Pharmacokinetics of unbound mycophenolic acid and 7-O-mycophenolic acid glucuronide in renal transplant recipients

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Pharmacokinetics of unbound

mycophenolic acid and 7-O-mycophenolic acid glucuronide in renal transplant

recipients

Development of an UPLC-MS/MS method and a population pharmacokinetic model

Ole Martin Drevland

Thesis for the degree of Master of Pharmacy Department of Pharmaceutical Biosciences

45 credits

School of Pharmacy

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

May 2021

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Pharmacokinetics of unbound mycophenolic acid and 7-O-mycophenolic acid glucuronide in

renal transplant recipients

Development of an UPLC-MS/MS method and a population pharmacokinetic model

by

Ole Martin Drevland

Thesis for the degree of Master of Pharmacy Department of Pharmaceutical Biosciences

School of Pharmacy

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

Supervisors

Associate professor Ida Robertsen

Department of Pharmaceutical Biosciences, School of Pharmacy, UiO Professor Hege Christensen

Department of Pharmaceutical Biosciences, School of Pharmacy, UiO

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Pharmacokinetics of unbound mycophenolic acid and 7-O-mycophenolic acid

glucuronide in renal transplant recipients – development of an UPLC-MS/MS method and a population pharmacokinetic model

Ole Martin Drevland http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

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Forord

Denne masteroppgaven er utført ved avdeling for farmasøytisk biovitenskap, under veiledning av førsteamanuensis Ida Robertsen og professor Hege Christensen. Arbeidet startet høsten 2020 og ble fullført våren 2021.

Først og fremst vil jeg takke Ida for fantastisk hjelp, veiledning og støtte underveis. Du er alltid tilgjengelig, i godt humør og ser aldri problemer, kun utfordringer. Denne oppgaven hadde ikke blitt det samme uten deg. Takk til Hege for gode samtaler, råd og din positive innstilling. Den har virkelig smittet over.

Vil også rette en stor takk til PK-gruppen sine stipendiater, Kine og Markus, for hjelp med både stort og smått, og at døren alltid har vært åpen hos dere. Takk også til ingeniør Eline som har hjulpet til med alt jeg måtte lure på av labarbeid og utstyr.

Takk til mine medstudenter Tine Herlofsen og Vilde Fjell for at dere har vært med på å gjøre dette til et veldig fint år, det har vært en fryd å dele kontor med dere. Til slutt vil jeg takke familien for at dere alltid støtter og motiverer meg. Takk til min kjære Julie for all

tålmodighet og støtte igjennom samtlige fem år.

Oslo, Mai 2021 Ole Martin Drevland

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List of abbreviations

AcMPAG Acyl glucuronide

AIC Akaike information criteria

AUC Area under the curve

bMPA Protein bound MPA

bMPAG Protein bound MPAG

CI Confidence interval

CKD Chronic kidney disease

CL Clearance

CLhep Hepatic clearance

CLint Clearance intrinsic

Cmax Maximum concentration

CNI Calcineurin inhibitor

CsA Cyclosporin A

Cu Unbound concentration

CV Coefficient of variation

CYP Cytochrome P450

d6 Deuterium labeled

DM-MPA 6-O-desmethyl-MPA

EC-MPA Enteric coated mycophenolic acid

ED Equilibrium dialysis

EHC Enterohepatic recirculation

EMA European medicine agency

ESRD End stage renal disease

fu Free fraction

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Gdur Duration of bile secretion

GFR Glomerular filtration rate

HSA Human serum albumin

IL Interleukin

IMPDH Inosine monophosphate dehydrogenase

IS Internal standard

LLOQ Lower limit of quantification

LSS Limited sampling strategy

m/z Mass-to-charge ratio

MMF Mycophenolate mofetil

MPA Mycophenolic acid

MPAG 7-O-MPA-glucuronide

MRP-2 Multi-drug resistance protein-2

MS/MS Tandem mass spectrometry

NPAG Non-parametric adaptive grid

NSA Non-specific adsorption

PBS Phosphate buffered saline

PK Pharmacokinetic

PopPK Population pharmacokinetic

PUF Plasma ultrafiltrate

QC Quality control

r Correlation coefficient

RMSE Root mean square error

RNA Ribonucleic acid

RPM Rounds per minute

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RRT Renal replacement therapy

SD Standard deviation

SNP Single nucleotide polymorphism

SRM Selected reaction monitoring

T1/2 Elimination half life

TDM Therapeutic drug monitoring

Tgal Time for bile secretion

Tlag Lag time

Tmax Time to reach maximum concentration

TNF Tumor necrosis factor

UF Ultrafiltration

UGT Uridine diphosphate

glucuronosyltransferases

ULOQ Upper Limit of Quantification

uMPA Unbound MPA

uMPAG Unbound MPAG

UPLC Ultra Performance Liquid Chromatography

Vd Volume of distribution

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Abstract

Introduction: Throughout the years, mycophenolic acid (MPA), the active compound of mycophenolate mofetil, has gained widespread acceptance as the antimetabolite of choice in solid organ transplant treatment regimens. MPA is glucuronidated to the main metabolite mycophenolic acid glucuronide (MPAG), which exhibits enterohepatic recirculation (EHC).

Both MPA and MPAG displays a high protein binding of 97-99% and 82%, respectively. The aim of this thesis was twofold; to develop and validate an UPLC-MS/MS method for determination of the unbound concentration of MPA and MPAG and to develop a pharmacokinetic population (PopPK) model describing both unbound and total concentration of MPA and MPAG using samples from renal transplant recipients included in the DayNight study.

Methods: Centrifree^® Ultrafiltration Device (filtration unit) was used to separate the unbound concentrations of MPA and MPAG. In the development of the sample preparation method, centrifugation conditions, volume yield of plasma ultrafiltrate, time of centrifugation and application volume as well as non-specific adsorption onto the membrane or the walls of the filtration unit were tested. The final method was validated according to European Medicine Agency (EMA) guidelines for bioanalytical method validation and further applied to measure unbound MPA concentrations in samples from 9 renal transplant recipients included the DayNight study. A popPK model of unbound and total concentrations of MPA and MPAG was developed using the data from the DayNight study.

Results: The centrifugation conditions that were implemented into the final sample preparation method were centrifugation of the filtration unit at 1000 g at 37°C for 10 minutes, with an application plasma volume of 250 µL. Within‐series and between‐series performance of the unbound MPA and MPAG assays were assessed with resulting coefficients of variation < 6.3%, and < 8.7%, respectively. The mean accuracy ranged from 94.5-105.3% for unbound MPA and from 96.0–106.8% for unbound MPAG. The mean ± SD free fractions of MPA and MPAG in the patients included in the DayNight study were 1.18% ± 0.15% and 26.5% ± 5.1%, respectively. The final popPK model was best fitted with a two-compartment model for MPA and a one-compartment model for MPAG, with an absorption phase with lag time and a gallbladder compartment connected with a gut compartment to describe the EHC of MPAG.

The protein binding was modeled with a linear approach. Unbound and total concentrations of

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XIII MPA and MPAG were adequately predicted for some patients, while a tendency of underprediction was observed in other patients.

Conclusion: The validated method to determine unbound concentrations of MPA and MPAG in plasma utilizing ultrafiltration and subsequent UPLC-MS/MS analysis showed acceptable accuracy and imprecision and satisfied the EMA requirements. In addition, a popPK model for unbound and total concentrations of MPA and MPAG in renal transplant recipients was developed. However, further optimization and the inclusion of more patients are required to finalize the model before a potential use to guide individualized dosing of MPA in the clinic.

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Sammendrag

Innledning: Gjennom de siste tiårene har mykofenolat syre (MPA), den aktive metabolitten til mykofenolat mofetil, fått bred aksept som den valgte antimetabolitten i de fleste behandlingsregimer etter organtransplantasjon. MPA blir glukuronidert til hovedmetabolitten mykofenolat-glukuronid (MPAG), som gjennomgår enterohepatisk resirkulasjon. Både MPA og MPAG utviser stor grad av proteinbinding på henholdsvis 97-99% og 82%. Målet med denne oppgaven var todelt; å utvikle og validere en UPLC-MS/MS-metode for bestemmelse av ubundet konsentrasjon av MPA og MPAG, og å utvikle en farmakokinetisk populasjonsmodell (PopPK) som beskriver både ubundet og total konsentrasjon av MPA og MPAG ved å benytte plasmaprøver fra nyretransplanterte pasienter inkludert i DagNatt-studien.

Metode: Centrifree® Ultrafiltration Device (filtreringsenhet) ble brukt til å separere ubunden konsentrasjonen av MPA og MPAG. Under utviklingen av prøveopparbeidelsen ble sentrifugeringsbetingelser, volumutbytte av plasma-ultrafiltrat, sentrifugeringstid og påføringsvolum så vel som uspesifikk binding på membranen eller veggene i filtreringsenheten testet. Den endelige metoden ble validert i henhold til European Medicine Agency (EMA) retningslinjer for bioanalytisk metodevalidering, og videre anvendt til å måle ubundet MPA- konsentrasjon i prøver fra 9 nyretransplanterte pasienter inkludert i DagNatt-studien. En popPK-modell av ubundne og totale konsentrasjoner av MPA og MPAG ble utviklet ved hjelp av konsentrasjonene fra DagNatt-studien.

Resultater: Sentrifugeringsbetingelsene som ble implementert i den endelige metoden var sentrifugering ved 1000 g i 10 minutter (37°C) med et påføringsvolum på 250 µL. Inter- og intradag analyser ble gjennomført for ubundet MPA og MPAG med resulterende variasjonskoeffisienter på henholdsvis <6,3% og <8,7%. Gjennomsnittlig nøyaktighet varierte fra 94,5-105,3% for ubundet MPA og fra 96,0-106,8% for ubundet MPAG. Gjennomsnittlig (±

SD) fri fraksjon av MPA og MPAG var henholdsvis 1,18% ± 0,15% og 26,5% ± 5,1%

(gjennomsnitt ± SD) i pasientene inkludert i DagNatt studien. Den endelige popPK-modellen var en to-kompartment modell for MPA og en en-kompartment modell for MPAG, med en absorpsjonsfase med Tlag og et galle-kompartment koblet med et tarm-kompartment for å beskrive MPAG sin enterohepatiske resirkulering. Proteinbindingen ble modellert med en lineær tilnærming. Ubundet og total konsentrasjon av MPA og MPAG ble tilstrekkelig predikert

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XV av modellen for noen pasienter, mens en tendens til underpredikering ble sett hos andre pasienter.

Konklusjon: Den validerte metoden for å bestemme ubundne konsentrasjoner av MPA og MPAG i plasma ved bruk av ultrafiltrering og påfølgende UPLC-MS/MS-analyse viste akseptabel nøyaktighet og presisjon, og oppfylte EMA-kravene for bioanalytiske metoder. I tillegg ble det utviklet en popPK-modell for ubundet og total konsentrasjon av MPA og MPAG hos nyretransplanterte pasienter. Det er imidlertid nødvendig med ytterligere optimalisering og inkludering av flere pasienter før modellen potensielt kan benyttes i klinisk for å veilede individtilpasset dosering av MPA.

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1 Introduction

1.1 Chronic kidney disease

Chronic kidney disease (CKD) occurs as a consequence of various heterogeneous diseases which over a long period of time changes the function and structure of the kidney irreversibly (1, 2). Diabetes is the main cause of CKD in most countries, and accounts for 40% of new cases.

Hypertension, obesity, smoking and proteinuria are other underlying causes. Renal function will also naturally be impaired with increasing age, and it has been shown that glomerular filtration rate (GFR) decreases by 1 mL/min annually from the age of 40 (2). The best indicator available that describes renal function is GFR, which is the total amount of fluid that is filtered through all the functional nephrons per unit of time (3). Upon loss of the functional nephrons, GFR will be reduced and the remaining nephrons will be overloaded (3). The actual definition and classification of CKD has evolved over time, but the current international guidelines define CKD as impaired renal function shown by a GFR <60 mL/min/1,73 m², and/or signs of ongoing renal damage (e.g. albuminuria) with a duration of at least 3 months, to be able to distinguish it from acute kidney injury. CKD is currently divided into 6 stages based on the degree to which GFR is impaired (4, 5). Figures from Norway based on the Nord-Trøndelag survey (HUNT) showed that the prevalence of CKD is about 10% (6). In the same study, 1% of the surveyed population had severe to very severe renal impairment (stage 4 and stage 5, Table 1), while about 3% of the population had moderate impairment of the renal function (stage 3a and stage 3b, Table 1) (6). There is a clear link between CKD, cardiovascular events and death. This applies regardless of the underlying cause. The demographic development observed in the society today is thus worrying. With an aging population and an increased prevalence of chronic diseases such as hypertension and morbid obesity, there will most likely be a sharp increase in people with CKD in the years to come (7, 8). If kidney failure (stage 5, Table 1) occurs, renal replacement therapy (RRT) will be required. There are two modalities of RRT, renal transplantation and dialysis.

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2 Table 1. Classification of Chronic kidney disease, according to international guidelines (4).

Stage Description GFR

(mL/min/1,72m²)

1 Kidney damage with normal or increased GFR ≥90

2 Kidney damage with mild decreased GFR 60-89

3a Mildly to moderately decreased GFR 45-59

3b Moderately to severely decreased GFR 30-44

4 Severely decreased GFR 15-29

5 Kidney failure/ESRD <15

ESRD, end-stage renal disease; GFR, glomerular filtration rate. Modified from Levey et al. (1) and Webster et al. (2).

1.2 Renal transplantation

The first successful renal transplantation took place in Boston in 1954 and was performed on two monozygotic twins (9). Outstanding graft function was obtained and the recipient had a functioning graft without rejection for 26 years (10). Only 2 years after the historic event in Boston the first renal transplantation in Norway and the Nordic countries was performed at Ullevål hospital, Oslo. The patient lived for 30 days with the new kidney (11). All renal transplantations in Norway have since 1983 been performed at Oslo University Hospital, Rikshospitalet. The center performs between 250 to 300 renal transplantations every year. In 2019 the number was 258, making the center the largest in Northern Europe and one of the largest worldwide (12). With 48.3 renal transplants per million inhabitants Norway has one of the highest kidney transplantation rates in Europe, together with countries like Spain, France and Netherlands (12, 13).

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Figure 1. Prevalence of patients receiving renal replacement therapy in the period 1990-2019. Tx:

Transplantation, HD: Hemodialysis and PD: Peritoneal dialysis. (Data from the Norwegian Renal Registry, January 2021 (12))

1.2.1 Immunosuppressive treatment

To avoid post-transplant rejection, a strict, immunosuppressive regimen is required. Current immunosuppressive therapy has reduced the number of rejection episodes after kidney transplantation to <10%, and 1-year graft and patient survival is now > 96% (14). After organ transplantation, it is important to find the combination of drugs that gives the patient adequate immunosuppression to avoid rejection and limit the adverse effects. This balance can be challenging to obtain as these drugs induce a number of adverse effects also in therapeutic doses (15). Standard immunosuppressive therapy for kidney transplant patients in Norway consist of a combination of four different types of drugs: mycophenolic acid (MPA), calcineurin inhibitor (CNI), corticosteroid and interleukin-2 (IL-2) antagonist (15). At time of transplantation, all patients receive induction treatment with intravenously methylprednisolone as well as a specific T- or B-cell inhibitory drug depending on the patient's immunological risk profile. With a standard immunological risk profile, the preferred drug is the IL-2 antagonist basiliximab (14).

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4 Prednisolone is initially used in high doses and gradually reduced to 5 mg which is the recommended maintenance therapy. CNIs is considered the most important immunosuppressive drug following kidney transplantation. These are T-cell specific drugs that inhibit cell activation, and have no effect on hematopoiesis or the function of phagocytic cells.

A major challenge with CNI is renal toxicity. The pathophysiology behind this is still debated.

In addition, these drugs often cause hypertension, hyperlipidemia, increased risk of cancer and post-transplant diabetes mellitus (14, 16). The combination of CNI, MPA and corticosteroids is a lifelong maintenance therapy, and due to different mechanisms of action this combination will have additive and synergetic effects (16).

1.3 Mycophenolate

A post-penicillium hunt starting in the 1940s eventually resulted in the discovery of a strain of Penicillum brevicompartum that produced a material inhibiting growth of Staphylococcus aureus. The active agent was a known compound named MPA discovered as early as in 1893 by the Italian physician Bartolomeo Gosio. MPA has gone from being an obscure fungal metabolite, to a powerful member of the physicians armamentarium (17). Throughout the years, MPA has gained widespread acceptance as the antimetabolite of choice in solid organ transplant treatment regimens (18). Currently, two MPA compounds are available on the Norwegian market; mycophenolate mofetil (MMF), marketed as CellceptÒ and enteric coated mycophenolate sodium (EC-MPA), marketed as MyforticÒ.

1.3.1 Mechanism of action

MMF is a prodrug of MPA, a potent, non-competitive, reversible inhibitor of inosine monophosphate dehydrogenase (IMPDH). By inhibiting IMPDH, MPA suppresses cell- mediated immune responses an antibody formation (19). IMPDH is a major rate-limiting enzyme heavily involved in de novo synthesis of guanine nucleotides, and is largely expressed in human immunocytes (20). De novo purine synthetize is crucially important for proliferative responses for T- and B- lymphocytes, whereas other cell types can utilize salvage pathways.

Consequently, the cytotoxic effects are more potent on lymphocytes than on other cell types in the body (21). Another factor that increases the selectivity of MPA, is the existence of isoforms of the target enzyme. IMPDH-II, an isoform of IMPDH, which is expressed in activated B- and T-lymphocytes is inhibited five times more effectively by MPA than IMPDH-I which is

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5 expressed in most cells throughout the body (19). MPA also inhibits the recruitment of lymphocytes and monocytes in vivo, and the glycosylation and expression of adhesion molecules. Inhibition by MPA also leads to a reduced amount of nitric oxide produced by the inducible nitric oxide synthase, which gives rise to an anti-inflammatory effect and reduced tissue damage (22). The mechanism of action of MPA is illustrated in Figure 2.

Figure 2. The mechanism of action of mycophenolic acid (MPA). Mycophenolate mofetil (MMF) is converted to the active metabolite MPA by carboxylesterases (CES). MPA inhibits inosine monophosphate dehydrogenase (IMPDH) which catalyze the oxidation of inosine monophosphate (IMP) to xanthine monophosphate (XMP). This is the rate limiting step in de novo purine synthesis. XMP is subsequently dehydrogenated to guanosine monophosphate (GMP). GMP is converted to guanosine triphosphate (GTP) and deoxyguanosine triphosphate (dGTP). The strong inhibition of IMPDH cause low availability of nucleotides (GMP, GTP and dGTP), and consequently prevents DNA synthesis and cell proliferation.

Modified from Broen et al. (23).

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1.3.2 Pharmacokinetics

Absorption

Absorption characteristics of MPA vary significantly between the two oral dosage forms available (EC-MPA and MMF). Both time to reach maximum concentration (Tmax), trough concentration and systemic exposure have been shown to vary between the two different formulations (24). MMF is, as previously mentioned, the 2-morpholinoethyl ester of MPA, with a structural formula shown in Figure 2. MMF is a prodrug of MPA produced with the intention of increasing the amount of drug that reaches the systemic circulation when administered orally.

Following oral administration, MMF is hydrolyzed to MPA by esterases in the small intestine, ventricle, blood and liver. Drugs absorbed intestinally are taken up directly into the portal circulation. Due to the high esterase concentrations present in the liver, it is most likely that the conversion to MPA mainly takes place there (25). MMF is not quantifiable in plasma after oral administration but is measurable after intravenous administration. This undergird the almost complete first pass de-esterification of the drug (26). After ingestion of MMF, MPA display a mean oral bioavailability of 94%. A lower mean oral bioavailability of 80.7% (90% CI 74.0, 88.8) has been observed in renal transplant patients (24). Possible explanations for the observed lower bioavailability in this patient population can be poor postoperative absorption or the fact that there is metabolism through the gastrointestinal tract via glucuronidation of MPA (24).

Tmax for MPA usually occurs 1-2 hours after MMF dosing, while for the main metabolite 7-O- MPA-glucuronide (MPAG) it takes 1-4 hours before Tmax is reached (24). After ingestion of EC-MPA, MPA has a mean absolute bioavailability of 72%. EC-MPA shows an average lag time (Tlag) in the increase of MPA concentration from 0.25 to 1.25 hours, and Tmax occurs 1.5- 2.75 hours post dosing (24).

Distribution

In whole blood, 99.99% of MPA is found in the plasma fraction with only 0.01% in erythrocytes and other cellular elements, which supports the rational of using plasma or serum as the matrix for quantitation of MPA concentrations (27). The mean MPA apparent volume of distribution (Vd) is 4.0 ± 1.2 L/kg. In a population pharmacokinetic model, the volume of the central compartment was shown to be 91 L, while the volume of the peripheral compartment was 237 L (28). MPA will, in patients with normal liver and kidney function, bind excessively to albumin in the order of 97-99%, and will not bind significantly to alfa1-acid glycoprotein (27).

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7 MPAG also displays a high protein binding to human serum albumin (HSA), approximately 82% in stable renal transplant patients (26).

In vitro studies have shown that although binding of MPA to albumin is constant in the therapeutic range (1-60 mg/L), HSA concentration can determine the free fraction of MPA.

This is exemplified by the fact that the free fraction of MPA decreased from 53 to 0.92% as the HSA concentration increased from 0.7 to 69 g/L (27). As it is the free MPA concentrations that determine the immunosuppressive effect, factors that can alter the protein binding can influence the pharmacodynamics of the drug (27, 29). Examples of such factors can be significantly renal dysfunction, liver disease and hypoalbuminemia (24). The potential of drug-drug displacement from plasma proteins has been investigated in vitro (27). The binding of MPA was unaltered by warfarin, phenytoin, tacrolimus, cyclosporin A and prednisone in normal therapeutic concentrations (27). More interesting, the free fraction of MPA increased around 3-fold as the concentration of the metabolite MPAG increased to 475 mg/L. MPAG concentrations in this range is above therapeutic levels. However, in presence of severe renal impairment, concentrations higher than those have been reported to displace MPA from its plasma protein binding sites have been reported (26).

Metabolism and excretion

The primary metabolic pathway of MPA is glucuronidation by the uridine diphosphate glucuronosyltransferases (UGTs). The glucuronidation takes place both in the gastrointestinal tract, kidney and liver (Figure 3) (24). UGT1A9 produces the main metabolite, MPAG, which is a phenolic glucuronide with no pharmacological effect on IMPDH (30). The isoenzymes UGT1A7, UGT1A8 and UGT1A10 are also, to a small extent, involved in the formation of the main metabolite. MPAG is generally present in the plasma with concentrations 20- to 100-fold higher than MPA (24). Three other minor metabolites are formed, a 7-O-glucoside, 6-O- desmethyl-MPA (DM-MPA) and an acyl glucuronide (AcMPAG). The 7-O-glucoside metabolite has, similar to MPAG, no inhibitory effect on IMPDH, while AcMPAG has shown to inhibit IMPDH in a concentration-dependent manner (31). DM-MPA is a phase I metabolite of MPA and was the last one to be detected and identified. The cytochrome P450 (CYP) 3A isoforms appear to be involved in 50% of DM-MPA formation (24). To study drug metabolism and excretion there has been performed studies with single dose oral administration of radiolabeled MMF in humans (26). In one of these studies, four healthy fasting men were given

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8 MMF 1 g as a solution. A mean of 93% of the recovered radioactivity was in urine, while the total recovery of the dose in the faeces averaged 5,5%. Of the material recovered in urine, MPAG was the largest contributor with 87% (26). Both MPAG and AcMPAG are mainly excreted in the urine via tubular secretion mediated by multi-drug resistance protein-2 (MRP- 2), the same transporter involved in the biliary excretion of MPAG (24).

Figure 3. A simplified illustration that depict the metabolic pathway of mycophenolate mofetil (MMF).

MMF is converted to the active metabolite MPA by carboxylesterases (CES 1 and CES 2) both in hepatocytes and enterocytes. The main metabolite MPAG is mainly produced in the liver by UGT1A9. UGT1A7, UGT1A8 and UGT1A10 are also to some extent involved in the formation of MPAG, but it is mainly in intestinal cells. Thereafter, MPAG is either eliminated through bile via multidrug resistance-associated protein 2 (MRP-2) or excreted into urine. MPAG excreted into bile is deconjugated back to MPA and reabsorbed in the colon. MPAG; 7-O-MPA-glucuronide, AcMPAG; acetyl 7-O-MPA-glucuronide, DM- MPA; desmethyl-MPA. Modified from Lamba V. et al. (32)

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9 Enterohepatic recirculation

After oral administration, MMF has a sharp initial peak in the plasma concentration-time profile at around 1 hour, but there is also a secondary peak occurring around 6 to 12 hours post dosing (33). Enterohepatic recirculation is responsible for the presence of the secondary peak, by biliary excretion of the major metabolite MPAG followed by a deconjugation back to MPA and reabsorption in the colon (Figure 3). The conversion back to MPA appears to be performed by glucuronidase enzymes encoded by bacteria located in the gastrointestinal tract (24, 26). The effect of enterohepatic recirculation has been investigated in a cross-over study by using cholestyramine with single doses of MMF (26). As a strong anionic exchange resin, cholestyramine is expected to bind MPA and prevent its reabsorption. The mean systemic exposure of MPA was decreased by approximately 40%, and a substantial part of this decline can be related to a reduction in MPA concentrations from 6 hours onwards. It is important to note that there was no difference in Cmax with or without cholestyramine, indicating that the absorption of MMF not was affected (26).

1.3.3 Interindividual pharmacokinetic variability

All humans differ in their responsiveness to drugs and there is thus often a need to tailor the doses to each individual patient. Interindividual variability (variability between subjects) in drug response is a challenge in clinical practice, especially for drugs with a narrow therapeutic window, and can make it difficult to find the optimal dose of a drug. There are a variety of reasons why people differ in their responsiveness to a given dose, e.g. genetics, age, gender, body weight, concomitant drug therapy, and other environmental factors (34).

Several important factors contribute to interindividual pharmacokinetic variability of MPA. It has been shown that dose-normalized systemic exposure of MPA can vary more than 10-fold (35). Concomitant immunosuppressant therapy is well known to influence the pharmacokinetics of MPA. Published data show that patients receiving tacrolimus or sirolimus achieve higher concentrations of MPA than those receiving cyclosporine A (CsA) (24).

Initially, it was hypothesized that this increased concentration of MPA was due to an inhibitory effect on UGT1A9 by tacrolimus (36). It has now been demonstrated that CsA inhibit the drug transporter MRP-2, which will lead to decreased biliary excretion, and less reabsorption of MPA via enterohepatic recirculation (37). Glucocorticoids induces a variety of drug metabolizing enzymes including UGTs, and can therefore interfere with MPA disposition (24).

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10 Active biliary secretion and glucuronidation are important processes under genetic control and another source to interindividual variability. In human liver microsomes, a 17-fold variation in UGT1A9 expression has been demonstrated (38). A part of this variability is linked to single nucleotide polymorphisms (SNPs) in the promotor region of the UGT1A9 gene. Of these SNPs, -275T>A and -2152C>T have shown best correlation with UGT1A9 expression and these mutations are seen in 15% of the Caucasian population (24, 38). Approximately 2-fold higher protein levels of UGT1A9 have been seen in the carriers, and patients with these SNPs have shown a reduction in MPA systemic exposure, explained with a decrease in enterohepatic recirculation. Genetic variability has also been reported in the gene encoding MRP-2, but the effect on biliary excretion, active tubular secretion and gastrointestinal reabsorption is yet to be known. Food, dialysis, gender, ethnicity, co-morbidities, hypoalbuminemia and renal/hepatic impairment are other important factors that complicate the disposition of MPA and cause interindividual variability (24). The potential impact of one or more factors is difficult to predict in a given patient, therefore therapeutic drug monitoring of MPA has been suggested to allow for individualization of MMF dose and to reduce variability.

1.3.4 Therapeutic drug monitoring

Various transplant centers have implemented therapeutic drug monitoring (TDM) for CNIs (tacrolimus and cyclosporin) and sirolimus in their posttransplant treatment protocols, but for MPA the role of TDM is still undergoing continued debate, and has not been well established (39). Renal transplant recipients usually receive MMF in doses of 0.75-1g twice daily depending on which CNI they are on (24). Despite extensive intra- and interindividual variability in terms of pharmacokinetics and -dynamics, there is currently no individual dose adjustment of MPA (40). The most common is to base TDM of immunosuppressants on single concentrations taken immediately before dosing, so-called through concentrations. For MPA, on the other hand, it has been challenging to show a correlation between the trough concentration and protection against rejection episodes (41). The systemic exposure (AUC) of MPA is more strongly correlated and predictive of rejection than the MPA through concentration but is impractical to perform in routine clinical practice. Therefore, limited sampling strategies have been proposed. Basically, limited sampling strategies use a limited number of timed samples (suitable for an ordinary clinical visit) and either a Bayesian analysis or a multiple regression-generated equation to forecast the actual MPA exposure (42). AUC

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11 should lay between 30 to 60 mg*h/L after a transplant to reduce the risk of rejection and toxicity (43). The upper limit (>60 mg*h/L) is based on lack of evidence for further reduction in risk of rejection, but is not a defined upper limit towards toxicity (39). Further research should attempt to define variables that can modulate the pharmacokinetics of MPA, which would in turn affect the utility MPA TDM.

1.3.5 Adverse effects and toxicity

Adverse effects observed following treatment with MMF is gastrointestinal-related. Nausea, diarrhea, abdominal pain but also hematological events as anemia and leukopenia are often observed. As all other immunosuppressive drugs, MPA will also increase the risk of infections such as cytomegalovirus and herpes zoster (24, 44). There have been difficult to find a significant correlation between adverse effects and pharmacokinetic variables as trough concentrationsand systemic exposure (AUC). Some studies show that patients with anemia and leukopenia had significantly higher MPA AUC and trough concentrationscompared with those not experiencing these effects (45). Other studies, on the other hand, show a less pronounced relationship. Free concentration of MPA may be better than AUC and trough concentrations in predicting leukopenia and other toxic events caused by MPA (24). MPA has shown no significant nephrotoxicity, unlike e.g. calcineurin inhibitors (19).

Glucuronidation is known to be one of the most important phase II metabolic pathways for drug substances in humans. This process is often considered to be a detoxification process since the resulting glucuronide often possess less intrinsic biologic activity than their corresponding aglycones. Acyl glucuronides have been linked to a wide range of adverse drug effects, including hypersensitivity reactions and cellular toxicity. The cellular toxicity is due to the electrophilic properties of the acyl glucuronides which can result in a covalent bond to lipids, proteins and nucleic acids (46). There is a hypothesis that AcMPAG is causing gastrointestinal disturbances, due to a proinflammatory effect. In a model with isolated human mononuclear leukocytes, AcMPAG induced the release of IL-6, TNF-α and cytokine messenger RNA expression. Significant interindividual variability in the proinflammatory response was found (30-fold for IL-6, 3-fold for TNF-α), which may explain why only some are bothered with gastrointestinal symptoms, but further studies are required (47).

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12

1.4 Drug protein binding

Drug substances are in vivo distributed to various compartments of the body - unbound in plasma, bound to various constituents of plasma such as plasma proteins and erythrocytes, unbound in tissue and bound to tissue components. Due to the rapidly reversible drug binding, a dynamic equilibrium occurs between the various compartments where the drug can reside. It is generally assumed that only the unbound fraction of the drug can diffuse across membranes, and ultimately interact with a receptor that provides the pharmacological effect (48, 49) (Figure 4). There will arise an equilibrium between the drug bound to the plasma protein and the unbound fraction, and this equilibrium will rapidly respond to changes in the environment.

Temperature, pH, drug concentration, other drugs present and the concentration of the protein itself are examples of external factors that could affect the equilibrium (50). Most drugs are reversibly bound to plasma proteins, but the extent will vary with each drug. Most acidic, neutral and basic drugs bind to albumin, although a few basic drugs bind excessively to alfa1- acid glycoprotein (51). Some substances with a large degree of lipophilic properties can also interact with lipoproteins. This is a slightly different interaction. Instead of a reversible binding to specific binding sites on the protein, the drug is distributed between the lipoprotein and the aqueous plasma phase. The fraction distributed to the lipoprotein is available for cellular uptake as opposed to drugs bound to albumin or alfa1-acid glycoprotein (52).

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13

Figure 4.Schematic relationship of drug equilibria in blood, tissue and eliminating organ. Cu: unbound concentration, Crbc: concentration bound to red blood cells (erythrocytes), Cprotein: concentration bound to plasma proteins, Ctissue: concentration bound to tissue, Celi: concentration eliminated, Ctarget: concentration bound to the target molecule and giving response. Modified from Nilsson LB. (50) and Egeland EJ. (53)

Free fraction (fu) is a proportionality factor between bound and unbound drug, and in practice fu is derived by measuring total- (C) and unbound concentrations (Cu) at several concentrations (Equation 1) (34).

f_!= ^"_"^! (Eq.1) Fu is constant at low concentrations but will increase if the concentration approaches the concentration of the binding protein. The typical concentration of albumin in plasma is approximately 650 µmol/L (54). Lower albumin levels, which can occur in several different disease states, may result in increased fu which may, in theory, lead to an increased clearance.

However, an inflammatory condition and/or interaction with endogenous substances may result in a decreased clearance (due to changes in the metabolic capacity), which can be relatively greater than the change we see with increased fu (55, 56). Equation 2 (Eq. 2) shows how hepatic clearance (CLHep) can be estimated based on the well-stirred model,

CL_#$% =_&,()^&∗()^!^∗"*^"#$⁾

!∗"*_"#$) (Eq. 2)

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14 where Q is blood flow to the liver, fu is the free fraction of a drug in the blood and clearance intrinsic (CLint) is the ability to metabolize the drug. Depending on the drug's extraction ratio, changes in fu can both give an increase or no change in clearance. At high extraction ratio, a change in fu will not have a significant impact as blood flow is the capacity limiting factor. On the other hand, at low to intermediate extraction ratio, a change in clearance will in theory be observed when fu is altered (34, 57).

The clinical relevance of changes in plasma protein binding has been a topic surrounded by some controversy the last decades (57, 58). It is intuitive to believe that when a substance is displaced from its plasma protein it leads to an increase in unbound concentration, thus an increment in effect and risk of toxicity. More often than not, this is not the case. For drugs taken orally and mainly eliminated by the liver and for all low extraction ratio drugs, independent of administration route, changes in fu will not alter the unbound exposure of the substance and the clinical relevance will be of minor interest (57). However, this should not create uncertainty on the importance of protein binding measurements. For example, TDM techniques utilizing total concentrations of highly protein bound drugs, will be affected by changes in fu. In these cases, the total concentration may be artificially increased or decreased, thus leading to unnecessary dose adjustments (57, 59). By determining unbound drug concentrations such situations can be avoided.

1.4.1 Determination of unbound concentrations of drugs

Determining protein binding and free fraction is one of the major challenges that a bioanalytical scientist may face. Numerous different techniques have been used to best determine the unbound concentration of a drug. All these techniques require a physical separation of the unbound fraction from the drug binding proteins. The techniques that have achieved the most recognition throughout the years are equilibrium dialysis (ED) and ultrafiltration (UF). Both techniques have their clear advantages and pitfalls and can therefore be useful in different situations. The most common challenges observed with these techniques are non-specific adsorption (NSA) and equilibrium disturbance, which along with the two different techniques will be discussed in more detail below (50, 60).

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15 Equilibrium dialysis

The device used in ED consists of two chambers separated by a semipermeable membrane. One of the chambers contains plasma and a buffer solution is in the second chamber. The buffer should have properties that are as similar as possible to plasma, and isotonic phosphate-buffered saline (PBS) (pH 7,4) is usually applied (50). The principle of the technique is based on the fact that the free fraction of a drug will reach equilibrium across the semipermeable membrane which is permeable to small molecules but not to macromolecules such as drug binding proteins (61). When equilibrium is reached, components will have the same concentrations on both sides of the membrane i.e. the drug concentration in the buffer will be equal to the unbound drug concentration in the plasma compartment. The time it takes to reach equilibrium can vary between 4-24 hours depending on the substance being analyzed and ED manifold. This makes ED an insufficient and laborious method (50). One of the biggest disadvantages with ED is that, at equilibrium, the new drug concentration will be lower than the original concentration in the plasma compartment due to some drug distribution into the buffer compartment. Consequently, both the drug concentration in the buffer and the plasma compartment must be determined, and a ratio between these two concentrations will be the free fraction of the drug. To determine the unbound concentration, the total concentration in the plasma compartment also need to be measured and multiplied with the free fraction (Eq.1). It is important to point out that the ultrafiltrate of plasma is a better matrix compared with buffer, but this requires obtaining larger plasma volumes from the patients and makes the method even more laborious (50). The main advantage with ED is that the NSA is assumed to have minor significance, and the reasoning behind this is the emergence of a new equilibrium. ED has been considered to be the preferred method for determining the unbound concentration of a drug, but as described above the method has its clear disadvantages (50, 60).

Ultrafiltration

UF is a fast and user-friendly method to determine the unbound concentrations of drugs (62).

The method is performed by transferring the plasma sample to the top of a two-part receptacle.

The two parts are separated by a membrane with a molecular weight cut-off between 3-30 kDa.

After centrifugation for 10-30 minutes, the unbound concentration can be determined directly by analyzing the plasma-ultrafiltrate (PUF) produced at the bottom of the device. For ultrafiltration to be a reliable analysis method, some assumptions must be made; (i) the membrane has to be equally permeable to the substance and water; (ii) no leakage of proteins

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16 through the membrane; (iii) as the protein concentration increases above the membrane during the separation process, the equilibrium constant has to remain unchanged; (iiii) the drug does not bind to the membrane (63). The fact that UF is a rapid and convenient method together with the opportunity to determine the unbound concentration directly from PUF has made the method more popular in recent years. Another advantage is that the equilibrium will be constant during the whole process (50). UF is not flawless, and as all the other methods used to determine unbound drug concentration, it has some pitfalls. The most mentioned disadvantage is the susceptibility to NSA (60). NSA involves that the unbound part of the drug adsorb onto the membrane or the walls of the sample reservoir. The walls are usually made of material such as polypropylene or polycarbonate. Thus, lipophilic substances have greater susceptibility to NSA than hydrophilic ones (62). As studies have shown that 20-30% of drugs are affected by NSA, it is useful to perform experiments that examine this carefully (63).

Many external factors will influence the reliability of UF, and pH- and temperature control are the most important ones. There is consensus to mimic the physiological conditions as much as possible during UF. The normal physiological body temperature is 37°C, while the plasma pH fluctuates between 7.35-7.45 (62). The plasma pH is regulated by the amount of dissolved CO2. This gives rise of a sensitive buffer system with H⁺ and HCO3-. For acidic drugs such as MPA that binds to albumin, the free fraction often increases with increasing pH, but a decrease has also been seen for some drugs (50). Temperature also affects the drug-protein interaction, and the free fraction increases with a raise in temperature. Analysis performed in room temperature has been shown to reduce the free fraction by approximately 50% compared with experiments performed in 37°C. The factors listed above underlines the importance of applying physiological conditions in order for the results to be relevant in a pharmacokinetic and pharmacodynamic perspective (50).

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1.5 Population pharmacokinetic modelling

There are several methods for analyzing pharmacokinetic data. Non-compartment analysis makes no assumptions about the underlying model and can, by using measured plasma concentrations, compare key pharmacokinetic parameters and variables for an individual. These simplified models are useful to describe observed data, but are challenging to use if only sparse data are available, and also provide limited ability to do simulations (64). Compartmental models on the other hand assumes that the body is divided into homogenous delimited units, called compartments. A basic principle is that the concentration in a compartment, as a function of time, is a result of mass transport in and out of this. By expanding the model with several compartments and incorporating relevant covariates, a more advanced model with greater predictability is achieved. This approach gives the researcher a unique opportunity to investigate a system that can be difficult to study experimentally (34, 65).

Population pharmacokinetics (PopPK) use the techniques of model development to describe the variation in plasma concentrations between individuals in a population of interest, for a given relevant dose of a drug. The goal is to identify covariates that perhaps can be associated with possible sources of variability, particularly interindividual variation (66, 67). PopPK has a direct role in clinical care by being used to guide patients dosing strategies, as well as being increasingly used in the last 2 decades to solve pharmacokinetic problems that arise in new drug development and the regulatory process (67). A major advantage with the PopPK approach is that both sparsely and intensively collected data can be used. This makes it easier to examine vulnerable patient populations where it can be difficult to collect enough data due to medical or ethical considerations. All data, from traditional pharmacokinetic studies to routine trials, can basically be used in pharmacokinetic population model (66).

Nonparametric approaches

There are several advanced modeling software that can be used in population modeling (e.g.

NONMEM, AdeptII, WinNonMIX, Pmetrics). An important fundamental difference in population modeling is the distinction between parametric and nonparametric approaches, this distinction is related to the underlying statistical presumptions used in the estimation methods (67). Nonparametric modeling makes no assumptions about the distribution to the model parameters (e.g. Gaussian or lognormal), and is thus better able to detect and describe e.g. multimodal distributions and will in this way be able to identify subpopulations. By

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18 using this approach, the single-most likely distribution of parameter values for the entire population studied can be obtained (68). Because of their statistical attributes, nonparametric models are particularly relevant for designing precise dosage regimens for the individual patient (68).

Population pharmacokinetic models of unbound mycophenolic acid in renal transplant recipients

As previously mentioned, TDM for MPA is still undergoing continued debate and has yet to be well established as a part of the clinical practice. The popPK approach has been used extensively in recent years to investigate the pharmacokinetics of total MPA in, e.g., renal transplant recipients. Due to the technical complexity of measurements regarding unbound concentrations of drugs, only a handful of studies have included unbound MPA in their popPK models (69-73). The development of popPK models that include total and unbound concentrations of MPA and MPAG will potentially be helpful to guide patients dosing strategies and identify clinically significant covariates that can explain the considerable inter-individual variation that MPA exhibits.

1.6 AIM

A broader knowledge on the underlying mechanism of the large intra- and interindividual variability in MPA pharmacokinetics is needed. Under different circumstances, the total concentration may be artificially increased or decreased, leading to unnecessary dose adjustments. By determining unbound drug concentrations, such situations can be avoided.

The primary aim of this thesis was to develop and validate a method to determine unbound concentrations of MPA and MPAG in plasma utilizing ultrafiltration and subsequent UPLC- MS/MS analysis. The validated method was further applied to measure unbound MPA concentrations in samples from renal transplant recipients included the DayNight study. The secondary aim was to perform the initial development of a population pharmacokinetic model for unbound and total concentrations of MPA and the main metabolite MPAG in renal transplant recipients using data from the DayNight study.

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2 Materials and methods

2.1 Materials

2.1.1 Chemicals

Chemicals Supplier

Acetic acid 100% (CH3COOH) Merck, Darmstadt, Germany Acetonitrile (ACN), hypergrade Merck, Darmstadt, Germany

Ammonia 25% (NH3) Merck, Darmstadt, Germany

Methanol (MeOH), hypergrade Merck, Darmstadt, Germany

Milli-Q water Merck, Darmstadt, Germany

Mycophenolic acid (MPA) Merck, Darmstadt, Germany

Mycophenolic acid β-D-glucuronide (MPAG) Cerilliant Corporation, Texas, USA

Mycophenolic acid β-D-glucuronide-d3 (MPAG-d3) Toronto Research Chemicals, Ontario Canada Mycophenolic acid-d3 (MPA-d3) Cerilliant Corporation, Texas, USA

Plasma Blood bank, Ullevål, Oslo Norway

Ringer-acetate Fresenius Kabi, Halden, Norway

2.1.2 Equipment

Equipment Supplier

96-well plate, 600 μL well volume Sarstedt, Nümbrecht, Germany Aluminum foil for 96-well boards VWR, Pennsylvania, USA

Centrifreeâ Ultrafiltration Device Merck Millipore, Darmstadt, Germany Centrifuge (Heraeus Megafuge 16R-centrifuge) Thermo-Fisher Scientific, Waltham, MA Column: Acquity UHPLC HSS T3 1,8 μm 2,1 mm x 50 mm Waters, Milford, MA

Corning centrifuge tubes (15 and 50 mL) Corning Inc., MA, USA

Dispenser pipette (Multipette M4) Eppendorf, Hamburg, Germany

Eppendorf tubes Eppendorf, Hamburg, Germany

Guard column: Acquity UHPLC HSS T3 1,8 μm 2,1 mm x 15 mm

Waters, Milford, MA

Invitrogen HulaMixer Thermo Scientific, IL, USA

Julabo SW 22 water bath JULABO GmbH, Seelbach, Germany

Phoenix control 2, fume hood Phoenix control

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21

Pipette (Finntip F2) Thermo-Fisher Scientific, Waltham, MA

Pipette tips Sarstedt, Nümbrecht, Germany

Semimicro balance CPA 225D Sartorius AG, Goettingen, Germany

Software, analysis instrument Xcalibur (version 4.1), Thermo Scientific

Software, pharmacokinetics Microsoft Excel (Version 16.44),

R (Version 1.2.5033), Pmetrics (1.5.2)

TSQ Altis Thermo-Fisher Scientific, Waltham, MA

Vanquish Flex UHPLC Thermo-Fisher Scientific, Waltham, MA

Vanquish trays, v-shaped wells Thermo-Fisher Scientific, Waltham, MA Whirlimixer (Vortex Genie 2) Scientific Industries (ELMIS), NY, USA

2.1.3 Solutions

Mobile phase A: 50 mM ammonium acetate buffer (pH 4.4, 5% Acetonitrile)

Chemical Volume

Ammonia 25% 1.4 mL

Acetic acid 100% 3.2 mL

Acetonitrile, hypergrade 50 mL

MQ-water 950 mL

Mobile phase B: 100% Acetonitrile

Acetonitrile 100% 1000 mL

Precipitation solution with internal standard:

Acetonitrile 95%, Methanol 5 % with 0.025 µg/mL MPA-d3 and 0.25 µg/mL MPAG-d3

MPA-d3, 100 μg/mL in methanol 50 μL MPAG-d3, 50 mg/mL in methanol 50 μL

Methanol, hypergrade 9.9 mL

Acetonitrile, hypergrade 190 mL

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22

2.2 Methods

In this thesis, a new sample preparation method was developed to determine unbound concentrations of MPA and MPAG in plasma. The method development was based on a previously developed and validated ultrafiltration method of midazolam (74) and a validated ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for quantification of total concentrations of MPA and MPAG in plasma (75). CentrifreeÒ Ultrafiltration Device (Merck Millipore Group, Darmstadt, Germany) was used in the sample preparation to determine the unbound concentrations of MPA and MPAG and will from now on be referred to as “the filtration unit”.

2.2.1 Sample preparation – total MPA and MPAG concentrations

A previously validated method was used to determine the total concentrations of MPA and MPAG during method development and in patient samples (75). Protein precipitation was used as sample preparation of the analytes before a UPLC-MS/MS analysis. Plasma samples were thawed at room temperature and mixed on a whirl-mixer (Vortex-Genie 2) for a few seconds.

Plasma samples (20 μL) were transferred to a 96-well tray. Then 200 μL of cold precipitation solution (95% acetonitrile and 5% methanol) was added with 100 ng/mL and 1 μg/mL deuterium-labeled internal standard (MPA-d3 and MPAG- d3) in each well. Self-adhesive aluminum foil was placed on the tray before vortexed for 2 minutes on a whirl-mixer (Vortex- Genie with insert for 96-well trays). The tray was stored at -20°C for one hour followed by centrifugation for 10 minutes at 4000 RPM (4°C). The supernatant (20 µL) was added to 100 µL mobile phase A before 1 µL was injected into the UPLC-MS/MS system.

2.2.2 UPLC-MS/MS conditions

After sample preparation, the samples were quantified by UPLC/MS-MS (Vanquish UPLC coupled with Altis triple quadrupole mass spectrometer, Thermo-Fisher, Waltham, MA). An injection volume of 2 μL sample prepared solution was injected into the apparatus. It was used a C18-column (Acquity UPLC HSS T3 1,8 μm 1mm x 15 mm, Waters, Milford, MA) with a guard column (Acquity UPLC HSS T3 1,8 μm 2,1 mm x 150 mm guard column, Waters, Milford, MA). Mobile phase A and B consisted of 50 mM ammonium acetate with 5%

acetonitrile and 100% acetonitrile, respectively. The gradient elution used under the analysis is described in Table 2. Electrospray ionization was in positive mode, and selected reaction

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23 monitoring was used as the monitoring technique (Supplementary, Table S1 and S2). The total time of analysis per sample was 3 minutes, and retention times were 1.7 minutes for MPA and MPA-d3 and 0.4 minutes for MPAG and MPAG-d3.

Table 2. Gradient elution for separation of MPA and MPAG.

Time (min.)

Mobile phase A (%)

Mobile phase B (%)

Mobile phase velocity (%)

0-0.3 70 30 0.400

0.3-2 70-10 30-90 0.400

2-2.1 10 90 0.400

2.1-3 70 30 0.400

2.3 Method development for determination of unbound MPA and MPAG concentrations

2.3.1 Initial sample preparation – unbound concentrations

The initial sample preparation conditions to determine unbound concentrations of MPA and MPAG was based on a previously validated ultrafiltration method of midazolam (74) and previously validated method for quantification of total concentrations of MPA and MPAG (75).

To assure that the physiological equilibrium between unbound and bound MPA was maintained, plasma samples were placed in a water bath holding 37°C for 30 minutes. A total of 500 µL plasma was added to the filtration unit and centrifuged in a fixed-angle rotor at 1500 g at 37°C for 30 minutes (Figure 5). Plasma ultrafiltrate (PUF) (50 µL) was added 200 µL precipitation solution with deuterated internal standard (95-5 acetonitrile-methanol with 0,025 µg/mL MPA-d3 and 0.25 µg/mL MPAG-d3). The samples were vortex-mixed momentarily, followed by a 1-hour cooling at -20°C. Samples were then centrifuged for 10 minutes at 4000 rounds per minute. The supernatant (50 µL) was added to 50 µL mobile phase A (50 mM ammonium acetate with 5 % acetonitrile) before 2 µL was injected into the UPLC-MS/MS system as described in 2.2.2.

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24

Figure 5: Centrifree® Ultrafiltration Device is loaded with plasma in the upper part. The plasma ultrafiltrate is formed after centrifugation and contains the unbound concentrations of MPA and MPAG.

2.3.2 Calibration curve in Ringer acetate

To simplify the sample preparation and make it less laborious, the use of Ringer acetate as an alternative matrix for PUF in the calibration curve was tested. The calibrator concentrations were made in Ringer acetate and PUF in concentrations of 0.025, 0.05, 0.1, 0.2, 0.4, 0.8, 1.6 and 3.2 mg/L for MPA, and 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0 and 32.0 mg/L for MPAG. The quality control (QC) samples were prepared in both matrices in concentrations of 0.05, 0.2, 0.8 and 3.2 mg/L for MPA and 0.5, 2, 8, 32 mg/L for MPAG. The samples were prepared and analyzed as described in 2.3.1 and 2.2.2. QC-samples prepared in both Ringer acetate and PUF were compared to the calibration curves in the opposite matrices. Accuracy and precision were assessed. Deviation from nominal concentration within ±15% (±20% for Lower Limit of Quantification (LLOQ)) and a coefficient of variation (CV) less than 15% (<20% for LLOQ) was considered acceptable.

Pharmacokinetics of unbound mycophenolic acid and 7-O-mycophenolic acid glucuronide in renal transplant recipients

Pharmacokinetics of unbound

mycophenolic acid and 7-O-mycophenolic acid glucuronide in renal transplant

recipients

Development of an UPLC-MS/MS method and a population pharmacokinetic model

Ole Martin Drevland

Thesis for the degree of Master of Pharmacy Department of Pharmaceutical Biosciences

45 credits

School of Pharmacy

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

Pharmacokinetics of unbound mycophenolic acid and 7-O-mycophenolic acid glucuronide in

renal transplant recipients

Development of an UPLC-MS/MS method and a population pharmacokinetic model

Ole Martin Drevland

Thesis for the degree of Master of Pharmacy Department of Pharmaceutical Biosciences

School of Pharmacy

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

Forord

List of abbreviations

Abstract

Sammendrag

Table of contents

1 Introduction

1.1 Chronic kidney disease

1.2 Renal transplantation

1.2.1 Immunosuppressive treatment

1.3 Mycophenolate

1.3.1 Mechanism of action

1.3.2 Pharmacokinetics

1.3.3 Interindividual pharmacokinetic variability

1.3.4 Therapeutic drug monitoring

1.3.5 Adverse effects and toxicity

1.4 Drug protein binding

1.4.1 Determination of unbound concentrations of drugs

1.5 Population pharmacokinetic modelling

1.6 AIM

2 Materials and methods

2.1 Materials

2.1.1 Chemicals

2.1.2 Equipment

2.1.3 Solutions

2.2 Methods

2.2.1 Sample preparation – total MPA and MPAG concentrations

2.2.2 UPLC-MS/MS conditions

2.3 Method development for determination of unbound MPA and MPAG concentrations

2.3.1 Initial sample preparation – unbound concentrations

2.3.2 Calibration curve in Ringer acetate