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

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