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Linköping University Medical Dissertations No. 982 Pharmacogenetic studies of paclitaxel in ovarian cancer – focus on interindividual differences in pharmacodynamics and pharmacokinetics Henrik Gréen Division of Clinical Pharmacology Department of Medicine and Care Faculty of Health Sciences Linköping University SE-581 85 Linköping, SWEDEN Linköping 2007 Cover: Upper left – The author with his daughter Linnea reading Pharmacology by Rang et al. Upper right – Pacific yew tree, Taxus brevifolia Lower left – The chemical structure of paclitaxel Lower right – Woman with DNA spiral by Don Carroll, reprinted with permission from Concept Images. © Henrik Gréen, 2007 All rights reserved. ISBN: 978-91-85715-84-8 ISSN: 0345-0082 Published articles have been reprinted with permission from the publishers: Paper I © 2006 Elsevier, Cancer Letters. Paper II © 2006 American Association of Cancer Research. Paper III © 2006 John Wiley & Sons Limited. Printed in Sweden by LTAB Linköpings Tryckeri AB, Sweden, 2007. 862. To my family Anna, Linnea & Julia Mamma & Pappa - Every active drug is a poison, when taken in large enough doses; and in some subjects a dose which is innocuous to the majority of people has toxic effects, whereas others show exceptional tolerance of the same drug Sir Archibald E. Garrod (1858–1936) - Alla är vi olika - TABLE OF CONTENTS ABSTRACT ..................................................................................................... 9 POPULÄRVETENSKAPLIG SAMMANFATTNING............................. 11 PAPERS IN THE PRESENT THESIS ....................................................... 13 ABBREVIATIONS ....................................................................................... 14 INTRODUCTION......................................................................................... 15 INDIVIDUALIZATION OF CANCER CHEMOTHERAPY ....................................... 15 GENETIC VARIATIONS IN DRUG METABOLIZING ENZYMES AND TRANSPORTERS ........................................................................................ 17 PHENOTYPING AND GENOTYPING ................................................................. 19 PACLITAXEL ................................................................................................. 20 The Clinical Use of Paclitaxel and Ovarian Cancer................................................... 21 Paclitaxel Pharmacokinetics....................................................................................... 24 Metabolism of Paclitaxel............................................................................................ 24 Resistance to Paclitaxel.............................................................................................. 28 Decreased sensitivity to apoptosis-inducing stimuli ......................................................... 29 Tubulin Alterations ........................................................................................................... 29 P-glycoprotein and other drug transporters....................................................................... 30 AIMS OF THE THESIS............................................................................... 37 MATERIAL AND METHODS.................................................................... 39 PATIENTS...................................................................................................... 39 METHODOLOGICAL OVERVIEW .................................................................... 41 Single Strand Conformation Analysis........................................................................ 41 DNA-sequencing........................................................................................................ 42 Solid Phase Extraction ............................................................................................... 44 High Performance Liquid Chromatography............................................................... 45 UV detectors ..................................................................................................................... 46 Fluorescence detectors ...................................................................................................... 47 Mass spectrometry ..................................................................................................... 47 STATISTICS ................................................................................................... 50 RESULTS AND DISCUSSION ................................................................... 51 THE SCARCITY OF β-TUBULIN MUTATIONS IN OVARIAN TUMORS................ 51 ALLELE FREQUENCIES OF SNPS IN ABCB1, CYP2C8 AND CYP3A4 IN A SWEDISH POPULATION AND IN OVARIAN CANCER PATIENTS ................... 52 CORRELATIONS OF ABCB1 GENOTYPES WITH THE RESPONSE TO PACLITAXEL ............................................................................................. 54 QUANTIFICATION OF PACLITAXEL AND ITS HYDROXYMETABOLITES IN HUMAN PLASMA ...................................................................................... 59 Extraction of paclitaxel from human plasma ............................................................. 59 Quantification of paclitaxel and metabolites using HPLC......................................... 60 Quantification of paclitaxel and metabolites using LC/MS....................................... 63 CORRELATIONS OF PACLITAXEL PHARMACOKINETICS TO ABCB1, CYP2C8, CYP3A4 GENOTYPES AND CYP3A4 PHENOTYPE ................... 67 CORRELATIONS BETWEEN PACLITAXEL EXPOSURE, RESPONSE AND GENOTYPES OF ABCB1, CYP2C8 AND CYP3A4.................................... 71 CONCLUSIONS............................................................................................ 75 FUTURE ASPECTS ..................................................................................... 77 ACKNOWLEDGEMENTS.......................................................................... 79 REFERENCES .............................................................................................. 82 ABSTRACT Ovarian cancer is one of the most common female cancer diseases in the world today and in Sweden more than 800 new cases are diagnosed every year. The standard treatment consists of chemotherapy with paclitaxel in combination with carboplatin after initial cytoreductive surgery. The response to treatment and the severity of adverse drug reactions after chemotherapy varies greatly among individuals, and one of the most important factors responsible for these differences is now recognized to be the genetic variability. One of the major obstacles to successful treatment is drug resistance. Several potential mechanisms have been suggested for the resistance to paclitaxel, such as mutations in the target protein β-tubulin, single nucleotide polymorphisms (SNPs) in the gene ABCB1, which encodes the transport protein P-glycoprotein. P-glycoprotein can mediate efflux of various drugs from cancer cells as well as from the circulation into the intestinal lumen, and overexpression and/or high activity leads to drug resistance and/or increased elimination. Another reason might be the high interindividual variability of paclitaxel plasma concentrations, which has been suggested to be influenced by variability in metabolic enzymes, such as CYP2C8 and CYP3A4, and transport proteins e.g. P-glycoprotein. In the studies constituting this thesis we have investigated the possibilities of predicting the pharmacokinetics of paclitaxel as well as the tumor response and adverse drug reactions after chemotherapy in the preparation of personalized chemotherapy. We studied the correlation between the response and the presence of mutations in the dominant β-tubulin gene and SNPs in ABCB1. DNA from 40 ovarian tumors was screened for sequence variations in the β-tubulin gene without finding any, showing that β-tubulin mutations are rare and unlikely to be a clinically relevant resistance mechanism for paclitaxel. The SNPs G2677T/A and C3435T in the ABCB1 gene were determined in 53 ovarian cancer tumors from patients with poor (progressive disease or relapse within one year) or good (disease-free survival of more than one year) response to paclitaxel-carboplatin chemotherapy. Patients homozygously mutated for G2677T/A had a higher probability of responding to chemotherapy. There was also a dose-dependent influence of the number of mutated alleles on the response to paclitaxel treatment. No correlation was found for the C3435T variant. By using a newly developed quantitative LC/MS method for the simultaneous determination of paclitaxel and its hydroxymetabolites in human plasma we assessed the individual elimination of paclitaxel in 33 ovarian cancer patients. The patients were genotyped for SNPs in the ABCB1, CYP2C8 and CYP3A4 genes and their in vivo CYP3A4 enzyme activity, tumor response and toxicity, especially the neurotoxicity, were determined. Patients heterozygous for G/A in position 2677 in ABCB1 had a significantly higher clearance of paclitaxel than patients with the wild type or homozygously mutated, but not compared to patients carrying the G/T alleles. A lower clearance of paclitaxel was also found for patients heterozygous for CYP2C8*3 when stratified according to the ABCB1 G2677T/A genotype. The CYP3A4 enzyme activity in vivo affected the relative influence of CYP2C8 and CYP3A4 on the metabolism, but not the total clearance of paclitaxel. The exposure to paclitaxel was correlated to the neurotoxicity, but not to the treatment response. In conclusion, our findings suggest that the SNP G2677T/A in the ABCB1 gene, but not β-tubulin mutations, might be a predictor for paclitaxel response and that the interindividual variability in paclitaxel pharmacokinetics might be predicted by ABCB1 and CYP2C8 genotypes and provide useful information for individualized chemotherapy. POPULÄRVETENSKAPLIG SAMMANFATTNING Ovarialcancer (äggstockscancer) är en av de vanligaste cancerformerna hos kvinnor i Sverige idag. Behandlingen består vanligen av tumörreducerande kirurgi följd av kemoterapi med paklitaxel och karboplatin. Målsättningen med detta avhandlingsarbete har varit att förbättra cytostatikabehandlingen (cellgiftsbehandlingen) med framförallt paklitaxel vid ovarialcancer genom att lägga grunden för individualisering av doser och förutsäga tumörsvaret vid behandlingen. Ett problem med dagens cancerbehandling är att många cancerceller så småningom blir resistenta mot olika cytostatika. För att angripa den mest resistenta cellen innan den induceras att öka uttrycket av, eller utveckla, fler resistensmekanismer vore det en fördel om vi före behandlingen kunde prediktera vilken dos av cytostatika som är bäst lämpad för individen samt om tumören kommer att reagera på behandlingen eller ej. En av de viktigaste faktorerna för skillnader i behandlingseffekt tros vara genetiska variationer mellan olika individer. I våra studier har vi använt genetiska metoder för att studera om vi kan prediktera tumörsvaret vid behandlingen genom att bestämma mutationer i genen för paklitaxels målprotein, β-tubulin, samt bestämma genetiska variationer i ABCB1-genen, kodande för transportproteinet P-glykoprotein. Tanken är att ett förändrat målprotein eller en förändrad förmåga hos cancercellerna eller kroppen att transportera ut paklitaxel skulle leda till en skillnad i påverkan på tumören. DNA från 40 ovarialtumörer analyserades utan att en enda sekvensvariation hittades i genen för β-tubulin, vilket tyder på att genetiska förändringar i genen för β-tubulin sannolikt inte är en klinisk relevant resistensmekanism. De normalt förekommande genetiska variationerna G2677T/A och C3435T i ABCB1-genen bestämdes i DNA från 53 ovarialtumörer där behandlingen endera givit en bra (tumörfri minst ett år) eller dålig (progression av tumören eller tumörfri mindre än ett år) antitumöreffekt. Patienter som var dubbelmuterade i position 2677 dvs hade endera T/T eller T/A (A/A hittades inte i materialet) i denna position hade en högre sannolikhet att få ett bra anti-tumörsvar vid behandlingen. Även antalet muterade baser påverkade utfallet, ju fler muterade baser i position 2677, desto högre sannolikhet att få ett bra svar på behandlingen. Andelen T eller A var också högre i den grupp av patienter som fått en lyckad behandling. För att kunna prediktera patientens individuella förmåga att bryta ner paklitaxel studerade vi inverkan av sekvensvariationer i generna för de nedbrytande enzymerna, CYP2C8 och CYP3A4, och transportproteinet Pglykoprotein (genen ABCB1) på eliminationen av läkemedlet i kroppen. Vi utvecklade en metod för att mäta paklitaxelkoncentrationerna i blodet och använde den för att studera hur snabbt 33 ovarialcancer patienter eliminerade cytostatikat från blodbanan. Hos dessa patienter bestämde vi förekomsten av kända genetiska variationer i generna ABCB1, CYP2C8 och CYP3A4 samt deras CYP3A4 enzymaktivitet i kroppen. Biverkningarna och tumörsvaret vid behandlingen utvärderades också. Eliminationen av paklitaxel hos dessa patienter var beroende av vilken bas som fanns i position 2677 i ABCB1genen och förekomsten av den genetiska varianten CYP2C8*3. Enzymaktiviteten hos CYP3A4 kunde inte påvisas påverka eliminationen av paklitaxel utan snarare vilket enzym, CYP2C8 eller CYP3A4, som var relativt dominant i respektive patient. Exponeringen av paklitaxel korrelerade till den neurologiska påverkan som patienten orsakades av cytostatikat, men kunde inte korreleras till tumörsvaret vid slutet av cytostatikabehandlingen. Sammanfattningsvis ger patientens genetiska variationer i ABCB1, men inte β-tubulin, information om behandlingsutfallet. Genetiska variationer i CYP2C8 och ABCB1 påverkar patientens förmåga att eliminera paklitaxel och kan förhoppningsvis användas för att individualisera doserna. Vår förhoppning är att resultaten i denna avhandling skall kunna användas för att individualisera och ytterligare förbättra cytostatikabehandlingen vid ovarialcancer. PAPERS IN THE PRESENT THESIS This thesis is based on the following Papers, which will be referred to by their Roman numerals: I. β-Tubulin Mutations in Ovarian Cancer Using Single Strand Conformation Analysis – Risk of False Positive Results from Paraffin Embedded Tissues. Henrik Gréen, Per Rosenberg, Peter Söderkvist, György Horvath, and Curt Peterson. Cancer Letters 236(1):148-154, 2006. II. mdr-1 Single Nucleotide Polymorphisms in Ovarian Cancer Tissue – G2677T/A Correlates with Response to Paclitaxel Chemotherapy. Henrik Gréen, Peter Söderkvist, Per Rosenberg, György Horvath, and Curt Peterson. Clinical Cancer Research 12(3 Pt 1):854-859, 2006. III. Measurement of Paclitaxel and its Metabolites in Human Plasma using Liquid Chromatography / Ion Trap Mass Spectrometry with a Sonic Spray Ionization Interface. Henrik Gréen, Karin Vretenbrant, Björn Norlander, and Curt Peterson. Rapid Communications in Mass Spectrometry 20(14):2183-2189, 2006. IV. Pharmacogenetics of Paclitaxel in the Treatment of Ovarian Cancer – a Pilot Study in Preparation of Individualized Chemotherapy. Henrik Gréen, Peter Söderkvist, Per Rosenberg, Rajaa A. Mirghani, Per Rymark, Elisabeth Åvall Lundqvist, and Curt Peterson. Submitted. ABBREVIATIONS The most important abbreviations used in this thesis are listed below: ABCB1 APCI APS ATP AUC bp BSA CI CYP dNTP dHPLC ESI HPLC LC/MS mdr-1 MRP NSCLC OATP PCR P-gp PPi RFLP RR SNP SSCA SSI TFA or mdr-1, the gene encoding P-glycoprotein Atmospheric pressure chemical ionization Adenosine 5’ phosphosulfate Adenosine triphosphate Area under the concentration curve Base pair Body surface area Confidence interval Cytochrome P450 Deoxynucleotide triphosphate Denaturing high performance liquid chromatography Electrospray ionization High performance liquid chromatography Liquid chromatography / Mass spectrometry or ABCB1, the gene encoding P-glycoprotein Multi drug resistance protein Non-small cell lung cancer Organic anion transport protein Polymerase chain reaction P-glycoprotein Pyrophosphate Restriction fragment length polymorphism Relative risk Single nucleotide polymorphism Single strand conformation analysis Sonic spray ionization Trifluoroacetic acid INTRODUCTION It is well known that most drug therapies are associated with significant interindividual variability in their therapeutic effect and adverse drug reactions. By far the most important factor responsible for these differences is now recognized to be the genetic variability. Today there are numerous examples of sequence variations in genes encoding drug-metabolizing enzymes, drug transporters or drug targets known to affect the treatment outcome. Although many non-genetic factors such as age, kidney and liver functions, concomitant therapy, drug interactions, and the nature of the disease influence the effects of medications, the list of genetic variants that affect the patients’ response is continuously growing (Daly 2003; Eichelbaum et al. 2006; Evans & McLeod 2003; Robert et al. 2005; Roses 2000). Pharmacogenetics is the study of how genetic differences influence the variability in patients’ responses to drugs (Roses 2000) and this thesis focuses on the pharmacogenetics of paclitaxel and the methodology for studying paclitaxel pharmacokinetics. Individualization of Cancer Chemotherapy Cancer chemotherapy has from its beginning in the 1940s, improved with the introduction of new substances such as the anthracyclines in the 1960s, the platinum complexes in the 1970s and the taxanes in the end of the 1980s. Even more important, the use of existing agents has become more and more INTRODUCTION | 15 efficient through the years. The treatment of cancer has gone from daily doses to intermittent treatment in the 1950s, when Skipper et al. showed that normal cells recover faster than cancer cells from cytotoxic drug exposure (Skipper et al. 1964). Combination chemotherapy, where the principle idea is to combine drugs with different mechanisms of action and dose-limiting toxicities, was introduced in the 1960s and is still today the gold standard when treating numerous types of cancer (Devita et al. 1970). A requirement for the introduction of more effective cancer chemotherapy regimens has also been improved supportive care such as the improvement of antibiotics, antiemetics, supply of blood products etc. In later years the identification of disease specific targets such as the Bcr-Abl tyrosine kinase in chronic myeloid leukemia and the development of specific inhibitors like imatinib have been shown to be successful (Druker 2002). However, at the same time as new chemotherapeutic agents are developed today, it is equally important that scientists try to improve the use of existing agents in the fight against cancer. The aim of cancer chemotherapy is rather straightforward; to kill cancer cells. However, there are relatively small differences between the normal cells and the neoplastic cells, giving these drugs a narrow therapeutic index in which the effective dose is not much different from a toxic dose in each patient. Given the high variability in interindividual response and elimination it is difficult to design a fixed dose suitable for all patients. A standard dose can be effective in some patients and toxic in others, or even worse, toxic without any tumor effect. The standard dosage used in chemotherapy today is often based on the body surface area (BSA) of the patient, calculated from the height and weight (Dubois & Dubois 1916). However, the variations in body surface are far less than the variations in drug metabolizing enzymes, drug transporters and the variability seen in pharmacokinetic parameters (Felici et al. 2002). One of the major causes of failure in chemotherapy in the clinical setting is the development of drug resistance. Failure of a patient’s tumor to respond to a specific therapy can be a result of several different mechanisms such as inability to deliver the drug to the tumor site, low intracellular concentration of the drug due to efflux pumps, rapid elimination of the drug from the circulation, etc. (Gottesman 2002). Resistance is, however, a quantitative problem and experimentally good dose-effect correlations have been shown in 16 | INTRODUCTION vitro (Liliemark & Peterson 1991). A higher dose would therefore have a higher probability of killing the most drug-resistant clone in the tumor mass. The use of high-dose chemotherapy, often in combination with hematopoietic rescue, has been studied showing an enhanced effect on the early remission of the tumor but without a benefit on long-term survival (Nieboer et al. 2005; Rodenhuis 2000). However, the results are from small studies on high risk or relapsed patients, who have probably already developed resistance to the chemotherapy. Recently scientists have tried to use dose-escalating regimens where the dose gradually is increased from course to course as the progression of side effects is monitored until the maximum tolerated dose is reached (with an ´acceptable´ degree of toxicity). Unfortunately this is the same principle used to induce drug resistance in cell lines in vitro and would therefore probably result in a selection of drug-resistant cell clones. Our belief is that higher doses, at least for some patients, should be used in the chemonaive setting and would there probably have a higher impact on the overall survival. We therefore postulate that the most important course of chemotherapy is the first, before any exposure to the drug and any selection of drug-resistant clones. It is highly desirable to a priori evaluate the patients’ predisposition for toxicity, response and pharmacokinetics of the drug to be able to administer the most suitable drug at the highest tolerable dose. Genetic Variations in Drug Metabolizing Enzymes and Transporters A genetic polymorphism is defined as the occurrence in the same population of a sequence variation of which at least two alleles exist at a high frequency, conventionally above 1% (Lewin 1990; Meyer 2000). Most genetic polymorphisms involve single base pair (bp) differences and are generally referred to as single nucleotide polymorphisms, abbreviated SNPs (Kruglyak & Nickerson 2001). Genetic polymorphisms affecting pharmacotherapy were first discovered in the 1950s due to incidental observations during World War II that some patients or volunteers suffered unpleasant and disturbing adverse effects when given standard doses of primaquine (Clayman et al. 1952; Hockwald et al. 1952; Meyer 2000; Meyer 2004). It was later shown that the differences were due to INTRODUCTION | 17 a genetically determined deficiency of glucose 6-phosphate dehydrogenase in some individuals (Alving et al. 1956; Meyer 2000). The sequencing of the human genome has allowed the identification of thousands of SNPs (Venter et al. 2001), which can play an important role in the expression level and activity of the corresponding proteins. When these polymorphisms occur in genes encoding drug metabolizing enzymes or transporters, it may change the disposition of the drug and, as a result, its efficacy may be compromised or its toxicity altered. Polymorphisms can also occur at the level of proteins directly involved in drug action, either when the protein is the target of the drug or when the protein is involved in the repair of drug-induced cell damage (Robert et al. 2005). The largest group of drug metabolizing enzymes is the cytochrome P450s (CYP), and all genes in the CYP families 1-3 are polymorphic. In the genes encoding the CYPs, SNP frequencies differ between ethnic groups and the functional importance of the various alleles also differs. In general, four phenotypes can be identified: poor metabolizers lacking functional enzyme; intermediate metabolizers, who are heterozygous for one deficient allele; extensive metabolizers, who have two normal alleles; and ultrarapid metabolizers, who have multiple gene copies. For one of the best described genetic polymorphisms in a drug metabolizing enzyme, CYP2D6, the rate of metabolism of a drug can differ 1000-fold between phenotypes (IngelmanSundberg 2004). Transport proteins have an important role in the disposition of and response to many drugs. Members of the adenosine triphosphate (ATP)binding cassette family (ABC) of membrane transporters are among the most extensively studied (Brinkmann et al. 2001; Choudhuri & Klaassen 2006; Evans & McLeod 2003; Marzolini et al. 2004). In the human ABC transport family, 49 genes have been identified and divided into seven families (Choudhuri & Klaassen 2006). A member of the ABCB family, P-glycoprotein (P-gp), is encoded by the ABCB1 gene (also called mdr-1). The ABCB1 gene contains several polymorphisms that have been shown to affect both the expression of P-gp as well as the efflux of several drugs (Marzolini et al. 2004). Genetic variations in genes encoding drug targets (i.e. receptors or cell cycle proteins) can have a profound impact on drug efficacy and over 25 examples have already been identified. For example, sequence variations in the 18 | INTRODUCTION gene for the β2-adrenoreceptor affects the response to β2-agonists and the renoprotective action of angiotensin-converting enzyme inhibitors are affected by SNPs in the gene for angiotensin-converting enzyme (Evans & McLeod 2003). Phenotyping and Genotyping Most methods for determining a patient’s ability to metabolize or transport a drug or predict its response utilize phenotyping and/or genotyping of the involved mechanism (Daly 2004; Linder et al. 1997; Mathijssen & van Schaik 2006). Phenotyping usually involve either the direct measurement of the enzyme activity in a tissue sample or the use of a probe drug (the metabolism of which is solely dependent on a specific enzyme) and the subsequent determination of the substance and metabolites in either plasma or urine for assessment of the in vivo activity (Daly 2004). Genotyping is the determination of specific genetic sequence variations, usually functionally important SNPs in the gene encoding a specific protein (Linder et al. 1997). Phenotyping has the advantage of the actual estimation of the enzyme activity and for some methods the in vivo measurement and the overall process of the drug metabolism. Phenotyping is mainly applicable to polymorphism affecting drug disposition and not response. Other difficulties with phenotyping are the usually complicated protocols, risk of adverse drug reactions, incorrect assessment due to coadministration of drugs and drug-drug interactions (if this is not what’s to be determined) and confounding effects of the disease (Linder et al. 1997). Genotyping is now more widely used than phenotyping and has the advantage that it only has to be performed once and requires a small amount of DNA-containing material such as blood or saliva. However, one should always consider the presence of unknown sequence variants that can affect the activity. Most genotyping methods involve the amplification of a specific gene segment using a polymerase chain reaction (PCR) with a subsequent detection of the sequence variant by methods such as PCRrestriction fragment length polymorphism (PCR-RFLP) analysis or single strand conformation analysis (SSCA) or more specialized and high-throughput detection methods such as single bp extension, microarrays and pyrosequencing (Daly 2004). INTRODUCTION | 19 The method(s) of choice will depend on a number of factors such as the specific drug studied, the enzyme(s) involved, number of samples, equipment available, etc., and combining phenotyping and genotyping may be appropriate for some drugs. Paclitaxel In the late 1950s the U.S. National Cancer Institute (NCI) initiated a program to screen 35,000 plant species for anti-cancer activity. As part of this program the U.S. Forest Service collected bark from the Pacific yew, Taxus brevifolia, and shipped it to NCI. Extracts from the bark were found to have anti-tumor activity and in 1971 the active ingredient was identified as paclitaxel (Wani et al. 1971); Figure 1. When the NCI screening program was closed in 1981 paclitaxel was the only compound left to be tested in humans (Stephenson 2002). Figure 1. The chemical structure of paclitaxel. In 1979 Susan Horowitz identified paclitaxel’s unique mechanism of action. Paclitaxel prevents cell division by promoting the assembly of stable microtubules from α- and β-tubulin heterodimers and inhibiting their depolymerisation (Schiff et al. 1979). This is an opposite effect to the vinca alkaloids, which inhibit the polymerization of the microtubule (Rang et al. 1995). The microtubules are part of the cytoskeleton and are involved in mitosis, cell shape determinations, cell locomotion and movement of 20 | INTRODUCTION intracellular organelles (Alberts 1994). A typical microtubule lasts for about 10 minutes before it is disassembled and the proteins are used to build a new one (Goodsell 2000). The cellular target for paclitaxel is β-tubulin (Manfredi et al. 1982) in the microtubule. Exposed cells are arrested in the G2/M-phase of the cell cycle (Schiff & Horwitz 1980) and eventually the cells undergo apoptosis. Mechanisms for paclitaxel-induced apoptosis, independent of microtubule stabilization, have also been suggested, such as a direct phosphorylation of bcl-2, induction of cytotoxic cytokines and pro-apoptotic proteins in a concentration-dependent manner (Torres & Horwitz 1998; Wang et al. 2000). However, the exact mechanisms for paclitaxel-induced cytotoxicity is still not clear (Zhao et al. 2005). In the mid-1980s paclitaxel was first tested on humans and the results of the phase I trial were reported in 1987 (Wiernik et al. 1987). Acute hypersensitivity reactions caused some delay in the trials, but they were later attributed to the solvent Cremophor EL. In 1991 NCI started collaborating with Bristol Myers Squibb for the development of the drug, marketed as TaxolTM (Stephenson 2002). In 1996, a phase III study showed that survival time for women with advanced ovarian cancer treated with the first line paclitaxel-cisplatin combination was extended by 50% over the standard cisplatin-cyclophosphamide treatment (McGuire et al. 1996). Shortly thereafter paclitaxel in combination with cisplatin was introduced as the primary treatment of carcinoma of the ovary in patients with advanced disease or residual disease after initial surgery. The Clinical Use of Paclitaxel and Ovarian Cancer In Sweden today, paclitaxel is primarily used for the treatment of ovarian cancer, breast cancer, non-small cell lung cancer (NSCLC) and AIDS-related Kaposi's sarcoma (Läkemedelsindustriföreningen 2006). In this thesis we focused on the treatment of ovarian cancer. Paclitaxel in combination with carboplatin is today considered the standard chemotherapy of ovarian cancer after cytoreductive surgery. Paclitaxel is used in standardized doses according to body surface area (BSA) although this gives a high variability in drug concentrations. Carboplatin on the other hand is almost entirely eliminated by renal excretion and the myelosuppression is proportional to the exposure of the drug. Therefore the dose of carboplatin is individualized according to INTRODUCTION | 21 Calvert’s Formula Carboplatin dose (mg) = AUC x (GFR+25 mL/min) Calvert’s formula in which the dose depends on the glomerular filtration rate (GFR) of the patient and the desired exposure of the drug expressed as area under the concentration curve (AUC) (Alberts & Dorr 1998; Calvert et al. 1989). In Sweden, paclitaxel is usually given at a dose of 175 mg/m2 during a three-hour infusion followed by a one-hour infusion of carboplatin aiming at an AUC of 5 or 6 mg*min/mL administered every three weeks for a minimum of six courses. For paclitaxel alternative infusion periods (1, 3 or 24 hours) and course intervals (every third week or weekly) are being considered, and especially for patients with poor general conditions, the dose of paclitaxel is usually reduced to 135 mg/m2. The weekly regimen, where paclitaxel usually is given at a dose of 80 mg/m2 and carboplatin every third week has been suggested as a more dose-dense alternative (Marchetti et al. 2002). The incidence of ovarian cancer is among the highest in the world in North America, Israel and Scandinavia (Parkin & International Agency for Research on Cancer 2002). The age standardized incidence rate in Sweden 2004 was 16.6 annual cases per 100,000 females with a median age of diagnosis of 60-64 years (The National Board of Health and Welfare 2005). Ovarian cancer can be divided into three broad subgroups – epithelial, stromal and germ cell tumors – each with different clinical behavior. Epithelial ovarian cancer is the most common, constituting more than 85% of the ovarian cancers (Agarwal & Kaye 2003). Both the stromal and germ cell ovarian cancers consist of diverse groups of diagnoses of which most are rare and with different prognosis (Henriksson et al. 1998). The focus in this thesis will be on the epithelial ovarian cancers. The epithelial ovarian tumors are characterized according to FIGO stage (I-IV, subgroups A-C), histology and grade of differentiation (well-, moderately- or poorly-differentiated). The FIGO staging is based on findings at the surgery and describes the spread of the disease. Stage I constitutes tumors localized to the ovaries, while stage II tumors have spread to the surrounding tissue, stage III to the peritoneal cavity and stage IV tumors have distant metastases. The tumors are subdivided into the following histological types: serous, mucinous, clear cell, endometrioid, undifferentiated, or adenocarcinoma (Heintz et al. 2003). One of the most important prognostic 22 | INTRODUCTION factors in the treatment of ovarian cancer is the extent to which the initial surgery reduces the tumor mass. Median survival for patients where the remaining tumor is larger than 2 cm is 12-16 months as compared to 40-45 months if the tumor is reduced below 2 cm (Mutch 2002). Other important prognostic factors are FIGO stage (see Table I) and histology where endometrioid and mucinous histotypes have a better prognosis, whereas serous and undifferentiated cancers have a worse prognosis (Heintz et al. 2003). However, progression-free survival and overall survival are improved in all patients by chemotherapy after initial surgery (Agarwal & Kaye 2003). Table I. Five-year survival and complete response by FIGO stage. Complete 5-year FIGO stage response survival I 94,8 % 82.3 % II 82,7 % 69.2 % III 51,7 % 31.6 % IV 23,5 % 13.4 % Note: Patients treated in 1996-1998. FIGO Annual report (Heintz et al. 2003) First-line chemotherapy with paclitaxel-carboplatin combination given every three weeks for six courses yields response in about 80% of ovarian cancer patients (Bolis et al. 2004). However, the median progression-free survival is only 15 months in these patients, since most relapse (Vasey et al. 2004). Interestingly, these patients can be re-treated with the same drugs (paclitaxel-carboplatin) with response rates that are proportional to the treatment-free interval. Patients who have a treatment-free interval of two years will have a complete response rate of ~75%, but this falls to 35% for a treatment-free interval of six to nine months (Gronlund et al. 2001). This reduced response rate is due to the development of drug resistance and patients with a progression free interval less than six months is determined clinically drug resistant. This results in a five-year survival of only 35% in relapsed patients with advanced ovarian cancer (Goldspiel 1997), despite the fact that most tumors are chemosensitive during the first-line treatment. INTRODUCTION | 23 Paclitaxel Pharmacokinetics Paclitaxel is insoluble in aqueous solution and is therefore formulated in 50% ethanol and 50% Cremophore EL (a polyoxyethylated castor oil derivative). Approximately 95% of the substance is bound to plasma protein and the volume of distribution is about 55 L/m2 (Wiernik et al. 1987). After a 3-hour infusion the plasma concentration of paclitaxel is decreased in a triexponential fashion. The initial phase has a t½ of 10–30 minutes, the second phase has a t½ of about 1–4 hours and the final elimination t½ is about 8-34 hours (Huizing et al. 1995b). No significant change in pharmacokinetics has been observed between different courses (Huizing et al. 1997a; Huizing et al. 1997b). For a 3-hour infusion of 175 mg/m2 the AUC ranges from 6 – 30 µmol*h/L and a clearance of 15–36 L/h (Gianni et al. 1995; Huizing et al. 1997a; Huizing et al. 1993; Huizing et al. 1997b; Nakajima et al. 2005; Nakajima et al. 2006). The pharmacokinetics is nonlinear with a disproportional increase in AUC and Cmax with an increase in dose (Gianni et al. 1995; Huizing et al. 1997a). The nonlinear pharmacokinetics of paclitaxel has been ascribed to a saturable elimination (transport) (Sonnichsen et al. 1994) and/or a saturable distribution (binding) (Karlsson et al. 1999), but also as caused by the micelle forming vehicle Cremophor El (Sparreboom et al. 1999). Several pharmacokinetic parameters have been shown to be correlated with the effect of paclitaxel. Higher plasma concentration and especially the duration of paclitaxel concentrations above a threshold correlates with the response to chemotherapy (Huizing et al. 1997a; Huizing et al. 1993; Mielke et al. 2005a) as well as with the toxicity (Gianni et al. 1995; Huizing et al. 1993; Mielke et al. 2005b). Metabolism of Paclitaxel Paclitaxel is primarily eliminated through hepatic metabolism and biliary clearance. In the liver paclitaxel is metabolized by two CYP-enzymes. The formation of 6α-hydroxypaclitaxel is catalyzed by CYP2C8 (Cresteil et al. 1994; Rahman et al. 1994) and p-3’-hydroxypaclitaxel is formed by CYP3A4 (Cresteil et al. 1994; Harris et al. 1994b). Both metabolites can be further oxidized to 6α, p-3’-dihydroxypaclitaxel by CYP2C8 and CYP3A4, respectively (Sonnichsen et al. 1995); see Figure 2. The metabolites are less cytotoxic and present in plasma in lower concentrations than paclitaxel and therefore believed to have 24 | INTRODUCTION no significant clinical effect on the tumors (Harris et al. 1994a; Kumar et al. 1995; Monsarrat et al. 1993; Sparreboom et al. 1995a). The major part of both paclitaxel and the metabolites are excreted in the feces, indicating extensive biliary clearance (Monsarrat et al. 1993; Monsarrat et al. 1998). Total urinary excretion is ~ 5% and in most patients the fecal excretion is ~ 80% of the dose (Rowinsky et al. 1990; Walle et al. 1995). The major part of the dose is excreted as metabolites indicating that CYP2C8 and CYP3A4 are important in the elimination of paclitaxel. In bile from patients treated with paclitaxel the most abundant metabolites are 6α-, p-3’-dihydroxypaclitaxel and 6αhydroxypaclitaxel. Paclitaxel and p-3’-hydroxypaclitaxel have also been found but at a much lower concentration (Harris et al. 1994a; Monsarrat et al. 1993; Monsarrat et al. 1998). Paclitaxel CYP2C8 CYP3A4 6α-hydroxypaclitaxel p-3’-hydroxypaclitaxel CYP3A4 CYP2C8 6α-, p-3’-dihydroxypaclitaxel Figure 2. The metabolism of paclitaxel by CYP2C8 and CYP3A4 in the liver. CYP2C8 is a major human hepatic P450 enzyme, constituting about 7% of the total microsomal CYP content in the liver (Totah & Rettie 2005). Extrahepatic expression of CYP2C8 mRNA has also been detected in the kidney, intestine, brain, mammory glands, ovaries as well as the adrenal glands (Klose et al. 1999). Substrates for CYP2C8 include both endogenous substances such as arachidonic acid and retinoic acid as well as therapeutic agents, e.g. paclitaxel, ibuprofen, repaglinide, cervastatin and amodiaquine (Totah & Rettie 2005). The gene for CYP2C8 is located on chromosome 10q24 and consists of 9 exons (Klose et al. 1999). Several SNPs have been identified in both coding and non-coding regions, some of which correspond to proteins with reduced enzymatic activity; see Table II (Bahadur et al. 2002; Dai et al. 2001; Hichiya et al. 2005; Soyama et al. 2002b). CYP2C8*3, expressed most commonly in Caucasian populations, has a dual amino acid change INTRODUCTION | 25 Arg139Lys and Lys399Arg (Dai et al. 2001). In vitro the CYP2C8*3 allele has shown decreased Vmax (Soyama et al. 2001) and activity (Dai et al. 2001) for the conversion of paclitaxel to 6α-hydroxypaclitaxel but maintains the catalytic activity towards amiodarone (Soyama et al. 2002a) as compared to the wild type. Liver microsomes from individuals heterozygous for CYP2C8*3 also had lower activity as compared to the wild type (Bahadur et al. 2002), although the activity did overlap and the results could not be reproduced in a smaller study (Taniguchi et al. 2005). CYP2C8*3 also has a reduced turnover for arachidonic acid in vitro but CYP2C8*2 which has a reduced paclitaxel activity, did not (Dai et al. 2001). Both these findings indicate that CYP2C8 exhibits ligand-dependent polymorphisms probably due to unique binding sites for different substrates (Totah & Rettie 2005). Altered drug metabolism for CYP2C8*3 has also been shown in vivo for ibuprofen and repaglinide (GarciaMartin et al. 2004; Niemi et al. 2003). Table II. CYP2C8 allele nomenclature and altered activity. Allele CYP2C8*1B CYP2C8*1C CYP2C8*2 CYP2C8*3 CYP2C8*4 CYP2C8*5 CYP2C8*6 CYP2C8*7 CYP2C8*8 CYP2C8*9 CYP2C8*10 CYP2C8 P404A Nucleotide change C-271A C-370A A805T G416A, A1196G C792G 475 Del A G511A C556T C556G A740G G1149T C1210G Amino acid change I269F R139K, K399R I264M Frame shift G171S R186X R186G K247R K383N P404A Enzyme activity 1 Increased Km Decreased activity None None Decreased Note: 1 Altered activity for at least one substrate in vivo or in vitro. In most people, CYP3A4 is the most highly expressed CYP enzyme in the liver although the interindividual protein expression varies as much as 40-fold (Lamba et al. 2002; Smith et al. 1998). The enzyme is also expressed in the lungs, stomach, small intestine, nasal mucosa and colon, of which the expression in the small intestine is believed to be the most important extrahepatic expression for drug metabolism (Ding & Kaminsky 2003). CYP3A4 is involved in conversion of the majority of drugs with known metabolic pathways. The CYP3A4 activity can be inhibited by e.g. grapefruit 26 | INTRODUCTION juice or ketoconazole and induced by substances such as barbiturates and St John’s wort (Whitten et al. 2006). The reason for the CYP3A4 metabolic versatility is believed to be due to a large active site that permits the binding of structurally diverse molecules. The enzyme is also known to accommodate several ligands simultaneously in the active site which may result in enhanced activity or reduced product formation, depending on the concentration of the substrate and the nature of the second or third ligand (Anzenbacher & Anzenbacherova 2001; Lamba et al. 2002). Genetic variability has been established in the CYP3A4 gene. However, due to the low frequencies and limited effect of these SNPs on the enzyme expression and catalytic function, the genetic variations are unlikely to be the major cause of interindividual differences in CYP3A4 activity (Lamba et al. 2002; Rodriguez-Antona & Ingelman-Sundberg 2006). The most common allelic variant CYP3A4*1B, A392G in the flanking region (located in the nifedipine response element and present at ~5% in Caucasians), has been shown to affect the activity and correlates to lower hydroxylation of quinine and less binding of nuclear proteins (Lamba et al. 2002; Rodriguez-Antona & Ingelman-Sundberg 2006). Although several other alleles have been reported, the responsibility of a particular allele(s) for the variable activity has not yet been established (Anzenbacher & Anzenbacherova 2001). Due to the inability to determine the CYP3A4 activity by genotyping, several probes have been proposed for measurement of CYP3A4 activity in vivo (Kivisto & Kroemer 1997; Masica et al. 2004; Smith et al. 1998). Classically the erythromycin breath test, endogenous/exogenous cortisol hydroxylation and midazolam clearance have been used to assess CYP3A4 activity in vivo. More recently alprazolam, triazolam and quinine have been validated as CYP3A4 probes (Anzenbacher & Anzenbacherova 2001; Masica et al. 2004; Mirghani et al. 2003; Smith et al. 1998). Some probes do correlate with each other although for some comparisons there is only a weak correlation (Kenworthy et al. 1999; Kinirons et al. 1999; Lown et al. 1995; Masica et al. 2004). Noteworthy is that midazolam and erythromycin appear to bind to different domains within the active site and that the CYP3A4 catalyses different types of reactions such as Ndemethylation of erythromycin and the hydroxylation of midazolam and quinine (Kenworthy et al. 1999; Smith et al. 1998). With respect to anticancer treatment, the CYP3A4 activity is expected to influence the response to INTRODUCTION | 27 several drugs such as paclitaxel, docetaxel and imatinib (Green et al. 2006; Rodriguez-Antona & Ingelman-Sundberg 2006). Docetaxel (an analog to paclitaxel preferably used in the treatment of breast cancer) is metabolized to an inactive hydroxymetabolite by CYP3A4. The clearance of docetaxel can be predicted by CYP3A4 activity measured by the erythromycin breath test and midazolam clearance and the greatest toxicity was found in patients with the lowest enzyme activity (Goh et al. 2002; Hirth et al. 2000). Similarly, Yamaoto et al. individualized docetaxel doses based on CYP3A4 activity measured by urinary cortisol metabolite concentrations after administration of cortisol and thereby decreased the pharmacokinetic variability of docetaxel when compared to BSA-based dosing (Yamamoto et al. 2005). If and to what extent interindividual variations in enzymatic activity of CYP2C8 and CYP3A4 are influencing the pharmacokinetics of paclitaxel has not yet been fully revealed. Resistance to Paclitaxel Drug resistance is a major obstacle to successful treatment of cancer patients and is believed to cause treatment failure and death in more than 90% of ovarian cancer patients with metastatic disease (Agarwal & Kaye 2003). Several potential mechanisms have been reported to account for cellular resistance to paclitaxel including decreased tubulin binding due to sequence variation (Monzo et al. 2002), alterations in microtubule dynamics (Orr et al. 2003), decreased sensitivity to apoptosis-inducing stimuli (Blagosklonny & Fojo 1999), and overexpression of the transport protein P-glycoprotein (Gottesman 2002). Non-cellular mechanism for failure to response can broadly be divided into two groups: pharmacokinetics and tumor microenvironment. Experimental studies show that, for many cytotoxic agents, the number of cancer cells killed is proportional to the drug exposure. Inadequate intratumor drug concentrations due to interindividual differences in pharmacokinetic variables might therefore explain some cases of lack of response. These factors include difference in renal clearance (as with carboplatin), hepatic drug metabolism (CYP2C8 and CYP3A4 for paclitaxel) and tumor vascularity (Agarwal & Kaye 2003; Iyer & Ratain 1998). The tumor micro-environment can also affect the tumor response. Hypoxia has been suggested to be associated with radiosensitivity as well as chemosensitivity, 28 | INTRODUCTION and cell-cell interaction in vitro has also been shown to correlate to chemosensitivity (Agarwal & Kaye 2003). This might be due to reduced proliferation during hypoxia, diminished free-radical production, enhanced drug detoxification and/or hypoxia-induced factor-1 mediated induction of survival factors and inhibition of apoptosis (Teicher 1994). Decreased sensitivity to apoptosis-inducing stimuli A number of proteins involved in the apoptotic signaling pathways and pathways downstream of drug-target have been suggested to affect the resistance to paclitaxel. These include oncogenes such as Ras and Akt, tumorsuppressor genes such as p53 and Pten, whereas others are components of the apoptotic mechanism such as survivin, Bak and Bcl-2 (Agarwal & Kaye 2003). Up-regulation of anti-apoptotic Bcl-2 and down-regulation pro-apoptotic proteins such as Bak have been shown to affect paclitaxel sensitivity in vitro (Ferlini et al. 2003; Jones et al. 1998; Mano et al. 1999). However, in vivo the correlation is not obvious and the paclitaxel sensitivity has been shown to be independent of Bcl-2 and a high as well as a low expression of Bcl-2 has been found to be a good prognostic factor (Marx et al. 1997; Poelman et al. 2000). Their exact role in clinical paclitaxel resistance therefore remains to be defined. Tubulin Alterations Tubulin alterations such as changes in tubulin expression and mutations in the tubulin genes have been suggested as a resistance mechanism for paclitaxel (Orr et al. 2003). However, the significance of these alterations has yet not been established mainly due to the existence of several tubulin isoforms, encoded by a large gene family consisting of both functional and nonfunctional genes with high homology. In humans, six different β-tubulin isoforms have been identified and are classified as follows (Roman numerals represent the protein class and Arabic numerals the gene): class I, M40; class II, β9; class III, β4; class IVa, 5β; class IVb, β2; class VI, β1. The M40 gene has four exons and encodes a protein, 444 amino acids long. The other isotypes show similar sequences, but specific regions of variability beyond amino acid 430 can be used to group the isotypes. The expression of these isoforms is tissue-dependent, but classes I and IVb are ubiquitously expressed and class I (gene M40) constitutes the major fraction of the β-tubulin INTRODUCTION | 29 isoforms (Sullivan & Cleveland 1986). A higher expression of different βtubulin isoforms, mainly classes III and IVa, have been found in paclitaxelresistant cell lines as compared to the parental cell line (Kavallaris et al. 1997; Nicoletti et al.; Orr et al. 2003). In clinical samples, class III overexpression has been associated with poor response to chemotherapy although conflicting results have been published (Ferrandina et al. 2006; Mozzetti et al. 2005; Nicoletti et al. 2001). Mutations in the β-tubulin genes, especially gene M40, resulting in tubulin units with different affinity for paclitaxel have also been suggested as a resistance mechanism. In resistant cell lines, point mutations have been found at several locations in the β-tubulin gene M40 as well as in Kα1-tubulin (Giannakakou et al. 2000; Giannakakou et al. 1997; GonzalezGaray et al. 1999; Martello et al. 2003). Several research groups have studied the presence of tubulin mutations in clinical samples. Monzo et al. (1999) identified β-tubulin mutations in 33% of patients with non-small-cell lung cancer, which correlated to poor response to paclitaxel containing therapy (Monzo et al. 1999). Many groups have attempted to confirm this initial study; but conflicting results have been reported (Kelley et al. 2001; Kohonen-Corish et al. 2002; Sale et al. 2002b; Tsurutani et al. 2002). The homology of the tubulin genes and the questioned accuracy of the original gene sequence for βtubulin M40 (J00314) have made it difficult to draw any decisive conclusions (Kelley 2002; Monzo et al. 2002; Sale et al. 2002a). P-glycoprotein and other drug transporters Alterations in drug influx and efflux due to cell membrane transport proteins such as P-glycoprotein (P-gp), multidrug resistance protein (MRP), organic anion transporters (OATP) 1B1 and 1B3 can affect intracellular drug concentrations and might have an effect on the chemosensitivty to paclitaxel (see Figure 3). The uptake of paclitaxel into the cells has been suggested to be either a diffusion process or mediated by OATP1B1 or OATP1B3, which mediate the cellular uptake of a large number of structurally diverse endogenous substances and xenobiotics (Tirona & Kim 2002). The expression of these transporters is restricted to the basolateral membrane of the hepatocytes. However, the expression in the ovaries has not been investigated (Konig et al. 2000a; Konig et al. 2000b). OATP1B3 has been shown to mediate the influx 30 | INTRODUCTION of paclitaxel and suggested to be the a key regulator for hepatic uptake of paclitaxel (Smith et al. 2005). The gene encoding OATP1B3 (SLCO1B3) contains several SNPs which affect the transport function of the protein (Letschert et al. 2004; Tirona & Kim 2002). The effect of these SNPs on the clearance of paclitaxel remains unclear. Figure 3. Schematic representation of the transport and metabolism of paclitaxel in the hepatocyte. The OATP located on the basolateral membrane of the hepatocyte facilitate the uptake of paclitaxel from the portal circulation. Paclitaxel is metabolized by CYP2C8 and CYP3A4 in the hepatocyte. P-gp mediates the canalicular efflux of paclitaxel and metabolites from the hepatocytes into the bile. P-gp is a 170 kDa transport protein encoded by the ABCB1 gene (mdr-1) and contains 1280 amino acids constituting 12 transmembrane domains and 2 ATP-binding motifs (Figure 5). Cytotoxic drugs of natural origin with very different chemical structures and mechanisms of action, such as vinca alkaloids, anthracyclines, epipodophyllotoxins, docetaxel and paclitaxel, can be extruded by P-gp through the cell membranes of resistant cells and crossresistance occurs (Germann 1996; Gottesman & Pastan 1993). Variable levels of P-gp have been observed in different multidrug resistant cell lines and an increased expression of P-gp generally correlates with an increase of drug resistance (Fujimaki et al. 2002). However, the opposite might not be true due to the presence of other resistance factors (Sonneveld 2000). P-gp is also expressed in non-malignant tissue and found in the canalicular surface of the hepatocytes, the apical surface of proximal tubular cells in the kidneys, and the brush border surface of enterocytes (Thiebaut et al. 1987). The localization of P-gp to the liver, kidneys and intestine makes it a major determinant for absorption, hepatic and renal elimination of several drugs. In INTRODUCTION | 31 P-gp P-gp P-gp P-gp Figure 4. Schematic representation of the transport of paclitaxel by P-glycoprotein. Elimination of paclitaxel from the liver to the bile, from the central nervous system and from the ovaries as well as the reduced reabsorption of paclitaxel in the small intestine is believed to be associated with P-gp transport. addition, P-gp is expressed in the epithelium of the brain choroid plexus (the blood-cerebrospinal fluid barrier) and on the luminal surface of blood capillaries of the brain (the blood-brain barrier) as well as other tissues with blood-tissue barriers such as the placenta, the ovaries, and the testis (Thiebaut et al. 1987) making it an important protein for the protection of xenobiotic sensitive organs. Paclitaxel is a P-gp substrate and the transport function might affect paclitaxel pharmacokinetics and pharmacodynamics in several ways; see Figure 4. Overexpression of P-gp on tumor cells resulting in an enhanced efflux of the drug is a known in vitro resistance mechanism for paclitaxel (Germann 1996; Gottesman & Pastan 1993). Paclitaxel can also be transported by the efflux protein MRP-7 but none of the other MRPs has been shown to transport paclitaxel in vitro (Allen et al. 2000; Hopper-Borge et al. 2004). Also the basal expression of P-gp has been shown to be important 32 | INTRODUCTION for the sensitivity to paclitaxel in vitro, and at high drug concentrations the transport of paclitaxel by P-gp has been shown to be saturable (Allen et al. 2000; Jang et al. 2001). Clinical resistance and poor response to paclitaxel have also been correlated with a high expression of P-gp in tumors (Kamazawa et al. 2002; Penson et al. 2004; Yeh et al. 2003) indicating an importance of the protein in the in vivo environment. P-gp also affects the pharmacokinetics of paclitaxel. Co-administering a P-gp inhibitor during paclitaxel treatment results in a decreased clearance of paclitaxel and marked increase in paclitaxel exposure (Berg et al. 1995). Studies in mice have also shown that P-gp limits the oral uptake of paclitaxel and that P-gp mediates the direct excretion of the drug from the systemic circulation via the hepatocytes and bile into the intestinal lumen as well as the absorption (or re-absorption) of paclitaxel in the intestine (Sparreboom et al. 1997; van Asperen et al. 1998). Figure 3 illustrates the transport and metabolism of paclitaxel in the hepatocytes. Paclitaxel penetrates to the cerebrospinal fluid although at very low concentrations (Gelderblom et al. 2003). The net penetration is dependent on the P-gp efflux and blocking the transport significantly increases the concentrations of paclitaxel in the central nervous system (Fellner et al. 2002; Kemper et al. 2003). In knockout mice (mdr1ab -/-) the penetration of paclitaxel to the central nervous system increases even further (Kemper et al. 2003). However, the importance of P-gp function for the neurotoxicity and the anti-neoplastic effect on brain tumor of paclitaxel remains unclear. In the late 1980s the first study of the effect and presence of SNPs in the ABCB1 gene (Figure 5) was presented and some sequence variants showed an altered resistance phenotype (Kioka et al. 1989). However, it was not until 2000 when Hoffmeyer et al. systematically screened the mdr-1 gene for sequence variations that the functional consequence of the SNPs were shown in vivo. This study indicated that the synonymous SNP in exon 26, C3435T, correlated with the expression level of P-gp in the intestine. Individuals homozygous for this SNP had lower P-gp expression and showed higher plasma levels of the P-gp substrate digoxin (Hoffmeyer et al. 2000). The C3435T SNP affects the mRNA stability and the 3435T allele is correlated to a lower expression (Wang et al. 2005a). More than 25 SNPs have been reported for the ABCB1 gene of which more than 20 are in the coding region (Marzolini et al. 2004; Pauli-Magnus & Kroetz 2004; Sakaeda et al. 2002). INTRODUCTION | 33 Figure 5. Secondary structure of P-glycoprotein with coding region SNPs (with an allele frequency of more than 1% in Caucasions). Non-synonymous amino acid changes are shown as filled balls with the indicated amino acid change and position. Synonymous changes are shown as encircled balls with the indicated nucleotide change and position in the mRNA. The ATP binding domains are indicated by the two boxes. However, most SNPs are present at very low frequencies and there are large interethnic variations (Marzolini et al. 2004). In Caucasians the SNPs C1236T, G2677T/A (Ala893Ser/Thr) and C3435T have been studied most frequently since they have been shown to correlate with the P-gp expression and phenotype (Hoffmeyer et al. 2000; Tanabe et al. 2001). These three SNPs are also linked to each other and the effect of one SNP might be difficult to distinguish from the others (Kroetz et al. 2003). A total of 64 haplotypes have been described and the two most common are the wild type allele (15% in Caucasians) and ABCB1*13 containing six sequence variants, three intronic 1236T, 2677T and 3435T (32% in Caucasians) (Kroetz et al. 2003). How these SNPs affect the transport function of P-gp has not been studied extensively in vitro (Kim et al. 2001; Kimchi-Sarfaty et al. 2002; Kroetz et al. 2003; Morita et al. 34 | INTRODUCTION 2003). Kimchi-Sarfaty et al. (2002) showed that the wild type P-gp causes a slightly higher efflux of paclitaxel than cells carrying a plasmid containing the 2677T variant (Kimchi-Sarfaty et al. 2002). However, for most other substrates such as verapamil, vinblastine, calcein-AM, prazosin, bisantrene, forskolin, digoxin (0.1 μM) and cyclosporin A the transport was not affected, but only one concentration was tested for each substrate (Kimchi-Sarfaty et al. 2002; Kroetz et al. 2003; Morita et al. 2003). Unexpectedly an enhanced efflux has been reported for digoxin in a very high concentration (50 μM) in cells expressing the P-gp Ser893 variant (2677T), indicating differences in substrate specificity for the different variants of P-gp (Kim et al. 2001). For paclitaxel the SNP G1199T/A (Ser400Ile/Asn) has been shown to affect the sensitivity of cells expressing the variants. Cells expressing the 1199A variant were more resistant to paclitaxel than wild type cells, while cells with the 1199T variant were more sensitive to paclitaxel. The SNP also affected the resistance to vinblastine, vincristine and doxorubicin, but not topotecan in the same way (Crouthamel et al. 2006). However, the effect of these SNPs on the response to paclitaxel and the pharmacokinetics of paclitaxel were not known when this thesis was initiated. INTRODUCTION | 35 36 | INTRODUCTION AIMS OF THE THESIS The general aim of this thesis was to study the pharmacogenetics of paclitaxel in preparation for personalized chemotherapy. Specific aims: 1. To investigate the presence of mutations in the β-tubulin gene M40, as a potential resistance mechanism, and its correlation to the clinical outcome of ovarian cancer patients treated with paclitaxel in combination with carboplatin 2. To evaluate the effects of the single nucleotide polymorphisms G2677T/A and C3435T in the ABCB1/mdr-1 gene on the response to paclitaxel treatment in ovarian cancer and establish the allele frequencies of these SNPs in a Swedish population 3. To develop a method for quantification of paclitaxel and its metabolites in human plasma 4. To evaluate the effects of sequence variations in the CYP2C8, ABCB1, and CYP3A4 genes and the CYP3A4 phenotype on the pharmacokinetics of AIMS OF THE THESIS |37 paclitaxel as well as the toxicity and anti-tumor response during treatment with paclitaxel in combination with carboplatin 38 | AIMS OF THE THESIS MATERIAL AND METHODS Patients The patients in the present investigation were diagnosed with gynecological cancer, predominantly epithelial ovarian cancer. A summary of the subjects and their diagnoses included in the individual papers are presented in Table III. Table III. Diagnoses of the patients included. Paper I II IV Epithelial Ovarian Cancer 40 51 26 Fallopian Tube Carcinoma 2 Peritoneal Cancer 4 Corpus Uteri Carcinoma 1 Cervix Uteri Carcinoma 1 Ovarian or Peritoneal Cancer 1 Total 40 53 33 In Paper I, epithelial ovarian cancer patients were selected from four patient groups based on their received chemotherapy and the therapeutic effect. The patients in the first group had been treated with paclitaxel in combination with carboplatin and achieved a complete response and were tumor free for at least 18 months. The second group was treated with the same regimen but had tumor progression during treatment or a relapse within nine months. The other two groups had the same clinical outcomes as the two first groups but were treated with non-tubulin affecting chemotherapy. In MATERIAL AND METHODS | 39 Paper II, a total of 53 patients were included from a biobank at Sahlgrenska University Hospital, Gothenburg or from the Linköping University Hospital. Fifty-one were diagnosed with epithelial ovarian cancer and two had fallopian tube carcinoma; both diagnoses were expected to have the same clinical response and exclusion of the latter two did not affect the results presented. In Papers II and IV the patients were treated with paclitaxel 175 mg/m2 in combination with carboplatin except for a few patients with general poor condition where the paclitaxel dose was reduced to 135 mg/m2. In Paper IV the patients were mainly diagnosed with either ovarian cancer or peritoneal cancer. In rare cases, cancer arises in the peritoneum – the inner lining of the abdominal cavity. Peritoneal cancer is believed to be very similar to ovarian cancer both in clinical behaviour, response to treatment and biology. These patients were therefore included in the same group as the ovarian cancer patients. Two patients with other diagnoses were included in Paper IV. In this study the pharmacokinetics was the main objective and as long as the patients had a gynecological cancer, the diagnosis was not expected to affect the pharmacokinetics. However, these two patients were excluded from the tumor response evaluation. To determine the allele frequencies of investigated SNPs in a reference population, DNA samples from a regional biobank were used (Paper II and IV). The biobank consists of DNA from 800 individuals, of which we used 200, living in the southeast region of Sweden (50% males and 50% females). The individuals had been randomly selected from the Swedish population register and after informed consent anonymously included in the biobank. The relationship between the allele frequencies in the reference population and in the ovarian cancer patients were investigated to determine if the SNPs in some way correlated to ovarian carcinogenesis. The regional ethics committee in Linköping approved all the studies including patient material and written informed consent was obtained from each patient in Paper IV. 40 | MATERIAL AND METHODS Methodological Overview In this section only a brief summary and the principal aspects of the methods used in this thesis will be presented (Table IV). The details of the methods are given in the individual Papers. Table IV. Methods used in this thesis. Methodology Single Strand Confirmation Analysis DNA sequencing Solid Phase Extraction (SPE) High Performance Liquid Chromatography (HPLC) Mass spectrometry Papers I I, II and IV III and IV III and IV III and IV Single Strand Conformation Analysis In Paper I, Single Strand Conformation Analysis (SSCA) was used to screen the β-tubulin gene M40 for mutations. The investigation of unknown sequence variations in biological samples is a powerful method for the discovery of individual differences in biological pathways associated with disease and/or altered drug response. For a long time, manual direct sequencing was the only available method for detection of genetic variations. Therefore several methods such as SSCA, denaturating gradient gel electrophoresis and denaturating High-Performance Liquid Chromatography (dHPLC) were developed for detection of whether or not a certain DNA sequence contains sequence variations or not. In 1989 Orita et al. first presented a polymerase chain reaction (PCR) based SSCA method as an efficient and sensitive method for the detection of DNA polymorphisms (Orita et al. 1989). The method is based on the principle that single-strand DNA molecules take on a specific sequence-dependent secondary structure under non-denaturating conditions. These secondary structures results in altered gel mobility for variant single-strand DNA fragments under non-denaturating conditions, compared to the mobility of the wild type fragments. In brief, during a PCR reaction the fragments are labeled using radioactive nucleotides, denaturated and separated on nondenaturating polyacrylamide gels followed by gel drying and autoradiography. The shifted fragments are then excised and sequenced using conventional MATERIAL AND METHODS | 41 techniques. The optimal length for the detection of SNPs was found to be 150-200 bp and the results are affected by temperature, gel matrix and ionic strength of the running buffer. The sensitivity for single nucleotide variations with SSCA is dependent on these variables and has been reported to be 7095% in unselected samples (Sheffield et al. 1993). DNA-sequencing The nucleotide sequence of different genes and PCR-products was determined by manual sequencing using the dideoxy chain termination method (Sanger et al. 1977) either using radioactive labeled (Paper I) or fluorescent labeled nucleotides (Papers II and IV). The determination of known SNPs in the ABCB1, CYP2C8 and CYP3A4 genes was achieved using pyrosequencing. The Sanger sequencing method is based on the incorporation of dideoxynucleotides and thereby stopping the elongation of the DNA molecule during PCR. In contrast to deoxynucleotides, dideoxynucleotides have a hydrogen atom attached to the 3’ carbon atom instead of a hydroxylgroup which prevents the elongation and thereby terminates the growing DNA chain (Sanger et al. 1977). By labeling the PCR product with either radioactive or fluorescent nucleotides the length of the different terminated DNA chains (usually one reaction for each nucleotide) can be determined by size separation. The pyrosequencing technique (Ronaghi et al. 1998) is usually suited for sequencing of shorter fragments as compared to the Sanger sequencing method. The principle (see Figure 6) is based on the extension of single nucleotides using the polymerase reaction and the release of pyrophosphate (PPi). In brief, a sequencing primer is annealed to a single-strand PCRfragment and deoxynucleotide triphosphates (dNTPs) are added in a userpredefined order. If the nucleotide is complementary to the base on the DNA strand, the released PPi is converted to ATP by ATP-sulfurylase in the presence of adenosine 5’ phosphosulfate (APS). The ATP generated will be used by luciferase to convert luciferin to oxyluciferin during which light is produced. The light created in the reaction is proportional to the amount of incorporated nucleotide and detected by a charge coupled device (CCD) camera. Unincorporated dNTPs and ATP are degraded between each cycle by 42 | MATERIAL AND METHODS apyrase. The result is displayed in a pyrogram and the height of each peak is used for the determination of the sequence (see Figure 6). It should be noted that deoxyadenosine alfa-thio triphosphate (dATPS) is used as a substitute for the natural deoxyadenosine triphosphate (dATP) which is efficiently used by the DNA polymerase, but not recognized by the luciferase. Figure 6. In the pyrosequencing reaction, a DNA polymerase incorporates dNTP if complementary to the template strand upon release of PPi. Adenosine 5’ phosphosulfate (APS) and PPi is converted by sulfurylase to ATP, which is used by luciferase to convert luciferin to oxyluciferin during which light is produced. The light registered by a CCDcamera is displayed in a Pyrogram where the height of the peak is proportional to the amount of dNTP incorporated. MATERIAL AND METHODS | 43 Solid Phase Extraction The preparation of biological samples is an important step in the quantification of low molecular weight analytes in human plasma. The objective is to remove macromolecules (e.g. proteins and nucleic acids) prior to High Performance Liquid Chromatography (HPLC) analysis. Due to the relatively high content of organic solvents in the mobile phases and the organic substituents in the stationary phases, macromolecules in the biological matrix can denaturate and precipitate onto the packing material of the analytical column leading to increased back-pressure and changes of the chromatographic separation. The preparation of human plasma samples usually includes precipitation of macromolecules, liquid-liquid extraction and/or solid phase extraction (SPE). For the preparation of human plasma samples prior to quantitative analysis we used a SPE for paclitaxel and its metabolites. A SPE procedure normally consists of five steps: sample pre-treatment, SPE activation and conditioning, sample application, matrix washout and elution of the analytes. The sample pretreatment usually includes centrifugation of the sample (to avoid clogging of the SPE column) and pH adjustment to make the analytes suitable for extraction. Prior to applying the sample on the SPE column, the cartridge requires activation and conditioning. Activation means that a proper interface is established between the stationary phase and the sample solvent, in most cases water. During conditioning of the column, proper absorption conditions are established. When applying the sample to the SPE column, the analytes are retained by the stationary phase. The fraction of plasma matrix bound to the column is washed out as extensively as possible. Aqueous mobile phases, preferably buffered, should be used for elimination of proteins. Analytes are then eluted by adding an organic eluent dependent on the structure and affinity of the substances. 44 | MATERIAL AND METHODS High Performance Liquid Chromatography The purpose of all chromatography is basically to separate analytes from each other and from matrix components. According to the International Union of Pure and Applied Chemistry (IUPAC), chromatography is a physical method of separation in which the components to be separated are distributed between two phases, one of which is stationary (the stationary phase), while the other (the mobile phase) moves in a definite direction (Ettre 1993). In High Performance Liquid Chromatography (HPLC), the mobile phase is a liquid delivered under high pressure to ensure constant flow and reproducible chromatography, while the stationary phase is packed into a column capable of withstanding the high pressures necessary. The mobile phase in HPLC may be water, buffers or organic solvents, either on their own or mixed with one another. The column materials used today are called microparticulate column packings and are commonly uniform porous spherical silica particles, with a diameter of 3 to 5 µm (or smaller). Usually specific chemical groups are bound to the surface of the silica particle, to produce the bonded phase. A variety of structures have been developed for HPLC in which hydrocarbon chains of different lengths e.g. C2, C8, C12 and C18 are the most common. HPLC methods can be subdivided into normal phase, where the polarity of the stationary phase is higher than that of the mobile phase, and reverse phase, where the polarity of the stationary phase is less that of the mobile phase. Different mechanisms of separation such as ion exchange, absorption, size exclusion, etc. can be achieved depending on the structure and composition of the stationary and mobile phases. However, in all cases the separation occurs if the components of a mixture interact to a different extent with the Figure 7. Schematic description of an HPLC system. MATERIAL AND METHODS | 45 mobile and/or stationary phases and therefore take different times to move from the sample injection to detector (Ardrey 2003; Lindsay et al. 1992). An HPLC system generally consists of six components (more or less automated): a pump, a sample injector, a mobile phase, a stationary phase, a detector and an integration system (see Figure 7). The pump delivers the mobile phase through the column at a rate of 0.1-5 ml/min depending on the system using a fixed composition of the mobile phase (isocratic) or a changing composition (gradient). The injection system consists of a valve and an injection loop where the sample is introduced either manually or by an autoinjector. When the valve is turned the loop becomes part of the mobile phase delivering system and the analytes are applied to the column. After passing through the column the separated solutes are sensed by an in-line detector and the output of the detector is recorded by a computer for integration. A number of devices can be used as detectors in HPLC. The function of a HPLC detector is basically to monitor the analytes emerging from the column. The signal from the detector should be proportional to characteristics of either the analytes or the mobile phase. Detectors used in this thesis are UV detectors, a fluorescence detector and an ion-trap mass spectrometer (MS) and they will briefly be presented here (Ardrey 2003; Lindsay et al. 1992). UV detectors The most popular detector in drug analysis has been the UV detector (see Figure 8). The principle is that the mobile phase from the column is passed through a small flow cell and the absorbence of the UV radiation passed through the cell is monitored. The detector has a wide applicability and it is selective in the sense that it will only detect those solutes that absorb UV light such as alkenes, aromatics etc. The absorption is described by Lambert-Beer’s law (A=abε) and proportional to the length of the flow cell (a), the molecular absorptivity (ε) and the concentration of the solute (c) (Lindsay et al. 1992). 46 | MATERIAL AND METHODS Figure 8. Schematic layout of a UV detector. Fluorescence detectors Many compounds are fluorescent, i.e. they are able to absorb radiation during which the molecule enters an excited state. Subsequently when the molecule returns to its normal state, some of the energy is released as light of a higher wavelength which can be used for detection (see Figure 9). Naturally fluorescent molecules usually have conjugated cyclic structures. Fluorescence detectors can be much more sensitive than UV detectors for favorable compounds and the detector can be highly selective since both the optimal excitation and emission wavelengths can be chosen (Lindsay et al. 1992). Figure 9. Schematic layout of a fluorescence detector. Mass spectrometry Mass spectrometry (MS) is a technique designed to separate gas phase ions according to their mass over charge ratio (m/z). Interest in the combination of HPLC and MS (i.e. LC/MS) has increased tremendously in analytical laboratories during the last two decades and has become a routine method in drug monitoring and clinical pharmacology for both quantitative and qualitative analysis. The mass spectrometer works with charged molecules, i.e. ions, and uses electrical and magnetic fields to move the ions from the position of the formation to the detector. The intensity of the signal is proportional to the concentration or amount of ions that reaches the detector. In all MS techniques the mass separation works at high vacuum and the molecules needs to be ionized before entering the mass spectrometer, making the performance of the interface between HPLC (liquid system and molecules at atmospheric pressure) and the mass spectrometer (ionized molecules and vacuum) of great importance. In general the mobile phase matrix must be eliminated and analytes must be presented as ions in a gas phase (Ardrey 2003). Several types of interfaces have been developed and today atmospheric pressure ionization (API) techniques such as electrospray ionization (ESI), MATERIAL AND METHODS | 47 atmospheric pressure chemical ionization (APCI) and sonic spray ionization (SSI) are the most commonly used interfaces. In the APCI, the LC eluent is first vaporized and the analytes are ionized in a gas phase in the presence of a corona discharge electrode by proton transfer, charge exchange, electron capture or adduct ion formation (Arinobu et al. 2002). In the ESI interface the ions are already present in the mobile phase, which is then vaporized using heat and a charged capillary tip. At the tip of the capillary the surface of the droplets containing the ionized compound will become charged. Due to solvent evaporation the density of the charge will increase until there is an explosion of the droplet and the analyte ions enter into a gas phase (Bakhtiar & Nelson 2000). The SSI interface is similar to an ESI interface, with the exception that charged droplets are formed without applying an electric field. Instead the compounds are ionized using only a very high gas flow (Bjorkman et al. 2002). Having produced the ions in the interface they are analyzed in the mass analyzer by determining the m/z and the relative intensity for each ion. There are a number of different mass separation devices each with its own advantages and disadvantages. In brief, they can be divided into: sector instruments, quadrupole instruments, time-of-flight instruments and ion trap instruments or combinations of these (Ardrey 2003). Sector instruments are the simplest form of mass analyzer and use electric and magnetic fields to bend the flight path of accelerated ions. The radius of the obtained flight path is dependent on the mass-over-charge ratio which is the principle of separation. In quadrupole instruments four rods (or more) are arranged in parallel of which the opposite two are connected. A voltage consisting of both a radio frequency and a direct-current component is applied to each pair of rods (the radio frequency being 180° out of phase for the two pairs) and for a specific value of these voltages, ions of a particular m/z follow a stable trajectory through the space between the rods and reach the detector. The mass analyzer used in Paper III and IV is a quadrupole ion trap, simply ion trap, and is referred to as a three-dimensional quadrupole (3DQMS). Similar to a quadrupole it is oscillating potentials and radio frequencies that can selectively differentiate mass-over-charge ratios. The ion trap consists of a ring electrode and two end cap electrodes as shown in 48 | MATERIAL AND METHODS Figure 10. The two end caps are either grounded or biased with DC or AC potential and the ring electrode operates with a sinusoidal radio frequency applied to it. After the ions are introduced into the ion trap they follow a stable but complex trajectory and are trapped inside the mass analyzer until a radio frequency potential is applied to the ring electrode so that ions are selectively ejected, depending on the m/z ratio, from the trap towards the detector (Ardrey 2003). By passing a pulse of electrons into the ion trap, it is possible to fragment ions in it. The formed fragment ions can be trapped and then selectively ejected from the ion trap for detection. This procedure is referred to as MS2 or MS-MS analysis. The processes that constitute an MS2 experiment in an ion trap can be divided into the following steps: trapping of ions formed in the interface, isolation of ions to be fragmented (i.e. m/z for the precursor ions) and ejection of all other ions, collision induced dissociation (CID – fragmentation) of the precursor ions, and detection of the fragments produced. The usage of MS2 in mass spectrometry usually increases the peakto-noise ratio and is a useful tool in both qualitative and quantitative analysis. Figure 10. Schematic layout of an ion trap mass spectrometer. MATERIAL AND METHODS | 49 Statistics The statistical analysis in this thesis was performed with EpiInfo 2002 and the SPSS software package version 11 or higher (SPSS Inc. Chicago, IL, USA). The allele distributions presented in Papers II and IV were found to be in Hardy-Weinberg equilibrium using χ2-test. The significance of differences in nominal data was calculated using generalized Fisher’s exact test (Papers II and IV). The relative risk (RR) of not responding to treatment with a 95% CI was calculated when applicable (Paper II). The χ2-test for linear-by-linear association was used to analyze the significance of trends in different tables. The Mann-Whitney U-test is a non-parametric test and used to identify differences between two independent groups (Paper IV). Linear regression was used when investigating the correlation of two continuous variables (Paper IV). Kaplan-Meier plot, Log Rank and Breslow tests were used to visualize and test differences in fractions of patients with progression-free survival between different genotypes. The P-values for the 2-sided significance were always presented. The precision of the LC/MS method was expressed as a coefficient of variation (CV) at each concentration by calculating the standard deviation as a percentage of the mean calculated concentrations, while accuracy of the method was evaluated by expressing the mean calculated concentration as a percentage of the nominal concentration (Paper III). 50 | MATERIAL AND METHODS RESULTS AND DISCUSSION The Scarcity of β-tubulin Mutations in Ovarian Tumors In Paper I we investigated the presence of mutations in the β-tubulin M40 gene as a potential resistance mechanism for paclitaxel. We investigated all the exons including the exon/intron boundaries of the gene without finding any sequence variations in genomic DNA from 40 chemonaive ovarian tumors. However, in eight samples from paraffin-embedded tumors we found numerous false positive mutations i.e. the shift was not reproducible in independent PCRs with original tumor DNA. In the first study of β-tubulin, Monzo et al. found mutations in about 30% of the patients with NSCLC (Monzo et al. 1999). The interpretation of these results has been questioned due to the use of non-specific primers (Hasegawa et al. 2002; Kelley et al. 2001) and, as shown in Paper I, the use of paraffin-embedded tissue might also explain the high frequency in that material. Our results in Paper I in ovarian cancer are in accordance with the results reported by several groups that the mutations in the β-tubulin gene are rare in clinical samples (Kelley et al. 2001; Kohonen-Corish et al. 2002; Lamendola et al. 2003; Mesquita et al. 2005; Sale et al. 2002b). Other groups have found synonymous mutations/polymorphisms in the β-tubulin gene, especially at codons 180 (GTC>GTT), 195 (AAT>AAC) and 217 (CTG>CTA) (Hasegawa et al. 2002; Sale et al. 2002b; Tsurutani et al. 2002). Several of these RESULTS AND DISCUSSION | 51 sequence variations are present in pseudogenes and no correlation to response was found. It is noteworthy that almost all these sequence variations were found in Asian, African or Oceanic populations and none in Caucasian populations which might explain the discrepancies between the studies. However, we cannot exclude that mutations occur later during the tumor progression or in other β-tubulin isoforms. In vitro studies in cell lines indicate that paclitaxel can induce mutations in β-tubulin with altered sensitivity to paclitaxel as a result (Giannakakou et al. 1997; Gonzalez-Garay et al. 1999; Wang et al. 2005b), although mutations have not been found in resistant or relapsed tumors (Lamendola et al. 2003; Mesquita et al. 2005; Sale et al. 2002b). The scarcity of true somatic mutations as well as non-synonymous polymorphisms in clinical samples indicate that it is not likely that sequence variations in the tubulin gene constitute a resistance mechanism to paclitaxel in human tumors and therefore cannot be used to predict the clinical response to drugs targeting β-tubulin. Allele Frequencies of SNPs in ABCB1, CYP2C8 and CYP3A4 in a Swedish Population and in Ovarian Cancer Patients The allele frequencies of SNPs in ABCB1, CYP2C8 and CYP3A4 were investigated in a Swedish reference population and ovarian cancer patients (Table V) to study if the SNPs were correlated to the disease, if there was a linkage between the sequence variations and if the allele frequencies were comparable with previous findings. Pyrosequencing was used to detect the SNPs in paraffin-embedded material, freshly frozen tumors as well as DNA extracted from blood samples. Pyrosequencing was chosen as a method since it is a fast method, it gives a short sequence of the PCR-product – confirming a correct amplification, it gives a quantitative assessment (peak height) of the analysis, and several SNPs can be run in each reaction. dHPLC was excluded due to the fact that it only detects heterozygous individuals unless known PCR products are added and RFLP was not chosen due to the laborious protocol and the manual evaluation of gels. All the studied SNPs were present in Hardy-Weinberg equilibrium and there was no difference in the distribution between healthy volunteers and ovarian cancer patients (although the CYP2C8 promotor SNPs are borderline cases) or between males and females. 52 | RESULTS AND DISCUSSION The allele frequencies were in accordance with previous frequencies found in Caucasian populations (Bahadur et al. 2002; Marzolini et al. 2004; Solus et al. 2004). Table V. The SNP frequencies in a Swedish population (n=195) and the allele distribution of the different SNPs in ovarian cancer patients. Nucleotide Swedish ref. Alleles/SNPs Ovarian Cancer Patients change population n=195 freq. +/- 95% CI Wild type Heterozygous mutated Homozygous mutated T-370G C-271A A805T G416A, A1196G C792G 475 Del A G511A C556T C556G C1210G 10% +/- 3,0% 29% +/- 4.5% 0% 11% +/- 3.1% 6,0% +/- 2.4% 0% 0% 0% 0% 0% 21 22 33 27 29 33 33 33 33 33 12 10 0 6 4 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 A-392G 4,4% +/- 2.0% 30 3 0 G 56%+/- 4.9% T 42% +/- 4.9% A 2% +/- 1.5% T 55 % +/- 4.9% Paper II: 14 Paper IV: 5 28 G/T 17 G/T 3 G/A 9 T/T 2 T/A 8 T/T CYP2C81 *1C *1B *2 *3 *4 *5 *6 *7 *8 P404A CYP3A4 *1B ABCB1 Ex21 G2677T/A Ex26 C3435T Paper II: 8 27 18 Paper IV: 4 19 10 1 Note: CYP2C8*1C and *4 were present in a linkage disequilibrium and CYP2C8*1B was present mutually exclusive of *1C, *3 and *4. In the ABCB1 gene the G2677T and C3435T were found to be in linkage disequilibrium (D’ = 0.92, Table VI) in agreement with previous findings (Marzolini et al. 2004; Pauli-Magnus & Kroetz 2004; Sakaeda et al. 2002). Table VI. The distribution of ABCB1 SNPs G2677T/A and C3435T in a Swedish population (n=195). C3435T G2677T/A C/C T/C T/T Σ G/G 39 20 2 61 G/T 3 59 28 90 T/T 1 0 34 35 G/A 2 3 0 5 T/A 0 4 0 4 A/A 0 0 0 0 Σ 45 86 64 195 RESULTS AND DISCUSSION | 53 Correlations of ABCB1 Genotypes with the Response to Paclitaxel In Paper II we showed that the non-synonymous SNP G2677T/A significantly correlated with the outcome of paclitaxel-carboplatin treatment in the Swedish ovarian cancer population studied. The 53 patients included in this study were subdivided according to their response to treatment. Patients having a complete response and remained relapse-free for at least one year were considered to be good responders and patients who had progressive disease during treatment or relapsed within 12 months were considered to be poor responders to paclitaxel-carboplatin treatment. The probability of responding was higher for patients who were homozygously mutated for G2677T/A (T/T or T/A) as compared to patients carrying the wild-type alleles and heterozygous patients (Figure 11A). Nine of 28 patients with good response were homozygously mutated as compared to two of 25 patients with poor response. The fraction of mutated alleles was also higher in the group of patients with good response (Figure 11B) and there was also an influence of the number of mutated alleles on the response to paclitaxel-carboplatin treatment (Figure 11C). Figure 11. The effect of the ABCB1 SNP G2677T/A on the treatment outcome. A) The frequency of good response (white) and poor response (black) among homozygously mutated individuals (T/T or T/A) as compared to the individuals having G/G or G/T at position 2677 (Fisher’s exact test P=0.04, relative risk (RR) = 1.81, 95% CI: 1.17 <RR<2.79). B) The nucleotide frequencies in the two treatment groups. A significantly higher fraction of T or A at position 2677 was found in the group of patients that had responded well to treatment compared to those who responded poorly (Fisher’s exact test P=0.03, RR = 1.59, 95% CI: 1.03<RR<2.45). C) The gene-dose effect of the number of variant alleles significantly correlated with the success of paclitaxel treatment (χ2 -test for linear-by-linear association, P=0.03). 54 | RESULTS AND DISCUSSION We also investigated the effect of the ABCB1 G2677T/A genotype on the progression free survival using a Kaplan-Meier analysis (Figure 12). Although not significant, the curves show a trend in time to progression due to ABCB1 genotype (P = 0.19, log-rank test; P = 0.09, Breslow). The mean time to progression for the patients with the G/G genotype was 1.2 years, for heterozygous patients with G/T genotype 1.4 years, and for homozygously mutated patients (T/T or T/A) the mean time to progression was 2.0 years. We could not show any effect of the C3435T SNP on the response to paclitaxel-carboplatin treatment. Figure 12. The effect of the ABCB1 SNP G2677T/A on progression-free survival. To our knowledge this was the first study investigating the effect of the ABCB1 genotype on the success of paclitaxel-carboplatin treatment. Several other groups had investigated the effect of the ABCB1 genotype on the response to chemotherapy. Obata et al. investigated the correlation between the response in 60 advanced ovarian cancer patients and SNPs in ABCB1, MRP1, MRP2 and LRP (Obata et al. 2006). They found a correlation between an SNP in exon 17 of MRP1 and the response at the end of chemotherapy but not to the disease-free survival. No correlation between the ABCB1 genotype and the response was found, although they did not genotype for the SNP G2677T/A and only 20 of 60 patients received taxane (paclitaxel or RESULTS AND DISCUSSION | 55 docetaxel) containing chemotherapy. In a study on acute myeloid leukemia (AML) patients Illmer et al. found that patients wild type for C1236T, G2677T/A and C3435T had a higher risk of relapse (Illmer et al. 2002). This is in accordance with our results showing a better prognosis for homozygously mutated ovarian cancer patients treated with paclitaxel-carboplatin. In contrast, Kim et al. found that AML patients wild type for ABCB1 G2677T/A and C3435T had a better prognosis (Kim et al. 2006). The discrepancies between these two AML studies might be due to ethnic differences in Caucasians and Koreans as well as the use of different P-gp substrates in the treatment regimens, idarubicin (Kim et al. 2006) versus daunorubicin and doxorubicin (Illmer et al. 2002). Most other studies have only investigated the effect of the C3435T polymorphisms which is linked with G2677T/A as shown by us and others (Marzolini et al. 2004; Pauli-Magnus & Kroetz 2004; Sakaeda et al. 2002). It has been proposed that the effect observed when studying one of the SNPs might be due to the other one (Kroetz et al. 2003), although the SNPs might be associated with different haplotypes in different populations. Considering this linkage, our results in Paper II are similar to those by Jamroziak et al. showing that children with acute lymphoblastic leukemia carrying C/C in position 3435 had a worse prognosis after anthracycline-containing treatment (Jamroziak et al. 2004). Kafka et al. also showed that breast cancer patients homozygously mutated in position 3435 and treated with anthracyclines alone or in combination with taxanes had a higher probability of complete clinical response (Kafka et al. 2003). The investigation by Illmer et al., these two studies and our results all suggest that patients having one or more wild type alleles of the SNPs are at higher risk of not responding to treatment. In contrast to this, Isla et al. did not find any association between the C3435T polymorphism, and the response to docetaxel-cisplatin treatment of NSCLC and Goreva et al. found that patients homozygously mutated for G2677T/A and C3435T had a higher risk of drug resistance (Goreva et al. 2003; Isla et al. 2004). However, one should keep in mind that there might be ethnic differences in the association of these SNPs to different haplotypes as well as their importance in drug therapy. Different amino acids at position 893 (Ala, Ser or Thr, nucleotide position 2677) might have different effects on different drugs and the concentration of the drug may also be important for the impact of the genetic polymorphism. 56 | RESULTS AND DISCUSSION After the publication of Paper II two groups have published similar studies, but with different results (Ludwig & Kupryjanczyk 2006; Marsh et al. 2006). Marsh et al. assessed the ABCB1 G2677T/A genotype in patients included in the SCOTROC1 trial, a large and well-defined phase III clinical trial designed to compare the effects of paclitaxel versus docetaxel in combination with carboplatin in ovarian cancer (Vasey et al. 2004). Although the paclitaxel treatment regimens in these studies were similar, there are several differences between the studies which might explain the discrepancies in the results. Compared to our national case-referent study in the southern part of Sweden the SCOTROC1 is an international multicenter study and since our material was from a biobank we probably have an overrepresentation of large tumors. We also have a higher proportion of patients with non-radical surgery than the SCOTROC1 study as well as a higher fraction of patients with FIGO stage IIIc. It has been shown that a higher percentage of tumors with FIGO stage IIIc have a gain in the 7q21 region (Partheen et al. 2004), where the ABCB1 gene is located, which might explain a higher impact of the ABCB1 SNPs in these patients. We assume that Marsh et al., although not stated, subdivided the SCOTROC1 patients into paclitaxel- and docetaxel-treated patients because paclitaxel and docetaxel might not be affected in the same way by these SNPs. In the study by Ludwig et al. they investigated the distribution of the G2677T/A SNP in Polish ovarian cancer patients with FIGO stage IIb-IV and poor tumor differentiation (Ludwig & Kupryjanczyk 2006) without finding any association of the T/T genotype to the treatment response. However, they did find a higher risk of death for these patients (Cox’s proportional hazards model). There are some differences in the populations studied in this study as compared to ours. However, the information given in the letter by Ludwig et al. is scanty and therefore it is difficult to draw any conclusions. The results in the two studies were also evaluated in different ways and the number of patients in each response group in the study by Ludwig et al. is not presented so it is difficult to evaluate the results. The polymorphisms of the ABCB1 gene have been investigated in numerous studies giving conflicting results for several P-gp substrates such as digoxin, fexofenidine, cyclosporine, and tacrolimus, as well as expression and function of P-gp (see review by Marzolini et al. 2004). So far, the relevance of RESULTS AND DISCUSSION | 57 the ABCB1 genotype for different P-gp substrates and for response to chemotherapy remains elusive and further studies are needed. We believe that the discrepancies between our relatively small study, the Polish study and the pharmacogenetic follow-up study of the SCOTROC1 trial may be explained by differences in the composition and/or subdivision of tumors and/or patients as well as the different ways of evaluating the results. However, these three studies show the importance of validating the results of a small pilot study in larger independent studies as well as the need to use well-defined populations and evaluation procedures, and that analyses on relatively small clinical groups may more easily generate statistical significances by chance. The use of the ABCB1 genotype as a predictor for paclitaxel response is still elusive and future studies will hopefully clarify the significance of these SNPs. 58 | RESULTS AND DISCUSSION Quantification of Paclitaxel and its Hydroxymetabolites in Human Plasma To investigate the effect of different factors such as genetic variations in genes encoding drug transporters and metabolic enzymes on the pharmacokinetics of paclitaxel we needed a method to quantify paclitaxel and its metabolites in human plasma. The final method used for measurement of paclitaxel and its metabolites in this thesis is presented in Paper III. Numerous methods have been developed for the quantification of paclitaxel in plasma using different techniques such as liquid chromatography (Theodoridis & Verpoorte 1996), capillary electrophoresis (Hempel et al. 1996), enzyme-linked immunosorbent assay (ELISA) (Svojanovsky et al. 1999) and radioimmunoassay (Leu et al. 1993). However, it has been suggested that the preferable technique to measure the concentrations of paclitaxel and its metabolites is by HPLC with a UV detector, fluorescence detector or mass spectrometer (Theodoridis & Verpoorte 1996). We therefore started by developing an extraction method and a HPLC-method for quantification of paclitaxel and its metabolites in human plasma. Extraction of paclitaxel from human plasma Liquid-liquid extraction (LLE) as well as solid-phase extraction (SPE), online (Sparreboom et al. 1995b) and off-line, has been used for sample clean-up and extraction of paclitaxel from human plasma. For extraction of paclitaxel from plasma using LLE several organic solvents such as ethyl-acetate, diethylether, methyl tert-butyl ether and acetonitrile have been used (Andreeva et al. 1997; Martin et al. 1998; Poon et al. 1996; Sottani et al. 2000; Wang et al. 2003). We investigated the use of LLE for the extraction of paclitaxel and its metabolites by mixing a number of different organic solvents with plasma, adding potassium carbonate, centrifugation (4000g, 10min) followed by freezing. The upper organic level was carefully transferred to a tube, evaporated to dryness and reconstituted in mobile phase and checked for the extraction recovery and purity using the HPLC-method described below. Isopropanol, ethanol, acetonitrile, ethanol/acetonitrile (60/40), acetone, methyl tert-butyl ether and ethyl-acetate were investigated; however, the extracts using these solvents were not clean enough for positive identification RESULTS AND DISCUSSION | 59 of the metabolites and in most cases the recovery was less than 50% for the metabolites, although the extraction of paclitaxel was satisfactory for some of the solvents (data not shown). Therefore SPE was tested for the extraction of paclitaxel and its metabolites. We adopted the SPE method from Huizing et al. with slight modifications (Huizing et al. 1998; Huizing et al. 1995a). In this method CN solid-phase columns are used and the plasma samples are buffered prior to application to the column, washed with buffer, methanol and hexane before elution with acetonitrile. The recovery of paclitaxel and its metabolites were originally between 83-117% but lower in Paper III: paclitaxel range 63-74%, p-3´-hydroxypaclitaxel 65-71%, and 6α-hydroxypaclitaxel 7285%. The difference is most likely due to different batches of SPE-columns as previously described (Huizing et al. 1998). We also investigated the use of other stationary phases such as C2, C4, C8, C18, phenyl and NH2. However, we were not able to get a satisfactory clean-up or a high enough recovery from these columns. In spite of the batch dependent recoveries of the substances from the CN columns, we adopted the SPE method by Huizing et al. 1995a. Quantification of paclitaxel and metabolites using HPLC The HPLC-method we developed for the quantification of paclitaxel and metabolites in plasma were also based on the method by Huizing et al. 1995a although the mobile phase was changed and docetaxel used as internal standard. We used a Chromeleon system consisting of a Gynkotek model 480 pump, a Gynkotek Gina 50 auto-sampler and a Gynkotek 340U UV-detector. The column was an Apex I C8 (150x3 mm 5µm) and the flow of the mobile phase (acetonitrile/20 mM ammonium acetate pH 5.0 45/55) was 0.7 ml/min. Fifty µl of the extracted sample was injected on the column. Typical chromatograms recorded at 227 nm with the corresponding retention times for each substance are shown in Figure 13. The lower limit of quantification for this method was 10 ng/ml of paclitaxel and the metabolites in human plasma (within-day variations <15%). However, after analyzing samples from the first three patients in Paper IV we concluded that this was not sensitive enough for the metabolites although the quantification of paclitaxel was satisfactory. We believed that to evaluate the metabolic pathways and the influences of the CYP2C8 and the CYP3A4 enzyme activities in the individual 60 | RESULTS AND DISCUSSION 7,5 5 2,5 5 2,5 Rt 6.5 min docetaxel Rt 7.2 min paclitaxel 7,5 Rt 3.8 min p-3’-hydroxypaclitaxel 10 Intensity (AU) Intensity (AU) 10 Rt 4.7 min 6α-hydroxypaclitaxel Spiked plasma Blank plasma 0 0 -2,5 -2,5 0 2 4 6 Time (min) 8 10 0 2 4 6 8 10 Time (min) Figure 13. Typical chromatograms for the HPLC method developed during this thesis. The spiked plasma sample contained 50 ng/ml of each metabolite and 250 ng/ml of paclitaxel as well as docetaxel. AU – Arbitrary unit, Rt – Retention time. patient it was important to quantify the metabolites. The concentration of the metabolites, especially p-3´-hydroxypaclitaxel, was found to be below 10 ng/ml within two to three hours after the end of infusion in some patients. We therefore decided to investigate the use of other detectors. Since paclitaxel contains several conjugated double bonds we concluded that the substance might be fluorescent and that fluorescence detection might improve the sensitivity. The fluorescence of paclitaxel was investigated by registering the fluorescence intensity from 210-800 nm while the molecule was exposed to light with a wavelength of 200-290 nm (corresponding to the maximum in the absorption spectrum of paclitaxel); see Figure 14. Maximum fluorescence intensity was reached using an excitation wavelength of 230 nm and an emission wavelength of 310 nm. Replacing the UV detector in the abovementioned system with a Waters 474 fluorescence detector and using the 230 nm for excitation and 310 nm for emission gave us a fluorescence method for the detection of paclitaxel and metabolites (mobile phase - acetonitrile/20 mM ammonium acetate pH 5.0 42.5/57.5). A chromatogram is shown in Figure 15. Unfortunately this method resulted in additional peaks in the chromatogram of which several were interfering with the peaks of our RESULTS AND DISCUSSION | 61 2000 280 1500 1000 240 500 Excitation wavelenght (nm) Emission intensity (mV) 2500 200 0 200 400 600 800 Emission wavelength (nm) Figure 14. Fluorescence spectra of paclitaxel. analytes. However, the data indicated that the sensitivity for especially p-3´hydroxypaclitaxel improved with the use of a fluorescence detector. We could then either try to clean up the sample even more, try to change the chromatography to separate the interfering peaks or use another type of detector. Due to the difficulties of extracting paclitaxel from human plasma and the number of interfering peaks we turned to the use of LC/MS. Spiked plasma 10 10 7,5 7,5 Intensity (AU) Intensity (AU) Blank plasma 5 2,5 Rt 5.0 min p-3’-hydroxypaclitaxel Rt 6.0 min 6α-hydroxypaclitaxel Rt 8.5 min docetaxel 5 Rt 9.6 min paclitaxel 2,5 0 0 0 5 10 15 Time (min) 20 0 5 10 15 20 Time (min) Figure 15. Typical chromatograms for the fluorescence method developed during this thesis. The spiked plasma sample contained 10 ng/ml of each substance. AU – Arbitrary unit, Rt – Retention time. 62 | RESULTS AND DISCUSSION Quantification of paclitaxel and metabolites using LC/MS The available LC/MS system in our lab consists of a LaChrome chromatograph with a Merck-Hitachi M-8000 LC/MSn (ion trap) equipped with exchangeable SSI and ESI interfaces. In most previously reported LC/MS methods, paclitaxel either forms [M+H]+ ions or [M+Na]+ ions. To form the [M+H]+ ion in the interface, usually an acidic mobile phase is used, and when the [M+Na]+ ion is desired sodium salts are added. In our method development we tried to use acidic mobile phases to form the [M+H]+ ion. However, in our system we could not achieve reproducible formation of the protonated ion between different batches of acidic mobile phase. The instability was probably due to the formation of a lower or higher amount of [M+Na]+. In an effort to stabilize the system, different ions were added to the mobile phase to push paclitaxel to form other adducts, and those that could be detected in our system were [M+K]+ (KCl 2 mM/MeOH 30/70), [M+Na]+ (NaCl 2 mM/MeOH 30/70), and [M+Acetate]- (ammoniumacetate 20 mM/MeOH 30/70). The highest sensitivity of these adducts was reached with the [M+K]+ ion although the sensitivity for the [M+H]+ ion was higher than any of the other ions formed, probably due to fragmentation of the latter in the ion source. In an attempt to stabilize the formation of the [M+H]+ ion we returned to the use of acidic mobile phases and tried to suppress the formation of the [M+Na]+ ion by adding crown ethers or trifluoroacetic acid (TFA). Although TFA caused a decrease of the absolute signal, the signal-tonoise ratio for paclitaxel was increased, and the formation of the [M+Na]+ ion was suppressed resulting in a stable formation of the [M+H]+ ion. The mobile phase was optimized for TFA concentration and pH. The MS conditions were optimized using a continuous infusion of a solution containing paclitaxel while running the optimized mobile phase components. Both the SSI and ESI interface was evaluated for the method and the SSI interface was about four times more efficient in ionizing paclitaxel. We also had the opportunity to test the ionization on a Perkin Elmer SCIEX equipped with both an ESI and an APCI interface. In this system, the ESI was more efficient in ionizing paclitaxel than the APCI interface. The SSI interface was then considered the best choice. The use of an MS2 method instead of a MS1 method further increased the sensitivity. The HPLC column was chosen to retain and separate the analytes since some of them have identical mass-to-charge ratios after RESULTS AND DISCUSSION | 63 Figure 16. Fragmentation of paclitaxel and its metabolites in our ion trap – mass spectormeter. fragmentation in the ion trap. The monohydroxymetabolites have the same molecular mass but they fragment into ions with a different m/z. However, p3’-hydroxypaclitaxel dissociates into fragments with the same m/z as fragments from paclitaxel and 6α-hydroxypaclitaxel dissociates into fragments with the same m/z as dihydroxypaclitaxel (Figure 16). To achieve single-peak mass chromatograms two methods were run in parallel, one for paclitaxel and dihydroxypaclitaxel and one for the monohydroxymetabolites. A typical chromatogram of the method is shown in Figure 17. The method was validated as described in Paper III. The CV for the precision was below 15% and accuracy was between 83-113% with the exception of the within-day accuracy for the lowest quality control sample for paclitaxel which was 151%. The within-day accuracy was then rerun the following three days with satisfactory result (80%, 103% and 88%) and the between-day accuracy was 93% for this concentration. For most samples re-injection data was also higher than 100% but this is probably due to a slight increase in concentration when the sample equilibrates with the atmosphere in the sample vial. Since there were a lot of quality controls run during the validation of this method a few analyses did show a fairly high variation. However, this reflects the actual 64 | RESULTS AND DISCUSSION 12 10 8 6 900 800 700 600 500 400 300 4 200 2 100 0 0 0 3 6 Intensity (AU) for paclitaxel 14 1000 - Rt 6.1 min Paclitaxel 16 - Rt 5.1 min 6α-hydroxypaclitaxel 18 - Rt 3.6 min p-3'-hydroxypaclitaxel Intensity (AU) for the hydroxymetabolites 20 9 Time (min) Figure 17. Typical chromatograms for the ion trap method presented in Paper III. The substances were isolated from plasma from an ovarian cancer patient and quantified as 42 ng/ml p-3’-hydroxypaclitaxel, 36 ng/ml 6α-hydroxypaclitaxel and 1440 ng/ml paclitaxel. In the chromatogram the baseline for the 6α-hydroxypaclitaxel is elevated with 2 arbitrary units (AU) and that for paclitaxel with 200 AU for illustrative purposes. Note that the peak at 6.1 min in the p-3´-hydroxypaclitaxel chromatogram is from “overspill” of paclitaxel (more than 100-fold intensity in the correct method) and that the peak at 7.5 min in the 6αhydroxypaclitaxel chromatogram is from the internal standard, which has a 13C fragment with the same m/z as the metabolite. AU – Arbitrary unit, Rt – Retention time. precision and accuracy of the method and we felt that since we are running a method for the quantification of paclitaxel concentrations over a range of 1 to 10,000 and including all the hydroxyl metabolites we needed five quality control samples for each analyte (although a more stable, reproducible and sensitive method would (always) have been desirable). Our method (Paper III) for quantification of paclitaxel and metabolites in human plasma is to our knowledge the first reported method using an ion trap mass spectrometer with an SSI interface and TFA as a suppressor of paclitaxel Na-adduct formation. The advantage of the method is the high sensitivity, the quantification of paclitaxel and its metabolites; a drawback is the spiking of RESULTS AND DISCUSSION | 65 the chromatograms which is discussed in the paper and led to reanalysis of some samples. The method presented in Paper III however suited our purposes for the quantification of paclitaxel and its metabolites in plasma. The concentration of paclitaxel and its hydroxymetabolites was determined in more than 450 human plasma samples used in Paper IV. The lowest concentration of paclitaxel found in samples taken 24 hours after the end of paclitaxel infusion was 17 ng/ml and the lowest measured concentration of p-3’hydroxypaclitaxel was 0.75 ng/ml although this metabolite was not detectable in some samples. Considering the performance of the method presented in Paper III the quantification of paclitaxel, the findings in Paper IV concerning the exposure and clearance of paclitaxel should be valid. 66 | RESULTS AND DISCUSSION Correlations of Paclitaxel Pharmacokinetics to ABCB1, CYP2C8, CYP3A4 Genotypes and CYP3A4 Phenotype In Paper IV we investigated the effects of CYP3A4, CYP2C8 and ABCB1 sequence variations and the CYP3A4 activity in vivo on the pharmacokinetics and clinical effects of paclitaxel in the treatment of ovarian cancer. A total of 33 ovarian cancer patients were included and assessed for their individual capacity to eliminate paclitaxel. Genotyping of SNPs in the CYP3A4, CYP2C8 and ABCB1 genes was done by pyrosequencing and the CYP3A4 activity in vivo was assessed using quinine as a probe drug. The patients were monitored for response during the chemotherapy as well as for adverse drug reactions, especially neurological toxicity, evaluated as Nscore (Cassidy et al. 1998) and the patients’ own rating of their discomfort due to neurological adverse reactions. The results presented in Paper IV indicate that the clearance of paclitaxel is correlated to the SNPs G2677T/A in ABCB1 and CYP2C8*3. Patients heterozygous for G/A in position 2677 in the ABCB1 gene had a significantly higher clearance (median 26.0 L/h) compared to patients carrying the wildtype G/G alleles (median 18.9 L/h) or patients homozygously T/T mutated (median 17.4 L/h); however, no significant difference could be shown compared to the G/T heterozygous patients (median 21.1 L/h) (Figure 18A). We also found indications that CYP2C8*3 and CYP3A4*1B correlated with the clearance of paclitaxel, although not statistically significant. To further investigate this, we stratified the data according to the ABCB1 SNP G2677T/A; however, the only combination where the number of patients was high enough for statistical analysis (n>2) was the combination of 2677G/T and CYP2C8*1/*1 versus 2677G/T and CYP2C8*1/*3. Patients carrying the 2677G/T and CYP2C8*1/*3 alleles had a significantly lower clearance of paclitaxel (median 14.7 L/h) than patients carrying the 2677G/T and CYP2C8*1/*1 alleles (median 22.8 L/h) (Figure 18B). The clearance of paclitaxel did not correlate to the genotypes ABCB1 C3435T, CYP2C8*1B, CYP2C8*1C or CYP2C8*4, nor to the CYP3A4 enzyme activity in vivo. RESULTS AND DISCUSSION | 67 Figure 18. The influence of the ABCB1 G2677T/A and CYP2C8*3 SNPs on the clearance of paclitaxel. A) The clearance of paclitaxel due to the ABCB1 genotype in position 2677. B) The effect of CYP2C8*3 on the clearance of paclitaxel is shown for patients with the ABCB1 genotype 2677G/T. A low CYP3A4 enzyme activity in vivo correlated with a high AUC0-24h for 6α-hydroxypaclitaxel (R = 0.671 , P < 0.001, data not shown), indicating that in patients with low CYP3A4 activity a higher proportion of paclitaxel is converted by CYP2C8 or that the elimination of 6α-hydroxypaclitaxel is decreased. A lower CYP3A4 enzyme activity in vivo also correlated to a higher ratio of the AUC0-24h for 6α-hydroxypaclitaxel divided by AUC0-24h for p-3’hydroxypaclitaxel (6α-OH-pac/p-3’-OH-pac) (R = 0.525, P = 0.002, data not shown). This indicates that the CYP3A4 enzyme activity in vivo influences which pathway, CYP2C8 or CYP3A4, is relatively more dominant in each patient. CYP2C8*1C also correlated with a higher 6α-OH-pac/p-3’-OH-pac ratio (P = 0.048, data not shown), although no other pharmacokinetic parameter investigated was affected by this SNP. Patients having the combination 2677G/T and CYP2C8*1/*3 had a higher AUC0-24h for 6α-hydroxypaclitaxel in plasma than CYP2C8 wild-type patients (SNP combination 2677G/T and CYP2C8*1/*1) (P = 0.032, data not 68 | RESULTS AND DISCUSSION shown), which is in contradiction to the lower clearance for these patients. A high CYP3A4 enzyme activity in vivo correlated to a low AUC0-24h for p-3’hydroxypaclitaxel (R = 0.354, P = 0.051, data not shown). Both these findings indicate that a high enzyme activity for the metabolizing enzymes is correlated with a low concentration of the metabolite in plasma, which is difficult to explain. However, the metabolism mainly occurs in the liver and the metabolites are believed to be directly eliminated via the bile (Gianni et al. 1995), so the plasma concentration might not reflect the metabolite formation in the liver. Gianni et al. reported that measured metabolite concentrations represent an overflow at times of relative saturation or blockage of biliary elimination (Gianni et al. 1995). We therefore speculate that if paclitaxel, at times of low enzymatic elimination, is saturating the biliary elimination of the metabolites, then these substances would be present at a higher concentration in plasma when we have a low clearance of paclitaxel. The saturation effect has also been suggested by others (Nakajima et al. 2005). None of the other genotypes could be correlated to the AUC0-24h for either of the metabolites. In one investigation of the influence of genetic variations on the pharmacokinetics of paclitaxel, Yamaguchi et al. found that ovarian cancer patients having the ABCB1 genotype -129T/C, 1236C/C and 2677A/A had the highest clearance and the lowest AUC of paclitaxel (Yamaguchi et al. 2006). This is in agreement with our results in Paper IV showing that patients carrying 2677G/A had the highest clearance. However, Nakajima et al. did not find an association between the ABCB1 genotype (T-129C, C1236T, G2677T/A and C3435T) and the clearance of paclitaxel, although they found that patients mutated in position 3435 had a higher AUC for p-3’hydroxypaclitaxel compared to the wild-type patients (Nakajima et al. 2005). Nor did Sissung et al. find an association between the genotype of ABCB1 and the pharmacokinetics of paclitaxel. However, like our study and the others mentioned, this is a small study and the statistics approach significance (P = 0.18) for the correlation between G2677T/A and C3435T and the AUC of paclitaxel (Sissung et al. 2006). A correlation has also been found between altered metabolite ratios and the haplotype CYP3A4*16B as compared to wild-type (Nakajima et al. 2006). CYP3A4*1B was not found in the material and no difference in paclitaxel clearance was found between the CYP3A4 haplotypes. This is similar to our results, where we found a correlation RESULTS AND DISCUSSION | 69 between CYP3A4 activity in vivo and the 6α-OH-pac/p-3’-OH-pac ratio, but no correlation to the clearance of paclitaxel. Due to the scarcity of CYP2C8 SNPs in a Japanese population (Hichiya et al. 2005), neither of these Japanese studies found a high enough number of variant CYP2C8 alleles for a statistical analysis. A larger study done in a Caucasian population investigated the clearance of unbound paclitaxel in cancer patients receiving paclitaxel as an i.v. infusion for 1, 3 or 24 h at doses of 80-225 mg/m2 (Henningsson et al. 2005). Although they found a high interindividual variation in clearance of unbound paclitaxel (10-fold), no statistically significant association was observed between the pharmacokinetics of paclitaxel and any genotype investigated (CYP2C8*2, CYP2C8*3, CYP2C8*4, CYP3A4*3, CYP3A5*3C and ABCB1 C3435T). This is in contradiction to our findings concerning a reduced elimination of paclitaxel in patients heterozygous for CYP2C8*3. Most in vitro studies as well as findings for repaglinide (Niemi et al. 2003) and ibuprofen (Garcia-Martin et al. 2004) suggest that CYP2C8*3 can affect the pharmacokinetics of its substrates. The differences between our study and the results presented by Henningsson et al. might be due to the use of a wide range of dosages and infusion times, since the SNPs might have different impacts at different concentrations of paclitaxel, although their study has the advantage of measurement of unbound paclitaxel. We also found an impact of the ABCB1 G2677T/A SNP, which Henningsson et al. did not genotype for, and we had to stratify the data accordingly to evaluate the effect of CYP2C8*3. In summary, it seems as if the genotype of ABCB1, especially G2677T/A, plays a role in the pharmacokinetics of paclitaxel. CYP2C8*3 also seems to be important, although the effect might be blurred by other genotypes. We might also speculate that it is important to evaluate the SNPs at each dosage and infusion period, and that multiple pathways are involved, which leads to the need for larger studies where the SNPs can be stratified or the usage of multivariate analysis would be possible. Our study is small and we therefore consider it a pilot study and the results need to be confirmed in larger patient populations. In Paper IV we found that patients with the ABCB1 genotype 2677G/A had the highest clearance. These patients would then be expected to receive the lowest exposure of paclitaxel and thereby the lowest anti-tumor effect. In 70 | RESULTS AND DISCUSSION Paper II we found that patients carrying the T/A combination were good responders. No G/A heterozygous patients were found in Paper II and no patients with the T/A combination were found in Paper IV. The results concerning the A-alleles in Paper II and Paper IV might be in contradiction to each other, however, the only allele in common is the A-variant and the effects might be influenced by the other allele. The effect might also be dependent on the ABCB1 haplotypes in these patients or an association to SNPs in other genes. In Paper IV patients carrying the T/T alleles had the lowest clearance of paclitaxel as compared to the other genotypes, although the difference was not statistically significant. These patients would then be expected to have the highest exposure of paclitaxel and a high anti-tumor activity, which would be in accordance with the higher response found in Paper II. However, the correlation to the response might be due to an influence both at the tumor site as well as an effect on the pharmacokinetics. It is also likely that both the response and the pharmacokinetics of paclitaxel are influenced by other factors. Correlations between Paclitaxel Exposure, Response and Genotypes of ABCB1, CYP2C8 and CYP3A4 In the prospective study (Paper IV) the toxicity was monitored in the patients using the CTC scale version 2 and 23 patients underwent a more extensive evaluation of the neurotoxicity both as Nscore (Cassidy et al. 1998) and the patients’ own rating of their inconvenience. The tumor response was evaluated as normalized CA-125 value prior to the fourth course of chemotherapy and at the end of chemotherapy according to the RECISTcriteria. The severity of the neurotoxicity at the final course of chemotherapy was correlated with the AUC0-24h for paclitaxel as shown in Figure 19B (R = 0.48, P = 0.02). No significant correlation could be found at first tumor evaluation, after course 3 or 4, although there is a positive coefficient of the slope; see Figure 19A (R = 0.24, P = 0.263). The inconvenience of the neurological adverse events as evaluated by the patients’ own rating significantly correlated to the exposure of paclitaxel at the first tumor evaluation but not at the final course of chemotherapy (Figure 20). The motor neuropathy was increased in RESULTS AND DISCUSSION | 71 Figure 19. Correlation between the exposure of paclitaxel (AUC0-24h) and the severity of the neurotoxicity as evaluated using Nscore at A) the first tumor evaluation (course 3 or 4) and at B) the final course of chemotherapy. patients heterozygous for CYP2C8*3 (P = 0.034, data not shown). The hematological toxicity was also associated with the CYP2C8*3 genotype, especially the leukocytes (P = 0.067), neutrophils (P = 0.086) and platelets (P = 0.02, see Table VII). Half of the CYP2C8*3 heterozygous patients (n = 3) suffered extremely high hematological toxicity while the other half (n = 3) had a minor effect on their blood counts as shown in Table VII. Whether this is Figure 20. Correlation between the paclitaxel exposure (AUC0-24h) and the patients’ own rating of their inconveniences due to neurological adverse effects at A) the first tumor evaluation (course 3 or 4) and at B) the final course of chemotherapy. 72 | RESULTS AND DISCUSSION related to the lower clearance of paclitaxel for these patients, a predisposition of hematological toxicity or just occurred by chance could not be established. Previous studies have found a correlation between a high paclitaxel exposure and low blood counts (Gianni et al. 1995; Huizing et al. 1993). Although not significant, patients with ABCB1 G2677T/A wild type seemed to have less sensory neuropathy compared to patients with heterozygous or homozygous mutations (P = 0.186, data not shown). For other adverse effects registered using the CTC scale no linear correlations could be found between genotype and toxicity. Table VII. The distribution of haematological toxicity using the CTC grade in patients carrying the CYP2C8*3 allele as compared to the rest of the patients. Leukocyte Toxicity Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 P = 0.067 CYP2C8*3 Neutrophile CYP2C8*3 Platelet *1/*1 *1/*3 toxicity *1/*1 *1/*3 toxicity 8 1 12 4 1 3 0 0 1 2 Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 P = 0.086 4 4 6 7 5 3 0 0 0 3 Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 P = 0.020 CYP2C8*3 *1/*1 *1/*3 15 10 1 0 0 2 1 2 1 0 In accordance with our results, Mielke et al. showed that the neurotoxicity is associated with high paclitaxel exposure, especially the total exposure of the drug (Mielke et al. 2005b). Neuropathy has also been shown to correlate to the genotype of ABCB1. Patients wild type for C3435T did not develop neuropathy as fast as other patients (Sissung et al. 2006). Although we did not evaluate the neurotoxicity in the same way, we found a slight indication that patients wild type for G2677T/A have less neurotoxicity than others. Since the two SNPs are linked, this might be an indication of the involvement of ABCB1 in the development of neurotoxicity after paclitaxel chemotherapy. In Paper IV we could not find a correlation between the tumor response and the genotypes of ABCB1, CYP2C8 or CYP3A4, although we found a correlation in Paper II where patients homozygously mutated in G2677T/A in the ABCB1 gene had a higher probability of responding to chemotherapy. The discrepancy between the studies might be due to the different ways of evaluating the response in the Papers, i.e. response at the end of chemotherapy using the RECIST criteria and CA-125 values as compared to RESULTS AND DISCUSSION | 73 progression-free survival. The study presented in Paper IV also included about half the number of patients in the response analysis as compared to Paper II and in the latter the significance only reaches the level of P < 0.05. It is noteworthy that in Paper II only primary treated patients were included while patients included in Paper IV were both previously treated and nontreated patients. However, in Paper IV we found a slight indication that wild type patients had less neurotoxicity than other patients which might be an indication of a higher P-gp transport in the blood-brain barrier and an indication of the same mechanism as in Paper II. In contradiction to the neurotoxic response, we found no correlation between the AUC0-24h of paclitaxel and the tumor response. Previous studies have shown a correlation of paclitaxel exposure and overall survival in NSCLC patients (Huizing et al. 1997a) as well as the response at the end of chemotherapy in advanced cancers (Mielke et al. 2005a). However, considering that in Paper IV only a small number of patients (n=29) were eligible for response analysis, including both previously non-treated and treated patients, as mentioned above, and the impact of carboplatin, the significance might be lost for the evaluation of the tumor response. In summary, it seems as if the SNP G2677T/A in the ABCB1 gene correlates to the pharmacokinetics of the drug as well as the response. CYP2C8*3 seems to correlate with a lower clearance of paclitaxel in accordance with in vitro findings although the clinical effect has not been as clearly established. The exposure of paclitaxel also correlates to the neurotoxicity after paclitaxel containing chemotherapy. 74 | RESULTS AND DISCUSSION CONCLUSIONS Based on the results from Papers I-IV, the following conclusions can be drawn: - - - Mutations and polymorphisms in the β-tubulin gene M40 are rare, especially in Caucasians, and unlikely to be a clinically relevant resistance mechanism. In the original article concerning sequence variations in the β-tubulin gene, a high frequency of mutations were found which correlated to response. The results from our study show that the use of paraffin-embedded material might explain the high incidence of mutations in that study. The SNP G2677T/A in the ABCB1 gene correlate to the response evaluated one year after the end of paclitaxel-carboplatin chemotherapy. The probability of responding to treatment is higher in homozygously mutated patients (T/T or T/A) and there is a dosedependent influence from the number of mutated alleles on the response to paclitaxel treatment. The G2677T/A SNP may be important for the function of P-glycoprotein and resistance to paclitaxel and provide useful information for individualized therapy. The SNPs G2677T and C3435T in the ABCB1 gene are present in linkage disequilibrium. CONCLUSIONS | 75 - - - - A sensitive LC/MS method using a sonic spray ionization interface and an ion trap mass spectrometer was developed for the simultaneous quantification of paclitaxel and its hydroxymetabolites in human plasma. The mobile phase contains TFA as a suppressor of Napaclitaxel ion formation in favor of the protonated ion which increases the sensitivity and stabilizes the ionization. The method covers a wide concentration range of paclitaxel suitable for the quantification of the substances in clinical studies. The SNPs G2677T/A in ABCB1 and CYP2C8*3 influence the clearance of paclitaxel. This indicates that a patient’s individual pharmacokinetics of paclitaxel might be predicted by determining the genotype of ABCB1 and CYP2C8. The SNP G2677T/A in ABCB1 gene correlates to the pharmacokinetics of paclitaxel as well as the response of paclitaxel containing treatment, indicating its importance in paclitaxel pharmacogenetics. The correlation of CYP2C8*3 with a lower clearance is in accordance with the in vitro activity of this enzyme variant. The exposure of paclitaxel correlates to the neurotoxicity as evaluated by both Nscore and the patients’ own rating of their inconvenience due to neurological adverse reactions. The CYP3A4 enzyme activity in vivo did not affect the clearance of paclitaxel but rather the metabolite pattern. A low CYP3A4 enzymatic activity was correlated to a high 6α-hydroxypaclitaxel concentration indicating a lower elimination of this metabolite or that a higher fraction of paclitaxel was converted by CYP2C8. 76 | CONCLUSIONS FUTURE ASPECTS Pharmacogenetics and individualized treatment/medicine is expected to become more and more important in the future as a way to improve and predict the outcome of chemotherapy. Hopefully the findings in this thesis will be part of the basic studies for individualization of paclitaxel-carboplatin chemotherapy. The results presented here show that patients’ individual elimination of paclitaxel might be predicted by determining the genotype of ABCB1 and CYP2C8, which might be important parameters for individualization of the paclitaxel dose. The studies also show that some patients have a higher probability of responding to treatment and will benefit from the regimen, while others might benefit from other chemotherapeutic agents. From our findings and other studies of β-tubulin mutations, we can conclude that they are rare and probably not a clinically relevant resistance mechanism and would probably not need any future investigation. On the other hand the correlation of the ABCB1 genotype and the response to paclitaxel-carboplatin chemotherapy needs to be further clarified. It would probably be beneficial to investigate the distribution of SNPs in the ABCB1 gene in material from a larger uniformly treated population with a welldefined protocol for response evaluation to validate or rule out the ABCB1 genotype as a predictor of paclitaxel response. Hopefully these investigations would not only include the SNPs studied in our report but others like FUTURE ASPECTS | 77 G1199T/A in ABCB1 (which in vitro correlates to the resistance of paclitaxel) and CYP2C8*3, which affect the clearance of the drug, as well as variations in other genes to identify a “gene-profile” for patients that will benefit from paclitaxel chemotherapy. The quantification of paclitaxel and its metabolites in plasma would probably be improved by an extraction method with a higher extraction ratio and a more sensitive MS-instrument, the former being a matter of time and the latter of money. However, the method developed suited our purposes for the quantification of the substances in the pharmacogenetic study of paclitaxel’s pharmacokinetics. The correlation of the clearance of paclitaxel and the ABCB1 and CYP2C8 genotypes needs to be confirmed in an independent and larger pharmacokinetic study. Hopefully this will lead to the development of individualized dosage of paclitaxel. However, prior to that one needs to establish a suitable target exposure and validate the impact of different genotypes on the clearance and elimination of paclitaxel. It has previously been suggested that a concentration of more than 50 nM of paclitaxel for at least 20 hours or 100 nM for 15 hours is correlated to a good response to paclitaxel-carboplatin treatment, although this has to be confirmed in future studies. How much and in what way each genotype influences the clearance and elimination of paclitaxel needs to be established using pharmacokinetic modeling of data from clinical studies such as our pharmacokinetic investigation. It will probably be possible to use physiologically based models to construct a method for dose determination, where different variables can be estimated both from physiological data from the patients such as weight and height as well as SNP data from validated genotypes influencing the elimination. However, there is still a long way to go before we can individualize the dose of paclitaxel. Hopefully our studies will be a part of the basic studies for this field of research. 78 | FUTURE ASPECTS ACKNOWLEDGEMENTS The studies in this thesis were conducted at the Division of Clinical Pharmacology, Department of Medicine and Care, Faculty of Health Sciences, Linköping University, Linköping, Sweden. During the course of a project like this a great number of people have made invaluable contributions both towards the work itself and in creating an environment where it has been both possible to work and enjoy living in moments of both triumph and disappointment. Despite my best intentions an acknowledgement can never be more than a modest token of the gratitude and esteem which I truly feel towards all those who have made this work possible. I wish to take the opportunity to express my sincere appreciation and gratitude to all who contributed to this thesis, especially: My supervisor, Professor Curt Peterson, for taking me under your wings and letting me become one of your PhD student. For your outstanding supervision, endless support, encouragement and for always taking the time to discuss problems both large and small. Professor Peter Söderkvist, my co-supervisor, for all the “genetic” support, methodological and scientific discussions. ACKNOWLEDGEMENTS | 79 The real patient doctor Per Rosenberg, my second co-supervisor, for introducing me to the world of gynecological oncology, willingness to discuss research and for making this thesis possible. Malin Lindqvist, my officemate and (former) Phd student colleague for fruitful discussions about anything and everything, a growing friendship and not hitting me to many times going out and in of the office. Björn Norlander for all the help with chromatography and for always being there for me when I needed somebody to discuss methodology with. I hope your new “occupation” will be as fruitful and enjoyable as possible for you! You’re the best! My new office-mate, Karin Skoglund, for putting up with me as a supervisor and all the controls during your first project, for help with all the other small lab projects and for all the discussions in the office. Kourosh Lotfi, a good friend and colleague, for the time in Nice and for our mutual effort in the quest for new knowledge and wild game. Anna Fyrberg, Britt Sigfridsson, Anna-Lena Zackrisson and Gunilla Graffner for a friendly atmosphere in the lab and all the help over the years. Björn Carlsson and Maria Kingbäck, my fellow mass spectrometrists, for all the time we’ve spent together disassembling and assembling the LC/MS. Anita Thunberg for all the help with economic and administrative matters, efficiently and swiftly as always. All the rest of the staff and students at the Division of Clinical Pharmacology – I hope I’ll have more time to help you with your computer and technical problems in the future. Martin Fransson, Ingemar Rundquist, Thomas Walz, Troels Bergmann and Professor Kim Brøsen for present and future collaboration. 80 | ACKNOWLEDGEMENTS My co-authors, Per Rymark, Karin Vretenbrant, György Horvath, Rajaa Mirghani, and last but not least special thanks to Elisabeth ÅvallLundqvist for excellent reviews of both study design and manuscript as well as scientific discussions. Mats Borén and Johan Hurtig – for being good friends. I’ve always enjoyed our scientific, non-scientific and “most-certainly” non-scientific discussions in the sauna, in the stream, in the boat or over a barbeque. All the research nurses who participated in the studies: Dagmar Gutemark and Britt-Lena Staberg in Linköping, Susanne Skarps in Västerås, Ninni Petersson and Anne Brandt in Stockholm, who monitored the patients, cared about the study and took care of all the blood samples and a lot of paperwork. I am extremely lucky to have such a wonderful family that has supported me during these years and I would therefore express my enormous gratitude to them: My parents, Ingvor and Krister Gréen, for their unconditional support and endless encouragement to pursue new knowledge. My one and only true love, Anna, for your endless love and encouragement through rough times and tender moments. Our two children, Julia and Linnea, for their endless and unconditional love. For always having time to play with dad and hugging him when coming home from work and for their aid in pushing the buttons on dad’s computer. The studies presented in this thesis have financially been supported by the Swedish Cancer Society, Gunnar Nilsson’s Cancer Foundation, the County Council in Östergötland, the Anna Cederberg Foundation, Lions Foundation, the Foundation for Strategic Research, Graduate School of Biomedical Research and Faculty of Health Sciences, Linköping University. ACKNOWLEDGEMENTS | 81 REFERENCES Agarwal, R. and Kaye, S. B. (2003) Ovarian cancer: strategies for overcoming resistance to chemotherapy. Nat Rev Cancer, 3: 502-516. Alberts, B. (1994) Molecular biology of the cell. New York, Garland. Alberts, D. S. and Dorr, R. T. (1998) New Perspectives on an Old Friend: Optimizing Carboplatin for the Treatment of Solid Tumors. Oncologist, 3: 15-34. Allen, J. D., Brinkhuis, R. F., van Deemter, L., Wijnholds, J., and Schinkel, A. H. 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