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Pharmacokinetics-pharmacodynamics of tedizolid in plasma and sputum of adults with cystic fibrosis

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Pharmacokinetics-pharmacodynamics of tedizolid in plasma and sputum of adults with cystic fibrosis

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Content 1

Pharmacokinetics-Pharmacodynamics of Tedizolid in Plasma and Sputum of Adults with
Cystic Fibrosis

A young Jenny Park
Molecular Pharmacology and Toxicology
Master of Science
USC Graduate School
University of Southern California
May, 2018

2

First and foremost, I wish to express my sincere gratitude to my advisor, Dr. Paul M.
Beringer for giving me the opportunity to work on this project and for being a great
mentor. This thesis would not have been possible without his continuous support,
patience and guidance. I would also like to extend my sincere gratitude and
appreciation to Dr. David Z. D’Argenio for sharing his knowledge and valuable
guidance. In addition, I would like to thank those who contributed to this project: Joshua
Wang PharmD, Jordanna Jayne, Lynn Fukushima RN, and Dr. Adupa Rao MD.
Most importantly, I would like to thank my family, friends, and Wesley for their endless
support and encouragement.
3

TABLE OF CONTENTS
SUMMARY…………………………………..…………………………………..……….. 4
INTRODUCTION……………………………………………………………………........ 5
MATERIALS AND METHODS……………………………………………………......... 19
RESULTS……………………………………………………......................................... 24
DISCUSSION……………………………………………….......................................... 28
REFERENCES………………………………………………........................................ 34
FIGURES AND TABLES………………………………………………......................... 45

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SUMMARY
Over the past decade, the prevalence of infections involving Methicillin-resistant
Staphylococcus aureus (MRSA) in patients with cystic fibrosis (CF) has increased
significantly. Tedizolid (TZD) demonstrates excellent activity against MRSA and a
favorable safety profile. The pharmacokinetics (PK) of several antibiotics has shown to
be altered in CF patients. The purpose of this study was to characterize the
pharmacokinetics-pharmacodynamics (PK/PD) of TZD in this population. Eleven
patients with CF were randomized to receive tedizolid phosphate 200 mg PO or IV once
daily for 3 doses, with a minimum 2-day washout, followed by crossover to the
remaining dosage form. Plasma and expectorated sputum were collected following the
third dose of each dosage form for analysis. Population pharmacokinetics (PPKs) were
described by a 2-compartment model. The estimated population mean ± standard
deviation of total clearance, central volume of distribution, and absolute bioavailability
were 9.72 ± 1.62 L/h, 61.6 ± 6.94 L, and 1.07 ± 0.165, respectively. The estimated
sputum partition coefficient was 2.875. The total clearance is higher in CF patients when
compared with healthy volunteers; however, it is similar to published data in patients
with complicated skin and skin structure infections (cSSSI). This study demonstrates the
oral bioavailability of TZD is excellent in patients with CF, and the plasma PKs are
similar to those reported for patients with cSSSI.

Keyword: tedizolid, pharmacokinetics, pharmacodynamics, cystic fibrosis
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INTRODUCTION
I. Overview of Cystic Fibrosis
Cystic fibrosis (CF) is a lethal autosomal recessive disorder characterized by a chronic
cycle of airway infection, inflammation and obstruction, leading to progressive loss of
lung function and ultimately respiratory failure (Chmiel et al., 2002). It affects 1:2000
Caucasian individuals in North America and more than 70,000 worldwide (Coutinho et
al., 2008). Recent advances in the understanding and management of the disease have
improved the life expectancy of patients with CF, where median survival age
approaching close to 40 years of age. CF is caused by mutations in the gene encoding
cystic fibrosis transmembrane conductance regulator (CFTR), leading to misfolding or
absence of the protein that regulates transportation of chloride and bicarbonate ions
across the epithelial membrane and fluid homeostasis. More than 1,900 mutations have
been identified to cause CF lung disease (De Boeck et al., 2014), the most prevalent
one being F508del.
CFTR mutations are categorized into 6 classes according to the mechanism by
which the mutation affects the synthesis or function of the CFTR protein (De Boeck et
al., 2014). Different mutations are associated with different levels of disease severity.
Mutations that belong to class I result in lack of CFTR synthesis, mutations in class II
cause misfolding of the CFTR protein, mutations in class III result in defective protein
regulation, mutations in class IV result in changes in the structure of CFTR that restricts
the transport of chloride ions through the channel, mutations in class V cause reduced
production of normal CFTR, and lastly, mutations in class VI reduce the stability of
CFTR protein due to increased turnover (De Boeck et al., 2014). F508del is a class II,
6
processing mutation in which the CFTR protein is synthesized but is misfolded and
therefore, unable to reach the surface membrane (Maiuri et al., 2015). While mutations
in the CFTR gene affect multiple organs in the body including pancreas, liver, intestines
and reproductive systems, the hallmark of cystic fibrosis is respiratory complications.
Abnormalities in the ion channel lead to airway surface dehydration and impaired
mucociliary clearance. Mucociliary clearance is the primary innate defense mechanism
against pathogens in the lung, and ineffective clearance of mucus causes accumulation
of thick and viscous mucus plaques on airway surfaces (Robinson and Bye, 2002).
Infections in CF patients occur when the inhaled pathogens are deposited on the
surface of thickened mucus plaques and become chronically colonized. Chronic
bacterial colonization and inflammation of the airway are the major causes of morbidity
and mortality in CF patients. In addition, in healthy subjects, airway surface liquid (ASL)
acts as the initial defense of the airway, and contains macrophages, neutrophils, and a
number of antimicrobial peptides such as lactoferrin, lysozyme, and ß-defensins
(Berkebile and McCray, 2014). In CF, pH of the airway surface liquid becomes reduced
due to the lack of CFTR-mediated bicarbonate ion secretion (Shah et al., 2016). The
acidification of airway surface liquid inactivates pH-sensitive antimicrobial host defense
peptides, further compromising the host defense and predisposing CF patients to
respiratory infections (Berkebile and McCray, 2014). While the microbiome of CF airway
continues to change, key pathogens in the lung include Staphylococcus aureus,
Haemophilus influenzae, and opportunistic pathogens such as Pseudomonas
aeruginosa and Stenotrophomonas maltophilia (Chmiel et al., 2014, Salsgiver et al.,
2016). In addition to antibiotic therapy, current approaches to management of CF lung
7
disease include (1) Lumacaftor/ivacaftor (Orkambi ®), which enhances the CFTR
channel activity for those with two copies of F508del mutation; (2) Dornase alfa, which
is a mucolytic agent that cleaves extracellular DNA to reduce mucus viscosity; (3)
inhaled mannitol, which increases mucus clearance in the lung; and (4) azithromycin,
which has both antibacterial and anti-inflammatory properties (Buckland, 2016,
Southern and Barker, 2004).

II. Acute Pulmonary Exacerbation in CF
Acute pulmonary exacerbation (APE) is the most common complication among patients
with CF and has been associated with decreased survival (Sanders et al., 2010).
Although a standard definition of APE has not yet been established, it is most generally
defined as recurrent episodes of exacerbated respiratory symptoms requiring
hospitalization, which contribute to deterioration of lung function and ultimately
respiratory failure (Waters et al., 2012). Symptoms of APE include, but are not limited
to, increased sputum production, shortness of breath, chest pain and decline in lung
function (Flume et al., 2009). One in four CF patients treated with intravenous antibiotics
for pulmonary exacerbation failed to recover to baseline pulmonary function within the 3
months after treatment, and the failure to recover to baseline suggests that patients may
never be able to regain their lung functions (Sanders et al., 2010). Pulmonary
exacerbations have also been associated with changes in the microbiome of the airway
or infection with new bacterial pathogens (Goss and Burns, 2007). Current approaches
for the management of APE include intensive antibiotic therapy at the onset of
exacerbation and airway clearance therapy (Smyth and Elborn, 2008). The primary
8
goals of these therapies are to control the lung infections and symptoms, and to restore
the patient’s lung function back to its baseline value (Boyle, 2007).

III. Drug disposition in Cystic Fibrosis
Due to pathophysiological changes that occur with CF, dispositions of a number of
antibiotics have shown to be altered in these patients. (Touw, 1998, de Groot and
Smith, 1987). In particular, the bioavailability of highly lipophilic drugs, such as penicillin,
cyclosporine, and nonsteroidal anti-inflammatory drugs, is reduced due to pancreatic
insufficiency (Knoop et al., 2003), as pancreatic lipases are the key enzyme for
intestinal absorption of fat. Vitamin D deficiency is also common in CF patients despite
vitamin D supplementation largely due to malabsorption of fat-soluble vitamins
(Chesdachai and Tangpricha, 2016). Pancreatic insufficiency is the most common
gastrointestinal complication associated with CF, as CFTR proteins are highly
expressed in the pancreas (Li and Somerset, 2014). Pancreatic status is linked to
specific mutations in the CFTR genes; analysis of CFTR mutations in CF patients with
pancreatic insufficiency (PI) and pancreatic sufficiency (PS) identified that CF patients
with class I, II, III or VI mutations developed PI, while those with class IV or V mutations
had normal pancreatic function (Singh and Schwarzenberg, 2017, Wilschanski and
Novak, 2013). The pathological changes of the gastrointestinal tract include gastric acid
hypersecretion, bile acid malabsorption and damage to the proximal small intestinal
mucosa (Rey et al., 1998, Spino, 1991). In addition, total drug clearance is moderately
enhanced in CF patients. Approximately 30 to 40% of CF patients have hepatobiliary
disease caused by elevated levels of hepatic enzymes and reduced production of
9
proteins such as albumin and prothrombin (Rey et al., 1998) leading to increased
hepatic clearance. Volume of distribution (Vd) is another PK parameter reportedly
increased in patients with CF due to the increased amount of lean body mass per
kilogram body weight (Touw, 1998, Spino, 1991, de Groot and Smith, 1987). Enhanced
total clearance leads to low serum concentration, which can reduce the efficacy of a
drug. Therefore, patients with CF often require higher doses of antibiotics to reach
therapeutic concentrations.

IV. MRSA in Cystic Fibrosis
Staphylococcus aureus is a Gram-positive bacterium and is one of the earliest
pathogens to infect and colonize the airways of infants with CF (Goss and Muhlebach,
2011, Coutinho et al., 2008). Methicillin-resistant Staphylococcus aureus (MRSA) is
resistant to multiple antibiotics, including erythromycin, cephalosporin, and other beta-
lactams, and such resistance is conferred by the production of a specific penicillin-
binding protein (PBP) called PBP 2a (Trust, 2008, Conly and Johnston, 2003). MRSA
infects a wide range of patients who are more susceptible to infection, such as
individuals with CF. CF patients are at increased risk for colonization with MRSA due to
chronic use of antibiotics (Nadesalingam et al., 2005) and repeated hospitalization
(Jennings et al., 2017). The increasing prevalence of respiratory infections with MRSA
presents a major challenge to the treatment of patients with cystic fibrosis. Persistent
infection with MRSA in CF patients has been associated with adverse clinical outcomes,
including a more rapid decline in lung function and higher mortality (Dasenbrook et al.,
2008). Based on Kaplan-Meier analysis and Cox regression models, CF patients with
10
MRSA infections were associated with 6.2 years shorter life span than those without
MRSA, suggesting that MRSA is an independent factor contributing to worse survival
(Dasenbrook et al., 2008). Furthermore, MRSA was also associated with higher risk for
mortality when compared with methicillin-sensitive S. aureus (MSSA) (Dasenbrook et
al., 2008).
For many years, vancomycin has been the first-line therapy for the treatment of
serious MRSA infections. However, reduced susceptibility and risk of nephrotoxicity
associated with vancomycin limit its use (van Hal et al., 2013). Oxazolidinone is a class
of synthetic antibiotics that exerts its antibacterial effect by inhibiting bacterial protein
synthesis via binding to 23S ribosomal RNA of the 50S ribosomal subunit. Currently,
two oxazolidinone-class antibiotics are approved by the FDA: linezolid (LZD) and TZD.
The first-generation oxazolidinone, LZD, exhibits potent activity against MRSA and has
been demonstrated to be efficacious in the treatment of acute pulmonary exacerbations
in CF (Chmiel et al., 2014). The PKs of LZD have been extensively studied in healthy
volunteers and various other special populations, including CF. Population PKs of LZD
in adults with CF was performed using 2-compartment model with time-dependent
clearance inhibition (Keel et al., 2011). LZD demonstrated good penetration into lung
and other tissues, as evidenced by the mean sputum:plasma ratio of 1.4 in patients with
CF (Saralaya et al., 2004). Several case reports have documented successful
eradication of MRSA in the respiratory tract of CF patients with LZD (Serisier et al.,
2004, Ferrin et al., 2002). Monte Carlo simulation demonstrated that the standard
dosing regimens (600 mg, q12h) would be sufficient to attain a pharmacodynamics
11
(PDs) target against MRSA isolates with minimum inhibitory concentrations (MICs) of ≤
2 µg/ml in adults with cystic fibrosis (Keel et al., 2011).

I. Tedizolid
Tedizolid phosphate is a second-generation oxazolidinone that demonstrates 4- to 16-
fold greater inhibitory activity when compared with linezolid (Livermore et al., 2009,
Prokocimer et al., 2012). TZD is administered as an inactive prodrug, tedizolid
phosphate, but rapidly converts to its active compound due to endogenous
phosphatases (Rybak and Roberts, 2015). Tedizolid phosphate is a phosphate-ester
prodrug of TZD; the addition of a phosphate group increases aqueous solubility and
therefore enhances bioavailability (Jornada et al., 2015). TZD is the most active
metabolite of tedizolid phosphate. It is currently approved for the treatment of acute
bacterial skin and skin structure infections (ABSSSIs) and TZD has shown to be non-
inferior when compared with LZD (Roger et al., 2017). TZD has potent activity against a
wide range of multidrug resistant pathogens, including MRSA, VRE and some LZD-
resistant strains (Roger et al., 2017). The recommended dosage is 200 mg
administered once daily orally as tablet or as an intravenous infusion over 1 hour for 6
days, and dose adjustment is not necessary in patients with renal or hepatic
impairment. Similar to LZD, TZD also inhibits monoamine oxidases; however, the free
concentration relative to the IC50 is significantly lower suggesting a reduced potential for
drug interactions with selective serotonin reuptake inhibitors (SSRIs) (Flanagan et al.,
2013). This is of relevance to patients with CF due to the high prevalence of depression
(29-46%), which is often treated with SSRIs/SNRIs (Quittner et al., 2008). PK studies in
12
healthy volunteers demonstrate TZD exhibits high oral bioavailability (~91%) and a
prolonged half-life enabling once daily dosing (Wong and Rab, 2014). TZD is 70-90%
plasma protein bound, and primarily eliminated via the liver as an inactive sulfate
conjugate (Ong et al., 2014).

II. PK analysis
PKs describes the biological fate of a drug after administration into the body, which is
governed by the time course of a drug’s absorption, distribution, metabolism and
excretion (ADME). Patient demographical and pathophysiological factors, as well as
pharmaceutical properties of a drug, can significantly influence the rate and extent of
these kinetic processes. The PK parameters representing the ADME processes are
estimated through collecting blood samples and other relevant, accessible biological
samples at defined intervals. Analysis of PK data involves determining the relationship
between the dose administered and the observed time course of drug concentrations,
and quantitatively expressing the behavior of a drug in the body. Several methods have
been developed to analyze the PK data. One approach is model-independent, non-
compartmental analysis (NCA), which is a convenient and frequently used method of
characterizing the kinetics of a drug. The non-compartmental method allows for
calculation of basic PK parameters without making any compartmental assumptions or
using complicated mathematical expressions (Gabrielsson and Weiner, 2012). A large
number of concentration-time data are needed for NCA as this approach heavily relies
on measured drug concentration data for analysis. A few underlying assumptions in
NCA are that a drug exhibits linear, first-order distribution and elimination, and is
13
eliminated strictly through the plasma. In contrast, in compartmental analysis,
compartments are simplified, theoretical spaces, and each compartment represents a
number of tissues with similar blood flow and drug affinity in the body, and a drug’s
kinetics are described using nonlinear regression. A key assumption in compartmental
modeling is that a compound is well mixed within each compartment. Systems of
differential equations are used to describe the rate of transfer into and out of the
compartment(s). The number of compartments in the model vary depending on the rate
of drug distribution (Hedaya, 2012). In an one-compartment model, a drug is assumed
to rapidly distribute to all parts of the body, and the body acts as one homogenous
compartment. If a drug distributes at different rates, then a two-compartment or multi-
compartmental model may be more appropriate. A two-compartmental model consists
of central and peripheral compartment: the central compartment represents the blood
and other well perfused tissues in which the drug rapidly equilibrates, such as heart,
liver and kidney, and the peripheral compartment represents poorly perfused tissues,
such as fat and muscle, where drug distribution is slow. For drugs that follow two-
compartmental kinetics, the plasma drug concentration declines biexponentially, each
exponential decay representing a distribution phase and elimination phase. The
reversible transfer of drug between the central and peripheral compartments is
governed by the rate constants.

III. Population pharmacokinetic (PPK) analysis
Individual patients can have substantial variability in response to a treatment. PPK is the
study of variability in the disposition of drugs in a particular population. It investigates
14
the inter-individual variabilities and the sources of those variabilities of the drug
concentration profiles in a target patient population when a fixed dose of drug is given
(Aarons, 1991, Mould and Upton, 2013). PPK is widely used in the drug development
process and in the field of clinical pharmacology, as these differences in drug exposure
can have a significant influence on efficacy and clinical response. One of the
advantages of PPK analysis is that PK parameters for a large population can be
estimated using sparse and unbalanced data collected from a relatively small number of
patients (Weber and Rüppel, 2013). It uses statistical techniques to predict PK
parameters and quantify both predicted (i.e. subject-specific covariates, such as sex,
age, body weight, and disease severity) and unpredicted (i.e. random effects) variability.
Understanding these PK variabilities ensures appropriate dose adjustments based on
demographical or pathophysiological factors, or on concomitant mediation (Whiting et
al., 1986). Therefore, PPK analysis is a vital component of drug development.
Several algorithms are available for the analysis of PK data. The traditional
method to perform PPK is using a standard-two-stage (STS) method. In the standard-
two-stage approach, the first stage involves estimating individual subject’s model
parameters and the residual variability, and in the second stage, population parameters
are estimated by pooling the individual estimates and calculating the mean and
variances of all the individual parameters (Colucci et al., 2011). The expectation
maximization (EM) algorithm is an iterative method used to find the maximum likelihood
estimates in the nonlinear mixed effects model. The EM algorithm also consists of 2
steps; in the first step (expectation step), a conditional mean and covariance of each
parameter is estimated using the predicted parameter values and the observed data. In
15
the second step (maximization step), population mean, covariance and error variance
parameters are computed to maximize the log-likelihood function found on E step
(Colucci et al., 2011, D'Argenio, 2009).

IV. Pharmacodynamics (PDs)
PDs describes how the drug exerts its pharmacological effect in the body. For
antibacterial agents, PDs studies characterize the rate and the extent of the
antimicrobial effects at the infection site, which includes bactericidal activity and post-
antibiotic effect (Levison and Levison, 2009). Post-antibiotic effect refers to the
persistent suppression of bacterial growth even after a drug level falls below the MIC at
the site of infection. MIC is a measure of the lowest concentration required to stop the
visible growth of bacteria and represents the potency PDs marker of antibacterial
agents (Nielsen et al., 2011). For treatment of lung infections such as in cystic fibrosis,
determination of pulmonary penetration of antibiotics is important in predicting optimal
dosing regimens. Since CF infection and inflammation predominately occur in the
endobronchial space, expectorated sputum is the ideal sample to measure drug
concentrations for PD analysis.

V. Monte Carlo simulation
In PKs, Monte Carlo simulation (MCS) is a computer-based statistical and mathematical
technique used to understand and predict the results of various therapeutic approaches,
such as different dosing regimens, and the probability of attaining the therapeutic target
(Roberts et al., 2011). Once a PPK model has been established and in vitro
16
susceptibility data are available, MCS can then be used to maximize PK data and to
establish robust exposure-response relationships. By simulating different dosing
regimens, it provides quantitative evidence to support dose selection for future studies
in phase 2 and 3 clinical trials that would maximize the therapeutic efficacy and safety
(Mould and Upton, 2013). It also takes into consideration both interindividual variability
in PK parameters and variation in the MIC of the pathogen (Nielsen et al., 2011) to
provide more accurate prediction.

VI. Clinical PKs of TZD
Several studies have assessed the PKs of TZD in healthy volunteers and patients with
cSSSIs. Prokocimer et al first evaluated the PPK, safety and efficacy of oral tedizolid
phosphate in patients with cSSSIs (Prokocimer et al., 2011). The disposition of TZD
was described using a 2-compartment model with two sequential linear absorption
compartments and linear elimination. Two sequential absorption compartments
comprised of a lag compartment and an absorption compartment, and the values of the
first-order rate constant for transfer from the lag to the absorption compartment (klag)
and for transfer from the absorption to the central compartment (kabs) were assumed to
be the same within patients (Prokocimer et al., 2011). Addition of a third compartment
did not significantly improve the objective function value compared to the two-
compartmental model. Furthermore, when non-linear elimination and auto-inhibition of
clearance were assessed in the model, nonlinearity did not improve the model’s fit to
the data compared to a model with linear elimination. Therefore, the authors selected
the simplest model to model the disposition of TZD. The second study performed single-
17
and multiple-ascending-dose, non-compartmental PK analysis of oral TZD in healthy
subjects (Flanagan et al., 2014c). PK parameter values did not change significantly for
single and multiple doses, and approximately 31% of drug accumulation occurred at day
15 of multiple dosing. The linearity factor was estimated to be around 1 for the three
doses (200, 300, and 400 mg), demonstrating that TZD follows first-order kinetics.
Flanagan et al subsequently performed a PPK meta-analysis of oral and intravenous
TZD using data from 7 clinical trials involving healthy volunteers and adults with cSSSI
and ABSSSIs (Flanagan et al., 2014b). Pooled data consisted of full-profile PK data
from phase 1 studies and sparse samples collected after single or multiple dosing in the
phase 2 and phase 3 clinical trials. The authors described the PKs of TZD using a
linear, two-compartment model with sigmoidal absorption and first-order elimination.
Sigmoidal absorption represents zero-order drug release of orally administered TZD in
the gastrointestinal tract and subsequent first-order absorption. Ideal body weight and
total bilirubin were incorporated in the final model as covariates. In this PK model, the
estimated absorption rate constant was high (1.99 L/h) but was associated with high
inter-individual variability (194%). Estimated total and distributional clearance were
lower than the values estimated in patients with cSSSI (CLtotal, 6.69 and 8.28 L/h; CLd,
0.959 and 2.95 L/h).

VII. PK/PD Modeling & Simulation
Antibiotic therapy is considered successful when the unbound drug levels exceed the
MIC at the site of infection (Dalhoff, 2014). Therefore, to optimize dosing regimens for
treatment of infections (Li and Somerset, 2014), the relationship between
18
pharmacokinetics and pharmacodynamics (PK/PD) of antimicrobial agents at the
infection site must be well characterized (Drusano et al., 2001). Studying PK and PDs
properties of drugs involves developing a PPK model to define the time course of a drug
and identifying PK/PD exposure targets by integrating the data from preclinical infection
animal models and in vitro susceptibility tests. Unbound plasma drug concentrations are
often used as a surrogate of drug exposure at the site of infection, especially if direct
measurement of drug levels at the site of infection is not feasible (Gonzalez et al.,
2013). Pre-clinical, in vivo infection models have been extremely useful in providing
detailed information regarding the correlation between time course (PK) and killing
activity (PD) of antimicrobial agent and in identifying PK/PD index as well as the
magnitude of the PK/PD index most indicative of clinical success (Lodise and Drusano,
2014). Three PK/PD indices are commonly used to measure drug efficacy and to predict
success or failure of antibiotic therapy: ratio of peak drug concentration to MIC
(Cmax/MIC, concentration-dependent), ratio of area under the curve (AUC) to MIC
(AUC/MIC), and the time T of exposure of bacteria above the MIC (T>MIC, time-
dependent) (Toutain et al., 2002, Trivedi et al., 2013). For TZD, studies in a murine
thigh infection model of Staphylococcus aureus first identified the ratio of the area under
the unbound drug concentration-time curve to MIC (fAUC/MIC) to be the PK/PD index
most predictive of the therapeutic efficacy (Louie et al., 2011). A subsequent study in a
murine pneumonia model demonstrated that a ratio fAUC/MIC > 20 and 34.6, when
adjusted for protein binding, was necessary for stasis and 1-log unit kill, respectively,
against MRSA (Lepak et al., 2012).

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The present study was undertaken to characterize the PKs and PDs of
intravenous and oral TZD in patients with CF experiencing acute pulmonary
exacerbations. This was the first study to evaluate the PKs and PDs of TZD in this
patient population.

MATERIALS AND METHODS
Participants. Eleven adult patients with CF admitted to the hospital for pulmonary
exacerbation as defined by CF Pulmonary Guidelines and Fuchs et al (1994) were
enrolled in the study. The inclusion criteria were age > 18 years, diagnosis of CF based
on positive sweat chloride or known CF mutation, who were able to spontaneously
expectorate sputum, and CF patients with or without MRSA positive sputum. The
patients were excluded from the study if they had AST/ALT ratio greater than 3x ULN,
thrombocytopenia (platelets < 100,000), anemia (hematocrit <30), or any clinically
significant abnormality noted on physical exam thought to interfere with the conduct of
the study. Patients were also excluded if they were pregnant or were taking monoamine
oxidase inhibitors and/or serotonergic agents. The creatinine clearance (CLcr) was
calculated using the Cockcroft-Gault equation.

Study design. This was a prospective, multiple-dose, crossover study to characterize
the steady-state PKs and PDs of TZD intravenous/oral (IV/PO) in plasma and sputum of
adults with cystic fibrosis. The study was conducted from 2016 to 2017 at the Keck
Medical Center of USC. Patients were randomized to receive tedizolid phosphate 200
mg IV or PO once daily for three doses followed by a minimum two-day washout and
20
crossed over to receive the remaining dosage form. The IV formulation of TZD was
administered over 1 hour, and oral tablets were administered with food. Three doses
were given to allow patients to reach steady state. Sparse samples were collected for
expectorated sputum. Tedizolid phosphate was administered in addition to antibiotics
prescribed for treatment of a pulmonary exacerbation in hospitalized patients. The
protocol was approved by the University of Southern California Institutional Review
Board (IRB), and all participants provided written informed consent prior to the study.

Blood and sputum sampling. Plasma samples were obtained immediately before the
third dose of each dosage form (0 (pre-dose)), and at 0.5, 1, 2, 3, 4, 8, 24 and 48 hours
after start of the infusion or oral dose administration. In addition, sputum was collected
from all participants immediately before a dosage was given, and each patient was
assigned to two additional sputum sampling time points such that samples were
obtained at 0.5-1 h and 8 h, 2 h and 8 h, 2 h and 24 h, 3 h and 24 h, 3 h and 48 h, or 4 h
and 48 h. Sputum was diluted 3:1 with normal saline, then processed via mechanical
homogenization with an appropriate syringe and needle. All samples were processed
and stored at -80°C until further analysis.

Analytical method and quantification. TZD concentrations in plasma and sputum were
determined using LC-MS/MS by Covance Bioanalytical Laboratory (Durham, NC). The
validated bioanalytical method was linear (R
2
=0.99), with an upper limit of quantification
(ULOQ) of 5000 ng/mL and a lower limit of quantification (LLOQ) of 1.00 ng/mL. Intra-
and inter-assay precision (%CV) and intra- and inter-assay accuracy (%RE) for
21
standards and quality controls were observed to be ≤10% for both plasma and sputum
TZD. Concentration data below the lower limit of quantitation were treated as missing
for PK analysis. TZD plasma concentrations were quantifiable in all subjects up to the
last sampling time (48 h post-dose).

Plasma non-compartmental PK analysis. Non-compartmental PK analysis was
performed using Kinetica version 5.0 (Thermo Fischer Scientific). One patient was
excluded from the analysis due to not reaching steady-state. The maximum
concentration in plasma (Cmax) and time to maximum (Tmax) were directly obtained from
the measured data. Area under the curve (AUC24) at steady state was determined using
the log-linear trapezoidal method. Absolute bioavailability (F) was calculated as the ratio
of oral AUC0-24 to intravenous AUC0-24. Terminal elimination rate constant (λ) was
derived from the linear regression of the terminal slope of the log-linear plasma
concentration-time curve. The elimination half-life (t1/2) was then calculated using the
equation t1/2=ln(2)/ λ. Total clearance (CL) was calculated as Dose/ AUC0-24 and the
steady-state apparent volume of the plasma compartment (Vss) was calculated as the
MRT*CL.

Plasma PK analysis. PPK modeling was performed with ADAPT (version 5) software
(D'Argenio, 2009) using the maximum likelihood, expectation maximization (MLEM)
algorithm. One- and two-compartment models for plasma disposition were evaluated.
The model was fit to both oral and IV data simultaneously. Models considered for drug
absorption included, a single first-order absorption compartment, and two sequential
22
first-order absorption compartments as previously described (Prokocimer et al., 2011).
Models with absorption lags could not be supported by the data, given the timing of
samples in the absorption phase. A standard-two-stage (STS) approach with maximum
likelihood estimation method was initially used to generate seed values (‘initial
guesses’) for subsequent population PK analysis using the MLEM algorithm. Median
values of estimated PK parameters obtained from STS analysis were used as
population means, and variance elements of the population covariance matrix were set
at a standard deviation of 80% of the median values for analysis with the MLEM
approach. Model selection was based on Akaike information Criterion (AIC) and
Bayesian Information Criterion (BIC) scores, likelihood ratio test (LRT), and goodness-
of-fit plots. All PK parameters were assumed to be log-normally distributed, and the
residual error was represented using a combined additive and proportional error
variance model (Bensman et al., 2017). Area under the curve (AUC0-24) was calculated
for each patient as AUC0-24 = Dose*F/CLT, where F=1 for intravenous administration.
The estimates of IIV were expressed as a percentage of the coefficient of variation
(%CV).

Sputum PK analysis. Several models for the sputum biophase were linked to the final
plasma PK model and evaluated using model selection as described above. The PK
model parameters for the population PK analysis were fixed at the subject specific
values and fit simultaneously to the sputum data following both oral and IV
administrations. Models evaluated for the plasma-sputum exchange included, a single
sputum compartment with the same or different exchange rate constants, as well as a
23
model assuming rapid equilibrium between plasma and sputum represented by a
plasma-to-sputum partition coefficient (PSp).

Covariate relationship exploration. Several covariates were explored and incorporated
into the base model one at a time, including total body weight and lean body weight, to
examine its effect on drug clearance and volume of distribution. Potential covariates
were characterized by a linear function and LRT and AIC scores were used for model
selection. Likelihood ratio test and AIC scores were used for model selection. Model
discrimination was based on a decrease in interindividual variability and the objective
function value (OFV), and improvement in goodness-of-fit.

Monte Carlo simulation. A 5,000-patient Monte Carlo simulation was performed with the
SIM module of ADAPT (version 5) software. The mean PK parameter and covariance
matrix estimates obtained from the final PPK model were used to simulate two dosing
regimens: 200mg OD, which is the same dosing regimen as in this study, and 400mg
OD. Each simulation generated concentration-time profiles for 5,000 patients and
allowed calculation of the area under the curve. MIC distributions and frequency
distributions of TZD against MRSA were based on previously published susceptibility
data by Barber, et al. (Barber et al., 2016). TZD MIC breakpoints of MRSA isolates
ranged from 0.0625 to 1 µg/mL, with MIC90 of 0.5 µg/mL. Among these, the MIC
breakpoint of ≤0.25 µg/mL was most common. From this data, the fAUC/MIC was
calculated for each simulated patient. The target fAUC/MIC ratios were 20 and 34.6,
24
based on results from Lepak et al. The probability of target attainment was calculating
by counting the number of simulated patients who achieved the target PD endpoints.

RESULTS
Patients. A total of 11 patients with cystic fibrosis completed the study, and baseline
characteristics are listed in Table 1. All patients were pancreatic insufficient and were
receiving pancrelipase supplements.

Non-compartmental Analysis. PK parameters for two routes of administration (IV and
oral) of TZD are calculated at steady-state using a non-compartmental method and
listed in Table 2. Mean Cmax and Tmax of oral and intravenous TZD were similar to
reported values in the package insert (Cmax, oral 2.2 mg/L and 2.2 mg/L, intravenous
2.96 mg/L and 3.0 mg/L; Tmax, oral 2.4 mg/L and 3.5 mg/L, intravenous 1.3 mg/L and
1.2 mg/L, respectively), indicating that absorption characteristics of TZD are comparable
between CF patients and healthy volunteers. The calculated oral bioavailability also
supports the value reported in healthy volunteers (0.99 and 0.91, respectively). None of
the PK parameters were significantly different between the two formulations except for
apparent volume of distribution. The apparent volume of distribution calculated using
oral data was higher than the value calculated using IV data (104.2 L and 76.85 L,
respectively), suggesting that the oral data may be inconsistent with the IV data. Subject
10 demonstrated higher drug exposure with oral dose of TZD than with IV TZD.

25
PPK. The structural model of plasma and sputum TZD PKs is shown in Fig 1. Plasma
and sputum concentration-time profiles after the third dose are shown in Fig. 2. Plots of
observed and individual model predicted concentration versus time profiles for IV and
oral are presented in Fig.3 and 4.
A two-compartment model with first-order absorption and first-order elimination best
described the plasma data. The objective function value was significantly lower with a 2-
compartment model when compared with an one-compartment model (-67.6304 and -
1.93974, respectively). The resulting estimates of the PPK parameters of TZD are
summarized in Table 3. The mean (SD) maximum and minimum plasma concentrations,
time to reach Cmax, and area-under-the-concentration curve (AUC24) at steady state
following oral administration were 2.22 mg/L (0.745), 0.251 mg/L (0.140), 2.5 h (1.33),
and 22.1 mg/L x h (5.72) respectively. Model validation was performed by visual
inspection of TZD concentration profiles of model predicted and observed data. When
lean body mass (LBM) was incorporated into the base PPK model as a covariate to
investigate its influence on volume of distribution, LBM did not reduce the interindividual
variability or objective function value. Therefore, LBM was not retained in the final
model.
Expectorated sputum samples for determination of PKs were available for 10 of
the patients. One patient could not produce sputum. During acute pulmonary
exacerbation, there is extensive protein degradation in sputum. Based on the low
protein concentrations in CF sputum (1.21 g/L), (Sloane et al., 2005) the sputum protein
binding of TZD was estimated to be less than 6% and therefore, no correction for
protein binding was performed. Because the oral bioavailability of TZD was close to 1,
26
TZD IV and oral sputum data were pooled (Fig. 2C and D). The drug distribution to the
sputum compartment was assumed to be in rapid equilibrium with the central
compartment. Therefore, plasma-to-sputum partition coefficient (PSp) was selected to
model the time course of TZD concentrations in sputum. The population model estimate
of PSp is listed in Table 2. The partition coefficient using the ratio of the unbound
concentration in sputum to the unbound concentration in plasma was in agreement with
values found using the ratio of AUCsputum to AUCplasma (2.876 vs. 3.270). The sputum
maximum and minimum concentrations and area-under-the-concentration curve
(AUC24) at steady state following oral administration were 1.08 mg/L (0.60), 0.142 mg/L
(0.087), and 15.04 mg/L x h (8.92) (pooled data). Figure 5 shows the goodness-of-fit
plots of the final model for observed versus individual predicted concentrations (Fig. 5A
and B), conditional standardized residuals versus model prediction (Fig. 5C and D) and
observed versus population predicted concentrations (Fig. 5E and F) in plasma and
sputum. Figure 6 demonstrates that at standard dose of TZD, drug concentrations in
sputum fall below the MIC of 0.5 µg/mL before the end of the dosing interval, but
AUC/MIC ratio was high enough to have sufficient drug exposure in the lung
compartment (data not shown). However, at a MIC of 0.5 µg/mL, all of the patients
reached fAUC/MIC ratio >20, and ratio >34.6, indicating that all of the patients achieved
both the bacteriostatic and bactericidal targets.

Monte Carlo simulation. A Monte Carlo simulation was utilized to maximize the plasma
TZD concentration data and plasma-to-sputum partition coefficient values for patients
with CF. Using these simulated profiles, drug exposure (AUC) in sputum was calculated
27
(AUCsputum=PSp*AUCplasma). Figure 4 shows the percentages of simulated patients
achieving the target fAUC/MIC ratio of 20 and 34.6 in sputum at each dose regimen of
TZD and across a range of MICs from 0.063 to 1 µg/mL. Simulations predicted high
probabilities of target attainment (close to 100%) for bacteriostasis and 1-log unit kill at
both 200 mg OD and 400 mg OD. At a standard dose of 200 mg OD and MIC90 of 0.5
µg/ml, 99.9% and 98.6% of simulation CF patients achieved the PD target necessary for
bacteriostatic and bactericidal effect, respectively. At a higher dose of 400 mg, 100%
and 99.9% of simulated CF patients attained the target exposure necessary for stasis
and 1-log unit kill, respectively. The estimated cumulative response rate for
bacteriostasis and 1-log unit kill at 200 mg were 99.9% and 99.4%, respectively, and at
400 mg were 99.9% and 99.9%, respectively (Table 4).

Safety and tolerability. No serious adverse events were reported in this trial. Diarrhea
was reported as an adverse event possibly related to administration of tedizolid
phosphate. The following adverse events were unlikely related to tedizolid phosphate:
tachypnea, nausea, headache and hemoptysis. Adverse events unrelated to tedizolid
phosphate included hoarse voice, hypoglycemia, fatigue (n=2), gastrostomy tube
placement, intermittent hyperglycemia, abdominal pain with coughing, back pain with
coughing, headache, urticaria, hyperkalemia, tachycardia, hypomagnesemia, acute
kidney injury, substernal chest pain, constipation, dry cough, facial swelling, weight loss,
left inguinal pain, and left inguinal hernia repair.

28
DISCUSSION
The current study evaluated the PKs and PDs of the oxazolidinone antibiotic TZD
in patients with CF. PKs of tedizolid phosphate were not examined due to negligible
systemic exposures reported in previous studies (Chen et al., 2016, Flanagan et al.,
2014c). The principal findings of this study were that the bioavailability of TZD is
excellent in patients with CF. In addition, the total clearance is higher than previous
reports in healthy volunteers, but similar to patients with cSSSIs. Finally, the sputum
concentrations of TZD exceeded the corresponding unbound plasma concentrations.
The oral bioavailability was excellent in CF patients. The estimated absolute
bioavailability was consistent with the value reported in healthy volunteers (104% and
91%, respectively) (Flanagan et al., 2014a). The high bioavailability of TZD in patients
with CF indicates that no oral dose adjustments are needed. Administration of tedizolid
phosphate with food was previously shown to reduce Cmax and delay the drug
absorption when compared with the fasted condition (Flanagan et al., 2014c). The mean
Cmax values found in healthy volunteers and our CF patients are very similar (2.2 µg/mL
(0.6) versus 2.21µg/mL (0.7), respectively), and the mean Tmax values are not
significantly different between healthy volunteers and CF patients (3.5 h (1.0-6.0) versus
2.5 h (1.3), respectively) (Sivextro, 2015). The similarity in the absorption kinetics
suggests that food appears to affect the absorption of TZD similarly in CF patients. In
addition, pancreatic insufficiency does not appear to affect absorption of tedizolid in
patients with CF.
A non-compartmental analysis was first employed to provide an initial estimation
of PK parameters to be used for subsequent model-based PK analysis and to compare
29
with the values reported in the package insert. Parameter estimates derived from the
non-compartmental method, such as apparent Vd, bioavailability and AUC were
comparable with those reported in the package insert.
A series of candidate models were constructed to evaluate which model would
best describe the plasma data. A one-compartment model with first-order elimination
was initially selected to characterize the TZD PKs. However, a two-compartment model
fit the plasma data substantially better than the one-compartment model, as evidenced
by a significant decrease in the OFV and the AIC score (OFV from -1.939 to -67.673,
and AIC from 30.06 to -9.674). Therefore, disposition of TZD in plasma was described
by a 2-compartment model with first order absorption. The estimated total and
distributional clearances were higher in CF patients when compared with data reported
in healthy volunteers, while volume of distribution did not differ significantly. The
clearance of several drugs has been shown to increase in the CF population when
normalized for total body weight (Rey et al., 1998). Bulitta et al hypothesized that the
increased clearance reported for ceftazidime in patients with CF may be an artifact of
scaling to total body weight rather than lean body mass (LBM) (Bulitta et al., 2010).
However, we did not find a strong relationship between PK parameters and patient
demographics. This is likely related to the improved nutritional status of our patients as
evidenced by the normal body mass indexes. Therefore, this covariate was not selected
in the final model. Furthermore, the range of demographics was limited due to the small
sample size which precluded the ability to identify a relationship between any specific
demographic characteristic and the PK parameters.
30
The PKs of TZD in healthy volunteers and patients with ABSSSIs are well
described (Flanagan et al., 2014b, Prokocimer et al., 2011). Previously, Flanagan et al
performed a PK analysis using pooled data from healthy subjects, ABSSSI, and cSSSI
studies and reported the mean total and distributional clearance of 6.69 and 0.959 L/h
respectively, which are both lower than our model-estimated total and distributional
clearance of 9.72 and 4.13 L/h, respectively (Flanagan et al., 2014b). The mean value
for total clearance reported in the Sivextro USPI (5.9 L/h) was also lower than our
estimated total clearance (Sivextro, 2015). However, Prokocimer et al reported a total
and distributional clearance of 8.28 L/h and 2.95 L/h, respectively, in patients with
cSSSI. These values are similar to the data from the CF patients in the current
investigation and suggest that the altered PKs observed in CF may be attributed, in
part, to pathophysiological changes associated with the disease (Prokocimer et al.,
2011).
In our study, pulmonary penetration of TZD was assessed to examine the
disposition of the drug within the airway by obtaining expectorated sputum from the
patients. The time course of sputum concentrations appeared to follow that of plasma;
therefore, we chose to model the sputum exposure using a plasma-to-sputum partition
coefficient. When corrected for protein binding in the plasma (80%), the estimated
unbound sputum-to-plasma ratio was 2.87. High variability in TZD concentrations was
observed at several of the time points which is likely due to the small number of sputum
specimens collected. Nonetheless, the observed versus model predicted plots and
conditional standardized residual plots shown in Fig. 5 indicate that the final PK model
described the data with good precision. The mechanism of drug penetration into sputum
31
is unknown. The estimated mean sputum partition coefficient was not as high as the
epithelial lining fluid (ELF):plasma ratio of 41.2 reported in healthy volunteers (Housman
et al., 2012). In healthy subjects, TZD concentrations in ELF and alveolar macrophages
(AM) were approximately 40- and 20-fold higher than free plasma concentrations
(Housman et al., 2012). Lodise et al. proposed that the high pulmonary exposure may
be due to intracellular accumulation (Lodise and Drusano, 2014). However, a
comparative study of TZD lung penetration in neutropenic and immunocompetent
murine Streptococcus pneumoniae lung infection models demonstrated that ELF
penetration ratio between the two models were not significantly different (Abdelraouf
and Nicolau, 2017). Therefore, the mechanistic basis for the pulmonary exposures is
unclear and warrants further research.
Increasing incidences of antibiotic resistance highlight the need for optimization
of antibiotic dosing regimens. Inappropriate and sub-optimal dosing of antibiotics are the
major factors that have led to the emergence of antibiotic resistance (Asin-Prieto et al.,
2015). PD analysis was performed in combination with MCS to examine whether a
standard dosing regimen of 200 mg and 400 mg would be sufficient to attain the PDs
target and have bacteriostatic and bactericidal effects in the airways of CF patients. As
mentioned above, previous studies have identified that unbound AUC/MIC is the PK/PD
exposure target best correlated to therapeutic efficacy. All of the patients in this study
had fAUC/MIC ratios greater than 34.6 at a MIC of 0.5 µg/ml at the standard dose, with
average fAUC/MIC ratios of 135.5 at MIC of 0.5 µg/ml. This suggests that dose
adjustments are not necessary in future studies evaluating the efficacy and safety in
patients with CF.
32
There are several limitations to our study. We observed a large interindividual
variability (IIV) in the absorption rate constant; it is uncertain whether the observed large
IIV is inherent PK variability among CF patients or due to limited sampling during this
phase. Also, given the infeasibility of obtaining serial samples of expectorated sputum,
sputum samples were very sparse and unbalanced; several subjects produced very little
to no sputum. Consequently, the analysis of sputum data may have been biased. Large
variability in the sputum samples and limited sampling strategy make it difficult to
precisely define the sputum penetration of TZD. Therefore, based on our data it is
difficult to definitively determine the sputum penetration of TZD. Additionally, there were
no preclinical PDs studies identifying PDs targets for TZD against CF-related MRSA
strains. Therefore, although our study used PDs targets found in a murine pneumonia
model, it is uncertain whether this PD endpoint would be relevant to CF. No studies
have shown a relationship between this target AUC/MIC and successful clinical
outcome in CF. Further pre-clinical studies may be necessary to determine the target
CF-related PK/PD endpoint. Lastly, our study did not include a control population to
directly compare the PKs in patients with CF.
Although my research has demonstrated that pulmonary distribution of TZD was
reduced in CF patients, further experiments are needed to fully understand the
mechanism at hand, and to determine whether TZD can be prescribed to CF patients
with acute pulmonary exacerbation. First, appropriate pre-clinical animal studies are
needed to identify a PK/PD exposure target that correlates best with treatment success
in CF patients. Also, mechanistic understanding of TZD distribution to sputum in CF is
necessary to optimize antibiotic therapy. The pulmonary distribution of TZD may be
33
further characterized by rich sampling of epithelial lining fluid and alveolar
macrophages.
In conclusion, TZD in CF patients demonstrated excellent bioavailability with
plasma PKs similar to those reported for patients with cSSSI. Sputum TZD
concentrations exceeded those of plasma indicating that standard dose may be
sufficient to have adequate pulmonary exposure. Our simulation results further support
that at a standard dose, the pulmonary concentrations would be sufficient to achieve the
target PK/PD index against MRSA. Therefore, a standard dose of TZD may be used in
CF patients for future clinical studies to examine efficacy and safety of TZD in this
patient population. However, additional PK and PD studies are needed to identify the
target PK/PD.

34
REFERENCES
AARONS, L. 1991. Population pharmacokinetics: theory and practice. British journal of
clinical pharmacology, 32, 669-670.
ABDELRAOUF, K. & NICOLAU, D. P. 2017. Comparative In Vivo Efficacies of Tedizolid
in Neutropenic versus Immunocompetent Murine Streptococcus pneumoniae
Lung Infection Models. Antimicrob Agents Chemother, 61.
ASIN-PRIETO, E., RODRIGUEZ-GASCON, A. & ISLA, A. 2015. Applications of the
pharmacokinetic/pharmacodynamic (PK/PD) analysis of antimicrobial agents. J
Infect Chemother, 21, 319-29.
BARBER, K. E., SMITH, J. R., RAUT, A. & RYBAK, M. J. 2016. Evaluation of tedizolid
against Staphylococcus aureus and enterococci with reduced susceptibility to
vancomycin, daptomycin or linezolid. J Antimicrob Chemother, 71, 152-5.
BENSMAN, T. J., WANG, J., JAYNE, J., FUKUSHIMA, L., RAO, A. P., D'ARGENIO, D.
Z. & BERINGER, P. M. 2017. Pharmacokinetic-Pharmacodynamic Target
Attainment Analyses To Determine Optimal Dosing of Ceftazidime-Avibactam for
the Treatment of Acute Pulmonary Exacerbations in Patients with Cystic Fibrosis.
Antimicrob Agents Chemother, 61.
BERKEBILE, A. R. & MCCRAY, P. B. 2014. Effects of airway surface liquid pH on host
defense in cystic fibrosis. Int J Biochem Cell Biol, 52, 124-9.
BOYLE, M. P. 2007. Adult cystic fibrosis. Jama, 298, 1787-1793.
BUCKLAND, D. 2016. Recent advances in the management of cystic fibrosis.
Prescriber.
35
BULITTA, J. B., LANDERSDORFER, C. B., HUTTNER, S. J., DRUSANO, G. L.,
KINZIG, M., HOLZGRABE, U., STEPHAN, U. & SORGEL, F. 2010. Population
pharmacokinetic comparison and pharmacodynamic breakpoints of ceftazidime
in cystic fibrosis patients and healthy volunteers. Antimicrob Agents Chemother,
54, 1275-82.
CHEN, R., SHEN, K., CHANG, X., TANAKA, T., LI, L. & HU, P. 2016. Pharmacokinetics
and Safety of Tedizolid after Single and Multiple Intravenous/Oral Sequential
Administrations in Healthy Chinese Subjects. Clin Ther, 38, 1869-79.
CHESDACHAI, S. & TANGPRICHA, V. 2016. Treatment of vitamin D deficiency in
cystic fibrosis. J Steroid Biochem Mol Biol, 164, 36-39.
CHMIEL, J. F., AKSAMIT, T. R., CHOTIRMALL, S. H., DASENBROOK, E. C.,
ELBORN, J. S., LIPUMA, J. J., RANGANATHAN, S. C., WATERS, V. J. &
RATJEN, F. A. 2014. Antibiotic management of lung infections in cystic fibrosis. I.
The microbiome, methicillin-resistant Staphylococcus aureus, gram-negative
bacteria, and multiple infections. Ann Am Thorac Soc, 11, 1120-9.
CHMIEL, J. F., BERGER, M. & KONSTAN, M. W. 2002. The role of inflammation in the
pathophysiology of CF lung disease. Clinical Reviews in Allergy & Immunology,
23, 5-27.
COLUCCI, P., GRENIER, J., YUE, C. S., TURGEON, J. & DUCHARME, M. P. 2011.
Performance of different population pharmacokinetic algorithms. Ther Drug
Monit, 33, 583-91.
36
CONLY, J. M. & JOHNSTON, B. L. 2003. The emergence of methicillin-resistant
Staphylococcus aureus as a community-acquired pathogen in Canada. Can J
Infect Dis, 14, 249-51.
COUTINHO, H. D. M., FALCÃO-SILVA, V. S. & GONÇALVES, G. F. 2008. Pulmonary
bacterial pathogens in cystic fibrosis patients and antibiotic therapy: a tool for the
health workers. Int Arch Med, 1, 24.
D'ARGENIO, D. S., A.; WANG, X 2009. ADAPT 5 User's Guide:
Pharmacokinetic/Pharmacodynamic Systems Analysis Software. Biomedical
Simulations Resource, Los Angeles.
DALHOFF, A. 2014. Pharmacokinetics and pharmacodynamics of aerosolized
antibacterial agents in chronically infected cystic fibrosis patients. Clin Microbiol
Rev, 27, 753-82.
DASENBROOK, E. C., MERLO, C. A., DIENER-WEST, M., LECHTZIN, N. & BOYLE,
M. P. 2008. Persistent methicillin-resistant Staphylococcus aureus and rate of
FEV1 decline in cystic fibrosis. Am J Respir Crit Care Med, 178, 814-21.
DE BOECK, K., ZOLIN, A., CUPPENS, H., OLESEN, H. V. & VIVIANI, L. 2014. The
relative frequency of CFTR mutation classes in European patients with cystic
fibrosis. J Cyst Fibros, 13, 403-9.
DE GROOT, R. & SMITH, A. L. 1987. Antibiotic Pharmacokinetics in Cystic Fibrosis.
Clinical Pharmacokinetics, 13, 228-253.
DRUSANO, G. L., PRESTON, S. L., HARDALO, C., HARE, R., BANFIELD, C., ANDES,
D., VESGA, O. & CRAIG, W. A. 2001. Use of preclinical data for selection of a
37
phase II/III dose for evernimicin and identification of a preclinical MIC breakpoint.
Antimicrob Agents Chemother, 45, 13-22.
FERRIN, M., ZUCKERMAN, J. B., MEAGHER, A. & BLUMBERG, E. A. 2002.
Successful treatment of methicillin-resistant Staphylococcus aureus pulmonary
infection with linezolid in a patient with cystic fibrosis. Pediatr Pulmonol, 33, 221-
3.
FLANAGAN, S., BARTIZAL, K., MINASSIAN, S. L., FANG, E. & PROKOCIMER, P.
2013. In vitro, in vivo, and clinical studies of tedizolid to assess the potential for
peripheral or central monoamine oxidase interactions. Antimicrob Agents
Chemother, 57, 3060-6.
FLANAGAN, S., FANG, E., MUNOZ, K. A., MINASSIAN, S. L. & PROKOCIMER, P. G.
2014a. Single- and multiple-dose pharmacokinetics and absolute bioavailability
of tedizolid. Pharmacotherapy, 34, 891-900.
FLANAGAN, S., PASSARELL, J., LU, Q., FIEDLER-KELLY, J., LUDWIG, E. &
PROKOCIMER, P. 2014b. Tedizolid population pharmacokinetics, exposure
response, and target attainment. Antimicrob Agents Chemother, 58, 6462-70.
FLANAGAN, S. D., BIEN, P. A., MUNOZ, K. A., MINASSIAN, S. L. & PROKOCIMER, P.
G. 2014c. Pharmacokinetics of tedizolid following oral administration: single and
multiple dose, effect of food, and comparison of two solid forms of the prodrug.
Pharmacotherapy, 34, 240-50.
FLUME, P. A., MOGAYZEL, P. J., JR., ROBINSON, K. A., GOSS, C. H.,
ROSENBLATT, R. L., KUHN, R. J., MARSHALL, B. C. & CLINICAL PRACTICE
GUIDELINES FOR PULMONARY THERAPIES, C. 2009. Cystic fibrosis
38
pulmonary guidelines: treatment of pulmonary exacerbations. Am J Respir Crit
Care Med, 180, 802-8.
GABRIELSSON, J. & WEINER, D. 2012. Non-compartmental analysis. Methods Mol
Biol, 929, 377-89.
GONZALEZ, D., SCHMIDT, S. & DERENDORF, H. 2013. Importance of Relating
Efficacy Measures to Unbound Drug Concentrations for Anti-Infective Agents.
Clin Microbiol Rev, 26, 274-88.
GOSS, C. H. & BURNS, J. L. 2007. Exacerbations in cystic fibrosis · 1: Epidemiology
and pathogenesis. Thorax, 62, 360-367.
GOSS, C. H. & MUHLEBACH, M. S. 2011. Review: Staphylococcus aureus and MRSA
in cystic fibrosis. J Cyst Fibros, 10, 298-306.
HEDAYA, M. A. 2012. Basic Pharmacokinetics, Second Edition, Taylor & Francis.
HOUSMAN, S. T., POPE, J. S., RUSSOMANNO, J., SALERNO, E., SHORE, E., KUTI,
J. L. & NICOLAU, D. P. 2012. Pulmonary disposition of tedizolid following
administration of once-daily oral 200-milligram tedizolid phosphate in healthy
adult volunteers. Antimicrob Agents Chemother, 56, 2627-34.
JENNINGS, M. T., DASENBROOK, E. C., LECHTZIN, N., BOYLE, M. P. & MERLO, C.
A. 2017. Risk factors for persistent methicillin-resistant Staphylococcus aureus
infection in cystic fibrosis. J Cyst Fibros, 16, 681-686.
JORNADA, D. H., DOS SANTOS FERNANDES, G. F., CHIBA, D. E., DE MELO, T. R.,
DOS SANTOS, J. L. & CHUNG, M. C. 2015. The Prodrug Approach: A
Successful Tool for Improving Drug Solubility. Molecules, 21, 42.
39
KEEL, R. A., SCHAEFTLEIN, A., KLOFT, C., POPE, J. S., KNAUFT, R. F.,
MUHLEBACH, M., NICOLAU, D. P. & KUTI, J. L. 2011. Pharmacokinetics of
intravenous and oral linezolid in adults with cystic fibrosis. Antimicrobial agents
and chemotherapy, 55, 3393-3398.
KNOOP, C., VERVIER, I., THIRY, P., DE BACKER, M., KOVARIK, J. M., ROUSSEAU,
A., MARQUET, P. & ESTENNE, M. 2003. Cyclosporine pharmacokinetics and
dose monitoring after lung transplantation: comparison between cystic fibrosis
and other conditions. Transplantation, 76, 683-8.
LEPAK, A. J., MARCHILLO, K., PICHEREAU, S., CRAIG, W. A. & ANDES, D. R. 2012.
Comparative Pharmacodynamics of a new Oxazolidinone Tedizolid Phosphate
and Linezolid in a Neutropenic Murine Staphylococcus aureus Pneumonia
Model. Antimicrobial Agents and Chemotherapy.
LEVISON, M. E. & LEVISON, J. H. 2009. Pharmacokinetics and pharmacodynamics of
antibacterial agents. Infect Dis Clin North Am, 23, 791-815, vii.
LI, L. & SOMERSET, S. 2014. Digestive system dysfunction in cystic fibrosis:
challenges for nutrition therapy. Dig Liver Dis, 46, 865-74.
LIVERMORE, D. M., MUSHTAQ, S., WARNER, M. & WOODFORD, N. 2009. Activity of
oxazolidinone TR-700 against linezolid-susceptible and -resistant staphylococci
and enterococci. J Antimicrob Chemother, 63, 713-5.
LODISE, T. P. & DRUSANO, G. L. 2014. Use of pharmacokinetic/pharmacodynamic
systems analyses to inform dose selection of tedizolid phosphate. Clin Infect Dis,
58 Suppl 1, S28-34.
40
LOUIE, A., LIU, W., KULAWY, R. & DRUSANO, G. L. 2011. In vivo pharmacodynamics
of torezolid phosphate (TR-701), a new oxazolidinone antibiotic, against
methicillin-susceptible and methicillin-resistant Staphylococcus aureus strains in
a mouse thigh infection model. Antimicrob Agents Chemother, 55, 3453-60.
MAIURI, L., DE STEFANO, D., RAIA, V. & KROEMER, G. 2015. The holy grail of cystic
fibrosis research: pharmacological repair of the F508del-CFTR mutation. Ann
Transl Med, 3.
MOULD, D. R. & UPTON, R. N. 2013. Basic Concepts in Population Modeling,
Simulation, and Model-Based Drug Development—Part 2: Introduction to
Pharmacokinetic Modeling Methods. CPT Pharmacometrics Syst Pharmacol, 2,
e38-.
NADESALINGAM, K., CONWAY, S. P. & DENTON, M. 2005. Risk factors for
acquisition of methicillin-resistant Staphylococcus aureus (MRSA) by patients
with cystic fibrosis. J Cyst Fibros, 4, 49-52.
NIELSEN, E. I., CARS, O. & FRIBERG, L. E. 2011. Pharmacokinetic/pharmacodynamic
(PK/PD) indices of antibiotics predicted by a semimechanistic PKPD model: a
step toward model-based dose optimization. Antimicrob Agents Chemother, 55,
4619-30.
ONG, V., FLANAGAN, S., FANG, E., DRESKIN, H. J., LOCKE, J. B., BARTIZAL, K. &
PROKOCIMER, P. 2014. Absorption, distribution, metabolism, and excretion of
the novel antibacterial prodrug tedizolid phosphate. Drug Metab Dispos, 42,
1275-84.
41
PROKOCIMER, P., BIEN, P., DEANDA, C., PILLAR, C. M. & BARTIZAL, K. 2012. In
vitro activity and microbiological efficacy of tedizolid (TR-700) against Gram-
positive clinical isolates from a phase 2 study of oral tedizolid phosphate (TR-
701) in patients with complicated skin and skin structure infections. Antimicrob
Agents Chemother, 56, 4608-13.
PROKOCIMER, P., BIEN, P., SURBER, J., MEHRA, P., DEANDA, C., BULITTA, J. B. &
COREY, G. R. 2011. Phase 2, randomized, double-blind, dose-ranging study
evaluating the safety, tolerability, population pharmacokinetics, and efficacy of
oral torezolid phosphate in patients with complicated skin and skin structure
infections. Antimicrob Agents Chemother, 55, 583-92.
QUITTNER, A. L., BARKER, D. H., SNELL, C., GRIMLEY, M. E., MARCIEL, K. &
CRUZ, I. 2008. Prevalence and impact of depression in cystic fibrosis. Current
Opinion in Pulmonary Medicine, 14, 582-588.
REY, E., TRÉLUYER, J.-M. & PONS, G. 1998. Drug Disposition in Cystic Fibrosis.
Clinical Pharmacokinetics, 35, 313-329.
ROBERTS, J. A., KIRKPATRICK, C. M. & LIPMAN, J. 2011. Monte Carlo simulations:
maximizing antibiotic pharmacokinetic data to optimize clinical practice for
critically ill patients. J Antimicrob Chemother, 66, 227-31.
ROBINSON, M. & BYE, P. T. 2002. Mucociliary clearance in cystic fibrosis. Pediatr
Pulmonol, 33, 293-306.
ROGER, C., ROBERTS, J. A. & MULLER, L. 2017. Clinical Pharmacokinetics and
Pharmacodynamics of Oxazolidinones. Clin Pharmacokinet.
42
RYBAK, J. M. & ROBERTS, K. 2015. Tedizolid Phosphate: a Next-Generation
Oxazolidinone. Infect Dis Ther, 4, 1-14.
SALSGIVER, E. L., FINK, A. K., KNAPP, E. A., LIPUMA, J. J., OLIVIER, K. N.,
MARSHALL, B. C. & SAIMAN, L. 2016. Changing Epidemiology of the
Respiratory Bacteriology of Patients With Cystic Fibrosis. Chest, 149, 390-400.
SANDERS, D. B., BITTNER, R. C. L., ROSENFELD, M., HOFFMAN, L. R., REDDING,
G. J. & GOSS, C. H. 2010. Failure to Recover to Baseline Pulmonary Function
after Cystic Fibrosis Pulmonary Exacerbation. Am J Respir Crit Care Med, 182,
627-32.
SARALAYA, D., PECKHAM, D. G., HULME, B., TOBIN, C. M., DENTON, M.,
CONWAY, S. & ETHERINGTON, C. 2004. Serum and sputum concentrations
following the oral administration of linezolid in adult patients with cystic fibrosis.
Journal of Antimicrobial Chemotherapy, 53, 325-328.
SERISIER, D. J., JONES, G. & CARROLL, M. 2004. Eradication of pulmonary
methicillin-resistant staphylococcus aureus (MRSA) in cystic fibrosis with
linezolid. J Cyst Fibros, 3, 61.
SHAH, V. S., ERNST, S., TANG, X. X., KARP, P. H., PARKER, C. P., OSTEDGAARD,
L. S. & WELSH, M. J. 2016. Relationships among CFTR expression, HCO(3)(−)
secretion, and host defense may inform gene- and cell-based cystic fibrosis
therapies. Proc Natl Acad Sci U S A, 113, 5382-7.
SINGH, V. K. & SCHWARZENBERG, S. J. 2017. Pancreatic insufficiency in Cystic
Fibrosis. J Cyst Fibros, 16 Suppl 2, S70-s78.
SIVEXTRO, R. 2015. [package insert]. Whitehouse Station, NJ: Merck & Co. .
43
SLOANE, A. J., LINDNER, R. A., PRASAD, S. S., SEBASTIAN, L. T., PEDERSEN, S.
K., ROBINSON, M., BYE, P. T., NIELSON, D. W. & HARRY, J. L. 2005.
Proteomic analysis of sputum from adults and children with cystic fibrosis and
from control subjects. Am J Respir Crit Care Med, 172, 1416-26.
SMYTH, A. & ELBORN, J. S. 2008. Exacerbations in cystic fibrosis: 3--Management.
Thorax, 63, 180-4.
SOUTHERN, K. W. & BARKER, P. M. 2004. Azithromycin for cystic fibrosis. Eur Respir
J, 24, 834-8.
SPINO, M. 1991. Pharmacokinetics of drugs in cystic fibrosis. Clinical Reviews in
Allergy, 9, 169.
TOUTAIN, P. L., DEL CASTILLO, J. R. E. & BOUSQUET-MÉLOU, A. 2002. The
pharmacokinetic–pharmacodynamic approach to a rational dosage regimen for
antibiotics. Research in Veterinary Science, 73, 105-114.
TOUW, D. J. 1998. Clinical pharmacokinetics of antimicrobial drugs in cystic fibrosis.
Pharmacy World and Science, 20, 149-160.
TRIVEDI, A., LEE, R. E. & MEIBOHM, B. 2013. Applications of pharmacometrics in the
clinical development and pharmacotherapy of anti-infectives. Expert Rev Clin
Pharmacol, 6, 159-70.
TRUST, U. C. F. 2008. Methicillin-Resistant Staphylococcus Aureus (MRSA). .
VAN HAL, S. J., PATERSON, D. L. & LODISE, T. P. 2013. Systematic review and
meta-analysis of vancomycin-induced nephrotoxicity associated with dosing
schedules that maintain troughs between 15 and 20 milligrams per liter.
Antimicrob Agents Chemother, 57, 734-44.
44
WATERS, V., STANOJEVIC, S., ATENAFU, E. G., LU, A., YAU, Y., TULLIS, E. &
RATJEN, F. 2012. Effect of pulmonary exacerbations on long-term lung function
decline in cystic fibrosis. Eur Respir J, 40, 61-6.
WEBER, W. & RÜPPEL, D. 2013. Population Pharmacokinetics. In: VOGEL, H. G.,
MAAS, J., HOCK, F. J. & MAYER, D. (eds.) Drug Discovery and Evaluation:
Safety and Pharmacokinetic Assays. Berlin, Heidelberg: Springer Berlin
Heidelberg.
WHITING, B., KELMAN, A. W. & GREVEL, J. 1986. Population pharmacokinetics.
Theory and clinical application. Clin Pharmacokinet, 11, 387-401.
WILSCHANSKI, M. & NOVAK, I. 2013. The Cystic Fibrosis of Exocrine Pancreas. Cold
Spring Harb Perspect Med, 3.
WONG, E. & RAB, S. 2014. Tedizolid Phosphate (Sivextro): A Second-Generation
Oxazolidinone to Treat Acute Bacterial Skin and Skin Structure Infections. P t,
39, 555-79.

45
FIGURES AND TABLES
Table 1. Demographic characteristics of patients with CF for PK analysis (n=11)
Demographics Mean (SD)
Age (yr) 27.27 (4.78)
Gender (M/F) 6/5
Weight (kg) 59.99 (13.6)
BMI (kg/m
2
) 21.72 (3.60)
Lean body weight (kg) 46.55 (9.832)
CLcr (mL/min) 147.6 (32.43)

Table 2. Mean (SD) plasma TZD PK parameters in Adult CF patients by non-
compartmental method (n=10)

PK Parameters Oral Intravenous
Cmax (g/L)

2.211 (0.7848)

2.964 (0.6427)
Tmax (h)

2.448 (1.407)

1.343 (0.3841)
AUC 0-24 (g⋅h/L)

22.12 (6.5)

22.31 (5.187)
AUC0-∞ (g⋅h/L)

25.95 (8.707)

25.21 (6.931)
F

0.9977 (0.2284)

λ (h
-1
)

0.08758 (0.01461)

0.09767 (0.01737)
t½ (h)

8.134 (1.483)

7.345 (1.569)
CL (L/h)

8.505 (2.742)

8.421 (2.082)
Vss (L)

104.2 (30.48)

76.85 (17.59)
Cmax, maximum concentration; Tmax, time to reach Cmax; AUC, area under the
concentration-time curve; CL, total clearance; F, bioavailability; Vss, apparent volume of
the plasma compartment; T1/2, half-life of elimination; λ, elimination rate constant;

46
Table 3. Parameter estimates for the final PPK model for TZD
Final model
Model parameter Mean Interindividual variability
SD (CV%)
CLt (L/h) 9.72 1.62 (16.6)
Vc (L) 61.6 6.94 (11.3)
Ka 0.428 0.320 (74.8)
CLd (L/h) 4.13 3.69 (89.4)
Vp (L) 26.4 9.44 (35.8)
F 1.04 0.232 (22.4)
PSp 2.875 1.445 (50.3)
CLt, total clearance; Vc, central volume of distribution; Ka, absorption rate constant;
CLd, distributional clearance; Vp, peripheral volume; F, bioavailability; PSp, plasma-to-
sputum partition coefficient;
Note: Parameter estimate standard error could not be calculated due to the small
number of patients

47
Table 4. Estimate of cumulative attainment of the PD targets.
MIC
(mg/L)
Fraction of
distribution
at the
indicated
MIC
Probability of
target
attainment
(AUC/MIC
>20) at the
MIC (200 mg)
Fraction of
expected
response
(>20)
Probability
of target
attainment
(>34.6) at the MIC
(200 mg)
Fraction of
expected
response
(>34.6)
<0.063 0.0497 1 0.0497 1 0.0497
0.125 0.311 1 0.311 1 0.311
0.25 0.397 1 0.397 0.999 0.397
0.5 0.225 0.999 0.225 0.986 0.222
1 0.0166 0.973 0.0161 0.815 0.0135

estimate of cumulative
expected response rate at
200 mg 0.999
estimate of
expected
cumulative
response rate at
200 mg 0.994
MIC
(mg/L)
Fraction of
distribution
at the
indicated
MIC
Probability of
target
attainment
(AUC/MIC >20)
at the MIC
(400 mg)
Fraction of
expected
response
(>20)
Fractional target
attainment
(>34.6) at the MIC
(400 mg)
Fraction of
expected
response
(>34.6)
<0.063 0.0497 1 0.0497 1 0.0497
0.125 0.311 1 0.311 1 0.311
0.25 0.397 1 0.397 1 0.397
0.5 0.225 1 0.225 0.999 0.225
1 0.0166 0.999 0.0165 0.989 0.0164

estimate of cumulative
expected response rate at
400 mg 0.999
estimate of
cumulative
expected response
rate at 400 mg 0.999

48

Figure 1. Diagram of structural model describing the plasma and sputum
pharmacokinetics of TZD following IV and oral administration in each subject.

CLt: total clearance, CLd: distributional clearance, Vc: central volume of distribution, Vp:
peripheral volume of distribution, Ka: absorption rate constant, F: bioavailability, PSp:
plasma-to-sputum partition coefficient; r(t): intravenous infusion; b: bolus dose;

𝐶(𝑡)
'()*)+
,-
= 𝑃𝑆𝑝∗3
𝐴(𝑡)
(56'+6 ,,-
𝑉𝑐
∗0.2=
𝐶(𝑡)
'()*)+
>?
= 𝑃𝑆𝑝∗3
𝐴(𝑡)
(56'+6,>?
𝑉𝑐
∗0.2=

A(t) is the estimated amount (mg) of TZD in the central compartment (compartment 1
for IV, and compartment 4 for PO), C(t) is the measured TZD concentration in sputum.
Unbound drug concentrations in plasma were estimated by correcting for protein
binding (~80%).

49
Figure 2. Concentration-time profiles (mean ± SD) of 200 mg of TZD in (A) plasma, (B)
plasma for the first 8 hours, (C) sputum and unbound plasma, and (D) sputum and
unbound plasma for the first 8 hours in patients with CF following the third dose.

50
Figure 3. Individual predicted versus observed concentration-time profiles of TZD in CF
patients (n=11) following oral administration. Predicted profiles based on the individual
subject conditional mean estimates from the MLEM analysis. Note: Each subject’s oral
and IV data were modeled simultaneously.

S u b je c t 1
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 2
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 3
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tr a tio n (m g /L )
S u b je c t 4
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 5
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tr a tio n (m g /L )
S u b je c t 6
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tr a tio n (m g /L )
S u b je c t 7
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 8
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 9
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 1 0
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 1 1
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
51
Figure 4. Individual concentration-time profiles of TZD in CF patients (n=11) following
intravenous administration. Predicted profiles based on the individual subject conditional
mean estimates from the MLEM analysis. Note: Each subject’s oral and IV data were
modeled simultaneously.

S u b je c t 1
0 2 4 4 8 7 2 9 6
0
1
2
3
4
O bserved
Predicted
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 2
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 3
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 4
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 5
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 6
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 7
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 8
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 9
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 1 0
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
S u b je c t 1 1
0 2 4 4 8 7 2 9 6
0
1
2
3
4
Predicted
O bserved
T im e (h )
T Z D c o n c e n tra tio n (m g /L )
52
Figure 5. Goodness-of-fit plots of the final PK model Observed TZD concentrations
versus individual model predicted concentrations in (A) plasma (R
2
=0.91) (B) sputum
(R
2
=0.93); conditional standardized residuals versus model prediction in (C) plasma and
(D) sputum; observed TZD concentrations versus population model predicted
concentrations in (E) plasma and (R
2
=0.77) (F) sputum (R
2
=0.54)

53
Figure 6. MRSA MIC frequency distribution and mean (SD) of observed sputum TZD
concentration-time profile (MIC90=0.5 mg/L)

Sputum TZD 200 mg
0 10 20 30 40
0.0625
0.125
0.25
1
2
IV
PO
0.5 MIC
90
Time (h)
TZD concentration (mg/L)
0.063
0.125
0.25
0.5
1
2
MIC frequency
MIC (mg/L)
0.05
0.31
0.40
0.22
0.02
54
Figure 7. Target attainment probabilities for (A) bacteriostatic (fAUC/MIC >20) and
(B) bactericidal effect (fAUC/MIC>34.6) at MIC90=0.5 mg/L.

Abstract (if available)

Abstract Over the past decade, the prevalence of infections involving Methicillin-resistant Staphylococcus aureus (MRSA) in patients with cystic fibrosis (CF) has increased significantly. Tedizolid (TZD) demonstrates excellent activity against MRSA and a favorable safety profile. The pharmacokinetics (PK) of several antibiotics has shown to be altered in CF patients. The purpose of this study was to characterize the pharmacokinetics-pharmacodynamics (PK/PD) of TZD in this population. Eleven patients with CF were randomized to receive tedizolid phosphate 200 mg PO or IV once daily for 3 doses, with a minimum 2-day washout, followed by crossover to the remaining dosage form. Plasma and expectorated sputum were collected following the third dose of each dosage form for analysis. Population pharmacokinetics (PPKs) were described by a 2-compartment model. The estimated population mean ± standard deviation of total clearance, central volume of distribution, and absolute bioavailability were 9.72 ± 1.62 L/h, 61.6 ± 6.94 L, and 1.07 ± 0.165, respectively. The estimated sputum partition coefficient was 2.875. The total clearance is higher in CF patients when compared with healthy volunteers

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Asset Metadata

Creator Park, A young Jenny (author)

Core Title Pharmacokinetics-pharmacodynamics of tedizolid in plasma and sputum of adults with cystic fibrosis

School School of Pharmacy

Degree Master of Science

Degree Program Molecular Pharmacology and Toxicology

Publication Date 04/10/2019

Defense Date 04/10/2018

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

Tag cystic fibrosis,OAI-PMH Harvest,pharmacodynamics,pharmacokinetics,tedizolid

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Adams, James David, Jr. (committee chair), Beringer, Paul M. (committee chair), D'Argenio, David Z. (committee member), Louie, Stan Gee (committee member)

Creator Email ayoung.jenny.park@gmail.com,ayoungpa@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-11601

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