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List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals. Author S. Carlert was formerly named Forssén.

I Lindfors, L., Forssén, S., Westergren, J., Olsson, U. (2008)

Nucleation and crystal growth in supersaturated solutions of a model drug. J. Colloid Interface Sci., 325(2):404–413

II Carlert, S., Pålsson, A., Hanisch, G., von Corswant, C., Nilsson, C., Lindfors, L, Lennernäs, H., Abrahamsson, B. (2010) Pre-dicting intestinal precipitation – a case example for a BCS class II drug. Pharm. Res., 27:2119-2130

III Carlert, S., Åkesson, P., Jerndal, G., Lindfors, L., Lennernäs, H., Abrahamsson, B. In vivo canine intestinal precipitation of mebendazole –a basic BCS class II drug. Submitted

IV Carlert, S., Lindfors, L., Lennernäs, H., Abrahamsson, B. Eval-uation of the use of Classical Nucleation Theory for predicting intestinal precipitation of two weakly basic BCS class II drugs. Manuscript

Reprints were made with permission from the respective publishers.

Contents

Introduction ................................................................................................... 11 Oral drug absorption ................................................................................. 12

Biopharmaceutics Classification System ............................................. 12 Intestinal transport ............................................................................... 12

Basic physiology and function of the GI-tract ......................................... 14 Gastric and intestinal transit and environment .................................... 14 Intestinal fluids .................................................................................... 17

Physico-chemical aspects of solubility and dissolution ........................... 18 Solubility definitions ........................................................................... 18 pH-dependent solubility ....................................................................... 19 Dissolution of particles ........................................................................ 20 Strategies to enhance absorption by creating supersaturation ............. 20

Precipitation ............................................................................................. 21 Crystallization ...................................................................................... 21 Amorphous precipitation ..................................................................... 23 Salt formation ...................................................................................... 24 Solid state evaluation of precipitated material ..................................... 24

Methods for studying in vivo relevant precipitation ................................. 25 In vivo .................................................................................................. 25 In vitro ................................................................................................. 26 In silico ................................................................................................ 27

Aims of this thesis ......................................................................................... 29

Methods ........................................................................................................ 30 Crystallization theory ............................................................................... 30 Model drugs.............................................................................................. 31 Analytical methods ................................................................................... 32 In vivo experiments .................................................................................. 33

Investigation of AZD0865 human pharmacokinetic parameters and possible effect of crystallization .......................................................... 33 Effect of mebendazole small intestinal crystallization in dogs ............ 33

In vitro crystallization .............................................................................. 34 Crystallization of bicalutamide ............................................................ 34 Crystal nucleation ................................................................................ 34 Crystal growth ..................................................................................... 34 Intestinal precipitation model .............................................................. 35

In silico simulations ................................................................................. 35 In silico simulation of crystallization................................................... 35 In silico simulation of in vivo absorption............................................. 36

Results and discussion .................................................................................. 37 In vivo effect of crystallization ................................................................. 37

AZD0865 ............................................................................................. 37 Mebendazole ........................................................................................ 38

Prediction of rate of crystallization and absorption .................................. 39 Bicalutamide ........................................................................................ 39 AZD0865 ............................................................................................. 41 Mebendazole ........................................................................................ 43

Predicting in vivo crystallization with the in vitro-in silico approach and future work ........................................................................................ 47

Conclusions ................................................................................................... 49

Acknowledgements ....................................................................................... 51

References ..................................................................................................... 53

Abbreviations

ACAT Advanced Compartmental Absorption and Transit AUC Area under curve BA Bile acids BCS Biopharmaceutics classification system Cmax Maximum concentration CNT Classical Nucleation Theory D Diffusion coefficient DIF Dog intestinal fluid DSC Differential Scanning Calorimetry EtOH Ethanol fa Fraction absorbed FaSSIF Fasted state simulated intestinal fluid FaSSIF-V1 Fasted state simulated intestinal fluid, version 1 FeSSIF Fed state simulated intestinal fluid GI Gastrointestinal HIF Human intestinal fluid IR Infrared MMC Migrating motor complex Mw Molecular weight Peff Effective jejunal permeability pH Potential hydrogen (measure of acidity) pKa Acid dissociation constant PVP Polyvinyl pyrrolidone S0 Intrinsic solubility Tm Melting point UWL Unstirred water layer VM Molar volume XRPD X-Ray Powder Diffraction λ Surface integration factor γ Fluid/crystal interfacial tension σ Supersaturation

11

Introduction

The most common route of administration of drugs is the oral route, where drug molecules are dissolved before they are absorbed into the systemic circulation where they can act at their specific target. There are a number of causes for prolonging or preventing the permeation of molecules past the gut wall after dissolution, such as enzymatic/chemical degradation, adsorption, complex formation and precipitation into solid particles. For many drugs, there is a limited window of intestinal absorption, and the formulation de-velopment is often focused on maintaining the drug substance in solution at the preferred site of absorption.

The extent of availability of the active drug moiety at the site of action is defined as the bioavailability.1 The bioavailability can be described by the following schematic illustration (Figure 1) and Equation 1 where fa is the fraction of the dose absorbed across the apical cell membrane and EG and EH are the extraction of the drug over the gut wall and the liver, respectively. Given a recent estimation of 40% of the new chemical entities (NCE) on the market being solubility/dissolution rate limited, it is essential to be able to accurately predict the dissolution rate of pharmaceutical formulations, but also to predict the possible rate of precipitation of dissolved drug in order to assess the fraction absorbed of a new drug substance.2

( ) ( )HGa EEfF −⋅−⋅= 11 (1)

Figure 1. Drug absorption and bioavailability

fa

F

to faeces

Gut lumen

Gut wall

Portal vein Liver

1- EG 1- EH

12

Table 1. The Biopharmaceutics Classification System BCS Class Solubility Permeability I High High II Low High III High Low IV Low Low

Oral drug absorption

Biopharmaceutics Classification System The Biopharmaceutics Classification System, BCS, proposed by Amidon et al in 1995, divided pharmaceutical drugs into four different groups depend-ing on their solubility and permeability (Table 1).3 The model is built on a simplified model of the intestine, where drug is absorbed from a cylindrical tube with no regional differences in absorption. Particles are flowing with the fluid and no particle aggregation occurs. Solubility is independent of the particle size and the pH, and there is no metabolism or degradation of the drug. Given these conditions, the model relates the dose, dissolution rate and permeability rate through the cell membrane to the fraction absorbed of the substance. For BCS class I and III drugs, the solubility and dissolution rate at the physiological range of pH will not limit the extent of absorption of the drug. In BCS class II and IV, the slow dissolution rate and/or the low solu-bility could be rate limiting, and formulation strategies can often be used to decrease the risk of the issues. Due to physicochemical properties such as pH dependent solubility, some of these drugs will have a tendency to precipitate into solid drug, leading to a possible decreased rate and extent of absorption.

Intestinal transport In order to be absorbed over the intestinal epithelial tissue of the mucosa, molecules must first have to diffuse through the unstirred water layer (UWL) consisting of water, mucus and glycocalyx.4 The UWL is created by insuffi-cient stirring of the luminal content, and is believed to contribute to the resis-tance of absorption of rapidly absorbed substances.5 The influence of the UWL may be less prominent in vivo compared to in vitro due to more effi-cient motility and stirring.7-10

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Figure 2. Schematic illustration of the intestinal epithelium. The arrows indicate the four different drug transport routes: 1) Transcellular passive diffusion through the membrane; 2) Paracellular transport; 3) Carrier mediated transport; 4) Endocytosis. (Reprinted from Artursson et al with permission from Elsevier.6

The epithelial tissue of the small intestine mainly contains a single layer of enterocytes. The area available for absorption in the small intestinal mucosa is greatly increased due to the presence of circular folds (valves of Kerck-ring), villi and microvilli. The local permeability differs along the villus, where the crypt appears to be more easily permeated.11, 12 Drugs with a high permeability are mainly absorbed at the tip of the villus,13 while low per-meability drugs may diffuse down the axis of the villus to be absorbed over a larger area.6, 14, 15

Drug absorption can occur either through the epithelial cells (transcellular transport) or between them (paracellular transport), as presented in Figure 2. The transcellular transport can be divided into passive diffusion through the cell membrane, carrier mediated transport and endocytosis. Passive transport is generally believed to be the most important route of transport for drugs, where the diffusion rate through a membrane is directly proportional to the concentration gradient over the membrane. The rate of passive diffusion will also depend on the local structure of the membrane and the physicochemical properties of the molecule, such as lipophilicity, size and degree of ioniza-tion. Small, lipophilic and uncharged molecules tend to penetrate the epi-thelial barrier most readily. Since approximately 20-30% of commonly used drug molecules are weak acids or bases with pKa in the physiological pH range,16 the local pH at the site of absorption will determine the degree of ionization of the molecule, and influence the absorption rate.

Nutrients can be transported over the cells trough carrier proteins or pro-tein channels, which can be either active transport or passive diffusion. Ac-tive transport is an energy consuming process, which could transport mole-cules even against their concentration gradient. Facilitated diffusion is the spontaneous passage of molecules through the cells aided by a carrier protein or channel. Transport occurs along the concentration gradient without energy

14

cost. These proteins could also transport drugs of similar structure as the original substrates.17-20 However, transporters are often selective for one or a couple of substrates and saturation of the transporters can occur.21 Active transport of molecules from the inside of the enterocytes back into the lumen can also affect the absorption of a drug.22-24

Endocytosis has not been considered to influence the absorption of small drug molecules, but has gained interest as a route for absorption of targeted macromolecules.25

Paracellular transport is restricted by the tight junctions, protein clusters holding the enterocytes together. The barrier can be permeable to small, polar molecules and ions due to the aqueous environment of the channels lined by charged surfaces. The paracellular pathway has been considered of less importance for drug absorption in the upper part of the small intestine of molecules with a molecular weight over 200,14, 26 and drugs that are depen-dent on this route are often incompletely absorbed.27

Basic physiology and function of the GI-tract

Gastric and intestinal transit and environment The gastrointestinal tract is a continuous tube consisting of the mouth, pha-rynx, esophagus, stomach, small intestines (duodenum, jejunum and ileum), large intestines (cecum, ascending colon, transverse colon, descending colon and sigmoid colon), rectum and anus (Figure 3).

Figure 3. The GI tract.

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Orally administered fluids and solid material will be rapidly transported from the mouth to the stomach. The principal purpose of the residence time in the stomach is to disintegrate, wet, and dissolve solid food and other ad-ministered solid content. The gastric pH varies much over time, and will be strongly affected by ingestion of food. Hydrochloric acid is secreted into the stomach via parietal cells, leading to a low gastric pH value (median 1.7, but can vary between 0.8-7.2 in the fasted state. 28, 29, 30) After food is digested, the pH is buffered to higher pH value, with a median pH of 5.031, which will depend on the type of food and the volume ingested. Approximately 3-4 hours after food intake, the pH returns to the fasted state acidic pH.32 Hor-mones such as gastrin are also secreted into the stomach, mainly aiding in the regulation of acidic secretion and stomach emptying. The existence of gastrin was suggested as early as 1905.33 Secretion of gastric enzymes such as pepsin, discovered in 183634, aid in the degradation of food. The absorp-tion of drug molecules is generally minimal from the stomach due to the limited gastric area, and the rate of gastric emptying will therefore be impor-tant for drug absorption. The gastric pH has been reported to be similar in humans and dogs35, although the canine fasted state gastric pH has been re-ported to be more variable (pH 1-8)36 than in healthy fasted humans (pH 1.5-2.5)31 due to a lower basal secretion of gastric acid in dogs.37

In the fasted state, there is a cyclic motility pattern called the Migrating Motor Complex (MMC) that controls the gastric emptying pattern of resi-dual stomach content. The first phase consists of irregular contractions of low amplitude, followed by a phase of more regular contractions. The con-tractions push the stomach content to the distal part of the stomach and fur-ther into the intestines. The final phase MMC III, empties the stomach with a strong contraction.38, 39 A fourth phase is sometimes mentioned, which then refers to the transition state between phases III and I. Low volumes of iso-tonic fluid will not alter the MMC pattern.40, 41 Fluids are generally emptied from the stomach in an exponential pattern, regardless of presence of food, where the half-life of fluid emptying is reported to typically vary from 8 minutes to 15 minutes42, 43, but a half-life as short as 4-5 minutes has also been reported.44 The reason for the discrepancy is likely due to differences in timing of administration of fluid in relation to the different parts of the MMC cycle. Emptying half-lives of 61, 17 and 9 minutes have been reported in MMC phase I, II and III for 50 ml of fluid.40 When 200 ml was administered, the half-lifes were lower (23, 12 and 5 min). Hunt reported a halflife of 10 min for fluids.45 The pattern changed if the fluid was cold, not isotonic, acid-ic or contained for instance sugars42, 46. The MMC pattern in the stomach was interrupted by ingestion of food, leading to a prolonged residence time in the stomach for particles larger than 2 mm.35, 39 The motility pattern of dogs and humans appeared to be similar.35, 47

The length of the duodenum, the proximal part of the small intestine, is only approximately 22 cm in humans47, but the residence time in the duode-

16

num includes some major changes in the environment for a molecule or a particle. Bicarbonate is secreted in the duodenum similar to the secretion of hydrochloric acid in the stomach, leading to a sharp increase in pH. The bicarbonate secretion is hormonally regulated, and the median pH in the mid to distal duodenum has been reported to be 6.1.31 In the duodenal bulb, a number of studies have measured the pH to be highly variable with a range of 2.4-6.8.48-51 Both the pancreatic duct and the bile duct leads to the duode-num, where exocrine secretion of various enzymes and bile occurs. Bile plays a significant role in the solubilisation of lipophilic molecules, and is stimulated by secretin and cholecystokinin secretion.52-55 In the fasted state, the emptying of the gall bladder follows the cyclic pattern of the MMC.56, 57 The major component of the bile is bile acids, but other important constitu-ents are phosphatidylcholine, cholesterol and free fatty acids.58 The drug solubilisation effect of the bile can be derived from the amphiphilic nature of the bile salts.59 The bile salts can produce micelles either alone or together with phospholipids (mainly phosphatidylcholine) in mixed micelles.59-68 Drugs, especially lipophilic drugs, can be incorporated in these micelles and increase the apparent solubility of the drug in the intestines and act like a reservoir for drug absorption. Furthermore, drug molecules can be aided in the transport to the intestinal mucosa (the innermost layer of the gastrointes-tinal tract) by the micelles, thus facilitating the absorption of the drug.

The intestinal pH further increases down the intestinal tract. The pH in the fasted state is typically reported to be 6-7 in the jejunum69, but has also been reported to be as low as 3.1.49 In the distal ileum, the reported values are between 6.5 and 8.48, 70 In the fed state, the pH values are similar to the fasted state, but are overall considered to be slightly lower.71 The transit time through the small intestines takes approximately 4 hours72, and the transit time through the entire GI tract takes between 7 hours up to 3 days.30, 70 The small intestine is the principal site of drug absorption.

The intestinal pH appears to be slightly higher in dogs than in humans.35 Transit time through the intestines has been reported to be faster in dogs than the human transport. The small intestinal transport was less than half of the human transport (111 min)35, and the GI transit time in beagles was reported to be 6-8 h.70

The resting volume of fluid in the human stomach has been reported to be 20-45 ml42, 73, 74, and 41-180 ml in dogs.75-77 The traditional method of mea-suring gastric volume is by using a barostat, but other methods such as aspi-ration of gastric fluid through a tube has also been used.42 Resting intestinal volumes have scarcely been reported, but a study using magnetic resonance imaging determined the fasted gastric fluid volume to be 45 ml, and the col-lective volume of intestinal fluid to be 105 ml, divided into fluid pockets throughout the intestines.74 This is far less than expected from the FDA rec-ommendation of dissolution tests to be made in 900 ml of fluid.

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Intestinal fluids The fluid in the human small intestine contains the gastric fluid including foodstuff, secreted fluids such as bile from the gallbladder and pancreatic and intestinal juices. The concentration of bile acids in jejunum in the fasted state originated from the gallbladder has been reported to be between 0.8-5.5 mM in humans58, 78-80, and the main human bile acid is glycocholic acid (35-49%),80, 81 The concentration is highly variable, since the secretion of bile is governed by the input of lipids in the diet (fed state BA content 8.6-1582-85), but also by the MMC since bile is secreted in the fasted state of both humans and dogs with the cyclic pattern of the MMC.86-90 Together with phospholi-pids, the bile acids will form micelles in the small intestines. The concentra-tion of phospholipids in the fasted state has been reported to be 0.2 mM and in the fed state the concentration increased to 3 mM.85 Further down the gastrointestinal tract, bile acids are reabsorbed by active transport.

The rate of secretion of bile in dogs is reported to be higher than the se-cretion rate in humans.91, 92 The BA content in canines in the fasted state was reported to be between 2.4-9.4 mM, where the upper value was considered to be due to partial emptying of the gallbladder during the MMC cycle.93 The content of bile acids and phospholipids in fed state intestinal fluid (8 mM and 2 mM) is similar to fed state human intestinal fluid. The main BA in dog intestinal fluid is taurocholic acid. 58, 80

Intestinal fluids for in vitro use have also been developed. The least com-plex media contains only buffer, whereas more complex media also contains micelle forming additives such as BAs and lecithin (a source of micelle forming phospholipids). A standardized fasted state simulated intestinal fluid (FaSSIF) was suggested by Dressman et al, containing phosphate buffer with 3 mM of the BA taurocholate and 0.75 mM lecithin (FaSSIF-V1).30 The fluid was later refined to better reflect measured in vivo composition.94, 95 The new version (FaSSIF-V2) used maleate buffer instead of phosphate buf-fer, and changed the buffer capacity of the fluid. The concentration of tauro-cholate remained the same, while lecithin concentration was lowered to 0.2 mM. Two different version of Fed State Simulated Intestinal Fluid (FeSSIF) has also been suggested, where the last version (FeSSIF-V2) contains less bile components than the first version (10 mM taurocholate and 2 mM leci-thin), but compensate that by containing monoglycerides and free fatty acids that can enhance solubility of poorly soluble drugs.

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Physico-chemical aspects of solubility and dissolution

Solubility definitions According to the IUPAC definition, solubility is the analytical composition of a saturated solution expressed as a proportion of a designated solute in a designated solvent.96 While it may seem like a constant, solubility is a dy-namic equilibrium, and is only valid at a precise set of parameters. Changing the solvent environment in any way, such as changing the pressure, tempera-ture or ion content of the solvent, a new equilibrium between the solvent and solute will develop. In the case of water solubility, the equilibrium solubility, S0, of a drug is usually not strongly affected by pressure, but can often be greatly affected by temperature.97-100 Unless the ionic content changes pH or is a common ion for the solute, the ionic strength will normally not influence the solubility of a substance strongly.

While equilibrium solubility is a well defined property, it can also be use-ful to consider non-equilibrium, apparent solubility of a drug. The solubility of a solute will depend on the energy gain from increasing the system entro-py or randomness by mixing the solute with the solvent. The second impor-tant factor for solubility is related to the intermolecular forces between the solute molecules (and also between the solvent molecules). This is known as the enthalpy changes associated with separating a molecule from the solid and creating a cavity for the molecule in the solvent.100, 101 Therefore, differ-ent conformational phases of a solid will have different apparent metastable solubility, since the intermolecular bonds or forces will be different depend-ing on the conformation of the molecules in the solid. This is true for solid state polymorphs, solvates, salts as well as amorphous phases of the drug. In the latter case, the lack of long range ordering of the molecules will affect the apparent solubility. The amorphous drug solubility can also change over time since molecular rearrangement within the disordered amorphous phase can occur.102, 103

When the concentration of drug monomers exceeds the equilibrium solu-bility in the solvent, the concentration of the solution is considered to be supersaturated. Extent of supersaturation can be calculated using Equation 2, where Ctot is the total concentration of solute in solution and S is the solubili-ty of the drug in the system.

S

Ctot=σ (2)

A special case emerges when the solvent consists of complex forming

agents, such as the micellar formation of bile acids (BA) and phospholipids in the intestines, or BA and lecithin in FaSSIF. Here, the solubility in the

19

water phase remains equal to the solubility before mentioned, but the overall solubility of poorly soluble drugs in the solvent can be enhanced due to par-titioning of drug into micelles.

pH-dependent solubility An aqueous solubility of specific interest for the pharmaceutical develop-ment and the absorption of drugs is the pH-dependent solubility of acids or bases with acid dissociation constants, pKa’s, within the physiological pH range. It has been estimated that more than 70% of commonly used marketed drugs are acids or bases.16 A fictive drug solubility curve of a basic sub-stance with pKa 5 based on the Henderson-Hasselbalch equation (3) is pre-sented in Figure 4

+=

−

HA

Aa C

CpKpH log (3)

From Figure 4, it is clear that the substance has a much higher solubility

at the low pH expected in the stomach (pH~1.8) than in the intestines (pH~6.5-7.5). The opposite is valid for acids with pKa’s above 3-4.

Figure 4. pH dependent solubility of a base following the Henderson-Hasselbalch solubility equation.

pH

0 2 4 6 8 10 12 14

Con

cen

tra

tion

(µ

M)

1e-6

1e-5

1e-4

1e-3

1e-2

1e-1

20

In the case of solubility of acids or bases in the micellar environment of the intestines, the partitioning of drug into micelles will have an effect on the solubility curve. One way to describe the solubility dependence on both pH and micellar content is by using a modified Henderson-Hasselbalch equa-tion.104 The solubility of the acid or base will then be calculated to be in-creased in the GI positions that contains a significant amount of bile acids, typically in the region of pH>5.

Dissolution of particles The dissolution of particles in a solvent can be considered to follow a mod-ified version of Fick’s first law of diffusion as presented in Equation 4-6, which takes the effects of hydrodynamics into account. The dissolution is here described as a flow of molecules from the particles into the solution.105

( ) ( )undissundissNielsenbundiss

Mundiss RFSC

R

DVXQ ,02

3−⋅⋅= (4)

( ) ( )( ) 285.0*1 undissundissNielsen RPeRF += (5)

( ) ( )η

ρρD

gRRPe waterdrugundiss

undiss 9

2 3*

−= (6)

Here, Xundiss is the amount of substance in the particles, Cb is the bulk concentration, S0 is the solubility of the solid form, D is the diffusion coeffi-cient, VM the molar volume and Rundiss the average radius of undissolved particles. The correction term FNielsen was added due to the effect stirring has on the dissolution rate of large particles (>1 µm).106 ρ is the density, η is the dynamic viscosity of the fluid and Pe is the Peclet number.

Strategies to enhance absorption by creating supersaturation A number of different formulating approaches have been used in order to create supersaturation of drug in the gastrointestinal tract and thereby in-crease absorption of low solubility compounds. Perhaps the most commonly used is salt formation of the base or acid, with a prevalence of approximately 40% reported for commonly used marketed drugs.16, 107, 108 Salts with differ-ent counterions generally have different apparent solubility, and the choice of counterion in pharmaceutical development is governed by a combination of physicochemical/formulation issues such as solubility, chemical stability,

21

rate of dissolution and melting point,16 and the in vivo appropriateness of the salt.109 Apparent solubility increase of the solid phase can also be utilized in vivo using other solid phases such as solvates and metastable polymorphs110-

113, prodrugs114, 115, cocrystals116 and amorphous form117-124 of the drug. The latter is normally stabilized by making a molecular dispersion between a carrier, typically a polymer, and the drug. This locks the solid drug in an amorphous solid state with an apparent higher solubility than the crystalline solubility, if the carrier is carefully chosen.

Other formulation strategies involve finding some kind of solvent that the drug molecule prefers to the environment in the gastrointestinal tract, giving the drug a possibility to be diluted and absorbed without precipitation. This includes cosolvents125, lipidics126-130 and complex forming agents such as cyclodextrins124, 131 or combinations thereof such as self emulsifying drug delivery systems containing both lipids and cosolvents. 132-134

The gain in using these formulation approaches will be strongly depen-dent on the apparent solubility of a possible solid state in the intestinal fluid and on the ability of the environment to maintain supersaturation. There is also a risk of decreasing the absorption rate of the substance due to the for-mulation content, such as the reduction of the thermodynamic activity of the drug as well as decrease in absorption rate due to a reversed solvent drag because of hyperosmolarity of the administered solution.135 For some ex-tremely lipophilic drugs coadministered with lipids, an additional oral route of administration of drugs is possible via the lymphatic uptake.136-138 This access route provides passage to the systemic circulation while avoiding the hepatic first pass metabolism.139

Precipitation

Crystallization Crystallization is the ordering of molecules into solid phase from a solution, melt or, more unusually, a gas. Long range order of molecules can be di-vided into unit cells, the smallest repeatable unit of a crystal. Drug molecules can often form different unit cells, leading to different polymorphs of the same drug. The polymorphs will have different properties such as solubility and X-ray diffractograms due to intermolecular forces. They will also often be distinguishable by different dissolution rates and crystal shapes. The ef-fect of polymorphism on the absorption of drug molecules was published as early as the 1960’s.111 The number of possible polymorphs will depend on the structure of the molecule, and as much as six different polymorphs have been reported for spironolactone.140

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Crystallization will be governed by two different forces – thermodynam-ics and kinetics. Thermodynamically, ordering of molecules from a solution will occur when the concentration of molecules is sufficiently high, and hence the heat of fusion in the process is high enough to overcome the cost of reducing the entropy of the system. The rate of formation of a crystal will, however also be dependent on kinetics such as the ability to have super-cooled rain below the normal freezing point of water that doesn’t crystallize in the air. Crystallization occurs in two steps, the formation of a cluster due to density fluctuations in the solution or melt, and subsequent growth of this cluster. The shape and degree of ordering in the cluster has been under de-bate141, 142, but the nucleation has been modeled using the following equa-tion143

( )nbn SCkJ 0−= (7)

where J is the number of nuclei formed per unit volume per unit time, kn is a rate constant, Cb is the bulk concentration, S0 is the intrinsic solubility of the substance and n is an empirical exponent. A more fundamental method of describing crystallization is by using Classical Nucleation Theory (CNT), developed from the early works of Gibbs on thermodynamic descriptions of heterogeneous systems.144-149 The model has branched into many different ones, depending on the use of empirical constants or fundamental, descrip-tive parameters.

According to CNT, clusters will form and redissolve until a critical size is achieved and an energy barrier is overcome for production of stable clusters. At this point, the rate of addition of molecules to the cluster will exceed the rate of removal, and the particle will start to grow. Particle growth in aqueous solutions can be described by a modified version of Fick’s first law of diffusion, that state that growth will be directly proportional to the radius of the molecule and the excess of molecules in solution (relative to S0) if the growth is strictly controlled by diffusion. Nucleation and growth occur simultaneously in supersaturated solutions. Nucleation is traditionally measured by measuring the induction time – the time it takes for particles to be measurable from supersaturated solutions.150-

157 Clearly, nucleation can occur while particles are still too small to be measured, but there have been attempts at separating the two processes of nucleation and growth in order to test if CNT is a good description for nuc-leation of proteins.158-161 The method involved creating a supersaturation, allowing the system to nucleate a set amount of time and then lowering the supersaturation down to a level where particles grew but no more nuclei were formed (i.e. the metastable zone). At the end of the experiment, par-ticles were counted, and the result was compared to what would be expected from CNT (Figure 5).

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Figure 5. The Galkin and Vekilov experimental method used for measuring nuclea-tion rate of substances with normal temperature dependence of the solubility. Super-saturation was created and controlled by altering the sample temperature. Nucleation was induced during Δt1 and subsequent growth of particles was made within the metastable zone of the substance tested. Reprinted from Galkin and Vekilov with permission from ACS.158

CNT only applies to homogenous nucleation, and does not involve crystals growing on foreign surfaces or facilitated nucleation known as secondary nucleation on or in the proximity of already formed crystals. Theoretical descriptions of secondary nucleation have been presented.162-164 Growth has also been described by models other than diffusion controlled growth traditionally used together with CNT. Models using two-dimensional growth through surface nucleation of either one growth site165 or multinuc-lear sites166 have been developed.

The crystallization rate of a drug molecule can be affected by foreign mo-lecules as well as macroscopic surfaces. It is difficult to distinguish the effect molecules will have on the homogenous or secondary nucleation or the growth since the processes normally occur simultaneously. The role of po-lymers on the reduction of crystallization rate has been extensively investi-gated, ranging in explanations such as hydrogen bonding between the drug and the polymer167, 168 and by the polymer occupying adsorption sites on the crystal.169 Surfactants such as the BA taurocholic acid and sodium lauryl sulfate (SLS) commonly used in dissolution experiments have been reported to increase the crystallization rate170, while others have reported a reduction in precipitation rate in the presence of micelles compared to buffer sys-tems.171, 172 Components of intestinal fluids have also been reported to affect excipient-mediated supersaturation stabilization of drugs.173

Amorphous precipitation Amorphous solids have no long range ordering in the particle, and the per-fect amorphous state would be a completely randomized conformation of

24

molecules in the particle. Creating an unordered state usually requires less energy than creating a crystal, even though the crystal will minimize the Gibbs free energy of the system. Therefore, if the concentration of drug mo-lecules exceeds the apparent solubility of the amorphous phase, amorphous particles will form prior to crystalline particles. Although the formation of the amorphous phase could theoretically be governed by the same rules as crystals (i.e. nucleation and growth due to energy barriers of removing the solute from the solution), no such energy barrier has been reported and amorphous particles will form instantly if concentration above amorphous solubility is obtained.

The amorphous state will create a supersaturated solution in comparison to the intrinsic solubility outside the particles, and the amorphous state can dissolve and crystallize over time.

Salt formation Precipitation of salts in the GI tract is possible for ionizable drug molecules at sections of the gut where the drug is charged and it encounters counterions leading to a salt with lower solubility than the base or acid. The conversion could also occur for salts with a higher solubility than the salt forming in vivo.108 This could occur for both acids and bases, and could be amplified by adding additional counterion in the formulation of the drug (the common ion effect). A drug salt can dissociate into solid base or acid if the solubility of the free base or acid is lower than the salt solubility and the solubility differ-ence between the two forms is large enough.16 Thus, it is possible for both formation and dissociation of a salt in the GI tract depending on the local pH in the gut and the physicochemical properties of the drug substance.

Solid state evaluation of precipitated material There are a number of different techniques that can be used in order to de-termine what solid state form a precipitate has. Traditionally, this type of fingerprint analysis has been made by the use of IR or Raman spectroscopy or microscopy, XRPD or some kind of thermal method such as DSC. The methods all have advantages and disadvantages. IR and Raman spectroscopy are used to study the vibrational and rotational modes in a molecule, giving information of the conformation of the molecule in a crystal. XRPD meas-ures the intensity of diffracted light at different angles from the light source, also giving information of the crystal structure. Both IR spectroscopy and XRPD are sensitive to water content of the samples blocking the signal of the crystals, and Raman spectroscopy is sensitive to background fluores-cence coming from fluids such as intestinal fluids. Although all three me-thods can give information of orientation of molecules in the crystals, refer-ence samples are needed in order to determine the solid state conformation

25

of a sample. Thermal methods such as DSC detecting events of conforma-tional shifts and melting could also be used to determine differences in solid state form, but it can be sensitive to the prior treatment of the sample, possi-bly leading to less conclusive data than IR and Raman spectroscopy or XRPD.

Methods for studying in vivo relevant precipitation

In vivo Traditionally, precipitation of drugs in the GI tract has been used as a means to explain increases in exposure when comparing formulations or different digestive states. In vitro dissolution studies were compared to differences in bioavailability or maximum plasma concentration of drug, and precipitation was considered a likely reason for the differences in exposure although no actual evidence of in vivo precipitation were presented.16, 174, 175 Precipitation of bases in the intestines has been most extensively investigated, mainly because the solubility can be high in the acidic stomach while it is at its min-imum at the intestinal pH range, and precipitation may thereby limit the amount of dissolved molecules at the main site of absorption. Direct mea-surements of in vivo precipitation are few, but generally involve removal of fluids and solids from the gut through tubes such as the Loc-I-Gut® system176 for determination of solid content in the fluid and solid state form of the precipitate.177, 178 A similar study was performed where amount of drug dis-solved in the stomach and the duodenum was determined from aspirated

Figure 6. Schematic drawing of the Loc-I-Gut® tube. The distal balloon is inflated when collecting intestinal fluid to prevent intestinal fluid to continue down the GI tract. The perfusate inlet and outlet can be positioned at suitable part of the proximal small intestine. Reprinted from Persson et al with permission from Springer.58

26

samples together with plasma concentration, where a direct correlation be-tween dissolved amount of drug in the stomach and the plasma concentra-tions was found.179

The percentage of solid drug found in vivo has so far been limited. For a dipyridamole dose of 90 mg, only 7% was found as solid drug in the human duodenum, and for a ketoconazole dose of 300 mg, the maximum percentage of solid drug was 16%.178

In vitro Precipitation of drugs has been extensively investigated in vitro, initiated by the risk of intravenous precipitation of solid drug from solutions upon dilu-tion. The methods used were either static, where the drug solution was mixed in a beaker with fluid simulating blood, or dynamic where the two fluids were mixed in a flow resembling the infusion of drug solution into the veins. 180-184

The basics of these methods have later been used to investigate the drug precipitation in the GI tract. A number of static and semi-dynamic methods of intestinal precipitation models have been developed with varying degree of complexity. In order to test a large number of formulations, a static me-thod was developed using a 96-well plate, but the method was limited to using a solution of the drug as initial starting point.185 The solution was di-luted with a suitable simulated intestinal fluid and dissolved drug substance over time was measured. The in vitro method was not designed to fully pre-dict in vivo intestinal precipitation, but could differentiate between different formulations or state of ingestion. More complex multicompartmental mod-els have also been developed. 186-193

A schematic illustration of such a model is presented in Figure 7 where drug is dissolved in a gastric compartment, transferred to an intestinal com-partment containing buffer and in this case also further transferred to an ab-sorption compartment with a donor and acceptor compartment divided by a Caco-2 cell layer.186 This model has been used to predict human fraction absorbed of relatively water-soluble drugs, but the correlation was less accu-rate for poorly soluble substances.186-189 Further development of models using FaSSIF or FeSSIF have been devel-oped without190 and with191 an absorption compartment, the latter where absorption was simulated by pumping fluid into the absorption compartment and the removed fluid was replaced by new dissolution medium. (Figure 8)

27

Figure 7. Scheme of the drug absorption predicting system by Kobayashi et al.186 Reprinted with permission from Elsevier.

Figure 8. Schematic illustration of compartmental dissolution/precipitation models adapted from Kostewicz et al and Gu et al.

The two models predicted diametrically opposed conclusions of the precipi-tation of the model substance dipyridamole, showing the influence of ab-sorption on the precipitation rate, but very little in vivo data was available to verify the results.

Manual sampling and analysis of precipitated content has in two other non-absorption models been replaced by in-line detectors of free concentra-tion of drug192 and solid particle analysis.193

In silico A number of different simulation models have been used to model in vivo absorption. One of the most commonly used models is the ACAT model where the gut is described as 9 different compartments: stomach, duodenum, 2 compartments in the jejunum, 4 compartments in the ileum and colon.194 All compartments are assumed to be instantly mixed, and there is a flow of molecules and particles through the compartments. Absorption is directly proportional to the luminal concentration of drug molecules. The commer-cially available simulation program Gastroplus® is based on this model, and predicts precipitation with a “precipitation time” arbitrarily set by the user.

Gastriccompartment

Intestinalcompartment

Absorption compartment

Reservoir for dissolution

fluid

28

Others have also based absorption simulation tools on similar more or less complex compartmental models where crystallization theories such as Clas-sical Nucleation Theory (CNT) and growth theory have been used for de-scribing small intestinal precipitation, but the simulation tools have so far only been tested on substances that are expected to be incompletely dis-solved in the stomach.104, 195-197 The type of crystallization theory described by Equation 7 has also been used to describe small intestinal precipitation, but has not been coupled to an absorption simulation to date.193 The precipi-tation models presented are only describing precipitation of crystalline solid form of the drug and not taking into account amorphous precipitation.

When simulating absorption of drugs, correct input parameters such as so-lubility and permeability rate in each compartment are imperative for quan-titative plasma concentration simulations. This is especially true when eva-luating effects of drug precipitation, as the precipitation rate will be strongly dependent on drug concentration, which is directly dependent on solubility, dissolution rate and permeability rate.

The lack of quantitative in vivo data of the actual intestinal concentrations and extent of small intestinal precipitation has made it difficult to validate the simulation tools. Indirect evaluations based on comparing predicted and measured drug plasma concentrations generally suffers from the multitude of factors beyond intestinal drug precipitation that is affecting drug absorption. However, reasonable agreement between simulated and reported in vivo fraction absorbed and plasma concentration has been published.196-198

29

Aims of this thesis

The main objectives of this thesis were to increase the understanding of small intestinal precipitation of poorly soluble pharmaceutical drugs, inves-tigate occurrence and effects on absorption of crystalline small intestinal precipitation and creating and evaluating methods of predicting crystalline small intestinal precipitation. In vivo, in vitro and in silico methods have been used to achieve this goal:

• To develop and evaluate a theory for crystalline precipitation of drugs in aqueous solutions (Papers I and IV)

• To develop and evaluate methods to measure the rate of the different parts of crystallization, hence facilitating investigations of what parts of crystallization could be affected by additives (Paper I).

• To investigate the predictive use of traditional in vitro precipitation methods when comparing precipitation rate to human in vivo plasma concentration data (Paper II).

• To measure the extent of in vivo intestinal crystalline precipitation

(Paper III).

• To investigate the possibility of determining solid state form of pre-cipitated drug in simulated and real intestinal fluid (Papers III and IV).

• To investigate the influence of different media and parameter set-

tings in in vitro precipitation methods (Papers II, III and IV) and to evaluate the use of in vitro precipitation methods as a means to create input parameters for in silico prediction using a model that in-tegrates CNT in the ACAT model of small intestinal in vivo precipi-tation and absorption (Paper IV).

30

Methods

Crystallization theory The crystalline precipitation from a solution follows a two-step process, nucleation and subsequent particle growth. In the calculations of crystalliza-tion rate used in Paper I, II and IV, a version of the Classical Nucleation Theory (CNT) was used. The theory was described in detail in Paper I. The theory describes the homogenous nucleation of drug particles, i.e. the nuc-leation of particles in the bulk, unaffected by other particles in the proximity of the nucleus. The nucleation rate, J, or rate of production of critical clusters that will continue to grow per unit time and unit bulk volume, is a function of the steady state concentration of critical clusters that overcomes the ener-gy barrier for creating stable nuclei (Cn*), and the transport of monomers to these critical clusters as described in Equation 8

( )*

*

24

R

ZCDCRNJ nbA

+=

∗

λπ

(8)

( )

( )0

*

/ln

/2

SCTk

NVR

bB

AMγ=

(9)

( )TkGCC Btotn /exp ** Δ−= (10)

( )

( )( )20

23

/ln3

/16

SCTk

NVG

bB

AMπγ=Δ ∗

(11)

( ) ( )( )( )AM

bB

NV

SCTkZ

/8

/ln2/3

20

2/3

πγ=

(12)

where NA is Avogadro’s constant, R* is the radius of the critical cluster, D is the monomer diffusion coefficient and Cb is the monomer concentration in the bulk solution. λ is a factor used to correct for dissolved molecules that do not immediately attach to the clusters as they arrive at the cluster surface

31

(growth governed by surface integration). The expression for the critical cluster (R*) in Equation 9 contains the interfacial tension, γ, which is the surface free energy between the solid and the liquid phase, VM which is the molar volume of the drug crystal, kB which is Boltzmann’s constant, S0 which equals the intrinsic solubility and the absolute temperature, T. The concentration of critical nuclei (Cn*) is given by Equation 10 and 11, where ∆G* is the free energy of forming a critical cluster from free monomers, Ctot is the total concentration of substance in the system (approximately the free monomer concentration). The final factor in the expression for nucleation rate (Equation 8) is the Zeldivich factor, Z (Equation 12), which corrects for deviations from the Boltzmann expression in Equation 10 and dissociation of critical clusters into subcritical ones. Further growth of the critical clusters can be described by a modified version of Fick’s law of diffusion, given in Equation 13, where R represents the radius of the growing particle.

( )0SCR

DV

dt

dRb

m −+

=λ

(13)

Only free monomers will add to the driving force for crystallization, ac-

cording to the theory. Therefore, free monomer concentration must be calcu-lated if the drug molecules are distributed into micelles present in real or simulated intestinal media. This can be done by using Equation 14, where Xmic+free is the total amount of substance in solution, φ is the volume fraction of bile micelles, V is the volume of the solution and Kmic is the bile micelle partitioning coefficient.

( )( )( )11

1

−+−⋅

== +

mic

freemicfreeb KV

XCC

φφ

(14)

This will be especially important for lipophilic and poorly soluble sub-

stances (BCS class II and IV) that exhibit a large difference between water and intestinal fluid solubility.85

Model drugs The drugs used in the in vivo, in vitro and in silico methods in papers I, II, III and IV were bicalutamide (AstraZeneca R&D, UK), AZD0865 (AstraZeneca R&D, Sweden) and mebendazole (Sigma, St Louis, USA). They are all poor-ly soluble drugs according to the Biopharmaceutical Classification System, and belong to BCS class II due to high permeability and low solubility. Bica-lutamide is aprotic, but AZD0865 and mebendazole are weakly basic.

32

Figure 9. Chemical structures of the model drug substances: a) bicalutamide, b) AZD0865, c) mebendazole

All model substances have the ability to form different solid state poly-morphs or salts depending on the experimental settings at time of crystalliza-tion. Given in Table 2 are physicochemical properties of the most likely solid form precipitated in in vitro precipitation experiments.

Although an excellent in vitro model drug compound, bicalutamide solu-tions could not be manufactured at sufficiently high concentrations for it to be of interest as an in vivo model compound. Therefore, only AZD0865 and mebendazole were used as model compounds in Papers II-IV.

Analytical methods The concentrations of bicalutamide, AZD0865 and mebendazole in water, buffers, real and simulated intestinal fluids were quantified using liquid chromatography (HPLC and UPLC) with ultraviolet (UV) or mass spectro-metry (MS) detection. Table 2. Physicochemical properties of the model substances including mo-lecular weight (Mw), acid dissociation constant (pKa), water solubility at 37°C of precipitated polymorph or salt (S), solubility in FaSSIF-V1 at 37°C (SFaSSIF), lipophilicity (logP), melting point (Tm) and melting entropy (ΔHm) Substance Bicalutamide AZD0865 Mebendazole Mw (g/mol) 430.4 366.5 295.3 pKa - 4.9a 3.5b S (µM) 14.5a 36.7c 4.3c SFaSSIF (µM) - 80.1c 6.5c logP 3.8a 4.2a 2.8a Tm (°C) 193a 246a >190d ΔHm (kJ/mol) 42a 54a 50d a Inhouse data AstraZeneca R&D b 199 c Determined in Paper IV d 200

F

FF

N

NH

O

CH3 OH

S

OO

F N

NH

NH

O

O

O

CH3N

N

NH

NH

O

OH

a) b) c)

33

In vivo experiments

Investigation of AZD0865 human pharmacokinetic parameters and possible effect of crystallization Clinical studies with a range of doses administered as both a solution and a rapidly dissolving salt of AZD0865 were evaluated for evidence of small intestinal precipitation (Paper II). The highest dose (4 mg/kg) was consi-dered to give an intestinal concentration high above the solubility in the in-testinal fluid (i.e. supersaturation), but below the amorphous solubility of the substance. The plasma concentration of AZD0865 was measured and the Area Under Curve (AUC) from the concentration-time curve and maximum concentration (Cmax) were plotted against dose to investigate if any deviation from linearity due to small intestinal crystallization could be detected.

Effect of mebendazole small intestinal crystallization in dogs Two different canine models were used to evaluate the extent and effect of mebendazole crystallization in the small intestine (Paper III). In the first model, the drug was administered orally as solutions to three Labrador dogs, and samples were withdrawn from mid-jejunum through a tube inserted into an intestinal stoma. The solutions were expected to achieve supersaturations of a factor of approximately 1 and 20 in the jejunum (T1 and T2, Paper III), and the intestinal concentration was expected to be below the amorphous solubility of mebendazole. The mid-jejunal samples were analyzed for solid and dissolved mebendazole as well as pH of the chyme in order to determine the amount of solid drug present in the jejunum and the potential for intes-tinal supersaturation of mebendazole. Supersaturation in a number of indi-vidual samples was determined from the concentration and solubility of me-bendazole in the intestinal fluid. Plasma concentration of drug was also measured in the study and bioavailability in the dogs was calculated. In the second study, a solution was administered either intravenously or directly into the duodenum of four Labrador dogs in order to bypass the stomach and eliminate effects of differences in gastric emptying on precipitation rate. The solution used here (T3 and T4, Paper III) had similar concentration of me-bendazole to the higher dose in the first study (T2, Paper III), but was ex-pected to produce higher small intestinal supersaturation of mebendazole due to lower intestinal dilution factor compared to the oral study. The small in-testinal concentration of mebendazole was still expected to be below the amorphous solubility. Plasma concentration was measured as a function of time and bioavailability was calculated.

34

In vitro crystallization

Crystallization of bicalutamide Crystallization experiments were made with bicalutamide in order to deter-mine the limit where no nuclei are formed in a supersaturated solution over a 72 hour time period (Paper I). The choice of time span was chosen on a prac-tical experimental basis. Supersaturated solutions of different concentrations were made and filtered after 72 h. The concentration of dissolved bicaluta-mide in the solutions was measured.

Experiments measuring the crystallization rate of bicalutamide were also made with a similar method. Supersaturated solutions of 440 µM were fil-tered after varying incubation times and the dissolved concentration was measured. Supersaturated solutions were made in water with and without the polymer polyvinyl pyrrolidone (PVP) to see the effect PVP had on the crys-tallization rate of bicalutamide. Polymers are often included in API formula-tions, and the fundamental interaction between the API molecule and the polymer was therefore of interest.

Crystal nucleation Crystal nucleation rate of bicalutamide was measured according to a mod-ified version of the method previously published by Galkin and Vekilov for proteins (Paper I).158 Supersaturated solutions of bicalutamide in water and in an aqueous solution containing a small amount of the polymer PVP were here created and maintained for four different set amounts of nucleation times in a 96 well plate. After the nucleation, the solution was diluted down into the metastable zone of bicalutamide where already formed particles were allowed to grow. The particles were then counted and the result was compared to the expected results from calculations with CNT.

Crystal growth Crystal growth rate was studied for all three of the model substances bicalu-tamide (Paper I), AZD0865 (Paper IV) and mebendazole (Paper IV). Since there is an energy barrier for creating new crystal nuclei, and a metastable zone where no new nuclei form in a supersaturated solution, growth of par-ticles could be separated from nucleation by observing the growth of par-ticles in a supersaturated solution within the metastable zone. The method used was a turbidimetric method previously published97, where nanoparticles were created and added to a supersaturated solution. Since the amplitude of scattered light is directly proportional to the total volume of particles, the growth of the initial particles could be measured and compared to theoretical calculations of crystal growth using Equation 13.

35

Intestinal precipitation model A traditional small intestinal precipitation model was used for measuring the expected precipitation rate of AZD0865 (Paper II and IV) and mebendazole (Paper III and IV). The method was used to investigate the effects of chang-ing in vitro parameters such as stirring method and rate of addition of intes-tinal fluid to gastric fluid on the crystallization rate and solid state form of the precipitate. In Paper II, AZD0865 was dissolved in a simulated gastric fluid (SGF) and concentrated FaSSIF-V1 (cFaSSIF) was added resulting in a supersaturated AZD0865 solution in simulated intestinal fluid with pH 6.5. The experiment was designed to simulate the in vivo mixing of gastric fluid with bicarbonate and bile in the upper small intestine to create a similar envi-ronment to the small intestine. The concentration of dissolved AZD0865 was measured as a function of time at different initial concentrations to assess the crystallization rate. The same method was used for both AZD0865 and me-bendazole in Paper IV, where the experimental factors of concentration of drug, rate of addition of cFaSSIF to SGF, stirring and filtration of sample after initial mixing phase were varied. This was done mainly to investigate the impact of factors relevant to differences in individual physiology on crystallization rate. The experimental design for investigating the effect of the parameters was a D-optimal design created and evaluated in MODDE 9 (Umetrics AB, Sweden) by the partial least squares method. In Paper III, the in vitro precipitation method was slightly altered for mebendazole, using real dog intestinal fluid (DIF) instead of the simulated intestinal fluid. The drug was dissolved in an organic solvent and small amounts were added to DIF, creating supersaturated solutions of different concentrations.

In silico simulations

In silico simulation of crystallization A number of different experiments were made in order to receive quantita-tive input data to in silico simulations of crystallization rate of all model drugs. For all three substances, solubility was measured using a turbidimetric method (Papers I and IV), but for AZD0865, a more traditional solubility test was also performed on precipitated particles due to differences in solid state between the powder and the precipitated particles (Paper IV). The crys-tallization parameter λ was also determined for all three model substances using the crystal growth method described under the section of in vitro expe-riments (Papers I and IV). In experiments where crystal nucleation could be excluded, λ could be fitted from the crystal growth curves using Equation 13201 (Papers I and IV). For bicalutamide, the monomer diffusion coefficient

36

was determined by pulsed gradient NMR (Paper I), but for AZD0865 and mebendazole, the diffusion coefficient used in calculations was calculated by the Stokes-Einstein equation (Equation 15).

3

0

4

36

A

M

B

N

V

TkD

ππη

=

(15)

The only remaining unknown parameter for simulation of crystallization using the CNT in Equation 8-13 is the interfacial tension, γ. This parameter was measured for bicalutamide using the crystal nucleation experiment de-scribed in the in vitro section above (Paper I). The in vitro results from crys-tallization experiments with bicalutamide in Paper I (measurement of me-tastable zone width, crystallization rate and nucleation rate) were simulated using CNT (Equations 8-13) to verify the use of CNT to describe bicaluta-mide crystallization. For AZD0865 and mebendazole, the value of γ was extracted from fitting the crystallization theory (Equations 8-13) to the con-centration versus time curves resulting from the intestinal precipitation mod-el described (Paper IV). The effect of changing in vitro parameters on γ of AZD0865 and mebendazole was investigated in Paper IV.

In silico simulation of in vivo absorption The in silico simulations of absorption of AZD0865 and mebendazole pre-sented in Paper IV were created using an in silico model based on the ACAT model. The model included 9 different gut compartments: stomach, duode-num, 2 jejunal compartments, 4 ileum compartments and colon. Absorption from the colon was, however, turned off. Compartmental volumes were in Paper IV adjusted in calculations of mebendazole absorption in order to match measured in vivo jejunal concentrations with in silico calculated je-junal concentrations. For absorption simulations, estimations of human and dog permeability constants (Peff) were necessary. Human effective permea-bility (Peff) was estimated either from two computational models correlating molecular descriptors to human effective jejunal permeability (Paper III) or from a previously established inhouse correlation between the apparent per-meability of a number of drug substances in Caco-2 cells (Papp) and the Peff. Dog Peff was estimated from a correlation between human and canine Peff.

202

116.052.3 ,, −= humaneffdogeff PP

(16)

Classical nucleation theory was integrated into the ACAT model, simulat-ing the rate of crystalline precipitation in the different compartments depend-ing on regional solubility of the substances and amount of bile acid present.

37

Results and discussion

In vivo effect of crystallization

AZD0865 Increasing intestinal concentrations should lead to a linear increase in driv-ing force for absorption. Therefore, it was expected that pharmacokinetic parameters such as Cmax and AUC would also increase linearly with dose if no intestinal precipitation occurred. If a large enough portion of the dose precipitated, the Cmax and AUC were expected to drop at higher doses. Using clinical data of a wide dose range of solutions of AZD0865, as well as salts dissolving quickly in the stomach (Paper II), it was shown that there was a linear increase of both AUC and Cmax up to a dose of 4mg/kg of AZD0865 (Figure 10). Hence, it was concluded that no significant precipitation oc-curred within this dose range.

Figure 10. Mean exposure data in humans of AZD0865 at different doses. a) Cmax b) AUCτ including the linear regression lines. Different symbols represent different clinical trials: ○ first trial, doses 0.08-4.0 mg/kg in solution (n=4) ■ second trial, doses 0.13-0.83 mg/kg in solution (n=6) ♦ third trial, 0.36-2.0 mg/kg in mesylate salt tablet (n=6).

Dose (mg/kg)

0 1 2 3 4 5

Cm

ax (

µm

ol/

L)

0

5

10

15

20

25

Dose (mg/kg)

0 1 2 3 4 5

AU

Cτ

(µm

ol*

h/L

)

0

20

40

60

80

100

120

a b

y=4.1+25.8xy=0.51+5.1x

38

Figure 11. Individual intestinal concentration of mebendazole in DIF over time in canine study with jejunal collection of fluid. ● low dose of mebendazole (concentra-tion 52 µM in administered solution) (T1), no supersaturation expected ■ and ▼high dose of mebendazole (concentration 1.3 mM in administered solution)(T2), individual trials. Dog G was not available for the second administration of the high dose. The dark and light grey lines represent the fraction of the total bioavailable amount over time in the respective trials, deconvoluted from mean i.v. plasma expo-sure curves in other canines. Time dependent bioavailability in the trials was con-verted to the fraction of the total bioavailable amount absorbed over time.

Mebendazole Two solutions with different drug concentrations were administered orally to canines in the study where sampling was made from stomas in the mid-jejunum of three dogs (Paper III). The solution with lower drug concentra-tion was not expected to result in any significant supersaturation in the small intestines, whereas the second one with higher concentration was expected to result in supersaturations up to a factor of 20. The mid-jejunal concentration of mebendazole was variable, but remained high in the fluid up to 1.5 h (Figure 11). Figure 11 also shows the individual fraction of the total bio-available amount over time. The measured supersaturation in the chyme samples were up to 5 and 10 in individual dogs.

These data clearly showed that there was potential for crystalline precipi-tation of mebendazole in the dogs, since supersaturated levels of mebenda-zole was present over an extensive period of time in some dogs. In individu-al G, it was also concluded that the late initialization of sampling was due to late gastric emptying. The amount of solid mebendazole was also measured in the samples, and after administration of the higher dose on average 11% of the dose was collected as solid mebendazole (Figure 12).

The mean (SD) absolute bioavailability was 41 (±21) % after oral admin-istration of the high dose. Bioavailability of mebendazole was also calcu-lated for the dog study using duodenal administration of a solution. The mean (SD) bioavailability was here 25% (±14).

Time (min)

0 50 100 150

Con

cent

ratio

n (µ

M)

0

50

100

150

200

250

300

350

400

Fra

ctio

n o

f to

tal b

ioav

aila

ble

am

oun

t

0.0

0.2

0.4

0.6

0.8

1.0

Time (min)

0 50 100 150

Con

cent

ratio

n (µ

M)

0

20

40

60

80

100

120

140

160

Fra

ctio

n o

f to

tal b

ioav

aila

ble

am

oun

t

0.0

0.2

0.4

0.6

0.8

1.0

Time (min)

0 50 100 150

Con

cent

ratio

n (µ

M)

0

50

100

150

200

250

300

Fra

ctio

n o

f to

tal b

ioav

aila

ble

am

oun

t

0.0

0.2

0.4

0.6

0.8

1.0

U R G

39

Figure 12. Individual percentage of dose collected from jejunum in solution (light grey) and as solid drug (black) in low (1) and high (2+3) dose.

Figure 13. Experimental results from nucleation experiments at a supersaturation of 30 for 0 (●) and 0.01 % (w/w) PVP (○) using PVP coated plates performed at two different occasions as indicated by the bars giving the standard error of the mean. Data from experiments with 0 % PVP (■) on uncoated plates are also shown. The solid line is the result from nucleation and crystal growth theory using λ=6.5 µm and a crystal-water interfacial tension γ=22.1 mN/m. The crosses (+) are results from calculations using γ =22.3 (lower) and γ =21.9 (upper) and the dotted lines are linear regression to those data points.

Prediction of rate of crystallization and absorption

Bicalutamide The crystallization rate of bicalutamide in water and in an aqueous solution containing a low amount of PVP was investigated using a number of in vitro experiments (Paper I). The experimental data were compared to CNT (Equa-tions 8-13) using measured values of D and VM, and the crystallization

Dog ID

U1 R1 G1 1 U2 R2 G2 U3 R3

% o

f to

tal a

dmin

iste

red

do

se0

10

20

30

40

50

Time (h)

0.0 0.5 1.0 1.5

Num

ber

of

crys

tals

pe

r m

L

0

20

40

60

80

100

40

Figure 14. Bulk concentration of bicalutamide a) after 72 h incubation vs. initial concentration in a polymer-free system b) vs. time for initial concentration 440 µM and 0 (■), 0.001 % (w/w) (○) and 0.01 % (w/w) PVP (●). The solid lines represent calculated bulk concentrations in 0% PVP using CNT. In the calculations, the solu-bility of crystalline material of 14.5 µM, λ=6.5 µm and the crystal-water interfacial tension γ=22.1 mN/m was used together with experimental data for other parameters in the theory. The different symbols in a) indicate that experiments have been per-formed at separate occasions. The dotted line in b) is a guide for the eye for 0.01% (w/w) PVP.

parameter λ was determined from a separate crystal growth experiment. The interfacial tension (γ) was determined using the crystal nucleation method described in the in vitro method section. The experimental value of γ was 22.1 mN/m. The precision of the experiment was excellent, as all measured number of crystals formed per volume and time were described by a γ be-tween 21.9 and 22.3 mN/m (Figure 13). Although such a range could influ-ence the nucleation rate, the impact on total crystallization rate of bicaluta-mide within this range would be insignificant. The rate of nucleation was not influenced by addition of the polymer PVP.

Using the measured crystallization parameters, the rest of the crystalliza-tion experiments were simulated using γ=22.1 mN/m. The CNT model could successfully predict the metastable zone width and crystallization rate (Fig-ure 14) at this concentration. It was concluded that the slower crystallization rate of bicalutamide in the presence of PVP (Figure 14b) compared to the PVP free environment was due to effects on the crystal growth of particles, since the nucleation rate of bicalutamide was not affected by the polymer (Figure 13).

Initial concentration (μM)

0 200 400 600

Con

cen

tra

tion

afte

r 72

h (

μ M)

0

200

400

600

Time (hours)

1 10 100

Co

nce

ntra

tion

(μM

)

0

100

200

300

400

500

a) b)

41

Figure 15. Mean (±SD) concentration of drug dissolved in FaSSIF over time from supersaturated solutions using the a) stirring in vitro model (n=3) and b) shaking in vitro model (n=3). Initial concentrations AZD0865: ● 0.35 mM, ■ 0.70 mM, ▲ 1.1 mM, ▼ 0.70 mM with PEG400 and EtOH. Dotted line represents the equilibrium solubility in FaSSIF. Initial concentrations calculated from concentration in gastric fluid, before concentrated FaSSIF was added.

AZD0865 The clinical studies of AZD0865 showed no effects of precipitation given that the in vivo AUC and Cmax had a linear correlation with dose up to doses with expected small intestinal concentrations up to 1.6 mM (Paper II). This was in contrast to the result from the in vitro precipitation method devel-oped, where rapid crystallization was seen at in vivo relevant concentrations and time frames. The first in vitro study of AZD0865 (Paper II) compared two different approaches where stirring using a foreign object (paddle) was compared to stirring using a shaking bath (Figure 15). From the in vivo study, small intestinal concentrations of 0.030-1.6 mM were expected, and the in vitro experiments covered a large range of these concentrations (0.35-1.1 mM). The results showed that this method clearly overpredicted the in vivo small intestinal precipitation. The highest in vitro concentration, which was lower than the expected highest in vivo concentration, predicted that more than 50% of the dose would precipitate within 10 minutes in both ver-sions of the in vitro method. If this had reflected the in vivo precipitation rate, it should have resulted in an in vivo effect on the absorption rate.

It was concluded that this type of conventional precipitation method could not predict in vivo behavior. The main reason for the discrepancy was likely the lack of an absorption phase, which could be included in the in vitro setup or simulated using an in vitro-in silico approach.

The use of CNT and growth equations (Equations 8-14) as a description of crystallization in in silico absorption simulations was therefore evaluated

Time (min)

0 10 20 30 40 50 60

Co

nce

ntr

ati

on

(µ

M)

0

200

400

600

800

1000

1200

Time (min)

0 10 20 30 40 50 60

Co

nce

ntr

ati

on

(µ

M)

0

200

400

600

800

1000

1200

b)a)

42

Figure 16. Interfacial tension (γ) versus starting concentration in in vitro precipita-tion experiment for AZD0865. Solid lines are the model fit with 95% confidence interval according to the PLS model.

Figure 17. Regression coefficients together with confidence intervals obtained from the PLS model for AZD0865 precipitation showing the relative influence of the different experimental parameters on γ. The different factors investigated were concentration of drug (Concentration), mixing rate of cFaSSIF and SGF (Time), stirring of the intestinal fluid (Stirring) and filtration of the intestinal fluid after the initial mixing phase of 10 minutes (Filtration).

(Paper IV). CNT was fitted to the in vitro concentration-time curves from precipitation experiments in order to investigate the effect of different expe-rimental settings on the only unknown parameter; interfacial tension (γ). Four different in vitro parameters were varied in the experiments, namely concentration, rate of adding simulated intestinal media to the gastric fluid, stirring and filtration of the intestinal fluid after mixing. The results showed

Concentration (µM)

400 600 800 1000

γ (m

N/m

)

13

14

15

16

17

18

19

γ=0.0054*CAZD0865+11.552

Con

cent

ratio

n

Tim

e

Stir

ring

Filt

ratio

n

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

43

Figure 18. Simulated versus in vivo values of AUC and Cmax for different doses of AZD0865. Two γ values were tested for estimated intestinal concentrations outside the in vitro concentration range ○ γ obtained by extrapolation from the linear rela-tionship of γ versus drug concentration × γ obtained from the closest experimentally determined drug concentration in the in vitro experiments. Included in the graphs are the lines of unison between simulated and measured results.

that there was a linear correlation of γ versus concentration for AZD0865 (Figure 16) within the concentration range investigated. Rate of mixing the concentrated FaSSIF with the simulated gastric fluid was also significant, although the effect it had on γ was less pronounced (Figure 17).

Absorption simulations of in vivo doses showed a linear relationship be-tween AUC and Cmax versus dose (Figure 18) when using γ predicted from the linear relationship between γ and drug concentration in simulated intes-tinal fluid. The agreement between the simulated and real AUC was excel-lent, but the simulation of Cmax provided somewhat lower values than meas-ured in vivo although not as a result of prediction of intestinal precipitation but due to other limitations of the model. Simulations were also made using the γ values from the in vitro experimental concentrations closest to the ex-pected in vivo concentrations (Figure 18). The results show that effects of precipitation on absorption would be expected at this crystallization rate within the dose range tested, which shows the importance of determining γ at concentrations as close to the expected in vivo concentration as possible.

Mebendazole For mebendazole, the first in vitro precipitation experiments investigated the impact of using DIF as a model for the intestinal fluid (Paper III). The drug substance was dissolved in DMA and added to DIF on a shaking bath. The in vitro precipitation rate showed that extensive precipitation was expected within 10 minutes at initial supersaturations of both 7.2 and 18.9.

In vivo AUC

0 20 40 60 80 100 120

Sim

ula

ted

AU

C

0

20

40

60

80

100

120

In vivo Cmax

0 5 10 15 20 25

Sim

ula

ted

Cm

ax

0

5

10

15

20

25

44

Figure 19. Regression coefficients together with confidence intervals obtained from the PLS model for mebendazole precipitation showing the relative influence of the different experimental parameters on the increase of γ. The different factors investi-gated were concentration of drug (Concentration), mixing rate of cFaSSIF and SGF (Time), stirring of the intestinal fluid (Stirring) and filtration of the intestinal fluid after the initial mixing phase of 10 minutes (Filtration). Included is also the interac-tion parameter of concentration times stirring (C*S).

This result did not reflect the in vivo studies, where supersaturations of up

to a factor of 10 were measured in the canine small intestinal fluid. It was concluded that DIF was unfit as a model fluid in in vitro precipitation expe-riments of mebendazole. This result was surprising, since others have re-ported similar solvent shift methods as a possible way to achieve high super-saturations in human intestinal fluid.171-173 A likely reason for the faster than expected crystallization was that initial nucleation was started when the two in vitro fluids were mixed. Unless perfect mixing was more or less instant, the local supersaturation created from the highly concentrated organic phase upon dilution would lead to an extremely fast nucleation.

The influence of experimental parameter settings on γ retrieved from me-bendazole in vitro precipitation experiments was also investigated (Paper IV). The experimental factors that were varied were identical to the factors investigated for AZD0865 (concentration, mixing rate of cFaSSIF and SGF, stirring and filtration) and FaSSIF was used as intestinal fluid. The effect of concentration on γ resulted in the most significant positive relationship (Fig-ure 19). All four in vitro factors investigated were significant for mebenda-zole, as well as an interaction parameter (concentration * the rate of adding cFaSSIF to SGF). The original D-optimal design developed contained only a limited concentration range due to experimental limitations. Within this re-gion, the correlation between concentration and γ was linear.

Con

cent

ratio

n(C

)

Tim

e

Stir

ring

(S)

Filt

ratio

n

C*S

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

45

Figure 20. Interfacial tension (γ) versus starting concentration in in vitro precipita-tion experiment for mebendazole. The line is a power equation fitted to the experi-mental data.

Additional experiments made at higher concentrations further investigated the effect of concentration on γ. The result showed that the correlation be-tween γ and concentration was not linear outside the initial concentration range (Figure 20).

The experimental result was fitted to a power model. Using this model, γ was estimated at the expected canine in vivo small intestinal concentrations of mebendazole (360-495 µM for the oral study with jejunal sampling, γ=27.0-28.9 mN/m, and 600 µM for the study with duodenal administration, γ=30.2 mN/m). Absorption simulations of the in vivo studies were made with these γ values, but also with the highest γ received from the in vitro experi-ments (26.2 mN/m).

Individual plasma concentration data from repeated administrations in each dog from the study using duodenal administration of solutions were compared to calculations of plasma concentration (Figure 21). The predicted plasma concentrations using the higher γ value (30.2 mN/m, extrapolation at expected intestinal concentration) had higher Cmax than the measured in vivo Cmax, whereas for the lower value (26.2 mN/m, highest experimentally de-termined value) the Cmax and AUC were consistently underpredicted. The simulated results show that significant crystallization is expected to occur at γ=26.2 mN/m, whereas γ=30.2 mN/m predicted very little or no crystalliza-tion. Thus, it seemed as the extrapolation of γ provided a too high value or that the estimated intestinal drug concentration was too high due to difficul-ties in predicting intestinal secretion.

Concentration (µM)

50 100 150 200 250 300 350

γ (m

N/m

)16

18

20

22

24

26 Line: 7.3466*Cmebendazole0.2208

R2=0.992

46

Figure 21. Mean (SD) canine plasma concentration of mebendazole over time after repeated duodenal administration to four different dogs (dots) together with in silico predictions of the plasma concentrations using ─ γ=26.2 mN/m (highest experimen-tal value) and ─ γ=30.2 mN/m (extrapolated value).

Simulations were also made of the canine study using oral administration

of solution where sampling was made from mid-jejunum in the dogs. The retrieved in vivo fraction of solid drug was compared to the fraction of solid drug in the first simulated jejunal compartment up to 90 minutes (Table 3). The highest levels of supersaturation in jejunum in vivo and in silico were also compared. In general, the supersaturation levels tended to be underpre-dicted and the amount of drug precipitation overpredicted when γ = 26.2 mN/m was used (highest experimentally determined γ), showing the impor-tance of determining γ at in vivo relevant in vitro concentrations. When γ was extrapolated from intestinal concentrations, both the levels of supersaturation and the amount of precipitated mebendazole in the small intestines corres-ponded very well with the in vivo values.

Time (min)

0 50 100 150 200 250 300

Pla

sma

con

cen

tra

tion

(nM

)

0

20

40

60

80

Time (min)

0 50 100 150 200 250 300

Pla

sma

con

cen

tra

tion

(nM

)

0

20

40

60

80

100

Time (min)

0 50 100 150 200 250 300

Pla

sma

con

cen

tra

tion

(nM

)

0

10

20

30

40

50

Time (min)

0 50 100 150 200 250 300

Pla

sma

con

cen

tra

tion

(nM

)

0

50

100

150

200

250

300

Dog 1 Dog 2

Dog 3 Dog 4

47

Table 3. Measured and predicted in silico amount of precipitated mebenda-zole and supersaturations in the jejunum of individual dogs after oral admin-istration of 2.4 µmol/kg of mebendazole using either the highest experimen-tally determined γ value (26.2 mN/m) or γ extrapolated from expected indi-vidual intestinal drug concentrations for each dog. Individual

dog (body

weight, kg)

% Precipitated drug

in jejunum from in

vivo sampling

% Precipitated drug in

jejunum from in silico

simulations

Highest σ

measured

in vivo

Highest simulated σ

in first jejunal com-

partment

γ experi-

mental

γ extra-

polated

γ experi-

mental

γ extra-

polated

R (21) 44.6 92.9 74.2 5.3 5.2 6.6

U (36) 59.1 97.9 75.5 9.4 4.9 11.1

G(28) 68.7 97.2 27.9 10.4 4.2 10.2

Predicting in vivo crystallization with the in vitro-in silico approach and future work The in vitro-in silico method used for both AZD0865 and mebendazole could reasonably well describe the crystallization and absorption of the drugs if correct parameter settings were used in the in vitro method. Thus, this indicates that this might be a useful approach in predicting intestinal drug precipitation and its influence on oral drug absorption. However, more work is required to refine and further validate the approach. The interfacial tension was primarily dependent on the in vitro concentration of drug in the simulated intestinal fluid in precipitation experiments where dissolved con-centration of drug was measured as a function of time and CNT coupled with crystal growth was fitted to the resulting concentration-time curves. The initial drug concentration in the simulated intestinal fluid should be as close to the expected in vivo concentration of drug in the small intestine as possi-ble. In order to maximize the concentration range possible to investigate in in vitro precipitation experiment, and thus enabling high supersaturations to be investigated, it was recommended to use instant mixing of simulated in-testinal fluid and gastric fluid without filtration in the in vitro precipitation experiment (Paper IV). The model could also benefit from more frequent sampling in order to visualize more rapid crystallization, but the in vivo rele-vant slow initial mixing of gastric and intestinal fluids will limit the benefit of faster sampling due to concentration fluctuations in the initial precipita-tion phase important for γ determination. Stirring was optional after initial mixing was made. The effect of this parameter was small comparing to the effect concentration had on γ.

In vivo intestinal concentrations will decrease over time, and therefore the γ value should also vary with the in silico intestinal concentration in the in

48

silico model according to the in vitro result. This was not simulated in these studies, but the error in the calculations could be considered of minor impor-tance since the decrease in crystallization rate due to lower concentration more than outweighs the increase in crystallization rate due to decreases in γ at lower concentrations.

The findings of a concentration dependent γ is in contrast to the original theoretical model of CNT, and there is a need for more accurate theoretical models. This effect was not evident in experiments with bicalutamide in water (Paper I), but the experiments with bicalutamide were mainly designed to see if CNT could describe crystallization at a specific concentration. Only limited data of in vitro precipitation experiments in water were available, indicating that the concentration effect on γ could not be derived from effects of FaSSIF (Paper IV). Effects of stirring and filtration on γ that could be seen for mebendazole and not AZD0865 could also not be explained within the current theoretical framework, and it is currently unknown why the ef-fects were only seen on one of the model drugs. No complete theory could be presented to support the new data. However, more complex growth mod-els are available and could be included in future calculations to alter the rate of particle growth, possibly leading to better predictions.165, 166

There is a lack of quantitative in vivo data of administrations of solutions to compare an in vitro-in silico prediction of precipitation with. Future work could therefore include studies on a large number of active pharmaceutical ingredients (API’s) for conclusions of new physiological and/or crystalliza-tion model suitability.

49

Conclusions

The general aim of this project was to develop and evaluate methods to pre-dict and assess the risk of small intestinal crystallization of drug affecting the absorption of API’s. From the work presented in this thesis, it can be con-cluded that:

• CNT can describe the crystallization rate of API’s at a specific de-gree of supersaturation. The theory can, however, benefit from fur-ther development, since the findings in this work indicate a concen-tration dependency for the fundamental theoretical constant γ, used in CNT.

• Methods for measuring nucleation and growth of particles were suc-cessfully developed in this work, which can be used to evaluate theoretical descriptions of crystallization and investigate the effect of additives on the different parts of crystallization of API’s.

• Traditional in vitro methods of predicting small intestinal precipita-tion excluding an absorption phase overestimated the in vivo intes-tinal precipitation.

• Absorption effects of in vivo crystallization of weakly basic BCS class II substances administered in solution were limited for the in-vestigated substances. Identification of a more “critical space” for intestinal drug precipitation by means of BCS and nucleation drug properties require further quantitative in vivo studies of small intes-tinal precipitation of a larger number of API’s.

• No conclusive method for solid state determination of low amount

of drug in simulated or real intestinal fluid is currently available. XRPD or similar solid state determination tool on wet samples can, however, indicate polymorphic form precipitated from solutions.

• The most critical parameter for in vitro precipitation experiments is the initial concentration in the simulated intestinal fluid. Other biore-levant factors such as stirring and rate of mixing gastric fluid with intestinal fluid had only minor effect on prediction of in vivo absorp-

50

tion rate of the investigated model drugs. Though refinement is needed for the theoretical description of crystallization in the absorp-tion model, the in vitro-in silico approach can be used in absorption simulations if simulation parameters are evaluated from in vitro pre-cipitation experiments using expected in vivo intestinal concentra-tions.

51

Acknowledgements

The studies included in this thesis were carried out at the Department of Pharmacy at Uppsala University, at the Clinical Pharmacology Units of As-traZeneca R&D in Lund and Gothenburg (Sahlgrenska University Hospital), at the Quintiles AB facilities in Uppsala, including the Research Unit at Uppsala University Hospital or at AstraZeneca R&D, Mölndal.

I wish to extend my deepest gratitude to:

Mina handledare, professor Hans Lennernäs och professor Bertil Abra-hamsson, för era förmågor att lyfta fram aspekter och tankar som inte fun-nits åtkomliga i min hjärna. Tack för all tid ni lagt ner på mina virriga tan-kar! Professor Lennart Lindfors, medförfattare och inspiratör, för din outsinliga entusiasm och ditt medryckande engagemang. Du är en sann mentor som inte räds diskussioner på alla nivåer och har en genuin vilja att lära ut. Professor Ulf Olsson, medförfattare, för din smittande glädje och kluriga kommentarer. Dr Jan Westergren, medförfattare och bollplank, för din fantastiska förmå-ga att beskriva och undervisa. Du är en sann inspiration! När det blir tyst hör man kugghjulen snurra… Eva Nises Ahlgren och Ulla Wästberg Galik för hjälp med allehanda klu-riga administrativa problem. Pernilla Åkesson, medförfattare, för all hjälp med de fyrbenta vännerna, och alla skratt som delats. Gunilla Hanisch, medförfattare, för alla substansdiskussioner och för att du alltid bryr dig! Anders Borde, för all din hjälp med analysinstrument och problem, samt för ditt odödliga engagemang.

52

Dr Christian von Corswant, medförfattare, för många kloka tankar och inspirerande ledarskap. Alla medarbetare på AstraZeneca, särskilt Pia Skantze, Urban Skantze, Dr Karin Assarsson, David Elmqvist, Dr Anna Petterssen och Dr Maria Lindqvist, utan vars hjälp jag aldrig hade kunnat utveckla alla testa alla metoder i mitt arbete. Dessutom vill jag tacka alla fantastiska människor som hållit mitt humör uppe under åren – ni vet förhoppningsvis vilka ni är! Dr Eva Karlsson för alla kloka ord, råd, virriga orienteringshistorier, skratt och all omtanke. Matti Ahlqvist för adekvat (om än kort) chefsskap, men framför allt för din vänskap och stöd. Dr Christer Tannergren för alla diskussioner om simuleringar och din värdefulla humor. Bli inte för snäll! Gunilla Jerndal, medförfattare, för alla analyser jag kommit farande med som du hjälpt till med. Alltid med ett glatt humör! Catarina Nilsson, medförfattare, för gott samarbete och skickligt utförda studier. Dr Olof Svensson för din hjälpsamhet med diverse experimentella designer och statistik. Java Gospel, för att ni hållit igång min kropp och knopp i gungande jubel under tuffa och fantastiska tider. Jag saknar er! Maria Carlsson, Lisa Holmdahl, Sandra Folke, Maria Olsson, för att ni alltid finns där för mig oavsett humör. Våra resor har varit oumbärliga! Min familj, mamma, pappa, Mattias, Maria, Birk, Ylva, Linda, Johan, Nils, Arvid och Elias, för att ni finns där i vått och torrt, hjälper till när det behövs och inte klagar alltför mycket när jag snurrar in mig i min egen värld. Vilket stöd! Alla goa grannar på ön, för att ni ger mig balans, skratt, tårar, och härlig vänskap. Alla båtpendlare för att ni hållit uppe mitt humör eller låtit mig vara ifred med mitt skrivande i tider av stress. Johan och Noah – ni är mitt allt!

53

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