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Factors impacting drug disposition and clinical outcomes: age, hepatic metabolism, renal elimination and pharmacogentetics

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Factors impacting drug disposition and clinical outcomes: age, hepatic metabolism, renal elimination and pharmacogentetics

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Content FACTORS IMPACTING DRUG DISPOSITION AND CLINICAL OUTCOMES:
AGE, HEPATIC METABOLISM, RENAL ELIMINATION AND
PHARMACOGENTETICS

by
Shanshan Liu

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PHARMACEUTICAL SCIENCE)

August 2009

Copyright 2009 Shanshan Liu

ii
Acknowledgements
There are many people I wish to thank for their help and support during my Ph.D.
training. I know it clearly that it is everybody’s help that makes who I am today.
I save the fondest memories for Dr. Stan Louie, my mentor. I am indebted to his
support and trust in my efforts. He also provided a nurturing environment where I thrive.
Dr. Louie inspired me to have passion for life-long learning. His hands-on experience and
sensibility kept me on the right track. Dr. Louie supported me in my pursuit to continue
my medical training, even when he knew by doing so this I would divert my attention. In
addition to all of this, Dr. Louie is the friend whom I trust. He has tirelessly shared with
me his wisdom in all aspects of life. To this I am truly grateful!
I am especially thankful to Dr. Gilbert Burckart, my previous mentor. I was lucky
enough to be picked up by Dr. Burckart from hundreds of candidates applying to the
Ph.D. program here at USC. Dr. Burckart has been generously supporting me financially
and academically till he left for a new position at FDA. He patiently guided me during
the first few years when I literally knew nothing about bench work.
I would also like to express my gratefulness to my committee advisors: Dr. Wei-
Chiang Shen and Dr. David D’Argenio. Serving as my committee members meant extra
time to guide me. Fortunately for me, both Dr. Shen and Dr. D’Argenio are not only
supportive but are very insightful in their guidance. They have been actively providing
critique and advices for my projects, which is critical for me to deliver a thesis in
excellent quality. In addition, I owe a tremendous debt of gratitude to Dr. D’Argenio,
who has guided me hand by hand on PK modeling using ADAPT. Being proficient with

iii
this powerful modality differentiates me as a professional in pharmacokinetics. I will
have fond memories of the time we spent together.
I am indebted to my collaborators Drs. Paul Beringer and Miguel Giocoechea. Dr.
Beringer led the efforts in the cystic fibrosis project, from study concept, execution of the
clinical trial, performing each experiment and data analysis, to the final manuscript
writing. Dr. Giocoechea provided the opportunity participating in the tenofovir
nephrotoxicity project, and has been always prompt in communicating with me and
providing important information to carry out my research work. This work will continue
where the data will help patients who are afflicted with HIV.
I am also deeply indebted to the members of the laboratory whom have touched
me with their constant support and encouragement: Drs. Haejung An, Luke Bi, Jerika
Lam, Jared Russel, Dr. Nick Mordwinkin, Dr. Dolph Ellefson, and Carol Lin. The five-
and-half-year Ph.D. experience not only helps me gain tremendous strength in research
but also a thorough understanding of teamwork. I am lucky to be part of this harmonious
family where people are willing to help each other with their own expertise. Lacking
bench-work experience in my previous background, I have been asking them all kinds of
technical questions, from cell culture experiments to LCMS, from solid phase extraction
to preparing stock solutions, and I always got helpful answers. Everybody in the lab takes
initiatives for routine lab maintenance such as filling pipettes, regular lab cleaning,
ordering lab supplies and etc, and the well-coordinated work like this makes life easy for
everyone. In addition, my dear lab-mates also uphold me with supportive friendship,
always encouraging me and cheering for every achievement I made. I don’t know why I

iv
could be so lucky to have the chance working with these brilliant and kind people, but I
do know that the only way to pay back is to offer my help and support to my dear friends
in any way I can, and I am heart-fully willing to do so.
Finally, I save my deepest gratitude for my family: my father, Yifu Liu, my
mother, Huawan Zhou and my sisters Yinhong Liu and Yinbo Liu for their constant love,
support and encouragement. Although they are far away in China, their love and caring
are always with me. I am also grateful to my husband, Zhimin Zhou for taking care of the
family, gracing me with a beautiful home and for sticking by me through the difficult
times. I couldn’t have achieved these without his essential advice and support.

v
Table of Contents
Acknowledgements ii
List of Tables ix
List of Figures x
Abbreviations xii
Abstract xvi
Chapter 1: overview of impacting factors in drug disposition 1
1.1 Introduction 1
1.2 Renal elimination and its impact on drug disposition 2
1.3 Important membrane transporters for drug elimination 3
1.3.1 MRPs 4
1.3.2 P-glycoprotein 7
1.3.3 OATs 9
1.3.4 OATPs 10
1.4 Overview of hepatic metabolism 11
1.5 Cytochrome P450 11
1.6 Pharmacogenetics and its impact on pharmacokinetics 14
Chapter 1 endnotes 16
Chapter 2: Impact of age on drug disposition and outcomes in cancer patients 17
2.1 Introduction 17
2.1.1 Age-related changes in pharmacokinetics 18
2.1.2 Advanced urothelial cancer and its management 20
2.1.3 Advanced breast cancer and its management 21
2.1.4 Advanced colorectal cancer and its management 22
2.1.5. Gemcitabine 23
2.1.6. Paclitaxel 25
2.1.7 Docetaxel 26
2.1.8 Capecitabine 27
2.2 Method: 29
2.2.1 Subjects 29
2.2.1.1 Subjects for gemcitabine and paclitaxel study 29
2.2.1.2 Subjects for docetaxel study 30
2.2.1.3 Subjects for capecitabine study 30

vi
2.2.2 Study design 31
2.2.2.1 Study design for gemcitabine and paclitaxel study 31
2.2.2.2 Study design for docetaxel study 32
2.2.2.3 Study design for capecitabine study 32
2.2.3 Analytical measurements 33
2.2.3.1 Determination of plasma concentrations for gemcitabine
and beta-uridine 33
2.2.3.2 Determination of plasma concentrations for paclitaxel
and docetaxel 34
2.2.3.3 Determination of plasma concentrations of capecitabine
and its metabolites 35
2.2.4 Pharmacokinetic modeling 36
2.2.5 Statistical analysis 37
2.3 Results 37
2.3.1 Results of gemcitabine study 37
2.3.1.1 Patient characteristics 37
2.3.1.2 Pharmacokinetics of gemcitabine and dFdU 38
2.3.1.3 Pharmacokinetics of paclitaxel 39
2.3.2 Results of docetaxel study 41
2.3.2.1 Patient characteristics 41
2.3.2.2 Pharmacokinetics of docetaxel 42
2.3.3 Results of capecitabine study 43
2.3.3.1 Patient characteristics 43
2.3.3.2 Pharmacokinetics of capecitabine and its metabolites 43
2.3.3.3 Clinical outcomes of colorectal cancer 45
2.4 Discussion 46
2.5 Conclusion 53
Chapter 2 endnotes 55
Chapter three: Impact of age and duration after liver transplantation
on phenotype of major CYP450 enzymes 57
3.1 Introduction 57
3.2 Method 58
3.2.1 Liver Transplant Patients 58
3.2.2 Healthy Subjects 59
3.2.3 Study Protocol 59
3.2.4 Data Analysis 60
3.2.5 Statistical Analysis 61
3.3 Results 61
3.4 Discussion 67
3.5 Conclusion 72
Chapter 3 endnotes 73

vii
Chapter four: Impact of mutational disease on drug disposition and outcomes 74
4.1 Introduction 74
4.2 Methods 76
4.2.1 Subjects 76
4.2.2 Study Protocol 77
4.2.3 Analytical measurements 79
4.2.3.1 Determination of P-gp efflux activity by flow cytometry 80
4.2.3.2 Determination of plasma and urine fexofenadine 80
4.2.3.3 Determination of plasma and urine iothalamate concentrations 82
4.2.4 DNA isolation and genotyping 83
4.2.5 Pharmacokinetics 83
4.2.6 Statistical analysis 84
4.3 Results 85
4.4 Discussion 89
4.5 Conclusion 93
Chapter 4 endnotes 94
Chapter 5: Inhibition of MRP transporters and its impact on TFV-related
nephrotoxicity in HIV-infected patients 95
5.1 Introduction 95
5.2. Methods 97
5.2.1 Study subjects (Goicoechea, Liu et al. 2008) 97
5.2.2 Protocol of the clinical study 98
5.2.3 Protocol of the cellular experiments 99
5.2.3.1 Cell culture 99
5.2.3.2 Intracellular TFV accumulation assays 100
5.2.4 Analytical measurements 101
5.2.4.1 Creatine clearance 101
5.2.4.2 Determination of plasma TFV concentrations 101
5.2.4.3 Genotyping 102
5.2.4.4 Determination of intracellular TFV accumulation 102
5.2.5 Pharmacokinetic modeling 103
5.2.6 Statistical analysis 104
5.3 Results 105
5.3.1 Patient characteristics (Goicoechea, Liu et al. 2008) 105
5.3.2 Changes in renal function (Goicoechea, Liu et al. 2008) 106
5.3.3 TFV Pharmacokinetics 106
5.3.4 Intracellular TFV accumulation in MDCK and HEK cells 109
5.3.5 SNPs in MRP2 and MRP4 and their impacts on TFV pharmacokinetics 111
5.4 Discussion 114
5.5 Conclusion 122
Chapter 5 endnotes 123

viii
Chapter 6: Conclusion 125
Bibliography 131

ix
List of Tables
Table 1: Patient characteristics and pharmacokinetic parameters for
gemcitabine, dFdU and paclitaxel in gemcitabine study 41
Table 2: Patient characteristics and pharmacokinetic parameters for
docetaxel in docetaxel study. 42
Table 3: Patient characteristics and pharmacokinetic parameters for
capecitabine, DFCR, DFUR and 5FU in capecitabine study. 45
Table 4: Patient demographics from each study group in cocktail study 62
Table 5: Subjects characteristics in cystic fibrosis study 87
Table 6: Fexofenadine pharmacokinetics, cystic fibrosis vs healthy volunteers 88
Table 7: Fexofenadine pharmacokinetics, effects of inhibitor 89
Table 8: TFV pharmacokinetics in study groups from CCTG584, CCTG585
and CCTG578. 108
Table 9: TFV Pharmacokinetics for different genotypes in
ABCC4 559G→T SNP 112
Table 10: TFV Pharmacokinetics for different genotypes in
ABCC2 1249G→A SNP 113
Table 11: TFV Pharmacokinetics for different genotypes in
ABCC2 3972 C→T SNP 113
Table 12: TFV Pharmacokinetics for different genotypes in
ABCC4 4131 T→G SNP 114

x
List of Figures
Figure 1: Transporters in renal tubular cells 5
Figure 2: Structure of gemcitabine, paclitaxel and docetaxel 26
Figure 3: Structures of capecitabine and 5FU and the 3-step
conversions in vivo 28
Figure 4: Comparisons dFdU pharmacokinetics between groups 39
Figure 5: Comparisons of paclitaxel pharmacokinetics between groups 40
Figure 6: Comparisons of Capecitabine pharmacokinetics between groups. 44
Figure 7: The chlorzoxazone metabolic ratio OLTx patients within
and after 30 days postoperatively and in healthy control subjects. 63
Figure 8: The urinary recovery of 4-hydroxymephenytoin in OLTx patients,
within and after 30 days postoperatively, and in healthy control subjects. 64
Figure 9: The caffeine metabolic ratio in OLTx patients, within
and after 30 days postoperatively, and in healthy control subjects. 65
Figure 10: The debrisoquin recovery ratio in in OLTx patients, within
and after 30 days postoperatively, and in healthy control subjects. 66
Figure 11: The dapsone recovery ratio in in OLTx patients, within
and after 30 days postoperatively, and in healthy control subjects. 67
Figure 12: P-gp efflux activity measured by in CD4+ and CD8+ T cells
for cystic fibrosis patients and healthy volunteers. 86
Figure 13: Fexofenadine plasma concentration-time profiles for
cystic fibrosis and healthy control subjects. 87
Figure 14: Regimen type and change in renal function in
HIV infected patients. 107

xi
Figure 15: Comparisons of TFV Kel and T1/2 among CCTG study groups 109
Figure 16: Intracellular TFV accumulation in MDCKII, MDCKII-MRP2
(A and B), HEK293 wt, HEK293-463 and HEK293-5I (C and D) after
exposure to TFV 10 µM alone and in combination with RTV 20 µM
or MK571 20 µM. 111

xii
Abbreviations
5FU 5’-fluorouracil
ABC ATP binding cassette
ABCB1 ATP binding cassette B1
ABCC ATP binding cassette C
ACN Acetonitrile
ADV Adefovir
AUC Area under the curve
AZT zidovudine
BSA Body surface area
cAMP Cyclic adenosine monophosphate
CCTG California Collaborative Treatment Group
CDA Cytidine deaminase
CDV Cidofovir
CF Cystic fibrosis
CFTR Cystic fibrosis transmembrane conductance regulator
C-G Cockcroft-Gault
cGMP Cyclic guanosine monophosphate
CLtota Total clearance
Cmax Maximum concentration
cMOAT Canalicular multispecific organic anion transporter
CsA Cyclosporine A

xiii
CSF Colony stimulating factors
CV Coefficients of variation
CYP450 Cytochrome P450
dAMP Deoxyadenosine monophosphate
dCTP Deoxycytidine triphosphate
ddC 2’,3’-dideoxycytidine
DFCR 5’-deoxy-5’-fluorocytidine
dFdC Gemcitabine
dFdU 2’-dexoy-2’,2’-difluorouridine
DFUR 5’-deoxy-5’-fluorouridine
DMEM Dulbecco’s modified eagle medium
dNTP Deoxynucleoside triphosphate
DPD Dihydropyrimidine dehydrogenase
EM Extensive metabolizer
F Oral bioavailability
FBS Fetal bovine serum
FXF Fexofenadine
GFR Glomerular filtration rate
HARRT Highly active antiretroviral therapy
HEK293 Human embryo kidney 293
HIV Human immunodeficiency virus
HPLC High performance liquid chromatography

xiv
IM Intermediate metabolizer
IULN Institutional upper limit of normal
Kel Elimination constant
LC/MS Liquid chromatography coupled to a tandem mass spectrometry
LOQ Lowest limit of quantification
LPV Lopinavir
MDCKII Madin-Darby Canine Kidney II
MDR1 Multiple drug resistance 1
MDRD Modification of Diet in Renal Disease
MEOH Methanol
MLEM Maximum likelihood expectation maximization
MRP Multiple resistance protein
MSDs Membrane spanning domains
MTX Methotrexate
NBDs Nuclear bounding domains
NNRTI Non-nucleotide reverse transcriptase inhibitor
NSAIDs Nonsteroidal anti-inflammatory drugs
OAT1 Organic anion transporter 1
OATP Organic Anion Transporter Peptide
OLTx Orthotopic liver transplantation
PAH p-aminohippurate
PB Probenecid

xv
P-gp P-glycoprotein
PI/r Protease inhibitor containing RTV
PK Pharmacokinetics
PM Poor metabolizers
PXR Pregnane X receptor
QC Quality control
RSD Relative standard deviations
RTV Ritonavir
SNP Single nucleotide polymorphism
STS Standard two-stage
SWOG Southwest Oncology Group
T1/2 Half-time
TDF Tenofovir disoproxil fumarate
TFV Tenofovir
Tmax Time to achieve Cmax
UEM Ultra extensive metabolizer
Vd Volume of distribution

xvi
Abstract
Objective: This dissertation aims to evaluate factors impacting drug disposition
and clinical outcomes.
Methods: Pharmacokinetic studies were conducted for gemcitabine in urothelial
cancer, paclitaxel in breast cancer and capecitabine in colorectal caner among patients
stratified into young and elderly groups. Cocktail consisting of chlorzoxazone (CYP2E1),
caffeine (CYP1A2), dapsone (CYP3A4),

mephenytoin (CYP2C19) and debrisoquine
(CYP2D6) were administrated to OLTx patients to evaluate enzyme activity relating to
recipient’s age and post-operative duration. To assess renal P-gp activity in cystic fibrosis
(CF) patients, disposition of fexofenadine was evaluated and P-gp efflux activity in
peripheral T cells was measured by flow cytometry. To assess interactions between
tenofovir and ritonavir, renal function of HIV-infected patients were evaluated and
tenofovir disposition was compared relating to regiments administrated. Kidney cell lines,
MDCKII and HEK293, were treated with tenofovir alone or in combination with
ritonavir or MK571 to assess the impact of ritonavir on intracellular tenofovir
accumulation.
Results: Compared to patients less than 60, elderly patients (>70 years) had
significantly greater AUCs and less clearance for paclitaxel and capecitabine and greater
AUC of dFdU. The CYP2E1 activity was significantly elevated and CYP2C19 function
was markedly reduced in OLTx patients, where CYP2D6 capacity was impaired in

older
OLTx patients within 30 days postoperatively compared to healthy controls. The P-gp
efflux activity in peripheral T cells was not significantly different between CF and HV.

xvii
Fexofenadine pharmacokinetics was similar in CF and HV when administered alone or
with probenecid. ABCB1 3435 C/T carriers showed increased basal P-gp activity in
peripheral T cells and decreased fexofenadine AUC. Patients receiving concurrent
TDF+PI/r treatment had greater reductions in CrCl and lower TFV Kel compared to
patients taking TDF+NNRTI. TFV was substrate of MRP2, MRP4 and MRP5, where
RTV was a potent inhibitor of MRP2 leading to nephrotoxicity with TDF-RTV
coadministration.
Conclusion: Impaired renal elimination and hepatic metabolic capacity are major
factors accounting for altered pharmacokinetics in elderly patients. Inhibition of
transporters contributes to variations in drug disposition. Genetic polymorphisms in
enzymes and transporters add to complexities of inter-individual differences in
pharmacokinetics and clinical outcomes.

1
Chapter 1: overview of impacting factors in drug disposition

1.1 Introduction
Drug disposition of various compounds is influenced by a myriad of factors,
which include renal and hepatic function. Renal filtration function is further influenced
by age and the gender, which dictates the glomerular filtration rate (GFR). However
renal function is not GFR alone, where some compounds undergo active tubular secretion.
Activate secretion is not limited to the kidney, but is found in biliary elimination as well.
This process is dependent on the influx and efflux transporters that are found on the
basolateral and apical membrane of the cell, respectively. Therefore the impact of efflux
transporters such as multiple drug resistant proteins (MRPs) and P-glycoprotein (Pgp)
and influx transporters such as organic anionic tranporter-1 (OAT1) and organic anionic
transporter protein (OATP) is critical. The expression level and functional activity of
these influx or efflux transporters are important, and this introduces the impact of genetic
polymorphism in light of the drug disposition, which is the basis of “pharmacogenetics.”
In addition, the activity of these membrane bound protein is another important factor to
be considered, since it has been shown that transporters’ activity can be inhibited or
induced by many other compounds and this introduces the impact of drug-drug
interaction on pharmacokinetics.
Hepatic function is another important factor, which substantially contributes to
variations in drug disposition. The cytochrome P450 (CYP450) system is one of the key
elements in catalyzing metabolism reactions. The expression level as well as the activity

2
of these CYP450 enzymes is subject to regulations of various factors, which could be age,
gender, genetic polymorphism, and drug-drug interactions.
In this thesis, I would demonstrate important contributions of each of the areas
that were presented. In doing so, it will be evident that drug disposition is dependent on
various factors, which ultimately impact clinical outcomes. With knowledge of how these
factors impact pharmacokinetics and drug disposition, clinicians may be able to optimize
drug therapy according to patients’ genetic and clinical profiles, which might help to
achieve maximum efficacy and minimize unnecessary toxicity.
1.2 Renal elimination and its impact on drug disposition
The kidney can filter approximately 100 cc/minute of blood, and thus it is not
surprising that this organ is critical in drug elimination. Alternation of this status has
been shown with age, ethnicity, sex, concurrent disease, medication and etc. In addition,
only amphiphilic and hydrophilic agents can be renally eliminated and thus this is only
one part of the total drug clearance equations.
Kidney plays a critical role in maintaining adequate levels of essential nutrients
while at the same time eliminating toxins, xenobiotics, natural end products, and drug
metabolites that may be potentially injurious to the body. The process of renal drug
elimination takes place in each of the nephrons, which are the functional units found in
the kidney and includes glomerular filtration, tubular secretion and tubular reabsorption
(Vander 1995). Glomerular filtration is a simple unidirectional diffusion process and is
governed by GFR and the level of drug protein binding (fu: fraction unbound). In contrast,
renal tubular secretion and reabsorption are bidirectional processes that mainly take place

3
in proximal tubular cells. Although the exact amount of tubular secretion or reabsorption
is difficult to be determined, the net sum of tubular secretion/reabsorption could be
estimated by subtracting the GFR portion from the total renal clearance. The process of
tubular secretion/reabsorption often involves both passive diffusion and carrier-mediated
membrane transport processes, where the passive diffusion process is dependent on series
of factors including molecular weight, lipophilicity and pKa of drugs as well as urine
flow rate and urine pH; while carrier-mediated transport involves various transporters
expressed on basolateral and apical membranes of renal tubular cells. Changes or
interactions in any of the aforementioned factors could potentially influence renal drug
elimination.
1.3 Important membrane transporters for drug elimination
There are numerous transporters expressed in renal tubules and biliary ducts and
they play important roles in drug elimination. There are a number of nomenclatures with
regards to the transporters, and they are often classified as influx transporters, which are
usually organic anionic transporters, for example, OAT1 and OATP and by contrast,
efflux transporters, which are either P-gp or MRPs (Lee and Kim 2004). Alternatively,
these transporters can also be classified into organic anion transporter system and organic
cationic transporter system. In general, influx transporters are located on the basolateral
membranes, where the efflux transporters are located on the apical membranes. These
transporters are normally oriented in directions in such a way to minimize intracellular
drug exposures.

4
In kidney, renal tubular secretion of organic anions can be functionally described
by two distinct processes: (a) cellular uptake of organic anions across the basolateral
membrane through influx transporters and (b) transport of organic anions into urine
across the apical membrane via efflux transporters. Because of the intracellular negative
potential, the uptake of negatively charged anions across the basolateral membrane is an
energy-dependent process. For example, the OAT1-mediated uptake of p-aminohippurate
(PAH) is coupled with an outward α -ketoglutarate ( α-KG) gradient which is sustained by
the Na+/ α-KG co-transport system driven by the inward Na+ gradient, established and
maintained by the Na+/K+-ATPase (Pritchard and Miller 1993). In contrast, the efflux of
anionic compound on the apical membrane of renal tubular cell is considered to be
mediated by anion exchange or facilitated diffusion which is not ATP dependent
(Masereeuw and Russel 2001)
Considering the important role that transporters play in the drug trafficking across
renal tubular cells or biliary tube cells, changes in the expression and activity of these
transporters can substantially impact drug elimination and consequently affect systemic
exposure to the drugs. The important transporters in renal tubular cells are illustrated in
the figure 1. Each important transporter will be discussed in details in the following
sections.
1.3.1 MRPs
Multiple resistant protein or MRP transporters are members of the ATP-binding
cassette (ABC) superfamily. In vitro, the MRPs can collectively confer resistance to
natural product drugs and their conjugated metabolites, platinum compounds, folate
antimetabolites, nucleoside and nucleotide analogs, arsenical and antimonial oxyanion,
peptide-based agents, and, under certain circumstances, alkylating agents. The MRPs are
also primary active transporters of other structurally diverse compounds, including
glutathione, glucuronide, and sulfate conjugates of a large number of xeno- and
endobiotics. In vivo, several MRPs are major contributors to the distribution and
elimination of a wide range spectrum of compounds and their respective metabolites. The
MRPs can be further divided into two subfamilies “long” (MRP1, -2, -3, -6, and -7) and
“short” (MRP4, -5, -8, -9, and -10). Short MRPs typically have ABC transporter structure
with two polytropic membrane spanning domains (MSDs) and two nuclear bounding
domains (NBDs), while the long MRPs have an additional NH
2
-terminal MSD. All of the
long MRPs are capable of conferring resistance to certain natural product agents. By
contrast, the short MRPs are distinguished by their ability to confer resistance to
nucleoside-based agents, and by their ability to transport cyclic nucleotides (Kruh,
Belinsky et al. 2007).
Figure 1: Transporters in renal tubular cells adapted from Lee et al. (Lee and Kim 2004)

5

6
MRP2 is an important transporter in MRP family with regards to drug elimination.
MRP2 has a more restricted tissue distribution compared to other MRP transporters, with
high level of expression in liver, kidney, and small intestine. Unlike other MRP
transporters, MRP2 is mainly localized to the apical member of target cells. MRP2 is also
known as canalicular multispecific organic anion transporter (cMOAT) or ATP binding
cassette C2 (ABCC2), indicating an important role of MRP2 in hepatic elimination of
anionic conjugates (Oude Elferink and Jansen 1994). Defect in MRP2 expression in
human hepatic cells has been identified as the molecular defect associated with Dubin-
Johnson syndrome, which clinically present with significantly elevated levels of
conjugated bilirubin (Wada, Toh et al. 1998). MRP2 is also abundantly expressed in renal
tubular cells and mediates the transport of non-conjugated compounds such as PAH,
vinblastine (Evers, Kool et al. 1998), and human immunodeficiency virus (HIV) protease
inhibitors (e.g., saquinavir, ritonavir, indinavir) (Huisman, Smit et al. 2002).
MRP4 is another efflux transporter in the MRP family, which has low to moderate
expression in numerous tissues including kidney. Similar to MRP2, MRP4 is localized to
the apical member of proximal tubular cells and mediates in the transport of methotrexate
(MTX), estradiol-17 beta-D-glucuronide, cyclic adenosine monophosphate (cAMP), and
cyclic guanosine monophosphate (cGMP) (Chen, Lee et al. 2001; van Aubel, Smeets et al.
2002). In addition, MRP4 is also involved in the renal excretion of many antiviral drugs,
including adefovir, PMEG [9-(2-phosphonylmethoxyethyl)-guanine], AZT and
etc(Schuetz, Connelly et al. 1999)

7
Unlike MRP2, MRP5 is expressed widely in various tissues, which includes the
kidney. MRP5 appears to localize to the basolateral membrane upon expression in
MDCKII cells and mediates the transport of S-(dinitrophenyl)-glutathione (DNPSG) and
GSH (Wijnholds, Mol et al. 2000). In stably MRP5-transfected HEK293 cells (HEK293-
5I), these cells were found to be resistant towards nucleoside analogs such as adefovir, 6-
mercaptopurine (6MP) and thioguanine (TG) (Wijnholds, Mol et al. 2000). The efflux
activity appears to be mediated by ATP-dependent export of cAMP and cGMP
(Jedlitschky, Burchell et al. 2000)
Other important members in MRP family include MRP1, which was first
identified from a drug-selected human lung cancer cell line H69AR (Cole, Bhardwaj et al.
1992). MRP1 has later been found to have wide tissue expression with high levels in liver,
lung, kidney and etc (Zaman, Versantvoort et al. 1993), and mediate the transport of a
variety of glucuronide, sulfate, and GSH conjugates (Bodo, Bakos et al. 2003). Similarly,
MRP3 is also found to express in various tissues, including the liver, kidney, and
intestine (Kiuchi, Suzuki et al. 1998). Unlike other efflux transporters, MRP3 is mainly
localized to the basolateral membrane in these tissues and involves in the transport of
anionic glucuronide, glutathione conjugates and drugs such as MTX (Kool, van der
Linden et al. 1999).
1.3.2 P-glycoprotein
P-glycoprotein (P-gp), an encoded product of human ABCB1 (MDR1) gene, is
also a member of ABC family. P-gp was first isolated from colchicine-resistant Chinese
hamster ovary cells and was studied for its role in mediating the feature of acquired

8
multi-drug cross-resistance in certain cancers (Romsicki and Sharom 1998).
Subsequently, constitutional expression of P-gp was found in a variety of normal tissue,
such as the brush border surface of intestinal enterocytes, canalicular surface of
hepatocytes, apical membrane of proximal tubular cells in kidneys, blood-tissue barriers,
etc. P-gp is a cell membrane ABC protein, and it functions as an efflux pump, actively
expelling substrate molecules out of the cell (Thiebaut, Tsuruo et al. 1987). The wide
tissue distribution of P-gp indicates a physiologic function as a protective barrier in
maintaining homeostasis and facilitating excretion of xenobiotics from liver and kidney
(Kim 2002). Genetic polymorphism in MDR1 has been intensely explored to determine
whether inter-individual variability in drug disposition, efficacy and toxicity might be
correlated with functional difference.
The importance of P-gp in drug disposition was supported by an animal study
where Kwei et al reported that CF-1 mice, a mouse strain naturally deficient in mdr1a,
was shown significantly sensitive to neurotoxic effects of ivermectin, a P-gp substrate,
which was attributed to an 80-folder higher brain accumulation of ivermectin compared
to that of the wild-type mice (Kwei, Alvaro et al. 1999). The role of P-gp in drug
disposition was further confirmed by drug-drug interaction studies by Klein et al, who
demonstrated that concomitant administration of verapamil, a P-gp inhibitor, and digoxin,
a P-gp substrate, lead to a 40% increased of plasma digoxin levels, where the increase
was attributed to verapamil-mediated inhibition of P-gp in intestine, liver and kidney
(Klein, Lang et al. 1982).

9
1.3.3 OATs
OATs are a transporter family composed of mostly influx transporters that are
normally expressed on the basolateral membrane of renal tubules and many other tissues,
facilitating entry of organic anions into cells. The first OAT transporter oat1 was cloned
from rat (Sekine, Watanabe et al. 1997; Sweet, Wolff et al. 1997) and later a human
ortholog OAT1 was identified (Hosoyamada, Sekine et al. 1999; Race, Grassl et al. 1999).
OAT1 is mainly expresses on the basolateral membrane of renal tubular cell and is
considered to mediate intracellular transport of a variety of substrates including antiviral
agents, such as adefovir, cidofovir, zidovudine (AZT), acyclovir, and ganciclovir (Cihlar,
Lin et al. 1999; Takeda, Khamdang et al. 2002). Variants of OAT1 have been identified
due to alternative splicing (Hosoyamada, Sekine et al. 1999; Bahn, Prawitt et al. 2000),
for example, the OAT1-3 and OAT1-4 variants were reported to be non-functional in
COS 7 cells because of a depletion of 132pb nucleotide (Burckhardt, Wolff et al. 2002).
Other members of OAT family include OAT2, OAT3 and OAT4 which are also
important in transporting a variety of endogenic or exogenic compounds. Similarly,
OAT2 is expression is localized on the basolateral membranes of proximal tubular and
participates in the blood elimination of cephalosporin antibiotics (Khamdang, Takeda et
al. 2003) and nonsteroidal anti-inflammatory drugs (NSAIDs) (e.g., diclofenac, ibuprofen,
ketoprofen) (Khamdang, Takeda et al. 2002) through influx into the epithelial cells and
followed by efflux through MRP-mediated elimination. Human OAT3 is also highly
expressed on basolateral membrane of proximal tubular (Cha, Sekine et al. 2001) and
mediate the transport of a broad spectrum of substrates including PAH, estrone sulfate,

10
MTX, and cimetidine, NSAIDs, diuretics, bile salts, and tetraethylammonium (TEA). In
contrast, OAT4 is primarily localized to the apical membrane along the proximal tubular
(Babu, Takeda et al. 2002) and thus facilitate reabsorption of substrate compounds.
1.3.4 OATPs
Similar to OATs, OATPs are another important transporter family facilitating the
transmembrane uptake of organic anions that are more hydrophobic than the
corresponding substrates utilizing the OATs. They are usually influx transporters
localized to the basolateral membrane of renal tubule, biliary ducts, brain and many other
normal tissues. The nomenclature of human OATPs is designated alphabetically in
contrast to the OATPs in animals. Human OATP-A (also known as OATP or OATP-1)
is a human ortholog to rat Oatp3 (Walters, Craddock et al. 2000)and is widely expressed
in brain, liver, kidney and many other tissue, mediating the transport of a variety of
compounds including bromosulfophthalein(BSP), bile acids, steroid conjugates,
fexofenadine, ouabain, and rocuronium (Jacquemin, Hagenbuch et al. 1994; Cvetkovic,
Leake et al. 1999; Eckhardt, Schroeder et al. 1999). Similarly, human OATP-B, OATP-D
and OATP-E have wide tissue expression, but their physiological function needs to be
further elucidated {Tamai, 2000 #19}. The OATP family also includes OAT-K1 and
OAT-K2, which are exclusively expressed on apical side of the renal tubular cells (Saito,
Masuda et al. 1996; Masuda, Ibaramoto et al. 1999) and involved in MTX renal excretion
(Takeuchi, Masuda et al. 2000).

11
1.4 Overview of hepatic metabolism
Xenobiotics or endobiotics accumulation can be toxic and thus requires
detoxication through chemical modification. In order for lipophilic compounds to be
eliminated, they must be metabolically converted to more hydrophilic metabolites. The
rate by which this is achieved is a critical determinant of the duration and intensity of the
pharmacological action of drugs. Cellular metabolism occurs predominantly in the
smooth endoplasmic reticulum in hepatocytes. Although the metabolic enzymes are
found in abundant levels in hepatocytes, these enzymes are also expressed in various
levels in many other biological tissues.
Lipophilic or amphiphilic agents can undergo two type of metabolism, which are
more commonly referred to as phase I and phase II reactions. Phase I reactions are
predominately mediated by cytochrome P450 enzymes and involves hydroxylation,
reduction or oxidation. In contrast, phase II reactions usually involve the addition of
various components and are referred to as glucuronidation, sulfation, acetylation or
methylation reactions. These processes enhance the polarity or water solubility and
facilitate their removal through transport into the urine or bile.
1.5 Cytochrome P450
Cytochrome P450 (CYP450) proteins are a superfamily of heme-containing
enzymes mainly expressed in liver. Extra-hepatic cytochrome P450 enzymes have been
identified in a wide range of tissues, which include the small intestine, pancreas, brain,
lung, adrenal gland, kidney, bone marrow, mast cells, skin, ovary and testis. (Krishna and
Klotz 1994) The physiological roles that CYP450 enzymes play include catalyzing

12
metabolism of endogenous substrates such as fatty acids, eicosanoids, steroids, bile acids,
vitamin D3 derivatives, retinoids, and also include catalyzing synthesis of endogenous
hydrophobic lipids such as cholesterol, bile acids, steroid hormones, and fatty acids.
CYP450 enzymes are the critical elements involved in the metabolism reactions for
various exogenous compounds, such as drugs, environmental chemicals, pollutants, and
natural plant products.
The completion of the sequence of the human genome revealed the presence of
115 human CYP450 genes: 57 active and 58 pseudo-genes (Nelson 2002). CYP proteins
are arranged into families and subfamilies on the basis of percentage of amino acid
sequence identity (Nebert, Nelson et al. 1991; Nelson, Kamataki et al. 1993). Enzymes
that share over 40% identity are assigned to a particular family designated by an Arabic
numeral, whereas those sharing over 55% identity make up a particular subfamily
designated by a letter. The CYP isoenzymes in families 1–3 are responsible for 70–80%
of all phase I-dependent metabolism of clinically used drugs (Rendic and Di Carlo 1997).
Ninety percent of metabolic activity dependents on six enzymes: CYP1A2, CYP3A,
CYP2C9, CYP2C19, CYP2D6 and CYP2E1. There are substantial inter-individual
variations with regards to the CYP enzymatic function and patients can be divided into
ultra extensive (UEM), extensive (EM), intermediate (IM) or poor metabolizers (PM)
(Ereshefsky and Dugan 2000) based on the expression level and activity of the specific
CYP enzyme. The difference in CYP450 enzyme activity is one of the major causes for
variations in efficacy and toxicity of the drug that is the substrate to the specific CYP
enzyme.

13
There are complex regulatory mechanisms involved in the expression of CYP
enzymes. For instance, Goodwin et al found an important regulatory pathway controlling
expression of CYP3A subfamily in the liver and gut (Goodwin, Redinbo et al. 2002). The
molecular basis of the induction was involved with a ligand-activated transcription factor,
known as the pregnane X receptor (PXR), or steroid xenobiotic receptor (SXR), which
belongs to an adaptive orphan nuclear hormone receptor superfamily. After substrate
binding onto PXR as a consequence of displacement of suppressors of this nuclear factor,
the activated PXR will form a heterodimer with rexinoid acid (RXR) and translocates
into the nucleus and functions as transcriptional factor. The activated transcriptional
factor can promote CYP3A and MDR-1 expression. On one hand, the expression level of
many P450 enzymes can be induced or inhibited by certain substrates through regulatory
mechanisms similar to the PXR pathway (Schuetz 2001). The induction or inhibition of a
CYP450 enzyme by one compound can consequentially influence the metabolism of
other compounds that are also substrates of the CYP enzyme. For example, hepatic
concentrations of the human CYP3A enzymes are increased by consumption of drugs
such as rifampicin, a common prescription for bacterial infection. The induction of
CYP3A not only enhance rifampicin metabolism but also enhances clearance of other
agents that are CYP3A substrates as well. The ability of one CYP substrate to affect the
metabolism of another drug in this manner introduces the concept of drug-drug
interactions, which might complicate medication management (Tang and Stearns 2001).

14
1.6 Pharmacogenetics and its impact on pharmacokinetics
It is well recognized that different individual may response differently to a same
drug prescribed at the same dose. Many factors can potentially influence the efficacy and
toxicity of the medication, such as organ function, drug-drug interactions, nature of
concurrent diseases as well as genetic variations. During the process of drug absorption,
distribution, metabolism and elimination, drugs interact with a variety of participating
elements such as receptors, transporters and transforming enzymes. Genetic variations in
each of the aforementioned elements might change their expression and activity, and
potentially affect drug deposition, leading to different profiles in pharmacokinetics or
pharmacodynamics. It is estimated that in the general population, genetic polymorphisms
account for 20% to 95% of the total variability in drug disposition and clinical outcomes
(Kalow, Tang et al. 1998). In particular, exhaustive research has demonstrated the
correlations between differences in pharmacokinetics and genetic polymorphisms in
major CYP enzymes, P-gp, MRPs and etc. This introduces the concept of
pharmacogenetics, which aims to elucidate genetic determinants correlated to drug
efficacy and toxicity. The ultimate goal is to predict drug responses according to patients’
unique genetic makeup.
In the following chapters, I will discuss several factors that potentially affect drug
pharmacokinetics and drug disposition. Particulately, I will try to elucidate whether
advance age can influences disposition of chemotherapeutic agents used in cancer
patients; how age and post-operative duration in orthodontic liver transplant patients
impact phenotypic activity of major CYP enzymes; whether P-gp activity is altered in

15
cystic fibrosis (CF) patients as a consequence of mutation of a related ABC transporter,
cystic fibrosis transduction receptor (CFTR); and whether inhibition of MRP transporters
are involved in RTV-related nephrotoxicity in HIV patients. The clinical significance of
the research presented here is to provide scientific fundamentals for individualization of
drug therapy based on patients’ clinical and genetic profiles, so that optimizing
therapeutic effects and minimizing toxicity can be ultimately achieved.

16
Chapter 1 endnotes

1.1 Introduction
1.2 Renal elimination and its impact on drug deposition
1.3 Important membrane transporters for drug elimination
1.3.1 MRPs
1.3.2 P-glycoprotein
1.3.3 OATs
1.3.4 OATPs
1.4 Overview of hepatic metabolism
1.5 Cytochrome P450
1.6 Pharmacogenetics and its impact on pharmacokinetics

17
Chapter 2: Impact of age on drug disposition and outcomes in cancer patients

2.1 Introduction
The proportion of the aging (defined as >65 years) population has been increased
significantly in the past century, where it is estimated that by 2030, 20% of Americans
will be over 65 years old. Several cellular mechanisms have been described as a
consequence of aging. These pathophysiological processes include oxidative stress,
mitochondrial dysfunction, telomere shortening, various genetic mechanisms (McLean
and Le Couteur 2004) and contribute to the age-related adaptive responses including
changes in pharmacokinetics and pharmacodynamics. Elderly individuals are more
vulnerable to variety of diseases including cancer. A number of age-related factors have
been described which include alterations in drug absorption, distribution, metabolism and
elimination. These changes suggest that drug responses are potentially different between
elderly patients when compared to their younger counterparts. These potential differences
have led to the exclusion of older subjects from participating clinical trials, which
furthers the data abyss as how to adequately dose patients who advance in age.
Considering the relative scarcity of pharmacological information for old patients, it is of
great significance to conduct studies to explore differences in pharmacokinetics among
old patients. To define this, a series of chemotherapeutic regimens with agents that are
eliminated by different routes were undertaken in patients who are greater than 70 years
of age as compared to patients less than 60 years of age. The following studies aim to
evaluate clinical pharmacology and clinical outcomes of several chemotherapeutic

18
regimens in the management of selective cancers for patients 70 years and older. These
studies will greatly enrich knowledge and thus optimize cancer management in patient
population who are advancing in their age.
2.1.1 Age-related changes in pharmacokinetics
With advancing age, a number of physiological changes may contribute to
alterations of pharmacokinetic profile. These changes might substantially affect drug
absorption, distribution, metabolism and elimination, and consequently impact systemic
exposure to medications. Understanding the impact of these factors may provide
guidance to clinicians as how to adjust medication treatment accordingly in order to
achieve ideal disease control and avoid unnecessary side effect.
Oral absorption in the elderly might be affected by several age-related
physiological changes. Gastric acid secretion appears to declines with the normal aging
process (Feldman 1997). In addition, elderly patients tend to have slower gastric
emptying, decreased peristalsis and slower colonic transit mainly due to loss of neurons
in gastroenteric tract (Wiley 2002). These changes will consequentially affect time to
reach maximum concentration (Tmax) and maximum concentration (Cmax) following an
oral administration. Other factors might impact drug absorption in the elderly include
decreased splenic blood flow, decreased secretion of digestive enzymes, and atrophic
mucosal membranes (Yuen 1990).
Volume of distribution (Vd) is also subject to change where the changes can come
from several factors. It is estimated that the blood albumin concentration is about 10%
less in older people as compared to younger counterparts (Yuen 1990), this corresponds

19
to approximately a 10% increase in the unbound fraction of drugs (Grandison and
Boudinot 2000). The changes of body composition in elderly patients can in turn affect
volume of distribution (Vd). Usually there is a 20-40% increase in body fat, with
advancement in age, approximately 10-15% decrease in body water and lean muscle
mass often encounter (Beaufrere and Morio 2000). These changes can theoretically
increase the concentration of water-soluble drugs and prolong the elimination half-life of
lipid-soluble drugs.
Liver metabolism function can also change with age advancement. The process of
liver clearance of medication is primarily impact by hepatic blood flow, intrinsic
clearance (i.e. liver enzyme capacity and liver mass) and protein binding. It is well
established that elderly people can have up to 40% reduction in liver blood flow and a
similar decrease in liver mass (Le Couteur and McLean 1998), which is considered as the
major contributing factor causing impaired hepatic clearance of medications, especially
for those drugs that go through ‘flow-limited metabolism’(Rowland 1984). There are still
controversies regarding whether or not intrinsic activities of common CYP450 enzymes
will decline with age. In a study of 226 patients, the cytochrome P450 enzymes in liver
biopsy samples decreased by approximately 30% in patients over 70 years of age
(Sotaniemi, Arranto et al. 1997), but many other in vitro and in vivo studies suggested
that the intrinsic function of CYP450 enzymes keep intact with age advancement.
Renal elimination activity might be affected when people age. It is estimated that
for an individual over 50 years old, there is a roughly 25-30% reduction in renal mass and
the renal blood flow decreased by 1% per year (Vestal 1997). The glomerular filtration

20
rate (GFR) is estimated to decrease at a rate of 0.75 mL/min/year after age 40 (Lindeman,
Tobin et al. 1985). The age-related decline in renal function might theoretical impact
elimination of medications, especially for those drugs which are mainly eliminated
through kidney.
As previously mentioned, there is a lack of pharmacologic information for old
patients. Considering the age-related changes in drug pharmacokinetics, it is critical to fill
this information void as proportion of aging population continues to grow. Information
for this population will enhance efforts to optimize pharmacologic therapeutics in
patients who are most likely to receive drug therapy.
2.1.2 Advanced urothelial cancer and its management
Urothelial carcinoma can arise anywhere in the urothelium lining the urinary tract
from the urethra to the calyces in kidney, but it is most commonly found in bladder.
Histological diagnosis includes transitional cell carcinoma, adenocarcinoma and
squamous carcinoma. Urothelial carcinoma is usually multifocal and has the tendency to
recur. Advanced urothelial carcinoma refers to cases of metastatic diseases, locoreginally
bulky diseases, or recurrent diseases after radiotherapy or cystectomy.
Chemotherapy is the standard treatment for advanced urothelial cancer and is the
only modality that can significantly improve survival in these patients (Logothetis,
Dexeus et al. 1990) The classic chemotherapy regimen include M-VAC combination
consisting of cisplatin, methotrexate, doxorubicin and vinblastine (Sternberg, Yagoda et
al. 1985). Other combination regiment includes cisplatin and methotrexate (CM
combination)(Stoter, Splinter et al. 1987) or CMV which adds vinblastine (Harker,

21
Meyers et al. 1985). There are a number of new single agents, which have been shown
promising anti-tumor activity in advanced urothelial cancer, for example, carboplatin,
oxaliplatin, trimetrexate, piritrexim, gemcitabine, taxanes (paclitaxel and docetaxel),
ifosfamide, the epothilones and vinflunine. The modern doublet regiment adopting
combination of gemcitabine and cisplatin (GC combination) was found to be comparable
to M-VAC, and was shown to achieve similar efficacy and overall survival rate with
significantly less toxicity profile (von der Maase, Hansen et al. 2000). Therefore GC
combination has recently become a standard alternative to M-VAC in the management of
advanced urothelial cancer.
2.1.3 Advanced breast cancer and its management
Breast cancer is the most common cancer and the second most common cause of
cancer death among women in the United States. Among the diverse pathological
classifications, invasive ductal carcinoma is by far the most common type. The treatment
for breast cancer is based on multimodality approach that combines surgery, radiotherapy,
hormone therapy and chemotherapy. Treatment is tailored for an individual patient based
on tumor size, axillary lymph node involvement, estrogen receptor (ER) and progesterone
(PR) status (the most important variables identified by many historical studies), histologic
tumor type, standardized pathologic grade, and menopausal status. For metastatic breast
cancer, chemotherapy is the primary intervention that has been proved to increase
survival in these patients (O'Shaughnessy, Twelves et al. 2002).
For many years, anthracyclines has been considered as the most effective
chemotherapeutic drug for breast cancer, both in metastatic disease and in adjuvant

22
settings for local diseases. In the past few decades, a new class of anti-tumor drug, taxane,
including paclitaxel and docetaxel, has move to the front for the management of
metastatic breast cancer. Both paclitaxel and docetaxel haven shown promising clinical
outcomes, which are comparable or superior to those treated with single reagent
doxorubicin. In phase II clinical trials assessing efficacy of docetaxel, the response rate
was ranging 62-89% for previously untreated metastatic breast cancer (Hudis, Seidman et
al. 1996; Dieras, Guastalla et al. 2004) and 12-57% for those previously treated patients
when various schedules and doses were used (Ravdin and Valero 1995; Valero, Holmes
et al. 1995). In a phase III clinical trial comparing efficacies between docetaxel and
doxorubicin, the responsive rate was 48% in the docetaxel treatment group, which was
significantly higher than 33% achieved by using doxorubicin alone (Crown 1997).
Therefore, docetaxel used as a single chemotherapeutic agent or in a combined setting
has been used as the frontline agent for metastatic breast cancer.
2.1.4 Advanced colorectal cancer and its management
Colorectal cancer is the third most common cancer in both men and women in the
United States. The vast majority of colorectal cancers are adenocarcinomas, which arise
from preexisting adenomatous polyps that develop in the normal colonic mucosa. For
metastatic or recurrent colorectal cancer, which is not resectable, systemic chemotherapy
is the major treatment option that has shown to improve survival in these patients. For
many years, combined chemotherapy utilizing 5FU and folic acid supplement, leucovorin
(LV), was the first-line treatment for metastatic colorectal cancer, with median survival
time of 11.5 months and overall responsive rate of 23% in untreated patients (Advanced

23
Colorectal Cancer Meta-Analysis Project 1992). More recently, with the addition of
novel antitumor agents like irinotecan (CPT11) and oxaliplatin to the pre-existing
5FU/LV regiment, the average survival time has been substantially increased to 16-20
months. Even more encouragingly, after new biological agents (such as bevacizumab,
cetuximab, panitumumab) are introduced into clinical practice, patients with metastatic
colorectal cancer can live longer than 2 years (Wadhawan, Stephens et al. 2009).
Different from all other chemotherapeutic agents that usually are administrated via
intravenous route, capecitabine, a pro-drug of fluoropyrimidine, 5-fluorouracil, has
demonstrated its important role in the treatment of metastatic colorectal cancer as an
orally available antitumor drug. The FDA has approved the use of capecitabine, as a first
line therapy in patients with metastatic colorectal cancer when single-agent
fluoropyrimidine is preferred.
2.1.5. Gemcitabine
As mentioned above, gemcitabine is one of the chemotherapeutic reagents used in
a standard protocol for the treatment of advanced urothelial cancer. Gemcitabine is an
analogue of deoxycytidine (Figure 2), is activated intracellularly into diphosphate or
triphosphate nucleosides, which inhibits DNA and RNA synthesis. Gemcitabine exhibits
cell phase specificity, primarily killing cells undergoing DNA synthesis (S-phase) and
also blocking the progression of cells through the G
1
/S-phase boundary. The cytotoxic
effect of gemcitabine is attributed to a combination of two major actions. Firstly,
gemcitabine triphosphate (dFdCTP) directly competes with deoxycytidine triphosphate
(dCTP), the endogenous nucleotide, for incorporation into DNA strand. Secondly,

24
gemcitabine diphosphate inhibits ribonucleotide reductase (RNR), which catalyzes
synthesis of deoxynucleoside triphosphates (dNTP), thus it indirectly inhibits DNA
synthesis by reducing intracellular dNTP pools. The reduction of dCTP enhances the
incorporation of gemcitabine triphosphate into DNA (self-potentiation), which only
allows one additional nucleotide to be added to the growing DNA strands. After this
addition, further DNA synthesis is inhibited, since DNA polymerase epsilon is unable to
remove the gemcitabine nucleotide and repair the growing DNA strands (masked chain
termination). In addition, it has been shown that gemcitabine induces internucleosomal
DNA fragmentation in human acute lymphoblastic T-cell leukemia (CEM) cells,
indicating that apoptosis is another mechanism for gemcitabine- induced cell killing, as
DNA fragmentation is one of the defining characteristics for programmed cell death
(Huang and Plunkett 1995).
Gemcitabine is usually administrated via intravenous infusion over 30 minutes.
Gemcitabine is then catabolized into inactive uridine derivative (dFdU or 2’-dexoy-2’, 2’-
difluorouridine) mainly in liver. The active derivatives such as gemcitabine diphosphate
and triphosphate do not appear to circulate in plasma at measurable levels. Additional
metabolites have not been identified in either plasma or urine. The plasma protein
binding of gemcitabine is negligible. Urinary excretion of parent and dFdU accounted for
99% of the excreted dose, while less than 1% of the dose was eliminated in feces
(Storniolo, Allerheiligen et al. 1997).
Generally speaking, the toxicity profile of gemcitabine is mild to moderate, where
bone marrow suppression is a dose-limiting side effect usually presenting as mild to

25
moderate granulocytopenia and thrombocytopenia. Gastrointestinal toxicities include
nausea, vomiting, and diarrhea. Toxicities associated with allergic reaction include rash,
pruitus, desquamation, vesiculation, ulceration, and dyspnea. Twenty percent of patients
have also experienced flu-like symptoms such as fever, headache, back pain, chills,
myalgia, cough, rhinitis, and etc. Other toxicities include edema, alopecia, somnolence,
pulmonary edema and bronchospasm.
2.1.6. Paclitaxel
Paclitaxel (Taxol) is another chemotherapeutic reagent commonly used for the
management of a variety of cancers including advanced urothelial cancer. Paclitaxel is a
natural product with antitumor activity, first extracted from the bark of Pacific yew tree
(Figure 2). Paclitaxel exerts its anti-tumor properties by stabilizing microtubules and
preventing them from depolymerization (Kumar 1981). Shortening and lengthening of
microtubules polymers are critical for its primary function, trafficking proteins and
nucleic acids. For example, during cellular mitosis, flexible microtubules provide the
cellular structure that helps to align chromosomes along cell equator and later separate
chromosome duplicate into two daughter cells. The binding of paclitaxel onto
microtubulin prevents this structure from dissembling, and thus destroys the cell’s ability
to utilize its cytoskeleton system in a flexible manner. These properties can lead to
interference of normal mitosis process. In addition, paclitaxel also exert this function by
inducing apoptosis through binding and arresting bcl-2, an anti-apoptotic protein
(Ganansia-Leymarie, Bischoff et al. 2003).
Following intravenous infusion, paclitaxel is mainly converted into inactive
metabolites in liver by CYP2C8 and CYP3A4. Majority of the radioactive-labeled
paclitaxel was recovered in feces found as either the parent drug or hydroxyl metabolites
via biliary excretion, while renal elimination of paclitaxel is minimal (Sparreboom, van
Tellingen et al. 1998). Dose-limiting toxicity is myelosuppression with reversible
granulocytopenia, anemia, and thrombocytopenia. Compared with 24-hour infusion
protocol, the alternative 3-hour infusion causes less frequency in severe neutropenia,
providing a safer application scheme (Eisenhauer, ten Bokkel Huinink et al. 1994).
Pharmacodynamic study also suggested a strong association between decreases in white
blood cells and the time when plasma concentration of paclitaxel was above 0.05 µM
(Gianni, Kearns et al. 1995). Other common side effects include allergy, diarrhea,
alopecia, arthralgias, mucositis and etc.
Figure 2: Structure of gemcitabine, paclitaxel and docetaxel

Paclitaxel Docetaxel
Gemcitabine
2.1.7 Docetaxel
Docetaxel (taxotere) is a semi-synthetical analogue to paclitaxel (Figure 2) and
together with paclitaxel; they are members of the family of taxane. Docetaxel is an

26

27
esterified product of 10-deacetyl baccatin III, which is extracted from the renewable and
readily available European yew tree. Due to structure similarity, docetaxel and paclitaxel
share similar mechanism of action. Like paclitaxel, docetaxel is mainly metabolized in
the liver by the CYP3A4 and CYP3A5. Patients with significant hepatic function
deficiency tend to have decreased clearance and increased chance for docetaxel toxicity
(Clarke and Rivory 1999). Biodistribution of
14
C-labelled docetaxel showed that 80% of
the administrated dose was metabolized and eliminated to the faeces via biliary ducts
while only 5% of the total dose was recovered in the urine, indicating that urinary
excretion of docetaxel is minimal (Clarke and Rivory 1999). The common toxicities of
docetaxel include neutropenia, leukopenia, anemia, thrombocytopenia, melalgia,
arthralgia and etc. Other side effects include allergy, cutaneous reactions, pulmonary
edema and etc.
2.1.8 Capecitabine
Capecitabine (Xeloda) is an oral pro-drug of fluorouracil (FU), and has been
developed with the aim of improving tolerability and intratumor drug concentrations
through its tumor specific conversion to the active drug. Capecitabine is highly water-
soluble, and can be rapidly absorbed through intestine with almost 100% bioavailability.
Capecitabine has to be converted into 5FU, the only active metabolite, through 3
metabolic steps (Figure 3). After oral absorption, it is first converted by liver
carboxylesterase to form 5’-deoxy-5-fluorocytidine (5’-dFCR). 5’-dFCR is then
metabolized into 5’-deoxy-5-fluorouridine (5’DFUR) via cytidine deaminase (CDA)
which is a ubiquitous enzyme with high concentrations in the liver, plasma, and tumor
tissue. In the last step, 5’DFUR is converted intracellularly into 5FU through thymidine
phosphorylase. The expression level of thymidine phosphorylase is considered to be 3 to
10 times higher within various tumor cells compared to that in normal tissues (Miwa, Ura
et al. 1998). In this way, 5FU converted from capecitabine can be selectively released
and targeted inside tumor bed, which consequently lowers the systemic exposure and thus
cause less toxicity compared to that in the situation of intravenous 5FU administration
(Schuller, Cassidy et al. 2000).
Figure 3: Structures of capecitabine and 5FU and the 3-step conversions in vivo (Walko
and Lindley 2005)

28
5FU, the fluorinated analog of uracil, has been shown anti-tumor effect for
various cancers (Pinedo and Peters 1988). The mechanism of action relies on the
inclusion of the drug into replicating RNA and depletion of thymidine through binding
and inhibiting thymidylate synthase. 5FU is ultimately converted into inactive
metabolites mediated by dihydropyrimidine dehydrogenase (DPD) followed by renal
elimination. Radioactive-labeled capecitabine was found to have 99.5% recovery rate in
urine (Judson, Beale et al. 1999), indicating that renal excretion is major elimination
pathway for capecitabine and its metabolites.

29
2.2 Method:
2.2.1 Subjects
2.2.1.1 Subjects for gemcitabine and paclitaxel study
Fifty-six eligible patients and controls were recruited to evaluate clinical
pharmacology and outcomes of gemcitabine and paclitaxel in the management of
advanced urothelial cancer among elderly patients. This study was organized by
Southwest Oncology Group (SWOG). Prior to the start of this study, written informed
consent was obtained from each subject. Subjects were included in the study if they are
70 years or older, or younger than 60 years old. Patients should have a confirmed
diagnosis of urothelial (bladder, renal pelvis, ureter and urethra) cancer. The histology
classification includes transitional cell carcinoma, squamous cell carcinoma and
adenocarcinoma. Patients must have measurable disease. Chest X-ray and any scan
needed for assessment of disease must be performed within 28 days prior to registration.
Patients must have serum creatine and bilirubium equal or less than the institutional
upper limit of normal (IULN), and SGOT and SGPT equal or less than twice of IULN.
Patients must have an absolute granulocyte counts (AGC) of equal or greater than
1200/µL, platelet equal or greater than 100,000/µL. Patients must not have received prior
chemotherapy within the past 10 years and must have recovered from the effects of prior
surgery or radiation. Patients will be excluded if they are pregnant or nursing, HIV
positive or suffering from other life threatening intercurrent medical disorders.

30
2.2.1.2 Subjects for docetaxel study
Twenty four eligible patients and controls were recruited for the study that aimed
to evaluate clinical pharmacology and outcomes of docetaxel in the management of
advanced breast cancer in elder patients. Prior to the start of this study, written informed
consent was obtained from each subject. Patients must be female, and have confirmed
diagnosis of breast cancer. Each participant should be either 70 years or older, or younger
than 60 years old. Patients must have measurable disease and chest X-ray and any scan
needed for assessment of disease must be performed within 28 days prior to registration.
Patients must have serum creatine and bilirubium equal or less than the institutional
upper limit of normal (IULN), and SGOT, SGPT and alkaline phosphatase equal or less
than twice of IULN. Patients must have AGC of equal or greater than 1500 cells/µL,
hemoglobin no less than 9 gm/dL and platelet count equal or greater than 100,000
cells/µL. Patients should have no more than one regimen for advanced, recurrent or
metastatic diseases, however, prior adjuvant chemotherapy or adjuvant taxane, and prior
hormone therapy were allowed. Patients must have recovered from the effects of prior
surgery or radiation. Patients will be excluded if they have known brain metastases, or if
they are pregnant or nursing, HIV positive or suffering from other life threatening
intercurrent medical disorders.
2.2.1.3 Subjects for capecitabine study
Twenty-nine eligible patients and controls were recruited for the study that aimed
to evaluate clinical pharmacology and outcomes of capecitabine in the management of
advanced colorectal cancer in elder patients. Patients must have confirmed diagnosis of

31
metastatic or recurrent colorectal cancer, which is not resectable. Each participant should
be either 70 years or older, or younger than 60 years old. Patients must have measurable
disease and chest X-ray and any scan needed for assessment of disease must be
performed within 28 days prior to registration. Patients must have serum creatine and
bilirubin no more than twice the institutional upper limit of normal (IULN), SGOT and
SGPT up to twice of IULN and estimated renal creatine clearance more than 50 ml/min.
Patients must have an AGC of equal or greater than 1500 cells/µL and platelet count no
less than 100,000 cells/µL. Patients should not have prior chemotherapy for advanced
cancer, however, prior adjuvant chemotherapy was allowed if recurrence happened more
than 12 months after the last adjuvant treatment. Patients must have recovered from the
effects of prior surgery or radiation. Patients will be excluded if they have known brain
metastases, or if they are pregnant or nursing, HIV positive or suffering from other life
threatening intercurrent medical disorders.
2.2.2 Study design
2.2.2.1 Study design for gemcitabine and paclitaxel study
Each participant will be given premedication including Diphenhydramine 25 mg
IV, Cimetidine 300 mg IV (or Ranitidine equivalent), Dexamethasone 8 mg IV, 30 to 60
minutes before chemotherapy to prevent allergic reactions and Compazine 10 mg IV to
prevent vomit caused by anticancer drugs. All patients will receive paclitaxel 175 mg/m
2

on Day 1 (over 3 -hour IV infusion) followed by gemcitabine 1,000 mg/m
2
on Day 1 & 8
(over 30-minute IV infusion). Cycles will be repeated every 21 days. Each subject can
only have 6 cycles maximally.

32
Pharmacokinetic study was conducted on day 1, when peripheral blood was
withdrawn from each participant at predose time, 0 (start of paclitaxel IV infusion), 1, 2,
3 (end of paclitaxel IV infusion and start of gemcitabine IV infusion), 3.25, 3.5 (end of
gemcitabine IV infusion), 3.58, 3.75, 4, 4.25, 4.5, 5, 6, 8, 12 and 24 hour after the
beginning of paclitaxel IV infusion. Patient samples were centrifuged and plasma
supernatant was collected and stored in -80
o
C freezer until further analysis.
2.2.2.2 Study design for docetaxel study
Each participant will be given premedication including oral dexamethasone 8 mg
BID for 3 days starting from one day prior to the docetaxel treatment. All patients will
receive docetaxel 75 mg/m
2
on Day 1 (over 1-hour IV infusion). Cycles will be repeated
every 21 days until progression or unacceptable toxicity happens. Further treatment
should be at the discretion of the treating physicians. Patients may need to be given
colony stimulating factors (G-CSF or GM-CSF) if they suffered from sever neutropenia.
Pharmacokinetic study was conducted on day 1, when peripheral blood was
withdrawn from each participant at predose time, 1, 2, 4, 6 and 24 hour after the end of
docetaxel IV infusion. Patient samples were centrifuged and plasma supernatant was
collected and stored in -80
o
C freezer until further analysis.
2.2.2.3 Study design for capecitabine study
Each participant will be given capecitabine 2000 mg/m
2
per day (as 1000 mg/m
2

BID PO with food) for day 1 through 14. Cycles will be repeated every 21 days to a
maximum of 18 cycles.

33
Pharmacokinetic study was conducted on day 1, when peripheral blood was
withdrawn from each participant at predose time, 0.5, 1, 1.5, 2, 4, 6 and 24 hour after the
beginning of capecitabine oral administration. Patient samples were centrifuged and
plasma supernatant was collected and stored in -80
o
C freezer until further analysis.
2.2.3 Analytical measurements
2.2.3.1 Determination of plasma concentrations for gemcitabine and beta-uridine
Gemcitabine (dFdC) and its metabolite beta-uridine (dFdU) in plasma samples
were measured by HPLC with UV detection. To an aliquot of 100 µL plasma sample,
200µL of 10µg/mL 2’, 3’-dideoxycytidine (ddC) dissolved in 30% methanol (MeOH) as
internal standard was added and the entire sample was protein precipitated using 500µL
acetonitrile. The entire mixture was vigorous mixed and centrifuge at 15,000 rpm at 4
o
C
for 15 minutes. The resultant supernatant was removed and evaporated to dryness under
steady flow of filtered dry air and reconstituted with 150 µL of mobile phase which
consisted of 3% (v/v) acetonitrile in 50 mM ammonium acetate.
A total of 50 µL processed samples were injected into an Agilent 1100 HPLC
system linked with an Ultrasphere C
18
column (by Beckman Coulter) with the following
dimensions: 5 µM diameter, 250 x 4.6 mm. The flow rate was set at 1.5 mL/min. The
three compounds: dFdC, dFdU and ddC were detected at UV wavelength of 254 nM,
where the retention time was 5.5 minutes, 6.6 minutes and 9.3 minutes for dFdC, ddC and
dFdU, respectively.
The dFdC plasma standards were linear in the range of 0.25-100 µg/mL with
correlation coefficients of at least 0.99. The dFdU plasma standards were linear in the

34
range of 1.25-500 µg/mL with correlation coefficients of at least 0.99. The inter-day
coefficients of variation (CV) were 7.4% for dFdC and 9.5% for dFdU.
2.2.3.2 Determination of plasma concentrations for paclitaxel and docetaxel
Plasma concentrations of paclitaxel and docetaxel were determined using similar
LC-MS-MS assays. Plasma samples of paclitaxel were initially processed by taking a 50
µL aliquot of plasma where 50 µL of 500 ng/mL of docetaxel dissolved in 80% MeOH
was added as internal standard. For plasma samples of docetaxel, 50 µL of plasma aliquot
was mixed with 50 µL of 200 ng/mL of paclitaxel dissolved in 80% MeOH as internal
standard. The entire sample was then extracted by adding 1000 µL of acetonitrile where
the protein was precipitated. The protein precipitated samples were then centrifuged at
13,000 rpm for 5 minutes at 4
o
C. Clean supernatant was then transferred into a new tube
and evaporated to dryness using a steady stream of filtered dry air. Evaporated residues
was reconstituted in 100 µL of mobile phase and then recentrifuged at 13,000 rpm to
remove undissolved debris. Supernatant was transferred into HPLC vials and 40 µL was
injected into LC/MS/MS.
Analytes were separated using an Agilent 1100 HPLC system coupled with Sciex
API3000. The analytes were separated using a Hypersil BDS C18 column (dimension
(mm) 50 x 2.1, particle 3 μm; Thermo Fisher Scientific Inc). Analytes were eluted using
an isocratic mobile phase consisting of 40% acetonitrile and 60% 10 mM ammonium
acetate (v/v) with pH adjusted to 4.5. The flow rate was set to 0.3 mL/min. Eluents were
quantified using an API 3000 triple quadrupole mass spectrometer which is operated
under the positive mode using a turbo ion spray source. The mass transitions were

35
854.5→286.1 and 808.4 →226.0 for paclitaxel and docetaxel respectively. The retention
times were 3.83 and 3.52 minutes for paclitaxel and docetaxel respectively. The total run
time was 15 minutes.
The paclitaxel plasma standards were linear in the range of 100-10000 ng/mL
with correlation coefficients of at least 0.99. The docetaxel plasma standards were linear
in the range of 10-2000 ng/mL with correlation coefficients of at least 0.99. The average
inter-day coefficient of variation (CV) was 7 % for docetaxel.
2.2.3.3 Determination of plasma concentrations of capecitabine and its metabolites
Method of sample preparation prior to LCMS quantification was adopted from
Salvador et al (Salvador, Millerioux, et al. 2006). In brief, plasma samples were initially
processed by mixing 500 µL of sample aliquot with 20 µL of 4000 ng/mL zidovudine
(AZT) dissolved in 80% MeOH as internal standard. Following vigorous vortex, samples
were further mixed with 10 µL 1M citric acid and 400 µL of 18 mM ammonia acetate
with pH adjusted to 5. Plasma samples were vortexed and centrifuged at 5000 rpm for 5
minutes at 4
o
C. Clear supernatant were collected and processed further by solid phase
extraction using Atoll XWP cartridge (Interchim, France). Prior to solid phase extraction,
cartridges were preconditioned sequentially with 1mL water, 1mL MeOH and 500 µL 18
mM ammonia acetate with PH adjusted to 5. Samples were loaded to the cartridge and
filtered through them slowly under gentle vacuuming. Cartridges were then washed with
500uL of 10% (v/v) MeOH in 18 mM ammonia acetate with pH. Analytes were eluted by
washing cartridges with 1mL MeOH twice. Elutes were evaporated to dryness under
steady flow of filtered dry air and then reconstituted with 150 µL of 50% MeOH.

36
Samples were further centrifuged to remove undissolved debris and 20 µL of supernatant
was injected into HPLC.
Capecitabine, its metabolites (DFCR, DFUR, 5-FU respectively) and internal
standard (zidovudine, AZT) were separated on a Zorbax Bonus-RP Column,
4.6mmX150mm, 5uM packing (Agilent). The mobile phase contained 50% distilled
water and 50% MeOH for the first 1 minute where a gradient from 50% to 95% MeOH
over 4 minutes and the program was held for another 6 minutes, and then returned to 50%
distilled water and 50% methanol for the last 8 minutes. The flow rate was set at 0.25
mL/min. The mass spectrometer was operated under the negative mode using a turbo ion
spray source. The mass transitions were 358.4 →154.3, 245.0→129.2, 244.1→127.8,
129.2→41.8 and 266.2 →223.2 for capecitabine, DFCR, DFUR, 5-FU and AZT
respectively. The retention times were 16.16, 8.22, 7.52, 7.02 and 12.48 minutes for
capecitabine, DFCR, DFUR, 5-FU and AZT respectively
The capecitabine plasma standards were linear in the range of 1-5000 ng/mL with
correlation coefficients of at least 0.99. The DFCR and DFUR plasma standards were
linear in the range of 100-40000 ng/mL with correlation coefficients of at least 0.99. The
5FU plasma standards were linear in the range of 10-5000 ng/mL with correlation
coefficients of at least 0.99. The average inter-day coefficient of variation (CV) was 6 %,
6%, 7% and 10% for capecitabine, DFCR, DFUR and 5FU respectively.
2.2.4 Pharmacokinetic modeling
Pharmacokinetic analysis for gemcitabine, dFdU, paclitaxel, docetaxel,
capecitabine, dFCR, dFUR and 5FU was performed using non-compartment (model

37
independent) method provided by WINNONLIN software (Pharsight, NC). Total drug
exposure was estimated by Area Under the Curve (AUC) methods, which was
determined by the trapezoidal rule. Half-time (T
1/2
), total clearance (CL
total
), volume of
distribution (Vd) and AUC were the primary PK parameters estimated and used for
further calculations of secondary PK parameters, such as elimination constant (Kel).
2.2.5 Statistical analysis
Differences in demographics/clinical characteristics and analytes’
pharmacokinetic parameters between groups (patients 70 years or older versus patients
younger than 60 years old) were compared using Mann-Whitney t-test, unpaired t-test or
χ-square where appropriate. Correlations between selected PK parameters and age or
renal functions were performed using first order equation of nonlinear regression method,
followed by F test which measures the differences between model fitted slopes and the
value of zero. The significance level was assumed to be 0.05. Analyses were performed
using GraphPad Prism version 4.0 for Windows (GraphPad Software, San Diego, CA).
2.3 Results
2.3.1 Results of gemcitabine study
2.3.1.1 Patient characteristics
Among the total of 56 participants, there were 7 patients who had been excluded
due to improper sample storage or sample missing. Among the remaining 49 patients,
there were seven patients who are younger than 60 years old with average age of 51.4 ±
9.6 years old and 42 patients who are 70 years or older with average age of 76.6 ± 4.4
years old (P<0.001). There was no significant difference in height and weight between

38
the two study groups. Gender composition was not markedly different between the
younger patients and the older cohorts. The renal creatine clearance (Crcl) normalized by
body surface area (BSA) in patients who are younger than 60 years old is 84.7 ± 20.6
mL/min/1.73m
2
, which is significantly higher than that in patients 70 years or older (63.0
± 24.2 mL/min/1.73m
2
, P=0.005). However, renal function calculated by modified diet
for renal diseases (MDRD) was not significantly different between the two patient groups.
2.3.1.2 Pharmacokinetics of gemcitabine and dFdU
Maximum gemcitabine concentrations (Cmax), Tmax, Kel, t
1/2
, AUC, total
clearance and Vd were not significantly different between patients younger than 60 years
old and patients who are 70 years or older. Consistently, correlation analyses between age
and major gemcitabine PK parameters did not shown significance.
However, for dFdU, the metabolite of gemcitabine, the average AUC in patients
younger than 60 years old was 215.7 ± 113.4 hr* µg/mL, which was significantly lower
that in patients 70 years or older (319.3 ± 118.2 hr* µg/mL, P=0.02). Similarly, the Cmax
of dFdU were 34.36 ± 9.84 µg/mL and 41.90 ± 13.06 µg/mL for patients younger than 60
years old and those 70 years or older respectively, P value was 0.04 when one-tailed t-test
was used. The dFdU AUC was correlated with age, with P value of 0.02 and R
2
of 0.11.
The comparisons of CrCL, AUC and Cmax of dFdU between the study groups and
correlation between dFdU AUC and age were presented in Figure 4.

Figure 4: Comparisons of normalized CrCL (A), AUC of dFdU (B) and Cmax of dFdU
(C) between patients younger than 60 years old and those 70 years or older and
correlation analysis between dFdU AUC and age (D).

39

2.3.1.3 Pharmacokinetics of paclitaxel
The average paclitaxel AUC was 13873 ± 9229 hr*ng/mL in patients younger
than 60 years old, and 19438 ± 11874 hr*ng/mL in patients 70 years or older, the P
values were 0.054 and 0.027 respectively when two-tailed and one-tailed Mann Whitney
T tests were used. The elimination constants (Kel) was 0.131 ± 0.071 hr
-1
in patients 70
years or older, which was significantly lower than that in patients younger than 60 years
old (0.178 ± 0.055 hr
-1
, P=0.01). Similarly, the average half-life was 4.26 ± 1.50 hr and
<60 >70
0
10
20
30
40
50
60
70
80
P=0.04 one-tailed
Patient groups
Cmax of dFdU (ug/mL)
30 40 50 60 70 80 90
0
100
200
300
400
500
600
700
P= 0.02
R
2
= 0.11
age
AUC of dFdU
(hr*ug/mL)
(C) (D)
<60 >70
0
100
200
300
400
500
600
700
P= 0.02
Patient
120
groups
AUC of dFdU
(hr*ug/mL)
100
80
60
40
20
0
<60
140
P=0.005
Patient
>70
groups
Normalized-CrCL
(mL/min/1.73m
2
)
(A) (B)
6.18 ± 2.12 hr respectively in the young and older groups, which were marked different
from each other (P=0.01). In patients 70 years or older, the total clearance of paclitaxel
was 11583 ± 6558 mL/hr/m
2
, which was significantly lower than 14251± 5527 mL/hr/m
2

in patients younger than 60 years old (P=0.04, one-tailed t-test). Similarly paclitaxel
AUC were 13873 ± 9229 hr*ug/mL and 19438 ± 11874 hr*ug/mL for the younger and
older patient groups respectively, with P value of 0.027 (one-tailed t-test, Figure 5) All
non-compartment PK parameters for gemcitabine, dFdU and paclitaxel and the
comparisons between the two age groups are presented in Table 1.
Figure 5: Comparisons of Paclitaxel Kel (A), AUC (B), T
1/2
(C) and total CL (D)
between patients younger than 60 years old and those 70 years or older.

40

<60 >70
0.0
0.1
0.2
0.3
0.4
0.5
P=0.01
Patient Groups
Paclitaxel Kel (hr
-1
)
50000
40000
30000
20000
10000
0
<60
60000
P=0.054 two-tailed
P=0.027 one-tailed
Patient Groups
AUCall of paclitaxel
(hr*ng/mL)
>70
(A) (B)
<60 >70
0
10000
20000
30000
40000
P=0.088 two-tailed
P=0.044 one-tailed
Patient Groups
Paclitaxel total clearance
(mL/hr/m
2
)
10.0
7.5
5.0
2.5
0.0
<60
12.5
P=0.01
Patient Grou
>70
ps
Paclitaxel half-life (hr)
(C)
(D)

41
Table 1: Patient characteristics and pharmacokinetic parameters for gemcitabine, dFdU
and paclitaxel in gemcitabine study.

< 60 yrs ≥ 70 yrs P value
N 7 42 (37**)
Age (yrs) 51.4 ± 9.6 76.6 ± 4.4 <0.001
Weight (Kg) 80.66 ± 18.99 76.20 ±19.45 0.577
Gender (M/F) 1/6 10/32 0.576
CrCL (mL/min/1.73m
2
) 84.7 ± 20.6 63.0 ± 24.2 0.005
Gemcitabine PK
Cmax (ug/mL) 22.89 ± 10.90 29.86 ± 35.01 0.679
Kel (hr
-1
) 1.250 ± 1.136 1.718 ± 1.501 0.520
T1/2 (hr) 2.40 ± 4.03 1.02 ± 1.00 0.520
AUC (hr*ug/mL) 18.35 ± 9.01 23.25 ± 18.36 0.743
CLtotal (L/hr/m
2
) 64.66 ± 30.47 58.89 ± 26.40 0.786
Vd (L/m
2
) 153.44 ± 230.80 82.09 ± 95.27 0.284
dFdU PK
Cmax (ug/mL) 34.36 ± 9.84 41.90 ± 13.06 0.089 / 0.045*
T1/2 (hr) 2.40 ± 4.03 1.02 ± 1.00 0.996
Kel (hr
-1
) 0.092 ± 0.059 0.082 ± 0.022 0.383
AUC (hr*ug/mL) 215.7 ± 113.4 319.3 ± 118.2 0.025
Paclitaxel PK
Cmax (ng/mL) 3671 ± 1337 5080 ± 3103 0.273
Kel (hr
-1
) 0.178 ± 0.055 0.131 ± 0.071 0.013
T1/2 (hr) 4.26 ± 1.50 6.18 ± 2.12 0.013
AUC (hr*ng/mL) 13873 ± 9229 19438 ± 11874 0.054 / 0.027*
CLtotal (L/hr/m
2
) 14.25 ± 5.53 11.58 ± 6.56 0.088 / 0.044*
Vd (L/m
2
) 85.33 ± 41.13 96.00 ± 4.70 0.521
* One-tailed T test was used for the comparison.
** There were 37 patients in the older patient group that were available for paclitaxel PK
analysis.

2.3.2 Results of docetaxel study
2.3.2.1 Patient characteristics
Among the total of 24 participants, there were 4 patients who had been excluded
due to sample missing. Among the remaining 20 patients, there were 5 patients who were
younger than 60 years old with average age of 45.0 ± 10.3 years old and 15 patients who

42
were 70 years or older with average age of 77.1 ± 5.6 years old (P<0.001). There was no
significant difference in height and weight between the two study groups. The renal
creatine clearance normalized by body surface area (BSA) in patients who were younger
than 60 years old was 107.7 ± 44.0 mL/min/1.73m
2
, which was significantly higher than
that in patients 70 years or older (57.4 ± 12.8 mL/min/1.73m
2
, P=0.001). Similarly, renal
functions calculated by modified diet for renal diseases (MDRD) were 93.49 ± 22.1
mL/min/1.73m
2
and 66.7 ± 22.2mL/min/1.73m
2
for the young and older patient groups
respective, which was markedly different from each other (P=0.047).
2.3.2.2 Pharmacokinetics of docetaxel
The average docetaxel AUC, Kel, total clearance and Vd were 1207 ± 920.5
hr*ng/mL, 0.076 ± 0.066 hr-1, 74.2 ± 62.9 L/hr/m
2
, 1245 ± 965 L/m
2
respectively for
patients 70 years or older, and 1513 ± 1287 hr*ng/mL, 0.113 ± 0.136 hr
-1
, 60.3 ± 36.9
L/hr/m
2
, 1353 ± 1495 L/m
2
respectively for patients younger than 60 years old, and the
differences for each parameter were not significant between the two groups. The
demographic data and PK comparisons between groups are presented in the table below.
Table 2: Patient characteristics and pharmacokinetic parameters for docetaxel in
docetaxel study.

< 60 yrs ≥ 70 yrs P value
N 5 15
Age (yrs) 45.0 ± 10.3 77.1 ± 5.6 <0.001
Weight (Kg) 72.95 ± 27.26 71.60 ± 19.88 0.912
CrCL (mL/min/1.73m
2
) 107.7 ± 44.0 57.4 ± 12.8 0.001
MDRD(mL/min/1.73m
2
) 93.49 ± 22.08 66.74 ± 22.22 0.047
Cmax (ng/mL) 500.5 ± 661.5 233.8 ± 206.6 0.930
Kel (hr
-1
) 0.113 ± 0.136 0.076 ± 0.066 0.418
T1/2 (hr) 12.38 ± 8.24 9.11 ± 8.16 0.436
AUC (hr*ng/mL) 1513 ± 1287 1207 ± 920.5 0.566

43
Table 2, Continued
CLtotal (L/hr/m
2
) 60.28 ± 36.86 74.18 ± 62.93 0.786
Vd (L/m
2
) 1353 ± 1495 1245 ± 965 1.000

2.3.3 Results of capecitabine study
2.3.3.1 Patient characteristics
There were total of 29 patients enrolled in the study, in which 5 patients are
younger than 60 years old with average age of 55.0 ± 3.1 years old and 24 patients are 70
years or older with average age of 76.5 ± 4.6 years old (P<0.001). There was no
significant difference in height and weight between the two study groups. Gender
composition was not markedly different between the younger patient group and the older
patient group. The renal creatine clearance normalized by body surface area (BSA) in
patients who were younger than 60 years old was 87.4 ± 8.7 mL/min/1.73m
2
, which was
significantly higher than that in patients 70 years or older (63.8 ± 13.3 mL/min/1.73m
2
,
P=0.0008). However, renal function calculated by modified diet for renal diseases
(MDRD) was not significantly different between the two patient groups.
2.3.3.2 Pharmacokinetics of capecitabine and its metabolites
The summary of the non-compartmental PK analysis is summarized in the table
below. Notable in this analysis was a markedly lower capecitabine AUC for patients
younger than 60 yrs old compared to that in patients 70 yrs and older (4098 ± 2852
hr*ng/mL vs 10238 ± 6355 hr*ng/mL, P=0.046). The total capecitabine clearance was
507.8 ± 585.6 L/hr/m
2
in patients younger than 60 years old, which was significantly
higher than that in patients 70 years and older (146.1 ± 107.2 L/hr/m
2
, P=0.035). Cmax
was 3035 ± 2316 ng/mL for the patients younger than 60 years old and 9105 ± 7303
ng/mL for patients 70 yrs and older, and the P value was 0.08 for two-tailed t-test and
0.04 using one-tailed analysis. In patients younger than 60 years old, capecitabine
apparent volume of distribution was 582.2 ± 720.8 L/m
2
, which was significantly higher
than 150.0 ± 104.1 L/m
2
in patients 70 years and older (P=0.046, Figure 6). No
statistical significance was achieved between the two groups regarding to half-life and
Tmax. Capecitabine AUC was correlated with age with R
2
of 0.188 (P=0.02), while
capecitabine total clearance and apparent volume of distribution were both inversely
associated with age (P=0.003 and 0.004 respectively, Figure 6)
There were no significant differences in AUCs of DFCR, DFUR and 5FU
between patients less than 60 years old and those 70 years or older. Similarly, elimination
constants of DFCR, DFUR and 5FU were not marked different between the two age
groups.
Figure 6: Comparisons of Capecitabine Cmax (A), AUC (B), total CL(C) and Vd (D)
between patients younger than 60 years old and those 70 years or older and correlation
analysis between dFdU AUC and age (E) and CL and age (F).

44

<60 >70
0
5000
10000
15000
20000
25000
P=0.08 two-tailed
P=0.04 one-tailed
Patient Groups
Capecitabine Cmax
(ng/mL)
20000
15000
10000
5000
0
<60
25000
P=0.046
Patient Groups
AUCall of Capecitabine
(hr*ng/mL)
>70
(A) (B)
Figure 6, Continued

45

2.3.3.3 Clinical outcomes of colorectal cancer
The 2-year survival rate in patients 70 years and older was 36% compared to 27%
in patients younger than 60 years, with partial responsive (PR) rate of 25% vs 0%,
undefined PR 10% vs 0%, and complete responsive rates of 5% vs 0% respectively in
patients who are 70 years and older and those younger than 60 years old.
Table 3: Patient characteristics and pharmacokinetic parameters for capecitabine, DFCR,
DFUR and 5FU in capecitabine study.

< 60 yrs ≥ 70 yrs P value
N 5 24
Age (yrs) 55.0 ± 3.1 76.5 ± 4.6 <0.001
Weight (Kg) 82.16 ± 15.47 78.03 ±12.93 0.534
40 50 60 70 80 90
0
10000
20000
30000
P=0.02
R
2
=0.188
AGE(yr)
AUCall of Capecitabine
(hr*ng/mL)
40 50 60 70 80 90
0
250000
500000
750000
1000000
1250000
1500000
1750000
P=0.003
R
2
=0.287
AGE(yr)
Clearance-F of Capecitabine
(mL/hr/m
2
)
(E) (F)
<60 >70
0
400000
800000
1200000
1600000
P=0.035
Patient Groups
Clearance-F of Capecitabine
(mL/hr/m
2
)
1500000
1000000
500000
0
<60 >70
2000000
P=0.046
Patient Groups
Capecitabine Vd
(mL/m
2
)
(C) (D)

46
Table 3, Continued
CrCL
(mL/min/1.73m2) 87.43 ± 8.74 63.77 ± 13.26 0.0008
Capecitabine PK
Cmax (ng/mL) 3035 ± 2316 9105 ± 7303 0.080/0.040*
Kel (hr-1) 1.005 ± 0.338 1.020 ± 0.339 0.928
T1/2 (hr) 0.736 ± 0.174 0.776 ± 0.323 0.436
AUC (hr*ng/mL) 4098 ± 2852 10238 ± 6355 0.046
CLtotal-F (L/hr/m
2
) 507.78 ± 585.57 146.10 ± 107.22 0.035
Vd-F (L/m
2
) 582.24 ± 720.84 150.01 ± 104.06 0.046
DFCR PK
Cmax (ng/mL) 7168 ± 6699 6361 ± 3711 0.705
T1/2 (hr) 0.850 ± 0.130 0.899 ± 0.536 0.312
Kel (hr
-1
) 0.831 ± 0.131 0.976 ± 0.422 0.462
AUC (hr*ng/mL) 11766 ± 9648 12461 ± 7406 0.977
DFUR PK
Cmax (ng/mL) 10252 ± 5546 10373 ± 5901 0.967
T1/2 (hr) 0.936 ± 0.287 1.094 ± 0.759 0.795
Kel (hr
-1
) 0.793 ± 0.223 0.820 ± 0.339 0.867
AUC (hr*ng/mL) 20737 ± 12257 19852 ± 9382 0.977
5FU PK
Cmax (ng/mL) 2688 ± 3138 2074 ± 1569 0.665
T1/2 (hr) 1.171 ± 0.764 1.060 ± 0.490 0.795
Kel (hr
-1
) 0.752 ± 0.343 0.789 ± 0.353 0.795
AUC (hr*ng/mL) 4986 ± 5786 3677 ± 1861 0.840
* One-tailed T test was used for the comparison.
2.4 Discussion
The main findings of this project were that paclitaxel and capecitabine presented
with greater AUC and slower or less clearance in patients 70 years or older, compared to
those in patients younger than 60 years old. In addition, the average AUC and Cmax of
dFdU, the major metabolite of gemcitabine were greater in the old age group than those
in the controls. There were no significant differences in pharmacokinetic parameters for

47
gemcitabine, docetaxel or the three metabolites of capecitabine between patients younger
than 60 years old and those 70 years or older.
The major pathway for paclitaxel elimination is hepatic metabolism followed by
biliary elimination. The 48-hour urine recovery of radioactivity for paclitaxel was less
than 10% of the total dose, while more than 70% of the radioactivity was recovered in
feces (Huizing, Misser et al. 1995). Therefore, it was reasonable to expect that liver
function might be a critical factor for paclitaxel disposition. In a cohort study aiming to
evaluate safety and pharmacology of paclitaxel in the management of cancer patients
with moderate to sever liver dysfunction, Joerger et al suggested that there was an inverse
correlation between paclitaxel elimination capacity and liver function stratified according
to liver transaminase and total bilirubin concentrations (R2 = -0.38, P = 0.05), and that
the total bilirubin was a significant covariate to predict decreased elimination capacity
with population modelling (P = 0.002) (Joerger, Huitema et al. 2007). As less than 10%
of prescribed dose is recovered in urine, renal elimination therefore does not play an
important role in paclitaxel clearance, which indicates that impaired renal function might
not be as important as defective liver function regarding its impact on paclitaxel
pharmacokinetics. In this study, we showed that paclitaxel AUC was markedly higher in
patients 70 years or older compared to the younger patients when one-tailed T test was
used for the comparison, in the meanwhile, Kel, total CL of paclitaxel were significantly
lower in the older patients. These results indicate a slower clearance capacity of
paclitaxel in patients 70 years or older. Although renal function calculated by creatinine
clearance normalized by body surface area was significantly different between the two

48
groups, the slower clearance and more accumulation of paclitaxel in elderly patients was
more likely due to the age-related decline in liver function rather than the impaired renal
function, as renal elimination accounted for a very small portion of the total paclitaxel
clearance. As mentioned before, the age-related decline in liver metabolism function is
more likely due to a rough 40% decrease in blood supply to the liver and a similar degree
of reduction in liver mass among old population.
In addition to the aforementioned mechanism to explain the difference in
paclitaxel disposition among different age group, there are many other contributing
factors that might also play certain roles. In liver, paclitaxel goes through series of
hydroxylation to form inactive metabolites. CYP2C8 is the enzyme that catalyzes the
formation of 6 α-hydroxypaclitaxel, and CYP3A4 catalyzes the conversion to 3’-p-
hydroxypaclitaxel. These two enzymes both participate the formation of dihydroxylated
metabolite (6 α,3’-p-dihydroxypaclitaxel) (Huizing, Giaccone et al. 1997). Unchanged
paclitaxel and its metabolites are then excreted into bile, where P-gp might involve in the
efflux transportation across the biliary duct. It is easy foreseen that expression and
activity of CYP2C8, CYP3A4 and P-gp are other factors that might potentially affect
paclitaxel disposition. Nakajima et al reported that CYP3A4-16B harboring patients had
significantly reduced formation of 3'-p-hydroxylation of paclitaxel and 2.4 fold increase
in the conversion of 6alpha-hydroxypaclitaxel (Nakajima, Yoshitani et al. 2006). Dai et al
used Recombinant technology to express CYP2C8*2 and CYP2C8*3 cDNA in E.Coli
and assessed their activity to metabolize paclitaxel. They found that CYP2C8*3 and
CYP2C8*2 only showed 15% and 50% of the capacity to turn over paclitaxel compared

49
to the wild type CYP2C8*1 (Dai, Zeldin et al. 2001). Therefore, further exploration of
genetic polymorphisms in CYP2C8, CYP3A4 and ABCB1 gene will help to elucidate the
possible reasons leading to differences in paclitaxel PK data among elderly patients.
Similar to paclitaxel, docetaxel also mainly goes through hepatic metabolism
followed by biliary elimination and 48hour renal recovery of radioactivity is less than
10%, indicating that renal elimination does not play an important role in docetaxel
clearance (Ringel and Horwitz 1991). We would expect a similar change in docetaxel
PK data as that in paclitaxel found in the elderly patient. However, all major PK data for
docetaxel in our study did not show any marked difference between patients younger than
60 years old and those 70 years or older. There were 49 patients available for the
paclitaxel study, while only 20 patients were included for the docetaxel study. The
relatively weak statistic power due to limited sample size might be one of the reasons for
being unable to find any significant differences in docetaxel pharmacokinetics between
the two study groups. Regardless the marked decrease in renal function calculated by
either creatine clearance normalize by BSA or MDRD in patients 70 years or older, the
average AUC and CL of docetaxel were not significantly different between the elderly
patients and the controls. This further confirmed the fact that renal elimination accounted
for a very small portion of the total clearance of docetaxel.
Age-related decline in liver metabolism capacity might also account for the
finding that capecitabine AUC was markedly greater in patients 70 years or older, and
capecitabine total clearance was significantly lower in the same group compared to those
in patients younger than 60 years old. As briefly introduced before, capecitabine is an

50
oral pro-drug of 5FU and the activation process needs a 3-step conversion involving 3
different enzymes. The conversion from capecitabine to DFCR mainly happens in liver
via carboxylesterase, while by contrast, cytidine deaminase, the metabolic enzyme
catalyzing the conversion of DFCR to DFUR is ubiquitous, with high expression in liver,
plasma and tumor tissue; For the final metabolic step, DFUR is preferably activated to
5FU inside of tumor bed, due to high level expression of thymidine phosphorylase in
tumor tissue. As capecitabine is extensively metabolized in liver, it is easily foreseen that
liver function might be a contributing factor for variation in capecitabine PK profiles.
Twelves et al conducted a clinical study to evaluate capecitabine pharmacokinetics in
cancer patients with liver dysfunction due to liver metastases (Twelves, Glynne-Jones et
al. 1999). In that study, there were 13 patients with impaired liver function stratified
using a scoring system based on serum bilirubin, alkaline phosphatase and transaminase
levels. The Cmax and AUC of capecitabine, DFCR and DFUR were shown to increase by
various percentages in patients with liver dysfunction compared to those in patients with
normal liver function, however none of the aforementioned differences had reach statistic
significance. In our study, more accumulation and less clearance of capecitabine in
patients 70 years or older, indicated that there was less conversion of capecitabine into
DFCR in elder patients. Since the first metabolic step mainly takes place in liver, this
result is most likely due to age-related decreases in liver blood supply and liver mass,
which sequentially impairs liver metabolism capacity in the elderly patients. This finding
was consistent with the change in paclitaxel PK profiles in patients 70 years or older,

51
considering that both capecitabine and paclitaxel rely on normal liver function to be
metabolized.
Plasma concentrations of DFCR, DFUR and 5FU are subject to the impact of
multiple enzymes involved in the activation process of capecitabine to the final active
product. For example, plasma concentration of DFCR is affected at least by two enzymes,
namely carboxylesterase which converts capecitabine to DFCR and cytidine deaminase,
which further converts DFCR to DFUR. Age-related decrease in liver blood supply and
liver mass might have an impact on the first activation step, which mainly happens in
liver. However, by contrast, cytidine deaminase (CDA) is ubiquitously available, and
there is no unknown publication suggesting an apparent age-related change in the enzyme
activity of CDA. Therefore, age impacts the first and second metabolic steps in various
extents, which might explain the lack of difference in DFCR PK data between the two
age groups. Similarly, plasma concentrations of DFUR and 5FU are subject to the impact
of several enzymes, which include CDA, thymidine phosphorylase, dihydropyrimidine
dehydrogenase (DPD) and etc. These enzymes are ubiquitously expressed, with
thymidine phosphorylase selectively abundant in tumor tissue. By far, there is no research
suggesting any age-related changes in the intrinsic activity of these enzymes, therefore
differences in PK profiles of DFUR and 5FU were not apparent in patients with different
age.
Due to the fact that PK profiles of capecitabine and its metabolites are dependent
on intrinsic activities of several enzymes that participate in the three metabolic steps, the
expression levels and genetic polymorphisms of the aforementioned enzymes contribute

52
substantially to the inter-individual differences in disposition of capecitabine and its
metabolites. Lu et al reported a 44 fold difference in DPD activity in peripheral blood
mononuclear cells collected from patients from breast cancer (Lu, Zhang et al. 1998),
which was consistent with the finding where Peters et al showed a 1000 fold difference in
Cmax of 5FU among patients receiving protracted 5FU IV infusion (Peters, Lankelma et
al. 1993). Further exploration of genetic polymorphisms of the participating enzymes
involved in the metabolism of capecitabine and its metabolites will help to elucidate the
contributing factors that cause variations in PK profiles for capecitabine and its
metabolites.
Gemcitabine is another drug that utilizes CDA in its metabolism process. Over
90% of the administrated dose is inactivated to dFdU by CDA, which is widely expressed
in various tissues. The unchanged gemcitabine and dFdU are almost exclusively excreted
through renal elimination. In this study, PK parameters for gemcitabine were not
significantly different between patients 70 years or older and those younger than 60 years
old. The lack of marked differences in gemcitabine PK profiles was probably due to the
fact that CDA is widely expressed in the tissues and that CDA activity may not change
significantly with age.
Further analysis showed that the AUC of dFdU, the inactive metabolite of
gemcitabine, was significantly greater in patients 70 years or older when compared to that
in patients younger than 60 years old. There are two factors that might contribute to the
greater accumulation of dFdU in elderly patients. One is the impaired renal function in
elderly patients. Since majority of unchanged gemcitabine and dFdU are excreted through

53
renal elimination, renal function is a critical impacting factor for dFdU disposition. The
renal function calculated as Crcl adjusted by BSA was significantly lower in the elderly
patients compared to that in the younger patients, which was consistent with the change
in AUC of dFdU between the two study groups. On the other hand, CDA enzyme that
converts gemcitabine to dFdU, is highly polymorphic. Thus far, there are two
nonsynonymous SNPs, 79A>C (Lys27Gln) and 208G>A (Ala70Thr), have been
identified in the coding region of the human CDA gene, among which, in vitro studies
have shown marked reduction in CDA activity for 208 G>A variant (Yue, Saikawa et al.
2003) and marginal decrease in activity for 79A>C variant (Gilbert, Salavaggione et al.
2006). Sugiyama et al conducted a clinical trial aiming to evaluate correlations between
CDA genotypes and gemcitabine pharmacokinetics and they found that the AUC and CL
of gemcitabine in patients with 208AA genotype were 5-fold and 20% respectively of
those in patients with 208GG genotype, indicating that 208G>A SNP was correlated with
decrease enzyme activity of CDA (Sugiyama, Kaniwa et al. 2007). Further exploration in
CDA genetic polymorphisms will help to evaluate contributing factors that lead to greater
accumulation of dFdU in elderly patients.
2.5 Conclusion
Age-related decreases in liver blood supply and liver mass might account for the
greater value of AUCs and lowered clearance of paclitaxel and capecitabine in patients
70 years or older compared to those in patients younger than 60 years old. Greater
accumulation of dFdU in the elderly patients might be associated with reduced renal
function in the elderly patients as compared to their younger counterparts. In these studies,

54
it is clear that reduced renal clearance and impaired liver metabolism are the two factors
that affect drug disposition in elderly patients.
Genetic polymorphisms in major enzymes and transporters that participate in the
metabolism and elimination of gemcitabine, paclitaxel, docetaxel, capecitabine and their
metabolite need to be investigated to further explore contributing factors accounting for
variations in the PK profiles.

55
Chapter 2 endnotes
2.1 Introduction
2.1.1 Age-related changes in pharmacokinetics
2.1.2 Advanced urothelial cancer and its management
2.1.3 Advanced breast cancer and its management
2.1.4 Advanced colorectal cancer and its management
2.1.5 Gemcitabine
2.1.6 Paclitaxel
2.1.7 Docetaxel
2.1.8 Capecitabine
2.2 Methods
2.2.1 Study subjects
2.2.1.1 Subjects for gemcitabine and paclitaxel study
2.2.1.2 Subjects for docetaxel study
2.2.1.3 Subjects for capecitabine study
2.2.2 Study protocols
2.2.2.1 Study protocol for gemcitabine and paclitaxel study
2.2.2.2 Study protocol for docetaxel study
2.2.2.3 Study protocol for capecitabine study
2.2.3 Analytical measurements
2.2.3.1 Determination of plasma concentrations for gemcitabine and beta-uridine
2.2.3.2 Determination of plasma concentrations for paclitaxel and docetaxel

56
2.2.3.3 Determination of plasma concentrations of capecitabine and its metabolites
2.2.4 Pharmacokinetic modeling
2.2.5 Statistical analysis
2.3 Results
2.3.1 Results of gemcitabine study
2.3.1.1 Patient characteristics
2.3.1.2 Pharmacokinetics of gemcitabine and dFdU
2.3.1.3 Pharmacokinetics of paclitaxel
2.3.2 Results of docetaxel study
2.3.2.1 Patient characteristics
2.3.2.2 Pharmacokinetics of docetaxel
2.3.3 Results of capecitabine study
2.3.3.1 Patients characteristics
2.3.3.2 Pharmacokinetics of capecitabine and its metabolites
2.3.3.3 Clinical outcomes
2.4 Discussion
2.5 Conclusion

57
Chapter three: Impact of age and duration after liver transplantation on phenotype
of major CYP450 enzymes

3.1 Introduction
Transplantation is an established treatment for patients with

organ failure. With
the development of improved surgical skills,

the survival rate of transplant grafts has
increased, and, correspondingly,

the survival rate of patients has increased. A variety of
drugs

are used in these patients, and the effectiveness of drug therapy

varies with altered
pharmacokinetics and pharmacodynamics after

organ transplantation. Liver transplant
patients deserve special

attention since the liver is the major organ for biotransformation

of xenobiotics. Many factors in the process of liver transplantation

can affect its drug
metabolic function, and these factors include

organ preservation, reperfusion injury,
inflammatory changes,

and the immunologic response of the recipient. Recently
expanded

criteria for acceptance for orthotopic liver transplantation

(OLTx) have
included older donors and recipients, which could

influence the liver's drug metabolic
capacity.

The cytochrome P450 enzymes (CYP) are a superfamily of more

than 50 isoforms.
The major function of these enzymes is to

catalyze phase I biotransformation reactions
for xenobiotics.

The major human drug-metabolizing CYPs belong to families 1,

2, and 3,
among which CYP1A2, CYP2C19, CYP2D6, CYP2E1, and CYP3A

account for about
65% of the total activity(Chang and Kam 1999; Venkatakrishnan, Von Moltke et al. 2001;
Lewis 2004). Little information

concerning the changes in CYP enzymes after OLTx is

58
available.

Thorn et al reported that the mRNA expression from liver biopsies

of CYP3A4,
3A5, 2E1, and 1A2 increased during the first year

after OLTx (Thorn, Lundgren et al.
2004). However, the baseline mRNA expression was taken

from the reperfusion period
immediately after transplantation

and the follow-up study was at 1 year, which makes the
change

difficult to interpret. In addition, the relationship between

hepatic mRNA
expression and phenotypic expression of CYP activity

is not clear.

The use of pharmacologic agents as metabolic probes to quantitate the phenotypic
expression

of CYPs is a widely used approach to study changes in drug metabolism.

An
early study by our group demonstrated that antipyrine clearance

normalized rapidly after
OLTx (Mehta, Venkataramanan et al. 1986). An extension of these studies

included
multiple probes for CYP activity. The "Pittsburgh cocktail"

contains 5 probe drugs for the
above CYP enzymes including 100

mg caffeine, 250 mg chlorzoxazone, 100 mg dapsone,
10 mg debrisoquine,

and 100 mg mephenytoin, and it is a noninvasive method to evaluate

the in vivo phenotype of these enzymes independently and simultaneously (Frye, Matzke
et al. 1997).

Using this approach, we have previously reported the induction

of CYP2E1
in the first postoperative month in OLTx patients (Burckart, Frye et al. 1998).

The
objective of this study was to examine the changes in the

5 CYP enzyme phenotypes in
OLTx patients in relation to recipient

age and time after transplantation.
3.2 Method
3.2.1 Liver Transplant Patients
This study was approved by the Biomedical Institutional Review Board of the
University of Pittsburgh, and all patients undergoing OLTx at the University of

59
Pittsburgh Medical Center were considered to be eligible for study participation. The
transplant procedure has been described in detail previously, and the anesthetic used in
these patients was primarily isoflurane. All patients were treated with an
immunosuppressive regimen of tacrolimus and prednisone, with mycophenolic acid
added when necessary. Patients were studied only during periods in which they were
considered to be clinically stable. Patients were excluded from the study if they were
receiving agents known to produce significant induction (anticonvulsants or rifampin) or
inhibition (ketoconazole, fluconazole, cimetidine, or erythromycin) of CYP enzymes or if
they had an estimated creatinine clearance of less than 50 mL/min (Cockcroft and Gault
1976). Although multiple studies were not possible in all patients, attempts were made to
study the subjects in the early posttransplant period (1 to 2 weeks), 1 to 4 months after
transplant, and during long-term follow-up (6 to 12 months).
3.2.2 Healthy Subjects
Fourteen young healthy subjects and 7 elderly healthy subjects participated after
giving written informed consent. Subjects were instructed to abstain from caffeine or
alcohol-containing products for at least 3 days before each study visit.
3.2.3 Study Protocol
After an overnight fast, subjects received the oral 5-drug Pittsburgh

cocktail in the
General Clinical Research Center. The cocktail

included 250 mg chlorzoxazone, 100 mg
caffeine, 100 mg dapsone,

100 mg mephenytoin, and 10 mg debrisoquine. None of the 5
agents

given in this cocktail influence the metabolism of the other

agents given
concurrently (Frye, Matzke et al. 1997). The 5 drugs were taken simultaneously

with

60
approximately 240 mL of water. Venous blood was collected

through an indwelling
catheter before and at 4 and 8 hours after

drug administration. Plasma was harvested and
stored at –20°C

until analyzed. Urine samples were collected before drug administration

and for 8 hours after. The total volume was recorded, and an

aliquot was stored at –20°C
until analyzed.

The following drugs and metabolites were measured by high-performance

liquid
chromatography techniques: caffeine and paraxanthine

in plasma (Burckart, Frye et al.
1998),chlorzoxazone and 6-hydroxychlorzoxazone in plasma (Frye and Stiff 1996),

dapsone and dapsone hydroxylamine in urine (May, Porter et al. 1990), debrisoquin and

4-hydroxydebrisoquin in urine (Frye and Branch 1996), and 4-hydroxymephenytoin in

urine (Frye, Matzke et al. 1997). The within- and between-day coefficients of variation

of
these assays are less that 10%. Serum and 8-hour urine creatinine

determinations
provided the basis for the calculation of measured

creatinine clearance in each subject.

3.2.4 Data Analysis
Patients were separated into the young OLTx patients group,

with age equal to or
less than 50 years, and the older OLTx

patients group, with age greater than 50 years.
Fifty years

of age was chosen because of the previously published association

of poor
outcomes in liver transplant patients older than 50 (Facciuto, Heidt et al. 2000; Moreno,
Cuervas-Mons et al. 2003).

Subjects were excluded from analysis if they had a measured

creatinine clearance of less than 40 mL/min. The concentration

of paraxanthine (1,7
dimethylxanthine) divided by the concentration

of caffeine in the 8-hour plasma sample
was used to assess CYP1A2

activity. The ability to hydroxylate chlorzoxazone (CYP2E1)

was estimated by the 6-hydroxychlorzoxazone to chlorzoxazone

plasma ratio at 4 hours.
The ability to N-hydroxylate dapsone

(CYP3A) was estimated by the urinary recovery
ratio HAD/(HAD

+ DDS), in which HAD is the urinary recovery of dapsone
hydroxylamine

in an 8-hour urine sample and DDS is the 8-hour urinary recovery

of
dapsone. The activity of CYP2D6 was estimated by use of the

debrisoquin recovery ratio:
HDB/(HDB + DB), where HDB and DB

are the urinary recoveries of 4-
hydroxydebrisoquin and debrisoquin

in 8 hours, respectively. The total urinary recovery
of 4-hydroxymephenytoin

was used as the phenotypic measure of CYP2C19 activity
(Frye, Matzke et al. 1997).

3.2.5 Statistical Analysis
All phenotypic trait measure estimates in OLTx patients and

healthy control
subjects were compared by nonparametric 1-way

ANOVA (Kruskal-Wallis test) and
Dunns posttest. The differences

were considered statistically significant when P .05. All
calculations

were performed using PRISM software, version 4.00 (Graphpad

Software Inc,
San Diego, Calif).
3.3 Results
Thirty-eight liver transplant patients were recruited into this

study. The reasons
for OLTx included hepatitis B and C (10 young

OLTx patients and 5 older patients),
autoimmune diseases (3

young OLTx patients and 8 older patients), and other diseases

affecting the liver (5 young OLTx patients and 7 older patients).

Two patients were
studied on 5 separate occasions, 2 patients

were studied 4 times, 4 patients were studied 3
times, 9 patients

were studied twice, and 21 patients were studied 1 time. There

were a

61

62
total of 69 patient studies, among which 12 patient studies

had to be excluded from
analysis because the measured creatinine

clearance was less than 50 mL/min. The
demographics of the subjects

who were included in the analysis are presented in table 4.

The groups were analyzed as independent from one another since

the statistical analysis
after we deleted repeat studies demonstrated

that the observations were unchanged.
Table 4: Patient demographics from each study group in cocktail study
Within 30 days After 30 days

Young
OLTx
Elderly
OLTx
Young
OLTx
Elderly
OLTx
Young
healthy
controls
Elderly
healthy
controls
Study number 15 12 17 13 14 7
Age (yr)
41.0 ±
7.8
59.3 ±
7.4
41.4 ±
6.7
54.5 ±
4.8
21.6 ±
2.3
75.7 ±
8.3
Male 11 8 14 10 14 3
Female 4 4 3 3 0 4
Weight (kg)
83.2 ±
27.8
77.1 ±
17.3
77.6 ±
17.3
81.1 ±
4.4
72.5 ±
10.2
69.7 ±
3.1
CL
CR
(ml/min)
91.7 ±
40.0
61.7 ±
22.3
74.6 ±
20.1
76.9 ±
23.8
90.5 ±
10.6
113.8 ±
78.5
Donor age (yr)
43.6 ±
12.9
42.2 ±
21.3
42.0 ±
9.5
38.9 ±
18.1

Postoperative
days
13.7 ±
5.5
18.3 ±
6.3
124.4 ±
81.2
150.9 ±
57.5

Liver function
ALT
139 ±
107
85 ± 45 88 ± 90 63 ± 86
AST 50 ± 34 30 ± 15 60 ± 60 43 ± 36
GGTP
431 ±
408
243 ±
117
168 ±
362
168 ±
359

CYP2E1 enzyme activity as measured by the 6-hydroxychlorzoxazone

to
chlorzoxazone plasma ratio at 4 hours was significantly higher

in the young OLTx
patients within 30 days after transplantation

(mean ± SD, 6.4 ± 5.2), compared to either
young

healthy controls (0.8 ± 0.3, P < .001) or elderly

healthy subjects (0.9 ±0.2, P < .05;
Figure 7). Older

OLTx patients also had a markedly elevated ratio within the

first 30 days
postoperatively (5.2 ± 3.7, P < .001)

compared to young healthy controls, although this
difference

was not significant when compared to the elderly controls. After

the first
month postoperatively, the chlorzoxazone metabolic

ratio in young OLTx patients
remained high (4.5 ± 5.2),

and the difference was significant compared to young healthy

subjects (P < .01). However, for older OLTx patients after

30 days postoperatively, the
ratio decreased (1.0 ± 0.7)

to approximately that of the control subjects. There were no

significant differences in CYP2E1 activity between young and

older OLTx patients both
within and after 30 days postoperatively.
Figure 7: The chlorzoxazone metabolic ratio in young and older orthotopic liver
transplant patients, within and after 30 days postoperatively and the young and elderly
healthy control subjects. Differences between groups are designated as #P < .001, $P
< .01, *P < .05.

63

The CYP2C19 enzyme capacity as measured by the total urinary

recovery of 4-
hydroxymephenytoin was decreased significantly

within the first month after
transplantation both in young (37.0

± 31.2, P < .001) and older (61.4 ± 25.5, P

< .01)
OLTx patients, when compared to young healthy subjects

(131.1 ± 25.9; Figure 8). These
differences were not

significantly different than the elderly controls (104.8 ±

59.9). The
CYP2C19 activity in young OLTx patients remained

low after 30 days (55.3 ± 35.8) and
was still significantly

lower than that of the young healthy controls (P < .01).

The
CYP2C19 activity in older OLTx patients after 30 days postoperatively

increased (98.7 ±
41.7) and was not significantly different

from young or elderly healthy controls. No
marked difference

between young and older OLTx patients for urinary recovery of

4-
hydroxymephenytoin occurred before or after 30 days postoperatively.
Figure 8: The urinary recovery of 4-hydroxymephenytoin in young and older orthotopic
liver transplant patients, within and after 30 days postoperatively, and the young and
elderly healthy control subjects. Differences between groups are designated as #P < .001,
$P < .01.

64

CYP1A2 enzyme activity as measured by the caffeine metabolic

ratio was not
significantly different between the OLTx patients

and healthy control groups (Figure 9).
No difference was observed

when comparing young and older OLTx patients for
CYP1A2 activity.
Figure 9: The caffeine metabolic ratio in young and older orthotopic liver transplant
patients, within and after 30 days postoperatively, and the young and elderly healthy
control subjects.

Only 7 OLTx patients were observed to be phenotypic poor metabolizers

for
CYP2D6 activity, defined as a debrisoquin recovery ratio

less than 0.07 (Carcillo,
Adedoyin et al. 2003), and these patients were excluded from analysis.

For CYP2D6
activity, no significant difference was observed

when comparing young (0.58 ± 0.23) or
older OLTx patients

(0.58 ± 0.20) within 30 days after transplantation with

young (0.61 ±

65
0.14) or elderly healthy subjects (0.66

± 0.13; Figure 10). After the first month
postoperatively,

the debrisoquin recovery ratio in older OLTx patients (0.28

± 0.22)
decreased significantly compared to young or

elderly healthy controls (P < .05).
Figure 10: The debrisoquin recovery ratio in young and older orthotopic liver transplant
patients, within and after 30 days postoperatively, and the young and elderly healthy
control subjects. Differences between groups are designated as *P < .05.

The N-hydroxylation capacity of CYP3A was measured by the dapsone

recovery
ratio. The only significant difference in the dapsone

recovery ratio was found in the
young OLTx patients group within

30 days after transplantation (0.3 ± 0.2), which was

significantly lower than that in young (0.6 ± 0.1, P

< .05) or elderly healthy controls (0.6
± 0.1, P <

.05; Figure 11). Overall, there was no significant difference

between young and
older OLTx patients within or after 30 days

postoperatively.

66
Figure 11: The dapsone recovery ratio in young and older orthotopic liver transplant
patients, within and after 30 days postoperatively, and the young and elderly healthy
control subjects. Differences between groups are designated as *P < .05.

3.4 Discussion
In the early years of liver transplantation, the upper age limit

for patients receiving
OLTx was 45 to 55 years old. However,

the age limitation has been expanded given
recent results demonstrating

that OLTx in patients older than 60 years can be
accomplished

safely(Bjoro, Hockerstedt et al. 2000). In 2003, there were a total of 5671
liver transplant

patients, among whom 2738 (48%) were 50 to 64 years old and

473 (8%)
were older than 65 years. The putative aging-related

physiological changes that may
affect drug clearance include

a decrease in renal blood flow and glomerular filtration rate,

a decrease in hepatic mass/blood flow (Geokas and Haverback 1969), and an increase in

67

68
adipose

tissue and an increase in lean body mass relative to total body

mass, which may
alter the distribution volume of lipophilic

drugs (Greenblatt, Abernethy et al. 1986). The
clearance of many commonly used drugs metabolized

by CYP enzymes decreases with
aging, but many studies have suggested

that the biotransformation capacity of these CYP
enzymes is

maintained in the healthy elderly. For example, Hunt et al and

Simon et al
found that the metabolic capacity of CYP3A and CYP1A2

was preserved in older healthy
subjects (Hunt, Westerkam et al. 1992; Simon, Becquemont et al. 2001).

Donor age is another important factor that could potentially

influence the
posttransplantation activities of CYP enzymes.

Carcillo et al reported that for OLTx
recipients with stable

liver function, the relationship between the debrisoquin recovery

ratio and CYP2D6 mRNA concentration in peripheral blood mononuclear

cells, a
measure of extrahepatic CYP2D6 expression, was similar

to the relationship observed in
healthy subjects (Carcillo, Adedoyin et al. 2003). Moneketal reported

that an extensive
metabolizer CYP2D6 donor liver was given to

a poor metabolizer recipient who remained
phenotypically a poor

metabolizer (Monek, Paintaud et al. 1998). Even when an extensive
metabolizer donor liver

was matched to an extensive metabolizer recipient, 4 of 40
subjects

were found to be phenotypic poor metabolizers. These findings

suggested that a
balance of hepatic and extrahepatic metabolism

existed to determine the overall in vivo
metabolic activity of

CYP2D6 after liver transplantation, and the donor organs may

not be
the primary determinant of the overall enzyme activity

of the liver in a transplant patient.

Many factors could contribute to the functional change in CYP

enzymes after
OLTx. The process of liver transplantation may

affect enzyme function through organ
preservation (cold ischemia

time), reperfusion injury, inflammatory changes during or
after

the surgical procedure, and the immunologic response, which

might impair the liver
directly or through the involvement of

cytokines elaborated during or after the surgical
procedure.

Many studies suggested that cytokines play an important role

in the regulation
of mRNA expression or activity of CYP enzymes.

Proinflammatory cytokines, such as
the interferons, IL-1, IL-6,

tumor necrosis factor– , and transforming growth factor–ß

have been shown to downregulate the biotransforming capacity

of CYP enzymes in
cultured hepatocytes or in vivo animal and

human studies (Tinel, Elkahwaji et al. 1999;
Pan, Xiang et al. 2000; Frye, Schneider et al. 2002; Carcillo, Doughty et al. 2003). While
the time course of elaboration of

these cytokines locally after OLTx was unclear, they
could contribute

to the depression of CYP-mediated drug metabolism in the months

following the transplant procedure.

Other studies have also demonstrated the induction of CYP enzymes

by certain
cytokines (Abdel-Razzak, Loyer et al. 1993; Tindberg, Baldwin et al. 1996). The activity
of CYP2E1, as measured

by the 6-hydroxychlorzoxazone to chlorzoxazone plasma ratio

at 4 hours, was markedly induced within 30 days after liver

transplantation in both young
OLTx patients and older OLTx patients.

After 30 days postoperatively, the ratio in young
OLTx patients

remained at a high level. Our earlier report (Burckart, Frye et al. 1998),
which used the

same patient population, did not differentiate between the young

and older
OLTx patients. Our observation of elevated CYP2E1

function early after OLTx was
supported by the subsequent study

of Park et al (Park, Lin et al. 2003). They reported that
enhanced formation of NAPQI,

a hepatotoxic metabolite of acetaminophen produced by

69

70
CYP2E1,

was observed in the first 10 days after liver transplantation.

A possible
explanation for the elevated activity of CYP2E1 could

be cytokine-mediated induction.
Abdel-Razzak et al found that

in human hepatocyte cultures, CYP2E1 was the only CYP
isoenzyme

in which the expression of its mRNA was increased in the presence

of IL-4
(Abdel-Razzak, Loyer et al. 1993). Tindberg et al also reported that in rat brain cortical

glial cell cultures, CYP2E1 mRNA increased with application

of IL-1ß (Tindberg,
Baldwin et al. 1996).

CYP2C19 activity was markedly reduced within 30 days after OLTx

for both
young OLTx patients and older OLTx patients. After

30 days postoperatively, the
metabolite recovery in young OLTx

patients remained lower than the young healthy
subjects, while

the older OLTx patients had CYP2C19 activity in that time period

that
was similar to the healthy controls. The result was consistent

with the hypothesis that
CYP activity would be temporarily impaired

due to factors related to liver transplantation
(eg, cytokines,

organ reperfusion).

The activity of CYP2D6 capacity as estimated by the debrisoquine

recovery ratio
was reduced after 30 days postoperatively in

the older OLTx patients group compared
with either young healthy

subjects or elderly controls. CYP2D6 activity may also be
reduced

in young OLTx patients but not to the extent of the older OLTx

patients. Bendriss
et al measured CYP2D6 activity by the debrisoquine

recovery ratio in 9 liver transplant
patients followed for up

to 3 years after liver transplantation and found it to be stable

over
time (Bendriss, Bechtel et al. 1995). Our observation suggested that some factor altered

the debrisoquine recovery ratio long term in OLTx patients and

that older OLTx patients

71
might be sensitive to this effect. Further

study is needed to evaluate CYP2D6 activity
after prolonged

survival in OLTx patients.

The only significant difference in the dapsone metabolic ratio

was found in the
young OLTx patients group in which the ratio

was significantly reduced in the first month
after OLTx. However,

recent studies have suggested that dapsone was not a good probe

to
measure CYP3A activity since the process of N-hydroxylation

of dapsone involves
contributions from multiple CYP enzymes (Gill, Tingle et al. 1995).

Other studies have
also suggested that many probes for CYP3A

activity did not accurately predict therapy
with CYP3A4-metabolized

drugs (Stein, Kinirons et al. 1996; Gass, Gal et al. 1998).

For the 5 CYP enzymes studied, young and older OLTx patients experienced

similar changes for the metabolic capacity after liver transplantation. Donor ages between
young and older

OLTx patients were balanced. The results of this study indicated

that
older patients exhibited changes in CYP activity following

OLTx that were similar to
those observed in young patients.

Prospective studies evaluating the appropriate dosage for drugs

that are substrates
of CYP2E1, CYP2C19, and CYP2D6 should be

conducted in OLTx patients. Drugs that
are substrates for CYP2D6

are of particular concern since CYP2D6 activity in the early

postoperative period decreased with time following OLTx. These

changes in prospective
studies should be assessed in relation

to current knowledge of the pharmacogenetic
differences in expression

of these enzyme activities in donor and recipient.

72
3.5 Conclusion
Markedly elevated CYP2E1 capacity, as measured by chlorzoxazone

hydroxylation, was observed within 30 days after liver transplantation

for both young and
older OLTx patients, and the effect was prolonged

beyond 30 days posttransplantation in
the young OLTx patients compared to that in healthy controls.

CYP2C19 metabolizing
function, as measured by mephenytoin hydroxylation,

was significantly reduced during
30 days after liver transplantation compared to healthy controls.

CYP2D6 capacity was
impaired after 30 days postoperatively in

older OLTx patients compared to healthy
controls. Young and older OLTx patients experienced

similar changes after liver
transplantation in their metabolic

capacity of the major CYP450 enzymes.

73
Chapter 3 endnotes
3.1 Introduction
3.2 Methods
3.2.1 Liver Transplant Patients
3.2.2 Healthy Subjects
3.2.3 Study Protocol
3.2.4 Data Analysis
3.2.5 Statistical Analysis
3.3 Results
3.4 Discussion
3.5 Conclusion

74
Chapter four: Impact of mutational disease on drug disposition and outcomes

4.1 Introduction
Cystic fibrosis (CF) is one of the most lethal congenital disorders

in the United
States. This disease is an autosomal recessive

disorder with a carrier frequency of about
1/30 in Caucasians.

The pathogenesis of this disorder is associated with mutations

along
the cystic fibrosis transmembrane conductance regulator

(ABCC7 or CFTR) gene located
on the long arm of chromosome 7.

ABCC7 encodes for a cellular membrane chloride
channel, which

regulates ion transport. The most common form of mutation in

ABCC7 is
a deletion of 3 adjacent nucleosides, leading to the

absence of phenylalanine at position
508 of the protein (McKone, Goss et al. 2006).
Defects in the CFTR protein, normally present in the apical

membrane of airway
epithelial cells, result in a loss of the

periciliary liquid layer leading to desiccated
pulmonary secretions

and airway obstruction (Worlitzsch, Tarran et al. 2002). Inadequate
airway clearance provides

an environment supporting airway infection with Pseudomonas

aeruginosa. A frequent complication of CF is acute pulmonary

exacerbations
characterized by increased cough, sputum production,

shortness of breath, weight loss,
and a decline in pulmonary

function requiring the administration of systemic antibiotics.

The pharmacokinetics of a number of antibiotics in CF patients

have been shown to be
altered when compared with age-matched

control subjects (Spino 1991). In particular,
enhanced renal clearance of

many beta-lactam antibiotics (methicillin, ticarcillin,
cloxacillin,

ceftazidime, aztreonam) has been previously reported. The mechanism

75
underlying the altered renal clearance of certain drugs remains

unknown. Delineation of
the exact mechanism may provide insight

as to how to manage treatment in CF patients
who exhibit an

altered pharmacokinetic profile.
P-glycoprotein (P-gp), similar to CFTR, is a member of the adenosine

triphosphate (ATP)–binding cassette superfamily of transport

proteins. It is encoded by
the human multiple drug resistant-1

(ABCB1 or MDR1) gene and is expressed in a
variety of normal

tissues. Besides its function as an efflux transporter, P-gp

is also an
important membrane protein modulating cell swelling-activated

chloride channels (Spino
1991). The ABCC7 and ABCB1 genes are structurally

related, sharing a common domain
organization, and functionally

related, acting as regulators of heterologous ion channels
(Trezise, Ratcliff et al. 1997).

Coordinate regulation of the ABCB1 and ABCC7 genes is
supported

by 2 studies (Breuer, Slotki et al. 1993; Trezise, Ratcliff et al. 1997),
suggesting that in patients with CF, P-gp function

might be elevated as a compensatory
response to the defective

CFTR protein. The upregulation of P-gp is a potential
mechanism

to explain the increased clearance of certain drugs in CF patients.

The goal of this study was to investigate the role of renal

P-gp function in patients
with CF. The first specific aim was

to compare the pharmacokinetic profile of
fexofenadine between

CF patients and age-matched control subjects. Fexofenadine was

chosen because it is a known P-gp substrate (Perloff, von Moltke et al. 2002), and it is not

extensively metabolized in vivo. Tahara et al (Tahara, Kusuhara et al. 2006)demonstrated

that after oral administration of [
14
C]fexofenadine to healthy

volunteers, 92% of the total
dose was recovered, 12% in urine

and 80% in the feces, as unchanged drug. This unique

76
feature

of fexofenadine makes it a good probe for P-gp function. In

addition to P-gp,
fexofenadine is also a substrate for the organic

anion transporter (OAT-3) and organic
anion-transporting polypeptides

(OATP)-A and OATP-B (Cvetkovic, Leake et al. 1999;
Tahara, Kusuhara et al. 2006). In the kidney, OATs and OATPs are expressed

on the
basolateral membrane of the renal tubular cells and are

responsible for the uptake of
fexofenadine from the bloodstream

into tubular cells, whereas P-gp is expressed on the
apical

membrane of the renal tubular cells and is responsible for the

efflux of
fexofenadine into the lumen. To determine the relative

contribution of OAT, OATP, and
P-gp to the renal clearance of

fexofenadine, we administered fexofenadine alone and in
combination

with probenecid, which is regarded as an inhibitor of OATs and

OATPs
(Tahara, Kusuhara et al. 2006). The second aim was to measure and compare the P-gp
efflux

activity in peripheral blood T cells between CF patients and

healthy controls as a
surrogate marker of renal P-gp activity.
4.2 Methods
4.2.1 Subjects
Ten CF patients and 15 healthy controls matched by age were

recruited to
participate in the first phase of this study. Among

the same 25 participants, 8 CF patients
and 8 age-matched healthy

volunteers were selected into the second phase of this study.

CF patients were recruited from the Adult CF Center at the University

of Southern
California (USC). Prior to the start of this study,

written informed consent was obtained
from each subject. Subjects

were included in the study if they were older than 18 years

of
age and had a weight within 70% to 130% of ideal body weight.

In addition, CF subjects

77
were required to have a positive sweat

chloride test or known CF genotype to participate
in this study.

Subjects were excluded from the study if they had a history

of allergy to
fexofenadine, or probenecid. Other exclusion criteria

included pregnancy or nursing an
infant, having received an

organ transplant, having used drugs known to alter P-gp
expression

within 30 days of screening, or having had significant anemia

or renal or
hepatic insufficiency.
4.2.2 Study Protocol
The study was a controlled, prospective 2-phase study in subjects with CF and
healthy volunteers. In the first phase, ABCB1 genotyping and P-gp efflux activity,
measured by functional flow cytometry, were performed in 25 participants (10 with CF
and 15 healthy volunteers). In the second phase, 8 patients with CF and 8 healthy
volunteers received a single dose of fexofenadine alone or in combination with
probenecid according to random assignment. The subjects were then crossed over to
receive the remaining study treatment after a 1-week washout period.
The clinical portion of the study was performed at the General Clinical Research
Center (GCRC) at LAC + USC Medical Center. In the first phase of the study,
participants were admitted to the GCRC, and 2 aliquots of blood were collected from
each subject. One aliquot was stored at –80°C until the time of genotyping. The other
aliquot of heparinized peripheral blood was processed by flow cytometry for P-gp efflux
function within 2 hours of sample collection.
Eight CF patients and 8 age-matched healthy volunteers from the first phase of
the study were recruited for the second phase. Following an overnight fast, subjects were

78
admitted to the GCRC on each study day, and 2 peripheral intravenous catheters were
placed. On the day when subjects were randomized to receive fexofenadine plus
probenecid, the first dose of probenecid (1 g oral; Benemid, Merck, Sharp & Dohme,
West Point, Pennsylvania) was administered along with an oral fluid load, consisting of
600 mL of caffeine- and sugar-free liquids, 1 hour prior to the administration of
fexofenadine and the second dose of probenecid (1g oral) 12 hours later. At time zero,
180 mg fexofenadine (Allegra; Sanofi-Aventis, Bridgewater, New Jersey) was
administrated orally. An intravenous bolus dose of 456 mg iothalamate meglumine
(Conray, Mallinckrodt, St Louis, Missouri) was administered on each study day.
Additional fluid was administered orally at 150 to 200 mL/h for 6 hours following the
drug administration to ensure adequate hydration during the urine collection period. No
food was allowed until 2 hours after the administration of the fexofenadine dose. In
addition, no citrus-containing liquids or alcohol were allowed starting the evening before
and continuing the day of the study. Blood samples for pharmacokinetic analysis were
obtained before administration of fexofenadine and at the following times: 0.25, 0.5, 1, 2,
3, 4, 6, 12, 24, and 48 hours after dosing. Plasma was separated by centrifugation within
30 minutes of collection. Urine samples were obtained from spontaneous voiding before
fexofenadine administration and 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 12, and 24 hours after dosing.
Plasma and urine samples were then stored at –80°C until the time of analysis. The
protocol was repeated on day 8 when each subject was randomized to complete the
remaining study arm.

79
4.2.3 Analytical measurements
4.2.3.1 Determination of P-gp efflux activity by flow cytometry
The efflux of P-gp substrate rhodamine 123 (R123) from CD4+ helper T cells and
CD8+ cytotoxic T cells was measured as an index for P-gp efflux activity. R123 is a
fluorescent dye that is a known P-gp substrate (Donnenberg, Wilson et al. 2004). Efflux
of rhodamine 123 from T cells obtained from each study subject was measured in the
presence and absence of cyclosporine A (CsA). The CsA concentration of 5 µM was
chosen because it is known to be a potent inhibitor of P-gp at this concentration (Pallis,
Turzanski et al. 1999). Efflux was measured under basal and rhodamine 123–induced
conditions by quantifying rhodamine fluorescence within peripheral blood CD4+ and
CD8+ T cells using a FACScan flow cytometer (Becton Dickinson, San Jose, California).
The methodology used was adopted from Donnenberg et al (Donnenberg, Wilson et al.
2004) with 2 modifications. The first difference was with processing of the dry pellet,
which was stained with 2 µL each of conjugated monoclonal antibodies against surface
epitopes for CD4 (PE conjugated, BD PharMingen, San Diego, California), CD3 (ECD
conjugated, BD PharMingen), and CD8 (PC5 conjugated, BD PharMingen). The other
significant difference was in the quantification of P-gp activity under basal and induced
conditions. Specifically, the basal P-gp activity was defined as the difference between
median R123 fluorescence at load phase without CsA (A) and that with CsA (B) divided
by B [(B – A)/B*100]. The R123-induced P-gp activity was defined as the difference
between median R123 fluorescence at efflux phase without CsA (C) and that with CsA

80
(D) divided by D [(D – C)/D*100]. The difference between load and efflux was
calculated as (A – C)/A*100.
4.2.3.2 Determination of plasma and urine fexofenadine
Fexofenadine concentrations in plasma and urine samples were measured by
liquid chromatography tandem mass spectrometry (LCMS). One hundred microliters of
plasma was mixed with 50 µL of internal standard, consisting of 10 µg/mL terfenadine.
The entire mixture was protein precipitated using 600 µL acetonitrile and vigorously
vortexed. The solution was then centrifuged for 5 minutes at 13 000 rpm. A 250-µL
aliquot of supernatant was evaporated to dryness under a stream of filtered air and
reconstituted with 125 µL of 50% acetonitrile (ACN) and 0.1% formic acid (v/v).
For urine quantification of fexofenadine, 50 µL of 10 µg/mL terfenadine was
added to 100 µL urine. The urine mixture was protein precipitated using 600 µL
acetonitrile. After vigorously mixing, the entire solution was centrifuged for 5 minutes at
13 000 rpm. A 100-µL aliquot of the supernatant was diluted by adding 500 µL of the
mobile phase.
Chromatographic separation was achieved using a C18-Cyano (Thermo) column
with the dimension of 50 x 4.6 mm and particle size of 5 µ. An isocratic mobile phase
was used to elute the analytes, which consisted of 20% methanol and 80% of a solution
consisting of 50% ACN and 0.1% formic acid (v/v). The flow rate was set at 0.6 mL/min
with a splitter set at 50:50. The sample volume of 10 µL was injected. Fexofenadine and
terfenadine (internal standard [IS]) were eluted at 1.45 and 1.60 minutes, respectively,
with a total runtime of 3 minutes.
The analytes were quantified using an Agilent 1100 high-performance liquid
chromatography (HPLC) system (Agilent, San Jose, California) linked with an API3000
mass spectrometer (Applied Biosystems, Foster City, California). The mass spectrometer
was fitted with a Turbo ion spray interface that was set in positive ion mode at 5000 V
and the capillary temperature of 350°C with an auxiliary gas flow of 6000 mL/min. The
nebulizer gas, curtain gas, and the collision gas were set at 10, 12, and 12, respectively.
The mass transitions monitored were m/z 502.4 171.4 with collision energy of 55 eV for
fexofenadine and m/z 472.3 436.6 with collision energy of 40 eV for terfenadine. The
dwell time was set at 150 and 250 ms for fexofenadine and terfenadine, respectively.
Linearity in the calibration curve was found over the concentration ranges of 1 to
5000 ng/mL in plasma samples and 10 to 20000 ng/mL in urine samples. The correlation
coefficients (r) all exceeded 0.998. The accuracy of each standard concentration sample
was within 85% to 115%. Precision and accuracy of the method were established by
analyzing quality control (QC) samples, prepared like the calibration standards at the
following concentrations: 20 ng/mL (low), 2000 ng/mL (medium), and 4000 ng/mL (high)
fexofenadine concentrations in plasma and 50 ng/mL (low), 10 000 ng/mL (medium), and
15 000 ng/mL (high) fexofenadine concentrations in urine. For both plasma and urine
quality control samples, the interassay relative standard deviations (RSDs) were less than
9%, and the interassay bias (mean values of deviation from the true value) was less than
7%.

81

82
4.2.3.3 Determination of plasma and urine iothalamate concentrations
Iothalamate concentrations in plasma and urine samples were measured by HPLC
with UV detection. An aliquot of 500 µL of plasma was processed by adding 1 mL of 40
µg/mL theophylline in acetonitrile. Theophylline served as the internal standard, and
acetonitrile was used to precipitate proteins in the plasma sample. The entire mixture was
vigorously mixed and centrifuged at 15 000 rpm for 15 minutes. The resultant
supernatant was removed and filtered through a 0.2-µ filter (Nalge, Rochester, New
York). The filtered supernatant was evaporated to dryness under a steady flow of dry air
and reconstituted with 200 µL distilled water.
For urine sample preparation, 0.5 mL of 60 µg/mL theophylline in 8% perchloric
acid solution was added to 100 µL of each urine sample, and the entire solution was
vigorously mixed and centrifuged at 15 000 rpm for 15 minutes. The resultant
supernatant was filtered and transferred to HPLC vials for injection.
For both plasma- and urine-processed samples, 50-µL aliquots were injected into
an Agilent 1100 HPLC system fitted with a Zorbax SB C18 column with the dimension
of 250 x 4.6 mm and particle size of 4.6 µM. Iothalamate was eluted using the mobile
phase, which consisted of 18% (v/v) methanol and 82% (v/v) of a solution consisting of
50 mM sodium phosphate dibasic and 0.5 mM tetrabutyl-ammonium hydrogen sulfate.
The pH of the mobile phase was adjusted to 4.5. The flow rate was set at 1.5 mL/min.
Iothalamate and theophylline were detected using UV absorption at 254 nM. The
retention time of iothalamate and theophylline was 3.4 minutes and 8.3 minutes,
respectively, for plasma and urine samples. The plasma and urine standard curves were

83
linear in the range from 2 to 60 µg/mL and 10 to 250 µg/mL with correlation coefficients
of at least 0.99 and 0.98, respectively. The inter-day coefficient of variation (CV) for
plasma sample assays was 6.3%, whereas the CV for urine sample assays was 10.6%.
4.2.4 DNA isolation and genotyping
Genomic DNA was extracted from 8 mL of whole blood using the QIAamp DNA
Blood Maxi Kit (QIAGEN, Inc, Valencia, California) according to the manufacturer's
instructions for Maxi spin protocol. The DNA concentration was measured
photometrically and was subsequently diluted to a concentration of 2 ng/µL prior to
polymerase chain reaction (PCR) amplification.
Genotyping of SNPs was performed using the TaqMan assay with the ABI 7900
Real-time PCR system (Applied Biosystems). The method used in the present study is
described by Zheng et al (Zheng, Zeevi et al. 2004).
4.2.5 Pharmacokinetics
The pharmacokinetics of fexofenadine and iothalamate were determined using
model-independent and compartmental methods. The maximum plasma/serum
concentrations (Cmax) and the time to maximum concentration (Tmax) were observed
values. The areas under the plasma/serum concentration time curves were determined by
the linear trapezoidal rule. A 1-compartment model with first-order absorption
incorporating lag times was applied to the observed plasma and urinary concentrations of
fexofenadine samples using ADAPT II software (Release 5, Biomedical Simulations
Resource, University of Southern California, Los Angeles). A 1-compartment model was
applied to the observed plasma and urinary concentrations of iothalamate samples. The
cumulative urinary excretion of fexofenadine and iothalamate was determined from the
fitted urinary concentrations and measured urinary volumes. The clearance of
fexofenadine and iothalamate was modeled with renal (CLR) and nonrenal (CLNR)
processes. The total drug clearance was described as the sum of the renal and nonrenal
processes (CLT). Analysis was performed using the standard 2-stage (STS) approach.
The data were analyzed assuming a lognormal distribution using the full covariance
matrix. A linear variance model was implemented in the model.
4.2.6 Statistical analysis
For the fexofenadine pharmacokinetic study, a sample size of 8 subjects in each
group was chosen to enable detection of a 30% to 40% reduction in area under the plasma
concentration time curve (AUC) in patients with CF compared with healthy subjects ( =
0.05, 1 – β = 0.80). This degree of reduction is less than that previously demonstrated for
dicloxacillin and similar in magnitude to that of ticarcillin, ceftazidime, and trimethoprim
(Jusko, Mosovich et al. 1975; Leeder, Spino et al. 1984; de Groot, Hack et al. 1990;
Hutabarat, Unadkat et al. 1991). Differences in demographics/clinical characteristics and
fexofenadine pharmacokinetic parameters between groups (CF vs healthy volunteers)
were compared using the Mann-Whitney U test or chi-square where appropriate.
Differences in P-gp efflux activity between groups (CF vs healthy volunteers) or among
genotypes were compared using the t test or 1-way analysis of variance (ANOVA) where
appropriate. The significance level was assumed to be 0.05. Analyses were performed
using GraphPad Prism Version 4.0 (GraphPad Software, San Diego, California).

84

85
4.3 Results
In the first phase of the study, 25 participants were recruited, among whom 2
participants were excluded due to inadequate blood samples. The remaining 23
participants included 9 patients with CF and 14 healthy volunteers. The P-gp pumping
activity described by P-gp basal activity, R123-induced activity, and the difference
between load and efflux in CD4+ T cells and CD8+ T cells were calculated for the 2
groups and are presented in Figure 12. No significant differences between the 2 groups
were noted. The distribution of ABCB1 genotypes (e.g., C1236T, G2677T, and C3435T)
from the participants in both phases of the study was not significantly different between
the CF patients and the healthy volunteers. The ABCB1 3435 C/T carriers showed
significantly elevated basal activity (median [interquartile range] 53.6 [48.9-62.8]) in
peripheral blood CD4+ T cells compared with that in ABCB1 3435 C/C carriers (39.6
[29.8-49.1], P < .05). The R123-induced activity in peripheral blood CD4+ T cells was
61.3 (54.8-66.7) in ABCB1 3435 C/T carriers, which is significantly higher compared
with that in C/C carriers (42.3 [35.1-59.5], P < .05). The basal activity in peripheral blood
CD8+ T cells was also significantly higher in ABCB1 3435 C/T carriers (60.7 [55.9-
68.3]) compared with that in C/C carriers (46.3 [36.1-55.0], P < .05).A total of 16
subjects, including 8 CF patients and 8 healthy volunteers from the first phase of the
study, were recruited into the second phase. The demographic and clinical characteristics
of the 2 groups are summarized in Table 5. The healthy volunteers recruited were
significantly older than the CF patients, with median age of 31 versus 26 in CF patients
(P = .05); however, our inclusion criteria allowed for a range of 5 years because this
difference in age was not expected to result in clinically significant differences in renal
function. The lack of difference in glomerular filtration rate (GFR) was supported by the
estimated creatinine clearance using the Cockcroft-Gault equation (P = .65). The weight
and body mass index of the CF patients were significantly less (P = .002) than those of
the healthy volunteers, which likely reflects differences in nutritional status between the 2
groups. As a result of the differences in body size, the pharmacokinetic parameters were
normalized to body surface area. No significant between-group (CF vs healthy volunteers)
differences in GFR, as measured by iothalamate clearance, were noted (Table 5). In
addition, GFR was not significantly different between the fexofenadine-alone and
fexofenadine-plus-probenecid study days (median [interquartile] 113.8 [95.2-126.3]
mL/min/1.73 m2 vs 127.7 [95.7-142.5] mL/min/1.73 m2, respectively, P = .39).
Figure 12: P-gp efflux activity measured by rhodamine123 efflux using functional flow
cytometry in CD4+ T cells (a) and CD8+ T cells (b) for 9 cystic fibrosis (CF) patients
and 14 healthy volunteers (HV).

(a) (b)
The plasma concentration-time curves for fexofenadine (scatter plots and model-
fitted plots) on each study day for both groups are depicted in Figure 13. In both CF and

86
healthy volunteer groups, the plasma concentration-time curves on study days when
probenecid was administered were significantly higher than those when fexofenadine was
administered alone. No significant between-group differences in plasma concentration
time curves were noted on either of the 2 study days.
Table 5: Subjects characteristics in cystic fibrosis study
CF (n=8) HV (n=8) P
Males/Females 3/5 6/2 0.31
Age (years) 26 (25-28) 31 (29-38) 0.05
Height (cm) 163 (160-168) 174 (170-182) 0.04
Weight (kg) 54.5 (49.0-58.5) 80.1 (72.0-83.2) 0.003
BMI (kg/m
2
) 10.1 (18.8-21.5) 25.5 (24.5-26.2) 0.002
GFR (ml/min/1.73 m
2
) 113.8 (101.5-145.6) 105.8 (95.2-118.2) 0.65
(Data presented as median (interquartile range). CF, cystic fibrosis; HV, healthy
volunteer; BMI, body mass index; GFR, glomerular filtration rate.)

Figure 13: Fexofenadine plasma concentration-time profiles in 8 patients with (A) cystic
fibrosis and (B) 8 age-matched control subjects. Mean observed fexofenadine plasma
concentrations in fexofenadine-plus-probenecid and fexofenadine-alone treatment arms
are represented by triangles with standard deviation bars above and squares with standard
deviation bars below, respectively. The dotted lines and solid lines reflect the mean of the
model-fitted fexofenadine concentrations in fexofenadine-plus-probenecid and
fexofenadinealone treatment arms, respectively. Fexofenadine 180 mg and probenecid 1
g twice daily were administered to each participant.

87

88
A comparison of fexofenadine pharmacokinetic parameters between CF and
healthy volunteers on each of the 2 study days is summarized in Table 6. No between-
group differences in fexofenadine pharmacokinetic parameters were observed when
fexofenadine was administered alone or in combination with probenecid.
Table 6: Fexofenadine pharmacokinetics, cystic fibrosis vs healthy volunteers
Parameter CF HV P
FXF
Cmax (mg/L) 0.50 (0.32 – 0.63) 0.39 (0.30 – 0.65) 0.88
Tmax (h) 2.0 (2.0 – 3.0) 2.0 (1.4 – 3.0) 0.65
AUC
0-48h
(mg ·h/L) 2.36 (1.88 – 3.02) 2.05 (1.50 – 3.38) 0.65
Aeu
0-24h
(mg) 10.67 (8.25 – 16.16) 12.28 (8.21 – 18.25) 0.96
CL
T
/F (L/h/1.73m
2
) 81.16 (72.04 – 106.0) 73.27 (50.50 – 123.20) 0.72
CL
R
(L/h/1.73m
2
) 5.47 (3.90 – 6.32) 5.00 (4.28 – 6.27) 0.88
CL
R
/GFR 0.73 (0.50 – 0.96) 0.78 (0.67 – 1.56) 0.57
V
c
/F (L/1.73m
2
) 262.4 (123.8 – 451.4) 262.1 (210.1 – 401.6) 0.80
k
a
(h
-1
) 1.83 (0.43– 4.23) 3.89 (1.09 – 6.63) 0.20
FXF+PB
Cmax (mg/L) 0.70 (0.47 – 1.21) 0.51 (0.33 – 0.85) 0.38
Tmax (h) 2.0 (1.5 – 2.5) 2.0 (2.0 – 2.0) 0.65
AUC
0-48h
(mg ·h/L) 3.64 (2.81 – 5.62) 3.58 (2.44 – 6.51) 0.96
Aeu
0-24h
(mg) 5.31 (3.36 – 7.99) 6.43 (4.70 – 11.50) 0.51
CL
T
/F (L/h/1.73m
2
) 53.31 (34.02 – 69.09) 42.11 (26.88 – 80.78) 0.96
CL
R
(L/h/1.73m
2
) 1.69 (1.10 – 2.20) 1.31 (1.03 – 2.63) 1.00
CL
R
/GFR 0.23 (0.15 – 0.45) 0.18 (0.09 – 0.41) 0.72
V
c
/F (L/1.73m
2
) 142. 2(98.55 – 274.0) 255.1 (165.2 – 522.6) 0.16
k
a
(h
-1
) 1.25 (0.68 – 2.31) 1.87 (1.51 - 3.84) 0.19
(Data presented as median (interquartile range). FXF, fexofenadine; PB, probenecid;
GFR, glomerular filtration rate.)

The impact of probenecid on the pharmacokinetics of fexofenadine is summarized
in Table 7. The area under the plasma concentration-time curve of fexofenadine
(AUCFxF) was significantly higher when fexofenadine was coadministered with
probenecid versus when administrated alone (P = .01). The amount of fexofenadine

89
excreted unchanged in the urine, the total body clearance, the renal clearance, and the
ratio of renal clearance to GFR were all significantly lower when coadministered with
probenecid versus when administered with fexofenadine alone. No significant differences
were noted in other pharmacokinetic parameters, including the lag time, Cmax, the Tmax,
Vd, and the absorption rate constant between the 2 study days.
Table 7: Fexofenadine pharmacokinetics, effects of inhibitor
Parameter FXF FXF+PB P
Cmax (mg/L) 0.45 (0.29 – 0.64) 0.57 (0.44 – 1.10) 0.16
Tmax (h) 2.0 (1.8– 3.0) 2.0 (2.0 – 2.0) 0.57
AUC
0-48h
(mg ·h/L) 2.34 (1.59 – 3.16) 3.58 (2.81 – 5.62) 0.01
Aeu
0-24h
(mg) 10.67 (8.25 – 18.20) 6.02 (3.84 – 9.13) 0.006
CL
T
/F (L/h/1.73m
2
) 81.16 (61.10 – 112.50) 45.69 (33.14 – 69.09) 0.009
CL
R
(L/h/1.73m
2
) 5.21 (4.11 – 6.32) 1.56 (1.03 – 2.44) <0.0001
CL
R
/GFR 0.73 (0.50 – 0.96) 0.23 (0.12 – 0.50) 0.0002
V
c
/F (L/1.73m
2
) 262.1(187.9 – 427.1) 218.4 (118.2 – 380.6) 0.32
k
a
(h
-1
) 2.24 (0.59 – 5.53) 1.82 (1.17 – 2.67) 0.72
(Data presented as median (interquartile range). FXF, fexofenadine; PB, probenecid;
GFR, glomerular filtration rate.)

The impact of ABCB1 genetic polymorphisms on the pharmacokinetics of
fexofenadine was also examined by combining data from both study groups. The
normalized renal clearance of fexofenadine was not significantly different among
subjects with ABCB1 C1236T, G2677T, and C3435T genotypes; however, the median
fexofenadine AUC of 1.742 mg*h/L in ABCB1 3435 C/T carriers and 2.895 mg*h/L in
C/C carriers did reach statistical significance (one-tailed unpaired t-test).
4.4 Discussion
The main finding of this study was that the pharmacokinetics of fexofenadine, a
known P-gp substrate, were not altered in patients with CF when compared with age-

90
matched healthy volunteers. The renal clearance of fexofenadine was similar on both
groups, indicating that upregulation of renal P-gp is unlikely to explain the enhanced
clearance of certain antibiotics in patients with CF.
The lack of altered pharmacokinetics of fexofenadine in the present study was
consistent with studies conducted with other P-gp substrates in CF patients. Our group
previously demonstrated comparable absolute bioavailability and total body clearance of
azithromycin, a known P-gp substrate (Sugie, Asakura et al. 2004), between CF patients
and age-matched healthy volunteers (Beringer, Huynh et al. 2005). A recent comparative
pharmacokinetic study of levofloxacin demonstrated no significant difference in renal
clearance CF patients and age-matched healthy controls (Lee, Boyle et al. 2007). These
results were consistent with an animal study, which demonstrated that levofloxacin renal
clearance was unaffected by the administration of P-gp inhibitors (Yamaguchi, Yano et al.
2002), despite the fact that levofloxacin was shown to be a P-gp substrate in a kidney cell
line system (Ito, Yano et al. 1997). The similarity in the pharmacokinetics of
fexofenadine in the 2 groups in the present study was consistent with the findings from
the functional flow cytometry experiments, which demonstrated no significant
differences between CF patients and healthy volunteers.
The pharmacokinetic data of fexofenadine alone and in combination with
probenecid confirmed the results from prior studies. The fexofenadine renal clearance,
total clearance, total amount excreted in the urine, and peak concentration of
fexofenadine in healthy volunteers in our study were comparable to previously reported
values (Drescher, Schaeffeler et al. 2002). In our study, the fexofenadine AUC was

91
significantly increased, whereas the cumulative urinary excretion, total body clearance,
and renal clearance of fexofenadine were all markedly decreased when probenecid was
coadministrated. This result was in agreement with the previous study, which
demonstrated a 63% reduction in renal clearance of fexofenadine in the presence of
probenecid (Yasui-Furukori, Uno et al. 2005).
P-gp function is subject to the impact and regulation of many factors, including
disease status, concurrent drug administration, food, and genes. In this study, we
excluded subjects who took drugs that have known interference with P-gp function. We
controlled food intake of each participant on the study days to minimize its impact on P-
gp function. One potential confounding factor was the potential between-group
differences in cardiac output. Increased cardiac output is known to increase GFR in burn
patients. Cardiac output in 83 CF patients, determined by 2-dimensional echo Doppler,
demonstrated that cardiac index (3.7 L/min/m
2
) was slightly above the normal range
(Vizza, Sciomer et al. 2001). The mild increase in cardiac index suggests that this is
unlikely to account for the difference in renal clearance of certain drugs noted in prior
studies. Another possible confounding factor in comparing P-gp function between the 2
groups is the presence of ABCB1 polymorphisms. Hoffmeyer et al (Hoffmeyer, Burk et
al. 2000) reported that the digoxin plasma concentrations in ABCB1 3435 TT carriers
were significantly higher than in CC carriers (7 subjects in each group), indicating lower
P-gp activity associated with the ABCB1 TT genotype. These results were in contrast to
those of Kim et al (Kim, Leake et al. 2001), who evaluated ABCB1 gene polymorphisms
in 37 healthy European American and 23 healthy African American subjects and used

92
fexofenadine as a substrate to compare pharmacokinetic profiles between different
genetic variant groups. The study by Kim et al showed that the AUC of fexofenadine was
significantly lower in the ABCB1 3435 TT group (n = 12) than in the CC group (n = 6).
Our functional flow cytometry assay showed that the 2 P-gp-specific parameters (ie,
basal activity and R123-induced activity in CD4+ T cells) were significantly higher in
ABCB1 3435 C/T carriers than in C/C carriers. Similarly, in CD8+ T cells, the basal
activity was markedly higher in ABCB1 3435 C/T carriers than in C/C carriers. The
fexofenadine AUC in ABCB1 3435 C/T carriers was significantly lower than that in C/C
carriers, which further supports that P-gp activity was elevated in 3435 C/T carriers. As
the normalized renal clearance of fexofenadine did not show any difference between
ABCB1 3435 C/T and C/C, the decreased fexofenadine AUC in C/T carriers might be
attributed to elevated nonrenal P-gp activity.
In this study, the pharmacokinetic disposition of fexofenadine was found to be
similar in patients with CF and healthy volunteers. The renal clearance of fexofenadine
was not different between the 2 groups, indicating that renal P-gp activity was unlikely to
be the explanation for altered clearance of certain antibiotics reported in prior clinical
trials. Probenecid coadministration significantly increased fexofenadine AUC while
decreasing the cumulative urinary excretion, total body clearance, and renal clearance of
fexofenadine, confirming earlier observations. Genetic polymorphisms in ABCB1
C3435T were found to be associated with alterations in rhodamine 123 efflux from
peripheral blood T cells and reduced fexofenadine AUC.

93
4.5 Conclusion
In this study, the pharmacokinetic disposition of fexofenadine was found to be
similar in patients with CF and healthy volunteers. The renal clearance of fexofenadine
was not different between the 2 groups, indicating that renal P-gp activity is unlikely to
be the explanation for altered clearance of certain antibiotics reported in prior clinical
trials. Alternatively, CF studies performed in the past were in more severely sick patients
who might have low albumin and thus increased free fraction of substrate drugs. It is
likely that higher free faction of drugs can induce Pgp expression and increase total drug
clearance. In addition, more severely sick individuals are more likely to have
inflammatory cytokines, which has been shown to increase transporter expression
(Hartmann, Cheung et al. 2002). Probenecid coadministration significantly increased
fexofenadine AUC while decreasing the cumulative urinary excretion, total body
clearance, and renal clearance of fexofenadine, confirming earlier observations. Genetic
polymorphisms in ABCB1 C3435T were found to be associated with alterations in
rhodamine 123 efflux from peripheral blood T cells and reduced fexofenadine AUC.

94
Chapter 4 endnotes
4.1 Introduction
4.2 Methods
4.2.1 Subjects
4.2.2 Study Protocol
4.2.3 Analytical measurements
4.2.3.1 Determination of P-gp Efflux Activity by Flow Cytometry
4.2.3.2 Determination of Plasma and Urine Fexofenadine
4.2.3.3 Determination of Plasma and Urine Iothalamate Concentrations
4.2.4 DNA Isolation and Genotyping
4.2.5 Pharmacokinetics
4.2.6 Statistical Analysis
4.3 Results
4.4 Discussion
4.5 Conclusion

95
Chapter 5: Inhibition of MRP transporters and its impact on TFV-related
nephrotoxicity in HIV-infected patients

5.1 Introduction
Antiretroviral therapy has substantially improved survivals in patients with HIV
infection. However, many of these agents have been shown or indicated to cause acute or
chronic kidney disease. It has been well documented that long term usage of nucleotide
acid analogs, particularly adefovir (ADV) and cidofovir (CDV), is associated with renal
toxicity (Miller 2001). Similar to ADV and CDV, tenofovir disoproxil fumarate (TDF),
an oral prodrug of tenofovir (TFV), is also an acyclic nucleoside phosphonate with
similar antiviral activity. Kidney toxicity is a concern for individuals receiving TFV due
to the structure similarity among TFV, ADV and CDV. Direct renal tubular injury is the
hallmark toxicity of ADV and CDV (Miller 2001). In contrast, In vitro study showed that
tenofovir did not inhibit renal cell growth and epithelium integrity to the same extent as
ADV and CDV (Cihlar, Birkus et al. 2002), which was consistent with clinical studies
demonstrating a lack of renal insufficiency in patients treated with TFV combined with
lamivudine and efavirenz (Gallant, DeJesus et al. 2006). However, recent human clinical
studies have reported the association of TFV with renal insufficiency, particularly when it
was combined with ritonavir (RTV)-containing regimens (Rollot, Nazal et al. 2003;
Barrios, Garcia-Benayas et al. 2004).
Tenofovir is not a substrate of CYPs and is not extensively metabolized by liver.
Tenofovir clearance is mainly achieved through renal elimination, which is largely

96
composed of glomerular filtration, with 20%–30% elimination through tubular secretion.
Organic anion transporter (OAT)–1 expressed on the basolateral membrane of renal
tubular cells is considered to mediate the uptake of TFV from blood stream (Cihlar, Lin
et al. 1999; Cihlar, Ho et al. 2001). Like ADV, TFV may be effluxed by MRP2 out of the
apical membrane of renal tubule and into the lumen (Mallants, Van Oosterwyck et al.
2005). In addition, MRP4, another transporter located to the apical side of tubular
epithelial cells, was indicated to involve in the transmembrane trafficking of TFV
through an in vitro study utilizing MRP4 overexpressing adefovir-resistant lymphoblastic
leukemia cell lines (CEMr1) (Ray, Cihlar et al. 2006).
RTV is a proteinase inhibitor and commonly used as a booster agent in highly
active antiretroviral therapy (HARRT) regiment. In the meanwhile, recent data has shown
that RTV is a potent inhibitor of MRP2, with its potency 20-fold stronger compared to
cyclosporin(Gutmann, Fricker et al. 1999). Flaherty et al reported that coadministration
of TFV with 400/100 mg LPV/RTV given twice daily increases the area under the curve
of TFV (AUCTFV) by 32% and the trough concentration (Cmin) by 51% (Flaherty,
Kearney, et al. 2001). The accumulation of TFV seemed to be dose-dependent, supported
by the observation that Cmin of TFV increased by 520% and 760% with coadministration
of 133 mg and 166 mg RTV, respectively (Hillebrand, Burger, et al. 2004). Consistently,
clinical studies in HIV-infected patients have demonstrated reduced tenofovir renal
clearance among patients receiving a PI/r than among non–PI-treated patients (Kiser,
Carten et al. 2008), suggesting that impaired renal excretion leads to increased tenofovir
plasma concentrations. Changes of TFV pharmacokinetics as well as increased chance of

97
renal toxicity when TFV is co-administrated with RTV both indicate a possible drug-drug
interaction between TFV and RTV. Exploring the interaction between the
aforementioned two reagents will help to elucidate the mechanism of TFV-related
nephrotoxicity.
In this study, we will evaluate TFV pharmacokinetics and changes in renal
function for HIV-infected patients who were co-administrated with TDF and RTV. In
addition, we will conduct further mechanistic exploration to elucidate factors involved in
TFV-related nephrotoxicity. Safely use of these antiviral reagents in the management of
HIV patients needs a thorough understanding of risk factors that predispose to kidney
injury. Our research will greatly enrich current understanding of the TFV-related
nephrotoxicity and ultimately provide guidance for clinicians to optimize medical
management for these patients.
5.2. Methods
5.2.1 Study subjects (Goicoechea, Liu et al. 2008)
Subjects included in this analysis were identified from California Collaborative
Treatment Group (CCTG) 578, a prospective, randomized clinical trial of therapeutic
drug monitoring of antiretroviral therapy. In this study, patients were recruited from 5
HIV outpatient clinics in California on the basis of the need to initiate a new HIV
regimen and to improve medication adherence behaviors, as determined by the health
care provider and/or the patient’s self-description. Subjects were antiretroviral naive or
experienced and had baseline detectable plasma HIV-1 RNA loads regardless of whether
they were not currently receiving therapy or experiencing treatment failure with their

98
current regimen. All patients initiated a new protease inhibitor containing RTV (PI/r) or
non-nucleotide reverse transcriptase inhibitor (NNRTI)-based regimen at study entry.
Subjects were eligible for the present analysis if they had serum creatinine values
available at baseline and at week 48 of therapy. Because the renal effects of tenofovir
were hypothesized to be dependent on exposure, TDF-treated subjects were included if
they received continuous TDF treatment for at least 40 weeks; otherwise, all other
subjects receiving a PI/r or NNTRI-based therapy and not receiving TDF were analyzed.
The duration of antiretroviral therapy among the 3 treatment groups was comparable.
Appropriate written informed consent was obtained from all study participants.
5.2.2 Protocol of the clinical study
In CCTG 578, serum creatinine levels were routinely monitored (baseline and
weeks 2, 6, 12, 24, 32, 40, and 48) and measured at a central laboratory (Quest
Diagnostics). Estimates of renal function were based on creatinine values obtained at
weeks 24 and 48; however, if serum creatinine laboratory values were not available from
these time points, values at weeks 32 and 40 were used, respectively. For each participant,
blood samples for the measurement of plasma TFV concentration was collected at before,
2 and 4 h after a witnessed medication dose at study week 2. Peripheral blood was
collected from each participant and DNA sample was extracted for genotyping of single
nucleotide polymorphisms (SNPs) in selective drug transporters.

99
5.2.3 Protocol of the cellular experiments
5.2.3.1 Cell culture
Madin-Darby Canine Kidney II (MDCKII) and stably transfected MRP2 over-
expressing MDCKII (MDCKII-MRP2) cells were generously provided by Dr. Piet Borst
(The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital). Both MDCKII
and MDCKII-MRP2 were maintained in growth cultures using Dulbecco’s Modified
Eagle Medium (DMEM) supplemented with glucose 4.5 gm/L, 10% fetal bovine serum
(FBS), 1% nonessential amino acids, L-glutamine, 100 μg/mL of penicillin, and
streptomycin. The cells were incubated at 37ºC with 5% CO
2
and 95% air atmosphere.
Cells were grown to confluence, then passaged and harvested using Trypsin-EDTA 10x.
Over-expression of MRP2 in MDCKII-MRP2 cells was maintained by the addition of
G418 (GIBCO, CA, USA) to the growth medium and checked periodically using Western
blot analysis.
Human embryo kidney 293 (HEK 293), stably transfected MRP4 over-expressing
HEK293 cells (HEK293-463) and stably transfected MRP5 over-expressing HEK 239
cells (HEK293-5I) cells were generously provided by our collaborators. HEK293 wt,
HEK293-463 and HEK293-5I cells were maintained in growth cultures using Dulbecco’s
Modified Eagle Medium (DMEM) supplemented with glucose 4.5 gm/L, 10% fetal
bovine serum (FBS), 1% nonessential amino acids, L-glutamine, 100 μg/mL of penicillin,
and streptomycin. The cells were incubated at 37ºC with 5% CO
2
and 95% air
atmosphere. Cells were grown to confluence, then passaged and harvested using
Trypsin-EDTA 10x. Cells were checked periodically using fluorescent microscopy.

100
5.2.3.2 Intracellular TFV accumulation assays
Relationship between MRP2 function and intracellular accumulation of TFV was
investigated by utilizing MDCKII and HEK293 cell lines treated with TFV alone or in
combination with RTV or MK571. Surviving cells were harvested and lysed, and the
intracellular concentrations of TFV were determined using Liquid chromatography
coupled to a tandem mass spectrometry (LC/MS/MS).
In details, MDCKII wt and MDCKII-MRP2 cells were grown to form a confluent,
polarized monolayer, and then were treated for 4 hours with TFV 10uM alone, TFV
10uM in combination with RTV 20uM and MK571 20uM respectively. There are
triplicate for each treatment, of which, 2 flasks of cells were scalped after treatment and
were further processed for TFV quantifications, while the remaining one flask of cells
were trypsinized for cell counting.
In order to investigate whether MRP4 or MRP5 also involved in the drug-drug
interaction between TFV and RTV, HEK293 wt, HEK293-463 and HEK293-5I were
used in the experiment as well. In details, HEK293 wt, HEK293-463 and HEK293-5I
cells ere grown to form a confluent, polarized monolayer, and then were treated for 4
hours with TFV 10uM alone, TFV 10uM in combination with RTV 20uM and MK571
20uM respectively. There are triplicate for each treatment, of which, 2 flasks of cells
were scalped after treatment and were further processed for TFV quantifications, while
the remaining one flask of cells were trypsinized for cell counting.

101
5.2.4 Analytical measurements
5.2.4.1 Creatine clearance
Rates of creatinine clearance (CrCl) were estimated using the unabbreviated
Modification of Diet in Renal Disease (MDRD) equation (equation 7) and the Cockcroft-
Gault (C-G) equation which is further adjusted by body-surface area (BSA).
5.2.4.2 Determination of plasma TFV concentrations
Plasma concentrations of TFV were determined by a validated method utilizing
LC/MS/MS. To prepare samples, 100 µL of plasma sample was mixed with 100 µL of
500 ng/mL deoxyadenosine monophosphate (dAMP) and 100 µL of 500 ng/mL adefovir
both of which served as internal standards. An additional 800 μL of methanol was added
to precipitate the proteins. The entire mixture was vortexed and centrifuged for 5 minutes
at 14,000 rpm. The supernatant was transferred into a clean Eppendorf tube and
evaporated to dryness using a steady stream of compressed filtered air at room
temperature. The dried sample was reconstituted with 100 µL of 5% methanol mixed
with 95% deionized water (V/V) and vortexed to completely dissolve the residues. The
reconstituted samples are then centrifuged at 14,000 rpm for 5 minutes to separate
precipitate. The supernatant was then transferred into HPLC vials, and 30 mL of samples
were injected for analysis. Calibration standards were prepared in the exact same
procedure as plasma sample, except 100 mL of tenofovir standard solution dissolved in
methanol was added into blank human plasma. The calibration standards were processed
in parallel to the plasma samples.

102
To determine the plasma concentration of TFV, a LCMS consists of an Agilent
1100 (Santa Clara, CA) high performance liquid chromatography (HPLC) system
coupled onto a triple quadruple mass spectrometer (Sciex API 3000, Foster City, CA)
was utilized. A reversed phase Advanced Chromatography Technologies (ACE) C18
column with the following dimensions, 3.0 X 50 mm and 3 μm packing was used to
separate the analytes. The mobile phase consisted of 5.0 % of methanol and 95% of 20
mM ammonium acetate (V/V) adjusted to pH 4.5 with acetic acid, where the flow-rate
was set at 0.3 mL/min. The retention times of tenofovir, adefovir and dAMP were 4.0,
2.6 and 3.1 minutes, respectively. The mass spectrometer was operated in positive mode
and utilized turbo ion spray ionization source. The mass transitions were 288 →176,
274→162 and 332 →156 for tenofovir, adefovir and dAMP, respectively.
The calibration curve range for TFV in human plasma was 10-1000 ng/mL, with a
lower limit of quantification of 10 ng/ml. The assay was linear over this range (R
2
>0.99)
and demonstrated excellent inter-day accuracy and precision.
5.2.4.3 Genotyping
Genotyping for SNPs in selected transporters were performed using Illumina
Golden Gate SNP Genotyping Arrays in Norris Cancer Center of University of Southern
California.
5.2.4.4 Determination of intracellular TFV accumulation
To process cells for intracellular TFV quantification, scalped cells from each flask
were centrifuged for10 minutes at 1500rpm at 4
o
C and extracted with 3mL methanol for
30 minutes. Cell lysates were centrifuged for 10 minutes at 4500rpm at 4
o
C and

103
supernatant was aspirated and evaporated to dryness under steady stream of dry air.
Cellular lysate samples and calibration samples were subsequently processed via solid
phase extraction procedure utilizing a weak anion exchange cartridge (WAX Oasis®,
Waters Co., MA, USA) to separate TFV from TFV-MP and TFV-DP. Extracted samples
were evaporated to dryness under steady stream of dry air, reconstituted with 100uL 8%
methanol and centrifuged for 5 minutes at 13,000 rpm at 4
o
C to remove debris. An
aliquot of 20uL was injected into LC/MS/MS.
Tenofovir and its internal standard adefovir will be separated on an ACE C18
column 2*50 mm, 5 μm packing (Advanced Chromatography Technologies) and detected
by the API 3+ triple quadruple tandem mass spectrometer under the positive ion mode
using a turbo ion spray ionization source. The mobile phase consists 8% of methanol and
92% of 20 mM ammonia acetate (pH 4.5). The flow rate is 0.3 mL/min. The mass
transitions are 288 →176 and 274 →162 and the retention times are 4.0, and 2.5 minutes
for tenofovir and adefovir, respectively. The total run time is 5.0 minutes.
The calibration curve range for TFV in cellular lysate was 10-1000 ng/mL, with a
lower limit of quantification of 10 ng/ml. The assay was linear over this range (R
2
>0.99)
and demonstrated excellent inter-day accuracy and precision.
5.2.5 Pharmacokinetic modeling
The PK profile of tenofovir was determined using model independent and
compartmental methods. The areas under the plasma/serum concentration time curves
were determined by the linear trapezoidal rule. A one-compartment model with first order
absorption incorporating weight and creatinine clearance as covariance’s for volume of

104
distribution and elimination constant respectively was applied to the observed plasma
concentrations of tenofovir samples using ADAPT II software (release 9, Biomedical
Simulations Resource, University of Southern California, Los Angeles). Analysis was
performed using the standard two-stage (STS) and the maximum-likelihood expectation-
maximization (MLEM) approach. The data were analyzed assuming a lognormal
distribution utilizing the diagonal covariance matrix.
PK data from other studies with abundant sampling times was implemented to
improve the accuracy of the modeling. In detail, concentration-time data of 46 patients
from the CCTG578 TFV+RTV group was mixed with the data of 12 patients from
CCFTG 585 where patients were treated with TFV 300mg qd in combination with
LPV/RTV 800/200mg qd in 3 different formulas and each patient had 7 sampling times
within the range of 0-24 hour after dosing; Similarly, concentration-time data of 28
patients from the CCTG578 TFV+NNRTI group was mixed with the data of 7 patients
from CCFTG 584 where patients were treated with TFV 300mg qd alone and each patient
had 7 sampling times within the range of 0-24 hour after dosing.
5.2.6 Statistical analysis
The statistical analysis regarding change in CrCL over time was performed in
UCSD (Goicoechea, Liu et al. 2008). In brief, linear mixed-effects models were used to
study the relationship between HIV treatment regimen and CrCl over multiple time points
(at baseline, week 24, and week 48). Student’s t tests were used for comparisons of
change in CrCl between treatment groups at week 24 and 48. Statistical analysis was
performed using R (version 2.4.1).

105
Differences in demographics/clinical characteristics and TFV pharmacokinetic
parameters between groups were compared using Mann-Whitney t test, unpaired t test or
κ-square where appropriate. The significance level was assumed to be 0.05. Analyses
were performed using GraphPad Prism version 4.0 for Windows (GraphPad Software,
San Diego, CA).
5.3 Results
5.3.1 Patient characteristics (Goicoechea, Liu et al. 2008)
One-hundred ninety-nine patients participated in the parent study, of whom 48
who did not initiate or discontinued TDF before week 48, 3 who received TDF without a
PI/r or an NNRTI, and 2 who received TDF with a baseline CrCl rate less than 50
mL/min were excluded from this analysis. Of the remaining 146 patients, 51 received a
TDF+PI/r regiment, 29 received a TDF+NNRTI regiment, and 66 received a non–TDF
containing HIV treatment regimen. Of note, 4 patients treated with TDF and both an
NNRTI and PI/r were assigned to the PI/r group. No significant differences were noted
between treatment groups with respect to age, sex, race, or baseline CD4 T cell count.
Patients receiving TDF+NNRTI regiment were more often treatment naive and had
higher baseline HIV-1 RNA loads than did patients receiving a PI/r. The mean baseline
renal function was within the normal range and was similar between treatment groups.
Most patients in the TDF+PI/r group received lopinavir-ritonavir (75%), whereas
efavirenz (79%) was most commonly used in the TDF+NNRTI group. In the non-TDF
group, approximately half received a PI/r, and the rest received an NNRTI.

106
5.3.2 Changes in renal function (Goicoechea, Liu et al. 2008)
In univariate analysis, decreases in C-G estimates of CrCl were not significantly
different among the 3 groups during the first 24 weeks of therapy (mean [SE], -7.75 [2.2]
mL/min for TDF+PI group, -4.46 [3.1] mL/min for TDF+NNRTI group, and -1.14 [2.8]
mL/min for non-TDF group) (Figure 14). After week 48, the TDF+NNRTI and non-TDF
groups continued to have similar changes in renal function from baseline (mean [SE], -
6.24 [2.6] mL/min and -6.02 [3.5] mL/min, respectively [P = 0 .96]). However,
TDF+PI/r-treated patients had greater week 48 declines in CrCl than did patients
receiving a TDF+NNRTI-based regimen (mean [SE], -13.9 [2.4] mL/min and -6.24 [2.6]
mL/min, respectively [P = 0 .04]). In mixed-effects analysis, patients receiving TDF+PI/r
had an increased rate of decline in CrCl compared with the TDF+NNRTI group over 48
weeks (mean [SE], 7.66 [3.6] mL/ min/year [P = 0 .03]).
5.3.3 TFV Pharmacokinetics
Patients available for Pharmacokinetic (PK) modeling were as following: 7
patients from CCFTG 584 who has been treated with TFV 300mg qd alone; 12 patients
from CCTG 585 who has been treated with TFV 300mg qd in combination with
LPV/RTV 800/200mg qd in 3 different formulas; 46 patients from CCTG578 who has
been treated with TFV 300mg qd in combination with LPV/RTV 400/100mg Bid and 28
patients from CCTG578 who have been treated with TFV 300mg qd in combination with
NNRTI.

Figure 14: Regimen type and change in renal function. The solid line with white circles
represents individuals receiving a non–TDF based regimen, the dashed line with black
circles represents individuals receiving a TDF plus PI/r based regimen, and the solid line
with white triangles represents individuals receiving a TDF plus NNRTI based regimen.

The median (interquartile) elimination constant (Kel) were 0.0544 (0.0506-
0.0604), 0.0485 (0.0439-0.0588), 0.0618 (0.04965-0.0697) and 0.053 (0.0429-0.0646) h
-1

for CCTG584, CCTG585, CCTG578 TFV+NNRTI and CCTG578 TFV+RTV groups
(P< 0.05, using ANOVA test, in which, the P value between CCTG585 and CCTG 578
TFV+NNRTI was less than 0.05 two-tailed, and the P value between CCTG578
TFV+RTV and TFV+NNRTI was 0.05 one-tailed ); The median (interquartile) Half-life
(T1/2) were 12.73(11.47-13.69), 14.3 (11.8-15.79), 11.22 (9.94-13.95) and 13.1 (10.74-

107

108
16.18) h
-1
for CCTG584, CCTG585, CCTG578 TFV+NNRTI and CCTG578 TFV+RTV
groups (P< 0.05, using ANOVA test, in which, the P value between CCTG585 and
CCTG 578 TFV+NNRTI was less than 0.05 two-tailed, and the P value between
CCTG578 TFV+RTV and TFV+NNRTI was 0.049 one-tailed.) The volume of
distribution (Vd/F), TFV total clearance (CLtotal/F) normalized by body surface area
(BSA), Area under curve (AUC/F) normalized by BSA and Creatine clearance
normalized by BSA were not statistically significantly different among each study group.
PK comparisons are presented in the table 8. Plots of PK parameters in each study group
are presented in Figure 15.
Table 8: TFV pharmacokinetics in study groups from CCTG584, CCTG585 and
CCTG578.

CCTG584 CCTG585
CCTG578
TFV+NNRTI
CCTG578
TFV+RTV
P
Kel (h
-1
)
0.0544
(0.0506-0.0604)
0.0485
(0.0439-0.0588)
0.0618
(0.04965-
0.0697)
0.05295
(0.04285-
0.06455)
<0.05
T1/2 (hr)
12.73
(11.47-13.69)
14.3
(11.8-15.79)
11.22
(9.942-13.95)
13.1
(10.74-16.18)
<0.05
Vd/F(L)
563.4
(401-663.3)
824.3
(581-1131)
752.2
(579.3-967.9)
788.5
(594.4-964)
NS
CL total/F
(L/h/1.73m
2
)
29.11
(20.02-32.95)
38.3
(22.93-56.47)
39.54
(24.14-59.76)
39.34
(28.5-51.57)
NS
AUC/F
(mg/L*h/1.73
m
2
)
4.659
(4.116-6.772)
3.54
(2.401-5.914)
3.438
(2.269-5.627)
3.447
(2.629-4.798)
NS
CrCL
(L/h/1.73m
2
)
6.732
(5.425-7.773)
5.637
(4.345-6.655)
6.47
(5.217-7.514)
6.81
(5.527-7.89)
NS

Figure 15: Comparisons of TFV Kel and T1/2 among CCTG study groups

109

5.3.4 Intracellular TFV accumulation in MDCK and HEK cells
Intracellular TFV concentration was 2.98 fMole/10
6
cells in MDCKII cells treated
with 10 µM TFV alone, 6.34 fMole/106
c
ells and 3.99 fMole/10
6
cells in MDCKII cells
treated with 10 µM TFV in combination with 20 µM RTV and 20 µM MK571
respectively, which were 2.13 times (P=0.03 compared to cells treated with TFV alone)
and 1.34 times (P=0.14 compared to cells treated with TFV alone) respectively of the
TFV concentration in MDCKII cells treated with TFV alone.
In MDCKII-MRP2 cell lines, intracellular TFV concentration was 1.83
fMole/10
6
cells in cells treated with 10µM TFV alone, 5.49 fMole/10
6
cells and 3.94
fMole/10
6
cells in cells treated with 10µM TFV in combination with 20uM RTV and
20µM MK571 respectively, which were 2.97 times (P=0.001 compared to cells treated
with TFV alone) and 2.13 times (P=0.01 compared to cells treated with TFV alone)
respectively of the TFV concentration in MDCKII-MRP2 cells treated with TFV alone.
584 585 578-NNRTI578-RTV
0.00
0.05
0.10
0.15
* P<0.05 two-tailed
*
**
** P=0.05 one-tailed
Elimination constant Kel
(h-1)
30
*
20
10
0
584 585 578-NNRTI578-RTV
* P<0.05 two-tailed
**
** P=0.049 one-tailed
T1/2 (hr)

110
In HEK293-wt cell lines, intracellular TFV concentration was 94.01
fMole/10
6
cells in cells treated with 10µM TFV alone, 111.76 fMole/10
6
cells and 174.01
fMole/10
6
cells in cells treated with 10 µM TFV in combination with 20 µM RTV and 20
µM MK571 respectively, which were 1.19 times (P=0.39 compared to cells treated with
TFV alone) and 1.85 times (P=0.16 compared to cells treated with TFV alone)
respectively of the TFV concentration in MDCKII-MRP2 cells treated with TFV alone.
In HEK293-463 cell lines, intracellular TFV concentration was 16.99
fMole/10
6
cells in cells treated with 10µM TFV alone, 19.80 fMole/10
6
cells and 95.50
fMole/10
6
cells in cells treated with 10µM TFV in combination with 20µM RTV and
20µM MK571 respectively, which were 1.17 times (P=0.27 compared to cells treated
with TFV alone) and 5.62 times (P=0.001 compared to cells treated with TFV alone)
respectively of the TFV concentration in MDCKII-MRP2 cells treated with TFV alone.
In HEK293-5I cell lines, intracellular TFV concentration was 42.14
fMole/10
6
cells in cells treated with 10µM TFV alone, 42.65 fMole/10
6
cells and 84.07
fMole/10
6
cells in cells treated with 10µM TFV in combination with 20µM RTV and
20µM MK571 respectively, which were 1.01 times (P=0.86) and 2.00 times (P=0.01)
respectively of the TFV concentration in MDCKII-MRP2 cells treated with TFV alone.
These results are presented in figure 16.

Figure 16: Intracellular TFV accumulation in MDCKII, MDCKII-MRP2 (A and B),
HEK293 wt, HEK293-463 and HEK293-5I (C and D) after exposure to TFV 10 µM
alone and in combination with RTV 20 µM or MK571 20 µM.
MDCK-WT MDCK-MRP2
0
1
2
3
4
5
6
7
TFV 10uM
TFV 10uM+RTV 20uM
TFV 10uM+MK571 20uM
(A)
TFV concentration
(fMole/10
6
cells)
MDCK-WT MDCK-MRP2
0
1
2
3
TFV 10uM
TFV 10uM+RTV 20uM
TFV 10uM+MK571 20uM
(B)
Folder change of intracellular TFV
concentration
HEK-w t HEK-463 HEK-5I
0
25
50
75
100
125
150
175
200
225
TFV 10uM
TFV 10uM + RTV 20uM
TFV 10uM + MK571 20uM

111
(C)
TFV concentration
(fMole/10
6
cells)
HEK-w t HEK-463 HEK-5I
0
1
2
3
4
5
6
TFV 10uM
TFV 10uM + RTV 20uM
TFV 10uM + MK571 20uM
(D)
Folder changes of intracellular TFV
accumulation

5.3.5 SNPs in MRP2 and MRP4 and their impacts on TFV pharmacokinetics
Four SNPs have been evaluated for their impact on TFV pharmacokinetics. These
four targeted SNPs include ABCC4 exon 5 559G →T, ABCC4 3'UTR 4131 T →G,
ABCC2 exon 10 1249 G →A and ABCC2 exon 28 3972 C→T.
For ABCC4 G559T SNP, there were 4 GT carriers and 20 GG carriers in the
TFV-NNRTI treatment group. The Creatine Clearance normalized by BSA was 128.3 ±
35.94 and 100.3 ± 21.19 mL/min/1.73m
2
for GT and GG carriers respectively, and the P

112
value was 0.048 using one-way Mann-Whitney analysis. Among the 4 GT variants, the
TFV total clearance was 70.52 ± 16.89 L/hr/1.73m
2
, which was significantly higher than
that in GG carriers (39.07 ± 19.54 L/hr/1.73m
2
, P=0.022). Similarly, TFV AUC in GT
variants was significantly lower compared to that in GG carriers (P=0.022).
For ABCC4 3'UTR 4131 T →G SNP, there were 7 TT, 10 TG and 4 GG variants
in the TFV-RTV treatment group. For TT, TG and GG carriers, the creatine clearance
normalized by BSA was 104.7 ± 34.49, 115.9 ± 27.50 and 108.3 ± 23.76 mL/min/1.73m
2

respectively, which was not significantly different from each other, while by contrast, the
TFV total clearance were 30.33 ± 14.78, 45.46 ± 22.67 and 78.50 ± 2.06 L/hr/1.73m
2

respectively, which was significantly different (P=0.022, and the difference between TT
and GG carriers was less than 0.05). Similarly, TFV AUC was markedly different among
the three groups, in which difference in AUC between TT and GG carriers was
significant (P<0.05). Comparisons in TFV pharmacokinetics between or among different
SNP carrier groups are presented in Table 9-12.
Table 9: TFV Pharmacokinetics for different genotypes in ABCC4 559G→T SNP
ABCC4 Exon 5
559G→T
AB (GT) BB (GG) P
TFV+ NNRTI
N 4 20
CrCL(mL/min/1.73m
2
) 128.3 ± 35.94 100.3 ± 21.19 0.096/0.048
Kel (hr
-1
) 0.068 ± 0.009 0.057 ± 0.014 0.152
Vd/F (L) 1058 ± 190.1 727.5 ± 223.8 0.018
CLtotal/F(L/hr/1.73m
2
) 70.52 ± 16.89 39.07 ± 19.54 0.022
AUC/F (hr* mg/L) 2.006 ± 0.463 4.358 ± 2.090 0.022
TFV+RTV
N 3 37
CrCL(mL/min/1.73m
2
) 129.3 ± 42.77 112.8 ± 27.57 0.356

113
Table 9, Continued
Kel (hr
-1
) 0.059 ± 0.015 0.056 ± 0.021 0.608
Vd/F (L) 815.9 ± 312.7 803.9 ± 254.9 0.959
CLtotal/F(L/hr/1.73m
2
) 41.89 ± 17.69 44.63 ± 25.19 0.772
AUC/F (hr* mg/L) 3.841 ± 2.143 3.986 ± 2.459 0.772

Table 10: TFV Pharmacokinetics for different genotypes in ABCC2 1249G→A SNP
ABCC2 Exon 10
1249 G→A
AB (GA) BB (GG) P
TFV+ NNRTI
N 8 16
CrCL(mL/min/1.73m
2
) 112.5 ± 31.81 101.2 ± 21.98 0.561
Kel (hr
-1
) 0.059 ± 0.015 0.058 ± 0.014 0.887
Vd/F (L) 882.2 ± 285.8 732.8 ± 221.1 0.171
CLtotal/F(L/hr/1.73m
2
) 53.43 ± 27.39 39.75 ± 18.55 0.161/0.080
AUC/F (hr* mg/L) 3.446 ± 2.210 4.226 ± 2.077 0.404
TFV+RTV
N 9 30
CrCL(mL/min/1.73m
2
) 117.9 ± 34.73 113.0 ± 27.34 0.520
Kel (hr
-1
) 0.056 ± 0.017 0.056 ± 0.022 0.582
Vd/F (L) 810.0 ± 283.5 804.7 ± 255.3 0.934
CLtotal/F(L/hr/1.73m
2
) 44.16 ± 14.52 44.25 ± 27.12 0.441
AUC/F (hr* mg/L) 3.442 ± 1.422 4.162 ± 2.636 0.441

Table 11: TFV Pharmacokinetics for different genotypes in ABCC2 3972 C→T SNP
ABCC2 Exon 28
3972 C→T
AA (TT) AB (CT) BB (CC) P
TFV+ NNRTI
N 4 11 9
CrCL(mL/min/1.73m
2
) 112.3 ± 16.38 101.8 ± 19.87 105.6 ± 35.22 0.649
Kel (hr
-1
) 0.064 ± 0.013 0.058 ± 0.014 0.057 ± 0.015 0.648
Vd/F (L) 723.2 ± 280.2 779.6 ± 201.4 812.6 ± 308.4 0.781
CLtotal/F(L/hr/1.73m
2
) 40.71 ± 25.19 42.98 ± 15.70 47.54 ± 29.32 0.927
AUC/F (hr* mg/L) 4.105 ± 1.729 3.775 ± 2.076 4.139 ± 2.483 0.927
TFV+RTV
N 4 17 16

114
Table 11, Continued
CrCL(mL/min/1.73m
2
) 111.4 ± 31.54 114.7 ± 27.04 114.0 ± 30.75 0.994
Kel (hr
-1
) 0.054 ± 0.014 0.057 ± 0.026 0.057 ± 0.017 0.957
Vd/F (L) 702.1 ± 235.7 712.6 ± 201.1 909.0 ± 271.6 0.083
CLtotal/F(L/hr/1.73m
2
) 38.64 ± 11.44 42.59 ± 30.24 47.16 ± 21.63 0.563
AUC/F (hr* mg/L) 3.872 ± 1.629 4.684 ± 3.284 3.400 ± 1.428 0.563

Table 12: TFV Pharmacokinetics for different genotypes in ABCC4 4131 T→G SNP
ABCC4 3'UTR
4131 T→G
AA (TT) AB (TG) BB (GG) P
TFV+ NNRTI
N 7 10 4
CrCL(mL/min/1.73m
2
) 113.1 ± 38.68 99.35 ± 19.22 109.3 ± 20.44 0.572
Kel (hr
-1
) 0.061 ± 0.017 0.057 ± 0.012 0.062 ± 0.016 0.779
Vd/F (L) 840.6 ± 229.7 784.6 ± 250.0 711.6 ± 129.1 0.611
CLtotal/F(L/hr/1.73m
2
) 49.87 ± 26.72 44.87 ± 20.95 37.64 ± 11.53 0.818
AUC/F (hr* mg/L) 3.459 ± 1.721 3.883 ± 2.205 3.863 ± 1.174 0.818
TFV+RTV
N 11 18 4
CrCL(mL/min/1.73m
2
) 104.7 ± 34.49 115.9 ± 27.50 108.3 ± 23.76 0.591
Kel (hr
-1
) 0.048 ± 0.014 0.057 ± 0.016 0.069 ± 0.051 0.471
Vd/F (L) 671.8 ± 209.9 872.0 ± 298.7 801.9 ± 170.2 0.260
CLtotal/F(L/hr/1.73m
2
) 30.33 ± 14.78 45.46 ± 22.67 78.50 ± 2.06 0.022
AUC/F (hr* mg/L) 5.838 ± 3.473 3.571 ± 1.471 2.207 ± 1.10 0.022

5.4 Discussion
In the present longitudinal study with 146 participating subjects, patients
receiving concurrent TDF+PI/r treatment had greater reductions in CrCl than did patients
taking TDF-NNRTI or non–TDF based regimens. In addition, the elimination constant
(Kel) was significantly lower in TDF-PI/r treatment group compared to that in patients
receiving TDF-NNRTI regiment (P=0.05). Cellular experiments utilizing MRP2
overexpressing MDCK cells demonstrated a 2.97 fold increase of intracellular TFV

115
accumulation when cells were treated with TFV and RTV compared to those treated with
TFV only. We also identified novel correlations between ABCC4 559TT and ABCC4
4131GG mutants and increased TFV renal clearance, indicating an elevated efflux
function of these variants.
The observation of greater reductions in CrCL among patients receiving TDF-PI/r
coadministration compared to patients given TDF-NNRTI regiment was consistent with
previously published findings that approximately 70% of the reported cases of tenofovir-
induced nephrotoxicity were associated with concomitant low-dose ritonavir treatment.
For example, Rollot et al reported a case where a HIV-infected patient developed Fanconi
syndrome–type tubulopathy with nephrogenic diabetes insipidus and systemic
accumulation of didanosine after receiving TDF and PI/r treatment and they proposed
that the renal toxicity was caused by interaction of RTV on renal transporters (Rollot,
Nazal et al. 2003). Barrios et al reviewed 22 cases of tenofovir-related nephrotoxicity,
and suggested risk factors for developing TDF related renal toxicity, which included
concomitant use of PI/r (Barrios, Garcia-Benayas et al. 2004). In contrast, a placebo-
controlled phase III trial showed that TDF alone had a favorable safety profile (Schooley,
Ruane et al. 2002) Cihlar et al demonstrated that tenofovir alone exhibited low
cytotoxicity in various human cell types including renal tubular cells, compared to other
nucleoside reverse transcriptase inhibitors (Cihlar, Birkus et al. 2002). According to the
package insert provided by Gilead, the manufacturer of TDF, only supratherapeutic doses
of TDF administrated alone might cause renal toxicity in animals.

116
As TFV is not extensively metabolized in liver, systemic elimination of TFV is
mainly achieved through renal clearance. Our data demonstrated a significant reduction
in Kel and marked elongation of half-time of TFV in patients receiving TDF-PI/r
suggesting that coadiminstration of TDF and RTV might decrease TFV renal clearance.
This data again was consistent with previous findings where elevated TFV systemic
exposure was indicated in patients receiving coadministration of TDF and PI/r. Kearney
et al reported a drug-drug interaction study recruiting healthy volunteers as study subjects
and they found that TFV AUC, Cmax and Ctau were 32%, 15%, and 51% higher,
respectively, when TDF was coadministered with LPV/r (n = 24) (Kearney, Mathias et al.
2006). Similarly, Flaherty et al. had reported that TFV
Cmin
and TFV
AUC
increased by 51%
and 32% when co-administered with LPV/r 400/100 mg BID (Flaherty, Kearney et al.
2001). Our data was also similar to the finding by Kiser et al. demonstrating that
tenofovir renal clearance was 17.5% slower (P=0.04) in 30 subjects taking
lopinavir/ritonavir versus those not taking a protease inhibitor (Kiser, Carten et al. 2008).
In our study, CrCL was comparable in patients among different regiment groups
at study week 2 when TFV plasma samples were collected, therefore TFV elimination
through glomerular filtration should be comparable across different treatment groups as
well. It is thus easily foreseen that the difference in TFV clearance was a result of
variations in renal tubular secretion of TFV among patients receiving different antiviral
regiments. Renal tubular secretion of TFV is composed of the uptake process mediated
mainly by OAT-1 transporter located to the basolateral membrane of renal tubules (Cihlar,
Lin et al. 1999; Cihlar, Ho et al. 2001) and the efflux process where MRP2 and MRP4

117
expressed on the apical membrane have been shown to play roles in transmembrane
trafficking of TFV (Mallants, Van Oosterwyck et al. 2005; Ray, Cihlar et al. 2006;
Imaoka, Kusuhara et al. 2007). TDF, the prodrug is a substrate of P-gp, but TFV is not
(Ray, Cihlar et al. 2006). Since TFV is a substrate of multiple drug transporters, it is
reasonable to propose that altered tubular secretion or renal clearance of TFV when it is
co-administrated with RTV is due to transporter-involved drug-drug interactions between
TFV and RTV.
RTV has been shown to have various effects on the transporters involved in TFV
renal tubular secretion. Ray et al conducted an in vitro experiment demonstrating that
neither lopinavir nor ritonavir inhibited hOAT1, and that only at concentrations three
times of the average C
max
, they could inhibit human organic anion transporter 3, which
might contribute to TFV influx at a minor extent (Ray, Cihlar et al. 2006) (Ray 2006).
Therefore, TFV influx into renal tubular mediated by OAT1 transporter should not differ
significantly with coadministration of RTV regiment. RTV has been shown to inhibit
function of P-gp. Van Hesswijik et al reported that single-dose ritonavir and
lopinavir/ritonavir increased the AUC of fexofenadine, a substrate of P-gp, by 2.2- and
4.0-fold, respectively (P <0 .02) (van Heeswijk, Bourbeau et al. 2006). As TDF, not TFV
is the substrate of P-gp, the inhibition of RTV on P-gp expressed on GI tract might
increase oral absorption of TDF, leading to higher TFV systemic exposure and greater
AUC of TFV, but this interaction could not explain the reduced TFV renal clearance and
lower TFV kel in patients receiving TDF and PI/r together. It has been well documented
that RTV might be a potent inhibitor on MRP2 transporter (Miller 2001). Gutmann et al

118
used renal proximal tubules from killifish to study the impact of RTV on renal
transporters and found that luminal accumulation of fluorescein methotrexate, a substrate
of MRP2 was decrease with RTV administration, and they also concluded that the
potency of RTV inhibition was 20 fold stronger than cyclosporin (Gutmann, Fricker et al.
1999). However, there are still controversies regarding the effect of RTV on MRP4
transporters. An in vitro study suggested that ritonavir, atazanavir, and lopinavir do not
inhibit MRP4 in MRP4 overexpressing cells (Ray et al. 2006).
Given limited data that has suggested various impacts of RTV on the transporters
involved in TFV renal secretion, mechanistic exploration of TFV related nephrotoxicity
relies on a thorough understanding of how RTV interacts with these transporters,
especially the MRP transporters. In order to fulfill this purpose, we conducted a cellular
experiment to identify which MRP transporter(s) was/were involved in the drug-drug
interaction between TFV and RTV. We utilized MCDKII-MRP2 overexpressing cells,
HEK293-MRP4 overexpressing cells, HEK293-MRP5 overexpressing cells, as well as
the wild type counterparts to study the intracellular TFV accumulation after
coadministration of RTV or MK571, the latter is a known non-selective inhibitor on MRP
transporters. Our data showed that in MDCKII-MRP2 overexpressing cells, the
intracellular TFV accumulation after treatment with TFV 10uM alone was only 61.4% of
that in the wild type, indicating that MRP2 involved in TFV efflux out of MDCKII cells.
Similarly, in HEK293-MRP4 overexpressing cells and HEK293-MRP5 overexpressing
cells, the intracellular TFV accumulations were 18.1% and 44.8% respectively of that in
HEK293 wild type cells, suggesting that MRP4 and MRP5 were both important in the

119
efflux transport of TFV out of HEK293 cells. Our data also showed that compared to
MDCKII-MRP2 overexpressing cells treated with TFV 10uM alone, there were 2.97 and
2.13 fold increases in TFV accumulation when these cells were co-treated with TFV10
uM and RTV 20 uM or MK571 20 uM respectively. The fold changes of TFV
accumumlation in MDCKII-MRP2 cells were greater than those in MDCKII-wild type
cells. This finding indicated that RTV and MK571 were both potent inhibitor of MRP2
transporters, with RTV demonstrating a stronger inhibition on MRP2 compared to
MK571. In contrast to what we found in MDCKII cells, the intracellular TFV
accumulation in HEK293-MRP4 expressing cells and HEK293 MRP5 overexpressing
cells only had a 1.17 and 1.01 fold increase respectively when these cells were co-treated
with TFV and RTV compared to those when cells were treated with TFV alone. These
changes of TFV accumulation were very similar to those in HEK293 wild type cells.
However, HEK293-MRP4 and MRP5 cells showed 5.62 and 2-fold increases in TFV
accumulation when cells were co-treated with TFV and RTV or MK571 compared to
those when cells were treated with TFV alone, and these changes were substantially
greater than those found in HEK293 wild type cell. These data confirmed that MK571
was a potent inhibitor to MRP4 and MRP5, however, RTV failed to demonstrate potent
inhibition in MRP4 or MRP5. Our data was consistent with the aforementioned research
by Ray et al. suggesting lack of inhibition of ritonavir on MRP4 in MRP4 overexpressing
cells (Ray et al. 2006). Our finding also resonates well with a pharmacogenetic study,
where Izzedine et al (Izzedine, Hulot et al. 2006) evaluated genetic polymorphisms in
MRP2 and MRP4 transporters in13 HIV patients who developed TFV related

120
nephrotoxicity. They demonstrated a significant correlation between TFV renal toxicity
and incident rates of MRP2 G1492T SNP and MRP2 CGAC haplotype. In contrast, they
didn’t find any allelic association between TFV nephrotoxicity and SNPs in MRP4
transporter. Izzedine et al indicated that MRP2, instead of MRP4, played an important
role in TFV related nephrotoxicity where 12 out of the 13 patients with nephrotoxicity
received coadministration of TDF and PI/r. Our findings in this in vitro experiment
confirmed that MRP2, MRP4 and MPR5 all participate in TFV efflux transport out of
renal tubular cells, however, it is the selective inhibition of RTV on MRP2, but not on
MRP4 or MRP5 that accounts for increased TFV accumulation inside renal tubule cells,
which might sequentially lead to reduced TFV renal clearance as well as enhanced
chance of TFV-related nephrotoxicity when TDF is co-administrated with RTV.
As TFV renal secretion involves multiple drug transporters, it is easy foreseen
that genetic variations and expression levels of these transporters are other important
factors regulating TFV disposition in addition to the aforementioned drug-drug
interaction with RTV coadministration. To better understand how these genetic variations
affect TFV pharmacokinetics, we evaluated four SNPs in MRP2 and MRP4 transporters,
which include ABCC4 exon 5 559G →T, ABCC4 3'UTR 4131 T →G, ABCC2 exon 10
1249 G →A and ABCC2 exon 28 3972 C →T. We identified a novel relation between
ABCC4 559TT mutant and elevated TFV total clearance (P=0.022) among 24 HIV
patients who received TFV-NNRTI regiment, although the CrCL adjusted by BSA in the
4 TT carriers were marginally higher compared to GG carriers (P=0.047 one-tailed t test).
We also found that in 33 HIV patient receiving TFV-PI/r coadministration, the TFV total

121
clearance among the ABCC4 4131 TT, TG and GG carriers were significantly different
(P=0.022), with the TFV total clearance in GG mutants markedly higher than that in TT
carriers. As CrCL was not markedly different across the 3 groups, this data indicated that
ABCC4 4131GG mutants might present with elevated MRP4 efflux function. These
findings have not been suggested in previous publications. Intensive research has been
conducted exploring the phenotype-genotype relationship in major drug transporters.
Theoretically genetic polymorphisms in any transporter that mediates TFV trafficking
can affect TFV disposition. Therefore, it is not surprising to find correlations between
SNPs in MRP2 or MRP4 and TFV pharmacokinetics. Genetic variation in these
transporters not only impact TFV pharmacokinetics, but also might affect TFV
intracellular accumulation inside renal tubular cells and thus relates to its renal toxicity.
For example, Bleasby et al reported that the K(m) for the nucleoside phosphonate analogs
adefovir, cidofovir, and tenofovir seemed to be increased in the R50H-hOAT1
(SLC22A6 728G>A) variant compared with the wild type (Bleasby, Hall et al. 2005),
therefore this variant might be a protective allele related with less TFV nephrotoxicity.
Similarly, genetic variants in MRP2 or MRP4 with elevated efflux function might be
other protective alleles. By contrast, polymorphisms with impaired efflux function might
be associated with more TFV accumulation inside renal tubule and thus more TFV
related nephrotoxicity. For instance, Kiser et al demonstrated that ABCC4 3463G
variants had impaired efflux function, and was related with 15% lower TFV renal
clearance and 32% increase in TFV AUC, while ABCC2 -24T carriers had elevated
efflux function, with evidence that this polymorphism was associated with a 19%

122
increase in TFV renal clearance compared to the wild type (Kiser, Carten et al. 2008).
Our pharmacogenetic data suggested that ABCC4 559TT and ABCC4 4131GG mutants
might be associated with elevated efflux function and could lead to less TFV
accumulation inside of renal tubule cells. Further validation for these novel correlations
needs analysis based on larger sample pool. Also, stratification of renal toxicity according
to genetic polymorphisms will help to verify the aforementioned correlations between
genetic variations and TFV pharmacokinetics.
5.5 Conclusion
Patients receiving concurrent TDF+PI/r treatment had greater reductions in CrCl
compared to patients taking TDF+NNRTI or non–TDF based regimens. Elimination
constant (Kel) was significantly lower in TDF+PI/r treatment group compared to that in
patients receiving TDF+NNRTI regiment (P=0.05). In vitro mechanistic study confirmed
that MRP2, MRP4 and MRP5 were involved in the efflux transportation of TFV out of
renal tubule cells and suggested that it was the selective inhibition of RTV on MRP2
transporter that accounted for elevated accumulation of TFV inside of renal tubule and
cause decreased TFV renal clearance and TFV-related nephrotoxicity when TDF was co-
administrated with PI/r. Pharmacogenetic analysis suggested that ABCC4 559TT and
ABCC4 4131GG mutants were associated with increased TFV renal clearance, indicating
elevated efflux function in these variants.

123
Chapter 5 endnotes
5.1 Introduction
5.2 Methods
5.2.1 Study subjects
5.2.2 Protocol of the clinical study
5.2.3 Protocol of the cellular experiments
5.2.3.1 Cell culture
5.2.3.2 Intracellular TFV accumulation assays
5.2.4 Analytical measurements
5.2.4.1 Creatine clearance
5.2.4.2 Determination of plasma TFV concentrations
5.2.4.3 Genotyping
5.2.4.4 Determination of intracellular TFV accumulation
5.2.5 Pharmacokinetic modeling
5.2.6 Statistical analysis
5.3 Results
5.3.1 Patient characteristics
5.3.2 Changes in renal function
5.3.3 TFV Pharmacokinetics
5.3.4 Intracellular TFV accumulation in MDCK and HEK cells
5.3.5 SNPs in MRP2 and MRP4 and their impacts on TFV pharmacokinetics

124
5.4 Discussion
5.5 Conclusion

125
Chapter 6: Conclusion
The process of drug absorption, metabolism and elimination is facilitated by a
myriad of enzymes and transporters. The expression and activity of these enzymes and
transporters are subject to a number of factors, which include age, sex, concurrent
diseases or medications, drug-drug interactions and genetic polymorphisms. These
factors can potentially affect functions of these participating elements, leading to
interindividual variation in drug disposition. These differences can impact efficacy and
the potential to develop toxicity among different patients. In the previous five chapters, I
have illustrated in detail how these factors can impact drug disposition and consequently
impact clinical outcomes. In this concluding chapter, I will summarize their role and
subsequent impact on clinical outcomes. The major findings are summarized as
following:
Age is a potent factor that affects drug disposition and pharmacokinetics. We
evaluated drug disposition of gemcitabine, dFdU, paclitaxel, docetaxel, capecitabine,
DFCR, DFUR and 5FU in elderly patients with advanced cancers in urothelium, breast or
colorectum, respectively. In these studies, when compared to patients younger than 60
years old, patients older than 70 years had significantly higher paclitaxel and capecitabine
AUCs, which corresponded to reduced total clearance for paclitaxel and capecitabine in
these patients. Both paclitaxel and capecitabine are eliminated through hepatic
metabolism. In contrast, no significant differences between the younger and older
patients were detected in term of pharmacokinetics of gemcitabine and the three
metabolites of capecitabine, namely DFCR, DFUR and 5FU. Commonality among these

126
agents is that metabolic enzymes mediating the breakdown of gemcitabine, DFCR,
DFUR and 5FU are found in all tissues and not confined in liver only. This data
indicated that liver function was impaired in patients 70 years or older compared to those
younger than 60 years old, leading to slower metabolism and greater accumulation of
substrate drugs that are mainly converted in liver. This age-related decline in liver
function might be due to decreased liver mass and reduced blood supply to the liver in the
elderly, however, the intrinsic activity of CYP enzymes expressed in liver or outside of
liver might remain intact.
In addition, the elderly patients also presented with markedly greater
accumulation of dFdU, which is the main metabolite of gemcitabine and almost
exclusively eliminated through kidney. Although not presented in this thesis, elimination
of paclitaxel metabolites appears to be increased in the younger cohort and similar to
dFdU, paclitaxel metabolites are mainly excreted through renal elimination. This finding
was consistent with the change in renal function, where GFR adjusted by BSA was
significantly lower in patients 70 years or older compared to the younger controls. This
data suggested the age-related decline in renal function might affect elimination of drugs
that are mainly excreted through kidney. However, the impact of dFdU on the
pharmacokinetics of gemcitabine (dFdC) has not been completely determined yet. A
final analysis to evaluate this finding will be critical as accumulation of metabolites may
lead to accumulation of the active parent compound.
In this thesis, we also analyze the enzymatic activity in patients receiving liver
transplant. Our data showed that markedly elevated CYP2E1 capacity, as measured by

127
chlorzoxazone

hydroxylation, was observed within 30 days after liver transplantation

for
both young and older OLTx patients compared to healthy controls, where this elevated
enzymatic capacity was prolonged

beyond 30 days postoperatively in the younger OLTx
patients.

CYP2C19 metabolizing function, using the formation of mephenytoin
hydroxylation as the measure of enzymatic capacity,

was significantly reduced during 30
days after liver transplantation for both younger and elderly patients compared to healthy
controls.

CYP2D6 capacity was impaired after 30 days postoperatively in

older OLTx
patients compared to healthy controls. Young and older OLTx patients experienced

similar changes after liver transplantation in their metabolic

capacity of the major
CYP450 enzymes. The enzymatic activity may be a consequence of new liver that has
been implanted. Alternatively, the new organ may induce immune response against the
organ, and thus elaborate inflammatory cytokines, which may impact on the expression
of the CYP and efflux transporters. This issue needs to be further dissected to
comprehensively understand the changes that occur following transplantation. This data
does provide guidances as how to manage patients who have received OLTx. Drugs that
are substrates for these enzymes should be closely monitored for altered drug disposition.
In this thesis, we were able to evaluate the impact of genetic disorders with
regards to how they impact drug disposition. As stated in earlier chapters that patients
afflicted with cystic fibrosis have been described to have altered drug disposition as
compared to healthy volunteers. Altered clearance of certain antibiotics reported in prior
clinical trails might not be explained by changes in renal P-gp activity alone. We
evaluated renal clearance of fexofenadine, an antihistamine that is eliminated using P-gp,

128
in 8 CF patients where we found that pharmacokinetic disposition of fexofenadine was
similar to healthy volunteers. Our study appears to be significantly different from those
performed before. This may represent disease difference of these patients as compared to
those reported in older studies. The CF patients in our study were all given prophylactic
azithromycin and pancreatic enzymes. The impact of changes in management may alter
study outcomes. Azithromycin has anti-inflammatory activity that may reduce cytokine
expression that may promote CYP and efflux transporter expression. In addition, the
application of pancreatic lipase to the treatment regimen of these patients can increase
lipid and protein absorption. Patient demographics of this study indicated that patients
were less severely sick as compared to those in the older studies. Despite these changes,
this study had other limitations that may mask the outcomes; for example, larger sample
size is needed to evaluate the possible compensatory mechanism between CFTR and
ABCB1 gene in CF patients. The other issue is the role of azithromycin in modulating
ABCB1 expression, which has been suggested as possible mechanism of benefits for
azithromycin.
Inhibition of drug transporters by one compound is usually the cause for altered
pharmacokinetics of another compound that is the substrate of the involved transporters.
For example, in the CF study, coadministration of probenecid, a potent inhibitor of OAT-
1 and OATP, significantly increased fexofenadine AUC while decreasing the cumulative
urinary excretion, total body clearance, and renal clearance of fexofenadine, which enters
renal tubule cells through OAT1 and OATP transporters localized on the basolateral side.
However, sometimes the drug-drug iterations based on transporter inhibition was not

129
apparent, and need further investigations. For instance, we followed renal function of
HIV patients given different antiviral treatments and found that patients receiving
concurrent TDF+PI/r treatment had greater reductions in CrCl compared to patients
taking TDF+NNRTI or non–TDF based regimens. Elimination constant (Kel) was
significantly lower in TDF+PI/r treatment group compared to that in patients receiving
TDF-NNRTI regiment (P=0.05). This clinical phenomenon strongly indicated a potential
drug-drug interaction between TFV and RTV. To explore the possible mechanism
involved, we conducted in vitro cellular study, which suggested that MRP2, MRP4 and
MRP5 were involved in the efflux transportation of TFV out of renal tubule cells and
confirmed that it was the selective inhibition of RTV on MRP2 transporter that accounted
for elevated accumulation of TFV inside of renal tubules, leading to decreased TFV renal
clearance and TFV-related nephrotoxicity when TDF was co-administrated with PI/r.
Genetic polymorphisms of major drug metabolic enzymes and transporters are
potential sources of interindividual variations in pharmacokinetics. In this thesis we
presented data from the CF study, ABCB1 3435 C/T mutants were found to be associated
with reduced fexofenadine AUC and elevated rhodamine 123 efflux from peripheral
blood T cells, a probe parameter for P-gp efflux activity, when compared to the C/C
carriers. In addition, the data from TFV renal toxicity study showed that compared to
their wild type counterparts, ABCC4 559TT and ABCC4 4131GG mutants were
associated with increased TFV renal clearance, indicating elevated efflux function in
these variants. Considering the important role of pharmacogenetics, genetic
polymorphisms in major enzymes and transporters that participate in the metabolism and

130
elimination of gemcitabine, paclitaxel, docetaxel, capecitabine and their metabolites need
to be investigated to further explore contributing factors accounting for variations in the
pharmacokinetic profiles of these compounds.
In summary, drug disposition can be affected by a number of factors, among
which, age, changes in hepatic metabolism or renal elimination, inhibition of drug
transporters as well as genetic polymorphisms are major source of variations of
pharmacokinetics among different individuals. Impaired renal elimination and hepatic
metabolic capacity are major factors accounting for altered pharmacokinetics in elderly
patients. Inhibition of transporters contributes to variations in drug disposition. Genetic
polymorphisms in major enzymes and transporters add to complexities of inter-individual
differences in pharmacokinetics and clinical outcomes. Elucidation of these impacting
factors helps to understand possible alterations in drug disposition and predict sequential
effects in efficacy and toxicity, so that the ultimate goal to optimize drug treatment based
on patients’ genetic and clinical profiles can thus be achieved. However the data must to
be integrated to explain how this information will ultimately benefit patients. Future
studies will need larger number of patients to validate these findings.

131
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Abstract (if available)

Abstract Objective: This dissertation aims to evaluate factors impacting drug disposition and clinical outcomes.

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Creator Liu, Shanshan (author)

Core Title Factors impacting drug disposition and clinical outcomes: age, hepatic metabolism, renal elimination and pharmacogentetics

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Pharmaceutical Sciences

Publication Date 06/23/2009

Defense Date 05/05/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Age,drug disposition,hepatic metabolism,OAI-PMH Harvest,pharmacogenetics,renal elimination

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Louie, Stan (committee chair), D'Argenio, David (committee member), Shen, Wei-Chiang (committee member)

Creator Email shanshal@usc.edu,shanshan.liu0633@gmail.com

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