key: cord-1003884-bczngg44
authors: Abd‐Rahman, Azrin N.; Marquart, Louise; Gobeau, Nathalie; Kümmel, Anne; Simpson, Julie A.; Chalon, Stephan; Möhrle, Jörg J.; McCarthy, James S.
title: Population pharmacokinetics and pharmacodynamics of chloroquine in a Plasmodium vivax volunteer infection study
date: 2020-05-16
journal: Clin Pharmacol Ther
DOI: 10.1002/cpt.1893
sha: 36ee8452a947448145cb1211427dab7953c39f98
doc_id: 1003884
cord_uid: bczngg44

Chloroquine has been used for the treatment of malaria for more than 70 years; however, chloroquine pharmacokinetic (PK) and pharmacodynamic (PD) profile in Plasmodium vivax malaria is poorly understood. The objective of this study was to describe the PKPD relationship of chloroquine and its major metabolite, desethylchloroquine, in a P. vivax volunteer infection study. We analyzed data from 24 healthy subjects who were inoculated with blood‐stage P. vivax malaria and administered a standard treatment course of chloroquine. The PK of chloroquine and desethylchloroquine was described by a two‐compartment model with first‐order absorption and elimination. The relationship between plasma and whole blood concentrations of chloroquine and P. vivax parasitemia was characterized by a PKPD delayed response model, where the equilibration half‐lives were 32.7 h (95% CI: 27.4–40.5) for plasma data and 24.1 h (95% CI: 19.0–32.7) for whole blood data. The estimated parasite multiplication rate was 17 folds per 48 hours (95% CI: 14–20) and maximum parasite killing rate by chloroquine was 0.213 h(‐1) (95% CI: 0.196–0.230), translating to a parasite clearance half‐life of 4.5 h (95% CI: 4.1–5.0) and a parasite reduction ratio of 400 every 48 hours (95% CI: 320–500). This is the first study that characterized the PKPD relationship between chloroquine plasma and whole blood concentrations and P. vivax clearance using a semi‐mechanistic population PKPD modelling. This PKPD model can be used to optimize dosing scenarios and to identify optimal dosing regimens for chloroquine where resistance to chloroquine is increasing.

Globally, 225 million cases of malaria have been reported with 7.5 million cases due to Plasmodium vivax. 1 Malaria causes an estimated 405,000 deaths worldwide in which 67% involve children aged under 5 years. 1 Chloroquine was the cornerstone of malaria treatment but the spread of drug resistance has rendered chloroquine ineffective for the treatment of P. falciparum malaria in almost all regions of the world. However, chloroquine remains a valuable antimicrobial agent for the treatment of P. vivax malaria in most countries because of its low cost and well-understood safety profile.

Chloroquine resistance to P. vivax emerged in Papua New Guinea in the late 1980s 2, 3 , and evidence of reduced chloroquine efficacy has been reported in other malaria endemic countries. 4, 5 Evaluating the efficacy of antimalarial drugs in P. vivax malaria is difficult due to lack of a continuous in vitro P. vivax culture system, and by the inability of molecular genotyping to discriminate between recrudescence (i.e. blood-stage treatment failures), relapse and new infections.

Inadequate chloroquine dosing leads to sub-therapeutic exposure and increased risk of treatment failure and resistance development. The pharmacokinetic (PK) and pharmacodynamic (PD) properties of chloroquine and its active metabolite, desethylchloroquine, in P. vivax malaria have been characterized in only a few studies. 6-14 Of note, the relationship between the PK profile of chloroquine and parasite killing has not been described using semi-mechanistic PKPD modelling approaches. Knowledge on the PK profile of chloroquine and its metabolite, as well as their antimalarial effect against P. vivax, would provide a valuable tool for clinical prediction of the efficacy of various dosing regimens, and for optimizing the current dosing regimen, particularly to combat rising resistance to chloroquine.

This article is protected by copyright. All rights reserved In this study, we investigated the PKPD relationship of chloroquine and desethylchloroquine by analyzing data from a volunteer infection study (VIS) using the P. vivax induced blood-stage malaria (IBSM) model. 15 We used nonlinear mixed effects modelling to characterize the PK and PD properties of chloroquine and desethylchloroquine. The results from our modelling will help optimizing chloroquine dosing regimens in P. vivax malaria field studies.

Study design, procedure and main results were described in detail elsewhere. 15 Briefly, the study was a phase 1b, single-center, open-label trial (ACTRN12616000174482) in three cohorts of eight subjects each. The 24 healthy subjects were inoculated with ~564 viable P. vivax-infected erythrocytes administered intravenously on Day 0. Chloroquine was administered as chloroquine phosphate tablet (Avloclor ® ). Subjects received a total chloroquine dose of 1.55 g base for ≥60 kg adults or 25 mg base/kg for <60 kg adults orally over 3 days on Day 8, 9 or 10. The study was approved by the QIMR Berghofer Medical Research Institute Human Research Ethics Committee and the Australian Defence Human Research Ethics Committee. All subjects provided written informed consent before participating.

Samples for measuring chloroquine and desethylchloroquine concentrations in plasma and whole blood were collected before chloroquine administration and at 1, 2, 3, 4, 6, 12, 24, 48, 72, 96, 168, 240 and 480 h after first dose administration. Blood samples for P. vivax counts were collected before inoculation, daily from Day 4 until parasitemia was detected, twice-daily thereafter until treatment day, before first dose on treatment day, and at 2, 4, 8, 12, 16, 24, 30, 36, 48, 60, 72, 96, 120, 168, 240 and 480 h after the first dose.

Chloroquine and desethylchloroquine concentrations were measured using liquid chromatography tandem mass spectrometry. The lower limit of quantification (LLOQ) was 1.35 µg/L for chloroquine plasma, 1.0 µg/L for whole blood chloroquine, 1.3 µg/L for plasma desethylchloroquine and 1.75 µg/L for whole blood desethylchloroquine concentrations. P. vivax parasitemia was measured using quantitative real-time polymerase chain reaction (qPCR) targeting the 18S rRNA gene. 16 The limit of detection (LOD) was 10 parasites/mL.

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The population PKPD analyses included 1,084 drug concentration measurements (271 for each chloroquine and desethylchloroquine in plasma and whole blood) and 619 P. vivax parasitemia measurements. Of these measurements, 8.9% of chloroquine plasma, 11.8% of desethylchloroquine plasma, 8.9% of chloroquine whole blood and 10% of desethylchloroquine whole blood concentrations were below the LLOQ. Chloroquine and desethylchloroquine concentrations in erythrocytes were calculated and correlations between drug concentrations in whole blood and erythrocytes were determined (see Supplementary Supporting Information).

Approximately 25.5% of parasitemia levels were below the LOD, of which 26% were from measurements taken before chloroquine treatment. The remainder of parasitemia levels below the LOD (74%) occurred at median of 66 h (range: 24-120) after the first dose of chloroquine.

Recrudescence was not observed in any subject over the study period.

Nonlinear mixed effects modeling was performed using Monolix v4. 3 

Chloroquine and desethylchloroquine plasma and whole blood concentrations were converted into molar units using the molecular masses of 319.8 g/mol and 291.8 g/mol, respectively. The chloroquine phosphate doses were converted to the equivalent (base) chloroquine dose by multiplying chloroquine phosphate by 0.62 and converted into molar units. Correlations between chloroquine (Spearman's rho = 0.99) and desethylchloroquine (Spearman's rho = 0.98) whole blood and erythrocyte concentrations were high ( Figure S1 ). Given the strong relationship between whole blood and erythrocyte concentrations, whole blood concentration was used as a surrogate for parasite killing effect. PK modelling was conducted separately for plasma and whole blood concentration-time data. First, a population PK model of chloroquine was developed and then the PK model was extended to describe concurrent PK of chloroquine and desethylchloroquine. Desethylchloroquine is known to have a killing activity against chloroquine-Accepted Article sensitive P. falciparum strains. [19] [20] [21] [22] Based on the assumption that desethylchloroquine antimalarial activity is similar against P. vivax, desethylchloroquine PK data were incorporated into the PK model with the aim to be used in the PKPD model. Data below the LLOQ were treated using the left-censoring method, by maximizing the likelihood of the data being below LLOQ. 23 One-, two-and three-compartment PK models with different absorption (zero-order, first-order, simultaneous and sequential mixed zero-and first-order with or without lag time) and elimination (linear and parallel linear and saturable) models were tested. Inter-individual variability (IIV) was assumed to be log-normally distributed. Correlations between PK parameters were evaluated by estimating the off-diagonal components of the variance-covariance matrix. Inter-occasion variability related to was tested for the second, third and fourth doses versus the first dose. Age, sex and body weight were tested during covariate model building. Effect of covariates on PK parameters was evaluated with a stepwise forward-addition (P <0.05) and backward-deletion (P <0.01) approach. Continuous covariates were modelled as power function and categorical covariates as linear function (see Supplementary Supporting Information). Covariates were selected based on biological plausibility, statistical significance and clinical relevance. A covariate was retained in the final model if its contribution was clinically important, which was defined as 95% confidence interval (CI) of the covariate effect lying completely outside of a predetermined limit (±20%).

Each P. vivax parasitemia measurement was quantified in replicate per time-points and log 10transformed. The mean of the log 10 -transformed parasitemia per time-point per subject was used in the analysis. A sequential PKPD modelling approach was used in which the empirical Bayes estimates of individual PK parameters from plasma and whole blood samples were used as regression parameters. The initial aim was to include the antimalarial effect of desethylchloroquine in the PKPD model; however, desethylchloroquine was excluded from the analysis as it was assumed that its contribution to the parasite killing effect was minor based on its low concentrations in both plasma and whole blood. The low exposures of desethylchloroquine

This article is protected by copyright. All rights reserved relative to chloroquine suggest that chloroquine contributes most to the antimalarial effect. The left censoring method was used for parasitemia below the LOD.

As the maximum parasite killing rate of chloroquine ( ) is independent of whether plasma or E max CQ whole blood concentrations were measured in describing the parasite killing effect, a common parameter was used to fit the two models simultaneously. To technically enable the E max CQ simultaneous fit, the observed parasitemia levels were duplicated to link each set separately to the empirical Bayes estimates of the PK parameters for plasma or whole blood concentrations. We tested several PD models, including a direct effect ( ), a turnover, and an effect compartment E max model (see Supplementary Supporting Information). IIV of each PD parameter was described using a log-normal variance model, except for the log 10 -transformed baseline parasitemia, for which a normal distribution was assumed. An additive error model was used to present the residual RUV in the log 10 parasitemia measurements. Secondary parameters such as parasite multiplication rate over 48 h ( ), parasite reduction ratio over 48 h ( ), parasite PMR 48 PRR 48

clearance half-life ( ), and time above chloroquine plasma and whole blood concentration at PCt ½ half of maximum effect ( ) were estimated using individual PD parameters EC 50 CQ biological matrix obtained via empirical Bayes estimates (see Supplementary Supporting Information).

The appropriateness of the model was evaluated using basic goodness-of-fit diagnostics and visual predictive check (VPC). A VPC was performed by simulating 1,000 observations at each timepoint and 95% CI of the 5 th , 50 th and 95 th simulated percentiles were plotted against observations. In addition, successful convergence of the minimization routine, reasonable physiological plausibility and precision of parameter estimates were also taken into account during the model development. 

All subjects from the previously reported clinical trial (three cohorts, n = 8 per cohort) were included in the analysis. 15 Table 1 shows the demographic characteristics of the subjects, chloroquine dose and treatment day. The majority of the subjects (91.7%) received 1,000 mg chloroquine phosphate initially, followed by 500 mg or 375 mg at 6, 24 and 48 h after first dose administration. Chloroquine treatment commenced on Day 8 for Cohort 1 and on Day 10 for Cohorts 2 and 3, except for one subject (subject 205) who commenced treatment on Day 9.

Plasma, whole blood and erythrocyte concentrations of chloroquine and desethylchloroquine time profiles are presented in Figure S1 .

Chloroquine plasma and whole blood PK profiles were characterized by a two-compartment model with first-order absorption and elimination and a proportional residual error.

Desethylchloroquine plasma and whole blood PK profiles were described by a two-compartment model, with a first-order input from the chloroquine central compartment and first-order elimination ( Figure 1 ). Chloroquine whole blood concentrations were approximately 5.9-fold concentrations. VPCs indicate that the PK models are able to reproduce the observed data ( Figure   2 ).

Individual empirical Bayesian estimates of PK parameters from plasma and whole blood data were used simultaneously to describe the drug concentration-effect relationship. 

This article is protected by copyright. All rights reserved and whole blood (2.6, 95% CI: 2.5-2.7), corresponding to a PRR of 400 every 48 hours (95% CI: 320-500 The goodness of fit plots did not show any systematic deviation between observed parasitemia and population and individual predicted parasitemias ( Figures S7 and S7 ). The distributions of PWRES, IWRES and NPDE plotted against population prediction and time after first chloroquine dose were homogenous around the zero line, with exception of a few observations, mainly data below LOD that lay outside the -2 to 2 range. The major contributor of these observations was subject 205, who received his first dose of chloroquine on Day 9. The estimated for this E max CQ subject is higher than the rest of subjects (0.365 h -1 versus others: 0.172-0.292 h -1 ). The model predictive performance was confirmed with VPC plots (Figure 3 ) in which the 5th, 50th and 95th percentiles were within the 95% CI for both plasma and whole blood data. data, and to 0.184 h -1 for chloroquine whole blood data. In the second scenario, the changes in parasite sensitivity to chloroquine was investigated by increasing the while EC 50 CQ biological matrix holding constant the ( Figure 4b ). As expected, the treatment success rate at Day 28 E max CQ decreased as the increased. To achieve a treatment success rate above 90%, EC 50 CQ biological matrix an of ≤19.5 µg/L (0.061 µmol/L) was required when using chloroquine plasma data, and EC 50 CQ of ≤146 µg/L (0.456 µmol/L) when using chloroquine whole blood data.

This is the first study characterizing the PKPD relationship of chloroquine plasma and whole blood concentrations on clearance of P. vivax parasites using a semi-mechanistic population PKPD modelling. The final population PK model for plasma and whole blood samples was a two-

This article is protected by copyright. All rights reserved compartment model for both chloroquine and desethylchloroquine, with first-order absorption and elimination. A delayed effect model was used to describe the effect of chloroquine on the P. vivax kinetics with equilibration half-life of at least 24 h. Given chloroquine's long half-life, this delayed effect is not clinically relevant in uncomplicated malaria. However, a delayed effect is undesirable property of drugs used for treating severe malaria or for antimalarials with short half-lives. 24 The structural PK model described in this study was similar to two published studies. 9, 13 However, another published study indicated a preference for one transit compartment absorption model over the first-order absorption. 12 Differences in sampling time-points during the absorption phase may contribute to this discrepancy. These three published PK models were based on chloroquine and desethylchloroquine plasma concentrations. 9, 12, 13 The PK parameter estimates of plasma samples in this study are slightly different in terms of volume of distribution and clearance with those reporting a 1-compatment PK model, probably due to limited sampling 25, 26 and another study reporting a 2-compartment model. 27 Compared to our estimates of the PK parameters, hydroxychloroquine has a smaller total apparent volume of distribution but was similar in regard to absorption and elimination parameters.

This article is protected by copyright. All rights reserved The analysis presented in this report represents the first attempt to develop a PK model of chloroquine and desethylchloroquine using whole blood concentrations. PK parameter estimates of whole blood samples were generally lower than that of plasma samples. This is due to differences in the partitioning of chloroquine and desethylchloroquine between erythrocytes and plasma. In this study, chloroquine and desethylchloroquine whole blood concentrations were higher than in plasma, which is in accordance with the results of other studies. [28] [29] [30] [31] Consequently, the volume of distribution and clearance of chloroquine were six folds lower in whole blood than in plasma. Given the differences in PK parameter estimates between whole blood and plasma, the choice of biological matrix is important for monitoring chloroquine concentration. The World

Health Organization recommendations are that whole blood be the preferred biological matrix for measurement of chloroquine levels, as it is more convenient to collect and process than packed red cells, plasma or serum. 32 Furthermore, factors such as the interval from sampling to centrifugation, duration and force of centrifugation do not influence chloroquine concentrations in whole blood. 30, 33 The delay between appearance of chloroquine in plasma and whole blood and parasite killing was described using an effect compartment model. The utility of this delayed response model has been reported in modelling of P. falciparum malaria in patients receiving oral artesunate therapy. 34 The PKPD model developed in this study assumed that chloroquine is active against all asexual lifecycle stages of parasites. However, there is emerging evidence that P. vivax trophozoites and schizonts are insensitive to chloroquine. [35] [36] [37] No recrudescence was observed for any subject, rendering the estimation of chloroquine difficult. Chloroquine for whole blood was EC 50 EC 50 fixed to a value previously reported in the literature based on the assumption that the drug sensitivity of parasites at the time of recrudescence is similar to that calculated from relapse data. 14 was assumed to be six folds lower than the based on whole blood EC 50 CQ plasma EC 50 CQ whole blood to plasma ratio of chloroquine concentrations. Despite these limitations, this is the first study to characterize PKPD relationship of chloroquine plasma and whole blood concentrations on clearance of P. vivax parasites. This is also the first time that the parasite killing effect of chloroquine was investigated in a P. vivax induced blood stage VIS.

The time above was used as a PK determinant of chloroquine therapeutic EC 50 CQ biological matrix outcome. With a log 10 PRR 48 of 2.6 (95% CI: 2.5-2.7), 10 days are required for chloroquine to

This article is protected by copyright. All rights reserved clear parasites in a hypothetical patient with a baseline parasitemia of 10 12 . As chloroquine concentrations remain above for 14-18 days, this ensures that all asexual parasites are EC 50 removed from circulation, thereby curing the blood-stage infection. The estimated log 10 PRR 48 in this study is similar to the reported values of chloroquine-sensitive isolates (range: 2.6-3.6) 38, 39 , mefloquine (2.6) 38 and halofantrine (2.3) 38 . However, the log 10 PRR 48 estimated for chloroquine is lower than artesunate (3.2) 38 , artemether (3.2) 38 , artefenomel (range: 4.6-6.3) 40 and ganaplacide (range: 3.1-3.8) 41 but higher than primaquine (1.0) 38 , quinine (2.0) 38 , DSM265 (range: 0.8-1.5) 42 and sulfadoxine-pyrimethamine (range: 1.2-1.5) 38 in patients with P. vivax malaria. Simulations showed that chloroquine efficacy was sensitive to changes in . An reduction of 14% E max CQ E max CQ for plasma samples and 25% for whole blood samples resulted in treatment success rate of <90%.

In other reported in silico simulations, changes in were also found to have the largest E max CQ effect on treatment outcome. 43 Increasing the chloroquine dose in adults could restore treatment success rate in areas with emerging chloroquine resistance but with an increased risk of development of toxicity. Of note, simulations of treatment success in this study were based on recrudescence within 28 days of follow-up, and relapse within the treatment interval by reactivation of dormant hypnozoites was not taken into consideration.

In conclusion, we have successfully developed a population PKPD model describing the PK and PD properties of chloroquine treatment in a VIS using the P. vivax IBSM model. This model improves our understanding of the concentration-effect relationship of chloroquine in P. vivax malaria and can be applied to optimize chloroquine dosing regimens. The PKPD model of chloroquine developed, which includes estimation of parasite growth from samples collected pretreatment, can be used within a simulation framework to explore a range of scenarios. These include varying dosing schemes for chloroquine resistant P. vivax infection by varying the or E max , the effect of incomplete adherence to the standard three-day dosing regimen on outcome, or EC 50 varying the dosage schedule so as to minimize drug toxicity which is primarily related to C max could be simulated. Another model applicability, particularly the PK model, is the prediction of chloroquine dosing regimens for treatment of COVID-19. Simulations of various dosing schemes using the developed PK model to achieve exposure above in vitro inhibition of 1.13-7.36 EC 50 µM [44] [45] [46] at multiplicities of infection of 0.01-0.8 (assuming these values correspond to total plasma values) and putative whole blood in vivo of 6.78-44.16 µM (based on whole blood-to-plasma EC 50

This article is protected by copyright. All rights reserved ratio of 6:1 in this study), as well as prediction of chloroquine concentrations in the lungs (assuming similar lung-to-plasma correlations in animal studies 47, 48 ) could be performed.

What is the current knowledge on the topic?

Chloroquine has been extensively used for more than 70 years; however, the relationship between chloroquine PK and parasite killing has not been quantified using semi-mechanistic population PKPD modelling.

This study investigated the PKPD relationship between chloroquine plasma and whole blood concentrations and P. vivax killing effect in a volunteer infection study using induced blood-stage malaria model.

This is the first study characterizing the PKPD relationship of chloroquine plasma and whole blood concentrations on clearance of P. vivax parasites. This study demonstrates the importance of biological matrices in chloroquine PK monitoring and found a delayed effect of chloroquine on killing of P. vivax parasites.

The developed PKPD model provides a valuable in silico prediction tool for determining dosing strategies that reduce the development of resistance and ensure chloroquine therapeutic effect against P. vivax with decreased sensitivity to chloroquine. , apparent volume of distribution for peripheral V p1 DCQ /F 

This article is protected by copyright. All rights reserved areas represent the 95% confidence intervals for 50 th percentile and the light grey shaded area the 95% confidence interval for the 5 th and 95 th percentiles derived from 1,000 stochastic profiles simulated from the final population pharmacokinetic model. The black solid lines represent the median of simulated treatment success for chloroquine plasma data, the grey solid line represent the median of simulated treatment success for chloroquine whole blood data, the light grey shaded areas represent the simulated 95% confidence interval for the median of chloroquine plasma data, and the dark grey shaded areas represent the simulated 95% confidence interval for the median of chloroquine whole blood data.

Figure S1 

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This article is protected by copyright. All rights reserved relative bioavailability of first-order absorption; C max , maximum concentration; t max , time to reach maximum concentration; AUC 0−inf , area under the concentration-time curve from zero to infinity; t ½ , elimination half-life; RSE, relative standard error; CI, confidence interval.

a Secondary pharmacokinetic parameters were calculated from the empirical Bayesian post hoc estimates.

b All values are given as median (range) unless stated otherwise.

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Accepted Article

We thank Claire Wang and all the staff at QPID Laboratory involved in the qPCR analysis, and Brett McWhinney for plasma and whole blood PK analysis. We thank Jacinda Wilson and Dr Laura Cascales for medical writing services.

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This article is protected by copyright. All rights reserved