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Importance of Lysosomal Trapping and Plasmodium Parasite Infection on the Pharmacokinetics of Pyronaridine: A Physiologically Based Pharmacokinetic Model-Based Study
Pyronaridine is a blood schizonticide with a high blood-to-plasma ratio, effective against Plasmodium parasites. As a lipophilic, moderately strong base, it accumulates in low-pH compartments such as lysosomes and parasite food vacuoles, leading to tissue accumulation and differences in drug exposure between healthy individuals and patients with malaria. This study applied physiologically based pharmacokinetic (PBPK) modeling to evaluate the effects of lysosomal sequestration, red blood cell (RBC) accumulation, and parasitemia on pyronaridine pharmacokinetics.
Methods
Data were available from a phase I clinical trial and the PYRAPREG study. PBPK models were developed in PK-Sim® and MoBi®. A standard multicompartment structure was expanded by adding lysosome compartments to relevant organs. To account for malaria infection, Plasmodium parasite compartments were incorporated into RBCs, with volume scaled by parasitemia.
Results
Data from 52 healthy individuals and 25 patients with malaria were used for model optimization. Incorporating lysosomal sequestration was essential for capturing pyronaridine distribution. In patients with malaria, incorporating low hemoglobin (Hb) and drug accumulation in the parasite compartment enabled an adequate description of whole blood pharmacokinetics. Simulations showed that free pyronaridine concentrations in the parasite compartment were over 10-fold higher than that in whole blood. Higher parasitemia was associated with increased area under the curve (AUC)0–24h and Cmax, mainly on day 1, as parasitemia decreased rapidly. However, the subsequent decrease in Hb had the opposite effect, lowering AUC0–24h and Cmax on the following days.
Conclusions
This study demonstrates the value of PBPK modeling in elucidating key pharmacokinetic mechanisms, revealing the critical roles of lysosomal sequestration, Hb level, and parasitemia in pyronaridine disposition.
This study used PBPK modeling to investigate how lysosomal sequestration and malaria-associated pathophysiological factors, such as low hemoglobin levels and elevated parasitemia, affect the pharmacokinetics of pyronaridine.
The model identified lysosomal sequestration as a key driver of extensive tissue distribution, while drug accumulation in parasite compartments and changes in hemoglobin levels substantially impacted both systemic and target site exposure.
These findings demonstrate the value of PBPK modeling in elucidating the complex distribution mechanisms of pyronaridine.
1 Introduction
Malaria, caused by Plasmodium parasites infecting red blood cells (RBCs), continues to pose a global health challenge, especially in tropical and subtropical regions. Despite ongoing efforts to control and eliminate the disease, the rise of drug-resistant strains has complicated treatment strategies [1]. Pyronaridine-artesunate, the most recent addition to the World Health Organization (WHO)-recommended artemisinin-based combination therapies (ACTs) for uncomplicated P. falciparum and P. vivax malaria, offers a promising alternative in areas where resistance to other antimalarials is a growing concern [2].
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Pyronaridine serves as the long-acting component in this ACT, playing a crucial role in clearing residual parasitemia after the artemisinin component has been eliminated. Although it has been used as an antimalarial for decades, its clinical pharmacology and mechanism of action are not fully understood [3]. As a lipophilic, moderately strong base with four ionizable amines (pKa > 6) [4], pyronaridine preferentially accumulates in low-pH cellular compartments such as lysosomes [5, 6]. This lysosomal sequestration may result in extensive drug accumulation in, and slow release from, lysosome-rich tissues such as the lungs, liver, kidneys, and spleen [7]. These properties may explain findings from prior population pharmacokinetic (PK) studies, which reported a peripheral volume of distribution exceeding 6000 liters in both healthy individuals and patients with malaria, including adults and children [8, 9]. Moreover, lower drug exposure has been observed in patients with malaria compared with healthy individuals receiving comparable dosing regimens, suggesting that malaria infection may influence the disposition of the drug [3].
Pyronaridine is a highly effective blood schizonticide, exhibiting potent activity against the asexual blood stages of the Plasmodium parasite. Its mechanism of action involves inhibiting hemozoin formation within the parasite’s digestive vacuole, thereby preventing the detoxification of heme, a process similar to that of other quinolone-based antimalarials [10]. The pharmacological characteristics of the drug enable efficient delivery to the target site, with a high blood-to-plasma ratio supporting its distribution within the bloodstream [4]. The acidic environment of the parasite’s digestive vacuole (pH 5.1) promotes selective pyronaridine accumulation at its site of action [10]. In patients with malaria, factors such as low hemoglobin (Hb) and high parasitemia may alter drug distribution and elimination [11], complicating its PK and therapeutic profile.
This study aimed to evaluate the role of lysosomal sequestration in the distribution of pyronaridine to peripheral tissues and to explore how low Hb and variable parasitemia influence its distribution to RBCs in patients with malaria. To address these objectives, we employed a physiologically based pharmacokinetic (PBPK) modeling approach, incorporating a mechanistic model to account for lysosomal sequestration and the pathophysiological effects of malaria infection.
2 Method
2.1 Pharmacokinetic Data
2.1.1 Healthy Individuals
Pyronaridine PK data from healthy individuals were kindly provided by Shin Poong Pharm. (Seoul, Korea). Data were available from the single ascending dose (SAD) and the multiple ascending dose (MAD) studies of a phase I clinical trial (SP-C-001-03), conducted at the Clinical Trial Center of Seoul National University, Seoul, South Korea [12]. Participants received pyronaridine tetraphosphate-artesunate at doses of 6+2 mg/kg, 9+3 mg/kg, 12+4 mg/kg, or 15+5 mg/kg, either as a single dose or once daily for three consecutive days. The administered pyronaridine tetraphosphate dose corresponds to 56% pyronaridine base. Accordingly, a 60 kg individual in the respective dose groups received 200, 300, 400, or 500 mg of pyronaridine free base. Details of the study design have been published elsewhere [12].
Whole blood samples were collected at 0.33, 0.68, 1, 1.33, 1.67, 2, 2.5, 3, 4, 5, 8, 12, and 24 h, as well as on days 2, 3, 5, 7, and 10 after the first dose in the SAD study and after the third dose in the MAD study. Additional samples were collected prior to the second and last dose administration in the MAD study. Pyronaridine concentrations in whole blood were measured using a validated liquid chromatography–tandem mass spectrometry (LC-MS/MS) assay with a quantification range of 5.7–855 µg/L [13].
2.1.2 Patients with Malaria
Data from patients with malaria were available from the PYRAPREG study (PACTR202011812241529), a phase III, noninferiority, randomized, open-label clinical trial involving pregnant women with malaria in five malaria-endemic countries in sub-Saharan Africa [14]. The PK data included in this analysis are from a cohort of nonpregnant, human immunodeficiency virus (HIV)-negative women, recruited in the Democratic Republic of the Congo and in Mozambique. Patients with P. falciparum monoinfection confirmed by microscopy at any density and Hb concentration ≥ 7 g/dL were included. Patients received Pyramax® tablets (180 mg pyronaridine tetraphosphate + 60 mg artesunate per tablet) once daily for 3 days. The dose was based on body weight: two tablets for 24 to < 45 kg, three tablets for 45 to <65 kg, and four tablets for ≥ 65 kg, providing 200–400 mg pyronaridine free base.
Whole blood samples were collected at 1, 2, 6, 10, and 24 h after the first dose. Additional samples were taken prior to the last dose and on study days 7, 14, 21, 28, and 42. Pyronaridine concentrations in whole blood were measured using a validated ultra-performance LC-MS/MS assay with a quantification range of 0.5–500 µg/L [15].
2.2 PBPK Model Development
2.2.1 General Workflow
Whole-body PBPK models for pyronaridine were developed in PK-Sim® and MoBi® (Open Systems Pharmacology software, version 11.3) [16], to describe the PK of pyronaridine in typical healthy individuals and patients with malaria on the basis of anatomical and physiological parameters. The model consists of interconnected compartments representing major organs and tissues (e.g., liver, kidney, heart, lung, stomach, Intestine, spleen, muscle, fat, skin, and brain), each subdivided into vascular (blood cells and plasma), interstitial, and intracellular spaces to capture drug distribution at the tissue level (Fig. 1A).
Fig. 1
Physiologically based pharmacokinetic (PBPK) model structure
Tissues are linked via arterial and venous blood compartments, with drug transport governed by blood flow, passive diffusion, or transporter-mediated processes. Distribution is classified as perfusion- or permeability-limited on the basis of physicochemical properties. Metabolism occurs primarily in the liver, while renal clearance accounts for elimination. System-specific parameters, including organ volumes, blood flows, tissue partitioning, and enzyme expression levels, are integrated to mechanistically predict drug PK.
Individual-specific input parameters, including body weight, height, body mass index (BMI), and age, were based on population medians used for model evaluation and optimization. Protein expression profiles were based on whole-genome expression array data from ArrayExpress [17]. Compound-specific parameters, encompassing physicochemical properties related to absorption, distribution, metabolism, and excretion (ADME) processes of pyronaridine were derived from literatures. Owing to the lipophilic and basic nature of pyronaridine, tissue-to-plasma partition coefficients (Kp,organ) were calculated using Rodgers and Rowland model for moderate-to-strong bases [18, 19], and the cellular permeability was calculated using the charge-dependent Schmitt model [20], both under the framework implemented in PK-Sim® and MoBi®.
In the Rodgers and Rowland model, Kp,organ was described as the following equation:
where P is the n-octanol:water partition coefficient for the unionized compound in tissues, f represents the fractional tissue volume, with subscripts IW and EW referring to intra- and extracellular tissue water, respectively, and NL and NP denoting tissue neutral lipids and neutral phospholipids. [AP]T represents the tissue concentration of acidic phospholipids, and Ka is the drug’s affinity constant for acidic phospholipids.
For polyprotic base, X and Y in Eq. 1, accounting for drug ionization, were defined as follows:
where hematocrit (HCT) and blood-to-plasma concentration ratio (BPratio) were used.
Model development followed a three-step process. First, a PBPK model for healthy individuals receiving 6, 9, 12, and 15 mg/kg pyronaridine tetraphosphate was built on the basis of literature values. A standard healthy individual profile implemented in PK-Sim® (ICRP 2002) [21] was used, assuming a body weight of 60 kg and a Hb level of 13.6 g/dL. Second, lysosome compartments were added to the structural model of healthy individuals to assess the impact of lysosomal sequestration. Finally, the model developed for healthy individuals was applied to patients with malaria, receiving 540 or 720 mg pyronaridine tetraphosphate, with parasite compartments incorporated into the blood organs. To represent a typical patient with malaria, the model was adjusted for a reduced Hb level of 8 g/dL and a parasitemia of 2%, reflecting characteristics of uncomplicated malaria [2, 22]. In all models, the administered drug dose was implemented as a pyronaridine base.
2.2.2 Lysosome Compartment
To the model structure as described, an additional lysosome compartment was incorporated into relevant organs (Fig. 1B), following the method provided by ESQlabs GmbH [23]. These organs included the brain, fat, gonads, heart, kidneys, large, and small intestines (including their mucosal compartments), lungs, muscles, pancreas, skin, spleen, and stomach, as well as the pericentral and periportal zones of the liver. In each organ, the volume of lysosome compartment was set at approximately 2% of the intracellular volume, consistent with literature estimates indicating that lysosomes typically occupy 1–5% of the total intracellular volume in mammalian cells [24, 25]. This value was used as a representative average across tissues for modeling purposes. The lysosomal pH, which typically ranges from 4.5 to 5, was fixed at 4.51 (pHlys) [5].
Between the intracellular space and lysosomes, the pronounced pH gradient was assumed to be the primary driving force for the disposition of pyronaridine. For model simplification, the interactions with neutral lipids, neutral phospholipids, and acidic phospholipids were omitted. Given that lysosomes lack an extra-organelle compartment, the intracellular tissue water fraction was assumed 1 [26]. Consequently, the partition coefficient for lysosomes (Kp,lys) was simplified from Eq. 1 as follows:
where Qcell,lys represents the flow rate between intracellular space and lysosomes, and Ccell and Clys denote the drug concentrations in the intracellular space and lysosomes, respectively.
2.2.3 Plasmodium Parasite Compartment
To account for patients with malaria, a Plasmodium parasite compartment was incorporated into organs with larger RBC volume (> 0.01 L), including arterial blood, bone, fat, kidney, liver periportal, lung, muscle, skin, spleen, and venous blood (Fig. 1C). The volume of the parasite compartment (Vparasite) was determined on the basis of HCT and parasitemia as follows:
Here, Vblood represents the volume (L) of the blood compartment in respective organ, with the volume of RBCs determined by the HCT (L/L). Parasitemia can be quantified as the proportion of malaria-infected RBCs relative to total RBCs, typically assessed by counting parasitized RBCs among 500–2000 RBCs in a thin blood smear [27]. Consequently, Vparasite serves as an approximation of the volume of infected RBCs, assuming a uniform RBC density of 1 g/mL.
In ACT, the potent artemisinin derivative rapidly decreases parasite density. For artesunate, the parasite clearance time to achieve a 99% reduction in parasitemia (PCT99) has been estimated to be around 30 h [28, 29]. Since this rapid decline occurs early in treatment and was not captured, we accounted for the treatment effect by incorporating the expected dynamics of parasitemia as follows:
Owing to the acidic environment of the Plasmodium food vacuole, the pH of the parasite compartment (pHparasite) was set to 5.1 [30]. The partition coefficient for parasite compartment (Kp,parasite) was derived using the same approach as Eq. 5, accounting for pH differences between the parasites and RBCs. Drug distribution between the parasites and RBCs was modeled similarly to Eq. 6, with QRBC,parasite representing the flow rate between RBCs and parasites, and CRBC and Cparasite denoting drug concentrations in the RBCs and parasites, respectively.
2.3 Model Evaluation and Optimization
The model was evaluated and optimized using PK observations from both healthy individuals and nonpregnant, HIV-negative women with malaria. Parameter identification was performed for flow rates (Qcell,lys and QRBC,parasite), owing to the absence of established values in the literature. In addition, for a subset of highly variable parameters, including lipophilicity, specific intestinal permeability, BPratio, and partition coefficients (Kp,lys and Kp,parasite), parameter identification was performed. Parameter identification was carried out using Levenberg–Marquardt or Monte–Carlo algorithm with 1000 iterations to identify a global optimum.
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Model performance was evaluated by comparing predicted and observed concentrations, with predictions deemed acceptable if they fell within a 2-fold prediction interval margin [31]. Additional data from the literature on comparable populations were used for external validation. For healthy individuals, validation used data from Thai subjects (n = 15) [32] and from a phase I relative bioavailability study comparing tablet and granule formulations (n = 42) [33], each receiving a single 540 mg dose of pyronaridine. For patients with malaria, data from the 9 mg/kg dose group in a phase II dose-finding study (n = 5) were used [34], corresponding to a 540 mg dose. All external data were digitized using WebPlotDigitizer [35].
2.4 Target Site Exposure and the Effect of Malaria Severity
Concentration-time profiles were simulated for various matrices, including whole blood, RBCs, plasma, and the parasite compartment, to assess the best proxy for target site exposure. These concentrations were compared with the in vitro half maximal inhibitory concentration (IC50) values (mean ± SD: 10.95 ± 8.30 µg/L) derived from a susceptibility study on P. falciparum isolates across multiple African countries [36], to evaluate PK target attainment.
To investigate the impact of malaria infection on the whole blood PK profile of pyronaridine, simulations were conducted incorporating variations in Hb and parasitemia. Simulations were performed for patients with Hb levels of 13 g/dL (no anemia) and 7 g/dL (severe anemia) [37], in combination with parasitemia levels of 1% (low parasite burden) and 5% (high parasite burden) [27].
Secondary PK parameters, including area under the concentration-time curve from time zero to infinity (AUC0–inf), AUC 24 h post-dose (AUC0–24h), peak concentration (Cmax), time to Cmax (Tmax), and half-life (T1/2), were derived using noncompartmental analysis (NCA) via PKanalix® [38], on the basis of simulated concentrations data up to day 10.
3 Results
3.1 Pharmacokinetic Data
Data from 52 healthy individuals (28 from the SAD study and 24 from the MAD study) and 25 female patients with malaria were used for model evaluation and parameter identification. The demographic and baseline characteristics of the study participants are presented in Table 1. An overview of the physicochemical properties and ADME characteristics of pyronaridine, used as initial model inputs and estimated in the final model, is presented in Table 2. Blood-to-plasma ratios reported in the literature ranged from 2 to 15; an initial value of 3 was selected for model development [3]. As only Hb concentrations were available in the clinical datasets, HCT was approximated by assuming it is typically three times the Hb value [39]. For the calculation of partition coefficients, the two strongest basic pKa values of 10 and 9.88 were used [40]. The model incorporated hepatic metabolism of pyronaridine via CYP3A4, CYP1A2, and CYP2D6 as the primary elimination pathways [41] (see Table 2).
Table 1
Demographics (median, range)
Healthy volunteersa (n = 52)
Patients with malaria (n = 25)
Country (n)
South Korea
52
-
Democratic Republic of the Congo
-
23
Mozambique
-
2
Pyronaridine dose (n)b
6 mg/kg
13
-
9 mg/kg
13
-
12 mg/kg
13
-
15 mg/kg
13
-
360 mgc
-
1
540 mgc
-
14
720 mgc
-
10
Body weight (kg)
61.5 (50–70)
61.0 (41–82)
Height (cm)
171.0 (154.0–183.0)
162 (152–175)
Body mass index (kg/m2)
21.1 (18.1–23.8)
23 (17.3–30.5)
Female (%)
40.6%
100%
Hemoglobin (g/dL)
13.6d
11.7 (8.6–13.6)
aNumber derived from the summary table in the Phase I clinical trial report (SP-C-001-03)
bDose represents the amount of pyronaridine tetraphosphate containing 56% of the free base
cEquivalent to pyronaridine dose of 8.7–11 mg/kg
dHemoglobin values are absent in the phase I clinical trial report. Therefore, a value of 13.6 g/dL, as provided by the PK-Sim® standard database, was used in the model
Table 2
Input parameters and final parameters for the whole-body physiologically based pharmacokinetic (PBPK) model of pyronaridine
aIn vitro CL for liver microsomes normalized to enzyme concentration
bpHs of intracellular space and lysosomes are defined as 7 and 4.51, respectively
cpHs of red blood cells and parasites are defined as 7.22 and 5.1, respectively
dData are not available from the literature; the value was approximated to represent the rapid oral absorption reported in previous studies
Table 3
Secondary pharmacokinetic (PK) parameters in patients with varying degrees of malaria-induced hemoglobin reduction and associated parasitemia
Hb 7 g/dL
Parasitemia 1%
Hb 7 g/dL
Parasitemia 5%
Hb 13 g/dL
Parasitemia 1%
AUC0–24h,D1 (µg·day/L)
98
150
110
AUC0–24h,D3 (µg·day/L)
142
143
153
AUCinf (µg·day/L)
797
851
801
Cmax (µg/L)
218
380
282
Tmax (h)
50.3
2.25
50.3
T1/2 (d)
4.38
4.38
4.54
Hb hemoglobin (g/dL), AUC0–inf area under the concentration-time curve from time zero to infinity, AUC0–24h area under the concentration-time curve 24 h post-dose, Cmax peak concentration, Tmax time to peak concentration, T1/2 half-life
3.2 Effect of Lysosome Ion Trapping and Malaria Infection
The initial simulation for healthy individuals, without incorporating lysosome compartments, failed to capture the extensive distribution of pyronaridine, overestimating the initial rapid decline and underestimating the prolonged slow elimination. Adding lysosome compartments substantially improved the model fit (Fig. 2), with Kp,lys estimated at 9 × 105, consistent with the calculations from Eq. 5. The flow rate Qcell,lys was fixed at a large value, assuming an instantaneous equilibrium between the intracellular space and lysosomes. The final model provided an adequate description of the whole blood pyronaridine concentration in healthy individuals who received 6, 9, 12 and 15 mg/kg pyronaridine tetraphosphate (Fig. 3). The average fold errors (AFE) for concentrations at these dose levels were 1.21, 1.32, 0.78, and 0.72, respectively. External validation results are shown in Supplementary Fig. S1.
Fig. 2
Effect of lysosomal sequestration. Model predictions with (red) and without (blue) the effect of lysosomal sequestration. The predictions are based on a typical healthy individual receiving 540 mg of pyronaridine tetraphosphate. Black dots represent observations from phase I healthy volunteers (n = 13) receiving a 9 mg/kg dose
Model fit for healthy volunteers. Healthy volunteers received 6, 9, 12, or 15 mg/kg of pyronaridine tetraphosphate. The black line represents the model prediction, while the dots indicate observed concentrations in whole blood. The shaded area depicts the two-fold prediction interval
The model for healthy individuals overestimated the whole-blood concentration-time profile of pyronaridine in malaria patients, especially during the dosing period. Adjusting the individual-specific characteristics to reflect a typical patient with malaria, incorporating decreased Hb (moderate anemia [37]) and an additional parasite compartment improved the model fit (Fig. 4A). Owing to the lack of observations after the second or third dose administration in patients, Kp,parasite was fixed at 12.5 to achieve best model fit. The flow rate QRBC,parasite was fixed at a large value, assuming an instantaneous equilibrium between parasites and RBCs. In the final model, most observed data fell within the two-fold prediction interval, with an AFE of 1.63 (Fig. 4B). External validation results are shown in Supplementary Fig. S2.
Fig. 4
Effect of malaria infection. A Model predictions unadjusted (blue) and adjusted (red) for patient characteristics, including low hemoglobin and drug accumulation in parasites. B Final model fit for patients with malaria. Malaria patients received 540 or 720 mg pyronaridine tetraphosphate on the basis of body weight. The black line represents the model prediction, while the dots indicate observed concentrations in whole blood. The shaded area depicts the two-fold prediction interval for the final malaria-adjusted model
3.3 Target Site Exposure and Effect of Malaria Severity
Simulations of pyronaridine concentrations in whole blood, plasma, RBCs, and parasites are presented in Fig. 5. The total and free drug concentrations in the parasite compartment were more than 10-fold higher than those in whole blood, indicating extensive target site exposure. Using the plasma concentration as a surrogate for exposure-response assessment may lead to an underestimation of the actual drug effect. This finding supported whole blood as the preferred matrix for evaluating drug exposure and response.
Fig. 5
Predicted pyronaridine concentrations in various compartments. Model predictions of total pyronaridine concentrations (solid line) in whole blood, plasma, red blood cells and parasites, and free drug concentrations (dashed line) in parasites, compared with in vitro half maximal inhibitory concentration (IC50) (mean ± SD [36], shade area). As parasitemia approached zero after treatment, drug concentrations in the parasite compartment were no longer present and therefore not depicted
Simulations in patients with varying degrees of malaria-induced Hb reduction and associated parasitemia are provided in Fig. 6 and table 3. Drug exposure following the first dose was strongly influenced by parasitemia level, with Cmax being 74% higher and AUC0–24h,D1 being 53% higher at a parasitemia level of 5% compared with 1%, attributed to drug accumulation within parasites. As parasitemia rapidly declined after treatment, drug exposure following the second and third doses were primarily affected by Hb level, where a 50% reduction in Hb resulting in a 29% decrease in Cmax and a 7% decrease in AUC0–24h,D3.
Fig. 6
Simulations illustrating whole blood pyronaridine exposure across varying hemoglobin (Hb) and parasitemia levels
This study employed a PBPK modeling approach to elucidate the critical role of lysosomal sequestration in driving the extensive tissue distribution of pyronaridine. It also provided insights into the substantial differences in drug exposure observed between healthy individuals and patients with malaria during the dosing phase. Furthermore, this study explored the impact of Hb levels and parasitemia on the PK of pyronaridine, identifying both as key determinants of variability in drug exposure throughout the dosing period.
Our findings support lysosomal ion trapping as the primary mechanism driving the extensive distribution of pyronaridine. Lipophilic basic drugs with pKa values that allow partial ionization at intracellular pH can diffuse across lysosomal membranes in their uncharged form. Once inside the lysosome, where the pH ranges around 4.51, these drugs become protonated and are effectively trapped [5]. This mechanism leads to high drug concentrations in lysosome-rich tissues, such as the liver and lungs. Lysosomal trapping has gained attention for its role in repurposing antimalarial drugs, including chloroquine and pyronaridine [6, 42], for treating respiratory infections including coronavirus disease 19 (COVID-19) [43]. Achieving high drug exposure in the lungs is crucial for addressing these conditions, making lysosomal trapping a beneficial factor in attaining and maintaining effective tissue target-site concentrations.
Lysosomal sequestration of pyronaridine provides a plausible explanation for its high apparent volume of distribution, as the drug rapidly accumulates in tissues following administration. This sequestration may enhance efficacy for lysosome-targeted actions but may also increase the risk of localized toxicity. For example, elevated levels of liver transaminases have been observed in previous studies following the use of pyronaridine [44]. Furthermore, the slow release of sequestered pyronaridine from lysosomes into the cytoplasm contributes to its prolonged systemic presence, with detectable drug levels persisting in whole blood for at least 42 days after a 3-day treatment [15]. This extended exposure may underlie its long-acting therapeutic potential in preventing malaria recurrence [3].
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Pyronaridine exhibits a strong affinity for RBCs, with the reported BPratio ranging from 1.6 to 15 in humans. This high and variable BPratio reflects its preferential partitioning into blood cells. Accordingly, the distribution of pyronaridine is hypothesized to be influenced by Hb levels, as observed with other high BPratio drugs such as tacrolimus [45]. Our model supports this hypothesis, identifying changes in Hb as a key factor explaining the observed differences in whole blood exposure between healthy volunteers and patients with malaria. Although plasma protein binding was not explicitly assessed in this study, disease-related alterations in protein levels may also contribute to variability in systemic exposure and warrant further investigation. In addition, pyronaridine concentrations in whole blood may partly reflect accumulation in lysosome-rich cells such as white blood cells. However, in the context of malaria, changes in red blood cell physiology are likely the predominant driver of altered pyronaridine distribution.
Pyronaridine preferentially accumulates within the food vacuole of Plasmodium parasites and demonstrates a strong affinity for hemozoin, which is an important disposal product of the heme detoxification process by the parasite [10]. This interaction may enhance drug retention in RBCs, particularly in patients with high parasite burdens, as infected erythrocytes sequester the drug. However, malaria infection also leads to RBC destruction, causing anemia. The resulting reduction in RBCs may limit pyronaridine trapping, potentially lowering its overall retention in whole blood. Our simulations (Fig. 6) supported that the parasitemia level substantially influenced AUC0–24h following the first dose. However, owing to the rapid parasite reduction after drug treatment, changes in Hb have a greater impact on AUC0–24h after the second and third doses. The opposite effects of parasitemia and Hb level, both associated with malaria infection, may explain the high variability observed in the Cmax and AUC during the dosing period.
Our model simulations supported the use of whole blood for PK assessments for pyronaridine and highlights the potential risk of underestimating drug effects when relying on plasma measurements. Whole blood has been the preferred matrix for PK studies, as pyronaridine primarily accumulates within RBCs. Consequently, whole blood measurements are believed to provide a more accurate representation of the drug’s distribution and elimination. In contrast, plasma assays have been shown to inadequately capture this intracellular distribution, leading to an underestimation of total drug concentrations [46]. Measuring drug concentrations in whole blood enables the use of dried blood spot (DBS) sampling, which offers practical advantages such as simplified sample collection, preparation, and storage, particularly beneficial in resource-limited settings [47].
This study has several limitations. First, the model was developed and validated using a limited dataset, which may constrain the generalizability of the findings. Second, while Hb levels were incorporated to account for malaria-induced physiological changes, other potentially relevant factors, such as alterations in hepatic enzyme activity, plasma protein binding, or organ perfusion, were not explored, as Hb was considered the most mechanistically plausible driver of the observed changes in pyronaridine distribution. Third, parasitological data during the treatment period and artesunate PK data were not available for model development. This precluded a direct, individual-level evaluation of the interplay between drug exposure and parasite dynamics during the early treatment phase. To address this, we relied on published parasite clearance values for artesunate to inform our analysis.
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In conclusion, this study demonstrates the use of PBPK modeling in elucidating key PK mechanisms that are difficult to capture using population PK modeling approach. Specifically, it highlights the pivotal roles of lysosomal sequestration and variations in parasitemia and Hb levels in the distribution of pyronaridine. These factors may strongly impact drug accumulation at the target site, which is crucial for assessing treatment response in malaria.
Acknowledgements
We gratefully acknowledge the investigators, study teams, and participants of the PYRAPREG study and the phase I clinical trial, whose data made this analysis possible. We extend our appreciation to the PYRAPREG consortium for granting access to the clinical trial data and express our gratitude to Shin Poong Pharm. Co. Ltd for providing the Phase I clinical trial data.
Declarations
Funding
This work was produced by the PYRAPREG study which is part of the EDCTP2 program (grant number RIA2017MC-2025-PYRAPREG) and the Global Health EDCTP3 Joint Undertaking (Grant agreement ID: 101145638 PYRAPREG-extended), both programs supported by the European Union. The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP. T.D. was supported by the Swedish Research Council (VR 2022-01251).
Competing Interests
J.S. is an employee of Shin Poong Pharm. Co. Ltd, which provided the study drug. Alwin Huitema is an Editorial Board member of Clinical Pharmacokinetics. Alwin Huitema was not involved in the
selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. All other authors declare that they have no competing interests relevant to the content of this article.
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Availability of Data and Material
The raw data are available upon reasonable request by an email to the corresponding author.
Code Availability
The model files are provided as supplementary material. Other codes are available upon reasonable request.
Consent for Publication
The authors affirm that human research participants provided informed consent for publication of their data.
Authors’ Contributions
U.D.A., K.K., T.D., H.M.M., J.K.T., D.Y., F.A.K., V.M., E.S., A.V., and J.S. contributed to protocol development, study conduct, study registration, recruitment, and data collection. W.S. performed the bioanalysis and measured the study samples. W.C., A.H., and T.D. conducted the PBPK modelling. The manuscript was drafted and reviewed by W.C., A.H., and T.D. All authors reviewed, commented on, and approved the final submitted version of the manuscript.
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Ethics Approval/Consent to Participate
The PYRAPREG trial (PACTR202011812241529) was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the following committees: the Ethics Committee for Health Research in Burkina Faso, the National Health Ethics Committee in the Democratic Republic of Congo, the Ethics Committee of the Faculty of Medicine and Odontostomatology/Faculty of Pharmacy in Mali, the Gambia Government/MRCG Joint Ethics Committee, and the National Bioethics Committee for Health in Mozambique. Written informed consent was obtained from all participants prior to their inclusion in the study. A four-part phase I clinical trial (Protocol number SP-C-001-03) was conducted at the Clinical Trial Center of Seoul National University, Seoul, South Korea, in accordance with Good Clinical Practice (GCP) guidelines and the Declaration of Helsinki. Ethical approval was granted by the Institutional Review Board of Seoul National University. Written informed consent was obtained from each participant prior to study participation.
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Importance of Lysosomal Trapping and Plasmodium Parasite Infection on the Pharmacokinetics of Pyronaridine: A Physiologically Based Pharmacokinetic Model-Based Study
Verfasst von
Wan-Yu Chu
Wietse M. Schouten
Hypolite Muhindo Mavoko
Japhet Kabalu Tshiongo
Doudou Malekita Yobi
Freddy-Arnold Kabasele
Gustave Kasereka
Vivi Maketa
Esperança Sevene
Anifa Vala
Jangsik Shin
Umberto D’Alessandro
Kassoum Kayentao
Alwin D. R. Huitema
Thomas P. C. Dorlo
Chu W-Y, Dorlo TPC. Pyronaridine: a review of its clinical pharmacology in the treatment of malaria. J Antimicrob Chemother. 2023;78:2406–18.CrossRefPubMedPubMedCentral
4.
Charman SA, Andreu A, Barker H, Blundell S, Campbell A, Campbell M, et al. An in vitro toolbox to accelerate anti-malarial drug discovery and development. Malar J. 2020;19:1.CrossRefPubMedPubMedCentral
5.
Schmitt MV, Lienau P, Fricker G, Reichel A. Quantitation of lysosomal trapping of basic lipophilic compounds using in vitro assays and in silico predictions based on the determination of the full pH profile of the endo-/lysosomal system in rat hepatocytes. Drug Metab Dispos. 2019;47:49–57.CrossRefPubMed
6.
Lane TR, Dyall J, Mercer L, Goodin C, Foil DH, Zhou H, et al. Repurposing Pyramax®, quinacrine and tilorone as treatments for Ebola virus disease. Antivir Res. 2020;182:104908.CrossRefPubMed
7.
Siebert GA, Hung DY, Chang P, Roberts MS. Ion-trapping, microsomal binding, and unbound drug distribution in the hepatic retention of basic drugs. J Pharmacol Exp Ther. 2004;308:228–35.CrossRefPubMed
8.
Ayyoub A, Methaneethorn J, Ramharter M, Djimde AA, Tekete M, Duparc S, et al. Population pharmacokinetics of pyronaridine in pediatric malaria patients. Antimicrob Agents Chemother. 2016;60:1450–8.CrossRefPubMedCentral
9.
Barber BE, Webster R, Potter AJ, Llewellyn S, Sahai N, Leelasena I, et al. Characterising the blood-stage antimalarial activity of pyronaridine in healthy volunteers experimentally infected with Plasmodium falciparum. Int J Antimicrob Agents. 2024;64:107196.CrossRefPubMed
10.
Bailly C. Pyronaridine: an update of its pharmacological activities and mechanisms of action. Biopolymers. 2021;112:e23398.CrossRefPubMed
Tan B, Naik H, Jang I-J, Yu K-S, Kirsch LE, Shin C-S, et al. Population pharmacokinetics of artesunate and dihydroartemisinin following single- and multiple-dosing of oral artesunate in healthy subjects. Malar J. 2009;8:304.CrossRefPubMedPubMedCentral
13.
Naik H, Imming P, Schmidt MS, Murry DJ, Fleckenstein L. Development and validation of a liquid chromatography-mass spectrometry assay for the determination of pyronaridine in human blood for application to clinical pharmacokinetic studies. J Pharm Biomed Anal. 2007;45:112–9.CrossRefPubMed
14.
Djimde M, Tshiongo JK, Muhindo HM, Tinto H, Sevene E, Traore M, et al. Efficacy and safety of pyronaridine-artesunate (PYRAMAX) for the treatment of P. falciparum uncomplicated malaria in African pregnant women (PYRAPREG): study protocol for a phase 3, non-inferiority, randomised open-label clinical trial. BMJ Open. 2023;13:e065295.CrossRefPubMedPubMedCentral
15.
Schouten WM, Roseboom IC, Lucas L, Kabalu Tshiongo J, Muhindo Mavoko H, Kayentao K, et al. Development and validation of an ultra-performance liquid chromatography-tandem mass spectrometry method for the quantification of the antimalarial drug pyronaridine in human whole blood. J Pharm Biomed Anal. 2024;245:116154.CrossRefPubMed
16.
Lippert J, Burghaus R, Edginton A, Frechen S, Karlsson M, Kovar A, et al. Open systems pharmacology community-an open access, open source, open science approach to modeling and simulation in pharmaceutical sciences. CPT Pharmacomet Syst Pharmacol. 2019;8:878–82.CrossRef
17.
Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M, et al. ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 2011;39:D1002-1004.CrossRefPubMed
18.
Rodgers T, Rowland M. Mechanistic approaches to volume of distribution predictions: understanding the processes. Pharm Res. 2007;24:918–33.CrossRefPubMed
19.
Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94:1259–76.CrossRefPubMed
20.
Schmitt W. General approach for the calculation of tissue to plasma partition coefficients. Toxicol In Vitro. 2008;22:457–67.CrossRefPubMed
21.
Boecker BB. Reference values for basic human anatomical and physiological characteristics for use in radiation protection. Radiat Prot Dosimetry. 2003;105:571–4.CrossRefPubMed
22.
Bartoloni A, Zammarchi L. Clinical aspects of uncomplicated and severe malaria. Mediterr J Hematol Infect Dis. 2012;4:e2012026.CrossRefPubMedPubMedCentral
23.
ESQlabs GmbH. ESQlabs’ training platform “lysosomal trapping” [Internet]. [cited 2025 May 11]. Available from: https://learn.esqlabs.com/.
24.
Lloyd-Evans E, Waller-Evans H. Lysosomal Ca2+ homeostasis and signaling in health and disease. Cold Spring Harb Perspect Biol. 2020;12:a035311.CrossRefPubMedPubMedCentral
25.
Saftig P. Physiology of the lysosome. In: Mehta A, Beck M, Sunder-Plassmann G, editors. Fabry disease: perspectives from 5 years of FOS [Internet]. Oxford: Oxford PharmaGenesis; 2006 [cited 2025 Jul 24]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK11604/.
26.
Assmus F, Houston JB, Galetin A. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs. Eur J Pharm Sci. 2017;109:419–30.CrossRefPubMed
Kreeftmeijer-Vegter AR, van Genderen PJ, Visser LG, Bierman WFW, Clerinx J, van Veldhuizen CKW, et al. Treatment outcome of intravenous artesunate in patients with severe malaria in the Netherlands and Belgium. Malar J. 2012;11:102.CrossRefPubMedPubMedCentral
30.
Kuhn Y, Rohrbach P, Lanzer M. Quantitative pH measurements in Plasmodium falciparum-infected erythrocytes using pHluorin. Cell Microbiol. 2007;9:1004–13.CrossRefPubMed
31.
Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos. 2015;43:1823–37.CrossRefPubMedPubMedCentral
32.
Jittamala P, Pukrittayakamee S, Ashley EA, Nosten F, Hanboonkunupakarn B, Lee SJ, et al. Pharmacokinetic interactions between primaquine and pyronaridine-artesunate in healthy adult Thai subjects. Antimicrob Agents Chemother. 2015;59:505–13.CrossRefPubMed
Shin Poong Pharmaceuticals Co. Ltd. Application for Inclusion in WHO Model List of Essential Medicine - pyronaridine tetraphosphate/artesunate. 2016 Nov.
35.
Computer vision assisted data extraction from charts using WebPlotDigitizer [Internet]. [cited 2025 Aug 14]. Available from: https://automeris.io/.
36.
Pascual A, Madamet M, Briolant S, Gaillard T, Amalvict R, Benoit N, et al. Multinormal in vitro distribution of Plasmodium falciparum susceptibility to piperaquine and pyronaridine. Malar J. 2015;14:49.CrossRefPubMedPubMedCentral
Nutritional anaemias. Report of a WHO scientific group. World Health Organ Tech Rep Ser. 1968;405:5–37.
40.
Adegoke OA, Babalola CP, Oshitade OS, Famuyiwa AA. Determination of the physicochemical properties of pyronaridine–a new antimalarial drug. Pak J Pharm Sci. 2006;19:1–6.PubMed
41.
Lane TR, Massey C, Comer JE, Anantpadma M, Freundlich JS, Davey RA, et al. Repurposing the antimalarial pyronaridine tetraphosphate to protect against Ebola virus infection. PLoS Negl Trop Dis. 2019;13:e0007890.CrossRefPubMedPubMedCentral
42.
Liu X, Jusko WJ. Physiologically based pharmacokinetics of lysosomotropic chloroquine in rat and human. J Pharmacol Exp Ther. 2021;376:261–72.CrossRefPubMedPubMedCentral
43.
Homolak J, Kodvanj I. Widely available lysosome targeting agents should be considered as potential therapy for COVID-19. Int J Antimicrob Agents. 2020;56:106044.CrossRefPubMedPubMedCentral
44.
Compaoré YD, Zongo I, Somé AF, Barry N, Nikiéma F, Kaboré TN, et al. Hepatic safety of repeated treatment with pyronaridine-artesunate versus artemether-lumefantrine in patients with uncomplicated malaria: a secondary analysis of the WANECAM 1 data from Bobo-Dioulasso, Burkina Faso. Malar J. 2021;20:64.CrossRefPubMedPubMedCentral
45.
Størset E, Holford N, Midtvedt K, Bremer S, Bergan S, Åsberg A. Importance of hematocrit for a tacrolimus target concentration strategy. Eur J Clin Pharmacol. 2014;70:65–77.CrossRefPubMed
46.
Croft SL, Duparc S, Arbe-Barnes SJ, Craft JC, Shin C-S, Fleckenstein L, et al. Review of pyronaridine anti-malarial properties and product characteristics. Malaria J. 2012;11:270.CrossRef
47.
Lim MD. Dried blood spots for global health diagnostics and surveillance: opportunities and challenges. Am J Trop Med Hyg. 2018;99:256–65.CrossRefPubMedPubMedCentral
48.
Park SH, Pradeep K. Absorption, distribution, excretion, and pharmacokinetics of 14C-pyronaridine tetraphosphate in male and female Sprague-Dawley rats. J Biomed Biotechnol. 2010;2010:590707.CrossRefPubMedPubMedCentral
