Blood sampling and assay.
Just before first drug administration (time < 0 h), blood was collected for biochemistry and the remaining plasma was used as the first, pre-drug administration (time < 0 h) PK sample. Thereafter, PK samples were collected through venipuncture. Blood samples were collected at 2 and 4 h after each dose and just before doses 2, 3, 4, 5, and 6 to measure trough concentrations. Additional samples were taken at 0.25 h after doses 1 and 2. Samples were collected at 6, 8, and 12 h after the last dose, in addition to a scheduled sample at day 7 after initiation of treatment. Patients were admitted until the last sample was taken and parasite clearance achieved. A total of 4 ml of blood per sampling time was collected in glass lithium-heparin vacuum tubes with gel. The sodium heparin tubes were at room temperature (18°C to 25°C) prior to use. Samples were centrifuged without delay and the plasma was separated and frozen at −70°C. Samples were shipped on dry ice to the Department of Clinical Pharmacology, Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand, for drug measurements. The laboratory is accredited according to ISO15189 and ISO15190. Drug concentrations of ARM and DHA were measured in plasma using high-performance liquid chromatography coupled with tandem mass spectrometry (45
); meanwhile, LF was measured using automated solid-phase extraction (46
). The LLOQ were 1.43 ng/ml for both ARM (4.79 nM) and DHA (5.03 nM) and 24.86 ng/ml (47 nM) for LF. Samples that showed signs of extensive hemolysis were excluded from analysis. Quality control samples of LF, ARM, and DHA at three levels (low, middle, and high) were analyzed within each batch of clinical samples to ensure precision and accuracy during routine clinical drug measurements. The coefficients of variation for all analytes were lower than 5% for all quality control samples, which is well below the required precision of ±15% according to U.S. FDA regulatory guidelines (47
). Parasitemia density assessment was conducted by microscopy using Field's method, i.e., thin or thick blood film counts of asexual parasites and gametocytes every 8 h (±1 h) following the first dose administration until 72 h postdose (48
). After discharge from the hospital, samples for thick and thin blood films were collected on outpatient basis on days 7, 14, 21, 28, 35, 42, 49, and 56 or on any other day when clinically indicated.
The molar units of LF, ARM, and DHA concentration were transformed to their natural logarithms for this modeling analysis. All the BLLOQ data for ARM and DHA were included and explored with the application of the likelihood-based M3 method for censored observations using the Laplacian estimation method (55
). Conversely, LF BLLOQ data were not included, as they made up <10% of the total data, and the FOCEI (first-order conditional estimation with interaction) estimation method was used. The unexplained residual error was estimated using an additive error model on the logarithmic scale for all drugs, which equates to an exponential error model on an arithmetic scale. In the case of ARM and DHA, a separate additive error model was used for each analyte. Different structural absorption (first-order, first-order with transit compartment, and sequential absorption) and distribution (one-, two-, and three-compartment) models were explored for all drugs.
Because of LF data sparseness, particularly during the elimination phase, informative priors based on a previous study (15
) were applied to all parameter estimates (57
). The chosen prior model explored PK properties of LF in pregnant and nonpregnant women with uncomplicated Plasmodium falciparum
malaria in Uganda. The final model had pregnancy retained as a significant covariate on intercompartmental clearance. Hence, the frequentist prior estimation for LF's Q/F was recalculated to represent estimation for pregnant women in this study. The typical relative F was implemented as a fixed parameter for the parent analyte, i.e., LF and ARM (100% relative bioavailability). The stochastic model implemented consisted of BSV modeled as shown below (equation 2
), BOV, and residual variability. Individual parameters for both drugs were modeled as lognormally distributed around the population estimate, except for F of LF. Box-Cox transformation (58
) was explored for the distribution of BSV on F as shown below (equation 3
) to assess formally the assumption that PK parameters are lognormally distributed.
represents the individual parameter estimate, Ppop
represents the typical parameter estimate for the population, η represents the BSV, and λ represents the estimated Box-Cox transformation factor.
ARM is known to exhibit an autoinduction of its own clearance (17
). Enzyme kinetics was included in the ARM-DHA PK model, and an enzyme turnover model used previously by Hassan et al. and Smythe et al. was also adapted in this study (37
). ARM and DHA, expressed as molar concentrations, were characterized simultaneously assuming complete and irreversible in vivo
conversion of ARM into DHA.
The dynamics of the enzyme compartment over time was expressed as shown below (equation 4
is the amount of enzyme in the enzyme compartment, KENZ
is the first-order degradation rate constant of the enzyme, and EFF is the link between ARM concentration and its enzyme pool through increase in enzyme production rate. Linear and nonlinear relationships (Emax
model) describing the effect of ARM concentrations on the induction of its own clearance were explored.
The enzyme concentration was initialized at 1 in order to normalize it to unity at baseline; i.e., the zero-order production rate of the enzyme was set to KENZ
. This (enzyme) then modulates the preinduced ARM clearance (equation 5
The model was parameterized in such way that the enzyme half-life (t1/2ENZ
) was estimated as shown below (equation 6
The body size descriptor covariates (total body weight, IBW, FFM, and NFM), EGA, observed baseline parasitemia density, observed time-varying parasitemia density, temperature, and dosing occasion (OCC; i.e., each dose given was considered single dosing occasion) were considered for exploration of covariate analysis for LF, ARM, and DHA based on biological plausibility and previous findings.
For body size descriptors, different covariate implementations were explored: allometric scaling using total body weight, allometric scaling using IBW (60
) (equation 7
), allometric scaling using FFM (equation 8
), and allometric scaling using NFM (equation 9
), with Ffat
representing the contribution of fat mass normalized to the FFM estimated for (i) CL/F, Q/F, volume of distribution of central compartment (Vc
)/F, and Vp
/F for LF and (ii) apparent clearance and volume of distribution of central compartment for ARM (CLARM
/F and V2
/F) and DHA (CLDHA
/F and V3
is 37.99 kg/m2
is 35.98 kg/m2
, which represent the maximal and half-maximal weight-for-height standards, respectively.
All size descriptors were scaled to their respective medians (i.e., total body weight [59 kg], FFMmedian, IBWmedian, and NFMmedian) on PK parameters using allometric power exponents of 0.75 for clearances (CL/F, Q/F, CLARM/F, and CLDHA/F) and 1 for volumes of distribution (Vc/F, Vp/F, VARM/F, and VDHA/F).
An example implementation for body descriptor FFM on CL/F is shown below (equation 10
Later, all chosen covariates were explored with body weight maintained on clearances and distribution volumes using a standard allometric function when evaluating linear, exponential, and power relationships for the other covariates, which were normalized to their median values in the population.
Stepwise covariate modeling was applied for all continuous covariates using P
values of 0.05 (ΔOFV > 3.84; 1 degree of freedom) in the forward step and 0.01 (ΔOFV > 6.63) in the backward step (62
). EGA was additionally explored as a categorical covariate (trimester 2 versus 3) using a forward inclusion cutoff ΔOFV of >5.99 (2 degrees of freedom).
The final separate LF and ARM-DHA PK models were subsequently evaluated as a combined model in a simultaneous fit, to explore correlations between the PK parameters of both drugs, in particular bioavailability and absorption rate. Parameter correlations were explored using the variance-covariance matrix.