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Home , Desmethylcitalopram, Didesmethylcitalopram, XENON

Linköping University Medical Dissertations

No. 793

From achiral to chiral analysis of

Björn Carlsson

Department of Medicine and Care, Division of Clinical University Hospital, SE-581 85 Linköping, Sweden

Linköping 2003 ISBN 91-7373-550-7 ISSN 0345-0082 Printed in Sweden by UniTryck, Linköping 2003 ¡A mi amor! María Dolores

Con los años que me quedan, Yo viviré por darte amor Borrando cada dolor, Con besos llenos de pasión Como te amé por vez primera (G Estefan & E Estefan Jr)

¿Qué es la vida? Un frenesí. Vad är livet? Moln som fara, ¿Qué es la vida? Una ilusión, rop som inga röster svara. una sombra, una ficción, Intet verkligt är oss givet, y el mayor bien es pequeño; ty en dröm är hela livet, que toda la vida es sueño, Själva drömmen drömmar bara y los sueños, sueños son.

Pedro Calderón de la Barca, 1636-1673

Abstract

Within the field of depression the “monoamine hypothesis” has been the leading theory to explain the biological basis of depression. This theory proposes that the biological basis of depression is due to a deficiency in one or more of three key neurotransmitter systems, namely noradrenaline, and which are thought to mediate the therapeutic actions of virtually every known agent.

Citalopram is a selective serotonin- (SSRI) used for the treatment of depression and anxiety disorders. Citalopram is a racemic compound, in other words composed of a 50:50 mixture of two enantiomers (S-(+)-citalopram and R-(-)-citalopram) and with one of the enantiomers (S-(+)-citalopram) accounting for the inhibitory effect. At the time of introduction of citalopram the physician needed a therapeutic monitoring service to identify patients with interactions, compliance problems and for handling questions concerning polymorphic enzymes and drug metabolism. An achiral analytical separation method based on solid- extraction followed by high-performance liquid chromatography (HPLC) was developed for routine therapeutic drug monitoring (TDM) of citalopram and its two main demethylated metabolites.

As the data available on citalopram were from achiral concentration determinations and to be able to further investigate citalopram enantiomers effects and distribution, a chiral method for separation of the enantiomers of citalopram and its demethylated metabolites was established. The advances within chiral separation techniques have made measurement of the concentrations of the individual enantiomers in biological fluids possible. The process behind enantioselective separation is however not fully understood and the mechanism behind the separation can be further scrutinized by the use of multivariate methods. A study of the optimization and characterization of the separation of the enantiomers of citalopram, and on an acetylated E-cyclodextrin column, by use of two different chemometric programs - response surface modelling and sequential optimization was performed. Sequential optimization can be a quicker mean of optimizing a chromatographic separation; response surface modelling, in addition to enabling optimization of the chromatographic process, also serves as a tool for learning more about the separation mechanism.

Studies of the antidepressant effect and pharmacokinetics of citalopram have been performed in adults, but the effects on children and adolescents have only been studied to a minor extent, despite the increasing use of citalopram in these age groups. A study was initiated to investigate adolescents treated for depression, with respect to the steady-state plasma concentrations of the enantiomers of citalopram and its demethylated metabolites. The ratios between the S- and R-enantiomers of citalopram and didesmethylcitalopram were in agreement with studies involving older patients. The concentrations of the S-(+)- and R-(-) enantiomers of citalopram and desmethylcitalopram were also in agreement with values from earlier studies. The results indicate that the use of oral contraceptives may have some influence on the metabolism of citalopram. This might be because of an interaction of the contraceptive hormones with the polymorphic CYP2C19 enzyme.

Even though the SSRIs are considered less toxic compared with older monoamine-active like the /tetracyclic , the risk of developing serious side effects such as ECG abnormalities and convulsions has been seen for citalopram, when larger doses have been ingested. Furthermore, fatal overdoses have been reported where citalopram alone was the cause of death. Data on the of each of the enantiomers in humans have not been reported and no data on levels of the enantiomers in cases of intoxication have been presented. An investigation was initiated on forensic autopsy cases where citalopram had been found at the routine screening and these cases were further analysed with enantioselective analysis to determine the blood concentrations of the enantiomers of citalopram and metabolites. Furthermore the genotyping regarding the polymorphic enzymes CYP2D6 and CYP2C19 were performed. In 53 autopsy cases, we found increasing S/R ratios with increasing concentrations of citalopram. We found also that high citalopram S/R ratio were associated with high parent drug to metabolite ratio and may be an indicator of recent intake. Only 3.8 % were found to be poor metabolizers regarding CYP2D6 and for CYP2C19 no poor metabolizer was found. Enantioselective analysis of citalopram and its metabolites can provide valuable information about the time that has elapsed between intake and death. Genotyping can be of help in specific cases but the possibility of pharmacokinetic interactions is apparently a far greater problem than genetic enzyme deficiency. Publications

This thesis is based on the following publications, referred to in the text by their designated Roman numerals

I. Carlsson B, Norlander B. Solid-phase extraction with end-capped C2 columns for the routine measurement of racemic citalopram and metabolites in plasma by high- performance liquid chromatography. J Chromatogr B Biomed Sci Appl 1997;702(1-2):234-9.

II. Carlsson B, Norlander B. Optimization and Characterization of the Chiral Separation of Citalopram and its Demethylated Metabolites by Response Surface Methodology. Chromatographia 2001;53(5/6):266-272.

III. Carlsson B., Olsson G., Reis M., Wålinder J., Nordin C., Lundmark J., Scordo M.G., Dahl M.-L., Bengtsson F. and Ahlner J. Enantioselective Analysis of Citalopram and Metabolites in Adolescents. Ther Drug Monit 2001;23(6):658-64.

IV. Holmgren P., Carlsson B., Zackrisson A.-L., Lindblom B., Dahl M.-L., Scordo M.G., Druid H., and Ahlner J. Enantioselective analysis of citalopram and its metabolites in postmortem blood and genotyping for CYP2D6 and CYP2C19 Submitted

The papers are reproduced with the permission of the copyright owners Contents

List of abbreviations………………………………………………………….. 7 Introduction…………………………………………………………………… 9 Chirality………………………………………………………………………. 10 Drugs and Stereochemistry………………………………………...……… 14 Chirality and Psychopharmacology…………………………………… 14 Chirality andPharmacodynamics……………………………………. 16 Chirality andPharmacokinetics…………………………………….... 16 Selective serotonin reuptake inhibitors ...... ……………..…………………. 18 Cytochrome P450 enzyme system…………………………………………… 20 Citalopram a selective serotonin reuptake inhibitor…………………………. 23 Chemical andphysical properties…………………………………… 23 Citalopram and cytochrome P450 superfamily……………………… 23 Pharmacokinetics……………………………………………………. 26 ………………………………………………….. 27 Adverse drug reaction – Toxicology………………………………… 28 Chiral bioanalysis…………………………………………………………….. 29 Indirect methods………………………………………………………29 Direct methods………………………………………………………..30 Mechanistic aspects of enantioseparation……………………………. 30 Chiral stationary phases………………………………………………. 31 Cyclodextrins…………………………………………………………. 33 Fluorescence detection………………………………………………………… 36 Chemometrics…………………………………………………………………. 37 Systematic optimization…………………………………………….... 37 Experimental design…………………………………………………..39 Design ofexperiments...... ………………………………………… 39 Screening designs……………………………………………………… 40 Full factorial and fractional factorial design…………………………... 40 Optimization design…………………………………………………… 42 Central composite designs………………………………. 42 Modified sequential simplex approach………………… 45 Aims of the present study………………………………………………………. 50 Methods, results and discussion……………………………………………………. 51 Paper I……………………………………………………………………………… 51 Achiral determination of citalopram Citalopram extraction version I Solid-phase extraction Internal standard Achiral chromatography Summary of paper I………………………………………………………. 56 Paper II……………………………………………………………………………... 57 Optimization of the chiral separation of citalopram and its demethylated metabolites Chiral separation of citalopram Optimization with Modde Optimization with Multisimplex Separation mechanism Summary of paper II………………………….. …………………………. 65 Paper III…………………………………………………………………………….. 66 Citalopram and adolescents Citalopram extraction version II Results, citalopram and adolescents Other examples where chiral analysis has been of value Animal studies Summary of paper III…………………………………………………….... 71 Paper IV…………………………………………………………………………….. 73 Enantioselective analysis of citalopram in postmortem blood and genotyping Citalopram extraction version III Drug levels in post mortem whole blood versus plasma concentrations from patients with toxicological symptoms Genotyping Summary of paper IV……………………………………………………… 75 Conclusions…………………………………………………………………………. 76 Chemometrics-Experimental design Therapeutic drug monitoring: Achiral or chiral analysis of citalopram Forensic chemistry: Achiral or chiral analysis of citalopram Future studies………………………………………………………………….……. 77 LCMS On-line extraction and chiral analysis Free fraction Chiral switches Acknowledgements…………………………………………………………………. 79 References…………………………………………………………………………... 80 Appendix……………………………………………………………………….….… 91 List of Abbreviations

5-HT serotonin, 5-hydroxytryptamine Į separation factor, see appendix ĮCT separation factor for citalopram enantiomers ĮDCT separation factor for desmethylcitalopram enantiomers ĮDDCT separation factor for didesmethylcitalopram enantiomers Į1 separation factor 1 between enantiomeric pairs Į1 separation factor 2 between enantiomeric pairs ANOVA analysis of variance AUC area under the concentration time curve C2, C8, C18 2, 8 or 18 chains bound to silica Cit racemic citalopram DCit racemic desmethylcitalopram DDCit racemic didesmethylcitalopram S-Cit S-(+)-citalopram () R-Cit R-(-)-citalopram S-DCit S-(+)-desmethylcitalopram R-DCit R-(-)-desmethylcitalopram S-DDCit S-(+)-didesmethylcitalopram R-DDCit R-(-)-didesmethylcitalopram CitNO citalopram N- CitAld citalopram aldehyde CitProp citalopram propionic CCD central composite design CCC central composite circum scribed CCF central composite face centred CD cyclodextrin Į, E and J E-CD Ac acetylated E-cyclodextrin CN cyanogroup bound to silica CSP chiral stationary phase CYP cytochrome P 450 EM extensive metabolizer PM poor metabolizer UM ultrarapid metabolizer HPLC high performance liquid chromatography HSA human serum albumin IS internal standard LCMS liquid chromatography MAO B monoamine oxidase B MLR multiple linear regression NA noradrenaline, norephinephrine Pgp P-glycoprotein PLS partial least square PRESS prediction residual sum of squares (SS) RSM response surface method SSRI selective serotonin reuptake inhibitor TDM therapeutic drug monitoring UV ultra violet

7

Introduction

Stereochemistry (from the Greek “stereos”, meaning solid) refers to chemistry in three dimensions. Most molecules have a three-dimensional extension in space and if the can not be superimposed on its mirror image it is chiral (greek “cheir” meaning hand). These molecules related as mirror images are called enantiomers and they differ only in their spatial arrangement and how they direct plane polarized light. Over a century ago Pasteur pointed out the connection between chiral molecules and living matter. The chiral of living systems has evident implications on biological activity since all of the crucial biopolymers associated with life are homochiral. The building blocks of life, peptides, proteins and polysaccharides, are made up of L-amino and D-sugars. As a consequence, metabolic and regulatory processes mediated by biological systems are sensitive to stereochemistry and different responses can often be observed when comparing the activities of a pair of enantiomers. The stereochemistry involved in the concept of receptors and other targets for drugs has been recognized for a long time, however the involvement of stereochemistry in the process of metabolising drugs has only more recently been taken into account. A new tool is provided by the knowledge of the genetics behind polymorphic drug metabolizing enzymes and why some individuals are poor metabolizers and some are ultrarapid metabolizers and the consequences for drug effects and metabolism. Within the field of depression the “monoamine hypothesis” has been the leading theory to explain the biological basis of depression. This theory proposes that the biological basis of depression is due to a deficiency in one or more of three key neurotransmitter systems, namely noradrenaline, dopamine and serotonin, which are thought to mediate the therapeutic actions of virtually every known antidepressant agent. Among the selective serotonin-reuptake inhibitors introduced for treatment of depression is citalopram. Citalopram is a racemic compound, in other words a mixture of 50 % of each of two enantiomers and in this case with one of the enantiomers accounting for the inhibitory effect. The advances within chiral separation techniques have made the measurement of the concentrations of the individual enantiomers in biological fluids possible. The processes behind enantioselective separation are however not fully understood and the mechanism behind the separation of enantiomers can further be scrutinized by the use of multivariate methods. In this thesis analytical separation methods for achiral as well as chiral analysis of citalopram and its metabolites have been developed. These methods have further been used as tools in clinical as well as toxicological investigations concerning citalopram.

9 Chirality

Stereochemistry – chemistry in three dimensions

The term “chirality” is derived from the Greek word for hand ("cheir") and reflects the fact that the human body is a chiral structure, the right hand, for example, can be said to be the enantiomer of the left. Molecules that make up living things tend to be chiral: they have the property of “handedness” and a preference for one kind of mirror-image isomers or enantiomers (Testa, 1986). Life, in the way we know it, has reached a high degree of specialization. At the molecular level the macromolecules, for example proteins and nucleic acid that make up the structures of the body, all contain chiral structures that use only one of the possible stereoisomers. These biomolecules are made up of units that have the same configuration of chirality, the proteins are made up of the L-enantiomers of amino acids whereas most carbohydrates are made up of sugar monomers with D-configuration. Essential physiological processes are, therefore, homochiral, they show 100 % stereoselectivty and only involve one of all the possible stereoisomers of key molecules (Bonner, 2000; Popa, 1997).

We could say that life is homochiral, that it only accepts molecules with the right configuration. Through this, receptors and enzymes in our body have been obliged to be able to differentiate between these enantiomers or optical isomers and they can interact/bind to only one of these molecules. As a result, the structures with which drugs interact are “handed” and can tell the difference between drug enantiomers, just as clearly as people can tell if they put their left hands into right-handed gloves. Discrimination between molecules is therefore at the heart of biology and at the heart of pharmacology. Therefore, it is not surprising that the enantiomers of the chiral molecule can have vastly different effects and (Eliel and Wilen, 1994; Wainer, 1993). A good example of this is the interaction of the chiral compound carvone with the human olfactory receptors. Carvone exists as two enantiomers (R)- and (S)-carvone, were the (R)-enantiomer is responsible for the distinct taste and smell of oil of spearmint whereas the S-enantiomer taste and smell of caraway (Allenmark, 1991; Williams and Wainer, 2002).

The origins of stereochemistry stem from the discovery of plane polarized light and the discovery by Biot (in 1812) that a plate, cut at right angle to its crystal axis, rotates the plane polarized light through an angle proportional to the thickness of the plate. Biot extended his observation to solids and solutions. He recognized the difference between the rotation produced by quartz and that produced by the organic substances. Later on, Pastuer (1848), by recrystallizing racemic sodium ammonium tatrate, observed that two different crystal forms were formed. He was able to separate these two forms using a lens and a pair of tweezers and when he separately redissolved the two kinds of crystals, he found that they rotated plane polarized light differently. Pastuer postulated that the molecular structures of (+)- and (-)-tartaric acids must be

10 related as an object to its mirror without the knowledge of its molecular structure (Eliel and Wilen, 1994; Mason, 1986). This method of manual sorting the crystals was followed by a method where Pastuer discovered the use of an optically active alkaloid base to form diastereomeric salt of (+)- and (-)-tartaric acids that have different and a third where he found that Penicillium glaucum grown on racemic tartaric acid, preferentially uses (+)-tartrate as a carbon source (Mason, 1991). The molecular basis for this observation was solved in 1874 when van´t Hoff and Le Bel independently and almost at the same time proposed that the four valences of the carbon were not planar, but directed into a three-dimensional space. A tetrahedral structure, with the four different groups pointing toward the vertices of a regular tetrahedron, would give two distinctly different nonsuperimposable forms. van´t Hoff also defined the carbon atom as asymmetric (Wainer, 1993), see figure 1. In the literature, three main systems of nomenclature are used, each based on a separate set of principles. I. Stereoisomers can be defined according to how they rotate the plane of polarized light in one direction or another. The magnitude of optical rotation is dependent on a variety of factors, such as analyte concentration, solvent, temperature, , etc. Dextrorotatory [(+) or (d-)] denotes a clockwise rotation of the plane of plane polarized light, while levorotatory [(-) or (l-)] denotes a counter clockwise rotation (Allenmark, 1991). II. The second method is derived from knowledge of the absolute structural configuration of the stereoisomers. To determine the absolute configuration of a chiral center, the rules proposed by Cahn, Ingold and Prelog (Eliel and Wilen, 1994) are used. The enantiomers of a chiral compound are termed R (from rectus, Latin for right) and S (from sinister, Latin for left) based on the sequence of groups around its chiral center, see figure 2. III. The final method is based on the structure of the molecule relative to a reference molecule. A system commonly used in biochemistry where the amino acids and carbohydrate are termed L- and D- with reference in many cases to the dextrorotary form of glyceraldehyde (Eliel and Wilen, 1994).

Figure 1. An asymmetric tetravalent carbon atom and its mirror image.

11 3 3 2 F F 4 4 H Cl Br H Br Cl 1 2 1

Assign a sequence of priority to the Visualise so that the lowest priority atom or groups of atoms attached to the is directed away from you. Observe the chiral center. If the four atoms have arrangement of the remaining ligands. different atomic numbers, then priority is assigned from highest to lowest .

F F H H Cl Br Br Cl

If the path goes clockwise, then the absolute configuration is R. If the path goes counterclockwise, the absolute configuration is S.

Figure 2. The Can Ingold Prelog sy stem.

The chirality rule requires that the model is viewed from the side opposite to that occupied by the atoms with lowest priority. The remaining three ligands then present a tripodal array, with the legs extending towards the viewer. If the sequential arrangement of direction of these three atoms (1,2,3) is clockwise, the configurational descriptor is R (for Latin rectus meaning right), if it is counter clockwise, it is S (for Latin sinister meaning left) (Eliel and Wilen, 1994).

12 Basic terminology of stereochemistry

Isomers Compounds that have the same molecular formula but differ in the way the constituent atoms are linked together.

Stereoisomers Compounds having the same molecular formula but with the atoms in a different three-dimensional arrangement. Stereoisomers can be divided into two distinct categories, enantiomers and diastereomers.

Enantiomers Compounds that contain the same atoms linked together in the same way but in a different three-dimensional arrangement. Enantiomers have identical physical properties, but rotate the plane of polarised light in opposite directions.

Diastereoisomers Stereoisomers that are not enantiomeric to each other. They characteristically occur in molecules with two or more chiral centres. Such compound have 2n stereoisomers, where n is the number of chiral centers.

Achiral An entity, such as a molecule, is achiral if it is superposable with its mirror image

Chiral Not superposable with its mirror image, as applied to molecules, conformations, as well as macroscopic objects, such as crystals.

Homochiral Isometric molecules are homochiral if they have the same sense of chirality, that is, if they are all R or all S.

Chiral centre Atoms, usually carbon, attached to four different substitutions that could be swapped to create a new stereoisomer.

Racemate A mixture of all possible stereoisomers of a compound in equal proportions. It does not have optical activity.

Stereoselective Relating primarily to one specific stereoisomer. A biological reaction is stereospecific if either the substrate or its binding is chiral.

13 Drugs and Stereochemistry

The concept of stereochemistry has been understood by scientists for more than a hundred years. Many of the drugs used through the years were originally from extract of plants. In these cases the active moiety taken out was a pure enantiomer. The efforts by the organic chemists to copy the nature ran into trouble, since the asymmetric carbon atom with the binding of four different substituents gave rise to a racemic mixture with two mirror images if there was one asymmetric carbon atom in the molecule. For some reason, this knowledge of racemic organic compounds and racemisation seemed to be forgotten until the question of racemic compounds was raised by Ariens in the late 1980s (Ariens, 1984; Ariens and Wuis, 1987; Ariens et al., 1988). He asked the question why we in some cases had to give doses to the patient where half of is content had no effect or the opposite effect. He also proposed that the active enantiomer should be named the eutomer and the less active being called the distomer and that the ratio between the eutomer/distomer (the eudomistic ratio) should be calculated and could be used as to measure the difference in effect (Ariens, 1986). After this rediscovery of stereochemistry, the regulatory authorities defined more strict requirements about drug discovery and chiral compounds (Strong, 1999). A lot of efforts have, since this regulation, been made in organic chemistry labs to directly synthesize pure enantiomers or to separate the enantiomers on an industrial scale after synthesis of a racemic mixture. The possibility of imitating the nature and only synthesising the desired enantiomer has been possible by the work by the Nobel Prize winners Knowles, Noyori and Sharpless (http://www.almaz.com/nobel). Single– enantiomer drug sales show a continuous growth worldwide and many of the top- selling drugs are marketed as single enantiomer (Maier et al., 2001; Stinson, 1999).

Chirality and Psychopharmacology

The biosynthetic pathways for noradrenaline (NA) and serotonin (5-HT) are good examples of stereochemistry within mammalians. The precursor for NA is L-tyrosine, an aromatic amino acid present in body fluids. Tyrosine hydroxylase is the enzyme that then converts L-tyrosine to L-dopa (L- dihydroxyphenylalanine), which yields dopamine after enzymatic decarboxylation by DOPA decarboxylase. DOPA decarboxylase is totally stereospecific, acting only on the L-enantiomer. L-dopa is used as an antiparkinson agent while D-dopa is responsible for serious adverse reactions such as agranulocytosis (Scott, 1993; Williams and Wainer, 2002). Dopamine E-hydoxylase then converts, with a completely stereospecific enzymatic hydroxylation, dopamine into the chiral compound L-noradrenaline and is finally changed into adrenaline (epinephrine) by N- methylation (Allenmark, 1991; Rang et al., 1999). For 5-HT the biosynthesis follows a pathway similar to that of NA, except that the precursor amino acid is L- instead of L-tyrosine. Tryptophan is converted to 5- hydroxytryptophan by tryptophan hydroxylase and then decarboxylated stereospecific by DOPA decarboxylase to 5-HT. See figure 3.

14 CO2 CO 2 NH2 NH3 NH3 H Tyrosine H DOPA Dopamine hydroxylase decarboxylase E-hydroxylase HO HO OH OH OH

L-Tyrosine L-dihydroxy- Dopamine phenylalanine (L-dopa)

NH2 H H NHCH3 HO Phenylethanolamine HO N-methyl transferase

HO HO OH OH

L-Noradrenaline Adrenaline

H Tryptophan H H N DOPA hydroxylase N decarboxylase N H H CH2CCOOH CH CCOOH HO 2 HO CH2CH2NH2 NH2 NH2 L-Tryptophan 5-hydroxytryptophan Serotonin (5-hydroxy-)

Figure 3. Stereochemistry involved in the biosynthesis of noradrenaline and serotonin.

15 Chirality and Pharmacodynamics

Among the newer antidepressant drugs, acting on the NA or 5-HT system, many of them are racemic. For example pharmacodynamic investigations of their action on subtype show a marked difference between the enantiomers as well as the enantiomers (Baumann and Eap, 2001; Baumann et al., 2002). Another example is the selective noradrenaline reuptake inhibitor where the S,S-(+)-enantiomer is approximately 20 times as potent as the R,R-(-)-enantiomer in inhibiting noradrenaline uptake (Caccia, 1998). These examples also show that the system proposed by Ariens dealing with eutomer and distomer have had to be revised and the racemic drugs are better classified as follows:

I Enantiomer 1 possesses the activity of interest, enantiomer 2 possesses no activity II Enantiomer 1 possesses the activity of interest, enantiomer 2 possesses some activity of interest III Enantiomer 1 possesses the activity of interest, but that which enantiomer 2 possesses is an antagonist of enantiomer 1 IV Enantiomer 1 possesses the activity of interest and enantiomer 2 possesses a separate activity of interest V Enantiomer 1 possesses the activity of interest, but enantiomer 2 possesses separate undesirable activity

Chirality and Pharmacokinetics

Drug absorption, distribution, and excretion are generally processes which do not differentiate between enantiomers, but when the drugs interact with an enzyme or a transporter system, a chiral discrimination may be seen and these enantioselective processes may affect the pharmacokinetics of some drugs. The pharmacological effect of a drug is directly related to the free fraction rather than total concentration of the drug in plasma (Simonyi et al., 1986). The major component of plasma proteins responsible for binding of drugs is human serum albumin (HSA), which plays a fundamental role in the transport of drugs (mostly acidic compounds), metabolites, and endogenous ligands. Binding to HSA controls the free, active concentration of a drug, provides a reservoir for a long duration of action, and ultimately affects drug absorption, metabolism and excretion. Oxazepam and its derivates have shown stereoselective binding to HSA (Bertucci and Domenici, 2002; Kaliszan et al., 1995; Wainer, 1993). Alpha-1-acid glycoprotein, normally present in plasma at concentrations approximately 100 times lower than HSA, preferably binds basic drugs, and chiral discrimination between the enantiomers has been shown for , where R-methadone displays a significantly greater unbound fraction. This reflects the higher affinity of S-methadone to the main plasma binding protein (Baumann et al., 2002). Drug absorption and disposition are regulated, in part, by transport across epithelial barriers. Transport across specialized capillaries of the blood brain barrier is important

16 for the distribution of drugs to the brain (Evans, 2000; Owens et al., 2001; Schwab et al., 2002; Tanaka, 1999b). The single enantiomers escitalopram (S-Cit) and R- fluoxetine have been shown to be potent inhibitors of the protein (Owens et al., 2001). The transport mechanism for citalopram (Cit) over the blood- brain barrier is a non-stereoselective, bidirectional and symmetrical carrier-mediated mechanism without the influence of active efflux mechanisms (Rochat et al., 1999). The role of P-glycoprotein (Pgp), a drug transporter, in drug disposition includes a urinary excretion mechanism in the kidney, a biliary excretion mechanism in the liver, an absorption barrier and determinant of oral bioavailability, and the blood-brain barrier that limits the accumulation of drugs in the brain. The inhibition of the transporting function of Pgp can cause clinically significant drug interactions and can also increase the penetration of drugs into the brain, as well as their accumulation (Schwab et al., 2002; Tanigawara, 2000). The enantiomers of mefloquine, an antimalarial agent, interact stereospecifically with Pgp, were the (+)-mefloquine competitively displaced the Pgp substrate cyclosporine whereas (-)- mefloquine had no effect on cyclosporine-Pgp binding (Williams and Wainer, 2002).

Chiral discrimination can occur at both the substrate and product levels in drug metabolism where five different stereochemical conversions may be possible: I. Prochiral to chiral, a non-chiral compound can become chiral, e.g. the metabolism of to the hydroxylated metabolites (+)- and (-)-9-hydroxyrisperidone (Yasui- Furukori et al., 2001). II. Chiral to chiral, numerous of chiral compounds are converted to a chiral metabolite, e.g. S-fluoxetine to S-norfluoxetine (Caccia, 1998). III. Chiral to diastereomeric, can occur by conjugation with glucoronic acid, glucose, glutathione and glutamine which are agents derived from the chiral pools within the body and of fixed configuration (Caldwell, 1995). IV. Chiral to non-chiral transformation, chirality may be lost by oxidative metabolism at a chiral centre, e.g., oxidation of secondary to yield ketons (Caldwell, 1995). V. Chiral inversion can be exemplified by racemic ibuprofen when given to humans, a substantial fraction of the dose of R-(-)-ibuprofen (50 - 60 %) undergoes "metabolic inversion" to yield S(+)-ibuprofen (Evans, 2001).

17 Selective serotonin reuptake inhibitors

Psychologists and neurobiologists sometimes debate whether it is ego-damaging experiences and self-deprecating thoughts or biological processes that cause depression. The mind, however, does not exist without the brain. Considerable evidence indicates that regardless of the initial triggers, the final common pathways to depression involve biochemical changes in the brain (Stahl, 2000). For over 30 years, the leading theory to explain the biological basis of depression has been the “monoamine hypothesis of depression”. This theory proposes that the biological basis of depression is due to a deficiency in one or more of three key neurotransmitter systems, which are thought to mediate the therapeutic actions of virtually every known antidepressant agent. These systems are NA, dopamine and 5- HT. The development and introduction of selective serotonin-reuptake inhibitors (SSRIs), including fluoxetine, , , , and citalopram, represent an important advance in the pharmacotherapy of psychiatric disorders, not only depression, but also a wide range of psychiatric disorders from anxiety disorders to bulimia (Goldstein and Goodnick, 1998; Goodnick and Goldstein, 1998a; Goodnick and Goldstein, 1998b; Lane et al., 1995; Masand and Gupta, 1999; Murphy et al., 2000). The SSRIs are chemically unrelated to tricyclic, heterocyclic, and other first- generation antidepressants, (Hyttel, 1994) see figure 4. There is also an increasing use of these new agents for treatment of childhood anxiety disorders (DeVane and Sallee, 1996; Murphy et al., 2000). These five drugs have the predominate effect of inhibiting the neuronal reuptake of serotonin. SSRIs are the treatment of choice for many indications, including major depression, dysthymia, panic disorder, obsessive- compulsive disorder, eating disorders, and premenstrual dysphoric disorder, because of their efficacy, good side-effect profile, tolerability, and safety when overdosed, as well as with regard to patient compliance (Boerner and Moller, 1999; Pacher et al., 1999). Pharmacokinetic properties are different due to stereochemistry, metabolism, interaction/inhibition with cytochrome P450 enzymes (CYP), and participation in drug-drug interactions. An observable difference between the SSRIs is their different potential for drug-drug interaction within the CYP enzyme system (Hiemke and Hartter, 2000). Side effects of SSRIs include gastrointestinal disturbances, headache, sedation, insomnia, activation, weight gain, impaired memory, excessive perspiration, paresthesia, and sexual dysfunction (Edwards and Anderson, 1999; Masand and Gupta, 1999). SSRIs are prescribed alone and in combination with other psychotropic in the treatment of a variety of psychiatric disorders. Such combinations create the potential for pharmacokinetic interactions, by affecting the activity of the drug metabolizing oxidative enzymes (Naranjo et al., 1999; Preskorn, 1998; Sproule et al., 1997).

18 NC CH3

O NCH3 F3C

H O N CH3 Fluoxetine F Citalopram

H3C H NH N O O

O

F Paroxetine Cl Sertraline Cl

O N NH2

CH3 N O Cl

CH2CH2CH2N(CH3)2 F3C

Fluvoxamine

Figure 4. The chemical structures of the SSRIs fluoxetine, sertraline, paroxetine, fluvoxamine, and citalopram and the clomipramine Fluoxetine and citalopram are racemic drugs while sertraline and paroxetine have been launched as single enantiomers. Fluvoxamine is achiral.

19 Cytochrome P450 enzyme system

The reactions catalyzed by drug (xenobiotic)-biotransforming enzymes are generally divided into two groups, namely phase I and phase II reactions. Phase I reactions involve hydrolysis, reduction, and oxidation, whereas phase II biotransformation reactions include glucorinidation, sulfation, acetylation, methylation, conjugation with gluthatione (mercapturic acid synthesis), and conjugation with amino acids (such as , , and glutamatic acid). Phase I biotransformation of drugs often precedes phase II biotransformation and is slower. For this reason, phase I biotransformation (such as oxidation of drugs by cytochrome P450 enzymes) tends to be the rate-limiting step in the overall metabolism (Molinoff and Ruddon, 1996).

The CYP superfamily is a of heme-proteins involved in the metabolism of exogenous substances such as drugs and chemicals but also of endogenous substances such as prostaglandins, fatty acids and steroids. The similarities in amino acid sequence are the base for how these enzymes are divided into families and subfamilies within this CYP superfamily (van der Weide and Steijns, 1999) www.imm.ki.se/CYPalleles. The human hepatic CYP system consists of over 30 related isoenzymes with different, sometimes overlapping, substrate specificity. Approximately 40% of human CYP-dependent drug metabolism is carried out by polymorphic enzymes, which can cause abolished, quantitatively or qualitatively altered or enhanced drug metabolism, resulting principally in three types of phenotypes poor (PM), extensive (EM) and ultrarapid metabolizers (UM) in the population (Ingelman-Sundberg et al., 1999). All enzymes exhibit major variability in their activity between different subjects, which is partially caused by genetic factors (in particular for those enzymes exhibiting genetic polymorphism: such as CYP2D6 and CYP2C19) and by environmental factors (Lin and Lu, 1998; West et al., 1997). Among enzymes, CYP1A2, CYP2C19, CYP2D6 and CYP3A4 are the most important enzymes involved in the metabolism of antidepressants or in the occurrence of drug interactions (Brosen, 1996; Dahl, 2002; Kirchheiner et al., 2001; Meyer et al., 1996; Nemeroff et al., 1996; Poolsup et al., 2000; Tanaka and Hisawa, 1999).

It is widely accepted that drug metabolism is often responsible for stereoselective disposition and hence factors modulating the activity of drug-metabolizing enzymes can modify this effect (Baumann and Rochat, 1995; Eap et al., 2000; Kroemer et al., 1996). CYP activities in the brain have also been described where the local cerebral metabolism of psychotropic drug may have pharmacological and/or toxicological consequences (Voirol et al., 2000). While the genotype describes the native level of enzymatic activity, changes in the relative levels and activities of a metabolizing enzyme can be produced by drug interaction and/or clinical conditions such as disease progression or malnutrition, and can result in another phenotype (Dahl, 2002; Linder et al., 1999; Naranjo et al., 1999).

20 CYP1A2

CYP1A2 is expressed at a substantial level in human liver, accounting for approximately 13 % of the total CYP content (Scordo, 2003). Among the psychotropic drugs, fluvoxamine is a potent inhibitor of the enzyme (Jeppesen et al., 1996) while , clomipramine and undergoes demethylation (Bertilsson and Dahl, 1996; Coutts and Urichuk, 1999; Nemeroff et al., 1996). is known to be metabolized to a major extent by CYP1A2 (Bertilsson et al., 1994). Smoking and polycyclic hydrocarbons induce this enzyme. CYP1A2 shows a great inter-individual variability and it is also polymorphically expressed, with approximately 50 % of Caucasians being slow or intermediate metabolizers (Landi et al., 1999). Phenotyping with has revealed that there is marked inter-individual variability in the expression of CYP1A2 (Carrillo et al., 2000; Landi et al., 1999).

CYP2C19

CYP2C19 accounts for about 3 % of the total CYP content in the liver (Scordo, 2003). Psychotropic drugs such as hexobarbital, , citalopram, imipramine, clomipramine and amitriptyline are metabolized via CYP2C19 (Poolsup et al., 2000). Fluvoxamine and fluoxetine are moderate inhibitors (Jeppesen et al., 1996) and the antiepileptics carbamazepine and phenytoin induce CYP2C19 (Scordo and Spina, 2002). Deficiencies of these enzymes are inherited as autosomal recessive traits, which result from a variety of mutations. About 20 % of Japanese subjects are poor metabolizers of S-mephenytoin (an anticonvulsant metabolized by CYP2C19), whereas less than 3 % of Caucasians are affected. In Caucasians, a single base pair mutation (m1) is the major defect responsible for the PM phenotype, where the m2 mutation is more important in Orientals (de Morais et al., 1994a; de Morais et al., 1994b).

CYP2D6

CYP2D6 accounts for about 2 % of the total CYP content in the liver (Scordo, 2003). It is quantitatively one of the less prominent hepatic enzymes. CYP2D6 metabolizes a number of antidepressants, , beta-adrenoreceptor blockers, and antiarrhythmic drugs (Dahl and Sjöqvist, 2000; Otani and Aoshima, 2000; Poolsup et al., 2000). Deficiencies of these enzymes are inherited as autosomal recessive traits, which result from a variety of mutations leading to about 7 % of the Caucausians not having a functional enzyme (PM). In addition to the defective CYP2D6 alleles, several genes cause impaired enzyme activity where a subgroup of 10-15% of Caucasians are termed phenotypical intermediate metabolizers of drug substrates of CYP2D6 (Raimundo et al., 2000). On the other hand, there are some individuals that have unusually high levels of enzyme activity, presumably because of gene duplication, named ultrarapid metabolizers (UM) (Bernal et al., 1999; Dahl et al., 1995; Lundqvist et al., 1999). This high capability of metabolization is explained by a duplication or multiduplication of a functional CYP2D6*2 gene (Lundqvist et al., 1999).

21 Several antidepressants are inhibitors of the CYP2D6 enzyme, although the individual SSRIs differ in potency of this effect. Co-pharmacy should be considered as a possibility for pharmacokinetic drug interactions leading to increased plasma drug concentrations (Kirchheiner et al., 2001; Lam et al., 2002).

CYP3A4

CYP3A4 is the predominant CYP enzyme in the liver, accounting for approximately 30 % of the total P-450 content, and the enzyme is also expressed in gut mucosa (Scordo, 2003). Unlike other human CYPs (e.g. CYP2D6 and CYP2C19), there is no evidence that CYP3A4 exhibits genetic polymorphism. There are genetic variations in the flanking, intronic end exonic regions of the gene that may influence the level of functional CYP3A4 protein, however full length mRNA has been detected in all adults studied to date (Lamba et al., 2002). The new antidepressant is metabolized by this enzyme (Spina and Scordo, 2002). Most of the SSRIs with the exception of norfluoxetine do not inhibit this enzyme, and interactions between SSRIs and CYP3A4 would not appear to be significant (Brosen, 1998). Drugs can not only be substrates for CYP3A4 or an inhibitor. They can also be inducers of a CYP3A4 and thereby increase the activity of the enzyme. One example of this is carbamazepine that is both a substrate and inducer of CYP3A4 (Levy, 1995; Tanaka, 1999a).

22 Citalopram a selective serotonin reuptake inhibitor

Overview

Citalopram is a selective serotonin reuptake inhibitor with no, or only minimal, effect on NA and dopamine reuptake (Sanchez and Hyttel, 1999). The ability of Cit to potentiate serotonergic activity in the central nervous system via inhibition of the neuronal reuptake of 5-HT is thought to be responsible for its antidepressant action (Joubert et al., 2000). It has been shown that only one of the enantiomers, S-Cit, stands for the reuptake inhibitor effect (Hyttel et al., 1992). S-Cit has recently been introduced onto the market under the name of escitalopram (Cipralex®, Swedish trade name) (Montgomery et al., 2001).

Chemical and physical properties

Cit with the molecular formula of the racemate (RS)-1-[3-(dimethylamino)propyl]-1- (4-fluorophenyl)-1,3-dihydroisobenzofuran-5-carbonitril has one asymmetric carbon atom in the 1 position in the isobenzofuran ring, leading to a racemic formulation, of the S(+)-enantiomer (S-Cit) and the R-(-)-enantiomer (R-Cit). The molecular weight of the hydrobromide is 405.31 g/mol and the white to off-white crystals of the racemate have a of 185-188 qC. The hydrobromide salt is sparingly soluble in water and it is soluble and stable in ethanol for up to one year at +4 qC. The pKa for the base (molecular weight 324.39 g/mol) of Cit is 9.5. The structures of the enantiomers are chemically unrelated to other SSRIs and antidepressants (se figure 4 on page 19).

Citalopram and the cytochrome P450 superfamily

Fenotyping data obtained by using sparteine and mephenytoin showed that CYP2D6 and CYP2C19 partially contributed to the metabolism of Cit to desmethylcitalopram (DCit) and that the further metabolism of DCit to didesmethylcitalopram (DDCit) appeared to a large extent to be mediated via CYP2D6 (Sindrup et al., 1993). Further studies in human liver microsomes found that even CYP3A4, but not CYP1A2, was involved in the N-demethylation of Cit, (Kobayashi et al., 1997; Rochat et al., 1997) but the relative contribution of CYP2D6, CYP2C19 and CYP3A4 were not in accordance. Other studies in human liver microsomes have indicated that, at therapeutic concentrations, CYP3A4 was responsible for 40-50 % of the formation of DCit, while the contribution of CYP2C19 increased and that of CYP2D6 tended to decrease with increasing drug concentration (Olesen and Linnet, 1999; von Moltke et al., 2001). CYP2D6 exclusively mediated the second demethylation step, and citalopram N-oxide (Cit-NO) was also exclusively formed by CYP2D6.

23 As Cit is a racemic compound, the stereoselective metabolism has also been studied showing that the three enzymes CYP2D6, CYP2C19 and CYP3A4 favour a more rapid demethylation of the S-Cit over the R-Cit. Further metabolism of DCit to DDCit is also stereoselective but here the R-DCit is favoured over the S-DCit (Olesen and Linnet, 1999). Another metabolite that is not formed by these P450 enzymes is a propionic metabolite formed by deamination of Cit, DCit and DDCit via monoamine oxidase B (MAO B). This process is also stereoselective where the production of S-Cit propionic acid was 5.6 times higher than R-Cit propionic (Kosel et al., 2001; Rochat et al., 1998). In figure 5, the metabolic pathways are summarized. These in vitro experiments have been followed up by in vivo studies on healthy volunteers, showing that the AUC of S-, but not R-Cit is significantly higher in PM of CYP2C19 (Herrlin, 2001). Steady-state pharmacokinetics of the enantiomers of Cit, DCit and DDCit in healthy human subjects who were extensive metabolizers by CYP2D6 and CYP2C19, showed, after multiple doses of Cit, for 21 consecutive days (40 mg per day), that the (S)(+)-enantiomers were eliminated faster than their antipodes (Sidhu et al., 1997). A single dose of ketozonazole, a potent inhibitor of CYP3A4, does not have any effect on Cit pharmacokinetics (Gutierrez and Abramowitz, 2001). Cit and concomitant treatment with fluvoxamine, a CYP2C19 inhibitor, increased the average plasma levels of S-Cit and R-Cit (Bondolfi et al., 1996). Carbamazepine, an inducer of CYP3A4 gave a considerable decrease in Cit plasma concentrations (Leinonen et al., 1996; Leinonen et al., 1991; Steinacher et al., 2002) but in these studies, concentration determination of Cit enantiomers has not been performed.

24 NC CH NC CH NC 3 CYP2D6 3 O O NCH3 O NCH3 CH2OH O

Citalopram (Cit) CitNO CitOH MAO-A F MAO-B F F

CYP2C19 CYP? other CYP3A4 enzymes CYP2D6 NC CH 3 NC NC O NH O O MAO-A Aldehyde O COOH CH oxidase 25 MAO-B

DCit CitProp CitAld F F F CYP2D6 ? CYP? NC H unknown metabolite(s) O NH

MAO-A MAO-B Figure 5. DDCit Summary of the metabolic pathways for Cit and its metabolites. F Pharmacokinetics

The absorption of Cit is not affected by food, and its oral bioavailability is reported to be approximately 80 % (Joffe et al., 1998). Peak plasma levels occur at about 4 hours (range 1 to 6 hours) after single or multiple doses and the plasma concentrations of the metabolites are much less than the plasma concentration of Cit (Baumann and Larsen, 1995). Cit is bound to plasma protein to 80 %, while the protein binding of the demethylated metabolite is lower, 74 %. It is widely distributed among peripheral tissues, with the volume of distribution estimated to 14 L/kg (Fredricson Overo, 1982; Joffe et al., 1998; Kragh-Sorensen et al., 1981). The single- and multiple dose pharmacokinetics of Cit are linear and dose proportional over a range of 10-60 mg/day. Steady-state plasma levels are achieved in patients in 1- 2 weeks. At a daily dose of 40 mg, the average plasma concentration is about 83 ng/ml (255 nmol/L) (n=114) with a range from 20-200 ng/ml (62-620 nmol/L) (Fredricson Overo, 1982). However, no clear correlation has been established between plasma concentration and clinical response due to, among other things, that Cit is a racemic compound. The elimination half-life of Cit is approximately 33 hours (range 23-45 hours) (Kragh- Sorensen et al., 1981) which allows recommendation for once-daily dosing. The systemic Cit plasma clearance is 0.33 L/min. Cit is eliminated primarily via the liver (85 %) and the remainder via the kidneys, approximately 12 % (range 6-21 %) of the daily dose is excreted in urine as unchanged Cit (Kragh-Sorensen et al., 1981), see table 1. In the elderly, clearance after oral administration was reduced to 25-75 % of the values in younger volunteers and the half-life was longer. Steady state plasma concentrations of Cit were higher than expected but there were no signs or symptoms of adverse effects in patients receiving therapy for 3 weeks (Fredericson Overo et al., 1985; Gutierrez and Abramowitz, 2000). In patients with a mild to moderate renal dysfunction, the clearance after oral administration of Cit was decreased by 17 %, T1/2 was moderately increased and peak plasma concentration was unaffected. Impaired hepatic function reduced the oral clearance of Cit by 37 % and doubled the T1/2 (Joffe et al., 1998).

26 Parameter Value Bioavailability (F) 80 % Plasma protein binding 80 % Time to peak plasma level (Tmax) 2-4 h Mean peak plasma concentration Cit (Cmax) 311 nmol/L (40 mg/day) Volume of distribution (Vd) 12-16 L/kg Metabolizing enzymes CYP2C19, 3A4, 2D6, MAO-B Metabolites Desmethylcitalopram Didesmethylcitalopram Propionic acid derivates Citalopram-N-oxide Steady-state trough plasma concentration (mean±SD): Citalopram 130±70 nmol/L (20 mg) Desmethylcitalopram <50 % of parent Didesmethylcitalopram <10 % of parent Systemic Clearance (Cl) 26 (23-38) L/h Elimination half-life (T1/2): Citalopram 33 (23-45) h Desmethylcitalopram 50 h Didesmethylcitalopram 100 h Renal clearance (ClR) 2.8 to 3.3 L/h Urinary extraction of intact parent compound after oral 12 (6-23) % dose

Table 1. Summary of the pharmacokinetic parameters for racemic citalopram (Baumann and Larsen, 1995; Bezchlibnyk-Butler et al., 2000; Fredricson Overo, 1982; Kragh- Sorensen et al., 1981; Noble and Benfield, 1997; Rochat et al., 1997; Rochat et al., 1998; Joffe et al., 1998).

Pharmacodynamics

The pharmacological activity resides in the S-Cit. The metabolite S-DCit, although, less potent than the parent compound, is still a selective inhibitor of 5-HT reuptake. S- Cit increases the amount of 5-HT in the synapse, thus prolonging its activity at postsynaptic receptor sites. Inhibition of reuptake leads to a reduced serotonin turnover (Hyttel et al., 1992). That Cit is a very selective 5-HT reuptake inhibitor has been shown both in vitro and in vivo, see table 2. In vitro brain studies showed Cit to be 300 times more selective in inhibiting 5-HT reuptake than clomipramine, the TCA with highest affinity for 5-HT inhibition (Hyttel, 1994; Sanchez and Hyttel, 1999). The metabolites are less liphohilic than the parent compound, leading to them entering the brain less readily than Cit. Cit has only weak affinity to a series of receptors. Receptor binding studies indicate that Cit has no significant activity for muscarinic, histamine H1, serotonin 5-HT1A, 5-HT1B, 5-HT2, dopamine, Į1, Į2, E-adrenergic, and J- aminobutyric acid (GABA).

27 Racemate/Enantiomer Inhibitory constant, S/R activity 5-HT (nM)a eudismic ratiob Cit 1,8 (S)-Cit 1,5 (R)-Cit 250 167 DCit 14 (S)-DCit 9,9 (R)-DCit 65 6,6 a Inhibitory constant for inhibition of the synaptosomal uptake of serotonin as IC50, a higher concentration denotes a less effective inhibitor. b Ratio between the inhibitory constant for the (S)-enantiomer (eutomer) divided by the (R)-value (distomer).

Table 2. Pharmacological effects of citalopram enantiomers (Hyttel et al., 1992).

Adverse drug reactions-Toxicology

Cases of serotonin syndrome with fatal outcome, a rare but serious event that can occur when medications that act to increase 5-HT at the synaptic junction are coadministrated, have been documented for Cit in combination with and (Brosen and Naranjo, 2001; Neuvonen et al., 1993). When taken alone in overdose, Cit appears to have a relatively wide margin of safety (Muldoon, 1996; Personne et al., 1997). SSRIs are believed to have a more benign cardiovascular safety profile than do the tricyclic antidepressants (Pacher et al., 1999). The effects of Cit on cardiac conduction and repolarization have been extensively evaluated, both in prospective studies in volunteers and patients, showing that the only effect of Cit on ECG findings is a small reduction in heart rate (Rasmussen et al., 1999). Although these new SSRIs seem to be a better alternative regarding adverse drug reactions and interaction with other drugs, they are found among autopsy cases (Rogde et al., 1999). Overdose by an antidepressant was the probable cause of death in 2.1 % of the men and 7.9 % of the women (Isacsson et al., 1999). Cit alone has been found to be the probable cause of death in some autopsy cases (Anastos et al., 2002; Ostrom et al., 1996; Worm et al., 1998). Postmortem concentrations of Cit in blood, and different organs have been investigated but no study on the distribution of the Cit enantiomers has been performed (Anastos et al., 2002; Jenkins and Gubanich, 2002; Levine et al., 1996; Worm et al., 1998).

28 Chiral bioanalysis Background

Within the field of chromatographic chiral separation, the basic mechanism behind chiral recognition and other fundamental problems concerning separation of enantiomers of drugs, pesticides, pheromones and other bioactive substances have been solved during the last two decades. For pheromones and more volatile chiral substances, chromatography is still the method of choice. In the field of drug analysis, high performance liquid chromatography (HPLC) and capillary electrophoresis have been established as the major enantioseparation techniques within the last decade (Maier et al., 2001; Scriba, 2002). Thus, chiral separation has become one of the most active areas of . There are two main approaches used to separate enantiomers, direct and indirect. The indirect method is based on the formation of a pair of diastereoisomers of the racemic mixtures with a chiral reagent. The direct approach utilizes chiral discrimination achieved by a chiral selector. The chiral selector may be a mobile-phase additive or the stationary phase in the chromatographic column. Indirect methods

The indirect methods are divided into two categories: one is to derivatize the enantiomers using an achiral derivatizing reagent and to separate the derivatives using a chiral stationary phase (CSP). The other is to derivatize the enantiomers using a homochiral derivatizing reagent and to separate the derivatives using an achiral stationary phase (Haginaka, 2002). The crucial step for this is to convert the enantiomers of a compound into diastereoisomers which make them suitable for normal reversed or straight phase chromatography on a standard column such as C18. It is also possible to achieve temporary diastereoisomers by adding an enantiopure counter ion (acid or base) to the mobile phase. It is essential that the chiral derivatization reaction proceeds to completion since enantiomers may display different kinetics during reaction with another chiral molecule. There is always a risk that the transformation of enantiomers to diastereomers will lead to racemization of the compounds leading in turn to dubious results (Gorog and Gazdag, 1994; Subramanian, 1994). It is also important that the derivatization reagent is pure otherwise unwanted reaction products will be formed, see scheme 1.

[(+)-A+(-)-A] + [(+)-B+(-)-B] o Enantiomers (50:50 %) Reagents (98:2 %)

(+)-A-(+)-B + (-)-A-(+)-B + (+)-A-(-)-B + (-)-A-(-)-B I (49 %) II (49 %) III (1 %) IV (1 %) Reaction products Scheme 1. Derivatization of enantiomers with an impure chiral reagent.

29 Direct methods

A variety of CSPs are now available for the separation of enantiomers by high- performance liquid chromatography, and they have been shown to be very useful in the chromatographic resolution of racemic mixtures. The basic principle for the separation of the enantiomers is the temporary diasteromeric complexes that are formed on the column. The fundamental mechanism for chiral recognition is the “three point rule” (Allenmark, 1991). Chiral recognition requires a minimum of three simultaneous interactions between the chiral selector and one of the enantiomers in the racemate to obtain separation. At least one interaction must depend on the stereochemistry at the chiral center of the chiral selector and the enantiomer (Han, 1997). Forces such as electrostatic, bonding, repulsive/attractive van der Waal, S-S or dipolar interactions and inclusion phenomena, contribute to the recognition process (Maier et al., 2001). In the separation of enantiomers by chromatography, the separation factor, Į, is determined by the difference between the free energy of adsorption of each enantiomer. In HPLC, the enantioseparation is dominated by enthalpic contributions in most cases, because the experiment is commonly performed at comparatively low temperatures (Okamoto, 2002). Mechanistic aspects of enantioseparation

Understanding how and where chiral recognition by chiral selector molecules occurs may provide valuable information of the qualitative magnitude of enantioseparation, types of analytes separable on a given selector, predictability of elution order, and appropriate chromatographic conditions. Compared with the number of chiral selectors available, relatively few detailed studies on enantioseparation mechanisms are available (Maier et al., 2001). The most popular strategy to establish chiral recognition models for a given selector involves the collection of a representative body of chromatographic enantioseparation data with a series of analytes displaying incremental structural information. A more sophisticated strategy for development is chemometrically driven prediction of retention and enantioselectivity by the construction of quantitative structure activity relationships (QSAR). For macromolecules and polymeric selectors these studies are more problematic and elucidation of the chiral recognition mechanism of protein type selectors may be even more challenging. The “three-point-interaction model” applies mainly for the Pirkel-type and other brush-type CSPs (Lipkowitz, 2001).

30 Chiral stationary phases

Chiral selectors can be obtained from natural sources or can be generated from natural or synthetic building blocks. A large number of CSPs developed are made of natural material such as protein, and cyclodextrin, seminatural synthetic of amylase or pure synthetic products. Chiral selectors can be classified in many different ways. Wainer has suggested a classification scheme for high-performance liquid chromatography CSPs based on the mode of formation of the solute-CSP complex divided in to five categories (Wainer, 1993).

Type I. Where the analyte-CSP complexes are formed by attractive interactions like hydrogen bonding, S-S interactions and dipole stacking as represented by Pirkle-like CSPs. Type II. Where the analyte-CSP complexes are formed by attractive interactions and through the inclusion into a chiral cavity or ravine as represented by some cellulose- based CSPs. Type III. Where the primary mechanism involves the formation of inclusion complexes, as represented by cyclodextrins. Type IV. Where the analyte is part of a diastereomeric metal complex as in chiral exchange chromatography. Type V. Where the CSPs are a protein and the analyte-CSP complexes are based on combinations of hydrophobic and polar interactions. Another way to classify these different CSP would be according to their origin (Maier et al., 2001), see table 3.

31 Source Type Chiral selector

Natural Proteins Serum albumin Orosomucoid (Į1-acid glycoprotein) Ovomucoid Cellobiohydrolase I Avidine Chymotrypsine Ovantranferrin

Oligosaccharides Į-E-and J-Cyclodextrin

Antibiotics Vancomycin Teicoplanin Ristocetin Avoparcin Low Mw molecules Amino acids Cholic acids/bile acids Alkaloids Tartaric acid

Semisynthetic Modified oligosaccharides Derivatized cyclodextrins Cyclodextrin polymers

Modified Polysaccaride Polysaccharide carbamates Polysaccharide esters

Modified low Mw molecules Ion exchange selectors

Synthetic Synthetic low Mw molecules Pirkle type selectors Receptor molecules

LEC selectors a Crown ethers derivate

Helical synthetic polymers Polyacrylamides Polyacrylates Crosslinked tartaramides MIPs b

Table 3. Main groups of chiral selectors used for analytical HPLC, arranged according to their origin. Adapted from Maier et al (Maier et al., 2001). Mw=molecular weight a LEC= Ligand Exchange Chromatography, b MIPs= molecular imprinted polymers

32 Cyclodextrins

Cyclodextrins (CDs) are cyclic oligosaccharides that form cavities. They are produced by partial degradation of starch and enzymatic coupling of cleaved units into crystalline, homogenous toroidal structures of different molecular weight. The three most characterized CDs, denoted Į, E and J, contain six, seven and eight glucose units respectively. The different number of glucose units leads to different internal diameters and size of the cavities, see table 4. A Į-CD has a size suitable for complexing a single six-membered aromatic ring, a E-CD can easily accommodate a molecule with the size of a biphenyl or naphthalene and J-CD can contain molecules as large as substituted pyrenes (Han, 1997). Cyclodextrins are chiral structures and, for example, E-cyclodextrin has 35 stereogenic centres.

Cyclodextrin Number of Molecular Cavity Cavity Water glucose weight diameter volume units g/mol Å3 (g/100 mL) Į 6 972 0,57 nm 174 14,5 E 7 1135 0,78 nm 262 1,85 J 8 1297 0,95 nm 427 23,2

Table 4. Physical properties of Į-, E- and J-cyclodextrin (Schneiderman and Stalcup, 2000).

The seven Į-D-glucose units in E-CD are linked through the 1,4 position (Į-1,4- linked) adopting a chair conformation, forming a rigid torus-shaped molecule with a central cavity. The toroidal structure has a hydrophilic surface resulting from secondary 2-, 3-hydroxyl groups and primary 6-hydroxyl groups, making the cyclodextrin water soluble. All the hydroxyl groups of the glucose building blocks are oriented to the exterior of the molecule, with the primary hydroxyl groups located at the narrow opening of the torus and the secondary hydroxyl groups on the wide opening. The 2´-OHs are pointed in clockwise mode, while the 3´-OHs are counter clockwise. One or two of the 6´-OHs are used to attach the cyclodextrin through a spacer arm to the silica (Hinze et al., 1985). The cavity is composed of the skeletal , ether atoms and methylene giving it an apolar character. As a consequence, cyclodextrins can include other apolar molecules of appropriate dimensions and bind them through dipole-dipole interactions, hydrogen bonding or van der Waals forces (Armstrong and Li, 1987; Bressolle et al., 1996; Hinze et al., 1985), see figure 6. The high of secondary hydroxyl groups at the opening of the toroid acts as an energy barrier for polar molecules attempting to form complexes, and instead hydrogen bonding occurs. Amines and carboxyl groups interact strongly with these hydroxyl groups as a function of the pKa of the analyte and pH of the aqueous system. This relationship is important in understanding how to design mobile phases for chiral separations on bonded native cyclodextrins.

33 Inclusion complexation are considered to be the driving force to obtain enantioselectivity in the reversed phase mode but in most cases the cylindrical binding cavity is itself too symmetrical to induce large enantioselectivity. The hydroxyl groups in the 2- and 3-position situated on the rim of the toroidal structure make it possible for potential interactions between these hydroxyl groups and substituent(s) present in the guest enantiomer molecule (Hinze et al., 1985). The selectivity, resolution and retention times are, in this mode, dependent on the type and amount of organic modifier, buffer, flow rate, temperature and choice of CD. The competition between analyte and the organic modifier for the CD cavity controls the retention, as well as the chiral discrimination process, which in this instance depends on the relative hydrophobicity of the solvent and analyte (Armstrong and Zhang, 2001). H OH OH O H H O O H OH H H HO H H H OH O HO H H O H HO OH OH O H H H H H O HO H OH HO H O H O OH OH H H H H H H H O OH H OH H OH O O OH OH HO H H O H H H O H HO

X X C Y C Y Z Z +

Figure 6. E-CD and the inclusion complex mechanism between the toroidal structure and guest molecule.

34 The CD with the broadest applicability is the E form. It has demonstrated the greatest suitability for small analytes of general interest in the pharmaceutical, chemical and environmental areas. Several derivatives of the native CDs have been made to change the physical and chemical properties and to incorporate various special characteristics. This provides specific interactions with certain functional groups and produces highly selective separations for a vast number of analytes. The CD rim hydroxyls have been acetylated or derivatized with R,S-hydroxypropylether, S-naphtylethylcarbamate and R-naphtylethylcarbamate etc. In table 5, the currently available underivatized and derivatized CD-CSPs are listed.

Material Trade name Company E-CB Cyclobond I 2000 Asctec E-CB acetylated Cyclobond I 2000 Ac E-CB S-hydroxypropyl ether Cyclobond I 2000 SP E-CB R,S-hydroxyproyl ether Cyclobond I 2000 RSP E-CB S-naphtylethyl carbamate Cyclobond I 2000 SN E-CB R-naphtylethyl carbamate Cyclobond I 2000 RN E-CB 3,5-dimethylphenyl carbamate Cyclobond I 2000 DMP E-CB 3,5-dimethylcarbamate Cyclobond I 2000 DM J-CB Cyclobond II J-CB acetylated Cyclobond II Ac Į-CB Cyclobond III Į-CB acetylated Cyclobond III Ac

E-CB Nucleodex E-OH Macherey- Į-CB permethylated Nucleodex Į-PM Nagel E-CB permethylated Nucleodex E-PM J-CB permethylated Nucleodex J-PM

E-CB ChiraDex VWR J-CB ChiraDex Gamma

Table 5. Currently available CD columns (Armstrong and Zhang, 2001).

In HPLC these columns are mainly used in reversed phase modes but normal phase and polar organic mode have also been used. In normal phase mode the derivatized CDs are more useful than native CDs with chiral recognition and retention mechanisms arising from S-S interactions with the derivative group. In the polar organic mode, the mobile phase contains acetonitrile and small portions of methanol, triethylamine and glacial acetic acid, which act as hydrogen bonding modifiers. As in normal phase mode, the inclusion mechanisms do not seem to play an important role, instead hydrogen bonding with the secondary and primary hydroxyl groups of the CD cavity and the analyte act more like a lid on top of the cavity (Armstrong, 1998).

35 Fluorescence detection

When certain molecules or atoms are exposed to high-intensity light, the molecules or atoms absorb the energy and enter an excited state. As a molecule moves from this excited state back to its normal state, some of the energy is released in the form of fluorescence. Fluorescence is a type of luminescence. The atoms and molecules in different compounds emit different levels of fluorescence when exposed to the same energy levels (Kemp, 1975). The process of fluorescence detection involves several components and processes: an excitation source, filtering the source light, excitation of the analyte with a selected wavelength, collecting and filtering the emitted fluorescence, measuring the emitted fluorescence and amplifying the emitted signal, see figure 7. The typical energy source used for fluorescence detection is a lamp that provides an intense, stable spectrum of light in the UV and visible range. The fluorescence intensity is directly related to the intensity of the excitation spectrum, so high- sensitivity detectors use the most intense excitation source available (Lindsay, 1992). Common excitation light sources include the following. Vapour lamps: , cadmium, or . Arc lamps: or xenon. Of these light sources, the vapour lamps provide high intensity, narrow-band outputs in the UV and visible ranges. The arc lamps provide a wider, lower intensity spectrum. Among the arc lamps, the xenon lamp provides the widest spectrum, making it the excitation source of choice for general-purpose fluorescence detectors.

Radiation Slit

Filter Slit Filter

Flow cell Photomultiplier detector

Figure 7. Schematic layout of a fluorescence detector (Lindsay, 1992).

36 Chemometrics

Chemometrics can be broadly defined as the application of mathematical and statistical methods to chemistry (Deming, 1986). In pharmaceutical and biomedical analysis, we are often confronted with several variables at one time and hitherto the approach has been to scrutinize one variable (factor) at a time. There are however a lot of problems associated with this approach, as the following list demonstrates: Does not lead to optimum Inefficient Unnecessarily many runs No information about what happens when the factors are varied simultaneously No information about interactions between factors The information of the variability in the response is less

Chemometrics, on the contrary, works with the variables multivariately, analysing everything together, giving information about which factors have a real influence on the response, settings of the factors to achieve optimal conditions and the possibility of predicting values for the responses when the factors are varied within the model (Berridge et al., 1991; Deming, 1986; Wold, 1991). The application of chemometrics is greatly motivated by the fact that chemical and biological data that are produced in studies have to be used both efficiently and economically. Applications within pharmaceutical research and development areas range from analysis of drug-interactions, culture media and to how to optimize the process of the final product (Gabrielsson et al., 2002; Lapinsh et al., 2001; Walters, 1999). Chemometrics can work both with data analysis, utilizing inherent information in chemical and biological data in the best way or for planning and performing experiments in a way that the data contain maximum information about stated questions (Wold, 1991). Systematic optimization

Systematic optimization are carried out in the following sequence: Choice of objective function Selection of important factors Optimization

In order to find the most suitable factor combination, we can distinguish between simultaneous and sequential optimization approaches.

With simultaneous strategies, the relationship between responses and factors is studied by running experimental designs, constructing a mathematical model, and investigating the relationship by use of so-called response surface methods (RSM). Very often RSMs aim to judge this relationship graphically and the consequences are drawn from those plots.

37 Sequential strategies of optimization are based on an initial experimental design followed by a sequence of further measurement in the direction of the steepest ascent or descent. That is, no quantitative relationship between factors and responses is evaluated but the response surface is searched along an optimal path (invisible path) (Otto, 1999). The two strategies are exemplified in figure 8.

2 4 Simplex 2

Simplex 1 Simplex 3

1 3 5

Figure 8. With response surface methods, the response is described by a mathematical model (dotted contour lines of the response surface). By use of sequential strategy methods the response is measured along a search path, here along a Simplex path (See section Multisimplex) beginning with trials 1, 2 and 3 and followed by the next calculated trial 4.

38 Experimental design

The objective of experimental design is to plan and conduct experiments in order to extract the maximum amount of information from collected data in the presence of noise from the measured values, with the fewest number of experimental runs. The basic idea is to vary all relevant factors simultaneously, over a set of planned experiments, and then connect the results by means of a mathematical model. This model is then used for interpretation, prediction and optimization (Gabrielsson et al., 2002; Otto, 1999). Experimental design can be used in many areas within analytical chemistry. For the optimisation of drug separation with high-performance liquid chromatography, the designs employed include the central composites design (Do et al., 2001), full factorial design (Karlsson and Aspegren, 2000), Plackett-Burman (Altria et al., 1995; Bempong and Honigberg, 1996), simplex (Carlsson and Norlander, 2001; Fell et al., 1988) and D-optimal design (Hermann et al., 2002). Analytical variability can also be estimated for an analytical procedure with experimental design (Tsang et al., 1998; Wieling et al., 1996). Another area is robustness testing which is performed at the end of method development to test the susceptibility of an analytical procedure to small changes in the experimental conditions (Eriksson et al., 2000; Hund et al., 2000; Hund et al., 2002; Small et al., 1995). The three primary experimental objectives can be divided into screening, optimization and robustness testing. It can be concluded that the use of experimental designs and statistical data evaluation are, in conjunction, of great benefit in the optimisation and robustness evaluation of HPLC methods. Design of experiments

Prior to the experiments, one has to specify some input conditions about the purpose and objectives of the investigation/experiment such as identifying the number of factors, ranges for the factors and types of factors (quantitative or qualitative) and number of responses. Then the experimental design is created, and its experiments are carried out, either in parallel, or one after another. Each experiment gives some results, i.e., values of the response variables. Thereafter, these data are analyzed by regression analysis. This gives a model relating the changes in factors to changes in the responses. The model will indicate which factors are important, and how they combine in influencing the responses. During an investigation, one needs answers to the following questions: I Which factors have a real influence on the responses (results)? II Which factors have significant interactions (synergy or antagonism)? III What settings of the factors are required to achieve optimal conditions for the best performance of a process or a system or a product? IV What are the predicted values of the responses (results) for given settings of the factors? (Eriksson et al., 2000; Otto, 1999)

39 Screening designs

Screening is the first stage of an investigation where the goal is simply to identify the important factors: I Which factors are most influential? II What are their appropriate ranges? An important factor is a factor that causes substantial changes (effects) to the response when it varies. In the screening stage, one uses simple models (linear or linear with interactions), and experimental designs that allow the identification of the factors with largest effects in the fewest possible number of experimental runs. The model represents the relationship between the response y and the factors x1, x2 etc. Using the models, the data are separated into two parts; the systematic part explained by the model, and the “noise”, the remaining unmodelled part of data. Data (y) = Model [factor values (x), parameters (E)] + Noise (İ) (Wold, 1991).

Linear model: y=E0+E1x1+E2x2+…+İ Interaction model: y=E0+E1x1+E1x2+E12x1x2+…+İ

After some initial experiments have been performed, a common approach is to identify an interesting standard reference experiment and then to perform new, representative experiments around it. The experiments can be illustrated graphically as points in a co- ordinate system defined by the x-axes. With three (factors) variables x1, x2 and x3 the experiments will be distributed as in table 6 and figure 9. All variables are changed simultaneously to cover the entire area of interest. Another reason for this is that it makes it possible to examine interaction effects. A center point, the middle value for each variable interval, is also included to detect curvature in the experimental design. More center points will give a statistical measure of noise. The size and sign of the scaled and centered regression coefficients indicate the influence of variables on the response. Full factorial and fractional factorial design

Whether the objective is screening of process factors or optimization of a , the variables that influence the responses are the center of attention (Gabrielsson et al., 2002; Lundstedt et al., 1998). The number of experiments in an experimental design grows rapidly with increasing number of variables. For the purpose of screening, a full factorial design is not a realistic option. A reduced design, called a fractional factorial design, is then a more appropriate choice. For a fractional factorial design, experiments are chosen to give the maximum amount of variation with fewer experimental runs. The drawback is lost information caused by confounding of main effects with interaction effects if the resolution is lower than four (Gabrielsson et al., 2002).

40 Experiment Factor Factor Factor Response x1 x2 x3 1 -1-1-1y1 2* +1 -1 -1 y2 3+1+1-1y3 4* -1 +1 -1 y4 5* -1 -1 -1 y5 6 +1-1+1y6 7* +1 +1 +1 y7 8-1+1+1y8

Table 6. Full factorial design at two levels, 23 design, see also figure 9. The star labelled experiments represent the half-fraction factorial design of figure 9.

+1 +1

x 3 x3 +1 +1

x 2 x 2 -1 -1

-1x1 +1 -1 -1x1 +1 -1

Full factorial design Fractional factorial design

Figure 9. Using fractional design, the number of experiments is reduced by a number p according to a 2k-p. For p=1, so-called half-fraction design result.

41 Optimization designs

After screening, the goal of an investigation is usually to understand in more detail how the factors influence the responses and create a valid map of the experimental domain. To make predictions, optimize or find a region of operability the use of more complex models is needed. Quadric polynomial models are used and, due to the increased complexity, more experiments are required. 2 2 Quadric polynomial model: y=E0+E1x1+E2x2+E11x1 +E22x2 +E12x1x2+…+İ How can we find the optimum? Is there a unique optimum, or is a compromise necessary to meet conflicting demands on the responses? Different types of response surface modelling (RSM) designs can be used in this optimization process: three-level full factorial, central composite (CCD), Box Behnken and D-optimal. Presented here are two strategies for optimization: response surface modelling and simplex optimization. Both these designs support the estimation of quadratic terms (Gabrielsson et al., 2002).

Central composite designs

Central composite circumscribed (CCC) and central composite face centered (CCF) are the two most frequently used composite designs (Eriksson et al., 2000). The CCC (stare design) design prescribes experiments, the axial points, whose factor values are located outside the low and high settings of the factor definition whereas in the CCF design, the axial points are centered on the face of the cube, see figure 10.

42 x 3 x 3

x 2 x 2

x 1 x 1

Central composite circumscribed Central composite face centered (CCC) (CCF)

Figure 10. To the left central composite circumscribed (CCC) design and, to the right, central composite face centered design (CCF) with three factor/variables are graphically illustrated. Central composite design for three factors consists of a full factorial two level design and a star design. The experimental points are symmetrically distributed around a center-point.

Analysing the resulting experimental data

Analysing the resulting experimental data, that have been laid out according to a statistical design, usually consist of three stages (Eriksson et al., 2000). I. In the first stage, the raw data are evaluated. To see whether the data are normally distributed and to check that the data replicate error is not larger than the total variation in the design points. II. Thereafter, in the second step calculation of models that link the design data to the recorded responses, and the interpretation of these models. The data collected by the experimental design are used to estimate the coefficients of the model. The data obtained are analysed by means of multiple regression (MLR) or generalisations thereof such as partial least squares projection to latent structures (PLS) and will show which factors are important, and how they combine together to influence the results. III. The third stage is to use the obtained regression model. This involves prediction of optimum conditions for verifying experiments or finding settings for an optimization design by e.g. response surface plots. To determine an optimum in a response surface plot, it is necessary for the polynomial function to contain quadratic terms (Gabrielsson et al., 2002). The most interesting variables with regards to the optimum are plotted against the response in three dimensions. This graphically illustrates the relationship between

43 different experimental variables and the responses and enables identification of the region of optimum.

Revision of the fitted model

All results of model fitting, by MLR or PLS are displayed, in the same way, graphically and in lists. Both MLR and PLS compute regression coefficients from each response. Thus y is expressed as a function of the x´s according to the selected model (i.e. linear, linear plus interactions or quadratic). Goodness of fit statistics, and information about model adequacy are obtained by, calculating the predictive power Q2 of the model (according to cross validation) and the percent of the variation of the response R2 explained by the model. 2 2 In these calculations Q =1-PRESS/SS, R =(1-SSResid/SS) and PRESS the prediction residual sum of squares. Small deviations between the actual residuals and the predicted ones will render a low PRESS and high values of predicted variation (Q2). SS is the sum of squares of the total variation of a selected response, corrected for the mean. The total SS consists of two parts, one part resulting from the regression model (SSRegr) and another due to the residuals (SSResid). Small residuals will render a high degree of explained variation (R2). Large Q2, 0.7 or lager, indicates that the model has good predictive ability and will have small predictive errors. Examination of the regression coefficients plotted in a bar chart will indicate how the factors influence the response(s). This is normally used to detect strong interactions, while response contour plots are used to interpret their meaning. The analysis of variance (ANOVA) for selected responses, partitions the total variance of the response (SS) into a component due to the regression model and a component due to residuals. If replicate observations are available the residual sum of squares is further partitioned into pure error and lack of fit. Model adequacy can be further assessed by normal probability plots of residuals and by checking for optimal transformation of the response through the use of Box- Cox plots. For PLS, a summary of the fit by component and PLS score and loading plot’s are available. Effect plots are available for screening designs.

MLR

With Multiple Linear Regression, the coefficients of the model are computed to minimize the sum of the squares of the residuals, i.e. the sum of squared deviations between the observed and fitted values of each response. The least squares regression method yields small variances from the coefficients and small prediction errors. It is important to note that MLR separately fits one response at a time and hence assumes them to be independent (Eriksson et al., 2000).

44 PLS

When several responses (3 or more) have been measured, it is important to develop a model representing the relationship of all the responses to the factors. PLS takes MLR one step further, as it deals with many responses simultaneously, taking their covariances into account. This provides you with an overview of how all the factors affect all the responses. PLS is one of the most common methods for analyzing multivariate data, where a quantitative relationship between a descriptor matrix x and a response matrix y is sought. The best possible correlation between x and y is calculated using the least squares technique. Hence, the resulting PLS model is not always the best description of x and y, but rather of the relationship between them (Gabrielsson et al., 2002).

Modified sequential simplex approach

The simplex method of optimization of chromatographic resolution selectively directs the chromatographer through the response surface towards an optimum (Berridge, 1982). The simplex algorithm can handle only one response at a time, but there are usually many response variables to optimize simultaneously. A method of forming a joint response measure, from the individual response variables, is therefore needed. Zadhe introduced the concept “fuzzy sets” (Otto, 1988; Zadeh, 1965) that provides flexible and efficient techniques for handling different and conflicting optimization criteria. The modified sequential simplex algorithm differs from the standard method by the addition of the concept of contraction and extension (Berridge, 1982; Berridge et al., 1991). The enhancement seeks to direct the algorithm selectively towards optimum, the zone of data covered becoming progressively focused in the process.

45 Simplex optimization

The simplex method is based on an initial design of k+1 trials, where k is the number of variables. A k+1 geometric figure in a k-dimensional space is called a simplex. The corners of this figure are called vertices. A simplex is a geometric figure having one more vertex than its number of factors. Therefore a simplex in one dimension is a line, in two dimensions a triangle, in three dimensions a tetrahedron, see figure 11, and in multiple dimensions a hyper-tetrahedron. A geometric interpretation is difficult with more variables, but the basic mathematical approach outlined below can handle the search for optimum conditions (Otto, 1999).

Figure 11. Geometric interpretation of lower dimensional simplexes. The shapes of the simplex in a one, a two and a three variable search space, are a line, a triangle or a tetrahedron.

The basic simplex method

The basic simplex method is easy to understand and apply. The optimization begins with the initial trials. The trial conditions are spread out efficiently. These initial trials form the first simplex. The number of initial trials is equal to the number of control variables plus one. With two variables, the first simple design is based on three trials for three variables it is four trials, etc. This number of trials is also the minimum for defining a direction of improvement. Therefore, it is a time saving and economical way to start an optimization project. After the initial trials, the simplex process is sequential, with the addition and evaluation of one new trial at a time. The simplex searches systematically for the best levels of the control variables. The optimization process ends when the optimization objective is reached or when the responses cannot be further improved, see figure 12.

46 Start

Make first simplex Re-test Stop retained trial Yes Yes Rank trials in order: Let Nw be W B, N & W w Rank the No other trials: Reevaluation Objectives rule activated fulfilled B & Nw No ? ? Make reflection R

Replace W for R

Figure 12. Basic simplex rules. The calculations in the basic simplex algorithm are outlined in the flow chart. For each simplex, the following labels are used: W for the least favourable trial or the trial being rejected, B for the most favourable trial and Nw for the second least favourable trial (i.e. next to worst).

The first rule is to reject the trial with the least favourable response value in the current simplex. A new set of control variable levels is calculated, by reflection into the control variable space opposite the undesirable result. This new trial (R) replaces the least favourable trial in the next simplex. This leads to a new least favourable response in the simplex that, in turn, leads to another new trial, and so on. Through this, the simplex will move steadily towards more favourable conditions away from the least favourable conditions These steps are repeated until the simplex begins to rotate around the optimum or the response satisfies the experimenters´ needs (1996; Otto, 1999), se figure 13. The fixed step width of the fixed size simplex might lead to problems if the step width chosen is either too large or too small. In the first case, the optimum might be missed and in the latter the number of experiments required becomes very large. These disadvantages can be circumvented if the step width is tuneable, as with in the variable-size simplex or modified simplex method (Otto, 1999).

47 R

Nw

B W

Figure 13. Illustration of a simplex path with two variables. After three initial trials the corners of the triangle are classified as W for the least favourable trial or the trial being rejected,

B for the most favourable trial and Nw for the second least favourable trial (i.e. next to worst). Then the next trial 4 (R) is calculated and performed.

The modified simplex method

The modified simplex method has much in common with the basic method, but can adjust its shape and size depending on the response in each step. Several new rules are added to the basic simplex rules. These rules make the simplex expand, in a direction of more favourable conditions, or contract if a move was taken in a direction of less favourable conditions. The procedure for expansion and contraction enable the modified simplex both to accelerate along a successful track of improvement and to home in on the optimum conditions. Therefore the modified simplex will usually reach the optimum region quicker than the basic method will and will pinpoint the optimum levels more closely. Moves in a typical optimization sequence are easy to show for control variables (the response is shown separately). The degree of contraction depends on how unfavourable the new response is. With the modified simplex, the step width is changed by expansion (E) and contraction (C- and C+) of the reflected vertices, see figures 14 and 15.

48 E

R C C- W Figure 14. Illustration of the different moves with the modified simplex method, adding the expansion and contraction of the reflected vertices.

Control variable 2

Control variable 1

Figure 15. An example of a typical optimization sequence with the modified simplex method. Change in the levels for two control variables.

49 Aims of the present study

With the introduction of citalopram (Cipramil®) in Sweden, a lot of questions were raised about its effect due to the fact it being a racemic compound. All plasma concentration data for citalopram and its metabolites, used to correlate the clinical data and pharmacological effect, were all based on determination of the total concentration (sum of the two enantiomers) with achiral chromatography. The rediscovery of chirality and its implication on clinical pharmacology and a more rigorous view from the regulatory authorities, together with the awareness that racemic drugs have to be further elucidated, formed the grounds of this thesis. Specific aims:

Development of a routine therapeutic drug monitoring (TDM) method for determination of racemic citalopram and its two main demethylated metabolites.

Development of an enantioselective method for determination of the enantiomers of citalopram and the demethylated metabolites that are also enantiomers.

Studies of the distribution of the enantiomers in patients treated with citalopram.

Study of the distribution of the enantiomers in whole blood from autopsy cases.

50 Methods, results and discussion Introduction

At the time of introduction of citalopram the physician needed a therapeutic drug monitoring service to identify patients with interactions, compliance problems and to provide answers to questions concerning polymorphic enzymes and drug metabolism. Anyone engaged in routine therapeutic drug monitoring must keep in mind that the plasma concentration achieved and maintained is a direct consequence of a wide variety of processes in the body. These processes include drug absorption, distribution, metabolism and excretion in combination with the physiological status of the patient. When long term oral therapy is initiated, the drug will accumulate within the body until the time when the rate of elimination equals the rate of administration. It requires 5 half-lives of drug administration before a steady state concentration is reached and stabilized. It is important to note that the principles that govern the gradual accumulation of a drug to a steady state also apply when the drug is discontinued. For any changes of dosage, after the steady state has been achieved, these principles regulate the time to reach a new steady state. Paper I Achiral determination of citalopram Citalopram extraction version I

Solid-phase extraction

Solid-phase extraction (SPE) has become one of the most common sample clean-up and concentration techniques used by analytical chemists. Over the last two decades, the use of SPE has gained in popularity and is today considered as being the technique of choice for sample work-up. SPE offers several benefits over liquid-liquid extraction, such as improved recoveries, less solvent consumption, smaller sample volumes, and increased sample throughput. A large choice of sorbents such as highly cross-linked copolymers, functionalized copolymers, graphitized carbons or some specific n-alkylsilicas are now available for trapping analytes over a wide range of polarities. The method development is based on prediction from liquid chromatographic retention data or solvation parameters in order to determine the main parameters for extraction. These parameters are type and amount of sorbent, sample volume, which can be applied without loss of recovery, composition and volume of the clean-up solution, and composition and volume of the elution solution. Obtaining extracts free from matrix interferences in a few steps or even in one step when possible, has now been included in the development of the SPE procedure. New selective phases such as

51 mixed-mode and restricted access matrix sorbents or phases such as immunosorbents or molecularly imprinted polymers are also available. Additional selectivity is obtained by combinination of non-polar sorbents with ion-exchange or ion-pair sorbents. SPE is relatively easy to use and the technique is often incorporated into dedicated fully automatized sample preparation systems of various formats, with on- line coupling to liquid chromatography followed by various detection modes (Boos and Rudolphi, 1998a; Boos and Rudolphi, 1998b; Fritz and Masso, 2001; Hennion, 1999; Rouan et al., 2001; Takeuchi and Haginaka, 1999).

Conditioning Solid phases must be conditioned, solvated, or activated before the sample is applied. Failure to activate the column sufficiently results in poor retention of the molecules because sample capacity is reduced on a dry column. The conditioning of the column with an organic solvent, for example methanol or acetonitrile forces the bonded carbon stationary phase to stand up from the silica so they are ready to accept the molecules. A C2 column, with two-carbon chains bound to the silica, is used in this method. Because of the very short chain length of the bound group, leaving the silica surface somewhat exposed, C2 is a fairly polar sorbent with its polarity slightly greater than CN. C2 is often used as a replacement for C18 and C8 when analytes are too strongly retained by the latter. In this extraction method, version I the C2 column is conditioned with acetonitrile because methanol has the disadvantage that small amounts will remain on the column, after the flush step, due to hydrogen bonding to the silica surface. Acetonitrile is the preferred choice if secondary silanol interactions are to be utilized for sample clean up. The organic solvent that has passed through the column may interfere with subsequent hydrophobic binding of the analyte and must be flushed away, in this case with 25 mmol/L phosphate buffer pH 11.5.

Buffering/sample pretreatment The purpose of buffering the sorbent is to ensure optimal pH conditions for extraction. For hydrophobic extraction, retention is promoted when the functional groups of the analytes are not charged. For optimal retention of a drug it must be in a hydrophobic form, therefore the buffer must be at a pH at which the molecules are uncharged. The pKa of the analyte will determine the pH for buffering. Addition of an organic solvent, like methanol or acetonitrile, in low concentrations, to the matrix may also be useful for breaking the protein binding of the drug. The pKa of Cit is 9.5 and, by using a phosphate buffer of 11.5, it will be uncharged. The buffered plasma sample is passed through the column followed by two washing steps. See figure 16.

Washing The washing and elution stages of any extraction are extremely important and require knowledge of polarity of the analyte and relative polarity of solvent or solvent mixtures. Incorrect selection of a washing solution or elution solvent will result in low recovery, poor reproducibility, and/or dirty extracts.

52 In this method two washing steps are used, the first with water and the second with acetonitrile. The water will wash out the polar matrix and gradually change the pH from 11.5 (phosphate buffer) to approximately 6.5 (micro milieu at silanol level). The Cit molecules have now become positively charged and, after drying the column with air, they will be retained on the column by ion interaction with negatively charged secondary silanols. Uncharged non-polar molecules can now be removed by washing with acetonitrile. This step is called phase switching because there is a switch from interaction between the sorbent and water to an interaction between sorbent and a water miscible organic solvent. See figure 16.

Elution Ideal elution solvents selectively remove analytes and leave remaining interferences on the column. Hydrophobic bonds are broken by solvent elution, while ionic interactions are broken by high or low pH aqueous elution. The final elution solvent must simultaneously disrupt both these interactions, that is, a high (basic) or low (acidic) pH modified organic solvent. For the elution of Cit, DCit, DDCit and internal standard (IS-Cit) methanol with 1 % acetic acid was used to break hydrophobic and ionic interaction and effectively eluate the analytes. See figure 16.

53 CH H3C 3 N NC

O CH3

N CH3 O

NC F CH3 CH3 CH CH CH F 3 3 CH3 3 Citalopram, IS F=Cl O OH OO OOHO O HO Si O Si O Si O Si O Si O Si O Si O Si OH O O O O O O O O

SiH H Si SiH3 H Si SiH H Si SiH 3 3 3 3 3 SiH3 3

Washing with water

NC NC

O CH3 + N CH3 H F O

F

+ CH3 H3C N HCH3 CH CH CH CH3 CH3 CH CH CH 3 3 CH3 3 3 3 CH3 3 CH3 O O- OO OO O- O O OH OO OOHO O HO Si O Si O Si O Si O Si O Si O Si O Si OH HO Si O Si O Si O Si O Si O Si O Si O Si OH O O O O O O O O O O O O O O O O

SiH H Si SiH3 H Si SiH H Si SiH SiH H Si SiH3 H Si SiH H Si SiH 3 3 3 3 3 SiH3 3 3 3 3 3 3 SiH3 3 Acetonitrile washing ”phase switching” Elution with methanol/acetic acid

Figure 16. Extraction procedure for citalopram and metabolites on a C2 extraction column, showing the mechanisms involved.

54 Internal standard

When working with biological matrices, the addition of an internal standard (IS) will compensate for errors caused by inaccuracy in volume transfers in the extraction step, as well as in the injection step. One important criteria for the IS is that binding strength of the analytes and the IS to proteins and other components of the matrix should be similar. The physiochemical properties of the IS should be comparable with those of the analyte(s) so it behaves in a similar way to the analyte(s) during sample pre- treatment. Yet the properties must be sufficiently unique to allow the IS to be clearly distinguished from the other analyte in the HPLC separation. The IS can not be another drug on the market because there is always a risk that that drug has also been prescribed to the patient. In such a case, a quantitative determination will be impossible. In this method, the internal standard IS-Cit is the chlorinated version of Cit, see figure 16. Protein binding of analytes may be a problem and should be considered if recoveries differ greatly when comparing extractions from plasma and water.

Achiral chromatography

After evaporating the eluate, the samples were dissolved in the mobile phase for the achiral chromatography and injected. For many older chromatographic C18 columns, organic additives in the mobile phase are used to reduce secondary silanol interactions so as to obtain good performance. With newer generations of analytical reversed phase material however, this problem has been considerably reduced and amine additives are very seldom necessary (Tournel et al., 2001). At the time as this method was developed, secondary interactions were avoided by the use of a phenyl-based stationary phase. After the publication of this method a few improvements have been made. The dimension of the column has been changed from 8*100 mm to 3.9*100 mm, protected by a precolumn of 3.9*20 mm, making it possible to reduce injection volume from 20 µl to 10 µl while maintaining the same sensitivity.

55 As this method has been our routine method for several years, both internal and external controls can in addition to the validation presented in paper I, further demonstrate the accuracy and stability of the method. In figure 17, the spiked values versus found concentrations of Cit external controls, followed for 5 years and the internal controls for Cit, DCit and DDCit during the last two years can be seen.

External control for citalopram Citalopram internal controls from 2001 to september 1998 to december 2002 2002 700 600 y = 0,924x + 4,686 Cit high R2 = 0,9892 600 CV=4.0 % 50 0 Cit low 500 CV=5.5 % 400 DCit High 400 CV=5.4 % 300 DCit low

nmol/L 300 CV=10.7 %

Found nmol/L 200 DDCit High 200 CV=5.7 % 10 0 DDCit low 100 CV=7.6 % 0 0 200 400 600 0 Spiked value nmol/L 1 6 11 16 21 26 31 36 41 46 Number of controls

Figure 17. External control for citalopram for a 5 years analysed every quarter and low and high internal controls for Cit, DCit and DDCit during the last two years. New standardisation was carried out on every analysis batch. Summary of paper I

A routine TDM method has been established for achiral concentration monitoring of Cit, DCit and DDCit.

The introduction of solid–phase sample preparation utilizing “phase-switching” technique allowed a more effective washing procedure.

The use of a phenyl column made it possible to use a mobile phase without any amine additives.

56 Paper II Optimization of the chiral separation of citalopram and its demethylated metabolites

Chiral separation of citalopram

With the enantioselective chromatography column/phases available today, the possibility of finding a column that can solve the problem of separating the enantiomers of the parent drug is good. However, in bioanalysis of a drug there is always the possibility of finding metabolites of that drug in the sample and these may either be of interest to measure or they may interfere with the analysis of the parent drug. Among the different E-CD columns available, the acetylated and 3,5- dimethylcarbamate derivates of E-CD have been used for chiral separation of Cit (Carlsson and Norlander, 2001; Rochat et al., 1995a; Sidhu et al., 1997). A cellulose-based CSP (Chiracel OD) column has also been used for chiral separation of Cit, after derivatization to minimize retention times (Rochat et al., 1995b). The enantiomers of the propionic metabolite of citalopram (Cit-Prop) could also be separated, after derivatization, on the same column but with a different mobile phase. With the column, chirobiotic V, where vancomycin is the chiral selector used in the polar organic mode with a mobile phase of methanol, acetic acid and triethyl amine the eluation order of the enantiomers is reversed as compared to E-CD, the R- enantiomers eluates before the S-enantiomers (Kosel et al., 1998; Zheng et al., 2000). The separation of S-Cit and R-Cit have also been performed on a protein type Į1-acid glycoprotein column (Chiral-AGP), with 3.0 mM N-dodecyl-N,N- dimethylammonio- 3-propanesulfonate and 10 mM hexanoic acid in phosphate buffer pH 6.5 as the mobile phase. On the Į1-acid glycoprotein column it was not possible to separate the demethylated metabolites (Haupt, 1996). Detection principles used are either UV or fluorescence.

In the initial attempts to separate citalopram and its demethylated metabolites a screening was performed on different columns. With the AGP column it was not possible to obtain any separation of the two metabolites, which is why it was excluded. A batch of a column with vancomycin as chiral selector was also tried early on but the time to obtain a stabile base line could take a whole weak, so that it was impossible to use. The native form of E-CD could separate the enantiomeric pairs of Cit, DCit and DDCit, but one problem was not solved, i.e. the overlapping of different enantiomer pairs. Modde, in both screening and optimization design (CCF), was used in these attempts to find the optimum for separation on these columns but they were unsuccessful. The

57 derivatized forms of E-CD offer additional opportunities for chiral separation and among them the acetylated form of E-CD (E-CD Ac). The E-CD Ac, where the hydroxyl groups on the rim of the E-CD cone are modified to acetoxy groups, leading to a somewhat extended cone, gave promising results.

This study had two main objectives:

I. To further scrutinize and understand the separation mechanism within enantiomeric pairs, as well as between enantiomeric pairs of Cit, DCit and DDCit. II. To evaluate the use of response-surface methodology as an optimization tool for enantioselective analysis, and to compare response surface modelling versus sequential simplex optimization. Optimization with Modde

With the use of Modde, an initial screening design resulted in four factors i.e. pH (4,5- 6,5), buffer concentration (5-145 mM), methanol content (45-65 %) and temperature (5-35 qC) to be used in a full factorial optimization design (CCF). Responses were the adjusted retention time (tR´) of the different enantiomers, separation factors within enantiomeric pairs (ĮCT, ĮDCT and ĮDDCT) and between enantiomeric pairs (Į1 and Į2). With a CCF design this resulted in 27 experiments including 3 replicates at the center point. Evaluation of the model with linear, quadratic, and cross terms showed a good fit for all terms except for Į2 where the predictive ability Q2 was low, illustrated in graphic form in figure 18. Acceptable values for chemical data are R2 • 0.8 and Q2 • 0.5 and excellent when Q2 > 0.8 (Lundstedt et al., 1998). The low predictive ability for Į2 is also shown in the plot “lack of fit”, as the model also suffers from a significant lack of fit for Į2, as shown in figure 19 comparing ĮCT and Į2. Suspected outliers among the experiments can be found in a residual plot. In figure 20, the residual plots for ĮCT and ĮDCT show that experiment number 5 could be an outlier. Subsequently, the non significant quadratic and cross terms were removed to try to improve Q2 for Į2. In figure 21, an overview of factors and responses with A, all terms included and in B, non-significant terms removed. Every response can also be presented with main effects, cross and quadratic terms and confidence interval, se figure 22. The very low value of Q2 was not improved by modifying the model and as other Q2´s, for the other responses did not improve, the default model was used to further investigate the separation mechanism. Interpretation of the model by response surface plots gave that the optimum for separating all the six enantiomers has to be done with a mobile phase of 55:45 methanol:10 mM citric acid-triethylamine buffer pH 6,3 at 30 qC, see figure 23 and paper II, page 269.

58 Investigation: Citalopram (PLS, Comp=5) Summary of Fit 1,0 R2 2 R2 & Q2 Q

0,8

0,6

Modde 0,4 4.0

0,2

0,0 Log tR´ DCT DDCT DDDCT D1 D2 R-CT N=27 CondNo=6.1592 DF=12 Y-miss=0

Figure 18. Summary of fit: the levels of explained (R2) and predicted (Q2) variations were good for all responses except for Į2.

Investigation: Citalopram (PLS, Comp=5) Investigation: Citalopram (PLS, Comp=5) Lack of Fit for ĮCT Lack of Fit for Į2 0,014

0,05 0,012

0,010 0,04

0,008 0,03

0,006 0,02 0,004

0,01 0,002

0,000 0,00 SD-LoF SD-pe SD-pe * sqrt(Fcrit) SD-LoF SD-pe SD-pe * sqrt(Fcrit) N=27 R2=0,9587 R2Adj=0,9104 N=27 R2=0,7075 R2Adj=0,3661 DF=12 Q2=0,7920 RSD=0,0084 AlphaLev=0,05 DF=12 Q2=0,1317 RSD=0,0485 AlphaLev=0,05

Figure 19. ANOVA: Plot of lack of fit for ĮCT and Į2. If the standard deviation of lack of fit (SD-LoF) is smaller than the standard deviation of the pure error multiplied by the square rot of the critical F (SD-pe*sqrt(Fcrit) there is no lack of fit in the model. For ĮCT there is no lack of fit but for Į2 there is a lack of fit.

59 Investigation: Citalopram (PLS, Comp=5) ĮCT 99,9 99,5 99 98 12 N-Probability 95 4 90 7 23 Modde 4.0 14 80 626 70 2215 60 2524 185 50 2127 40 1720 30 38 1130 20 916 11 10 1 5 19 2 2 1 0,5

-1,0 -0,5 0,0 0,5 1,0 1,5 Standardized Residuals N=27 R2=0,9587 R2Adj=0,9104 DF=12 Q2=0,7920 RSD=0,0084 Investigation: Citalopram (PLS, Comp=5) ĮDCT 99,9 99,5 99 98 7 N-Probability 95 6 90 9 12 80 23 19 70 2622 60 182 16 24 Modde 4.0 50 1513 40 317 30 2514 4 27 20 11 20 21 10 8 5 10 2 5 1 0,5

-1,5 -1,0 -0,5 0,0 0,5 1,0 Standardized Residuals N=27 R2=0,9650 R2Adj=0,9242 DF=12 Q2=0 7719 RSD=00068

Figure 20. Residual N-plot for ĮCT and ĮDCT, showing the distribution of the residuals. For ĮDCT, experiment number 5 can be considered as an outlier.

60 Investigation: Citalopram (PLS, Comp=5) Normalized Coefficients pH Ko 0,4 Me Te 0,2 pH*pH Ko*Ko 0,0 Me*Me Te*Te pH*Ko -0,2 pH*Me pH*Te Modde 4.0 -0,4 Ko*Me Ko*Te Me*Te -0,6

-0,8

Log tR´ R-CT DCT DDCT DDDCT D1 D2 A N=27 CondNo=6.1592 DF=12 Y-miss=0

Investigation: Citalopram (PLS, Comp=3) Normalized Coefficients pH 0,4 Ko Me Te 0,2 pH*Ko pH*Me 0,0 Ko*Me

-0,2 Modde 4.0 -0,4

-0,6

-0,8

Log tR´ R-CT DCT DDCT DDDCT D1 D2 B N=27 CondNo=1.1331 DF=19 Y-miss=0

Figure 21. A. Normalized coefficient with all interaction and quadratic terms included. B. Normalized coefficient with non-significant quadratic and cross terms removed.

61 Factors thatarewithin theboundariesofconfidenceintervalhave nosignificance. barswiththeirconfidenceintervals. All factorsfortheresponse aredisplayedin 62 Coefficient plot.Plotof scaledandcentredcoefficientsfortheresponses Figure 22. -0,02 -0,01 -0,02 -0,01 -0,04 -0,02 -0,04 -0,02 0,00 0,01 0,00 0,01 0,00 0,02 0,04 0,00 0,02 0,04 cld&Cnee ofiinsfor Coefficients &Centered Scaled (PLS, Comp=5) Investigation: Citalopram980210RT cld&Cnee ofiinsfor Coefficients &Centered Scaled (PLS, Comp=5) Investigation: Citalopram980210RT cld&Cnee ofiinsfor Coefficients &Centered Scaled (PLS, Comp=5) Investigation: Citalopram980210RT cld&Cnee ofiinsfor Coefficients &Centered Scaled (PLS, Comp=5) Investigation: Citalopram980210RT F1 2083 S=,04ConfLev=0,95 RSD=0,0084 Q2=0,8232 DF=12 R2Adj=0,9101 R2=0,9585 N=27 F1 2015 S=,45ConfLev=0,95 RSD=0,0485 Q2=0,1552 DF=12 R2Adj=0,3665 R2=0,7076 N=27 pH pH

Ko Ko

Me Me

Te Te

pH*pH pH*pH

Ko*Ko Ko*Ko

Me*Me Me*Me

Te*Te Te*Te

pH*Ko pH*Ko Į Į Į pH*Me Į pH*Me CT CT 2 2 pH*Te pH*Te

Ko*Me Ko*Me

Ko*Te Ko*Te

Į Me*Te Me*Te CT and

Modde 4.0 Modde 4.0 Į 2. Figure 23

tR´ R-CT D CT D DCT

tR´ D D 1,20 1,19

1,19 1,18 40 1,18 1,17 1,17

1,16 1,16 20 4,5 4,5 6,5 1,15 1,15 5,0 5,0 6,0 1,14 5,5 5,5 20 5,5 140 140 120 40 120 100 1,14 100 60 80 6,0 pH 80 6,0 pH 80 5,0 pH 60 60 100 40 40 120 20 6,5 20 6,5 Concentration 140 4,5 Concentration Concentration

D DDCT D D D D D

1,10 1,05 1,160

1,155 1,00 1,05

1,150 0,95 1,00 1,145 4,5 0,90 6,5 6,5 5,0 1,140 6,0 0,95 6,0 140 5,5 20 5,5 20 5,5 120 100 40 40 80 6,0 60 60 60 pH 80 5,0 80 5,0 40 100 pH 100 pH 20 120 120 Concentration 6,5 Concentration 140 4,5 Concentration 140 4,5

A. Response surface for the retention time of the last-eluated enantiomer of citalopram (R- CT) and for the separation factors for DCT, DDCT, DDDCT, D1 and D2.Methanol content and temperature were held at their centre points, 55 % and 20 qC, respectively; the other factors were varied between their minimum and maximum values.

tR´ R-CT D CT D DCT tR´ D D 1,17 120 1,17 1,16 1,16 100 1,15 1,15 80 1,14 1,14 1,13 60 1,12 1,13 40 1,11 1,12 45 45 45 5 20 5 1,10 5 1,11 10 50 10 50 10 50 15 55 15 55 15 55 20 20 20 25 60 Methanol 25 60 Methanol 25 60 Methanol 30 30 30 Temperature 35 65 Temperature 35 65 Temperature 35 65

D DDCT D D D D D 1,12 1,16 1,10 1,11

1,15 1,08 1,10 1,14 1

a 1,09 f 1,06

l

A 1,13 1,08 1,04 1,12 1,07 1,02 1,11 1,06 45 35 65 45 5 50 30 5 50 10 25 60 10 15 55 15 55 20 20 55 20 15 25 60 Methanol 50 Methanol 25 60 Methanol 30 Temperature 10 30 Temperature 35 65 5 45 Temperature 35 65

B. Response surface for the retention time of the last-eluated enantiomer of citalopram (R- CT) and for the separation factors for DCT, DDCT, DDDCT, D1 and D2. pH and buffer concentration were 6,3 and 10 mM, respectively; the other factors were varied between their minimum and maximum values. 63 Optimization with Multisimplex

The control variables pH, buffer concentration, methanol content and temperature were the same as the factors in the Modde optimization and likewise the response variables were separation factors within enantiomeric pairs (ĮCT, ĮDCT and ĮDDCT) and between enantiomeric pairs (Į1 and Į2). The adjusted retention time was not used in this optimization process. The reference values from which the five initial trials were calculated were the same as the center point in the Modde optimization i.e. methanol content 55%, pH 5.5, temperature 20 qC and buffer concentration 75 mM. For the Multisimplex optimization 12 experiments were performed to obtain an adequate separation within and between enantiomer pairs. The resulting mobile phase was 57:43 methanol: 22 mM citric acid-triethylamine pH 6.3 at 13 qC. Comparing the results obtained with Modde 55:45 methanol: 10 mM citric acid-triethylamine pH 6.3 at 30 qC. The divergence between the results of these two optimization programs is to be found in the obtained optimal column temperature. One possible explanation, which has come forward after this study, is that the reduction of the separation factors (ĮCT, ĮDCT, ĮDDCT, Į1 and Į2) is caused by aging of the column, and can be compensated for by lowering the temperature of the column. As the optimization trials with the two programs were done on different occasions, with several months inbetween, a certain degree of aging may have occurred. The storage media for this type of column was methanol but today the manufacturer recommends 2-propanol, as it has less effect on the stationary phase. Separation factors (Į) have been used as responses in Modde and response variables in Multisimplex. Instead of separation factors, Kaiser peak separation index or resolution can be used. The Kaiser peak separation index is defined as the average valley depth expressed as a ratio to the average peak height of the two adjacent peaks (Owens et al., 1996), see appendix. In these chromatograms, the use of resolution would require that in every experiment, the single enantiomers have had to be injected to obtain the parameters for the calculations.

Separation mechanism

With the E-CD Ac, the separation was improved, as compared to E-CD. The acetylation of sterically fixed hydroxyl groups changes the nature of the interaction from a diastereomeric complex formed by simple hydrogen bonding to sites with hydrogen donor and acceptor mechanisms which are a much stronger type of interaction. The size of the CD cone and the possibility of a stronger type of diastereomeric interaction between the analytes and the chiral phase make the separation between enantiomers of the parent drug and metabolites possible. The temporary complex formation between the different citalopram enantiomers and the cyclodextrin cone (which is proposed in reversed phase mode) can not fully base line separate these six enantiomers. To obtain a complete baseline separation between the enantiomer pairs, pH has to be 4 (limit for the column), and a buffer concentration around 100 mM has to be used at low temperature. This will result in overlapping peaks from parent drug and metabolites and, to be able to quantify the enantiomers, a

64 mass spectrometric detector is needed. The mobile phase, however, in this application is not compatible with the mass spectrometric detector.

Summary of paper II

Modde vs Multisimplex

Both programs use response surfaces to determine an optimum. To be able to determine an optimum it is necessary that the polynomial function contains quadric terms (Lundstedt et al., 1998). Modde gives more information about the mechanism behind a chiral chromatographic separation, which has been shown in many articles. Multisimplex is a more straightforward way of finding an optimum, but a disadvantage is that there is also the risk that a local optimum has been found. Multisimplex and Modde complement each other. Chromatographers might find the optimum faster with Multisimplex but they are not gaining as much information about the separation mechanism as they do with Modde.

65 Paper III Citalopram and adolescents Citalopram extraction version II

The extraction procedure from paper I and the optimized chromatographic procedure for the chiral chromatography from paper II were intended to be used for determination of the enantiomers of Cit and its metabolites. At the very first analytical test, it turned out that the mix of the extraction methodology and the chiral separation were not compatible with regard to the resulting chromatography, see figure 24. Standard ethanol solutions of Cit, DCit and DCCit diluted in the mobile phases were used during the optimization study. The conclusion was drawn that the cause had to be found in the extraction procedure. An investigation was performed to find out how to avoid these disturbances. An unwanted residue of acetic acid after the evaporating step could lower the pH of the injection solution, leading to distortion in the chromatogram. This could be solved by using 2.5 % ammonia in acetonitrile as the eluating agent, a solvent that was discarded during the evaluation of the achiral chromatography due to losses in the evaporation step. The losses could however be avoided by the addition of 100 µl 10 mM citric acid to the tube used for collecting the eluate, but to obtain good extraction recovery 2*2 ml of 2.5 % ammonia in acetonitrile had to be used. The problem was not solved totally by these measures. It seems also that the phosphate buffer used contributed to the chromatographic problem and as a consequence this was substituted for by 30 mM NaOH. The change from methanol to acetonitrile improved the extraction in another way as methanol usually gives more dirty extracts than acetonitrile.

66 A B

Figure 24. A. Injection of a solution of Cit, DCit and DDCit in mobile phase. B. Injection of a plasma extract using extraction method I and re-dissolved in the mobile phase for the chiral separation of Cit, DCit and DDCit.

Results, citalopram and adolescents

As no investigations had been done regarding Cit enantiomers and adolescents, such a study was initiated. Together with the Child and Adolescent Department at Uppsala University Hospital, adolescent patients on Cit were recruited. The data on the concentration of the enantiomers were found to be in agreement with earlier studies on adults. However in these other studies, see table 7, additional CNS drugs were prescribed. In our study of adolescent patients, there were a few females using contraceptive hormones. In earlier studies a correlation between the ratio of the S/R Cit and S/R DCit had been found. In this study however an interesting finding was that the correlations were good for females not using contraceptive hormones but not for females using contraceptive hormones, se figure 25 and 26. A possible explanation may be an interaction of contraceptive hormones with the metabolism of citalopram. Several studies have shown that contraceptive hormones may interact with the polymorphic enzyme CYP2C19 (Hägg et al., 2001; Laine et al., 2000; Tamminga et al., 1999; Tanaka, 1999b).

67 Citalopram enantiomers #30 Patient number 18 Int_Chan_1 100 Citalopram enantiomers #1 Standard I Int_Chan_1 100 mV mV

80 80

60 60 R-DCIT - 12,844 - R-DCIT R-CIT - 16,113 R-CIT

40 40 S-DCIT - 11,789 S-DCIT S-IS-CIT - 19,358 - S-IS-CIT S-IS-CIT - 19,043 - S-IS-CIT R-IS-CIT - 21,493 - R-IS-CIT R-IS-CIT - 21,059 - R-IS-CIT S-CIT - 14,922 - S-CIT S-DCIT - 11,580 - S-DCIT

20 R-DCIT - 12,662 20 S-CIT - 14,654 S-CIT R-CIT - 15,874 - R-CIT R-DDCIT - 10,853 S-DDCIT - 10,015 - S-DDCIT R-DDCIT - 10,747 R-DDCIT S-DDCIT - 9,827

min -10 min -10 0,0 5,0 10,0 15,0 20,0 25,0 30,0 0,0 5,0 10,0 15,0 20,0 25,0 30,0

Figure 25. Standard I with 3.5 nmol/L of S- and R-DDCit, 7.5 nmol/L of S- and R-DCit, 15 nmol/L of S- and R-Cit and a patient with 4 S- DDCit, 7 R-DDCit, 27 S-DCit, 38 R- DCit, 40 S-Cit and 90 nmol/L R-Cit.

Correlation S/R ratio With contraceptive pill Correlation S/R ratio Without contraceptive pill

1,2 1,2

1 1

0,8 0,8

0,6 0,6

y=0.416*x+0.581 y=1.355 *x+0.095 0,4 2 0,4 2 S/R Desmethylcitalopram r =0.163 Desmethylcitalopram S/R r =0.926 r=0.403 r=0.962 0,2 p=0.2480 0,2 p=0.0005

0 0 0 0,2 0,4 0,6 0,8 1 0 0,2 0,4 0,6 0,8 1 S/R Citalopram S/R Citalopram

Figure 26. Correlation between the S/R ratio for Cit and S/R ratio for DCit for females using and those not using oral contraceptives.

68 Comparison with data from adults

Four studies have hitherto been published were the concentration of the enantiomers have been measured in patients on Cit medication. In table 7 a summary of the four studies is presented with the intention of giving an indication of what concentrations of S-Cit are to be expected.

Genotyping

In this study, samples for determination of the enantiomers of Cit and for genotyping of the polymorphic enzymes CYP2D6 and CYP2C19 were drawn from every patient. With the frequencies of 7 % for PM and 1-3 % for UM of 2D6 and 3 % of 2C19 PM the study had to have a design of at least 100 patients, if it were to be possible to draw any conclusions regarding these polymorphic enzymes. The genotyping results could however be a tool to explain possible outliers found among the patients in the enantioselective analysis. Other examples where chiral analysis has been of value

During the journey from achiral to chiral analysis of Cit, other problems/questions have emerged, such as inquiries from clinicians about Cit metabolism or interesting findings obtained with the achiral method, leading to a further analysis with the chiral method. The first example is from a patient with Crohn´s disease who was followed using plasma samples during a 24 hours period. In figure 27, the plasma concentration and ratio of the different enantiomers versus time is shown. These led to the dose given to the patient being increased to 120 mg daily. This plasma concentration profile could be compared with the study by Sidhu et al (Sidhu et al., 1997) on healthy human subjects, who were extensive metabolizers of CYP2D6 and CYP2C19 and who took multiple doses (40 mg per day) of Cit for 21 consecutive days. The results of this study showed the same stereoselectivity in the disposition of Cit, DCit and DDCit.

69 Concentration of the enantiomers Ratio between S och R enantiomers

250 1,00 0,90 200 R-Cit 0,80 0,70 S-Cit 150 0,60 S/R-DCit R-DCit 0,50 S/R-DDCit S/R

S-DCit Ratio nmol/L 100 0,40 S/R-Cit R-DDCit 0,30 50 S-DDCit 0,20 0,10 0 0,00 0 102030 0102030 Hours after dose Hours after dose

Figure 27. Concentration- and ratio-time curves on a patient suffering from Crohn´s disease on a Cit dose of 60 mg daily.

Carbamazepine, an inducer of CYP3A4, has been shown to affect the metabolism of Cit (Leinonen et al., 1996; Leinonen et al., 1991). Phenytoin is also an inducer of CYP3A4, but in contrast to carbamazepine, it is not itself metabolized via CYP3A4 (Tanaka, 1999a). Phenytoin is mainly metabolized by CYP2C19 and CYP2C9. In this example a sample from a patient on 20 mg of Cit and 100 mg of phenytoin daily was analysed by the enantioselective method with the following concentrations found [nmol/L] 17 S-DDCit, 41 R-DDCit, 124 S-DCit, 183 R-DCit, 114 S-Cit and 141 R-Cit. S/R ratios were S/R DDCit 0.41, S/R DCit 0.68 and S/R Cit 0.81, see figure 28. In contrast to the normal parent/metabolite ratio (Cit/DCit) of between 0.3-0.5, this patient showed a ratio of 0.83 indicating an induction of CYP3A4 metabolism. The ratio between the different enantiomers did not differ as compared to earlier studies.

70 CIPRALEX03033 #11 [modified by Administratör] Int_Chan_1 300 mV 1

250 R-DCIT11,677 - S-IS-CIT - 18,928 R-IS-CIT 20,50 -

200 S-DCIT 11,081 - R-CIT - 15,784 150 S-CIT - 14,849

100 R-DDCIT 9,333 -

50 S-DDCIT8,975 -

min -10 0,0 5,0 10,0 15,0 20,0 25,0 30,0

Figur 28. Chromatogram of a serum sample from a patient at steady state, before dose, receiving 20 mg Cit and 100 mg phenytoin daily with the following concentrations found [nmol/L] 17 S-DDCit, 41 R-DDCit, 124 S-DCit, 183 R-DCit, 114 S-Cit and 141 R-Cit. Animal studies

The chiral method has also been used in studies on rats. Enantioselective analysis of Cit and its metabolites in blood and brain has been used in a study on open-field behaviour in rats (Wikell et al., 1999). To further scrutinize the enantiomer-selective distribution of Cit beyond clinical doses, an investigation concerning the toxicological effects of Cit has been carried out (Kugelberg et al., 2001) on rats with a further extension as a model for investigating drug distribution postmortem. Summary of paper III

With this study on adolescents, the distribution of Cit enantiomers have been investigated and found to be in agreement with studies on adult patients. As the polymorphic enzyme CYP2C19 is involved in the metabolism of contraceptive hormones as well as of citalopram a possible interaction has been shown.

71 Carlsson B et al (2001) Rochat B et al (1995) Zheng Z et al (2000) Foglia JP et al (1997) Normalized to 20 mg/day Racemic drug dose in mg/day 10-30 (30r14) 20-60 (44r15) 20-40 (22r7) 20 (meanrS.D.) Diagnosis Major depressive Depressive patients Stroke-induced Dementia + behavioral disorder (dysthymia) depression disturbances Number19 29 16 8 Female 16 13 13 4 Male 31634 Age year (mean) 15-20(18) 20-65 61-88 76-90 (77,2) 15-90 Steady state >14 days TDM One week Two weeks SS Trough value (hours 10-30 12-24 Trough 12-15 Trough value after dose) Other drugs No (contraceptives) CNS drugs CNS, somatic + lorazepam Citalopram nmol/L

72 S (mean rS.D.) 62r53 103r66 88r40 100r76 54r41 S/R (mean rS.D.) 0,59r0,13 0,56r0,14 0,7r0,17 0,65r0,19 DCit nmol/L S (mean rS.D.)26r17 50r26 37r36 50r13 25r15 S/R (mean rS.D.) 0,82r0,13 0,72r0,2 0,95r0,2 1,08r0,3 DDCit nmol/L S (mean rS.D.) 4r1 ???? S/R (mean rS.D.) 0,46r0,14

Table 7. Data comparing the four investigations carried out using concentration determination of citalopram’s enantiomers. Recalculated to 20 mg daily of Cit. Data from (Carlsson et al., 2001; Foglia et al., 1997; Rochat et al., 1995a; Zheng et al., 2000). Paper IV

Enantioselective analysis of citalopram in postmortem blood and genotyping

The enantioselective analysis on this material was carried out before the clinical study in paper III. Genotyping for the polymorphic enzymes CYP2D6 and CYP2C19 has prolonged the process of publication of paper IV, thus explaining its later publication. Citalopram extraction version III

Extraction of whole blood was a new challenge. To be able to use solid phase extraction, and to extract the analytes with sufficient recovery the femoral whole blood samples had to be diluted, sonicated (treated by ultrasonic bath) and centrifuged before extraction on solid-phase columns (Verweij et al., 1996). Blood should be handled much like serum or plasma. The difference is the presence of whole red blood cells and, in this special case, that many postmortem samples are clotted.

Buffering/sample pretreatment

To optimize the extraction for whole blood samples, the sample pretreatment step was modified. As the concentrations in these samples could be assumed to be higher than in the TDM service 0.1-0.5 g blood (corresponding approximately to 0.1-0.5 ml) were used and the standard curves were prepared in the concentration range 0.01 to 0.16 µg/g (a30-500 nmol/L) for S- and R-DDCit, 0.05 to 0.8 µg/g (a150-2500 nmol/L) for S- and R-DCit and 0.1 to 1.6 µg/g (a300-5000 nmol/L) for S- and R-Cit. IS-Cit was added to the sample, followed by dilution with 3 ml 5:95 acetonitrile:0.025 M phosphate buffer. After vortex mixing, sonication and centrifugation the supernatant was poured on the conditioned solid phase column and the extraction procedures given in paper I were followed. As the determination of the enantiomers was performed before study III, a minor problem was seen regarding skew ness of the peaks when the samples from the whole blood extraction were re-dissolved in the mobile phase for the chiral chromatography (55:45 methanol:10 mM citric acid-triethylamine). This problem was solved by re-dissolving the samples in a “mobile phase” with higher content of buffer, 55:45 methanol:100 mM citric acid-triethylamine pH 6.3, which brought the pH to 6.3. In table 8, the extraction recoveries for the three extraction procedures are summarised.

73 Extraction I II III method Achiral method Chiral method Chiral method plasma plasma whole blood Cit 98 89 91 DCit 102 108 93 DDCit 96 102 94 IS 89 75 85

Table 8. Extraction recovery for the three extraction procedures. Drug levels in postmortem whole blood versus plasma concentrations from patients with toxicological symptoms

The findings that a S/R–Cit ratio close to 1.0 in postmortem whole blood is associated with an intake of the drug only a few hours or less before death can further be confirmed by some examples from routine toxicology. In table 9 six patients found to have toxic symptoms were analysed regarding the racemic plasma concentrations and later followed up by enantioselective analysis.

Sample Cit S/R Ratio DCit S/R Ratio DDCit S/R Ratio number Total Total Total concentration concentration concentration nmol/L (µg/g) nmol/L (µg/g) nmol/L (µg/g) 1 2472 (0.80) 1.03 318 (0.097) 1.37 43 (0.013) 2 981 (0,32) 0.94 198 (0.061) 0.92 <2 2.07 3 1591 (0.52) 0.99 239 (0.074) 1.88 23 (0.007) 4 640 (0.21) 0.90 115 (0.036) 0.69 <2 5 1188 (0.39) 0.89 191 (0.059) 0.84 15 (0.004) 1.14 6 2484 (0.81) 1.14 172 (0.053) 1.00 <2

Table 9. Plasma concentration and S/R ratio from six patients with toxic symptoms.

In case number 8 in paper IV, a higher concentration of DCit than Cit was found. Phenytoin was also found together with Cit in this case. Interaction with phenytoin, as in the example shown from a patient on citalopram and phenytoin, gives further support to that CYP3A4 being induced by phenytoin.

74 Genotyping

In this study, the patients were also genotyped for the polymorphic enzymes CYP2D6 and CYP2C19. With the frequencies of 7 % for PM and 1-3 % for UM of 2D6 and 3 % of 2C19 PM, the study had to have a design of at least 100 patients, if it were to be possible to draw any conclusions regarding these polymorphic enzymes. The low frequency approximately 3.5 % of PM in this study is however in agreement with an earlier study (Druid et al., 1999). In this paper pyrosequencing has been used for the genotyping of CYP2D6 but in the earlier paper the technique by Heim and Mayer (Heim and Meyer, 1990) was used. Summary of paper IV

Investigation of these autopsy cases showed that pharmacokinetic interactions have a greater impact on the cause of death than metabolic deficiency regarding the polymorphic enzymes CYP2D6 and CYP2C19. The enantioselective analysis of Cit and the S/R ratio can give additional information when interpreting the results, especially with regard to the time elapsed between intake and death.

75 Conclusions Chemometrics – Experimental design

Experimental design has been shown to be an effective way of planning and performing chromatographic optimization systematically. Optimization with response surface methodology using central composite or modified simplex design led to a refined chiral chromatographic process for the analysis of citalopram and its demethylated metabolites. Simplex design was a quicker way to obtain the optimum while the central composite design gave additional information about the separation mechanism on the acetylated E-cyclodextrin column. Therapeutic drug monitoring: Achiral or chiral analysis of citalopram

An achiral method for TDM may still have a function in the daily routine to identify problems e.g. with compliance. With the introduction of S-Cit (escitalopram) the possibility of finding a concentration-effect relationship may be possible and if so the chiral method could support clinicians in optimization of the effect. As racemic citalopram is still on the market, the chiral method can support the switch from racemic to pure enantiomer or vice versa. Patients may by mistake take the racemic form instead of the pure enantiomer or vice versa, where the chiral method may unveil the presence or not of R-Cit in the blood sample. Forensic chemistry: Achiral or chiral analysis of citalopram

The use of enantioselective analysis within forensic chemistry can serve as a complement to the achiral routine. In the case of Cit, the introduction of escitalopram and the concomitant prescribing of racemic generic products of Cit, a statement from the forensic laboratory including enantioselective analysis may give certain additional information about the cause of death. Furthermore citalopram is not, or will not be, the only drug that will go from racemic to single enantiomer (chiral switches) (Agranat et al., 2002; Baumann and Eap, 2001).

76 Future studies LCMS

It is possible to further improve the chiral method for Cit analysis by changing from fluorescence detection to mass spectrometric detection. The mobile phase used however has to be replaced because triethyl amine and to some extent citric acid is not suitable. Maybe another type of column has to be used instead of acetylated E- cyclodextrin with a mobile phase that is more compatible with a mass spectrometric detector. There is also the possibility of using the mass spectrometer, as it examines different masses, in order to measure different enantiomers in overlapping peaks if this is the only way to obtain baseline separation between the enantiomers. On-line extraction and chiral analysis

A more advanced method construction is that of automatizing the solid phase extraction followed by direct injection and separation on a chiral column. Another combination is on-line extraction with alkyl-diol silica (Öhman et al., 2001) followed by mass spectrometric detection.

Free fraction

A question to be solved is if the total concentration, the sum of free fraction and drug bound to plasma proteins, or free fraction only best reflect the effect/concentration in the brain. Chiral switches

Today most of the new drugs reaching the market are single enantiomers, rather than the racemic mixtures that dominated up to ten years ago. Many of the new single- enantiomer drugs were developed as such, but there are also important examples of new single-enantiomer drugs derived from “chiral switches” of established racemates with examples such as, omeprazole to esomeprazole or bupivacaine to ropivacaine the first local anaesthetic marketed as pure S-(-) enantiomer (Agranat et al., 2002; Agranat and Caner, 1999; Baker and Prior, 2002; Caldwell, 2001; Ekatodramis and Borgeat, 2001; Tucker, 2000; Williams and Wainer, 2002). Examples of “chiral switches” within the field of psychopharmacology are the switch from citalopram to escitalopram (Baumann et al., 2002; Montgomery et al., 2001; von Moltke et al., 2001) and that the enantiomer S-fluoxetine is in the clinical trial phase for migraine headaches, as well as R-fluoxetine as an antidepressant (Baumann and Eap, 2001). As patents for the racemic drugs are expiring and generic products will be on the market at the same time as the “chiral switches” there will be a need for

77 enantioselective analysis both within TDM and forensic medicine. It should not be forgotten that metabolic processes can give unwanted effects, e.g. chiral inversion as exemplified by thalidomide, which was withdrawn due to teratogenicity and neuropathy (Reist et al., 1998), where the non teratogenic form is converted to the teratogenic form in the body.

78 Acknowledgements

To my supervisor, Professor, Johan Ahlner. After many persuasive attempts from you and a trip to Barcelona to listen to the controversial speech by E.J. Ariens about racemic drugs, I finally gave up and began a somewhat slow journey with citalopram towards this thesis.

To Björn Norlander, my co-supervisor, co-author and roommate at work. For making sense of my fragmental or overheadish lines and for twenty years of fruitful scientific discussions, friendship and how to win the pools.

To Professor Curt Peterson. Head of the Division of Clinical Pharmacology. For giving me the time to complete my work

To my co-authors, specially Gunilla Olsson, Margareta Reis and Per Holmgren

To Eva Ekefeldt-Hultin and Karin Walldén. For not complaining about my absence during the last six months and for their help with analysis of citalopram.

To the Citalopram mafia Fredrik Kugelberg, Daniel Öhman and Finn Bengtsson.

To the rest of the staff and PhD students at the Division of Clinical Pharmacology

To the pensioners/veterans at the Division of Clinical Pharmacology, with special thanks to Åke Bertler for giving me the opportunity to begin my thesis and studies in the Spanish languish.

To my children Mario and Paloma. Although the have complained about the mess in the study room (Paloma) and about me not having time for soccer or ice hockey (Mario).

To my mother Stina and in remembrance of my father Bertil.

To my sister Eva, and my brothers Mats and Jan for support and always offering a helping hand when needed.

A Pepe Chermá y Dolores Yeste porque me dejaron traerme a su Fallera a Suecia. I por las peladillas, jamónes, chorizos y vinos.

To Lions, the Faculty of Health Sciences, Linköping University, the County Council of Östergötland, Anna-Lisa and Anders Olofssons’ fund for the investigation of mental health, H. Lundbeck (Copenhagen, Denmark), the National Board of Forensic Medicine and Laboratory Medicine Östergötland for financial support.

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Appendix

tR2 depth

tR1 height peak 1 height peak 2

t0

w w Inject 1 2 t0 Designates retention time of an unretained analyte that has travelled through the column at the speed of the mobile phase tR1 and tR2 Designate retention time of peaks 1 and 2 w1 and w2 Designate peak width of peaks 1 and 2 k´=( tR- t0)/ t0 The capacity factor, describes the retention of a compound in a chromatogram

Į= k´2/ k´1=(( tR2- t0)/t0)/(tR1- t0)/ t0)) The separation factor or selectivity, describes the separation of compounds relative to each other (Į•1)

t t w w Rs=( R2- R1)/(0.5( 1+ 2) The resolution, describes the separation of one peak from another (base line separation obtained if Rs •1.5)

Kaiser peak separation index=depth/((height peak 1+height peak 2)/2) Defined as the average valley depth expressed as a ratio to the average peak height of the two adjacent peaks

2 N=16(tR-w) The plate number, used to determine column efficiency

H=L/N The plate height, measures efficiency for unit column length.

91