Download Therapeutic Drug Monitoring in Psychiatry

 Linköping University Medical Dissertations No. 1152 Therapeutic Drug Monitoring in Psychiatry Some aspects of utility in clinical practice and research Maria Dolores Chermá Yeste Division of Drug Research ‐ Clinical Pharmacology Department of Medical and Health Sciences Faculty of Health Sciences Linköping University, SE‐581 85 Linköping, Sweden 2009 Supervisors Professor Finn Bengtsson Clinical Pharmacology, Linköping University Professor Johan Ahlner Forensic Genetics and Forensic Toxicology, Linköping University Faculty Opponent Associate professor Ylva Böttiger Clinical Pharmacology, Karolinska Institutet, Stockholm Examination board Professor Örjan Smedby Center for Medical Image Science and Visualization (CMIV), Linköping University Professor Sigvard Mölstad Primary Care Jönköping, Linköping University Professor Rune Dahlqvist Clinical Pharmacology, Umeå University
Associate professor Anne‐Marie Landtblom (substitute) Neurology, Linköping University Cover picture: The Mediterranean Sea in Cabanes, by Mario Carlsson Chermá, Aug‐09. Illustration page 4: Two of a loft of pictures drawn by Paloma Carlsson Chermá, at the age 6 and 8 years respectively. ©Maria Dolores Chermá Yeste, 2009 The published articles have been reprinted with permission of the publisher, Wolters Kluwer Health/Lippincott, Williams & Wilkins. Printed in Sweden by LTAB, Linköpings Tryckeri AB, Linköping, 2009. ISBN 978‐91‐7393‐537‐1 ISSN 0345‐0082 To José Daniel Chermá Mayor and Dolores Yeste Lara, my parents and Todo pasa y todo queda; pero lo nuestro es pasar, pasar haciendo caminos, caminos sobre la mar. …. Caminante, son tus huellas el camino, y nada más; caminante, no hay camino, se hace camino al andar. Al andar se hace camino, y al volver la vista atrás se ve la senda que nunca se ha de volver a pisar. Caminante, no hay camino, sino estelas en la mar. Everything passes and everything remains;
but it is our lot to pass,
to pass creating roads,
roads over the sea.
… Wayfarer, your footprints
are the road, nothing else;
wayfarer, there is no road,
the road is created as one walks.
As you walk, the road is created,
and when you look back
you see the path that you will never
tread againg.
Wayfarer, there is no road,
only ships’ wakes on the sea.
Antonio Machado (1875‐1939), “Proverbios y Cantares” An expert is a man who has made all the mistakes which can be made, in a narrow field. Niels Bohr (1885‐1962) Danish physicist, Nobel Prize in Physics 1922 The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them. Sir William Bragg (1862‐1942) British physicist and chemist, Nobel Prize in Physics 1915 Laughter is natureʹs best medicine... Anonymous CONTENTS
CONTENTS ................................................................................................................. 5 ABSTRACT .................................................................................................................. 7 POPULÄRVETENSKAPLIG SAMMANFATTNING (SWEDISH).................. 9 LIST OF PAPERS ...................................................................................................... 11 ABBREVIATIONS.................................................................................................... 12 INTRODUCTION..................................................................................................... 13 Psychiatric disorders ......................................................................................... 14 History of psychiatry ....................................................................................... 14 Classification of psychiatric disorders ............................................................. 15 Psychiatric epidemiology ................................................................................. 15 Psychoactive drugs............................................................................................. 16 History of psychoactive drugs ......................................................................... 16 Classification of psychoactive drugs................................................................ 17 Neurotransmitters .............................................................................................. 18 Pharmacokinetics ............................................................................................... 19 General............................................................................................................. 19 Chirality........................................................................................................... 22 Sources of pharmacokinetic variability......................................................... 23 Drug metabolism –CYP enzyme system and genetic variation...................... 24 Age ................................................................................................................... 25 Gender.............................................................................................................. 27 Smoking and diet ............................................................................................. 27 Nutritional status ............................................................................................ 28 Concomitant medication.................................................................................. 29 Compliance ...................................................................................................... 31 Individualised drug dosage ............................................................................. 32 Therapeutic drug monitoring (TDM) ............................................................ 33 Genotyping.......................................................................................................... 35 Escitalopram........................................................................................................ 37 Ziprasidone ......................................................................................................... 38 5 AIMS ........................................................................................................................... 40 MATERIAL AND METHODS ............................................................................... 41 Subjects and study design................................................................................ 41 Blood samples..................................................................................................... 42 Determination of drug concentrations .......................................................... 43 Genotyping.......................................................................................................... 43 Request form....................................................................................................... 44 Stratification procedures for data collected.................................................. 44 Internal validation ........................................................................................... 45 External validation .......................................................................................... 46 Screening for drug‐drug interactions............................................................. 47 Statistical analysis.............................................................................................. 48 Ethical considerations ....................................................................................... 49 RESULTS .................................................................................................................... 50 Paper I................................................................................................................... 50 Paper II ................................................................................................................. 54 Paper III ............................................................................................................... 57 Paper IV ............................................................................................................... 60 DISCUSSION ............................................................................................................ 63 Papers I and II..................................................................................................... 63 Papers III and IV ................................................................................................ 64 CONCLUSIONS ....................................................................................................... 68 REFLECTIONS AND FUTURE PROSPECTS..................................................... 69 ACKNOWLEDGEMENTS ...................................................................................... 72 REFERENCES ............................................................................................................ 75 APPENDIX (PAPERS I – IV)................................................................................... 85 6 ABSTRACT
Background and objectives: Several new psychoactive drugs for the treatment of psychiatric disorders have been introduced onto the market since the late 1980s. Basic aspects of pharmacodynamics and pharmacokinetics (PK) are investigated before approval for general prescription. Thus, a limited number of subjects are exposed to the drug before it is marketed and only sparse measurements of drug concentration are performed during phases II and III of drug development. The objective of this thesis was to provide further descriptive PK and linked patients data in naturalistic clinical settings. The PK of psychoactive drugs was also studied in the elderly and the young, major risk groups that are exposed in normal everyday clinical practice but that are underrepresented in the phases of drug development. The PK‐data were to be assessed by samples sent to the Therapeutic Drug Monitoring (TDM) laboratory service. In a subset of individuals, the genotypes of the cytochrome P450 (CYP) enzymes were described. Results: Serum concentration of the parent compound and its metabolites was provided from TDM‐data on antidepressant escitalopram (Paper I) and antipsychotic ziprasidone (Paper II). A large interindividual PK variability was found. The daily dose of the drug was higher than the defined daily dose (DDD) for both escitalopram and ziprasidone (median dose 20 mg and 120 mg, respectively). The median number of drugs per patient, apart from the studied drug, was 4 and 3, respectively (range 1‐18). If repeated eligible TDM‐
data were available, change in treatment strategies could be seen between the first and second sample for the patient, and the metabolite/parent compound (M/P) ratio had lower intraindividual than interindividual variation in the escitalopram study but opposite results were found in the ziprasidone study. The prescription of antidepressant drugs (ADs) in the nursing homes studied was 38 % (Paper III). The concentration of the ADs was higher, or much higher, than could be expected from the dose administered in 73 %. The majority of the elderly people were treated with citalopram. No clear time schedule for how long the drug treatment should continue was found in the patients’ current medical record. The median number of drugs per patient apart from the AD was 11 (range 4‐19), no monotherapy was found in these patients. The genetically impaired metabolic activity of CYP enzymes correlated to higher drug concentration as expected, in patients medicated with an AD that is substrate for the CYP enzyme genotype. 7 The concentrations of ADs were as expected from the dose administered in 63 % of the children/adolescents evaluated (Paper IV). The majority of TDM samples requested sertraline. PK outcome of sertraline was similar to the results in adult populations. Monotherapy was documented in 49 % (median number of drugs apart from AD was 1 per patient, range 1‐7). Changes in treatment strategies were also shown, if repeated TDM‐samples were available. The median variation of the M/P ratio for sertraline between the first and the last samples within the same patient was 20 % (the interindividual variation was 37 %). The poor metabolizers (PM) for CYP2D6 medicated with a CYP2D6 substrate had a lower dose than did non‐PM for the same drug. Conclusion: These studies provide reference data for the evaluation of the therapeutic response, i.e. a reference range of what is to be expected in a normal clinical setting, as well as the toxicological information concerning the psychoactive drugs studied. When available, the M/P ratio between two patients’ samples may assess patient compliance, as well as drug‐drug interactions. Thus, the use of TDM can be beneficial for individual dose optimisation and drug safety, above all in the studied populations, elderly people and children/adolescents, when the selection of doses requires a consideration of PK parameters. TDM may be a tool for research, increasing knowledge of the psychoactive drug in TDM service, as well as toxicology. A more frequent clinical use of TDM and pharmacogenetic testing in clinical practice would contribute to a better quality when treating with psychoactive drugs. 8 POPULÄRVETENSKAPLIG
SAMMANFATTNING (SWEDISH)
En rad nya psykofarmaka, dvs. läkemedel som används vid behandling av psykiatriska sjukdomar, har introducerats kontinuerligt under 1980‐talet. Att ett nytt farmaka godkänts för allmän förskrivning är inte det samma som att farmakokinetiken i människa (vad kroppen gör med läkemedlet) är fullständigt känd. Sparsam mätning av psykofarmakas koncentration i blodet görs under läkemedelsutvecklingen. Särskilda grupper av patienter som t.ex. äldre, barn och ungdomar, är sällan med i studier av nya psykofarmaka trots att de exponeras i klinisk praxis. Målen för den här avhandlingen var att beskriva koncentrationen i serum av nya introducerade psykofarmaka och dess metaboliter dvs. moder‐ och dottersubstans, i en grupp av patienter i den kliniska miljön (naturalistisk) för att undersöka mellan‐individ variabilitet, men också att med upprepade prover från samma individ skapa en inom‐individ uppföljning. Läkemedelsutfallet kontrasteras mot den relevanta kliniska informationen i remissen, för att försöka hitta förklaringen till koncentrationsutfall och variabilitet i den kliniska miljön dvs. med polyfarmaci (mer än två läkemedel samtidigt) och olika typer av ”samsjuklighet” (både psykiatriskt och somatiskt). Blodprover som har sänts till laboratoriet för analys av läkemedel för terapikontroll dvs. ”Therapeutic Drug Monitoring” (TDM), har vi använt i våra studier för att utvärdera farmakokinetiken av antidepressiva medlet escitalopam och antipsykotiska medlet ziprasidon. Blodprovet bör tas som dalvärde (dvs. strax innan nästa dosintag, allra helst innan morgondosen) och vid steady‐state (dvs. vid jämviktskoncentration). Hos äldre och ungdomar, har farmakokinetiken och användningen av antidepressiva (AD) medel också studerats via TDM‐prover. Farmakogenetiska metoder (genotypning) för att bestämma genetiska variationer av de enzymer som bryter ner läkemedlen i kroppen, cytokrom P450 (CYP) enzymer, har applicerats. Vi fann att de studerade nya psykofarmaka uppvisar stora skillnader i plasmakoncentration vid samma dos mellan patienter. Den vanligaste förskrivna dosen, både för escitalopram och ziprasidon, var den högst rekommenderade dosen i FASS och polyfarmaci var vanligt. Vid uppföljning av första provet med ytterligare ett prov har ändring av dosering visats som 9 en följd av det första utfallet. Variabilitet mellan två provtillfällen från samma patient (inom‐individ), mätt som kvoten mellan koncentration av dottersubstans och modersubstans (M/P), var lägre än mellan‐individ M/P i escitalopram studien dock var det motsatt i ziprasidon studien. I studien av äldreboende förskrevs AD medel till 38 % (ålder 71‐100, 78 % kvinnor). Koncentration av AD medel var högre eller mycket högre än förväntat i förhållande till intagen dos i 73 % av TDM‐proverna. Citalopram var den mest använd AD medlet. Information om när behandling med AD medel skulle sättas ut saknas i huvuddelen av patientjournalerna. Doser av AD medel som används hos äldre var i samma storlek som i FASS rekommenderade doser för vuxna yngre än 65 år. Läkemedelsanvändningen var hög, i genomsnitt 11 läkemedel per patient (4‐19) vid sidan av AD läkemedlet, och inga av patienterna behandlades enbart med AD läkemedlet i fråga. Hos barn och ungdomar (ålder 8‐20, 64 % flickor), var koncentration av AD medel som förväntat i förhållande till intagen dos i 63 % av fallen. Sertralin var det mest använda AD medlet och koncentrationen av läkemedlet i blodet stämde överens med publicerade data hos vuxna. Monoterapi dvs. endast behandling med ett AD läkemedel, var vanligt (49 %). Även här fann vi ändring av dosering vid uppföljning av första provet med ytterligare ett prov. Kvoten M/P för sertralin mellan två prov från samma patient varierade i genomsnitt 20 % (inom‐individ) dock var mellan‐individ variation 37 %. Patienter med genetisk variation i CYP genen som leder till en minskad metaboliseringsförmåga (långsam metaboliserare) hade högre läkemedelskoncentration i blodet än patienter som inte var långsamma metaboliserare för läkemedlen som bryts ned med dessa enzym. Slutsats: TDM inom psykofarmakologin har i allmänhet minskat efter att nyare ”mindre toxiska” psykofarmaka har introducerats och att koncentrations‐effektsamband inte har påvisats. Vi har visat att koncentrationsutfallet från TDM studierna kan användas som referensdata i klinisk praxis. Variation i M/P kvoten mellan två prov tillfällen, skulle kunna användas som ett mått på följsamhet (compliance) eller interaktion med andra läkemedel. Användning av TDM kan öka kunskapen om psykofarmaka, som stöd vid individual dosering och till hjälp vid oönskade/toxikologiska effekter. Kvalitén vid behandling med psykofarmaka i klinisk praxis skulle kunna öka med en mer frekvent användning av TDM tillsammans med genotypning. 10 LIST OF PAPERS
The present thesis is based on the following papers, which will be referred to in the text by their designated Roman numerals: I Therapeutic Drug Monitoring of Escitalopram in an Outpatient Setting. Reis M, Chermá MD, Carlsson B, Bengtsson F. Therapeutic Drug Monitoring 29(6): 758‐766, 2007. II Therapeutic Drug Monitoring of Ziprasidone in a Clinical Treatment Setting. Chermá MD, Reis M, Hägg S, Ahlner J, Bengtsson F. Therapeutic Drug Monitoring 30(6): 682‐688, 2008. III Assessment of the Prescription of Antidepressant Drugs in Elderly Nursing Home Patients. A Clinical and Laboratory Follow‐Up Investigation. Chermá MD, Löfgren U‐B, Almkvist G, Hallert C, Bengtsson F. Journal of Clinical Psychopharmacology 28(4): 424‐431, 2008. IV Concentration of Antidepressant Drugs in Children and Adolescents: a naturalistic clinical study. Chermá MD, Ahlner J, Bengtsson F, Gustafsson PA. Submitted. 11 ABBREVIATIONS
A.D. Anno Domini ADR Adverse drug reactions ATC Anatomical Therapeutic Chemical B.C. Before Christus BBB Blood‐Brain Barrier BMI Body Mass Index C/D Concentration‐over‐dose CIT Citalopram CL Clearance Cmax Maximum blood concentration CNS Central nervous system CYP Cytochrome P‐450 DCIT Desmethylcitalopram DDCIT Didesmethylcitalopram DDD Defined daily dose DSERT Desmethylsertraline DSM Diagnostic and Statistical Manual of Mental Disorders
EM Extensive metabolizer HPLC High‐performance liquid chromatography ICD International Classification of Diseases ITE Intention to treat M/P Metabolite/parent compound ratio, metabolic ratio OC Oral contraceptives p Probability PD Pharmacodynamic PE Patients evaluated PEM Protein‐energy malnutrition P‐gp P‐glycoprotein PK Pharmacokinetic PM Poor metabolizer S‐CIT Escitalopram S‐DCIT S‐desmethylescitalopram S‐DDCITS‐didesmethylescitalopram SERT Sertraline SMDZ S‐methyldihydroziprasidone SSRI Selective serotonin reuptake inhibitor t1/2 Half‐life TCA Tricyclic antidepressant TDM Therapeutic drug monitoring tmax Time to the maximum blood concentration UM Ultrarapid metabolizer Vd Volume of distribution WHO World Health Organization ZIP Ziprasidone 12 INTRODUCTION
Basic aspects of pharmacodynamics (PDs), pharmacokinetics (PKs), as well as clinical outcome of a drug, are investigated during the phases of drug development before approval for general prescription by drug regulatory agencies, (Figure 1). Only sparse measurements of concentration are performed during phases II and III of drug development {Balant et al., 1993a; Balant et al., 1993b; Peck et al., 1994}. However, a limited number of subjects are exposed to the drug before it is marketed. Specific groups, such as elderly people, children and adolescents are underrepresented in such studies but in normal everyday clinical practice these groups are also exposed. For drugs acting on the central nervous system (CNS), used in psychiatric care, this has been a particularly prominent feature in recent years. Underlying this fact has Clinical trials
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Figure 1. Stage of the drug development process. IND=investigational new drug application filed with the drug regulatory agencies (DRA), NDA=new drug application or biologics license application filed with DRA, Registration=DRA approval for one new drug 13 been the increased awareness for the very variable individual response to different psychoactive drugs, such as antidepressants and antipsychotics. Implementation of PK knowledge concerning a drug after post‐marketing surveillance, under clinical conditions, is needed: hence the importance of phase IV studies. Improving clinical drug utility for the health care system and for the patient’s safety makes essential a research strategy leading to further awareness for an already marketed psychoactive drug. Psychiatric disorders
History of psychiatry
Humoral concepts of illness developed in the 4th century B.C. by Hippocrates and expanded by Galen (130‐200 A.D.), persisted until the 17th and 18th centuries. The humoral concept of illness was the result of bodily humors (black bile, yellow bile, phlegm, and blood) imbalance and treatments were oriented to restore this balance. Depression resulted from an excess of black bile (melaina chole) and could be cured by treatments such as special diets, purgatives and bloodletting. The growing knowledge resulting from anatomical studies of the human body, within a mechanical worldview, resulted in multiple etiologies of mental illness, including spiritual, external th th environmental and internal, during the 17 and 18 centuries. Clear national th th trends can be identified during the 19 and 20 centuries. Germany developing the idea of Vitalism in which there is a transformation of the concepts of soul/spirit into a vital force in all organisms. In England, Battie (1704–1776) hypothesized that madness was the result of either the over excitement of the sensibilities or insensibility. In France, among others, Rousseau (1712‐1778) saw the psyche as arising from sensations to produce reason and emotions. American Benjamin Rush (1745‐1813), influenced by Pinel (1745‐1826) and others, advocated that mental illness was a disease of the brain {Merkel, 2003}. 14 Classification of psychiatric disorders
Psychiatric disorders are mental health disorders. Mental and behavioural disorders are understood as clinically significant conditions characterized by alterations in thinking, mood (emotions) or behaviour associated with personal distress and/or impaired functioning. Psychiatric disorders, such as schizophrenia and affective disorders, are concepts that tend to be defined by their symptoms. Compared to concepts that are defined by their etiology, such as Addisson’s disease, they are more difficult to describe and diagnose, and more open to misunderstanding and misuse {Burton, 2006}. Depression is characterized by sadness, loss of interest in activities, and decreased energy. Thoughts of death and suicide, disturbance of sleep and a variety of somatic symptoms may also be present. Schizophrenia is a severe disorder that typically begins in late adolescence or early adulthood. It is characterized by fundamental distortions in thinking and perception, and by inappropriate emotions. Behaviour may be seriously disturbed leading to adverse social consequences. In order to standardize the description and interpretation of psychiatric disorders, diagnosis and classification systems have been established. The most prominent diagnostic classification systems are: the International Classification of Diseases (ICD‐10) published by the World Health Organization (WHO) and the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV) by the American Psychiatric Association. These systems categorize mental disorders according to their predominant symptom. The ICD‐10 is the most frequently used system worldwide for all general epidemiological purposes and for clinical diagnosis, whereas DSM‐IV is the most frequently used system for research {APA, 2000; WHO, 1993}. The classifications should be seen as complementary rather than competitive. Both classification systems converged strongly in their last revisions, but significant differences still remain {Bertelsen, 2002; Paykel, 2002}. Psychiatric epidemiology
WHO Global Burden of Disease Study estimates that mental and addictive disorders are among the most burdensome in the world and their burden will increase over next decades {WHO, 2001b}. The point prevalence of unipolar depressive episodes estimates to be 1.9 % for men and 3.2 % for women, and that 5.8 % of men and 9.5 % of women will experience a depressive episode in a 12‐month period {WHO, 2000}. Schizophrenia is found with approximately 15 the same frequency in men and women. The point prevalence of schizophrenia is estimated to be 0.4 % {WHO, 2000}. It has been estimated that up to 10 % of children have a psychiatric disorder that could be responsive to pharmacotherapy {Riddle, 1998}. The most common of these conditions are attention‐deficit hyperactivity disorder, major depressive disorder and anxiety. Psychoactive drugs
History of psychoactive drugs
Since ancient times, people have used chemical agents that altered their behaviour. The earliest psychoactive drugs were derived from plants. Tea and opium were available in the Orient. Tobacco and coffee in the Americas and alcohol throughout the world. Despite the use of psychoactive drugs having been dated to approximately 10,000 B.C in early post‐glacial fishing sites on the south China coast {Merlin, 2003}, there were few effective and specific treatments for any psychiatric disorder before the 1950s {Cameron, 1999}. The first successful drugs in every major class were discovered between the 1950 and the 1970s. Adequate animal models for human psychiatric disorders do not exist as human psychopathology is nonreproducible in animals. The discovery “by chance” of therapeutic effects of drugs developed for other indications marked the early years of modern psychopharmacology. The first modern report on the treatment of psychotic excitement or mania with lithium salts was that of Cade (1949). In the early 1950s, some antipsychotic effects were obtained with extracts of the Rauwolfina serpentina plant (active alkaloid reserpine). Another important discovery of the early 1950s was the antipsychotic effect of chlorpromazine (Laborit, Delay and Deniker). In 1954, Berger introduced meprobamate that marked the beginning of modern sedatives with useful antianxiety properties. By the early 1960s, chlordiazepoxide and diazepam had been discovered. In the mid‐1950s, there were two important accidental findings, both of which proved to be related to neurotransmission by monoamines, and both of which revolutionezed the treatment of depression. A compound originally developed as an anti‐ tuberculosis drug, iproniazid, was observed to improve the mood of patients treated with it for their tuberculosis. This finding and other hints led Kline, 16 who had already worked with reserpine, to establish that this drug was effective against depression. It was later learned that iproniazid inhibits the activity of monoamine oxidase (MAOI). In 1958, Kuhn found that imipramine did not have a favorable effect on schizophrenic symptoms, instead, it was found to have significant antidepressant properties. It became the first in a series of variants known as tricyclic antidepressants (TCAs) that block neuronal uptake of both serotonin and norepinephrine. Following this lead, even more selective serotonin reuptake inhibitors (SSRIs) were developred in the early 1970s, arising from observations by Carlsson {Carlsson et al., 1997a; Carlsson et al., 1997b} that antihistamines inhibited the transport of serotonin or norepinephrine. The first marketed SSRI was zimelidine but it was with the introduction onto the market of fluoxetine in the late 1980s that the SSRIs became widely used. The wide use of fluoxetine was not because it was more effective in the treatment of depression and anxiety than the TCAs but rather because it was safer (more benign side effect profile) and easier to use. The most significant advances after the 1950s have been in reducing the unwanted side effects of these therapeutic agents {Hardman et al., 1996; Lingjaerde, 2001}. Classification of psychoactive drugs
Psychoactive substances are substances that affect mental processes, e.g. cognition or affect. Traditionaly, psychoactive drugs can be placed into four major categories: antipsychotics or neuroleptics (used to treat psychoses and mania), antianxiety‐sedative agents (used to treat anxiety disorders), antidepressants (mood‐elevating agents) and mood‐stabilizing (antimanic agents) {Hardman et al., 1996}. Such categoric distinctions have become less valid because many drugs of one class are used to treat disorders previously assigned to another class and, in addition, several categories are used for treating symptoms associated with behavioural disturbances e.g. dementia, eating disorders, insomnia and impulse‐control disorders {Sadock et al., 2006}. In order to improve quality of drug use in drug utilization research, the WHO have introduced the use of the Anatomical Therapeutic Chemical (ATC) Classification System for the classification of drugs (http://www.whocc.no/atcddd/). The ATC codes divide medicinal products into different groups according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. The psychoactive drugs are classified under “N” i.e. Nervous system: N05 psycholeptics (N05A antipsychotics) and N06 psychoanaleptics (N06A antidepressants). 17 Neurotransmitters
The major categories of substances that act as neurotransmitters are amino acids (glutamate, gamma‐aminobutyric acid=GABA, aspartate, glycine), peptides (neurotensin, somatostatin, vasopressin, etc) monoamines (dopamine, norepinephrine, epinephrine, histamine, serotonin=5‐
hydroxytryptamine=5‐HT) and acetylcholine. The main excitatory neurotransmitter in the brain is glutamate, while the main inhibitory neurotransmitter is GABA. The peripheral nervous system neurotransmitters are acetylcholine and norepinephrine. Neurotransmitters play a major role in controlling state of mind, i.e. consciousness, emotions and behaviour {Healy et al., 1997}; see Figure 2. Monoamines have been implicated in the pathophysiology of a number of psychiatric disorders. The most extensively studied associations are dopamine with schizophrenia and norepinephrine, epinephrine and serotonin with depression and anxiety. There is some “weaker” evidence of the involvement of monoamines in mania, as well as of dopamine in affective disorder generally and in substanse abuse. Clinically useful psychoactive drugs acts by interacting with brain neurotransmitters and receptors {Cameron, 1999; Hardman et al., 1996; Stahl, 1998}. Figure 2. Different suggested functions of monoamines in the field of personality biology. 18 Pharmacokinetics
General
Pharmacokinetics describes the time‐course of the various events that a dose of drug, and its metabolites in the body, may undergo: absorption, distribution, metabolism and excretion (Figure 3). In most cases, the concentration of a drug in the general circulation will be related to the concentration of drug at its site(s) of action and the concentration in the target organ is related to the effect of the drug. The drug at the site of action may then elicit a number of pharmacologic effects, i.e. pharmacodynamics. These pharmacologic effects can include the desired clinical effect or side/toxic effects, and in some cases there may be effects unrelated to either the desired effect or toxicity of the drug. Site(s) of action
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The majority of drugs are administered orally and are absorbed in the small intestine. The absorption from the gastrointestinal tract is determined by the rate of the passive diffusion. This rate is determined, apart from the nature of the membrane, by the physiochemical properties (degree of ionisation) of 19 the drug and the concentration gradient across the membrane. Psychoactive drugs must pass from the gastrointestinal tract through the liver (“first‐
passage”) into the systemic circulation. The fraction of the ingested drug that reaches the central circulation unchanged is the bioavailability. The distribution of a drug in the body is primarily influenced by its chemical characteristics, as well as binding to plasma proteins (e.g. albumin). Only the free form of the drug can move between the membranes (distributed in compartments) and give pharmacological effect. When distribution is complete, their concentration in plasma water and extracellular fluid is approximately equal. Psychoactive drugs have their main site of action in the brain. One important barrier is the blood‐brain barrier (BBB). The BBB is formed by the brain capillary endothelial cells that have high‐resistant tight junctions. Besides low passive permeability, the brain is protected from potentially harmful endogenous and exogenous substances by efflux transporter proteins, located in the brain capillary wall {Pardridge, 1996; Xie, 2000}. P‐glycoprotein (P‐gp) is a major drug efflux transporter, involved in the efflux of a wide variety of lipophilic drugs and endogenous substances {Schinkel et al., 1994}. P‐gp is a member of the ABC‐transporters (ATP‐binding cassette) and was first discovered to be overexpressed in multidrug‐resistant cancer cells; hence the common referral of P‐gp as the multidrug resistant gene (mdr) {Allikmets et al., 1996; Juliano et al., 1976}. Drug transporters are expressed in the brain but also in the intestine, liver and kidney, thus playing a key role in absorption, distribution and excretion of drugs. The majority of psychoactive agents are lipophilic (lipid‐soluble), leading to the ability to penetrate the membrane, to be absorbed and to enter into the target organ. However, to be eliminated principally by the kidney from the body, they have to be converted to a hydrophilic (water‐soluble) substance. The liver is the principal organ of drug metabolism, but other tissues display a considerable metabolic activity, e.g. the gastrointestinal system (gut wall), the lungs, the skin and the kidney. There are mainly two types of metabolic reactions, phase I and phase II. Phase I reactions (oxidation, hydrolysis, reduction) may result in both inactive and active metabolites. The most important enzymatic systems are the cytochrome P‐450 (CYP) enzymes that are responsible for more than 80 % of phase I reactions {Evans et al., 1999; Scordo et al., 2002}. These enzymes prepare very lipophilic molecules for phase II reactions by creating a conjugation site, often a reactive group such as a hydroxyl group. In the phase II reactions, the conjugation with a glucuronyl‐, 20 sulphate‐ or acetylgroups forming a more polar and water soluble molecule that can be more easily excreted in the urine and/or bile {Hardman et al., 1996}. concentration
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08
08
Figure 4. Time versus concentration after daily doses of an oral drug. Cmax=maximal blood concentration, tmax=the time to reach Cmax, Cmin=lowest blood concentration, Css=blood concentration at trough values under steady state conditions. Further key concepts are clearance (CL), a measure of the body’s ability to eliminate drug, and volume of distribution (Vd), a measure of the apparent space in the body available to contain the drug. Other Important PK parameters are the maximum blood concentration (Cmax) and the corresponding time (tmax) after an oral dose, as well as the time period at which blood concentrations stay above a certain limit, e.g. the lowest concentration for a certain pharmacological response (Cmin); see Figure 4. Designing appropriate drug dosage regimens involves knowledge of the half‐life (t1/2), the time it takes for the blood concentration to be reduced by 50 %. Thus, the time course of a drug in the body will depend on both the clearance and the volume of distribution. Figure 5. The chemical structure of racemic citalopram. Original citalopram figure from B. Carlsson (2003). 21 Chirality
Stereochemistry refers to chemistry in a three‐dimensional extension in space and if the chemical compound cannot be superimposed on its mirror image, it is chiral (greek “cheir” meaning hand). Compounds having the same molecular formula but with the atoms in a different three‐dimensional arrangement are called stereoisomers. Enantiomers are stereoisomers that are not superposable with their mirror image (Figure 5). Enantiomers have identical physical properties, but rotate the plane of polarised light in opposite directions. An equimolar mixture of two enantiomers is a racemate {Caldwell et al., 2001; Wainer et al., 1995}. The absolute structural configuration of a stereoisomer is termed R (latin “rectus” meaning right) and S (latin “sinister” meaning left) based on the sequence of groups around its chiral center Chirality can be observed when a chiral molecule is subjected to a chiral influence. PK differences may exist among enantiomers. When enantiomers interact with an enzyme, or transporter system, a chiral discrimination (stereoselectivity) may be seen {Wainer, 2001}. Metabolic transformation is often enantioselective {Wainer, 2001}. However, chiral discrimination may be seen in the drug absorption (i.e. active transport {Wade et al., 1973; Yan, 2002}), distribution (i.e. protein binding, tissue affinity and receptor affinity {Lam, 1988; Williams et al., 1985}) and, less commonly, in excretion {Williams et al., 1985}. Enantiomer 1
Enantiomer 2
Desirable effect
No effect
Same effect as 1
Desirable additive effect
Antagonist to1
Figure 6. Some enantiomeric interaction possibilities. 22 Other not wanted effect
Several psychoactive drugs, as well as their metabolites, are stereoisomers/enantiomers. A few psychoactive drugs are available in a pure enantiomeric form, but numerous others are administered as racemates. In a racemate, the enantiomers may also have different PD properties {Caccia, 1998; Cushny, 1908; 1909; Cushny et al., 1905; Mehvar et al., 1997; Nation, 1994; Tucker, 2000} and five possible combinations have been proposes {Caldwell, 2001}: see Figure 6. Sources of pharmacokinetic variability
A broad variety of physiological, pathological, genetic and environmental factors might affect the PKs of a drug (Figure 7). Physiological
Genetic constitution
-age
-gender
-polymorphism
PHARMACOKINETICS
Drug in blood
...............................
Drug
dose
Drug at receptor site
Drug
response
PHARMACODINAMICS
Environmental
Pathological
-diet
-diseases
-concomitant medication
-smoking habits
-alcohol consumption
Figure 7. Factors affecting drug blood concentration and drug response. 23 Drug metabolism
–CYP enzyme system and genetic variation
The major CYP enzymes that contribute to the metabolism of drugs in man are CYP3A, CYP2D6, CYP2C9, CYP2C19 and CYP1A2 {Bertilsson, 1995; Daly et al., 1993; Ingelman‐Sundberg et al., 1999; Xie et al., 2001}. The metabolic capacity of the hepatic CYP enzyme system is the most important source of PK variability for psychoactive drugs. There are wide interindividual differences in the expression and activity of CYP enzymes caused principally by genetic polymorphisms and environmental factors such as concomitant drug therapy that may induce or inhibit the enzyme activity. The potential of a drug to inhibit the metabolism of other drugs almost always exits for drugs metabolized by the same pathway but can also be present for entirely separate pathways {Patat, 2000; Spina et al., 2003}. Genetic polymorphism has been defined as the existence in the population of at least two alleles, neither of which is present with a frequency lower than 1 % {Kalow, 1991; Meyer, 1991}. Now, however, variations with lower allele frequency are included in the Single Nucleotide Polymorphisms (SNP) database, dbSNP BUILD, on the web site: http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp. Polymorphic enzymes carry out approximately 40 % of human CYP‐dependent drug metabolism {Evans et al., 1999}. Genetic polymorphism occurred for CYP2D6, CYP2C9 and CYP2C19 {Dahl, 2002; Droste et al., ; Scordo et al., 2004}. The different alleles and their corresponding phenotype for CYP enzymes are listed in the Home Page of the Human Cytochrome P450 (CYP) Allele Nomenclature Committee, on the web site: http://www.imm.ki.se/CYPalleles. The population is divided, based on the polymorphisms of drug metabolism, into at least two phenotypes: poor metabolizers (PM), lacking enzyme activity, and extensive metabolizers (EM), among the majority of individuals, who have a normal metabolic activity. Subjects with extremely high enzyme activity are referred to ultrarapid metabolizers (UM) {Eichelbaum et al., 1996; Meyer, 1994}. The proportion of different metabolizers in a population varies with ethnicity (Figure 8). CYP2D6 is of particular importance in psychopharmacology as it is implicated in the metabolism of various antidepressants and antipsychotic drugs. CYP2D6 may be inhibited by therapeutic concentrations of various drugs {Bertilsson et al., 1997; Glue et al., 1996}. CYP3A4 and CYP1A2 activities are not polymorphically distributed. They have a unimodal distribution but show large interindividual variability (about 20‐fold to 40‐fold) and are 24 influenced by a number of constitutional and environmental factors. CYP3A4 is involved in the metabolism of a number of psychoactive drugs including benzodiazepines and antidepressants. Unlike CYP1A2, CYP3A and CYP2C, CYP2D6 activity is not inducible {Dahl, 2002; Ingelman‐Sundberg, 2001}. The major CYP enzyme systems and their substrates are listed on the website: http://medicine.iupui.edu/flockhart/
Figure 8. The frequency of poor and ultrarapid metabolizers (circle resp. trapeze).
Age
PK, as well as PD differences can exist between infant, adult, and geriatric populations. All PK parameters can be dependent on age (Table I). Pediatric patients differ from adults, anatomically and physiologically. Changes in a pediatric patient’s body proportions and composition and the relative size of the liver and kidneys can alter the PKs of a drug. A child undergoes rapid changes in growth (quantitative change in the size of the body or any of its parts), most rapidly during the first years of life, and development (maturation, qualitative change in skills or functions), continues until late childhood. These changes are accompanied by changes in body composition, such as water, fat and protein. At birth, there is a greater percentage of body water and less body fat. The percentage of body weight that is water varies between 70 % to 75 % at birth and approximately 60 % in 25 older children, adolescents and adults (less than 40 % in obese adults) {Brozek et al., 1953; Forbes, 1962; Keys et al., 1953}. Adolescence is associated with major changes in hormone secretion, growth and behaviour. Although the hormonal changes associated with puberty might be expected to produce alternations in drug disposition, there is little evidence that this constitutes a major problem {Uges et al., 1987}. PK differences, which may be clinically important, can be seen between the elderly (65‐79 years old) and the oldest olds (80‐92 years old) {Lundmark et al., 2000b}. Age does not alter drug absorption in a clinically significant way (passive diffusion is not changed). Among the factors that can influence PK changes in older people are decreased percentage of total body water (≈50 %), increased percentage of body fat, decreased liver mass and blood flow, decreased cardiac output, and reduced renal function {Poole Arcangelo et al., 2006; van Boxtel et al., 2001}. Table I. Age‐related pharmacokinetic differences in relation to adults
Premature Neonate Infant Child Adolescent
Old age
Absorption
Gastric acidity
Gastric emptying time
GI motility
Pancreatic enzyme activity
GI surface area
Distribution
Body composition:Water
Blood-brain barrier
Plasma proteins
Metabolism
Liver
Elimination
Renal blood flow
Glomerular filtration
Tubular function
↓
↓
↓
↓↓
↑
↓
↓
↓
↓
↑
↓
↔
↓
↓
↑
↔
↔
↔
↔
↑
↔
↔
↔
↔
↔
↓↔
↓↔
↓↔
?*
↔↓
↑
↓
↓↓
70-75%
↓
↓
>
↔
↔
~60%
↔
↔
~60%
↔
↔
~50%
↓ (leakage)
↔ (↓ albumin)
↓
↓
↓
↔↓
↔
↓ ↔ **
↓
↓
↓
↓
↓
↓
↓
↓
↓
↔
↔
↔
↔
↔
↔
↓
↓
↓
↓=decreased, ↑=increased, ↔=unchanged, ?=uncertainty, *some enzymes remain constant (amylase), other decrease (lipase, trypsin), **decrease in hepatic blood flow often associated with decreased first‐pass metabolism.; phase I metabolism affected and II generally preserved Age categories: preterm/premature (less than 36 week gestational age), neonate (less than 30 day of age), infant (1 month until 1 year of age), child (1 year until 12 year of age), adolescent (13 year until 18 year of age) and old age > 65 years of age 26 There is preliminary evidence that CYP3A4 activity is lowest in neonates and increases to maximal levels in adults {Ratanasavanh et al., 1991}, and that the activity of CYP3A4 (but not CYP2D6 or CYP2C19) appears to decrease between 20 and 80 years of age {May, 1994}. Gender
Differences in physical constitution (body water, muscle mass, organ blood flow and organ function) and physiology (menstruation, pregnancy and menopause) can result in differences in PKs (and PDs) between men and women {Beierle et al., 1999}. Total drug absorption does not appear to be significantly affected by gender, although absorption rates may be slightly slower in women. Differences in renal elimination are generally only of minor importance. Differences in oral bioavailability, however, seem to be more important and are usually caused by gender differences in the activity of major intestinal and hepatic metabolic enzymes. Controversial findings may be found. Studies into the effects of gender on enzyme activity in humans suggest that females have higher activity of CYP2C19 compared with males, while activity of CYP2D6 does not differ between the sexes {May, 1994}. CYP3A4 activity in females is greater in vitro compared with males, and is similar or greater in clinical studies {Horsmans et al., 1992; Hunt et al., 1992a; Hunt et al., 1992b; May, 1994; Watkins et al., 1989}. Nonetheless, the absence of a sex difference has been reported by some authors {Kashuba et al., 1998; Kharasch et al., 1997; Kirkwood et al., 1991; Sitar, 1989; Wilkinson, 1996}. Females have been reported to have decreased CYP1A2 activity compared with males {Relling et al., 1992}. Some authors have shown that clearance of P‐gp substrates appears to be similar in men and women {Schwartz, 2003}, others that P‐gp in men seems to have a higher activity relative to women {Meibohm et al., 2002}. Smoking and diet
Life style appears to have considerable influence on expression or activity of CYP enzymes. The majority of PK interactions with smoking are the result of induction of hepatic CYP, primarily CYP1A2. Smoking may increase CYP1A2 activity (and possibly CYP2E1 and glucuronide conjugation) {Kroon, 2007; Zevin et al., 1999}. Consumption of cruciferous vegetables (e.g. cabbage, Brussels sprouts) and food cooked over charcoal, and diets high in protein and 27 low in carbohydrates may also increase CYP1A2 activity. Conversely, grapefruit juice, psoralens (found in parsley, parsnip and celery) and diets low in protein and high in carbohydrates may reduce CYP1A2 {Bailey et al., 1994; Kappas et al., 1978; Kappas et al., 1976; Yang et al., 1992}. Grapefruit juice can also inhibit the activity of CYP3A4 {Bailey et al., 1994}. Activity of CYP2E1 may be induced by fasting {Hong et al., 1987} and chronic intake of alcohol {Perrot et al., 1989} and inhibited by substances found in cruciferous vegetables {Koop, 1992; Yang et al., 1992}. Caffeine from dietary sources (mainly coffee and tea) is the most frequently and widely consumed CNS stimulant in the world. CYP1A2 participates in the metabolism of caffeine, this means there is a potential for PK interactions due to inhibition of drugs that are metabolised by, or bind to, this enzyme {Carrillo et al., 2000}. Nutritional status
Information on the influence of nutritional status, e.g. protein‐energy malnutrition (PEM) and obesity, on drugs is limited. Many phase I trials specifically exclude participants who are 15 % below or above their ideal body weight {Stoltz et al., 2004}. A tool for indicating nutritional status in adults is the body mass index (BMI). It is defined as the weight in kilograms divided by the square of the height in metres (kg/m2). During childhood and adolescence, the ratio between weight and height varies with gender and age {WHO, 2007c}. Underweight
Malnutrition may be a chronic or acute problem, and primary or secondary to other processes {Torun et al., 1999}. Malnutrition can be associated with variable but potentially important effects on the bioavailability, binding, hepatic metabolism, and renal clearance of drugs. Changes in drug disposition may vary with the degree of PEM. In severe PEM, drug absorption may be reduced. The protein carriers may be limited, low albumin means higher free fraction of drug) Even slower oxidative metabolism is described. These may result in higher drug concentrations. In mild to moderate malnutrition, changes in metabolism may be minimal or of limited clinical significance. However, clinical data to support this conclusion are very limited {Compher, 2004}. An adult with a BMI of 18.5 or less is generally considered as being underweight {WHO, 2009}. In pediatrics, underweight is defined as more than 28 2 standard deviations under the cut‐off points of weight‐for‐age reference range {WHO, 2007a}. Obesity
Knowledge of the influence of obesity on a drug’s PKs is limited. The altered pathophysiology of the obese body is likely to affect drug distribution within the tissues and drug elimination, whereas absorption does not seem to be modified {Blouin et al., 1999; Cheymol, 2000}. Body composition is characterised by a higher percentage of fat and a lower percentage of lean tissue and water. Although the cardiac output and total blood volume are increased, the blood flow per gram of fat is less than in nonobese individuals. Albumin concentrations do not appear to be altered as a result of “moderate” obesity, whereas α1 acid glycoprotein levels may be higher but this is uncertain. Discrepancy exists across studies of obesity. Some data suggest that the activity of CYP2E1 may be increased, whereas CYP3A activity may be reduced or unchanged. CYP1A2 activity appears to be unchanged. Different data on renal function in obese individuals have been provided. Glomerular filtration, as measured by creatinine clearance can be increased in obese individuals, but have also been reported to be unchanged in some obese patients. Indirect evidence suggests that tubular secretion may also be increased. Lipophilic drugs do not necessarily have larger distribution volumes in obese individuals and some are not even be stored in adipose tissue. An adult with a BMI of 30 or more is generally considered obese {WHO, 2009}. In fact, persons with obesity can be considered quite a heterogeneous group. Subjects with a BMI of 30‐35 may be quite different from those with a BMI of 45 or greater. A given BMI cannot differentiate the degree of fatness between individuals. Definitions of obesity in children have been less well defined. Overweight is defined as a more than 1 standard deviations over the cut‐off points of weight‐for‐age reference range and obesity more than 2 standard deviations {WHO, 2007a}. Concomitant medication
Oral contraceptives
The PK and clinical significance of the major drug interactions seen with oral contraceptives (OC) are that drugs may impair the OC efficacy, leading to breakthrough bleeding and pregnancy, and situations where OC may interfere with the metabolism of other drugs. The molecular basis of these interactions 29 seems to be inhibition or induction of CYP3A and 2C families. CYP3A is one of the major forms involved in 2‐hydroxylation of ethinylestradiol. Since many drugs share these catabolic pathways, their PKs (as well as PDs) will be affected by OC: some drugs exhibit reduced clearance (i.e. alprazolam, diazepam) during OC intake, and others accelerate clearance (i.e. morphine) {Back et al., 1990; Teichmann, 1990}. Herbal medicines
The concomitant use of herbal medicines and pharmacotherapy is wide spread. In general, use of herbal remedies and supplements is constantly rising in the western population and this may be potentially dangerous due to adverse effects and drug‐herb interactions. Approximately 25 % of patients hospitalized in internal medicine wards has been shown to consume some kind of herbal or dietary supplement {Goldstein et al., 2007}. Many herbs and natural compounds isolated from herbs have been identified as substrates, inhibitors, and/or inducers of various CYP enzymes {Zhou et al., 2004a}. St. Johnʹs wort (Hypericum perforatum) is a potent inducer of CYP3A4 {Zhou et al., 2004b}. It also contains ingredients that inhibit CYP1A2, CYP2C9, CYP2C19, CYP2D6 as well as P‐gp {Gurley et al., 2008; Hellum et al., 2009; Wenk et al., 2004; Xu et al., 2008}. Many other common medicinal herbs also exhibit inducing or inhibiting effects on the CYP system {Delgoda et al., 2004; Mannel, 2004}. Polypharmacy
Polypharmacy, defined as more than 2 concurrent drugs can cause PK interactions and drug incompatibility {Bjerrum et al., 1998}. The interactions may be drug‐drug, drug‐disease, drug‐food, drug‐alcohol, drug‐herbal product, drug‐nutritional status as well as multiple PD interactions might occur. One drug can potentiate or diminishes the action of the other drug by affecting its absorption from the site of administration, its disposition within the body, or its metabolism or excretion. The outcome of PK interactions depends on both the concentration of the inhibitor and the activity of that enzyme in the specific patient. Polypharmacy is widespread in populations around the world, especially among the elderly {Bjerrum et al., 1998}. Age‐
related changes make the elderly, especially those with chronic conditions, susceptible to many drug adverse effects. Polypharmacy increases the risk of adverse drug reactions (ADR), interactions and incorrect drug use {LeSage, 1991}. 30 A new psychoactive drug is studied as a single drug versus either a placebo and/or a comparator agent. Experience with any new psychiatric medication in combination with another medication is limited to a few short‐
term drug‐drug interaction studies, usually conducted in healthy young volunteers before drug registration and marketing {Preskorn, 1995}. Despite this, the use of combinations of psychoactive drugs has been common practice in adults {Rittmannsberger et al., 1999}. The use of combination therapy has expanded even in youths {Safer et al., 2003}. Compliance
Poor compliance is an important cause of both therapeutic failure and drug toxicity. Adherence to medication plays a crucial role in the ultimate effectiveness of psychopharmacological interventions and in preventing relapse {Marcus et al., 1998}. Noncompliance is the most common cause of no response to medication {Kruse, 1992; Pullar et al., 1990}, and rival PKs as the biggest single source of variance in drug response {Harter et al., 1991}. Factors contributing to noncompliance may be: lack of knowledge, poor motivation, decreased understanding, forgetfulness, lack of support from family and friends, lack of daily reminder routine, financial factors, perception that side effects outweigh benefits and poor outpatient education programmes. It would be odd science to try to assess drug actions when drug administration is both variable and unknown, given the dose‐response relation {Urquhart, 1994}. Medication compliance is typically poor in adult psychiatric populations, and in young people receiving treatment for chronic diseases. Noncompliance is extensive within medication for schizophrenia and mood disorders. Patients receiving antipsychotic medication take an average of 58 % of the recommended amount of medications and patients receiving antidepressant medication take an average proportion of 65 %, compared to patients with physical disorders who take 76 % of the recommended amount {Cramer et al., 1998}. Noncompliance includes overuse, abuse, forgetting to take the medications and alteration of schedules and doses. 31 Individualised drug dosage
A complete patient evaluation and correct diagnosis remains important for ensuring proper treatment and selection of an appropriate psychoactive drug. There is a common practice of administering a standard dose to all patients but, in order to give the right dose to the right patient, the drug dosage should be determined individually (Figure 9). The individualization of dosages has the obvious objective of maximizing the balance between the efficacy and safety of drug therapy. Many factors can affect a patient’s response to a drug. Figure 9. Schematic interrelationship in the dose‐utility paradigm. Brain, body and mind relationships are extremely complex in dealing with mood disorders, as is true of most psychiatric disorders. In psychopharmacology, a number of bottlenecks have been identified. There can be discrepancies between treatment recommendations and everyday clinical practice, or what the efficacy of a drug will be if patients do no take their medication (compliance). Genetic differences in drug metabolism are an important cause of unusual drug effects or concentration levels. It must be 32 taken into consideration that a prescribed drug is not the same thing as one active substance. Drug activity is the pharmacological activity of the active substance and its potential bioactive metabolites. Further, for psychoactive drugs are important the ability of the drug to move across the BBB and to stay in the brain long enough to exert its desired action. Although the drug concentration is important for the clinical response, it is not the sole determinant (e.g. co‐morbid conditions, receptor sensitivity). Response in psychiatry is based on subjective assessment. Nevertheless, objective data, such as drug concentration, can assist in the clinical decision, in absence of relevant biological measures {Yesavage, 1986}. Therapeutic Drug Monitoring (TDM)
The blood is a unique body fluid in that it stays in intimate contact with all tissues. The drug concentration in the blood will depend on absorption, distribution and elimination of the drug, and will continuously mirror the fate of the drug in various tissues and organs. The basic assumptions underlying TDM are that drug metabolism, as well as other factors that affect the drug PKs, varies from one patient to another and that the blood level of a drug is more closely related to the drug’s therapeutic effect or toxicity than is the dosage. TDM comprises the assessment and communication of drug levels in blood as well as recommendations for dose adjustments {Glassman et al., 1985}. The foundation of modern TDM was established in the early 1970s, with monitoring of epileptic patients on phenytoin {Bowers, 1998}. The TDM is by tradition based on concentration intervals (therapeutic range or index) within which most subjects are expected to have their optimal response (high enough to give the desired effect but enough to avoid toxicity). Recommended dosing regimens are designed to generate blood concentrations within a therapeutic range. Therapeutic range is a statistical concept. A commonly used measure is the lethal dose of a drug for 50 % of the population (Ld50) divided by the minimum effective dose for 50 % of the population (ED50). Thus defined, provides a very crude measure of the safety of any drug as used in practice. Consequently, some patients will exhibit such a response at blood levels below the lower limit of the range, while others will require blood levels exceeding the upper limit for therapeutic benefit. Therapeutic ranges, however, are only intermediate endpoints that must be used in the context of additional criteria 33 to assess the clinical efficacy of any given drug therapy. The therapeutic goal must be individualised. The field of TDM in psychiatry began with the tricyclic antidepressants {Alexanderson et al., 1969} and is based on indications of the existence of blood concentration‐effect relationships for a drug and motivated by a therapeutic range (changes in systemic concentration can lead to significant change in PD response, i.e. subtherapeutic or toxic effects) {Friedman et al., 1986; Preskorn et al., 1993}. For some of the TCAs, therapeutic ranges have been established {Preskorn et al., 1988}. The lack of easily defined therapeutic range among the new psychoactive drugs has not been shown convincingly for TDM {Rasmussen et al., 2000}. But a number of specific situations have been defined in which determination of blood concentrations has been proven useful, such as control of compliance, drug interactions, and identification of genetic peculiarities of drug metabolism {Baumann et al., 2004a; Hiemke, 2008c; a; Hiemke et al., 2004; Jerling, 1995a}. A new way of looking at TDM when a psychoactive drug becomes available for prescription and no definitive concentration‐effect relationships have been demonstrated is considered {Bengtsson, 2004}; see Figure 10. The first outcome of a patient’s TDM should thereby be in “reference” to whether the patient has the “expected” amount of drug in relation to dose prescribed compared with interindividual PKs data as reference values. If a second TDM sample is drawn from the same patient after a period of treatment, this outcome is compared with the previous one. This strategy provides a PK instrument to answer some questions related to the dosage of the patient. Therefore, if the metabolite of the parent compound has also been determined, the compliance may be scrutinized with the TDM‐
procedure based on metabolite/parent compound (M/P) ratio stability within individuals over time {Reis et al., 2004}. Finally, the simple act of ordering a blood drug level does not guarantee that the information will be meaningful or useful. The interpretation of blood concentrations can be profoundly influenced by factors such as the timing of the sample, the patientʹs clinical state, the drugʹs pharmacokinetics and metabolism, as well as the tube type and analytic methodology used. The likelihood of obtaining clinically meaningful and useful results can be maximized when these factors are taken into account {Friedman et al., 1986}. TDM must include sampling organization, measurement of psychoactive drugs and interpretation of the blood concentration, for individual dose adjustment. A TDM service connotes an organized system of care to ensure that the serum drug concentration will have maximal positive impact on patient care {Schumacher et al., 1998}.
34 Figure 10. TDM, inter‐/intraindividual reference. The primary TDM sample outcome will permit a PK interindividual comparison with the ”expected” TDM reference values. A secondary TDM sample outcome provides the possibility of PK interindividual comparison as well as comparison with the previous outcome (intraindividual reference). Dose‐adjustment over time is possible: to achieve the same amount (in some cases higher) of active drug in the body as the previous sample with good response. Genotyping
Pharmacogenetics is the use of genomics to determine a subject’s drug response and depends on the availability and reliability of genetic testing as well as the ability of providers to interpret test results {Kalow, 1962}. The genotype is the genetic constitution of an individual, either overall or at one or more specific loci. Genotyping is the determination of specific genetic sequence variations, of functionally important polymorphism, in the gene encoding of a specific protein {Linder et al., 1997}. Genotype is not affected by other factors, such as concomitant therapy. However, one must always consider the possible existence of unknown sequence variants. 35 In short, subjects who carry 2 copies of a functional allele are genetically classified as EM, while those carrying two defective alleles, are PM {Kirchheiner et al., 2004}. Moreover, in the case of CYP2D6, subjects carrying more than two copies of a functional allele are classified as UM. Fairly recently, a novel allelic variant of CYP2C19 associated with UM status has been described and denoted CYP2C19*17 {Rudberg et al., 2008; Sim et al., 2006}. The allelic frequency of CYP2C19*17 was different between Swedish and Chinese subjects being 18 % and 4 % respectively {Sim et al., 2006}, but lower in the Japanese population, 1.3 % {Sugimoto et al., 2008}. Probability
(%)
Therapeutic range
Effect
100
Toxicity
50
Concentration
in the blood
UM
EM
IM
PM
Drug
PM
IM
EM
UM
Prodrug
Figure 11. Concentration of a drug or prodrug, after a standard dose metabolised by a polymorphic enzyme. Indications for genotyping may be: identify patients who are PM (a decreased metabolic capacity may lead to high blood levels and increased risk of toxicity or, if the main compound is a pro‐drug that needs to be activated, to therapeutic failure), differentiate between patients who are UM (may lead to low blood levels of the drug causing therapeutic failure) or have noncompliance, and differentiate between genetic or environmental factors that affect drug metabolism (phenotype=genotype?) {Dahl et al., 2000; Droste et al., 2005}; see Figure 11. Additionally, dose adjustments would compensate for genetically caused differences in blood concentrations {Kirchheiner et al., 2004}. 36 Escitalopram
Escitalopram (S‐CIT) is the active substance, responsible for the antidepressant effect of citalopram {Montgomery et al., 2001}, which is a racemic mixture of S‐
(+)‐ and R‐(‐)‐citalopram in a 1:1 ratio. Escitalopram, the S‐enantiomer of citalopram, is a highly selective serotonin reuptake inhibitor. It was about 150 times more potent than the R‐enantiomer when comparing the inhibition of serotonin (5‐HT) reuptake in an in vitro rat brain synaptosome system {Hyttel et al., 1992; Sanchez et al., 2003a; Sanchez et al., 2003b}. S‐CIT was launched in 2002 in Sweden for major depression, panic syndrome with or without agoraphobia. Later, it has been approved for social phobia, generalized anxiety disorder and obsessive‐compulsive disorder. A summary of the relevant PK data for S‐CIT is shown in Table II {Burke, 2002; FASS, 2009; McRae, 2002; Sogaard et al., 2005}. The single‐ and multiple‐dose PKs of once‐
daily oral escitalopram 10–30 mg in healthy adult volunteers are dose‐
proportional. Concurrent ingestion of food with a single dose of S‐CIT 20 mg had no major influence on the PKs of S‐CIT. The excretion of S‐CIT and metabolites is primarily renal. No PK studies of S‐CIT have demonstrated interconversion of the S‐ and R‐enantiomers of S‐CIT in plasma or urine.{Murdoch et al., 2005; Sogaard et al., 2005}. CYP2C19
CYP3A4
CYP2D6
S-citalopram
CYP2D6
S-desmethylcitalopram
S-didesmethylcitalopram
Figure 12. Major CYP enzymes involved in the metabolic pathway of escitalopram (S‐citalopram) and its metabolites. Escitalopram is extensively metabolized by CYP enzymes in in vitro analyses of human liver microsomes {von Moltke et al., 2001}. S‐CIT is demethylated by CYP3A4, CYP2C19 and CYP2D6 to desmethylescitalopram (S‐DCIT), whereas only CYP2D6 mediates further demethylation to didesmethylescitalopram (S‐DDCIT) {Sindrup et al., 1993}; see Figure 12. S‐CIT is more potent than S‐DCIT and S‐DDCIT in serotonin reuptake inhibition, 7 and 27 times respectively, indicating that the metabolites do not contribute 37 significantly to the antidepressant effects of S‐CIT {FDA:, ; Waugh et al., 2003}. It has been shown in vitro that S‐CIT is a weak or negligible inhibitor of human CYP1A2, CYP2C9, CYP2D6, CYP2E1 and CYP3A4 {Brosen et al., 2001; Olesen et al., 1999}. Ziprasidone
Ziprasidone (ZIP) is an antipsychotic agent with combined dopamine and serotonin receptor antagonist activity. ZIP has a high affinity for dopamine, serotonin, and alpha‐adrenergic receptors and a medium affinity for histaminic receptors. ZIP has an affinity for 5‐hydroxytryptamine (serotonin) 2A (5‐HT2A) receptors that is more than 10‐fold higher than its affinity for dopamine (D2) receptors. ZIP is somewhat unique among the ʺatypicalsʺ in that it also displays some inhibition of synaptic reuptake of serotonin and norepinephrine. ZIP was launched in 2000 in Sweden for the treatment of schizophrenia. Later, it has been approved for treatment of acute manic and mixed episodes of bipolar disorders. A summary of the relevant PK data for ZIP is shown in Table II {Ballas et al., 2004; FASS, 2009; Gunasekara et al., 2002; Wilner et al., 2000}. ZIP has a relatively short half‐life and should be given in a twice‐a‐day (b.i.d.) regimen. ZIP tends to show predictable linear PK (apparent dose‐proportionality between the 20 mg and 60 mg dose levels). ZIP is likely to prolong the corrected QT (QTc) interval to a greater extent than other second generation antipsychotic agents. The pharmacological profile of ZIP suggests a low potential for bodyweight gain. Absorption of ZIP is increased up to 2‐fold in the presence of food. ZIP is extensively metabolised in the liver, predominantly via reduction by aldehyde oxidase and, to a lesser extent, via CYP3A4 and CYP1A2. ZIP is not expected to interact with drugs metabolised by CYP enzymes, and little potential exists for drug interactions with other highly protein‐bound drugs according to in vitro studies. Because ZIP is highly metabolised (less than 5 % of the drug being excreted unchanged), renal impairment is unlikely to have a significant impact on its PKs {Green, 2001; Nemeroff et al., 2005; Weiden, 2001; Weiden et al., 2002}. 38 Table II. Pharmacokinetic data of escitalopram and ziprasidone
Escitalopram (S-CIT)
Racemic mixture
Ziprasidone (ZIP)
S-(+)-enantiomer of racemic
citalopram
Active metabolite(s)
Yes (no clinically relevant
antidepressant effect)
tmax
Parent drug
~4 hours
Main metabolite ~6 hours
Time to SS
1 week
No
t1/2
Parent drug ~32 hours
Main metabolite ~54 hours
Bioavailability
80 %
6.6 hours
?
60 % (↑ up to 2-fold)**
Protein binding
80 %
>99 %
Vd, parent drug
12-26 L/kg
1.1 L/kg
Linear kinetic
10 to 30 mg daily
80 to 160 mg daily**
Possible target doses
(5) 10-20 mg daily
(40) 80-160 mg daily
Impaired renal function
↑ t1/2 (dose adjustment)
No dose adjustment?
Pathologic hepatic
function
Age
↑ t1/2
dose adjustment
↑ concentration (30%), ↑ t1/2
(dose adjustment)
No dose adjustment
Gender
No dose adjustment
No dose adjustment
Metabolites:
S(+)-desmethylcitalopram
(35 % of S-CIT)
S(+)-didesmethylcitalopram
(3 % of S-CIT)
Metabolism
CYP2C19, CYP3A4, CYP2D6
Reference values:
10 mg ≈50 nmol/L (20-125) a
5-26 nmol/L c
S-methyldihydroziprasidone*
(~105 % of ZIP)
Ziprasidone sulphoxide*
BITP sulphoxide
BITP sulphone
aldehyde oxidase-medianted
reduction plus S-methylation,
CYP3A4, CYP1A2
40 mg ≈40±10 nmol/L b
121-290 nmol/L c
?*
6-8 hours
?
1-3 days
SS=steady‐state concentration, *QTc‐prolongation, **taken with food, tmax = time to maximum serum concentration, t1/2 =elimination half‐life, Vd =volume of distribution, BITP=benzisothiazole piperazine. Based in a FASS2009, b Wilner et al., 2000, c Baumann et al., 2004 39 AIMS
The general aim of this thesis was to provide further descriptive pharmacokinetics (PKs) of psychoactive drugs and linked clinical data of patients in naturalistic clinical settings in Sweden. The PK‐data were to be assessed by means of determination of serum trough level concentrations (Cmin) under steady‐state conditions (Css) of parent compounds and, to some extent, metabolite(s) from an established Therapeutic Drug Monitoring (TDM) laboratory service. In a subset of individuals, the CYP2D6, CYP2C19 and CYP2C9 genetic expressions were to be assessed from blood samples acquired in parallel to the TDM‐samples. Specific aims were: Papers I and II ‐To describe and evaluate the interindividual variation of serum concentrations of escitalopram (S‐CIT), ziprasidone (ZIP) and their respective metabolite(s) in terms of trough values under steady‐state conditions, in heterogeneous cohort of patients using oral escitalopram (Cipralex®) and ziprasidone (Zeldox®), respectively. ‐To describe and identify factors or subgroups with possibly deviating PK. ‐To study the intraindividual PK variation over time in patients repeatedly referred to the TDM service. Paper III ‐To analyse the serum concentration of different antidepressant drugs in a group of elderly patients in nursing homes in the Council of Östergötland. ‐To investigate the use of antidepressant drugs in this group of patients. ‐To describe the CYP enzymes’ genotypes and their correlation with the concentrations of the antidepressant drugs. Paper IV ‐To describe and evaluate the interindividual variation of serum concentrations of different antidepressant drugs in terms of trough values under steady‐state conditions, in child and adolescent patients in the south‐east region of Sweden. ‐To describe and identify factors with possibly deviating PK. ‐To study the intraindividual PK variation over time in patients repeatedly referred to the TDM service. ‐To investigate the use of antidepressant drugs in this group of patients. ‐To describe the CYP enzymes’ genotypes and their correlation with the concentrations of the antidepressant drugs. 40 MATERIAL AND METHODS
Subjects and study design
The studies presented in Papers I, II and IV in this thesis were naturalistic, open‐labelled, retrospective and descriptive phase IV‐studies, based on routine TDM data from patients’ treated with the drug(s) studied. Primarily the psychiatrists were informed about the availability of the TDM service and the study purpose. The patients included in Paper I were patients treated with escitalopram. At the request of the treating physician, a blood sample was sent to the TDM laboratory. A total of 243 escitalopram analyses, from a total of 212 patients, were requested from clinics in Sweden, from 2002 to 2005. Ninety‐one different physicians have requested the TDM service (6 physicians requested more than 10 analyses each). The patients included in Paper II were patients treated with ziprasidone. At the request of the treating physician, a blood sample was sent to the TDM laboratory. A total of 545 ziprasidone analyses, from 370 patients, were requested from 97 different clinical centers in Sweden (13 centers requested more than 10 analyses each). The patients included in Paper IV were patients from Child and Adolescent Psychiatry Center in the South‐East of Sweden, treated with an antidepressant drug. At the request of the treating physician, a blood sample was sent to the TDM laboratory. A total of 262 analyses were requested from 9 different clinical centers (5 centres requested more than 10 analyses). The samples were collected from 211 patients, between 2002 and 2004. Paper III was a cross‐sectional study performed in selected elderly patients from nursing homes. All the elderly patients who were prescribed at least one antidepressant drug were selected. A total of 76 elderly people were selected after screening within 8 nursing homes in the County of Östergötland, during the last half of 2003. 41 Blood samples
Drug serum concentrations fluctuate after drug therapy has begun until there is an equilibrium, or steady‐state, between intracellular and serum concentrations. The t1/2 provides an approximation of how long it takes to attain a steady‐state after initiation of the therapy. In general, steady‐state blood concentrations of a drug are reached after the drug doses have been given for a length of time equal to 5 half‐lives of the drug {Greenblatt, 1993}; see Figure 4. The timing of the sample in relation to the previous dose influences the interpretation of a drug concentration measurement. When a patient takes a dose of a drug, the amount in the blood rises for a time period, peaks and then began to fall usually reaching its lowest level (trough) just before the next dose. Peak levels should be below toxic concentrations and trough levels should remain in the therapeutic range. Figure 13. TDM sample drawn as trough level. In the four studies of this thesis, requirements were made for blood samples to be taken as trough values under steady‐state conditions for determination of drug concentration (Figure 13). The recommendations to the 42 physicians requiring TDM were to take venous blood samples by cubital venipuncture. Whole blood was collected (7 mL) in an empty test tube (vacuum tube). For determination of the genotypes, a 10 ml blood sample was obtained in an ethylenediaminetetraacetic acid (EDTA)‐test tube. Determination of drug concentrations
Drug analyses were done at two accredited laboratories of Clinical Pharmacology in Sweden. An accredited analysis is a quality‐validated method with an internal control program, as well as participation in an external quality service. Paper I. Analysis of escitalopram and its metabolites. Drug concentrations were determined at the Department of Clinical Pharmacology, Linköping University, Sweden by high‐performance liquid chromatography (HPLC), according to a previously published method {Carlsson et al., 2001}. The lower limit of quantification for S‐CIT and its metabolites was 2 nmol/L. Paper II. Analysis of ziprasidone and main metabolite. Drug concentrations were determined at the Department of Clinical Pharmacology, Linköping University, Sweden by HPLC, with a newly established method for clinical TDM service. The lower limit of quantification for ZIP and metabolite S‐methyldihydroziprasidone (SMDZ) was 20 nmol/L. Papers III and IV
The drug samples were analyzed at the accredited laboratories at the Department of Clinical Pharmacology, Linköping University Hospital and Lund University Hospital, Sweden. The assays were parts of a clinical TDM service. In short, HPLC was used for the determination of the TCA, citalopram {Carlsson et al., 1997c}, fluoxetine, paroxetine, fluvoxamine, sertraline, venlafaxine and mirtazapine. The enantioselective HPLC method {Carlsson et al., 2001} was used also in Paper III for the citalopram samples. Genotyping
Genomic deoxyribonucleic acid (DNA) was isolated from peripheral leukocytes according to the DTAB/CTAB method {Gustincich et al., 1991}. Extracted DNA was stored frozen at –20° C until analyzed. Determination of the genotypes of CYP2D6, CYP2C19 and CYP2C9 was performed at the 43 Department of Forensic Genetics and Forensic Toxicology, Swedish National Board of Forensic Medicine, Linköping, Sweden. The identification of CYP2D6 alleles *2, *3, *4, *5, *6, as well as multiple copies, CYP2C19 alleles *2, *3, *4 and CYP2C9 alleles *2, *3, *4, *5 was done by use of Pyrosequencing™ technology {Eriksson et al., 2002; Soderback et al., 2005; Zackrisson et al., 2003}, a nonelectrophoretic, real‐time DNA sequencing. Request form
For Papers I, II and IV, a specifically designed request form was used to acquire relevant clinical data on the patient (Figure 14). The request form and sampling procedures were mailed to the treating physician at beginning of the study but were also available at the laboratory and mailed upon request of the clinics. Information was required not only on age and gender but also on dosage of the studied drug, duration of current treatment, time in relation to the previous dose, indication for treatment and taking of blood sample, concomitant drugs used (including oral contraceptives, oestrogen substitution therapy and herbal medicine), adverse effects, significant somatic illness, smoking habits, and weight and height of the patients. Hepatic and renal dysfunction were evaluated by the treating physician and indicated by a checkmark with the possibility of specifying the dysfunction. Global effectiveness evaluation was indicated on a visual analogue scale of 0 mm to 100 mm at the time of blood sampling (0= poor, 100= good). In Paper II, information on intake of food was requested. In Paper IV, a requirement was implentated that the prescribing physician ranked the indication for treatment, if there was more than one indication. For Paper III, the data were obtained from the available medical record forms at the nursing homes as well as a questionnaire about possible side effects and a gross evaluation of the clinical situation for the patient when data was collected, reported by the patient and/or nurse as yes/no. Stratification procedures for data collected
No control group was included in our design. Minimal inclusion and exclusion criteria were used in order to diminish the potential patient selection bias. 44 Figure 14. Request form: ziprasidone, escitalopram and for antidepressant drugs in Children and Adolescents. 45 Internal validation
A stratification procedure was implemented in order to increase data reliability and validity in the PK evaluations. In order to be eligible for scientific evaluation, samples had to comply with predefined inclusion criteria: measurable drug and/or metabolite concentration, information on dose regimen/daily dose prescribed and the blood sample should have been taken under steady‐state conditions and drawn at the trough level. Only one TDM sample per patient (the first eligible sample) was collected. These samples were referred to as the Patients Evaluated (PE); see Figure 15. Patients with repeated eligible TDM samples were entered into the inter‐ and intraindividual variability determination. PAPER I
PAPER II
PAPER IV
(2002-2005)
(2001-2004)
(2002-2004)
243 TDM samples
545 TDM samples
262 TDM samples
ITE
ITE
ITE
212 TDM samples
370 TDM samples
211 TDM samples
PE
PE
PE
155 TDM samples
121 TDM samples
153 TDM samples
Figure 15. Summary of the stratification procedure. ITE= Intention‐to Evaluate, 1 st sample per patient, PE= Patients Evaluated, trough values in steady‐state. 1 st sample per patient External validation
To control selection bias, the demographics of the patients, age and gender distribution in every TDM study, were compared with the prescription demographics of the drug(s) during the study period, to assess selection bias and whether the limited subpopulation in the study was representative for 46 extrapolation of the study results (Figure 16). This is possible in Sweden due to all prescription drugs being delivered through the National Corporation of Swedish Pharmacies (Apoteket AB, Stockholm). Sweden
im
ip
ch ram
i
lo
m ne
tri ipr
m ...
ip
r
lo am
fe in
pr e
am am
itr ine
i
no ptyl
rtr ine
ip
pr tyl
i
ot
rip ne
m tyli
ap ne
ro
t
flu ilin
ox e
e
ci tin
ta
lo e
pa pra
ro m
xe
se tine
r
flu tral
vo ine
es xam
ci
ta ine
lo
m pra
ia
ns m
m eri
irt
n
az e
ve api
nl ne
af
a
re xin
bo e
xe
tin
e
%
100
80
60
40
20
0
Council of Östergötland
%
100
80
60
40
20
im
i
ch pra
m
lo
in
m
e
ip
r
tri am
in
m
e
ip
ra
m
lo
in
fe
e
pr
a
am mi
ne
itr
ip
no tyl i
n
rtr
ip e
pr tyli
ne
ot
rip
t
m ylin
ap
e
ro
ti
flu line
ox
et
in
ci
e
ta
lo
pr
am
pa
ro
xe
t
se ine
rtr
al
flu
i
vo ne
x
es am
in
ci
e
ta
lo
pr
m
ia am
ns
m eri n
i rt
az e
ap
ve
in
nl
e
af
a
re xin
e
bo
xe
tin
e
0
Study
%
140
120
100
80
60
40
20
im
ch ipra
m
lo
i
m
ip ne
tri ram
m
ip ine
r
lo am
fe
pr ine
am am
itr ine
i
no ptyl
rtr ine
ip
pr tyl
ot
i
rip ne
m tylin
ap
ro e
flu tilin
ox e
et
ci
ta ine
lo
p
pa ra
ro m
xe
se tine
rtr
flu
a
vo line
x
es am
ci
ta ine
lo
m pra
ia
ns m
m eri
irt
n
az e
ve api
nl ne
af
a
re xin
bo e
xe
tin
e
0
Figure 16. Prescription of antidepressant drugs. Comparison between the data collected from the age group older than 65 years in Sweden, Council of Östergötland during 2003 and study III. Screening for drug-drug interactions
Polypharmacy is common in psychiatric patients and is of clinical importance. The drug PK was explored regarding concomitant medication to assess drug interaction in the clinical setting. Each request form was categorized into one of five groups, in order to simplify the database search routines and the evaluation. The five drug therapy groups were: 1) “monotherapy” (the drug studied as the only drug); 2) “CNS monocombination” (the drug studied plus, 1 CNS‐active drug); 3) “CNS polycombination” (the drug studied plus, more than 1 CNS‐active drug); 4) “Somatic monocombination ” (the drug studied 47 plus, 1 “somatic” drug); and 5) “polypharmacy” (the drug studied plus, mixed CNS‐active and “somatic” drugs or more than 1 “somatic” drug). CNS‐active drug was categorized as “N” according to the ATC system {WHO, 2001a} and “somatic” drug according to the other ATC groups. The screening procedure should show a variation in the drug concentration or metabolic ratio between monotherapy compared to polypharmacy and whether concomitant medication included an inhibitor or inductor. Statistical analysis
The bioanalysis results and the acquired request form information were entered in to a specifically designed application in a database (Access®, Microsoft, Redmond, WA), according to previously designed for PK studies in the naturalistic setting {Reis et al., 2003; Reis et al., 2002a; Reis et al., 2002b}. After analysis, the data were transferred from the database management software over to the program that carried out the statistical analysis. The statistical methods used in each study are described in the respective papers. Variables were tested for normality distribution using the Kolmogorow‐Smirnov test (yielding that data were non normally distributed). Covariations of variables were analysed using the Spearman rank correlation (rs) test or Pearson correlation (r) coefficient after a 10 logarithmic transformation had been performed. Besides descriptive statistics, analytic statistics were used. Mann‐Whitney’s U test and Kruskal‐Wallis were used in order to compare two respective more than two group characteristics. The p‐
values below 0.05 were accepted as statistically significant. Dose normalization: due to a wide prescribed dose range of the drugs studied, a correction for the serum concentration results in relation to the daily dose for both parent compound and metabolite(s) was calculated for group comparisons. Despite proportional dose‐concentration linearity was assumed, an approximation bias might be introduced. This may to be weighed against the advantages of more large subgroups of patients available to compare. The concentration‐over‐dose (C/D) acquired in this way approximates the drug concentration in nmol/L per mg per day of administered drug. The inter‐ and intraindividual variation was calculated by the coefficient of variation (CV) expressed in percent. 48 Ethical considerations
During the design and conduct of these studies, ethical awareness was considered important. Participants in the naturalistic studies in this thesis already had a treatment of the disease from their routine medical care. The intervention, the venipuncture, was a part of the checking of the patients’ therapeutic efficacy. Ethical dilemmas concerning genotyping as a genetic test with isolation of genomic DNA, had explicitly been taken into consideration. Genotyping of the drug metabolism enzyme genes may not be considered as an invasion of patient’s privacy since these genes have not been associated with increased risk of disease. However, patient’s integrity has been guaranteed by an anonymization procedure of the information from patients entered into the database. All four studies were approved by the Regional Ethics Committee, Faculty of Health Sciences, Linköping University, Sweden with the following registration numbers: 03‐407, 02‐246, 03‐139 and 02‐075. 49 RESULTS
Paper I
A summary of the demographics and clinical information from the request forms follow the stratification procedures as shown in Table III. In the work‐
up procedure aimed at providing valid data for the PK scientific evaluation, about 25 % of the samples had to be excluded from a scrutinized evaluation, due to a lack of essential information such as the actual dose of the escitalopram or the time for blood sampling in relation to the previous dose of the drug indicated on the request forms. 50
Number of request forms
40
Trough value; 10-30 hours
30
20
10
0
1 2 3 4 6 7 8 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 38 49 74
Number of hours between last dose and sampling
Figure 17. The timing of the sample in relation to the last intake of the drug in ALL collected samples. 50 Table III. Demographic data from the escitalopram analysis request forms
All samples
243
ITE samples*
212
PE samples**
155
Women
Men
166 (68)
77 (32)
144 (68)
68 (32)
105 (68)
50 (32)
All
Women
Men
48 (13-95)
45 (13-95)
50 (19-89)
47 (13-95)
45 (13-95)
49 (19-89)
48 (17-95)
47 (17-95)
57 (19-89)
All
Women
Men
Not registered, n (%)
20 (2.5-70)
20 (2.5-60)
20 (5-70)
1 (0.4)
20 (2.5-40)
18 (2.5-40)
20 (2.5-40)
1 (0.5)
20 (5-40)
20 (5-40)
20 (5-40)
---
Once a day
Twice daily
Not registered, n (%)
Median time elapsed (range), hr
All
Not registered, n (%)
Median time for treatment (range), day
All
Not registered, n (%)
Indication for treatment, n (%)
#
According to FASS
Other
Not registered, n (%)
Indication for analysis, n (%)
Routine check-up
Compliance
Undertreatment
Overtreatment
Other
Not registered, n (%)
Somatic status, n (%)
Healthy
Not healthy
Not registered, n (%)
Smoking habits, n (%)
Yes
No
Snuff/other
Not registered, n (%)
Hepatic function, n (%)
Normal
Pathologist
Not registered, n (%)
Renal function, n (%)
Normal
Pathologist
Not registered, n (%)
BMI, kg/m2, median (range)
All
Women
Men
Not registered, n (%)
Side effects, n (%)
Yes
No
Not registered, n (%)
Concomitant medication, n (%)
S-CIT only
S-CIT + 1 drug
S-CIT + ? 2 drug
Not registered, n (%)
Effectiveness, VAS
Median (range), mm
Not registered, n (%)
235 (97)
7 (3)
1 (0.4)
208 (99)
3 (1)
1 (0.5)
153 (99)
2 (1)
--
24 (1-74)
29 (12)
24 (1-74)
28 (13)
24 (11-29)
---
103 (5-1156)
78 (32)
93 (7-1156)
69 (33)
92 (14-681)
39 (25)
203 (93)
15 (7)
25 (10)
177 (94)
11 (6)
24 (11)
126 (93)
9 (7)
20 (13)
143 (64)
11 (5)
38 (17)
6 (3)
24 (11)
21 (9)
126 (66)
8 (4)
33 (17)
6 (3)
18 (9)
21 (10)
93 (66)
8 (6)
25 (18)
2 (1)
12 (9)
15 (10)
145 (70)
63 (30)
34 (14)
124 (69)
57 (31)
31 (15)
89 (66)
45 (34)
21 (13)
47 (20)
152 (67)
30 (13)
14 (7)
39 (20)
133 (67)
27 (14)
13 (6)
31 (21)
99 (67)
18 (12)
7 (4)
221 (98)
4 (2)
18 (7)
194 (98)
4 (2)
14 (7)
143 (99)
2 (1)
10 (6)
224 (99)
2 (1)
17 (7)
196 (99)
2 (1)
14 (7)
146 (100)
-9 (6)
25.2 (16.4-119.6)
24.8 (16.4-119.6)
25.9 (17.8-42.3)
45 (18)
25.2 (16.4-119.6)
24.7 (16.4-119.6)
25.7 (17.8-42.3)
40 (19)
24.9 (16.4-119.6)
24.1 (16.4-119.6)
25.8 (17.8-42.3)
29 (19)
45 (28)
113 (72)
85 (35)
40 (29)
98 (71)
74 (35)
26 (27)
70 (73)
59 (38)
33 (16)
42 (20)
131 (64)
37 (15)
27 (15)
39 (22)
115 (64)
31 (15)
21 (15)
29 (21)
89 (64)
16 (10)
50 (0-90)
66 (27)
50 (0-90)
53 (25)
57 (0-90)
43 (28)
Number of patients (n)
Gender, n (%)
Age, median (range), y
Dos, median (range), mg/daily
Dose regimen, n (%)
*ITE = one sample per patient. **PE samples = first individual steady state trough value samples. #Recommended
indications for treatment according to the Physicians’ Desk Reference (FASS) during the period the samples were
collected: major depression, panic syndrome, and social phobia.
51 An important task in this study was the collection of serum concentrations of S‐CIT and its metabolites in a substantial number of patients after oral escitalopram, in order to produce relevant material in the form of reference values for comparisons in routine TDM service, as well as the information provided on toxicology. The timing of the sample in relation to the last intake of oral escitalopram was widely spread (Figure 17). The trough value for a sample to be included in the scientific analysis was defined as sampled 10 to 30 hours post dose. The quantification of the parent compound and its metabolites at each dose level are displayed in Figure 18. 400
300
200
100
0
200
Serum S-DCIT concentration (nmol/L)
Serum S-CIT concentration (nmol/L)
500
5 mg
n=11
10 mg
n=40
15 mg
n=13
20 mg
n=54
25 mg
n=6
30 mg
n=19
150
100
50
0
40 mg
n=8
5 mg
n=11
10 mg
n=40
15 mg
n=13
20 mg
n=53
25 mg
n=6
30 mg
n=19
20 mg
n=53
25 mg
n=6
30 mg
n=19
40 mg
n=8
2.5
300
100
2.0
Ratio S-DCIT/S-CIT
Serum S-DDCIT concentration
(nmol/L)
200
30
20
10
0
1.5
1.0
0.5
0.0
5 mg
n=9
10 mg
n=29
15 mg
n=13
20 mg
n=46
25 mg
n=4
30 mg
n=17
40 mg
n=7
5 mg
n=11
10 mg
n=40
15 mg
n=13
40 mg
n=8
Figure 18. Escitalopram (S‐CIT), S‐desmethylcitalopram (S‐DCIT), S‐didesmethylcitalopram (S‐DDCIT) and S‐DCIT/S‐CIT ratio. Median concentration, 25th and 75th percentiles and min‐max values. Other important findings from Paper I can be summarised thus: ‐The median daily dose of oral escitalopram was 20 mg (54 % of the patients took 20 mg or more per day). ‐The concentration over dose (C/D) of S‐CIT and S‐DCIT was comparable with previously published data for citalopram assuming the S/R ratio for racemic citalopram 0.5‐0.6 and for racemic desmethylcitalopram 0.7. ‐The S‐DCIT/S‐CIT ratio 0.6 was higher than previously published data. ‐An extensive CV of concentration of S‐CIT and metabolites was found. 52 ‐The intraindividual CV for the ratio S‐DCIT/S‐CIT was 23 %, lower than the interindividual CV (Table IV).
‐The CV of C/D of S‐DCIT (36 %) was lower than for C/D of S‐CIT (71 %). ‐S‐DCIT/CIT ratio was higher in women than in men (0.64 versus 0.50). A lower S‐DCIT/S‐CIT ratio was seen in women taking oral contraceptives compared with women in the same age range without external hormone influences. ‐Higher serum concentrations and greater concentration variance were found in patients older than 65 years of age, but no gender‐related concentration differences were found. ‐Polypharmacy was present in this population. The median number of drugs apart from escitalopram was 4 per patient (range 1‐18). ‐In about 62 % of the patients with repeated eligible TDM data (16 patients), a change in treatment strategies was shown between the first and second sample of the patient. These changes have consisted of an increase in the doses. Table IV. Pharmacokinetic variability in the studies Escitalopram
Ziprasidone
Citalopram
Sertraline
16 (overall 155)
28 (overall 121)
44
108
C/D Parent drug
71 (overall 67)
49 (overall 62)
46
64
C/D Main metabolite
36 (overall 43)
37 (overall 56)
41
54
Metabolic ratio
62 (overall 74)
19 (overall 57)
74
37
C/D Parent drug
35
39
C/D Main metabolite
29
45
Metabolic ratio
23
59
n
Interindividual,CV %
Intraindividual, CV %
Inter‐ and intraindividual variation over time of dose‐normalized (C/D) serum
concentrations of parent drug, main metabolite and the metabolite/parent drug rations 53 Paper II
A summary of demographic and other essential clinical request form data from all samples to the patients evaluated (PE) are presented in Table V. Up to 27 % of the samples had been excluded in the work‐up procedure due to a lack of essential information in the request forms. Serum concentrations of ZIP and main metabolite in a large population of patients after oral ziprasidone were provided as reference values for comparisons in routine TDM service as well as on toxicology. The concentrations of ZIP, SMDZ and SMDZ/ZIP ratio are shown in Figure 19. The timing of the sample in relation to the last intake of oral ziprasidone was also wide (Figure 20). All serum samples taken between 11 to 13 hours post dose were defined as trough values. The serum concentration range for ALL collected data is described in Table VI. 500
700
450
600
350
SMDZ (nmol/L)
Ziprasidone (nmol/L)
400
500
400
300
200
300
250
200
150
100
50
100
0
40
60
80
120
160
200
240
Median
25%-75%
Non-Outlier Range
Outliers
Extremes
0
-50
40
60
80
Daily dose (mg)
120
160
200
240
Median
25%-75%
Non-Outlier Range
Outliers
Extremes
Daily dose (mg)
2,4
2,2
2,0
Ratio SMDZ/ZIP
1,8
1,6
1,4
1,2
1,0
0,8
0,6
0,4
0,2
0,0
40
60
80
120
160
200
240
Median
25%-75%
Non-Outlier Range
Outliers
Extremes
Daily dose (mg)
Figure 19. Ziprasidone (ZIP), S‐methyldihydroziprasidone (SMDZ) and SMDZ/ZIP ratio. Median concentration, 25th and 75th percentiles and min‐max values. 54 Table V. Demographic data from the ziprasidone analysis request forms All samples
545
ITE samples*
370
PE samples**
121
Women
Men
295 (54)
250 (46)
209 (56)
161 (44)
68 (56)
53 (44)
All
Women
Men
37 (9-81)
38 (9-71)
36 (14-81)
36 (9-81)
38 (9-71)
36 (14-81)
36 (15-68)
36 (15-68)
35(20-61)
All
Women
Men
Not registered, n (%)
120 (20-320)
120 (20-180)
120 (20-320)
16 (3)
120 (20-320)
120 (20-240)
100 (20-320)
8 (2)
120 (40-240)
140 (40-240)
120 (40-240)
---
Once a day
Twice daily
Three times daily
Four time daily
Not registered, n (%)
44
453 (89%)
12
1
35 (6)
29
304 (88%)
10
1
26 (7)
--121 (100%)
-------
All
Not registered, n (%)
Median time for treatment (range), day
All
Not registered, n (%)
Indication for treatment, n (%)
Schizophrenia or psychotic dis.
Other
Not registered, n (%)
Indication for analysis, n (%)
Routine check-up
Compliance
Undertreatment
Overtreatment
Intoxication
Other
Not registered, n (%)
Somatic status, n (%)
Healthy
Not healthy
Not registered, n (%)
Smoking, n (%)
Yes
No
Not registered, n (%)
Hepatic function, n (%)
Normal
Pathologist
Not registered, n (%)
Renal function, n (%)
Normal
Pathologist
Not registered, n (%)
BMI, kg/m2, median (range)
All
Women
Men
Not registered, n (%)
Side effects, n (%)
Yes
No
Not registered, n (%)
Concomitant medication, n (%)
Ziprasidone only
Ziprasidone + 1 other drug
Ziprasidone + ?2 drug
Not registered, n (%)
Effectiveness, VAS
Median (range), mm
Not registered, n (%)
12 (0.25-38)
114 (21)
12 (0.25-38)
90 (24)
12 (11-13)
---
152 (1-1328)
173 (32)
96 (1-1203)
140 (38)
111 (9-1124)
20 (16)
288 (93)
21 (7)
236 (48)
175 (93)
14 (7)
181 (49)
69 (90)
8 (10)
44 (36)
271 (68)
54 (14)
42 (10)
23 (6)
2 (1)
3 (1)
150 (27)
165 (65)
37 (15)
32 (12)
16 (6)
2 (1)
3 (1)
115 (31)
63 (65)
16 (16)
10 (10)
6 (6)
--2 (2)
24 (20)
252 (84)
48 (16)
245 (45)
148 (82)
32 (18)
190 (51)
75 (90)
8 (10)
38 (31)
177 (51)
171 (49)
197 (36)
107 (50)
107 (50)
156 (42)
50 (53)
45 (47)
26 (21)
158 (76)
50 (24)
337 (62)
96 (76)
30 (24)
244 (66)
46 (78)
13 (22)
62 (51)
142 (84)
28 (16)
375 (69)
79 (80)
20 (20)
271 (73)
36 (82)
8 (18)
77 (64)
29.5 (14.7-56.4)
29.8 (14.7-56.4)
29.2 (16.8-52.0)
278 (51)
29.1 (14.7-56.4)
29.3 (14.7-56.4)
28.9 (16.8-52.0)
212 (57)
29.7 (14.7-43.9)
29.9 (14.7-43.9)
29.4 (18.5-41.2)
49 (40)
79 (29)
195 (71)
271 (50)
58 (32)
123 (68)
189 (51)
15 (20)
59 (80)
47 (39)
33 (8)
67 (15)
337 (77)
108 (20)
20 (7)
49 (17)
222 (76)
79 (21)
10 (10)
18 (17)
76 (73)
17 (14)
54 (10-95)
319 (58)
54 (11-94)
218 (59)
50 (14-85)
56 (46)
Number of patients (n)
Gender, n (%)
Age, median (range), y
Dos, median (range), mg/daily
Dose regimen, n (%)
Median time elapsed (range), hr
*ITE = one sample per patient. **PE samples = first individual steady state trough value samples 55 Table VI. Serum concentration ranges in ALL collected samples for ziprasidone and metabolite n
Min
25th
Median
75th
Max Linearity,
percentile
percentile
r (p<0.05)
516
10
68
120
190
1400
0.37
ZIP
481
SMDZ
5
45
73
474
0.03
0.39
0.59
SMDZ/ZIP
Ziprasidone (ZIP) -methydihydroziprasidone (SMDZ) in
116
556
0.46
0.89
11.39
no
120
100
11-13
No of request forms
80
60
11-16
40
20
38,5
37,0
35,4
33,9
32,4
30,9
29,3
27,8
26,3
24,7
23,2
21,7
20,1
18,6
17,1
15,6
14,0
12,5
11,0
9,4
7,9
6,4
4,8
3,3
1,8
0,3
0
Number of hours between last dose and sampling
Figure 20. The timing of the sample in relation to the last intake of the drug in ALL collected samples. Trough values, 11 to 13 hours. Other important findings from Paper II can be summarised thus: ‐The median daily dose of oral ziprasidone was 120 mg. The most frequently prescribed dose was 160 mg. ‐Pronounced interindividual concentration variability at each dose level of ZIP and SMDZ was found. ‐The intraindividual CV of C/D of SMDZ and SMDZ/ZIP ratio was higher than the interindividual CV (Table IV). ‐The SMDZ/ZIP ratio was found to decrease with increasing concentration of ZIP (r= ‐0.51). 56 ‐Smoking women had a lower C/D of ZIP than non‐smoking women and significantly higher daily doses. ‐No significant difference between the first and the last sampling occasions was found regarding the BMI over time (n=37). ‐Polypharmacy was present in this population. The median number of drugs apart from ziprasidone was 3 per patient (range 1‐18). ‐In 54 % of the patients with repeated eligible TDM samples (28 patients), a change in treatment strategies between the first and second sample of the patient was found. In the majority of the patients the dose had been increased. Paper III
A summary of demographic and data adhering to general patient characteristics is presented in Table VII, as well as data collected with focus on the antidepressant drug treatment. The prescription of antidepressant drugs in the nursing homes studied was 38 %. SSRIs were prescribed to 86 % of the elderly, above all citalopram (62 %). The concentrations of the antidepressant drugs were higher, or much higher, than could be expected from the dose administered in 73 % of elderly people evaluated (Figure 21). Low
4%
Expected
23%
73%
High
High
26%
Low
11%
63%
Expected
Figure 21. Estimated concentration to dose in the elderly (left) and in the paediatrics (right).
57 Table VII. Summary of demographic and data concerning general patient characteristics (right) and summary of data collected with focus on the antidepressant drug treatment (left) Number (n) of patients
Gender, n (%)
71
Number (n) of patients
Diagnosis, n (%)
71
Women
57 (80)
Depression
41 (60.3)
Men
14 (20)
Dystymi
11 (16.2)
Age, mean in years (range)
Compulsion
1 (1.5)
Women
85 (72-100)
Panic
1 (1.5)
Men
84 (71-98)
Manodepressiva
1 (1.5)
Nursing home (n of beds), n
Other
13 (19.0)
(%)
VG (50)
19 (38)
Not register
3 (4.0)
K (36)
7 (19)
Treatment initiated by SPSY, n (%)
KG (30)
7 (23)
Yes
5 (8)
HG (26)
5 (19)
No
58 (92)
RG (25)
15 (60)
Not register
8 (13)
EB (12)
6 (50)
Aware of their ongoing medication, n (%)
BB (11)
8 (73)
Yes
16 (23)
ÖG (8)
4 (50)
No
50 (70)
BMI, kg/m2, mean (range)
Not answer
5 (7)
Women 26.6 (18.3-39.6)
The duration of current treatment, n (%)
Men 25.0 (20.1-37.8)
< 6 month
6 (13)
Food intake, n (%)
6-12 month
9 (19)
Self-eat
46 (65)
1-2 years
5 (11)
Self-eat after request
10 (14)
>2 years
27 (57)
Self-eat with assistance
10 (14)
Not register
24 (34)
Feeding
5 (7)
Evaluation of the clinical situation*
Coffee, n (%)
Interest in personal hygiene 51 (18-93)
Yes
61 (86)
Sadness 47 (17-75)
No
10 (14)
Anxiety
44 (6-77)
Smoking, n (%)
Social function
42 (6-78)
Yes
2 (3)
Agony
20 (5-56)
No
69 (97)
Insomnia
10 (3-45)
Physical activities, n (%)
Side-effects: YES, n (%)/not register
Confined to bed
4 (6)
Dizziness
26 (37)/5
Sitting
32 (45)
Obstipation
25 (35)/5
Walking/going
22 (31)
Xerostomia
21 (30)/4
0ut walking
13 (18)
Nausea
14 (20)/6
Blood pressurea
Dyspepsia
5 (7)/6
Diastolic
71 (50-100)
Concomitant drugs
Systolic
134 (100-220)
number of drugs, mean (range) 10.7 (4-19)
Chemistry parametersb
> 1 antidepressant drugs
2 patients
Haemoglobin (g/L)
125 (99-166)
number of CNS drugs, mean (range)
2.3 (1-5)
MCV (fL)
92 (82-107)
> 1 CNS drug 46 patients
Leukocytes (x109/L)
7.0 (3.0-15.0)
>2 CNS drugs 29 patients
Platelets (x109/L)
258 (105-446)
>3 CNS drugs
8 patients
ASAT (µkat/L)
0.33 (0.18-1.1)
>4 CNS drugs
4 patients
ALAT (µkat/L)
0.30 (0.10-1.5)
Na (mmol/L)
141 (127-147)
K (mmol/L)
4.0 (3.1-5.3)
Creatinine (µmol/L)
100 (56-282)
Albumin (g/L)
37 (28-46)
TSH (mU/L)
2.2 (0.2-28)
a mean in mmHg; b mean (range); SPSY=specialist in psychiatry; * VAS‐recording= 0‐100 mm, median (25th 75th percentiles)
58 The PK of citalopram in the elderly was analysed. Citalopram (CIT) is a racemic drug (50/50 of R‐ and S‐CIT enantiomers). The result of enantiomer analysis (S/R ratio) of citalopram, desmethylcitalopram (DCIT) and didesmethylcitalopram (DDCIT), a steady‐state trough value serum concentration in patients receiving different oral citalopram doses was presented in Table VIII. A significant correlation between the total racemate concentration for CIT and S/R‐CIT ratio (rs = 0.59) and for DCIT and S/R‐DCIT ratio (rs = ‐0.58) was found. The significant calculated Spearman correlation coefficient between the estimation of creatinine clearance and C/D of CIT and DCIT were –0.47 and –0.65, respectively (p≤0.001). A pronounced interindividual variation of C/D of CIT, metabolites and ratio was found (Table IV). Table VIII. Serum concentrations of citalopram (CIT), desmethylcitalopram (DCIT) and
didesmethylcitalopram (DDCIT) in patients receiving citalopram, in terms of steady-state and
trough values. Concentrations in nmol/L. *25th-75th percentiles
Oral Citalopram,
daily dose
median
10
percentiles*
mg
min-max
CV
20
median
mg
percentiles*
min-max
CV
30
median
mg
percentiles*
min-max
CV
40
median
mg
CIT
173
101-222
74-225
53%
258
175-344
81-613
49%
449
330-528
206-656
38%
690
S/Rcit
0.74
0.61-0.85
0.57-0.86
20%
0.77
0.47-0.95
0.31.01
36%
0.80
0.63-0.86
0.38-0.90
35%
1.00
DCIT
45
40-62
35-80
35%
91
71-119
38-307
45%
153
113-183
82-204
32%
176
S/RDcit
0.73
0.66-0.74
0.60-0.75
10%
0.63
0.54-0.74
0.35-0.95
26%
0.55
0.54-0.59
0.53-0.64
8%
0.56
DDCIT
12
10-18
9-22
38%
16
6-26
0-67
81%
29
24-34
12-51
44%
22
S/RDDcit
0.48
0.43-0.53
0.40-0.57
15%
0.47
0.34-0.56
0.00-1.00
44%
0.33
0.32-0.38
0.19-0.90
56%
0.38
DCIT/CIT
N
0.34
0.20-0.55
0.20-0.62
58%
0.38
0.26-0.53
0.17-0.89
45%
0.26
0.22-0.55
0.19-0.74
50%
0.25
4
30
5
1
Other important findings from Paper III can be summarised thus: ‐According to their medical records, the vast majority of the patients (96 %) were found to have an indication motivating antidepressant drug treatment. Depression was the indication in 60 % of the cases. ‐The duration of the antidepressant drug treatment was greater than 2 years in 57 % of patients. No clear time schedule for how long the drug treatment should continue was found in the patients’ current medical records. ‐A majority of patients were not aware of their ongoing medication. ‐The median prescribed dose of the antidepressant drugs was the same, or higher than the recommended daily dose in the Swedish Physician’s Desk 59 Reference or the defined daily dose (DDD) applicable for younger patients {WHO, 2001a}. ‐Possible adverse effects of the drug treatment could be retrieved in 77 % of elderly people. ‐Polypharmacy was present in this group of population. The median number of drugs per patient, apart from the antidepressant drug, was 11 (range 4‐19). ‐The results of all patients genotyped in the study are shown in Table IX. The genetically identified PMs for CYP2D6 displayed higher concentrations of fluoxetine (n=2) and venlafaxine (n=1) than did non‐PMs for the respective antidepressant drugs. The PM for CYP2C19 showed higher concentrations of citalopram than non‐PMs. Table IX. CYP‐genotype outcome in the elderly Number (n) of patients
Expected phenotype, n (%)
Poor metabolizers
Intermediate metabolizers
Extensive metabolizers
Ultrarapid metabolizers
CYP2D6
70
CYP2C19
70
CYP2C9
70
6 (8.5)
25 (35.7)
38 (54.3)
1 (1.4)
2 (2.8)
16 (22.8)
52 (74.2)
-
3 (4.2)
20 (2.8)
47 (67.1)
-
Paper IV
A summary of demographic and other essential clinical request form data from all samples to the patients evaluated (PE) is presented in Table X. Only 8 % of the samples had been excluded in the work‐up procedure due to a lack of essential information in the request forms. SSRIs were prescribed to 98 % of the patients in this study, above all sertraline (67 %). The concentrations of antidepressant drugs were as expected from the dose administered in 63 % of the patients evaluated (Figure 21). Focus was on the PK of sertraline (SERT). No relationship between any clinical information of the patient on the request forms and drug concentration of SERT and metabolite desmethylsertraline (DSERT) was found, except a significant difference in the ratio DSERT/SERT between monotherapy (n=41) and nonmonotherapy (n=38), 2.4 versus 2.1 (p=0.013), but no difference between doses was shown. The interindividual PK variability of C/D of SERT and DSERT, as well as the ratio are shown in Table IV. 60 Table X. Demographic data from the request forms All samples
262
ITE samples*
211
PE samples**
153
Women
Men
167 (64)
95 (36)
134 (64)
77 (36)
94 (61)
54 (35)
Women
Men
16 (8-20)
17 (11-19)
16 (8-20)
16 (8-20)
16 (11-19)
16 (8-20)
16 (8-19)
16 (11-19)
16 (8-19)
23.5 (0.17-74.3)
10 (4)
23 (0.17-74.3)
8 (4)
22.8 (10-26.7)
---
46 (8-658)
95 (36)
40 (8-658)
74 (35)
38 (16-658)
55 (36)
176 (69)
37 (14)
2 (1)
6 (2)
16 (6)
13 (5)
6 (2)
6 (2)
144 (70)
27 (13)
1 (0.5)
4 (1.9)
14 (6.8)
10 (4.9)
6 (2.9)
5 (2)
111 (74.5)
19 (12.8)
1 (0.7)
1 (0.7)
9 (6.0)
5 (3.4)
3 (2.0)
4 (3)
223 (88)
8 (3)
11 (4)
9 (4)
-3 (1)
8 (3)
178 (88)
6 (3)
11 (5)
5 (2)
-3 (1)
8 (4)
130 (88)
2 (1)
8 (5)
4 (3)
-3 (2)
6 (4)
195 (82)
42 (18)
25 (10)
160 (82)
34 (18)
17 (8)
119 (83)
25 (17)
9 (6)
42 (17)
199 (83)
21 (8)
36 (18)
160 (82)
15 (7)
28 (19)
117 (81)
8 (5)
241 (99)
2 (1)
19 (7)
193 (99)
2 (1)
16 (8)
142 (99)
2 (1)
9 (6)
241 (99)
2 (1)
19 (7)
193 (99)
2 (1)
16 (8)
142 (99)
1 (1)
10 (7)
20.2 (12.4-38.6)
20.1 (12.4-36.3)
20.3 (14.0-38.6)
58 (22)
20.1 (12.4-38.6)
19.9 (12.4-36.2)
20.3 (14.0-38.6)
46 (22)
20.4 (14.0-38.6)
20.0 (14.7-36.2)
20.7 (14.0-38.6)
24 (16)
63 (31)
143 (69)
56 (21)
52 (32)
110 (68)
49 (23)
37 (31)
81 (69)
35 (23)
Number of patients (n)
Gender, n (%)
Age, median (range), y
All Median time elapsed (range), hr
All
Not registered, n (%)
Median time for treatment (range), day
All
Not registered, n (%)
Indication for treatment, n (%)
Depression
Obsessive-compulsive disorder
Eating disorder
Dysthymic disorder
Panic attack
Social phobiai.
Other
Not registered, n (%)
Indication for analysis, n (%)
Routine check-up
Compliance
Undertreatment
Overtreatment
Intoxication
Other
Not registered, n (%)
Somatic status, n (%)
Healthy
Not healthy
Not registered, n (%)
Smoking, n (%)
Yes
No
Not registered, n (%)
Hepatic function, n (%)
Normal
Pathologist
Not registered, n (%)
Renal function, n (%)
Normal
Pathologist
Not registered, n (%)
BMI, kg/m2, median (range)
All
Women
Men
Not registered, n (%)
Side effects, n (%)
Yes
No
Not registered, n (%)
Concomitant medication, n (%)
Monotherapy
antidepressive + 1 other drug
antidepressive + ? 2 drug
Not registered, n (%)
Effectiveness, VAS
Median (range), mm
Not registered, n (%)
93 (49)
74 (39)
23 (12)
72 (27)
63 (8-93)
60 (23)
81
58
17
55
(52)
(37)
(11)
(26)
63 (8-93)
47 (22)
60
46
14
33
(50)
(38)
(12)
(22)
64 (8-93)
34 (22)
*ITE = one sample per patient. **PE samples = first individual steady state trough value samples
61 This study also provides information about the median prescribed dose for the antidepressants requested, according the first ranked indication for treatment. Monotherapy was documented in 49 % of the request forms. Other important findings from Paper IV can be summarised thus: ‐Depression was the first ranked indication for the antidepressant treatment (69 %). ‐Sertraline was the drug prescribed for several psychiatric disorders, majority in depression but also in eating disorder. ‐Escitalopram, clomipramine and mirtazapine were prescribed only for depression and fluvoxamine only for obsessive‐compulsive disorder. ‐Genotyping was possible to request by checkmarking a box on the request form. Unfortunately, only 43 samples out of 101 request forms where genotyping were marked, reached the laboratory. The genetically identified PMs for CYP2D6 (n=2) treated with fluvoxamine (CYP2D6‐substrate) had been prescribed 3‐fold lower doses than non‐PM. The rest of PMs had been prescribed sertraline (non‐CYP2D6 substrate). The calculated CYP2D6 allele relative frequencies, show *4 as the most common PM allele (20.9 %). ‐The median number of drugs besides the antidepressant drug was 1 per patient (range 1‐7). ‐The intraindividual correlation between C/D of the antidepressant drugs for the first and last samples was rs=0.66 (p=0.008). ‐The DSERT/SERT ratio varied no more than 20 % between the first and the last sample, in patients treated with sertraline (n=12). ‐In 55 % of the patients with repeated eligible TDM samples (22 patients), a change in treatment strategies was shown between the patients’ first and second samples. These changes have consisted of an increasing of the doses or change of the antidepressant drug. 62 DISCUSSION
Papers I and II
Pharmacokinetic data obtained from larger clinical populations, which mimic the real‐world clinical conditions, are rarely found in the literature. Furthermore, the drug concentrations levels of these studies may be used as the basis for individualized dose optimisation, e.g. if the serum concentration in a patient is proportional in relation to the dose administered. These concentration ranges are also important for the interpretation of concentration data in toxicologic cases. Consequently, these studies merit consideration, despite the limitations associated with naturalistic studies, such as selection bias secondary to lack of randomisation, problems in establishing unequivocal causal relationship due to confounders derived from uncontrolled conditions, the lack of full information given on the TDM request forms and patient compliance issues. Regarding age and gender from the data supplied by the National Corporation of Swedish Pharmacies (Apoteket AB, Stockholm), it seems reasonable to assume that no major demographic selection bias occurred in the studies of this thesis. As previously reported by other authors {Bates, 1998; DʹAngio et al., 1990; Lundmark et al., 2000b}, we also experienced a lack of data on request forms in these studies. As expected, a pronounced interindividual variability in all doses prescribed was evident at the steady‐state and trough serum levels of escitalopram and ziprasidone, as well as their metabolites (Table IV). This may be expected in a population where subjects with extreme clearances achieve a too high or a too low concentration when receiving standard doses. This variability depends not only on the PK items, but also on the lack of compliance, which is usually present in this population. In both studies, the daily dose of the drug was higher than the DDD, i.e. the assumed average maintenance dose per day for a drug used for its main indication in adults {WHO, 2007b}, and higher than reported by other studies at the time of the project. This finding may be explained due to naturalistic studies reflecting everyday clinical practice with patients suffering several degrees of the disease and a variety of co‐morbidities, resulting in the fact that 63 in clinical practice the prescribed doses tend to be generally higher than the manufacture’s preliminary recommendations {Citrome et al., 2009; Citrome et al., 2005; Dietlein et al., 2003}. The application of the approach of the TDM metabolite/parent compound (M/P) ratio for screening of noncompliance suggests that the lower intraindividual CV of the ratio S‐DICT/S‐CIT compared with interindividual CV in Paper I is in agreement with the theory that the metabolic capacity is fairly stable over time and may mark good compliance. The contrasting results with the antipsychotic ziprasidone in Paper II may agree with the higher rates of noncompliance in patients treated with antipsychotics compared with patients treated with antidepressants in the literature. This suspected noncompliance in the ziprasidone paper may be supported by the higher number of request forms with no measurable concentration of the parent compound (8 %), as well as the higher number of cases of suspected noncompliance indicated on the request forms (14 %) compared with the escitalopram paper: 6 % and 5 %, respectively. Possible influences of gender and oral contraceptives on the metabolic ratio of S‐CIT, suspected substrate saturation of aldehyde oxidase at higher doses of ziprasidone, as well as the possible influence of smoking on the concentrations of ZIP in women, might explain data observed in these papers. These observations cannot be taken as conclusive due to the limitations related to naturalisc studies, and probably the clinical impact is low.
These studies were not designed as an interaction study, but the method of interaction screening that has been used in our studies may “catch” a possible substance‐drug interaction that must be verified with controlled studies. Our screening will not give information on the influence of the drug studied on serum concentrations of other concomitantly administered drugs since serum concentrations of concomitant drugs were not measured. Papers III and IV
The sparse concentration measurements performed during the drug development in these populations studied made it relevant to investigate the PK data in these studies. These papers also provide information about the dosage of the drug in a clinical setting. According to the sales statistics, SSRIs were the most prescribed antidepressant drugs both in the elderly and in the pediatric population. As expected in clinical practice, profound interindividual variability at steady‐
64 state and trough serum levels of sertraline and citalopram, as well as its metabolites were also found in both populations (Table IV). The TDM outcome has provided new information: not expected drug concentration for the dosage administered in 77 % of the elderly and in 37 % of the pediatric population. The genetically impaired metabolic activity of CYP enzymes corresponded with higher drug concentration as expected in patients medicated with an antidepressant drug that is a substrate for the genotyped CYP enzyme, underlining that genetic factors should be considered for dose optimisation {Scordo et al., 2005}. Conclusions are limited due to the few cases and that not all CYP genotype PM patients were medicated with an antidepressant drug that is a substrate for the genotyped CYP enzyme. The frequencies of CYP2D6 alleles was in accordance with those of other authors {Zackrisson et al., 2009}. The results in Paper III, where the majority of patients have higher levels of the drug than expected in relation to dose, showed the need to follow‐up the treatment of depression in the elderly. The high blood levels of the drugs may be expected due to the decreasing clearance of drugs in general, but it is a warning signal that the aging process may not have been considered, above when the median daily dose for the antidepressant drugs in this study was according to the recommended dosage to younger patients in the Swedish Physician’s Desk Reference. SSRIs have a more benign profile of ADR that do the tricyclic antidepressants. Despite this, the majority of patients have reported a possible adverse effect among the scrutinized questions (dizziness, obstipation, dyspepsia, xerostomia, nausea). Whether or not the adverse effects mentioned above are consequences of the antidepressant treatment per se, of concomitant medication or comorbidy in this population is difficult to differentiate. However, the risk of drug‐related problems, such as falling and serious adverse drug reactions when polypharmacy and multimorbidity are present in this population cannot be ruled out. In some medical records, no clear diagnosis was found or the existing diagnosis was seen as being questionable. It is difficult to assess and assign psychiatric diagnoses in elderly people {Fastbom et al., 2004; Gottfries, 1997} and principles for treatment of depression are based on clinical trials where elderly people are underrepresented {Lasser et al., 1998}. On the contrary, the majority of patients in Paper IV had an expected drug concentration in relation to dose that suggests that the PKs of the drugs in this group were similar to those of adults, thus the interpretation of the results was based on adult PK data {Baumann et al., 2004b; Lundmark et al., 2000b; 2001; 65 Lundmark et al., 1989; Reis et al., 2003; Reis et al., 2002a}. However, the PK outcome of sertraline were similar to the results of other authors in adults population; see Table XI {Lundmark et al., 2000b; Reis et al., 2009}. Table XI. Sertraline serum concentration in different groups. *mean, **10th and 90th percentiles. Recommended therapeutic range (consensus) 33‐163 nmol/L (Bauman et al, 2004) Study IV
Lundmark et al, 2000
Reis et al, 2009
Dose, mg/daily
50
50
50
Age, yr; median (range)
16 (8-20)
52* (16-92)
43 (8-98)
Gender, % woman
63
69
66
TDM collected data
Yes
Yes
Yes
Patients, n
85
156
984
Sertraline:
median
51
40
40
25th-75th percentiles
27-71
25-60
13-99**
Desmethylsertraline:
median
109
102
79
th
th
25 -75 percentiles
71-127
62-154
31-196**
The observed proportional C/D linearity of the antidepressant drugs between the first and last samples from the same patient permit the opportunity to predict subsequent blood concentrations after antidepressant drug dose‐adjustments within the individual patient. However, the metabolic capacity (DSERT/SERT ratio) may provide information on patient compliance, as well as drug‐drug interactions if the patient is treated with other drugs or a concomitant drug is withdrawn. At the time of the project, no antidepressant drugs were approved for treatment of depression in children and adolescents in Sweden. Only sertraline and fluvoxamine were approved for treatment of obsessive‐compulsive disorder in pediatric populations. No information on dosage for depression and other psychiatric disorders was accessible in the Swedish Physicians’ Desk Reference. Thus, depression was the indication for treatment on 69 % of the request forms. Off‐label prescribing is widespread in practice and necessary because most drugs had not been studied adequately in children {Leslie et al., 2005; Serradell et al., 1993}. Despite the limitations associated with naturalistic studies, the information on dosages used in the different psychiatric disorders may be a guideline for clinicians to ensure the provision of safe and efficacious treatment for children and adolescents. These data are lacking for most of the 66 psychoactive drugs in these populations. The TDM service may provide also support to the treating physician at the off‐label prescribing. Among the data already presented above, the fact that, in 12 % of the patients in Paper I, 30 % of patients in Paper II and 27 % of patients in Paper IV, the treating physician had requested more than one sample during the time period of the study, and the change of treatment strategy shown after the first TDM sample, suggest the utility of TDM for the treating physicians for individual dose optimisation or when suspecting noncompliance. Genotyping can complement the information about a possible concentration deviation. In this thesis, missing data on request forms, inappropriate sampling and misunderstod performance of the TDM service have been disclosed. These conditions may decrease the usefulness of the TDM sample, and the clinical decisions concerning dose adjustment. 67 CONCLUSIONS
Valuable and reliable information may be extracted from TDM collected samples {Gex‐Fabry et al., 1997; Gross, 1998; Jerling, 1995b; Jerling et al., 1994}. The TDM databases are valuable tools for collecting new PK‐data from large‐
scale heterogeneous clinical populations after the introduction of a drug onto the market. Thus the development of a drug should be seen as a continuous process. The data collected by the TDM service may improve reference data for the evaluation of therapeutic response, as well as toxicological information concerning psychoactive drugs {Reis et al., 2007}. The relationships found in these TDM based studies between the drug serum concentration and the clinical information obtained simultaneously cannot be taking as being conclusive but may point towards future hypothesis testing studies. Variability of drug concentrations of psychoactive drugs is expected in TDM data, as a consequence of several factors that exist in the complex scenario in clinical practice. The TDM samples with the correct interpretation of results in order to answer the question of whether a concentration of a psychoactive drug is in the expected range with respect to drug dosage (or therapeutic range), together with complementary CYP genotyping, may be a tool for dose optimisation of the psychoactive drug since resistance or tolerance towards the drug may be disclosed. The optimisation of treatment may be aided by the use more or less regularly of TDM, for maintaining/adjusting doses that may increase rates of drug response and for questionable compliance (compare the TDM metabolite/parent compound ratio between the samples). However, the TDM service may provide support to the prescribing physician at the off‐label prescribing, which was found in this thesis, already in the early times of the introduction of a new psychoactive drug onto the market. TDM is also a valuable tool to optimise further drug medication and drug safety when the selection of doses requires a consideration of PK parameters as well as in the elderly and pediatric populations. The findings in this thesis have been awareness of the usefulness of the TDM service. In summary, the benefits of TDM data were individual dose optimisation and providing research information for the TDM service, as well as toxicology. A more frequent clinical use of TDM and pharmacogenetic testing in clinical practice would contribute to better quality in treatment with psychoactive drugs. 68 REFLECTIONS AND FUTURE
PROSPECTS
Not all patients have their optimal drug response at the same drug concentration but the drug response is more closely related to the concentration of drug in the blood than the prescribed doses. The concept of less toxic psychoactive drugs than previously and the lack of concentration‐
effect relationships in clinical trials have diminished the use of TDM in psychiatry, but the use and benefit of TDM has already been reported. The use of TDM was shown to increase the rates of antidepressant therapy response for TCA from 30 to 40 % to as high as 80 % {Tollefson, 1993} as well as the treatment adherence and response rate for sertraline therapy {Akerblad et al., 2003}. The interindividual variability of drug metabolism can result in subtherapeutic or toxic blood concentrations of psychoactive drugs from standard doses {Dahl et al., 2000; Hiemke, 2008b; c; a; Hiemke et al., 2004}. TDM can be used by the physicians to optimise dosage decisions with psychoactive drugs, in order to maximise efficacy and prevent toxicity, especially when individuals are nonresponsive to treatment or vulnerable to adverse reactions with standard doses due to age, disease states or complicated therapy (drug interactions). Active metabolites must be taken into consideration if present in concentrations sufficient to contribute to the pharmacological effect. Both active and non active metabolites are important in achieving less variable TDM technique metabolite/parent compound ratio, when screening for noncompliance, instead of the TDM technique presence/absence of parent compound in serum {Reis et al., 2004}. The use of TDM may help to assess compliance, but it is limited. The half‐life of most drugs in the blood is only a fraction of a day and there is good evidence that compliance in the days prior to a scheduled visit is much better than at other times. TDM is a tool when dosing uncertainty is a problem in both the very old and the very young as a result of erroneously measured, refused, vomited, repeated or forgotten doses. However, the CYP genotyping of a patient before starting a treatment with a psychoactive drug might help the treating physician in choosing the right drug and the right starting dose, so that a potentially harmful drug for the patient can be avoided. 69 Thus, TDM may increase knowledge of the blood concentration of prescribed doses in clinical setting and safety, when the occurrence of side effects may relate to the levels of the drug in the blood. Simultaneously it may help to solve some medical‐legal problems, such as too low or too high postmortem blood concentrations in the case of suicide or sudden death. The need for educational efforts regarding different aspects of TDM must be considered, not only for physicians but also for nursing and laboratory staff when the high rates of lacking data on request forms, inappropriate sampling, as well as misunderstod performance of the TDM service has been disclosed. Improvement at different levels within the healthcare system is needed to promote an effective TDM service for individualizing drug therapy. Inappropriate timing of sample and no sufficient communication between treating physician and laboratory staff, including the clinical pharmacologist, result in an inappropriate TDM use in psychiatric care {Mann et al., 2006}, and consequently in a decrease of belief in this service. Apart from educational strategies, the use of computerized systems to facilitate the communication between treating physician‐laboratory staff is necessary for improvement of TDM use in psychiatry. So is, a more structured‐stabilised protocol for taking the sample in relation to the treatment’s start/course/possible preceding dose change and the time for the answer from the laboratory with reliability interpretation. Faster answers to the treating physician’s request may result in faster changes of treatment strategy that may reduce the timecourse of disease and avoid possible adverse effects/toxicity. In psychiatrics, patients are frequently subjected to polypharmacy, as was found in the studies in this thesis. It is not rare that treatment involves more than one CNS‐active drug. CNS‐active drug combination treatment is little studied. How several simultaneously administered CNS‐active drugs influence their serum concentrations, i.e. PKs and consequently PDs, is not clear. TDM in psychiatry must take into consideration this fact in order to give clinically meaningful and useful results. The TDM of only one psychoactive drug must be changed to a TDM map of the CNS‐active drugs prescribed. It is possible to do this routinely now due to new techniques with short analysis time, higher resolution and multiple compounds in the same method {de Castro et al., 2008; de Castro et al., 2007; Zhang et al., 2007}. Even CNS‐active non licensed drugs will be taken in consideration. Assessment of a TDM service should include its impact, not only on patient care, but also on the economic efficiency of the delivery system. Reports of the cost‐effectiveness of TDM in psychiatry are limited {Burke et al., 1999; Lundmark et al., 2000a; Preskorn et al., 1991}. The economics of TDM in 70 psychiatry should be evaluated in a wide perspective for society, in the form of the possibility of reducing the number of failed treatments or the time to symptom remission and consequently, a more rapid return of the patient to work and his/her family (social cost?). It is also important to consider that a more frequent use of TDM (as control) in patients treated with antipsychotics, may disclose subtherapeutic/noncompliance, with the possibility of preventing violent acts due to the non following of treatment, especially in Forensic psychiatry. The drug response of psychoactive drugs does not depend only on the PK factors of the drug, but also on other factors, as well as the interindividual differences in receptor density or sensitivity, drug levels in the brain, differences in pathophysiology/disease or co‐morbid disease. The development of research methods must include the polymorphism of the enzymes involved in drug metabolism, as well as drug transporters and receptors. Attenuation of P‐gp function, for example through the use of pharmacological inhibitors, results in substantial changes in the PK and PD of various substrates {Loscher et al., 2005; Schinkel et al., 1996}. Together, more information about the PK of enantiomers, above all if the drug is administered as a racemate, is needed for appropriate interpretations to be possible At the end of this thesis I would like to outline that the research group to which I belong have plans in the future to increase both the safety and efficacy of drugs used in the treatment of psychiatric diseases. Increased knowledge concerning new psychoactive drugs, enantiomers, protein transporters and receptors are in focus at the present and will continue to be so in the future for our research group. 71 ACKNOWLEDGEMENTS
I would like to thank everyone who has contributed to my PhD education and my work, both during this thesis and before, but I cannot mention everybody. Especially I want to thank: Professor Finn Bengtsson, my supervisor, for introducing me to the field of psychopharmacology, for all creative ideas on scientific projects and guidance throughout the preparation of this thesis. I would like to have your “gift of the gab” Professor Johan Ahlner, my co‐supervisor, for invaluable support, valuable opinions (not only about science), guidance in the field of forensic pharmacology and for providing research facilities. To both of you, for your friendship and for your trust in my ability to succeed with this scientific work. Thank you for encouragement, scientific discussions, valuable comments, and for giving me freedom and responsibility. Curt Peterson, Professor in Clinical Pharmacology at Linköping University, for teaching and for recruiting me to the Department of Clinical Pharmacology. My boss, Thomas Bradley, at the Department of Clinical Pharmacology ‐
Center for Rational Use of Drugs, for generously support and providing research facilities. My co‐authors, without whom this thesis would not have been, for good collaboration and valuable opinions, especially associate professors: Margareta Reis, for fruitful discussion around naturalistic settings, statistical analysis, and….life; Staffan Hägg, my colleague, for constructive criticisms, engaging discussion and numerous interesting comments, and Per A. Gustafsson for quick feedback and improving the cooperation between “BUP” and clinical pharmacology. All staff and students, past and present, at the Department of Clinical Pharmacology for creating a stimulating and merry working place, for the willingness to help and for all the fun, specially Eva Ekerfeldt‐Hultin 72 (outstanding skills in the lab and support), Björn Carlsson (☺ ♥), Ingela Jacobsson (nice company in meetings), Kourosh Lotfi, Mikael Hoffmann, Björn Norlander (my mentor in quality and accreditation), Karin Walldén, Gunilla Graffner, Louise Carlsson, Maria Kingbäck, Daniel Öhman, Henrik Lövborg, “LiLi‐teamet” and Anita Thunberg (administrative matters). The staff at the Department of Forensic Genetics and Forensic Toxicology for a friendly and a good working atmosphere, especially Gunnel Ceder, Anita Holmgren, Wayne Jones and Gunnel Nilsson.
The psychopharmacology scientific group, for great motivation, scientific thinking and good collaboration, specially my previous colleagues in Clinical Pharmacology who are now on the staff of the Department of Forensic Genetics and Forensic Toxicology, Anna‐Lena Zackrisson, Fredrik Kugelberg and Martin Josefsson. I hope that we can keep on in the future with a lot of projects together. The Department of Clinical Chemistry in Linköping, especially Professor Elvar Theodorsson, for guidance during the beginning of my time as physician in Sweden. My previous colleagues at the Department of Clinical Biopathology at the University Hospital La Fe in Valencia (Spain) for teaching and introducing me to science, especially Miguel Bretó and Pascual Bolufer, and my friend M. Teresa Contreras. All my friends from Valencia and Cabanes, for all the fun events, especially my best and eternal friends with whom I share at lot of things, M. Carmen España and Estrella Borrás (my twin soul since childhood), and my friends‐
colleagues José M. Borrás, Flor Alonso, Carlos López and Alicia Zunzunegui (my private doctor). Thanks for not letting any distance come between us. My Swedish family for a warm welcome and introduction into the Swedish tradition, especially my brothers/sister‐in‐law families of Mats Allard, Eva Högsberg and Jan Hemmingson, and in memory of my parents‐in‐Law Stina and Bertil Carlsson. My beloved Spanish family. My parents Pepe and Dolores for endless love and support in every situation during these years (“por haber cuidado con 73 amor siempre de mi, sois mi refugio y mi abrigo”), my brother Antonio for always being there for me (“mi tete, tan lejano pero tan cercano en mi corazón”), my niece Alba and my sister‐in‐Law Amparo López de Briñas (for you taking care of them). My dear children, Mario and Paloma, for filling my life with other things than research and work, for bringing happiness into my life and for all that we share. Thank you for understanding when I was working. You are so lovely. I want to mention my beautiful cat Sissela for faithful and warm company during the long days of writing. One more time, Björn Carlsson, my love, my friend, my husband, my colleague…You recruited me to Sweden. Thank for your support during my sad/sulky moments both at work and in life in general, and for making everyday‐life fun and special. “Själva drömmen, ibland är de inte bara drömmar, utan verklighet” This work has been financially been supported by the Research Council in the South‐East of Sweden (FORSS), the government fund for clinical research administered by the Östergötland County Council (Sweden), the Lions Foundation, Laboratoriemedicinskt Centrum in Östergötland (Östergötland County Council) and the Swedish Research Council (Medicine) grant no. 2006‐
4345 and K2007‐62X‐20386‐01‐3 (Finn Bengtsson). Lundbeck AB (Sweden) and Pfizer AB (Sweden) have undertaken with an unrestricted commission the therapeutic drug monitoring service through an agreement made with Berzelius Clinical Research Center AB in Linköping. My sincere gratitude to the Ganadería (breeding of bulls) Germán Vidal in Cabanes (Castellón), especially Herminio Llorens for engaging collaboration (“gracias por tu atención”). 74 REFERENCES
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Papers I ‐ IV 85