PHARMACOKINETICS AND DISPOSITION

Population pharmacokinetics of artesunateand dihydroartemisinin during long-term oral administrationof artesunate to patients with metastatic breast cancer

Therese Ericsson & Antje Blank & Cornelia von Hagens &

Michael Ashton & Angela Äbelö

Received: 29 April 2014 /Accepted: 11 September 2014# Springer-Verlag Berlin Heidelberg 2014

AbstractPurpose The purpose of this study were firstly to characterizethe population pharmacokinetics of artesunate (ARS) and itsactive metabolite dihydroartemisinin (DHA) in patients withmetastatic breast cancer during long-term (>3 weeks) dailyoral ARS administration and secondly to study the relation-ship between salivary and plasma concentrations of DHA.Methods Drug concentration-time data from 23 patients, re-ceiving oral ARS (100, 150, or 200 mg OD), was analyzedusing nonlinear mixed effects modeling. A combined drug-metabolite population pharmacokinetic model was developedto describe the plasma pharmacokinetics of ARS and DHA inplasma. Saliva drug concentrations were incorporated as beingdirectly proportional to plasma concentrations.Results A first-order absorption model for ARS linked to acombined two-compartment disposition model for ARS andone-compartment disposition model for DHA provided thebest fit to the data. No covariates were identified that couldexplain between-subject variability. A time-dependent in-crease in apparent elimination clearance of DHA was ob-served. Salivary DHA concentrations were proportionally

correlated with total DHA plasma concentrations, with anestimated slope factor of 0.116.Conclusions Population pharmacokinetics of ARS and DHAin patients with breast cancer was well described by a com-bined drug-metabolite model without any covariates and withan increase in apparent elimination clearance of DHA overtime. The estimated DHA saliva/plasma ratio was in goodagreement with the reported DHA unbound fraction in humanplasma. Saliva ARS concentrations correlated poorly withplasma concentrations. This suggests the use of saliva sam-pling for therapeutic drug monitoring of DHA. However,further studies are warranted to investigate the robustness ofthis approach.

Keywords Artesunate . Breast cancer . Dihydroartemisinin .

Population pharmacokinetics . Plasma . Saliva

Introduction

Artemisinin is a sesquiterpene lactone endoperoxide isolatedfrom the Chinese medical plant Artemisia annua L. Semi-synthetic derivatives of artemisinin, such as artemether,artesunate (ARS), and dihydroartemisinin (DHA), have beendeveloped and are currently widely used for the treatment ofmalaria [1]. Artemisinin-based combination therapies (ACTs),including at least 3 days of treatment with an artemisininderivative, are recommended as the first-line treatment ofPlasmodium falciparum malaria [2]. The artemisinin-relatedcompounds have been proven to be safe and well tolerated inclinical settings with a very favorable risk benefit ratio regard-ing adverse reactions and side effects at doses and treatmentdurations employed in malaria treatment [3–6]. Lately, atten-tion has been focused on the demonstrated anticancer proper-ties of these compounds [7, 8]. Because of their favorablesafety profiles, at least in short-term use, artemisinin

Electronic supplementary material The online version of this article(doi:10.1007/s00228-014-1754-2) contains supplementary material,which is available to authorized users.

T. Ericsson (*) :M. Ashton :A. ÄbelöUnit for Pharmacokinetics and Drug Metabolism, Department ofPharmacology, Sahlgrenska Academy at the University ofGothenburg, Box 431, 405 30 Gothenburg, Swedene-mail: therese.ericsson@neuro.gu.se

A. BlankDepartment of Clinical Pharmacology & Pharmacoepidemiology,Heidelberg University Hospital, Heidelberg, Germany

C. von HagensDepartment of Gynecological Endocrinology and ReproductiveMedicine, Naturopathy and Integrative Medicine, UniversityWomen’s Hospital Heidelberg, Heidelberg, Germany

Eur J Clin PharmacolDOI 10.1007/s00228-014-1754-2

compounds could be candidates for adjunctive therapy ofvarious cancers. Several studies indicate cytotoxic activityagainst breast cancer cells, exerted by artemisinins bothin vitro and in vivo, including ARS and DHA [9–13]. Breastcancer is by far the most frequent cancer among women,accounting for approximately 25 % of all new female cancersreported worldwide [14]. As only 50–70 % of patients withbreast cancer receiving chemotherapy respond to first-linetreatment, the need to discover new, effective chemotherapeu-tic alternatives is imperative [13].

ARS is often considered a pro-drug for DHA following oralARS administration [15]. In vivo ARS is rapidly (t1/2!20–40 min) [15] and probably almost entirely converted to itsactive metabolite DHA, which accounts for the major antima-larial activity. DHA is further glucuronidated by UDP-glucuronosyltransferases (mainlyUGT1A9 andUGT2B7)witha half-life of 0.5–1.5 h [15, 16]. The pharmacokinetics of thesecompounds has been extensively studied in healthy volunteersand inmalaria patients after single dose or short-term (<1 week)regimens [3, 17–19]. A time dependency in the pharmacoki-netics of some artemisinin compounds has been demonstrated,notably with artemisinin and artemether [20–24]. Induction ofdrug-metabolizing enzymes has been suggested a probableexplanation [25–28]. Evidence for a similar time dependencyof ARS and DHA have been less convincing [23].

Until now, the human pharmacokinetics of artemisininendoperoxides has only been studied up to about 7 days ofadministration. The present study aimed to characterize thepopulation pharmacokinetics of ARS and DHA during long-term daily ARS administration in patients with metastaticbreast cancer and to evaluate potential time dependency inpharmacokinetic parameters. The implementation of salivadata aimed to investigate the correlation between plasma andsaliva drug concentrations, in order to evaluate future pros-pects for collecting saliva samples in pharmacokineticinvestigations.

Methods

Study design

Plasma and saliva samples from 23 patients were collected forthe population pharmacokinetic analysis. All subjects werewomen with metastatic breast cancer participating in a pro-spective open uncontrolled monocentric tolerability studyconducted at the Medical Clinic at the University of Heidel-berg, Germany (ClinicalTrials.gov NCT00764036). Regula-tory (EudraCT 2007-004432-23, Submission-No. CA:4033804) and ethics approval (University of Heidelberg;AFmu495/2007) was obtained. Written informed consentwas obtained from each subject prior to the inclusion in thepresent pharmacokinetic sub study.

Drug regimen and sampling

A first group of patients (n=6) were initiated on 100 mg ARSOD (Dafra Pharma International, Turnhout, Belgium). Subse-quently, two groups received either 150 mg (n=7) or 200 mg(n=10) ARS once daily. Venous blood (7.5mL) and saliva (1–5 mL) samples for pharmacokinetic analysis were drawn atpre-dose and at 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 2, 3, 4, 6, and 8 hafter the first ARS administration as well as after a dose atleast 3 weeks into the treatment. Additional sparse samplingwas performed on an irregular basis, with plasma and salivasampled 1–2 h after ARS administration. All plasma andsaliva were collected in pre-chilled tubes containing potassi-um oxalate/sodium fluoride. Collection of saliva was per-formed during or within 5 min after collection of bloodsamples. Following collection, all tubes were placed on icebefore centrifugation (10 min; blood at 1000–1200 g, saliva2400–2500 g) within 15 min of collection. Immediately aftercentrifugation, plasma and saliva were transferred into twoapproximately equal volume aliquots in screw-cap cryovialsand then immediately frozen at or below "75 °C. Sampleswere later shipped on dry ice to the Unit for Pharmacokineticsand Drug Metabolism at the University of Gothenburg, wherethey were stored at or below "75 °C until the day of druganalysis.

Sample analysis

Determination of ARS and DHA concentrations in plasma orsa l iva was per formed us ing a va l ida ted l iqu idchromatography-tandem mass spectrometric (LC-MS/MS)method [29]. Briefly, solid-phase extraction was used to ex-tract ARS, DHA, and the IS artemisinin from 0.3-mL humanplasma or saliva. Isocratic chromatography using a BetasilPhenyl/Hexyl (50 mm!2.1 mm, 5 μm) column protected bya Betasil Phenyl/Hexyl Drop-in (10 mm!2.1 mm, 5 μm)guard column (Scantec Lab, Gothenburg, Sweden) with amobile phase of acetonitrile-ammonium acetate 10 mM pH4 (50:50, v/v) delivered at a flow rate of 200 μL/min wereapplied. The LC system included a PerkinElmer Series 200Micro Pump and a temperature-controlled PerkinElmer Series200 LC Autosampler set at 8 °C and a vacuum degasser. AnAPI 3000 triple quadrupole mass spectrometer (AppliedBiosystems/MDS SCIEX, Foster City, USA) with anelectrospray ionization source (ESI) operated in the positiveion mode was used for the multiple reaction monitoring(MRM) MS analysis. Transit ions for artesunate,dihydroartemisinin, and internal standard (IS) were m/z402.5–267.1, 302.4–267.3, and 300.4–209.2, respectively.The lower limit of quantification (LLOQ) was 5 ng/mL forboth ARS and DHA. LLOQ and quality control (QC) samples(ARS:DHA 15:15, 300:750, and 750:1500 ng/mL, respective-ly) were analyzed together with the clinical samples. Inter-day

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precisions for ARS ranged between 1.4 to 4.5 % and 1 to3.1 % for plasma and saliva, respectively. For DHA valuesranged between 2 to 7.8 % and 1.1 to 2.2 % for plasma andsaliva, respectively. ARS intra-day precision (coefficient ofvariation) ranged from 0.9 to 3.1 % in plasma and 0.7 to 1.8 %in saliva. Corresponding numbers for DHAwere 1.4 to 4.5 %and 0.7 to 1.8 %. No matrix effects or ion suppression wereobserved for ARS, DHA, or IS in either plasma or saliva.

Population pharmacokinetic analysis

All ARS and DHA concentrations were transformed into theirnatural logarithms and analyzed using nonlinear mixed effectsmodeling as implemented in NONMEM version 7 [30]. Mod-el diagnostics were performed using Pearl-speaks-NONMEM(PsN, version 3.6.2) [31, 32] and Xpose (version 4.0.4) [33].The first-order conditional estimation (FOCE) method wasused throughout the modeling, except when categorical datawas included in which case the Laplace method was applied.Model discrimination was based on plausibility and precisionof parameter estimates, visual inspection of diagnostic plots,and the objective function value (OFV; computed byNONMEM as "2*Loglikelihood) [34]. When comparing hi-erarchical models, a decrease in OFV of 3.84 or more wasconsidered statistically significant at p<0.05 with 1° of free-dom difference.

Different approaches to handle observed data below thequantification limit (BQL) where investigated to avoid bias inparameter estimates due to multiple ARS samples being BQL.Firstly, using the M1 method, all BQL data was coded asmissing data. Secondly, using the M6 method, the first BQLvalue in a consecutive series was imputed as LLOQ/2, whileall other values below the LLOQwere omitted. The BQL datawas also modeled as censored data using the M3 method incombination with Laplacian estimation [35–37]. To discrimi-nate between the methods, visual predictive check was per-formed to evaluate the observed and model predicted fractionof censored data over time. Different disposition models wereinvestigated with all three methods to assure that the finalstructural model was not an artifact of BQL data.

Full drug concentration-time profile data were combinedwith sparse sample data. The structural model, describing thepharmacokinetics of ARS and DHA, was evaluated usingplasma data only. ARS and DHAweremodeled simultaneous-ly, applying ADVAN6 for all modeling approaches.

One- and two-compartment models with first-order absorp-tion and disposition were evaluated, assuming first-orderelimination from the central compartment. The base modelwas parameterized as oral (for ARS) or apparent (for DHA)elimination clearance (CL/F), apparent volume of distributiono f t h e c en t r a l compa r tmen t (VC /F ) , a ppa r en tintercompartmental clearance (Q/F), and apparent volume ofdistribution of the peripheral compartment (VP/F). From here

on, elimination clearance parameters will refer to the oralclearance of ARS (CLARS/F) and the apparent oral clearanceof DHA (fm*CLDHA/F), respectively, where F is the oralbioavailability of ARS and fm the fraction of ARS convertedto DHA, assumed to be unity in the present study [16, 38, 39].To further optimize the basemodel, several alternative absorp-tion processes for ARS were assessed, including first-orderabsorption with and without lag time, zero-order absorption,sequential zero- and- first-order absorption with and withoutlag time, sequential first-order and zero-order absorption,parallel zero- and first-order absorption, two parallel first-order absorption with and without lag time, Weibull-typeabsorption (one and two functions), and transit compartmentabsorption with a fixed number of 1–5 transit compartments.Partial pre-systemic conversion of ARS into DHAwas inves-tigated by applying a semi-mechanistic liver model structuredescribed by Gordi et al. [40] or by an alternative way wheredual first-order absorption of ARS and DHAwas consideredand the fractions of the dose absorbed either as ARS or asDHAwere estimated. Implementation of relative bioavailabil-ity, with a population value fixed to 100 % and between-subject variability (BSV) estimated, was investigated. Due tothe presence of double peaks in drug concentration-time pro-files during the first 3 h post-dose for both ARS and DHA in14/23 individuals, different models for enterohepatic recircu-lation were tested [41, 42].

BSV was investigated on all parameters as an exponentialrandom effect, assuming a log-normal distribution of theindividual parameters (Eq. 1):

Pi ! !P # eηi;P "1#

where Pi is the estimated parameter value for the ith individ-ual, !p represents the population mean of parameter P, and "i,Pis the deviation of Pi from !p. BSV is estimated from a normaldistribution with zero mean and variance #2. Residual vari-ability was modeled using an additive model with natural log-transformed data, which is essentially equivalent to an expo-nential error model on an arithmetic scale (Eq. 2):

lnCij ! lnCpred;ij $ $ij "2#

whereCij represents the jth observation for individual i,Cpred,ij

represents the predicted ARS or DHA concentration for indi-vidual i, and $ij represents the residual random error for the jthobservation of individual i.

Allometric scaling with body weight was evaluated onclearance parameters (power of 0.75) and apparent volumesof distribution (power of 1). Potential covariates were inves-tigated using a stepwise forward addition (p value $0.05,!OFV >3.84) and backward elimination (p value $0.001,

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!OFV >10.83) approach. The lower p value in the backwardstep was used to compensate for the relatively small popula-tion studied. The continuous covariate age, hemoglobin andliver status (ALAT/ASAT), and their relationships with phar-macokinetic parameters were evaluated using a linear functionwith covariates centered around their population medianvalues (Eq. 3):

Pi ! !p # 1$ !1 % COV ‐ COVmedian" #" # "3#

where COV being the continuous covariate centered to thepopulation median (COVmedian) and !1 is a factor describingthe correlation between the covariate and the parameter.

Visual inspection of scatter plots indicated a reasonablegood correlation between salivary and plasma DHA concen-trations (Fig. 1). Therefore, saliva data for DHAwas incorpo-rated in the final structural plasma model to evaluate theconcentration relationship between the matrices. Excessivelyhigh drug concentrations in saliva were observed in samplestaken at early time points from a few patients. The mostobvious explanation for this is residual amounts of ARS inthe mouth after intake of the tablets, which to some extent washydrolyzed to DHA. To account for this issue, all salivaconcentrations with saliva/plasma concentration ratios aboveor equal to an arbitrarily set value of 0.5 were excluded fromanalysis (3.9 % in total). The high value of 0.5 was chosen toensure that only samples that were not trustable due to residualamounts of drug in the mouth were excluded. A possibly morerealistic value may be lower due to the fact that only unboundfraction of drug enter saliva by passive diffusion. All plasmaand saliva data were fitted simultaneously, and concentrationsof DHA in saliva were proportionally scaled to plasma DHAconcentrations according to Eq. 4:

DHA S" # ! !1 #DHA P" # "4#

where DHA(S) represents salivary DHA concentrations,DHA(P) the total plasma concentration of DHA, and !1 theproportionality constant. Due to a large proportion of samplesbeing undetectable or BQL (75.6 %), saliva data for ARS wasnot included. Furthermore, visual inspection of scatter plots

indicated, as expected, a poor correlation between salivary andplasma ARS concentrations.

Potential time dependency in elimination clearance param-eters of ARS and DHA was evaluated. Two-dose occasionswere considered; occasion 1 including full concentration-timeprofile data after the first oral dose and occasion 2 includingfull concentration-time profile data after at least a 3-weekdaily medication. Due to the low fraction of sparse samples(18 %) at time points between the two occasions and at timepoints later than occasion 2 (Fig. 2), only full concentration-time profile plasma data from 22 patients was used. At occa-sion 1 (OCC equal to 0), elimination clearance was estimatedaccording to Eq. 1. The time-dependent change in the param-eter measured on occasion 2 (OCC equal to 1) was assessedaccording to Eq. 5:

CLi ! !P # 1$ !1" # # eηi;P "5#

where CLi is the estimated elimination clearance parametervalue for the ith individual and !1 is the relative change inclearance after at least a 3-week daily medication compared toafter the first oral ARS dose.

To evaluate the final models, basic goodness-of-fit plotsand simulation-based diagnostics were used. Visual predictivecheck (VPC) was performed to evaluate the predictive perfor-mance of the models using 1000 simulations at eachconcentration-time point. The 95 % confidence intervals(CI) around the simulated 5th, 50th, and 95th percentiles wereoverlaid with the same percentiles of observed data. Non-parametric bootstrap diagnostics, stratified on ARS andDHA in each matrix, was employed to assess the precision(relative standard errors, %RSE) of population parameterestimates.

Fig. 1 Observed concentrations of DHA in plasma versus observedDHA concentrations in saliva for all patients

Fig. 2 Observed concentrations of dihydroartemisinin (DHA) in plasmaover time for all patients. Rich data (full drug concentration-time profiles)is present after the first oral dose of artesunate (ARS) and after a dose atleast 3 weeks into the treatment. Additional sparse sample data is presenton an irregular basis representing relatively few data points

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Results

Twenty-three women with metastatic breast cancer were en-rolled in the study. Demographic data for the patients ispresented in Table 1. A total of 640 ARS and DHA plasmaconcentrations, respectively, and 614 DHA saliva concentra-tions were used in the pharmacokinetic analysis. Out of these,261 (40.8 %) ARS and 129 (20.2 %) DHA plasma concen-trations and 242 (39.4 %) DHA saliva concentrations werebelow the quantification limit (BQL). M1, M3, and M6 ap-proaches (described in theMethods section) were evaluated toaccount for the BQL data. Based on diagnostic goodness-of-fit plots and VPC plots showing observed and model-predicted fraction of censored data over time (Fig. 4), theM6 method was considered superior to the M3 method. Com-paring the M1 and M6 approaches, no remarkable differencecould be seen in the goodness-of-fit plots. However, consid-ering the large amount of BQL data for ARS, the M6 methodwas implemented, imputing only the first BQL sample in aconsecutive series as LLOQ/2, while omitting all other valuesbelow LLOQ.

The pharmacokinetics of ARS and DHA when includingboth full plasma concentration-time profile data and sparsesample plasma data was best described by a combined drug-metabolite model with a two-compartment disposition modelfor ARS and a one-compartment disposition model for DHA(Fig. 3). A simple first-order absorption model described theabsorption of ARS reasonably well, and based on OFV andgoodness-of-fit plots, it was not improved by other absorptionmodels tested. Including a lag time improved model fit with asignificant decrease in objective function value (%OFV="9.3).However, the precision of parameter estimates obtained wereslightly worse, and visual inspection of goodness-of-fit plotsshowed no improvement in the fit. Also, observed data did notsupport any delay in absorption and the estimated value of thelag time was very low (3.3 min). Therefore, lag time wasconsidered an unjustified parameter and excluded from thefinal model. Inclusion of a relative bioavailability, with apopulation value fixed to 100% and BSVestimated, improvedthe model fit (%OFV="62.6). Based onOFVand goodness-of-

fit plots, none of the models describing enterohepatic recircu-lation or partial pre-systemic conversion of ARS to DHAwereconsidered superior, and therefore not included in the finalmodel. Body weight as a fixed allometric function on clearanceparameters and apparent volume of distributions resulted in anincrease in objective function value (%OFV=3.7) and wasomitted in the analysis. Due to very low estimates, poorprecision (%RSE >50) and high eta shrinkage, BSV for oralelimination clearance of ARS (CLARS/F), intercompartmentalclearance (QARS/F) of ARS, apparent volume of distribution ofthe peripheral compartment (VP,ARS/F) for ARS, and apparentvolume of distribution for DHA (VC,DHA/F) could not bereliably estimated and were therefore not retained in the finalmodel. No statistically significant covariates were found. Onlythe covariate relationships hemoglobin on apparent volume ofdistribution for ARS (VC,ARS/F) and ASAT on bioavailabilitywere selected in the forward stepwise addition (p<0.05). How-ever, none of them was retained in the backward step with amore stringent statistical criterion (p<0.001), and the ob-served BSV in pharmacokinetic parameters could not beexplained by any covariate tested. The slope factor, describ-ing the relationship between salivary and plasma DHAconcentrations, was estimated to 0.116.

From here on, the final structural model when including fullplasma and saliva concentration-time profile data and sparsesample data will be referred to as model A. Goodness-of-fitdiagnostics of model A indicated an adequate description ofobserved plasma and saliva data (Supplement 1). All etashrinkages were below 30 %. Calculated epsilon shrinkagewas low (<2.5 %) indicating that model diagnostics can beassessed reliably. Both plasma and saliva concentrations wereadequately predicted as shown in the VPC plots (Fig. 4).Population parameter estimates and BSV estimates for modelA are presented in Table 2.

When analyzing only full concentration-time profile data(occasions 1 and 2), a time-dependent increase (24.9 %) inapparent elimination clearance of DHA (CLDHA/F) was ob-served after at least a 3-week daily medication (occasion 2)compared to after the first oral ARS dose (occasion 1). Asimilar 12.3 % increase in CLARS/F between the two dose

Table 1 Demographic data of study population before first intake of study medication

Daily dose (mg) Number Body weight (kg) Age (year) Hemoglobin (g/dL)a ALAT (U/L)b ASAT (U/L)c

100 6 76 (58–111) 53 (39–61) 12.3 (10.7–13.4) 35 (18–84) 31 (11–66)

150 7 68 (61–85) 60 (44–71) 13.3 (12.8–14.3) 21 (15–52) 24 (12–54)

200 10 62.5 (51–91) 54 (41–73) 13.0 (9.8–13.9) 23 (18–42) 23.5 (19–61)

All 23 67 (51–111) 57 (39–73) 13.1 (9.8–14.3) 24 (15–84) 25 (11–66)

Data is presented as median (range)a Normal ranges 12–15 g/dLbAlanine aminotransferase; clinical measure of liver status; normal ranges "35 U/LcAspartate aminotransferase; clinical measure of liver status;;;; normal ranges "35 U/L

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occasions was estimated, but due to an insignificant drop inOFV (%OFV="3.54), the time dependency in CLARS/F wasomitted. From here on, the final structural plasmamodel whenincluding only full concentration-time profile data and timedependency in CLDHA/F will be referred to as model B.Goodness-of-fit diagnostics of model B indicated an adequatedescription of observed plasma data (Supplement 2). Howev-er, when not including sparse data, parameters associated withthe peripheral compartment of ARSwere estimated with fairlypoor precision (high %RSE, Table 3). All eta shrinkages butCLARS/F (37 %) were below 30%. Calculated epsilon shrink-age was low (<4 %) indicating that model diagnostics can beassessed reliably. Population parameter estimates and BSVestimates of model B are presented in Table 3. The model

showed good predictive performance as shown in the VPCplots (Fig. 5).

Discussion

In the present study, the pharmacokinetics of ARS and itsactive metabolite DHA during long-term treatment were in-vestigated using nonlinear mixed effects modeling with dataobtained in 23 patients with metastatic breast cancer receivingoral ARS once daily. The pharmacokinetics of these com-pounds has been extensively studied, in healthy volunteersand patients with malaria during short-term treatment [3,17–19]. However, to date, no pharmacokinetic analysis of

Fig. 4 The upper panels show predicted-corrected visual predictivecheck of model A for artesunate (ARS) in plasma (a), dihydroartemisinin(DHA) in plasma (b), and DHA in saliva (c), respectively. Observationsand the 5th, 50th, and 95th percentile of observed data are presented ascircles and dashed lines, respectively. Solid lines represent the 50thpercentile of simulated plasma concentrations of ARS and DHA, respec-tively. Shaded areas represent the simulated 95 % confidence interval ofthe 5th, 50th, and 95th percentiles of simulated plasma concentrations.Dashed horizontal lines represent LLOQ for ARS (0.013 μM) and DHA

(0.018 μM), respectively. Time is presented as time after dose and isrestricted to 6.5 h for plasma ARS and salivary DHA and to 8.5 h forplasma DHA, respectively, excluding one observed DHA plasma con-centration at 10.4 h (plot B). The lower panels show observed fraction ofdata points below the limit of quantification (solid lines) and the 95 % CIof the simulated (n=1000) fraction below the limit of quantification(shaded area) for ARS in plasma (d), DHA in plasma (e), and DHA insaliva (f), respectively

Fig. 3 Structural model describing the plasma pharmacokinetics ofartesunate (ARS) and dihydroartemisinin (DHA) in patients with breastcancer. ka first-order absorption rate constant, VC apparent volume of

distribution of the central compartment, VP apparent volume of distribu-tion of the peripheral compartment, CL oral/apparent elimination clear-ance, Q apparent intercompartmental clearance

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ARS and DHA in long-term ARS treatment has been pub-lished nor in patients with breast cancer.

Tan et al. reported the population pharmacokinetics ofARS and DHA following single and multiple dosing of oralARS in healthy Korean subjects [43]. ARS was rapidlyabsorbed (ka=3.85 h"1) from a dosing compartment to acentral compartment, subsequently converted to DHA,which was described by a two-compartment dispositionmodel. Population parameter estimates presented in thepresent study (Table 2) are in good agreement with esti-mates of CL/F and V/F for ARS (1190 L/h and 1210 L) andDHA (93.7 L/h and 97.1 L) reported by them. Populationpharmacokinetics of ARS and DHA in pregnant and non-pregnant women with malaria has been studied by Morriset al. [44]. Generally, they reported lower CL/F and V/F forboth ARS and DHA, and the absorption of ARS was de-scribed by a mixed zero-order, lagged first-order process.Like Tan et al. [43] the final model presented here suggestsa first-order absorption process. However, the parameterrelated to absorption showed the most variability, withBSV for ka estimated to be 160 %. Inclusion of a lag timewas considered, but omitted in the final model. Due to thelack of observations in the very early absorption phase

(<15 min) and around the estimated lag time (3.3 min), therewas no informative data to support inclusion of a lag time.

Multiple peaks in the concentration-time profiles is a phe-nomenon for which the underlying physiochemical and phys-iological mechanisms are often multifactorial, but quite oftenexplained by enterohepatic recirculation [45]. This has beenreported for ARS in rat [46, 47]. However, different modelsapplied to describe enterohepatic recirculation of ARS werenot supported by the present data. In order to confirm thepresence of enterohepatic recirculation, inclusion of intrave-nous data is required and the occurrence of double peaks afterboth oral and intravenous administration [45].

Several studies have reported a remarkable time dependen-cy in the pharmacokinetics of artemisinin [20–22]. Metabolicautoinduction has been proposed as the underlying mecha-nism for the observed decline in artemisinin plasma concen-trations with time after repeated oral administration [25, 28].Less convincingly, a time-dependent decline in DHA plasmaconcentrations was suggested during a 5-day oral ARS treat-ment in six malaria patients [23]. Another pharmacokineticstudy reported a significant increase in DHA clearance (CL/F)after a 5-day oral DHA monotherapy in malaria patients,which was suggested to be the results of physiological

Table 2 Parameter estimates describing the population pharmacokinetics of artesunate (ARS) and dihydroartemisinin (DHA) in plasma and therelationship between salivary and plasma DHA (model A)

Population estimatesa 95 % CIb BSVa 95 % CI[%RSE]b [%RSE]b for BSVb

Artesunate plasma

CLARS/F (L/h) 1260 [11.1] 1249;1271 – –

VC,ARS/F (L) 1160 [16.6] 1145;1175 52.4 [31.1] 45.2;59.6

QARS/F (L/h) 258 [16.6] 255;261 – –

VP,ARS/F (L) 1320 [23.7] 1295;1345 – –

ka (h"1) 3.35 [33.9] 3.26;3.44 160 [37.9] 141;179

%ARS,plasma 0.762 [6.01] 0.758;0.766 – –

Bioavailability 1 FIX – 41.9 [33.5] 35.6;48.2

Dihydroartemisinin plasma

CLDHA/F (L/h) 118 [8.23] 117;119 23.1 [50.6] 18.7;27.5

VC,DHA/F (L) 98.0 [12.8] 97.0;99.0 – –

%DHA,plasma 0.558 [5.08] 0.556;0.560 – –

Dihydroartemisinin saliva

Ɵ1 0.116 [5.04] 0.115;0.116 – –

%DHA,saliva 0.654 [4.71] 0.652;0.656 – –

CLARS/F elimination clearance of ARS, VC,ARS/F apparent volume of distribution of the central compartment of ARS, QARS/F intercompartmentalclearance of ARS between the central and the peripheral compartment, VP,ARS/F apparent volume of distribution of the peripheral compartment for ARS,CLDHA/F elimination clearance of DHA, VDHA/F apparent volume of distribution of DHA, F relative bioavailability, ka first-order absorption rateconstant,Ɵ1 proportionality constant describing the relationship between salivary DHA and plasma DHA. The additive error (%) variance will essentiallybe exponential on arithmetic scale data. Coefficient of variation (%CV) for BSV was calculated as 100* ((emean variance estimate )"1)1/2 . Relative standarderror (%RSE) was calculated as 100*(standard deviation/mean value). The 95 % confidence intervals (CI) are given as the 2.5 to 97.5 percentiles ofbootstrap estimatesa Based on population mean values from NONMEMb%RSE and 95 % CI for parameters are based on 683 successful stratified bootstrap runs (out of 750 runs)

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changes associated with disease state and/or possibleautoinduction [48]. The same authors later published anotherstudy in which they reported no evidence of time-dependentpharmacokinetics of ARS or DHA after a 5-day oral ARStreatment in healthy subjects [49]. In the present study, a24.9 % increase in the apparent oral clearance of DHA was

observed after long-term (>3 weeks) oral ARS treatment(model B). Glucuronidat ion catalyzed by UDP-glucuronosyltransferases has been proposed as the primarymetabolic pathway involved in the clearance of DHA inhuman beings, with α-DHA-β-glucuronide as the major me-tabolite [16]. However, extensive phase I metabolism of DHA

Table 3 Parameter estimates describing the population pharmacokinetics of artesunate (ARS) and dihydroartemisinin (DHA) in plasma based on fullconcentration-time profile data from occasions 1 and 2 (model B)

Population estimatesa 95 % CIb BSVa 95 % CI[%RSE]b [%RSE]b for BSVb

Artesunate plasma

CLARS/F (L/h) 1100 [15.4] 1088;1112 17.7 [62.5] 13.7–21.7

VC,ARS/F (L) 1280 [19.6] 1262;1298 48.8 [52.1] 39.5;58.1

QARS/F (L/h) 213 [55.8] 202;224 – –

VP,ARS/F (L) 930 [203] 458;1402 – –

ka (h"1) 2.88 [32.1] 2.81;2.95 136 [40.0] 119;153

σARS,plasma 0.695 [6.55] 0.692;0.698 – –

Bioavailability 1 FIX – 32.6 [30.9] 28.0;37.2

Dihydroartemisinin plasma

CLDHA/F (L/h) 109 [15.3] 108;110 – –

VC,DHA/F (L) 99.6 [18.2] 98.4;101 – –

Ɵ1 0.249 [29.6] 0.243;0.255 – –

%DHA,plasma 0.549 [9.58] 0.545;0.553 – –

CLARS/F elimination clearance of ARS, VC,ARS/F apparent volume of distribution of the central compartment of ARS, QARS/F intercompartmentalclearance of ARS between the central and the peripheral compartment, VP,ARS/F apparent volume of distribution of the peripheral compartment for ARS,CLDHA/F elimination clearance of DHA, VDHA/F apparent volume of distribution of DHA, F relative bioavailability, ka first-order absorption rateconstant, Ɵ1 relative change in CLDHA/F between occasions 1 and 2. The additive error (%) variance will essentially be exponential on arithmetic scaledata. Coefficient of variation (%CV) for BSV was calculated as 100* ((emean variance estimate )"1)1/2 . Relative standard error (%RSE) was calculated as100*(standard deviation/mean value). The 95 % confidence intervals (CI) are given as the 2.5 to 97.5 percentiles of bootstrap estimatesa Based on population mean values from NONMEMb%RSE and 95 % CI for parameters are based on 746 successful stratified bootstrap runs (out of 1000 runs)

Fig. 5 Predicted-corrected visual predictive check of model B forartesunate (ARS) in plasma (a) and dihydroartemisinin (DHA) in plasma(b), respectively. Observations and the 5th, 50th, and 95th percentile ofobserved data are presented as circles and dashed lines, respectively.Solid lines represent the 50th percentile of simulated plasma concentra-tions of ARS and DHA, respectively. Shaded areas represent the

simulated 95 % confidence interval of the 5th, 50th, and 95th percentilesof simulated plasma concentrations. Dashed horizontal lines representLLOQ for ARS (0.013 μM) and DHA (0.018 μM), respectively. Time ispresented as time after dose and is restricted to 6.5 h for plasma ARS andto 8.5 h for plasma DHA, respectively

Eur J Clin Pharmacol

has been shown in rat liver microsomes, with mono-hydroxylated derivatives of DHA and deoxyDHA accountingfor approximately 70 % of all metabolites formed under exper-imental conditions [50]. A more recent study also identified 13phase I metabolites and 3 phase II metabolites of DHA in ratbile, urine, and plasma [51]. The phase I metabolites weremainly hydroxylated and deoxyl products that could formglucuronides in the subsequent phase II reactions. Therefore,it is not unlikely that the cytochrome P450 (CYP) superfamilyof monooxygenases might be involved in metabolic clearanceof DHA, especially in the induced state. No evidence of induc-tion of UGTs by ARS or DHA has been reported. However,published reports demonstrate inductive properties of the twocompounds on human P450s [28, 52], why it cannot be ruledout that the autoinduction phenomenon could be the potentialmechanism behind the increase in CLDHA/F observed here.Attempts to model the time course of an induction processwere, not surprisingly, unsuccessful, considering the paucityof sparse data during the 2 weeks after the first dose (Fig 2). Amechanistic induction model proposed by Gabrielsson et al.proved inconclusive (no change in OFV despite additionalparameters), although a 16 % increase in DHA clearance wasestimated (data not presented) [53]. Further, attempts to let thedegree of induction (change in CLDHA/F) be a function of dosedid not significantly improve fits (no change in OFV). Apply-ing Eq. 5 to each dose group of subjects did not prove incon-clusive (no change in OFV), although a 1.5 times greaterchange in CLDHA/F was estimated after at least a 3-week dailyARS medication for the 200-mg dose group (33.4 %) com-pared to the 100-mg (21.1 %) and 150-mg (22.1 %) dosegroups. These results suggest that the apparent oral clearanceof DHA not only show time dependency but also the change inCLDHA/F over time might also be dose dependent.

Use of saliva as substitute for blood offers an inexpensiveand noninvasive sampling alternative, which may even becarried out by patients themselves. Most drugs enter salivaby passive diffusion, a mechanism that allows only unboundand unionized drug in plasma to pass [54]. Ideally, a drug thatexhibits a constant saliva/plasma ratio that is consistent overconcentration and time would allow salivary concentrations topredict unbound plasma concentrations. Distribution of sev-eral drugs into saliva has been investigated, includingartemisinin. Gordi et al. observed a high correlation betweensaliva and plasma concentrations of artemisinin [55]. Salivaryartemisinin was comparable to its unbound concentration inplasma, suggesting saliva sampling to offer an easy way todetermine unbound plasma levels of this compound. DHA,being a less lipophilic compound than artemisinin, would beexpected to have a weaker saliva-to-plasma correlation. De-spite this, we found salivary DHA to be proportionally corre-lated with total plasma concentrations, with an estimated slopeof 0.116 (model B). This value is in excellent agreement withpreviously reported values of unbound fraction of DHA in

human plasma (12 and 9 % in Vietnamese and Caucasianvolunteers, respectively) [56]. To account for the issue withexcessively high drug concentrations in saliva at early timepoints, a pre-defined saliva/plasma concentration ratio of $0.5was considered as inclusion criteria as described in the methodsection. Based on reported values of unbound fraction ofDHA in human plasma, one would expect the saliva/plasmaconcentration ratio to be lower than 0.5 if the passage of DHAwas exclusively by passive diffusion. However, by choosing aratio of $0.5 as inclusion criteria, we ensured that only sam-ples that were not trustable due to residual amounts of drug inthe mouth were excluded, but at the same time ensured thatreliable samples were retained for analysis. It has been hy-pothesized that, during the distribution phase, drug concentra-tions in arterial blood exceed those in venous blood [57, 58].As drug concentrations in saliva are in equilibrium with that inarterial blood, this could contribute to a model underestima-tion of saliva concentrations at early time points if venousplasma concentrations are used as reference.

Conclusions

In conclusion, the population pharmacokinetic properties of ARSand its active metabolite DHA in plasma in 23 patients withbreast cancer were characterized by a combined drug-metabolitemodel. A 24.9 % increase in apparent elimination clearance ofDHA was observed after long-term (>3 weeks) daily treatmentwith oral ARS, suggesting a potential for autoinduction of me-tabolism. Also, the observed correlation between salivary andplasma DHA concentrations suggests a possible use of salivasampling in pharmacokinetic investigations.

Acknowledgments The authors give their appreciations to the diligentstaff at theMedical Clinic at University of Heidelberg. TE thanks RichardHöglund at the Unit for Pharmacokinetics and Drug Metabolism at theUniversity of Gothenburg for his valuable input during the modelingprocess. Also, appreciations to Dafra Pharma International, Research &Development (Turnhout, Belgium), who supplied the study medication.

Conflicts of interest The clinical study was supported by H. W. and J.Hector Stiftung, Weinheim, Germany, and Monika-Kutzner-Stiftung,Berlin, Germany. The co-author Antje Blank received personal fundingfrom the Medical Faculty of the University of Heidelberg. The authorsfurther certify that there is no other financial involvement or conflicts ofinterest regarding the material discussed in the manuscript.

Contribution of authors T.E.—drug quantitation in plasma and salivasamples, population pharmacokinetic analyses; drafted and finalizedmanuscript; corresponding author

A.Ä.—senior contribution to the population pharmacokinetic analy-sis, revised the final manuscript for important intellectual content.

A.B...—contributed to the planning and conduct of study, revised thefinal manuscript for important intellectual content

C.v.H.—main contributor to the study design and conductM.A.—contributed to the study design, interpretation of results, and

manuscript development.

Eur J Clin Pharmacol

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