � 20 0.05
12h 91h 20 0.05
3.4.2 Near Infrared Spectroscopy
Analysis of the samples was performed using a FT-NIR spectrometer, more
specifically a FTLA 2000-160 spectrometer from ABB Bomem (Québec, Canada). The
instrument is equipped with an internal quartz halogen source and an InGaAs detector.
Samples were measured through the side of the vial while there were positioned on a
rotating sample holder.
Each spectrum was the average of 64 scans collected in the range 4000 to 8000 cm-1
with a resolution of 8 cm-1. Data acquisition was performed using the Grams (Version
7.00/LT, Thermo Fischer Scientific, Waltham, USA) software package. NIR measurements
were performed after having stored the samples at the different relative humidities during 7
days.
20
3.4.3 Data analysis
The obtained NIR spectra were analyzed by Principal Component Analysis (PCA) and
Partial Least Square Projections to Latent Structures (PLS), using SIMCA-P 11.5 (Umetrics,
Umeå, Sweden). All spectra were baseline-corrected using standard normal variate
transformation. Regarding scaling methods, all NIR spectra were centred.
3.4.4 Karl Fischer titration
Coulometric Karl Fischer titration was used as a reference method to determine the
total moisture content in the samples. Measurements were performed using a Karl Fischer
Coulometer 831 from Metrohm (Copenhagen, Denmark) equipped with an autosampler.
First, 3 ml of formamid (Riedel-de Haën, Sigma Aldrich, Seelze, Germany) were added
to dissolve the cake. Of this reconstituted solution, approximately 1 ml was poured into the
titration cell which was filled with the Karl Fischer reagent.
3.4.5 XRPD
The powder patterns were obtained using a X-ray powder diffractometer X’pert PRO
MPD from PANalytical (Almelo, The Netherlands). XRPD patterns were obtained with Cu Kα
radiation (45 kV x 40 mA, λ=1.54 Å). The scans were conducted in the reflection mode in the
2θ range from 5° to 30° and counts were accumulated for 40s at each step with a step size of
0.01 °2θ. Samples were prepared by spreading a part of the sample on zero background
silicon wafers. Analysis of the data was performed with X’pert HighScore Plus (PANalytical,
Almelo, The Netherlands). Identification was carried out by comparing the diffraction
patterns with patterns found in literature (Botez et al., 2003; Burger et al., 2000; Cambell-
Roberts et al. 2002b, Nunes et al., 2004).
3.4.6 Raman spectroscopy
Using Raman spectroscopy, additional information concerning the solid state of the
content of the vials can be acquired. A Renishaw Ramascope System 1000 (Wotton-under-
Edge, Gloucestershire, United Kingdom) with NIR diode laser (λ=785 nm) was employed to
analyze the powder sample that was placed on a microscopy slide. The sample was analyzed
with the Raman microscope under a 20x objective. A Rencam Charged Coupled Device (CCD)
21
silicon detector was used to acquire the Raman shifts. The exposure time for data collection
was set at 240 s. All samples were analyzed over the range from 3200 to 100 cm-1 and with 1
accumulation per sample. Wire V.2.0 software was used for instrument control and data
acquisition. Identification was carried out by comparing the Raman spectra with reference
spectra found in literature (De Beer et al., 2007).
22
4. RESULTS AND DISCUSSION
In this part the results on the investigation of the solid state properties of freeze-
dried protein formulations are discussed. The first part focuses on the development and
validation of a NIR spectroscopic method which can be used to quantify water in freeze-
dried samples. In the next chapters, the results of the investigation of the freeze-dried
protein formulations with XRPD and Raman spectroscopy are summarised.
4.1 PCA ANALYSIS OF THE NIR SPECTRA
All 162 freeze-dried samples were measured with NIRS after 7 days of storage at the
different relative humidities. The samples differ in composition, storage condition (relative
humidity) and mannitol-sucrose ratio. Therefore a multivariate analysis was performed on
the data. The three different identifiers were added in the spreadsheet. Scaling of the data is
necessary to acquire a dataset suitable for analysis. Therefore, all data were centred.
4.1.1 Principal Component Analysis of untreated data
First, the untreated data were analysed. Untreated data are mainly influenced by the
physical state of the content of the vial. Analysis of the untreated data is useful to detect
production outliers. The NIR spectra were analysed in the range from 4000 to 8000 cm-1.
All samples are clustered around the centre of the plot; no outliers were detected.
When colouring the points in the PCA score plot of PC2 and PC1 according to composition
and adding labels showing the mannitol-sucrose ratio, clustering of samples having the same
composition and weight ratio is seen (Figure 4.1). An increase in mannitol content is noticed
when moving diagonally from the 4th to the 2nd quadrant. This distribution might explain a
difference in the presence of different forms of mannitol as peaks at 4370 and 4430 cm-1,
characteristic for mannitol hemihydrate and β-mannitol respectively (De Beer et al., 2007),
are noted in the loading plots of PC1 and PC2. Figure 4.2 shows the loading plot of PC1 and
PC2 in the range from 4000 to 5000 cm-1. No peaks indicating different polymorphs of
mannitol were found in the area from 5000 to 8000 cm-1.
23
-0,6
-0,4
-0,2
-0,0
0,2
0,4
0,6
-4 -3 -2 -1 0 1 2 3 4
t[2]
t[1]
R2X[1] = 0,968552 Ellipse: Hotelling T2 (0,95) R2X[2] = 0,0229506
BGILN
50
50
50
5050
5050
5050
50 505050
50
50
50 505070707070
7070
70
7070
707070
70
7070 707070
90
909090
90
9090
9090
90
90
90
90
90
9090
90 90 7070 7070 70707070
70 70
70707070 70 707070
70 707070707070
707070
7070
70
707070
7070
70 70 707070
70
70707070
70 707070 70
70
70
70
7070 70707070
70
70
70
70707070 70
70
707070 50
50505050 5050
5050 5050
5050
50
50 505050
7070 70
7070
7070707070
70
7070
70
70707070
90
90
909090
9090
90
90
9090
909090
90
909090
FIG. 4.1: PCA SCORE PLOT OF PC2 AGAINST PC1, COLOURED ACCORDING TO COMPOSITION
AND LABELED ACCORDING TO WEIGHT RATIO
FIG. 4.2: LOADING PLOT OF PC1 AND PC2 (R2X[1]=0.98, R2X[2]=0.02)
When colouring the points according to the relative humidity during storage, no
tendency is observed on the PCA score plot of PC2 and PC1. However, on the PCA score plot
of PC4 and PC3, a tendency is seen. When moving diagonally from the 4th to the 2nd
quadrant, an increase in relative humidity at which the samples were stored is noticed
(Figure 4.3). The loading plots (Figure 4.4) of these components show a peak at 5165 cm-1,
which corresponds to the OH combination band. We conclude that the scattering on the
score plot indicates a difference in the water content in the samples stored at different
relative humidities.
24
-0,3
-0,2
-0,1
-0,0
0,1
0,2
0,3
-0,2 -0,1 -0,0 0,1 0,2
t[3]
t[4]
R2X[3] = 0,00468591 Ellipse: Hotelling T2 (0,95) R2X[4] = 0,00301801
51135
FIG. 4.3: PCA SCORE PLOT OF PC3 AGAINST PC4, COLOURED ACCORDING TO STORAGE
CONDITION
FIG. 4.4: LOADING PLOT OF PC3 AND PC4 (R2X[3]=0.005, R2X[4]=0.003)
PCA analysis of the untreated data allowed to detect differences in composition and
storage condition between the different samples. The fact that no strong outliers were
observed is in good agreement with all samples showing a similar and good cake structure.
4.1.2 Principal Component Analysis of SNV corrected data
Before performing PCA analysis, all data were SNV (Standard Normal Variate)
corrected in the range 4000 - 8000 cm-1 in order to reduce the impact of the physical state
and to focus on chemical information. A common problem with spectroscopic data is
baseline shifts, which is particularly prevalent with reflectance methods such as NIR. SNV
25
correction reduces the baseline offset and adjusts all spectra based on an individual average
spectrum.
4.1.2.1 Comparison of the first and the second principal component
Figure 4.5 shows the scatter plot of PC1 and PC2. When colouring the points
according to composition, a random distribution of the samples along the first principal
component is seen. Besides this, a tendency for samples containing either insulin or human
growth hormone to have a lower PC2 is observed.
-1,0
-0,5
0,0
0,5
1,0
-2 -1 0 1 2 3
t[2]
t[1]
R2X[1] = 0,785564 Ellipse: Hotelling T2 (0,95) R2X[2] = 0,135781
BGILN
FIG. 4.5: PCA SCORE PLOT OF PC1 AND PC2 COLOURED ACCORDING TO COMPOSITION
This tendency can be explained when looking at the loading plot of PC2 (Figure 4.6).
The plot shows a strong maximum at 4800 cm-1, corresponding to a strong band in the
spectra of mannitol and sucrose – the C-H combination band. Samples containing protein
have a relatively lower amount of sugars and therefore have a lower PC2 than other
samples.
Investigation of the NIR spectra of the different samples shows the influence of the
relative amount of sugars on the spectra. Samples not containing protein have a clear peak
at 4800 cm-1, while the peak of samples containing protein has a different shape in the area
from 4600 to 4900 cm-1. This difference can be explained by the presence of some bands
characteristic for proteins in this region (Tantipolphan et al., 2008), which overlap the C-H
combination band. The spectra for the samples with different excipients are shown in Figure
4.7.
26
FIG. 4.6: LOADING PLOT OF PC2 (R2X[2]=0.14)
FIG. 4.7: NIR SPECTRA OF SAMPLES WITH VARIOUS EXCIPIENTS
Furthermore, we observe a peak at 4430 cm-1, corresponding to β-mannitol (De Beer
et al., 2007), on the loading plot of PC2. This peak suggests that samples with a high PC2, in
this case the B, L and N samples, contain β-mannitol. Besides this, a minimum at 4370 cm-1,
corresponding to δ-mannitol or mannitol hemihydrate is noted, suggesting that the I and G
samples, which have a low PC2, contain these forms of mannitol.
Differentiation between δ-mannitol and mannitol hemihydrate is possible by
analyzing the results of the XRPD measurements. In the XRPD patterns of the I and G
27
samples, only peaks at 9.7 and 24.7 °2θ, corresponding to δ-mannitol (Campbell-Roberts et
al., 2002a), were seen. No peak at 18.0 °2θ, which is a characteristic position for mannitol
hemihydrate (Nunes et al., 2004), was noted in the pattern of the samples containing
proteins. In the XRPD pattern of the B, L and N samples, peaks at 14.6 and 16.8 °2θ were
observed, indicating that these samples contain β-mannitol (Campbell-Roberts et al., 2002a).
In Figure 4.8, a comparison of the XRPD patterns of a B and an I sample is made.
0
1000
2000
Counts
Position [°2Theta] (Copper (Cu))
10 15 20 25 30
2009 04 01_B70X11a 2009 04 01_I70X11a
FIG. 4.8: COMPARISON OF THE XRPD PATTERN OF A B AND AN I SAMPLE
4.1.2.2 Comparison of other principal components
The PCA score plot of PC4 and PC1 shows a clustering according to storage humidity
(Figure 4.9). Samples stored at higher relative humidity tend to have a higher PC4. This can
be explained by interpreting the loading plot of PC4 (Figure 4.10). On the loading plot two
maximums at 5165 cm-1 and 6900 cm-1, corresponding to the combination peak of OH
vibrations in water and the first overtone of OH respectively, are observed. Samples with a
higher PC4 have been stored at higher relative humidity and therefore have a higher water
content. Even though PC4 represents only 3.2% of the variation in the data, this PC should be
taken into account when analyzing the data because of the clear features visible in the
loading plot.
28
-0,4
-0,2
-0,0
0,2
0,4
0,6
-2 -1 0 1 2 3
t[4]
t[1]
R2X[1] = 0,785564 Ellipse: Hotelling T2 (0,95) R2X[4] = 0,0323225
51135
FIG. 4.9: PCA SCORE PLOT OF PC4 AND PC1, COLOURED ACCORDING TO STORAGE HUMIDITY
FIG. 4.10: LOADING PLOT OF PC4 (R2X[4]=0.03)
Several trends were observed on the score plots of the different principal
components when performing PCA analysis of the SNV corrected data. The scattering can be
explained by looking at the loading plots. The different mannitol polymorphs are
represented in the second principal component. The water content of the samples is
represented in the fourth principal component.
29
4.2 DEVELOPMENT AND VALIDATION OF A NIR SPECTROSCOPIC METHOD FOR THE
QUANTIFICATION OF WATER
In this part the results concerning the development of a model for the quantification
of water based on the NIR spectra are discussed. First the correlation between the observed
and the predicted water content for all samples was investigated. In the second part the
development and the validation of a general model for the water quantification, based on
the samples containing only mannitol and sucrose, is discussed. Finally, the applicability of
the model when varying the sample composition is discussed.
4.2.1 First look at the Observed versus Predicted plot
A PLS model including 3 PLS components was built based on the SNV corrected NIR
spectra of all the samples in a range from 4500 to 7400 cm-1. Figure 4.11 shows a good
correlation between the water content observed with Karl Fischer titration and the water
content predicted based on the NIR spectra. A RMSEE (Root Mean Square Error of
Estimation) of 0.14% was obtained.
FIG. 4.11: PLOT OF THE WATER CONTENT OBSERVED WITH KARL FISCHER TITRATION VERSUS THE PREDICTED WATER CONTENT BASED ON THE NIR SPECTRA OF ALL SAMPLES
(RMSEE=0.14%)
The main contribution in the PLS model are the peaks at 5160 and 6900 cm-1,
corresponding to the OH combination band and the first overtone of OH respectively, which
are observed in the weight plot of PC1 (Figure 4.12).
30
FIG. 4.12: WEIGHT PLOT OF PC1 (R2X[1]=0.30)
4.2.2 Model for the quantification of water
4.2.2.1 Development of the model
Three different PLS models were developed based on the results of the B samples
measured on day 7. The same pre-processing technique, namely SNV transformation was
used in all cases and all data were centred. Three spectral ranges were evaluated: 4850-5400
cm-1, including the OH combination band; 4500-7400 cm-1, including the OH combination
band and the first overtone of OH vibrations in water and 4000-8000 cm-1, corresponding to
the complete range in which the NIR spectra had been taken. The choice of the best model
was based on the predictive ability of the model as reflected in the RMSEE (Root Mean
Square Error of Estimation). An overview of the PLS models is given in Table 4.1.
TABLE 4.1: RESULTS OF PLS MODELS IN DIFFERENT SPECTRAL RANGES
Wavenumber range (cm-1
) Number of PLS components RMSEE (%)
4850-5400 1 0.132 4500-7400 2 0.137 4000-8000 2 0.138
No significant difference was observed in the RMSEE: all values are between 0.13 and
0.14%. Considering the fact that the variation of the Karl Fischer measurements can be as
high as 0.4%, these are good results. The model ranging from 4500 to 7400 cm-1 was chosen
31
because in this area focusing is done on the OH combination band and the first overtone of
OH stretching, which are of particular interest in this project. Figure 4.13 represents this PLS
model with the values for the water content observed with Karl Fischer titration versus the
values predicted based on the NIR spectra.
The main contribution in the PLS model are the peaks at 5160 cm-1 and 6900 cm-1,
which correspond to the combination band of water and the first overtone of water, and
which are observed on the weight plot of PC1 (Figure 4.14).
FIG. 4.13: PLOT OF THE WATER CONTENT OBSERVED WITH KARL FISCHER TITRATION VERSUS THE PREDICTED WATER CONTENT BASED ON THE NIR SPECTRA OF THE B SAMPLES
(RMSEE=0.137%)
FIG. 4.14: WEIGHT PLOT OF PC1 (R2X[1]=0.52)
32
4.2.2.2 Validation of the model
No standard procedure is available for the validation of a quantitative NIR method
and therefore the validation of the method for the quantification of water presented in this
project was based on the different parameters described in the ICH (International
Conference on Harmonisation) guideline. It must however be stressed that this guideline
was written for chromatographic techniques and has been designed for the validation of
methods involving univariate calibration, while a multivariate approach is used with NIR
spectroscopy. The results of the NIR analysis were compared to the results for the water
content obtained with Karl Fischer titration and not to the real values of the water content
as these were unknown. The following validation parameters were evaluated in order to be
consistent with the recommendations of the ICH guideline: linearity, range of application,
limit of detection (LoD) and limit of quantification (LoQ), accuracy, precision (repeatability
and intermediate precision) and robustness.
Linearity. To evaluate the linearity of the method across the range, a regression line
between the values for the water content observed with Karl Fischer titration and the
predicted values based on the NIR measurements was developed and is showed in Figure
4.15.
FIG. 4.15: REGRESSION LINE BETWEEN THE WATER CONTENT OBSERVED WITH KARL FISCHER AND THE NIR PREDICTIONS
33
The method is linear when the regression line has a unity slope and a zero intercept,
indicating that the NIR measurements provide the same results as the Karl Fischer titration.
In this case, the regression equation is y=1x-5*10-8 with y being the observed value and x
being the predicted value. A regression coefficient (R²) of 0.98 was obtained. The slope of
the regression line is 1 and the y-intercept is 5.34*10-8, which does not differ significantly
from the theoretical zero value in case of linearity. Considering these results, the method is
qualified as linear.
Range. The range of an analytical method is the lower and upper limit of analyte
concentration that can be determined with an acceptable linearity, precision and accuracy
when applying the described method. The method was confirmed to be linear, accurate and
precise in the range from 0.55 to 3.38 % water.
Limit of detection (LoD). The LoD is the lowest quantity of a substance that can be
detected, but not necessarily quantitated as an exact value. The LoD was found to be 0.44%
and was calculated according to the formula below:
LoD=3.3σ/S
With: σ: standard deviation of the response
S: slope of the regression line
Limit of quantification (LoQ). The LoQ is the lowest quantity of a substance that can
be quantified. The LoQ was found to be 1.34% and was calculated according to the formula
below:
LoD=10.0σ/S
With: σ: standard deviation of the response
S: slope of the regression line
Precision. Precision expresses the closeness of agreement between a series of
measurements. In accordance to the ICH guidelines repeatability and intermediate precision
were evaluated. The results are shown in Table 4.2.
34
TABLE 4.2: RESULTS OF THE REPEATABILITY AND INTERMEDIATE PRECISION STUDY
Sample
code
Repeatability Intermediate Precision
Average water
content (%)
Absolute spread water
content (%)
Relative spread
(%)
Average water
content (%)
Absolute spread water
content (%)
Relative spread
(%)
B50X5c 0.74 0.73-0.77 98.7-104.1 0.73 0.70-0.77 95.9-105.5 B50X11c 1.58 1.57-1.58 99.4-100.0 1.56 1.54-1.58 98.7-101.3 B50X35c 2.76 2.69-2.85 97.5-103.4 2.72 2.59-2.85 95.2-104.8
To determine repeatability, three samples covering the specified range were
measured three times by the same person, with the same instrument and at the same time
point. The water content was determined based on the NIR spectra, using the model.
When evaluating intermediate precision, the effect of random events including
different analyzers, time points and instruments, on the results is assessed. In this case three
samples covering the specified range were measured three times by two different persons
and at two different time points.
For the two different parameters the average water content was calculated as well as
the absolute and relative spread of the water content. A maximum relative spread from 95
to 105% was found for the water content in the repeatability and intermediate precision
study. The largest spread in water content was found in the sample with the lowest water
content. Even though this sample has a water content which is lower than the LoQ, the
spread of the water content is still between the limits 100%±5%. Especially considering the
variation of Karl Fischer measurements this spread can be considered as a good result.
Accuracy. Accuracy explains how well a measured value corresponds to the real
value. During this project no real values were obtained for the water content and therefore
this parameter could not be evaluated statistically. However, according to the ICH
guidelines, accuracy may be inferred once precision and linearity have been established.
35
Robustness. When evaluating the robustness of a method, the reliability of the results
after variations in method parameters is assessed. This parameter is discussed in further
details in the paragraph below.
4.2.3 Applicability of the model when varying the sample composition
The next step was to investigate whether the validated model could be used to
predict the water content of samples with a different composition, in this case containing
protein or NaCl, or having a lower concentration of mannitol and sucrose.
To evaluate the robustness of the model, a prediction set consisting of the L, I, G and
N samples was specified. The model based on the samples containing only mannitol and
sucrose, which was validated above, was used to predict the water content of the new,
independent samples. A RMSEP (Root Mean Square Error of Estimation) of 0.21 % was
achieved, which is a good result. Figure 4.16 shows the plot of the observed water content
versus the water content predicted based on the NIR spectra.
FIG. 4.16: PLOT OF OBSERVED VERSUS PREDICTED WATER CONTENT FOR L, I, G, N SAMPLES (RMSEP=0.21%)
It can be seen from the weight plot (Figure 4.17) of PC1 that the main contribution to
the model is the water content as peaks at 5160 and 6900 cm-1 are noted.
36
FIG. 4.17: WEIGHT PLOT PC1 (R2X[1]=0.30)
Samples with a composition highly differing from the samples of the reference set
used to built the model were included in the prediction set. Samples in the prediction set
contained additional excipients such as NaCl (N samples), had a lower concentration of
mannitol and sucrose (L samples) or contained 15% (w/w) of proteins (I and G samples). The
obtained results show that a general model, based on samples containing only mannitol and
sucrose can be used to predict the water content of samples with a strongly differing
composition.
4.3 XRPD MEASUREMENTS
XRPD measurements were performed to determine the solid state form of the
content of the vials. Two parallels of all compositions were measured.
The XRPD patterns of the different samples were compared visually to reference
patterns of the different mannitol polymorphs found in literature. Nunes et al. (2004) define
peaks at positions 9.6, 16.5, 18.0 and 25.7 °2θ as being characteristic for mannitol
hemihydrate. Reference patterns of the different mannitol polymorphs are given by
Campbell-Roberts (2002a) with characteristic peaks at the following positions: 13.7 and 17.3
°2θ for the α-polymorph; 14.6 and 16.8 °2θ for the β-polymorph and 9.7 and 24.6 °2θ for the
δ-polymorph (Figure 4.18). The peak at 18.0 °2θ, rather than at 9.6 °2θ, seemed the most
37
useful for the identification of mannitol hemihydrate due to the possible confusion with the
peak of δ-mannitol at the same position.
FIGURE 4.18: XRPD PATTERN OF MANNITOL HEMIHYDRATE (a) AND STICK PATTERNS OF MANNITOL HEMIHYDRATE (b) AND THE DIFFERENT MANNITOL POLYMORPHS ALPHA (c),
BETA (d) AND DELTA (e) (Nunes et al., 2004)
Samples with a different composition were found to contain different polymorphs.
When comparing the patterns of samples of the two parallels having the same composition,
no difference in the presence of the different polymorphs was observed, except for some B-
samples. No influence of the relative humidity at which the samples had been stored on the
presence of different polymorphic forms was noted, except for the L-samples.
Peaks characteristic for β- and δ-mannitol were found in the patterns of the samples
containing only mannitol and sucrose (B-samples) (Figure 4.19). Besides this, a peak at
18.0 °2θ was found in these samples, indicating that they contained mannitol hemihydrate.
Exceptions are the patterns of samples B90 stored at 5 and 35% of the second parallel,
where no peak at 18.0 °2θ was observed, indicating that mannitol hemihydrate was not
present (Figure 4.20). The formation of the hydrate form of mannitol is influenced by
chance. Yu et al. (1999) describe the vial-to-vial variations in the amount of mannitol
hemihydrate present, even for samples from the same batch. The XRPD study confirmed the
presence of mannitol hemihydrate observed by NIR spectroscopy. In Figure 4.21, the
comparison between the NIR spectra of the corresponding samples of both parallels is
made. In the spectrum of the sample of parallel A, a peak at 4370 cm-1, characteristic for
38
mannitol hemihydrate (De Beer et al., 2007) can be seen. This peak is not present in the
spectrum of parallel B.
0
1000
2000
Counts
Position [°2Theta] (Copper (Cu))
10 15 20 25 30
2009 04 01_L70X11a 2009 04 01_N90X11a 2009 04 01_B90X5a
FIG. 4.19: XRPD PATTERNS OF B, N AND L SAMPLES
0
1000
2000
Counts
Position [°2Theta] (Copper (Cu))
10 15 20 25 30
2009 04 06_B90X5b 2009 04 01_B90X5a
FIG. 4.20: COMPARISON OF XRPD PATTERN OF SAMPLES FROM 2 PARALLELS. THE PEAK AT
18.0 °2θ, CHARACTERISTIC FOR MANNITOL HEMIHYDRATE, IS NOT PRESENT IN CASE OF THE SAMPLE OF PARALLEL b.
39
FIG. 4.21: COMPARISON OF NIR SPECTRA OF SAMPLES FROM PARALLEL a AND b SHOWING A DIFFERENCE IN PRESENCE OF MANNITOL HEMIHYDRATE
Based on the characteristic peaks in the patterns of the L- samples (Figure 4.19), it
was concluded that these samples contained β- and δ-mannitol. An additional peak at 18.0
°2θ was noted in the pattern of the L-sample stored at 35% relative humidity, indicating that
this sample contained the hydrate form of mannitol. The higher humidity level and the
resulting higher water content might have favoured the hemihydrate formation.
In the patterns of the N-samples (Figure 4.19) peaks at positions 9.7 and 24.7 °2θ,
characteristic for δ-mannitol, and at 14.6 and 16.8 °2θ, characteristic for β-mannitol, were
found. The NaCl-induced inhibition of the mannitol hemihydrate formation is mentioned by
Telang et al. (2003) and might explain the absence of mannitol hemihydrate in the N-
samples, which contain NaCl.
None of the samples containing either insulin or growth hormone was found to
contain the β polymorphic form of mannitol. Only peaks at positions 9.7 and 24.7 °2θ,
characteristic for δ-mannitol, were observed in the pattern of these samples (Figure 4.22).
Reports of the influence of proteins or amino acids on the crystallization of mannitol were
found in literature. Pyne et al. (2003) describe the influence of glycine on the mannitol
crystallization during the different stages of a freeze-drying process. Mannitol was found to
crystallize out as the δ-polymorph during primary drying. The inhibition of crystallization of
mannitol from vacuum-dried solutions with a β-lactoglobulin:mannitol ratio up to 1:5 is
40
described by Sharma and Kalonia (2004). Additionally, they observed that when mannitol
crystallized out from solutions with a higher content of mannitol, mainly the δ-polymorphic
form was found in the samples.
The presence of only the δ-polymorphic form in the samples containing either insulin
or hGH was concluded from the analysis of both the NIR spectra and the XRPD patterns. We
conclude that the presence of proteins in this study influences the crystallization of mannitol
and facilitates the formation of the δ-polymorphic form.
0
1000
2000
Counts
Position [°2Theta] (Copper (Cu))
10 15 20 25 30
2009 04 01_G70X11a 2009 04 01_I70X11a
FIG. 4.22: XRPD PATTERNS OF SAMPLES CONTAINING PROTEINS
4.4 RAMAN MEASUREMENTS
Raman spectroscopy is an additional method to investigate the different polymorphs
of mannitol. One parallel of all compositions was analyzed with the Raman microscope. PCA
analysis was performed on the data which were centred and SNV corrected in the range
from 1000 to 1200 cm-1.
On the score plot of PC1 versus PC2 (Figure 4.23) the following distribution is seen:
samples containing either insulin or hGH have a low PC1, while the samples B50 and N70
have an average PC1. The remaining samples have a high PC1. This distribution in the scatter
plot can be explained when having a look at the loading plot of PC1 (Figure 4.24). On this
plot a maximum at 1037 and 1135 cm-1, corresponding to β-mannitol, is observed, indicating
41
that the L, N and B samples contain β-mannitol. Besides this, a minimum at 1055 and 1148
cm-1, corresponding to δ-mannitol is observed, suggesting that the δ-polymorphic form is
present in the samples containing either insulin or hGH.
-10
-5
0
5
10
-20 -10 0 10 20
t[2]
t[1]
R2X[1] = 0,729002 R2X[2] = 0,166182 Ellipse: Hotelling T2 (0,95)
BGILN
505050 70
70
7090
90
90707070
70
70
70 7070
70
50
50
50
70
7070
9090
90
FIG. 4.23: SCORE PLOT OF PC1 AND PC2, COLORED BY COMPOSITION AND LABELED
ACCORDING TO WEIGHT RATIO
FIG. 4.24: LOADING PLOT OF PC1 (R2X[1]=0.73)
The results were confirmed when having a look at the raw spectra obtained with
Raman spectroscopy. The obtained spectra were compared with reference spectra found in
literature.
Reference spectra for the different polymorphs of mannitol are published by De Beer
et al. (2007) and are shown in Figure 4.25. The β-polymorphic form of mannitol has a
characteristic peak at 1037 and at 1135 cm-1, the δ-polymorphic form at 1054 and at 1147
cm-1, while mannitol hemihydrate has a characteristic peak at a wavenumber of 1140 cm-1.
42
FIGURE 4.25: RAMAN SPECTRA OF α-, β- and δ-MANNITOL AND MANNITOL HEMIHYDRATE (De Beer et al., 2007)
A Raman spectrum of sucrose is given by Xie et al. (2008) and shows no characteristic
peaks at the same position as the different polymorphic forms of mannitol or mannitol
hemihydrate. The characteristic peak of sucrose is situated at 832 cm-1.
No influence of the relative humidity at which the samples had been stored was
found on the formation of the different polymorphs of mannitol. In none of the samples a
peak characteristic for mannitol hemihydrate was observed. Samples composed of only
mannitol and sucrose (B and L samples) contained only β mannitol, indicated by peaks at
1037 and 1135 cm-1, except for the B samples composed of equal amounts of mannitol and
sucrose, which contained also the δ polymorphic form, since additional peaks at 1054 and
1147 cm-1 were observed. In the samples containing NaCl, mannitol was present in the β
polymorphic form, except for the samples with a mannitol-sucrose ratio of 7:3, which
contained both the β- and the δ-polymorphic form. Samples containing either insulin or hGH
had characteristic peaks at 1054 and 1147 cm-1, indicating that only the δ-polymorphic form
of mannitol was present. This finding confirms the results previously obtained with the NIR
and XRPD measurements. The Raman spectra of different samples are given in Figure 4.26.
43
FIG. 4.26: RAMAN SPECTRA OF SAMPLES WITH DIFFERENT COMPOSITION
44
5. CONCLUSION
The developed NIR method for the quantification of water in freeze-dried samples
was successfully validated according to the ICH guideline. A model was developed based on
the samples containing only mannitol and sucrose. The model was proven to be linear in a
range from 0.55 to 3.38% of water and to be accurate. The results of the precision study
showed a maximum relative spread of the water content from 95 to 105%. A LoD of 0.44%
and a LoQ of 1.34% was determined.
Furthermore, it was shown that the model could be applied successfully to samples
with varying composition. A RMSEP of 0.21% was achieved when using the validated method
to predict the water content of samples with a lower concentration of mannitol and sucrose,
containing NaCl, or containing proteins in a concentration of 15% (w/w).
From the investigation of the solid state properties of the freeze-dried samples it can
be concluded that the presence of proteins in this study facilitates the formation of the δ-
polymorphic form of mannitol. This finding was confirmed with NIR spectroscopy, XRPD as
well as with Raman spectroscopy.
45
6. REFERENCES
Blanco, M.; Valdés, D.; Bayod, M. S. ; Fernández-Mari, F.; Llorente, I. (2004). Characterization
and analysis of polymorphs by near-infrared spectrometry. Anal. Chim. Acta, 502, 221-227
Botez, C. E.; Stephens, P. W., Nunes, C., Suryanarayanan, R. (2003). Crystal structure of
anhydrous delta-D-mannitol. Powder Diffr., 18, 214-218
Burger, A.; Henck, J. O.; Hetz, S.; Rollinger, J. M.; Weissnicht, A. A.; Stottner, H. (2000).
Energy/temperature diagram and compression behavior of the polymorphs of D-mannitol. J.
Pharm. Sci., 89, 457-468
Campbell Roberts, S. N.; Williams, A. C.; Grimsey, I. A.; Booth, S. W. (2002a). Quantitative
analysis of mannitol polymorphs. FT-Raman spectroscopy. J. Pharmaceut. Biomed., 28, 1135-
1147
Campbell Roberts, S. N.; Williams, A. C.; Grimsey, I. M.; Booth, S. W. (2002b). Quantitative
analysis of mannitol polymorphs. X-ray powder diffractometry – exploring preferred
orientation effects. J. Pharmaceut. Biomed., 28, 1149-1159
Cao, W.; Mao, C.; Chen, W.; Lin, H.; Krishnan, S.; Cauchon, N. (2006). Differentiation and
quantitative determination of surface and hydrate water in lyophilized mannitol using NIR-
spectroscopy. J. Pharm. Sci., 95, 2077-2086
Carpenter, J. F.; Chang, B. S. (1996). Lyophilization of protein pharmaceuticals. In:
Biotechnology and biopharmaceutical manufacturing, processing and preservation; Avis,
K.E., Wu, V. L. (Eds.); Interpharm Press, Boca Raton, FL, USA; pp. 199-203
Carpenter, J. F. ; Pikal, M. J. ; Chang, B. S. ; Randolph, T. W. (1997). Rational design of stable
lyophilized protein formulations : some practical advice. Pharm. Res., 14, 969-975
Carpenter, J. F.; Izutsu, K.-I.; Randolph, T. W. (2004). Freezing- and drying-induced
perturbations of protein structure and mechanisms of protein protection by stabilizing
additives. In: Freeze-drying/Lyophilization of pharmaceutical and biological products, Rey, L.
and May, J. C. (Eds). Marc Dekker Inc., New York, USA, pp. 147-186
46
Chang, B. S.; Patro, S. Y. (2005). Freeze-drying process development for protein
pharmaceuticals. In: Lyophilization of protein pharmaceuticals; Costantino, H. R., Pikal, M. J.
(Eds.); AAPS Press, Arlington, VA, USA; pp. 113-138
De Beer, T. R. M.; Allesø, M.; Goethals, F.; Coppens, A.; Vander Heyden, Y.; Lopez De Diego,
H.; Rantanen, J.; Verpoort, F.; Vervaet, C.; Remon, J. P.; Baeyens, W. R. G. (2007).
Implementation of a Process Analytical Technology system in a freeze-drying process using
Raman spectroscopy for in-line process monitoring. Anal. Chem., 79, 7992-8003
De Maesschalk, R.; Cuesta Sanchez, F.; Massart, D. L. ; Doherty, P. ; Hailey, P. (1998). On-line
monitoring of powder blending with near-infrared spectroscopy. Appl. Spectrosc., 52, 731-
752
Eriksson, L.; Johansson, E.; Kettaneh-Wold, N.; Trygg, J., Wikström, C.; Wold, S. (2006). Multi-
and megavariate data analysis. Part I: Basic principles and applications. Umetrics AB, Umeå,
Sweden.
Food and Drug Administration (FDA) (2004). Guidance for Industry. PAT – A framework for
innovative pharmaceutical development, manufacturing, and quality assurance.
Grohganz, H.; Fonteyne, M.; Skibsted, E.; Falck, T.; Palmqvist, B.; Rantanen, J. (2009). Role of
excipients in the quantification of water in lyophilized mixtures using NIR spectroscopy. J.
Pharmaceut. Biomed., 49, 901-907
ICH Guideline: Q2B Validation of analytical procedures: methodology (1996).
Izutsu, K.-I.; Kojima, S. (2002). Excipient crystallinity and its protein-structure-stabilizing
effect during freeze-drying. J. Pharm. Pharmacol., 54, 1033-1039
Kamat, M. S.; Lodder, R. A.; DeLuca, P. P. (1989). Near-infrared spectroscopic determination
of residual moisture in lyophilized sucrose through intact glass vials. Pharm. Res., 6, 961-966
Kirsch, J. D.; Drennen, J. K. (1996). Near-infrared spectroscopic monitoring of the film coating
process. Pharm. Res., 13, 234-237
47
Larsen, S. S. (1973). Studies on stability of drugs in frozen systems VI: The effect of freezing
upon pH for buffered aqueous solutions. Arch. Pharm. Chem. Sci. Ed., 1, 41-53
Lu, X.; Pikal, M. J. (2004). Freeze-drying of mannitol – trehalose – sodium chloride-based
formulations: the impact of annealing on dry layer resistance to mass transfer and cake
structure. Pharm. Dev. Technol., 9, 85-95
Luypaert, J.; Massart, D. L.; Vander Heyden, Y. (2007). Near-infrared spectroscopy
applications in pharmaceutical analysis. Talanta, 72, 865-883
Milton, N.; Gopalrathnam, G. D.; Craig, G. D.; Mishra, D. S.; Roy, M. L.; Yu, L. (2007). Vial
breakage during freeze-drying: crystallization of sodium chloride in sodium chloride –
sucrose frozen aqueous solutions. J. Pharm. Sci., 96, 1848-1853
Nunes, C.; Suryanarayanan, R.; Botez, C. E. ; Stephens, P.W. (2004). Characterization and
crystal structure of D-mannitol hemihydrate. J. Pharm. Sci., 93, 2800-2809
Pikal, M. J. (2004). Mechanisms of protein stabilization during freeze-drying and storage: the
relative importance of thermodynamic stabilization and glassy state relaxation dynamics. In:
Freeze-drying/lyophilization of pharmaceutical and biological products, Rey, L. and May, J. C.
(Eds.). Marc Dekker Inc., New York, USA, pp. 63-107
Pyne, A.; Chatterjee, K.; Suryanarayanan, R. (2003). Solute crystallization in mannitol-glycine
systems – Implications on protein stabilization in freeze-dried formulations. J. Pharm. Sci.,
92, 2272-2283
Reich, G. (2005). Near infrared spectroscopy and imaging: basic principles and
pharmaceutical applications. Adv. Drug Deliver. Rev., 57, 1109-1143
Romero-Torres, S.; Wikström, H.; Grant, E. R.; Taylor, L. S. (2007). Monitoring of mannitol
phase behavior during freeze-drying using non-invasive raman spectroscopy. PDA J. Pharm.
Sci. Tech., 2, 131-145
Sharma, V. K.; Kalonia, D. S. (2004). Effect of vacuum drying on protein-mannitol
interactions: the physical state of mannitol and protein structure in the dried state. AAPS
Pharm. Sci. Tech., 5, no pp given
48
Skoog, D. A.; Holler, F. J.; Nieman, T. A. (1998). Principles of Instrumental Analysis, 5th
Edition. Harbourt Brace & Company, London, UK, pages 182-186.
Tang, X.; Pikal, M. J. (2004). Design of freeze-drying processes for pharmaceuticals: practical
advice. Pharm. Res., 21, 191-200
Tantipolphan, R.; Rades, T.; Medlicott, N. J. (2008). Insights into the structure of protein by
vibrational spectroscopy. Curr. Pharm. Anal., 4, 53-68
Telang, C.; Yu, L.; Suryanarayanan, R. (2003). Effective inhibition of mannitol crystallization in
frozen solutions by sodium chloride. Pharm. Res., 4, 660-667
Vora, K. L.; Buckton, G.; Clapham, D. (2004). The use of dynamic vapour sorption and near
infrared spectroscopy (DVS-NIR) to study the crystal transitions of theophylline and the
report of a new solid-state transition. Eur. J. Pharm. Sci., 22, 97-105
Wang, W. (2000). Lyophilization and development of solid protein pharmaceuticals. Int. J.
Pharm., 303, 1-60
Xie, Y.; Cao, W.; Krishnan, S.; Lin, H.; Cauchon, N. (2008). Characterization of mannitol
polymorphic forms in lyophilized protein formulations using a multivariate curve resolution
(MCR)-based Raman spectroscopic method. Pharm. Res., 25, 2292-2301
Yu, L.; Milton, N.; Groleau, E. G. ; Mishra, D. S. ; Vansickle, R. E. (1999). Existence of a
mannitol hydrate during freeze-drying and practical implications. J. Pharm. Sci., 88, 196-198
Zhou, X.; Hines, P.; Borer, M. W. (1998). Moisture determination in hygroscopic drug
substances by near infrared spectroscopy. J. Pharmaceut. Biomed., 17, 219-225
Zhou, G. X.; Ge, Z. H.; Dorwart, J.; Izzo, B.; Kukura, J.; Bicker, G.; Wyvratt, J. (2003).
Determination and differentiation of surface and bound water in drug substances by near
infrared spectroscopy. J. Pharm. Sci., 92, 1058-1065
Zografi, G. (1989). States of water associated with solids. Drug. Dev. Ind. Pharm., 14, 1905-
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