Research Article
20 October 2020

Evaluation of the Combination of Azithromycin and Naphthoquine in Animal Malaria Models

ABSTRACT

Combination therapy using drugs with different mechanisms of action is the current state of the art in antimalarial treatment. However, except for artemisinin-based combination therapies, only a few other combinations are now available. Increasing concern regarding the emergence and spread of artemisinin resistance in Plasmodium falciparum has led to a need for the development of new antimalarials. Moreover, the efficacy of current available chemoprophylaxis is compromised by drug resistance and noncompliance due to intolerable adverse effects or complicated dosing regimens. Therefore, new antimalarials that are more effective, safer, and more convenient are also urgently needed for malaria chemoprophylaxis. In this study, we assessed the combination of azithromycin and naphthoquine in animal malaria models. A dose-dependent interaction was observed in Peters’ 4-day suppressive test on P. berghei K173-infected mice. Moreover, at inhibition levels of ≥90%, synergistic effects were found for combinations at various ratios. At an optimal dose ratio of 1:1, the combination of azithromycin and naphthoquine acted synergistically even by 4 weeks after the first dose and provided a more effective and sustained prophylaxis than did naphthoquine alone in blood-stage P. berghei K173 and P. cynomolgi bastianelli L challenge models. The ability of the combination to delay and slow down resistance development in P. berghei K173 was also shown. These results showed clear evidence for the benefit of the combination therapy with azithromycin and naphthoquine in animal malaria models, providing some insight for further development of this therapy for malaria treatment and prophylaxis.

INTRODUCTION

Malaria is one of the leading global health threats, causing 228 million clinical episodes and 405,000 deaths in 2018 (1). The continued development of drug resistance in Plasmodium falciparum over the past decades has led to a global adoption of artemisinin-based combination therapies (ACTs) as a first-line treatment for uncomplicated malaria (2). However, the emergence and spread of artemisinin resistance in P. falciparum (35) has evoked a need for the development of new antimalarials, especially alternative treatments in case of standard ACTs treatment failure, and new combinations of available drugs that can be immediately deployable and do not rapidly induce resistance (69).
Another important consideration is that malaria incidence in travelers to areas of endemicity has continued to rise steadily (10, 11). With continued growth in international travel, more than 120 million international travelers arrive annually in countries where malaria is endemic, resulting in an average of nearly 10,000 travel-related malaria cases per year (12). Despite many decades of intense research and development effort, there is currently no effective malaria vaccine. Antimalarials remain the essential options for malaria prophylaxis, and nonuse or inappropriate use of chemoprophylaxis is a major risk factor of fatal malaria associated with travel (13). Unfortunately, the use of available antimalarials for chemoprophylaxis is restricted by drug resistance, intolerable side effects or adverse effects, and noncompliance due to daily or complicated dosing regimens (10, 11, 14). Thus, more effective, safer, and more convenient chemoprophylaxis drugs for malaria are urgently needed.
Azithromycin (AZ) is a widely used macrolide antibiotic with antimalarial activity (15). It has been known to inhibit ribosomal protein synthesis in the Plasmodium apicoplast by binding to the apicoplast ribosomal 50S subunit (16), leading to failure in the production of infective daughter merozoites in the asexual stage, as well as the reduction of gametocyte-ookinete transformation and sporozoite production in the sexual stage (1720), possibly by interfering with protein prenylation, which results in defects in cellular trafficking (21). Given its slow and relatively weak delayed death effect on asexual-stage parasites, AZ monotherapy is not useful for uncomplicated malaria and is only moderately effective for P. falciparum prophylaxis (22). However, it has been considered as a potential candidate for combination therapy, especially in pregnant women and children, owing to its multistage targeting action with unique mechanism, attractive safety profile, and excellent pharmacological properties (20, 23). AZ combination therapy with other antimalarials has been evaluated extensively in clinical trials over the past dozen years, but no reliable evidence has been found on the superiority or equivalence of AZ combination therapy with current partner drugs compared to the current first-line antimalarial combinations (22, 2430). A more appropriate partner for AZ in combination remains to be identified.
Naphthoquine (NQ) is a 4-aminoquinoline antimalarial first synthesized in 1981 by Li et al. (31). A single-dose, fixed coformulation of NQ with artemisinin (32) has been marketed under the name ARCO for 15 years. Based on the excellent efficacy and safety data of NQ-containing therapies from available studies involving more than 4,000 patients, NQ has been proposed as an emerging candidate for new ACT or triple therapy to respond to the concern on resistance (33). In addition, NQ has a long half-life up to 23 days in human (33, 34), and its excellent efficacy for seasonal malaria chemoprophylaxis with monthly single dose has been reported in China (35, 36). In a posttreatment follow-up study of artemisinin-NQ in Papua New Guinean children, the posttreatment prophylactic effect of NQ even was evident at day 63 or beyond (37). Based on these advantages, NQ should be a highly promising candidate for further development as a chemoprophylactic antimalarial.
Recently, a coformulated combination of AZ with NQ has been evaluated in a clinical trial for malaria prophylaxis in Southeast Asia, and it shown a good safety profile and efficacy in adults with a monthly dose of 400 or 800 mg (38). However, no study has yet investigated the interaction between AZ and NQ, and the possible benefits of combining NQ and AZ for malaria treatment or prophylaxis remains to be clarified. In the present study, we evaluated the combination of AZ and NQ in an animal malaria model. The pharmacodynamic interaction and its effect on the blood-stage prophylaxis and drug resistance development were investigated to determine whether the combination therapy with AZ and NQ can provide some benefits for malaria treatment or prophylaxis.

RESULTS

Drug interaction in P. berghei-infected murine model.

To investigate the pharmacodynamic-interaction profile between AZ and NQ in vivo, 25 combinations with different ratios of NQ and AZ doses (Table 1) were tested by a Peter’s 4-day suppressive test (39) using P. berghei K173-infected mice, and single drugs used alone were also tested as controls. The dose-response curves for single drugs or combinations with more than four dose levels in the same ratios were fitted for the calculation of combination index (CI) values (40) and further analysis (Fig. 1A and Table 2).
TABLE 1
TABLE 1 Summary of parasitemia inhibition and combination index values for azithromycin and naphthoquine combined at various doses and ratiosa
a
The background color indicates the same ratios indicated in Fig. 1. The averages and standard deviations shown were calculated from two independent experiments. CI, combination index; AZ, azithromycin; NQ, naphthoquine. *, The CI for 100% inhibition cannot be calculated.
FIG 1
FIG 1 Interaction between azithromycin (AZ) and naphthoquine (NQ) against P. berghei K173 infection in mice. Data points present the mean values from two independent tests. (A) Dose-response plot of AZ and NQ alone or in combination. The dose is the summed dose of AZ and NQ with the indicated ratios, which is the same hereafter. Each data point plotted is the average value from two independent experiments, and error bars indicate standard deviations. The result summary is listed in Table 2. (B) Combination index (CI) analysis. Fa means the inhibition level produced by a given dose (inhibition %). Trend lines indicate CI values at any given effect and symbols represent CI values derived from actual data points: CI = 1, additive interaction; CI > 1, antagonistic interaction; and CI < 1, synergistic interaction. Blue arrows indicate the cut-points between antagonism and synergy in the trendlines. (C) Normalized isobologram analysis. Daz and Dnq, (EDx)az, and (EDx)nq are the doses of AZ and NQ used in combination or alone to produce effect x. The gray dotted line indicates additive interaction, and colored symbols show data points of various ratios at the actual inhibition levels. The data points below the gray line indicate synergy, whereas the data points above denote antagonism. (D) Curve-shift analysis. Data derived from dose-response curves were normalized to the ED90 values of the single drugs (ED90 eq) and replotted. The gray dotted line indicates an inhibition level of 90%, and the blue arrow indicates the ED90 of AZ and NQ alone. Left and right shifts of the dose-response curves of the combination (colored) relative to the single drugs (gray) indicate synergy and antagonism, respectively.
TABLE 2
TABLE 2 Summary of results of dose-response curve fitting and curve-shift analysisa
AZ/NQ ratioED (mg/kg/day)ED90 (ED90 eq)r2
ED50ED90
0:11.103.7110.9557
1:05.9014.8210.9861
5:33.054.840.69360.9647
10:33.226.110.69780.9825
20:33.968.420.79050.9750
a
The ED50 or ED90 is the summed dose of AZ and NQ at the indicated ratios.
The calculated CI values and Fa-CI plot (i.e., a plot of the CI against Fa, the effect level produced by a given dose) shows a dose-dependent pattern of interaction between NQ and AZ among the different ratios (Table 1 and Fig. 1B). The drugs showed antagonistic interaction at lower inhibition levels and synergistic interaction at higher inhibition levels. In addition, a decrease in the AZ/NQ ratio resulted in a narrower dose range of synergy, as indicated by a right shift of the cut point between antagonism and synergism, which is addition (CI = 1). For combinations with AZ/NQ dose ratios of 20:3 and 10:3, the cut point occurred at similar inhibition level of approximately 61%, whereas for the 5:3 combination, it occurred at approximately 73% inhibition.
For all combinations with inhibition levels of >90%, synergistic interaction was observed, except for two combinations with an AZ/NQ ratio of >26:1, which was nearly additive (Table 1 and Fig. 1C). Further evidence for the synergistic interaction at inhibition levels of >90% was shown by curve-shift analysis (41), which indicated lower ED90 in normalized units (ED90 equivalents [eq]) for the combination than in single drugs (Fig. 1D and Table 2).
To confirm the synergistic interaction observed at high inhibition levels, three combinations with fixed dose AZ/NQ ratios were further assessed. Synergistic drug interaction at ED90 was clearly indicated by the CI values ranging from 0.50 to 0.79 for all combinations against the sensitive P. berghei strain K173 and 0.50 for the 1:1 combination against the chloroquine (CQ)-resistant P. berghei RCQ/K173 strain (Table 3). All tested combinations showed an ED90 comparable to that of NQ (P = 0.73784 in sensitive strains, P = 0.131353 in resistant strains) but significantly lower than that of AZ (Table 3). This finding suggested that although AZ alone showed weaker potency than NQ, combinations formulated by replacing some part of NQ with some amount of AZ still achieved potency comparable to that of NQ and provides another evidence of synergistic interaction. Of note, the synergistic interaction appeared to be stronger as the ratio of AZ to NQ increased, which indicated by “moderate synergism” (CI = 0.7 to 0.85) in the combination with lowest AZ proportion but “synergism” (CI = 0.3 to 0.7) in the other two combinations according to the semiquantitative measurement of the degrees of synergism or antagonism proposed by Chou (40). In addition, the combination with a 1:1 ratio showed slightly stronger synergistic effect against the CQ-resistant parasite than against the sensitive parent strain (CI = 0.50 versus CI = 0.61), as well as nonsignificantly but slightly lower ED90 than NQ in the CQ-resistant parasite (7.07 ± 3.21 versus 10.85 ± 1.29).
TABLE 3
TABLE 3 Summary of in vivo activities of azithromycin and naphthoquine alone or in combination against chloroquine-sensitive and -resistant murine malaria lines in Peters’ 4-day suppressive testa
AZ/NQ ratioP. berghei K173P. berghei RCQ/K173b
Mean ED90 ± SD (mg/kg/day)CIMean ED90 ± SD (mg/kg/day)CII90c
1:24.01 ± 0.48**0.79NDNDND
1:13.73 ± 0.12***0.617.07 ± 3.21*0.501.9
2:13.80 ± 0.40***0.50NDNDND
0:13.81 ± 0.02*** 10.85 ± 1.29* 2.8###
1:015.47 ± 1.20 20.07 ± 5.03 1.3
a
Means and standard deviations (mean ED90 ± the SD) were calculated from triplicate independent experiments. *, P < 0.05; **, P < 0.01; ***, P < 0.001 (compared to AZ alone [AZ/NQ = 1:0], according to Tukey’s HSD post hoc test after one-way ANOVA). ND, not determined.
b
A chloroquine-resistant line derived from P. berghei K173 was used. The resistance index (I90) for chloroquine tested in this experiment was 163.4.
c
The I90 is defined as the ratio of the ED90 of the resistant line to that of the parent line. ###, P < 0.001 (ED90 of the resistant line compared to that of the sensitive parent line, according to one-way ANOVA).

Synergistic prophylaxis against blood-stage parasite challenge in mice and rhesus monkeys.

To determine how long after oral administration the NQ and AZ remaining in the body can still interact synergistically and whether the combination can provide a more effective and sustained prophylaxis against blood-stage parasite challenge, prophylactic experiments were performed in mice and rhesus monkeys. First, three combinations with AZ/NQ dose ratios of 1:2, 1:1, and 2:1 were assessed in mice challenged with P. berghei K173 to select an optimal ratio for further study. After administration at 600 mg/kg/day once daily for 3 days, the 1:1 combination achieved 100% protection for 3 weeks after the first dose, whereas the 1:2 and 2:1 combinations only provided 100% protection in the first week and ca. 50 to 70% protection at 2 and 3 weeks after the first dose (Table 4). Log-linear analysis showed that the parasitemia outcome, treatment, and challenge time were not independent one another of but rather strongly associated with each other (Fig. 2A and B). Further analysis indicated that given the challenge time of 2 or 3 weeks after treatment, the higher counts of parasitemia-free animals in groups receiving the 1:1 combination versus those in groups receiving other treatments (Fig. 2C) and, given the treatment of 2:1 or 1:2 combination, the decrease in the count of parasitemia-free animals in groups with challenge time from 1 week to 2 and 3 weeks after treatment (Fig. 2D) are both statistically significant. These findings suggested that the frequency of parasitemia arising in animals was significantly associated with treatment and challenge time, the 1:1 combination of AZ and NQ providing the most effective and sustained prophylaxis in blood-stage parasite-challenged mice.
TABLE 4
TABLE 4 Observed frequency of parasitemia in mouse challenged with blood-stage P. berghei K173 after treatmenta
TreatmentChallenge time (wks after the first dose)No. of parasitemia-free mice (n = 10)Prophylactic efficacy (%)
YesNo
Placebo1010 
 2010 
 3010 
AZ/NQ = 2:11100100
 27370
 35550
AZ/NQ = 1:11100100
 2100100
 3100100
AZ/NQ = 1:21100100
 25550
 35550
a
Grouped animals (n = 10) were administered combinations at 600 mg/kg/day with a fixed ratio of AZ to NQ, as indicated, or vehicle as placebo for 3 days and then challenged at different times posttreatment. Parasitemia was checked at ca. 7 to 10 days after challenge.
FIG 2
FIG 2 Prophylactic efficacy of combinations with different ratios in P. berghei K173 challenge model. Data were obtained from one experiment, in which grouped mice (n = 10) were administered 600-mg/kg/day combinations at the indicated fixed-dose ratios of AZ to NQ or vehicle as a placebo for 3 days and then challenged at different times posttreatment. Parasitemia was checked in each animal for 7 to 10 days after challenge. Log-linear analysis was used to determine the relationship among the parasitemia outcomes in animals, treatments, and challenge times after treatment, and the results of a model fitting under different assumptions present in a mosaic plot are shown. (A) Mutual independence. (B) All two-way associations. (C) Conditional independence given challenge times. (D) Conditional independence given treatments. The size of each cell is proportional to the observed frequency, and lines with circles over them mean zero. Deviance residual is used to depict deviations from model expectations: blue (excess) and red (paucity) colors indicate deviations from the expectation. The cutoff points imply that the highlighted cells are those with residuals individually significant at approximately the α = 0.05 and α = 0.01 levels. P values refer to the probability of accordance between the observed frequency distribution and the frequencies expected under the given fitted models.
The 1:1 combination was then chosen to further define the effective dose range and be compared to NQ and AZ used alone. As shown in Fig. 3, the combination showed a significant dose-dependent prophylactic effect in the P. berghei K173 challenge model (P < 2.2 × 10−16). Administered at 600 or 800 mg/kg/day once daily for 3 days, the combination provided more than 90% protection for 3 weeks, and ca. 40 to 50% protection in week 4 (Fig. 3C and E), whereas at 75, 150, or 300 mg/kg/day, it provided ca. 40 to 80% and ca. 30 to 50% protection only in the first 2 weeks (Fig. 3H, F, and D). NQ at 300 mg/kg/day alone showed 90 and 60% protection only in the first 2 weeks, which was slightly, but not significantly, more effective or sustained prophylaxis than did the combination at the same total dose (Fig. 3B versus Fig. 3D, P = 0.4673). At a dose of 300 mg/kg/day AZ alone provided only a slight prophylactic effect below 30% in first 2 weeks (Fig. 3A). However, the combination of both achieved a significantly better prophylactic effect that was sustained for 4 weeks (Fig. 3B versus Fig. 3C, P = 6.551 × 10−9). In all of the experiments discussed above, no signs of toxicity were observed.
FIG 3
FIG 3 (A to H) Prophylactic efficacy of AZ and NQ both administered alone and in a 1:1 combination in a P. berghei K173 challenge model. Data were obtained from one experiment, in which grouped mice (n = 10) were administered the combination or single drugs at the indicated doses (mg/kg/day) for 3 days and then challenged and checked as described in Fig. 2. Animals supplied with vehicle only were included as placebo controls. Logistic model analysis was used to estimate the association between treatments, challenge times, and parasitemia outcomes in challenged mice, and results are shown in the combined plots of histogram and logistic regression. Observed data and model prediction results are shown in blue rectangles (top and bottom for parasitemia-free and parasitemia-positive animals, respectively) and red lines, respectively. Combinations versus 300 mg/kg/day NQ alone: ***, better (P < 0.0001); #, inferior (P < 0.05).
Similar results were also observed in P. cynomolgi bastianelli L-challenged rhesus monkey (Fig. 4). The combination (AZNQ) at 60 mg/kg/day once daily for 2 days provided full protection (3/3) for up to 3 weeks after the first dose and half protection (1/2) in the fourth week. AZNQ at 30 and 15 mg/kg/day provided partial protection (2/3) or no protection, respectively, at 3 weeks after the first dose. When used alone, 30 mg/kg/day NQ afforded only slight protection (1/3), which was weaker than that provided by AZNQ at same dose (AZNQ 30), whereas 30 mg/kg/day AZ afforded no protection.
FIG 4
FIG 4 Prophylactic efficacy of AZ and NQ both alone and in a 1:1 combination (AZNQ) in a rhesus monkey P. cynomolgi challenge model. Data were obtained from one experiment, in which 2 to 3 monkeys in each group were administered the combination or single drugs alone at the indicated doses (mg/kg/day) or vehicle as placebo for 2 day and then challenged at different times posttreatment. Parasitemia was checked in each animal in a 30-day follow-up period after challenge. Black inverted triangles indicate the treatment given on the first 2 days for all indicated dose (mg/kg/day), red inverted triangles indicate challenge on days after first dose, black lines indicate the dose-to-challenge interval, and gray lines indicate follow-up period for parasitemia monitoring. Circle or cross symbol indicate successful or failed prophylaxis. The sizes of the symbols represent the numbers of animals, as indicated. The actual intervals between challenge and failure or success are also depicted.

Delay resistance development in murine malaria parasite.

To confirm whether resistance development can be delayed or prevented by the combination of AZ and NQ, we compared the development of resistance in P. berghei K173 under selective pressure of AZ and NQ alone or in combination in parallel. Under single-drug selection pressure, the resistance appeared after five passages (i.e., the I90 values, defined as the ratio of the ED90 of the resistant line to the ED90 of the parent line, were 3.3 and 3.7 for AZ and NQ, respectively) and then increased exponentially with passages and reached moderate levels after 30 passages (I90 = 29.2 and 50.8 for AZ and NQ, respectively). In contrast, under the selection pressure of the 1:1 combination (AZNQ), the resistance did not appear until after 20 passages (I90 = 1.8), and only slight resistance occurred after 30 passages (I90 = 4.2). Further exponential regression analysis using I90 as a response variable against passages and drugs showed that, apart from passages (P = 3.964 × 10−13), changes in I90 were also affected by drugs (Fig. 5A, P = 4.475 × 10−7), and an interaction between passages and drugs was found (Fig. 5B, P = 0.00996). These findings indicated that the difference observed in resistance development and its rapidity (indicated by regression slope) under the selective pressure of different drugs should be significant.
FIG 5
FIG 5 Resistance development of P. berghei K173 under the selection pressure of AZ and NQ alone or in combination (AZNQ). P. berghei K173 were passaged in mice under a gradually increased selective pressure of different drugs, and the ED90 was determined after each five passages from one Peters’ 4-day suppressive test, with five dose groups tested and 10 animals in each group. The I90 was then calculated as described previously and used as a response variable against passage and drugs in exponential regression analysis to determine the effect of drugs on resistance development. (A) Box-and-whisker plot of I90 values for drugs. In each group, I90 values determined after 5 to 30 passages were pooled. The bold line in each box represents the median, the box itself indicates the interquartile range, and the whiskers extend to the minimum and maximum. (B) Plot of fitted exponential regression. In the regression equation, e is a natural number, ai is the slope change value in regression caused by a change in drug, and bi is the intercept of trendline for drug i. Colored symbols and lines represent actual data points and the fitted trendlines for each drug. The dotted line at I90 = 1 is the reference line for resistance indication, whereas data points above it indicate resistance. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

DISCUSSION

Combination therapy to increase efficacy, decrease toxicity, and minimize or slow down the development of drug resistance has become the standard choice for malaria treatment and is an important strategy for the development of new antimalarial drugs. However, accurate evaluation of the drug-drug interactions in a combination is complicated because of the influence of the drug ratios or doses used and potency desired. There have been many hypotheses, models, theories, and approaches, as well as some controversies, regarding drug combination analysis (4042). In the present study, the Chou-Talalay method was used because it has been well recognized as the most impactful approach to quantifying synergy and cited over 9,000 times in more than thousand biomedical journals over the past few decades (40, 43, 44).
Our data provided evidence of synergy at higher inhibition levels but antagonism at lower inhibition levels between NQ and AZ in combinations at various ratios, and a decrease in the AZ proportion will lead to a decrease in the dose range of synergy from lower inhibition levels to higher inhibition levels (Fig. 1B). A nonsignificant but visible enhancement of synergistic effect at the ED90 level was also observed with the increase in AZ proportion (see Results and Table 2). Apart from one report of synergistic interaction between AZ and NQ against P. falciparum in vitro, in which only one ratio of AZ to NQ (34:40) was tested (45), no other studies have been reported concerning the interaction between AZ and NQ. However, a previous study on combinations of AZ and CQ, another 4-aminoquinoline antimalarial drug very similar to NQ, reported a comparable finding in an in vitro test against P. falciparum. This study demonstrated synergistic effect at the FIC90 level but additive interaction at the FIC50 level, and the increase in AZ proportion led to greater synergy (23). The interpretation proposed by the authors is that the lysosomotropic property of AZ might cause its accumulation in the digestive vacuole of the parasite at a high concentration, which perhaps can indirectly increase the potency of CQ by affecting pH and/or the process of hemoglobin digestion (23). Although the drug-drug interaction in vivo might be different from that in vitro because the in vivo pharmacokinetics cause more complex changes in drug exposures over time, the considerable concordance between our results and the findings in the previous in vitro test of AZ and CQ indicated that, in addition to possible pharmacokinetics interaction in vivo, the interaction between AZ and NQ in our study could also be partially interpreted as similar to the in vitro interaction between AZ and CQ. However, the interpretation given above is not satisfactory because CQ and NQ are also lysosomotropic weak bases known to accumulate in the digestive vacuole and exert their antimalarial activity by binding to toxic heme released via hemoglobin proteolysis, thus interfering with parasite-mediated heme detoxification (33, 46, 47). Although the precise mechanism of AZ and CQ or NQ uptake in Plasmodium parasite remains unknown, it is believed that their accumulation in the digestive vacuole is largely dependent on the transmembrane pH gradient, and competition of intravacuolar accumulation between them will occur, as previously observed between AZ and ammonium chloride in rat alveolar macrophages (48). Thus, AZ accumulation in digestive vacuoles should diminish the accumulation of NQ in same cellular compartment and vice versa. The former obviously means a decrease in NQ potency, while the later implies an increase in AZ potency, because it means the higher proportion of AZ molecules were retained in the parasite cytoplasm to target the apicoplast (16, 17). Based on these considerations, we assume that an imbalance or balance between the accumulation of AZ and NQ in digestive vacuoles could be one reason for the different types of interaction that occurred in the combinations with different drug ratios and inhibition levels. When the increase in the total contribution of AZ to parasite inhibition caused by its decreased accumulation in digestive vacuoles exceeds the decrease in NQ contribution caused by the same reason, the interaction is a synergy; otherwise, it is addition or antagonism. The synergistic interaction at higher inhibition levels and the additive or even antagonistic interaction at lower inhibition levels observed in our study and a previous report about AZ and CQ indicated that the contribution of increased AZ potency tended to exceed that of decreased NQ or CQ potency at higher inhibition levels, such as at the ED90 level and above.
Of note, although greater synergy at the ED90 level and a wider dose range of synergy were observed with an increased AZ proportion, a further increase beyond a certain ratio does not seem to work better, as indicated by the only nearly additive interaction observed for two combinations with a AZ/NQ ratio f above 26:1 (Table 1 and Fig. 1C) and the similar inhibition level for the cut-point of CI = 1 that occurred in the Fa-CI trendlines of the combinations with AZ/NQ ratios of 20:3 and 10:3 (Fig. 1B). Moreover, an increase in the AZ proportion with the consequent synergy enhancement did not result in a significant difference in the efficacy of three fixed-ratio combinations (see Results and Table 3). This finding suggested that stronger synergy may not always lead to higher overall efficacy. One interpretation for this could be that at the dosage tested in our study, AZ predominantly exerted a delayed death effect by targeting apicoplast, which only causes growth arrest in daughter parasites and was much weaker and slower than the parasiticidal activity of NQ (16, 17). Furthermore, a large decrease in the NQ proportion might cause a large overall potency loss in a combination enough to offset the gain from a synergistic interaction.
Since a high level of parasitemia inhibition is always desired for malaria treatment, the synergy observed at the ED90 level or above in a wide range of dose ratios of AZ to NQ should be much more therapeutically relevant than the addition or antagonism observed at lower inhibition levels, and this may provide clearly positive evidence for the value of combination therapy with AZ and NQ.
Considering the mismatch between the terminal half-life of AZ and NQ, we further investigated how long after oral administration the AZ and NQ remaining in the body can still interact synergistically by using the blood-stage prophylaxis models. In a pilot experiment of P. berghei K173-challenged mouse models, the 1:1 combination provided the most effective and sustained prophylaxis (Fig. 2). This suggested that the AZ and NQ doses at this ratio maintained the best balance between the contributions of each drug, and the interaction between them to provide the best efficacy lasted the longest in vivo. In further tests performed with P. berghei K173-challenged mice and P. cynomolgi bastianelli-challenged monkeys, replacing half the amount of NQ with AZ did not significantly decrease effectiveness, whereas adding the same amount of AZ to NQ achieved a great improvement for 4 weeks after the first dose, although AZ alone presented only slight or no prophylactic effect and was much weaker than NQ (Fig. 3 and 4). This suggested that when combined with NQ at an optimal ratio of 1:1, the weak or ineffective AZ could exert an effect against the growth of blood-stage parasite comparable to that of NQ, and this positive interaction could occur even at 4 weeks after the first dose, although AZ has a much shorter half-life than NQ (6.42 h versus 198.6 h in normal mice after a single oral dose) (49, 50).
NQ or AZ resistance has not been documented in clinical setting, but previous laboratory studies have found that both NQ- and AZ-resistant lines could be rapidly selected, and an NQ-resistant P. berghei line was highly cross-resistant to CQ (I90 = 14.5, P < 0.01) (16, 51). Considering the widespread CQ resistance in areas where malaria is endemic, the highly CQ-resistant P. berghei RCQ/K173 was used to determine the synergistic antimalarial efficacy of the drug combination against CQ-resistant parasite. For the 1:1 combination, a slightly increased synergistic effect against the CQ-resistant parasite was observed. Moreover, compared to the significantly decreased sensitivity to NQ, the decrease in sensitivity to combination in the CQ-resistant parasite was slight and not significant (I90 = 1.9, P = 0.146097) (Table 3). The former suggested that the effect of the decrease in NQ potency caused by AZ on a CQ-resistant parasite might be smaller than that on a sensitive parasite, and the latter indicated that combination therapy should be more likely to avoid efficacy diminishment in CQ-resistant malaria treatment than NQ monotherapy. A parallel comparison of drug resistance development in P. berghei K173 under selective pressure of drugs was performed to further determine the effect of the combination on the development of resistance. The delayed and slower development of resistance under combination selection pressure compared to that under AZ and NQ pressure alone clearly proved that the combination delayed the emergence of resistance and slowed down the development of resistance (Fig. 5).
Taken together, these findings provide positive evidence of the benefits of combination therapy with AZ and NQ. Combination therapy using drugs with different mechanisms of action is the current state of the art in antimalarial treatment. However, apart from ACTs, there are relatively few other combinations available. In light of increasing concern regarding the emergence and spread of artemisinin resistance, the combination of AZ and NQ should be a viable candidate for alternative treatment development. In addition, the positive interaction between AZ and NQ after oral administration to provide prophylaxis lasting 4 weeks in mice and rhesus monkeys in this study, as well as the excellent prophylactic efficacy with monthly dose regimens reported in a previous clinical study (38), suggest that this combination may also be an ideal candidate for long-term chemoprophylaxis of malaria.

MATERIALS AND METHODS

Drugs.

Naphthoquine phosphate and AZ were obtained from Shanghai Sixth Pharmaceutical Factory (Shanghai, China) and Shanghai Modern Pudong Pharmaceutical Co., Ltd. (Shanghai, China), respectively. Drug suspension for oral administration were freshly prepared on the day of drug administration by grinding the drug mixed with small amount of Tween 80 and subsequently suspending it in double-distilled water to the desired concentration. The dose of NQ was calculated as the base.

Parasites and experimental animals.

The P. berghei K173, P. berghei RCQ/K173, and P. cynomolgi bastianelli L parasites were maintained as cryopreserved stabilates or by mechanical blood passage.
Kunming mice (4 to 5 weeks old, 18 to 22 g) and rhesus macaques (Macaca mulatta, 2.7 to 3.7 kg) were obtained from the Animal Research Center of Academy of Military and Medicinal Science (Beijing, China). Animals were housed in standard laboratory cages and maintained under 12-h light-dark cycle with ad libitum access to food and water at a constant temperature (25 to 28°C) and humidity (65 to 80%). All animal experiments were carried out in accordance with institutional guidelines for animal care, using protocols approved by the Institutional Animal Care and Use Committee at Beijing Institute of Microbiology and Epidemiology. Animals were infected with parasites by intraperitoneal or intravenous injection of cryopreservation or blood from an infected donor animal. Parasitemia was monitored microscopically in Giemsa-stained blood smears prepared from mouse tail and monkey ear blood. Drugs were administered by oral gavage by using a feeding needle for mice and a stomach tube for monkeys.

Antimalarial activity assay.

Antimalarial activity against P. berghei K173 infection in mice was assessed by Peters’ 4-day suppressive test (39). Mice intraperitoneally inoculated with 1 × 107 parasite-infected erythrocytes (iRBC) were randomly grouped (n = 10) and administered 0.2 ml of drug suspension once daily for 4 consecutive days or vehicle only for the nontreated control. On day 4 after infection, parasitemia was examined in each animal under a microscope at ×100 magnification (oil immersion). The parasitemia level was determined by counting the number of iRBC from more than 200 erythrocytes in random fields of the microscope. Average percentage of parasitemia inhibition for each test group was calculated as 100 × (AB)/A, where A is the average percentage of parasitemia in the nontreated control group, and B is the average percentage of parasitemia in the test groups. For the determination of effective dose, each drug was tested in five dose groups. The dose-response curve was fitted by a linear regression model based on the median-effect equation (40) (equation 1), in which D is the drug dose, fa is the percentage of parasitemia inhibited by D, fu is the percentage of uninhibited parasitemia (fu = 100 – fa), Dm is the median-effect dose that inhibits parasitemia by 50% (ED50), and m is the coefficient signifying the shape of the dose-effect relationship.
fafu=(DDm)mlog(fa100fa)=m×log(D)m×log(Dm)
(1)

Drug combination analysis.

The Chou-Talalay method (40) was used for combination analysis. Combination index (CI) values were used to determine an additive (CI = 1), antagonistic (CI > 1), or synergistic (CI < 1) interaction between drugs and were calculated using equation 2, in which Daz and Dnq, (EDx)az, and (EDx)nq are the doses of AZ and NQ used in combination or alone, respectively, to produce that same effect (i.e., % parasitemia inhibition).
CI=Daz(EDx)az+Dnq(EDx)nq
(2)
The Fa-CI plot and normalized isobologram was created to present an effect-oriented or dose-oriented view on drug interactions. Data points above or below the line of additivity indicate antagonism or synergy, respectively. Curve-shift analysis (41) was also performed to directly compare the dose-response curves and determine the drug interaction, in which the dose-response curves of single drugs or combinations were normalized by transforming the dose of AZ and NQ alone or in combinations to the equivalents of its own ED90 value (ED90 eq). A left shift in the dose-response curve indicates synergy.

Prophylactic effect evaluation in blood-stage parasites challenge models.

For study in P. berghei K173 challenge model, grouped mice (n = 10) were administered combinations or single drugs alone once daily for 3 consecutive days and then intraperitoneal challenged once with 1 × 107 iRBC at 1, 2, 3, or 4 weeks after the first dose. Parasitemia was checked ca. 7 to 10 days after challenge. Blood from cardiac puncture in mice confirmed to be parasite-free via microscopy in 100 random fields was inoculated into a healthy mouse, which was then examined for ca. 7 to 10 days to confirm the absence of infection. Prophylactic efficiency was expressed as (1 − PT/PC) × 100%, where PT and PC are the percentages of animals positive for parasitemia in treated and placebo groups, respectively.
For studies using the P. cynomolgi bastianelli challenge model, grouped monkeys (n = 2 to 3) were administered combinations or single drugs alone once daily for 2 consecutive days and intravenously challenged once with 1 × 106 iRBC at 3, 4, or 5 weeks after the first dose. Parasitemia was checked at 5 and 10 days after challenge, followed by every 2 to 3 days until 30 days after challenge. Prophylaxis was deemed successful if the animal remained parasite-free during the 30-day follow-up monitoring period.
Animals administered vehicle only as placebo controls were included for in experiments to validate the challenge.

Selection of drug-resistant P. berghei.

Selection of drug-resistant parasites was performed as previously described (50). Briefly, five P. berghei K173-infected mice were treated with the combination or with single drugs alone at the ED90 dose 3 to 5 days after infection. On day 7 after infection, parasitemia was checked in each mouse, and infected blood from mice with the highest parasitemia (>2%) was used to inoculate the next passage. The parasites were exposed to a gradually increased dose of drugs in subsequent passages. The level of resistance was evaluated after each five passages by calculating the I90, which is defined as the ratio of the ED90 of the resistant line to that of the parent line. The degree of resistance was categorized into four levels by the I90 values as follows: (i) I90 ≤ 1, susceptible; (ii) 1< I90 ≤ 10.0, slightly resistant; (iii) 10 < I90 ≤ 100.0, moderately resistant; and (iv) I90 > 100.0, highly resistant.

Statistical analysis.

Statistical analysis and data visualization were conducted using R (52). Dose-response curves were fitted as described above, and the goodness of fit was determined using r2. Averages and standard deviations of ED90 values in a fixed-ratio combination test were calculated from three independent experiments. Significant differences were determined using one-way analysis of variance (ANOVA), followed by Tukey’s honestly significant difference (HSD) post hoc test. Log-linear or logistic regression analysis was performed on the data from the prophylaxis studies to determine the statistically significant relationship between treatments, challenge times after treatment, and the frequency of parasitemia arising in animals, and a likelihood ratio test was performed for goodness-of-fit and comparison analyses (53). The R package vcd was used to produce mosaic plots with residual-based shadings for log-linear analysis (54, 55). The effect of drugs on resistance development was determined by exponential regression analysis using I90 as a response variable against passage and drugs, in which the significance of the total effects or of each effect of passages, drugs, and interactions between them on I90 was tested by the F statistic, and the significance of coefficients in the fitted model was checked by the t statistic.

ACKNOWLEDGMENTS

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
J.-Y.W. conceived and designed the experiments. G.-F.L., J.-H.Z., M.Z., and X.-G.J. performed the experiments. Z.-C.B. collected and analyzed the data. Z.-C.B. wrote the paper. All authors read and approved the final manuscript.
We declare that there are no conflicts of interest.

REFERENCES

1.
World Health Organization. 2019. World malaria report 2019. World Health Organization, Geneva, Switzerland. https://www.who.int/news-room/feature-stories/detail/world-malaria-report-2019.
2.
World Health Organization. 2015. Guidelines for the treatment of malaria, 3rd ed. World Health Organization, Geneva, Switzerland. http://www.who.int/malaria/publications/atoz/9789241549127/en/.
3.
Dondorp AM, Nosten F, Yi P, Das D, Phyo AP, Tarning J, Lwin KM, Ariey F, Hanpithakpong W, Lee SJ, Ringwald P, Silamut K, Imwong M, Chotivanich K, Lim P, Herdman T, An SS, Yeung S, Singhasivanon P, Day NP, Lindegardh N, Socheat D, White NJ. 2009. Artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med 361:455–467.
4.
Ashley EA, Dhorda M, Fairhurst RM, Amaratunga C, Lim P, Suon S, Sreng S, Anderson JM, Mao S, Sam B, Sopha C, Chuor CM, Nguon C, Sovannaroth S, Pukrittayakamee S, Jittamala P, Chotivanich K, Chutasmit K, Suchatsoonthorn C, Runcharoen R, Hien TT, Thuy-Nhien NT, Thanh NV, Phu NH, et al. 2014. Spread of artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med 371:411–423.
5.
Lu F, Culleton R, Zhang M, Ramaprasad A, von Seidlein L, Zhou H, Zhu G, Tang J, Liu Y, Wang W, Cao Y, Xu S, Gu Y, Li J, Zhang C, Gao Q, Menard D, Pain A, Yang H, Zhang Q, Cao J. 2017. Emergence of indigenous artemisinin resistant Plasmodium falciparum in Africa. N Engl J Med 376:991–993.
6.
Hanboonkunupakarn B, White N. 2016. The threat of antimalarial drug resistance. Trop Dis Travel Med Vaccines 2:10.
7.
Menard D, Dondorp A. 2017. Antimalarial drug resistance: a threat to malaria elimination. Cold Spring Harb Perspect Med 7:a025619.
8.
Achan J, Mwesigwa J, Edwin CP, d’Alessandro U. 2018. Malaria medicines to address drug resistance and support malaria elimination efforts. Expert Rev Clin Pharmacol 11:61–70.
9.
Haldar K, Bhattacharjee S, Safeukui I. 2018. Drug resistance in Plasmodium. Nat Rev Microbiol 16:156–170.
10.
Mace KE, Arguin PM. 2017. Malaria surveillance—United States, 2014. MMWR Surveill Summ 66:1–24.
11.
Angelo KM, Libman M, Caumes E, Hamer DH, Kain KC, Leder K, Grobusch MP, Hagmann SH, Kozarsky P, Lalloo DG, Lim P-L, Patimeteeporn C, Gautret P, Odolini S, Chappuis F, Esposito DH, GeoSentinel Network. 2017. Malaria after international travel: a GeoSentinel analysis, 2003–2016. Malar J 16:293.
12.
Tatem AJ, Jia P, Ordanovich D, Falkner M, Huang Z, Howes R, Hay SI, Gething PW, Smith D. 2017. The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics. Lancet Infect Dis 17:98–107.
13.
Lüthi B, Schlagenhauf P. 2015 Jan-Feb. Risk factors associated with malaria deaths in travellers: a literature review. Travel Med Infect Dis 13:48–60.
14.
Tan KR, Arguin PM. 2019. Travel-related infectious diseases, malaria. In Brunette GW, Nemhauser JB (ed), Centers for Disease Control and Prevention CDC yellow book 2020: health information for international travel. Oxford University Press, New York, NY. https://wwwnc.cdc.gov/travel/yellowbook/2020/travel-related-infectious-diseases/malaria. Accessed 12 November 2019.
15.
Gingras BA, Jensen JB. 1992. Activity of azithromycin (CP-62,993) and erythromycin against chloroquine-sensitive and chloroquine-resistant strains of Plasmodium falciparum in vitro. Am J Trop Med Hyg 47:378–382.
16.
Sidhu AB, Sun Q, Nkrumah LJ, Dunne MW, Sacchettini JC, Fidock DA. 2007. In vitro efficacy, resistance selection, and structural modeling studies implicate the malarial parasite apicoplast as the target of azithromycin. J Biol Chem 282:2494–2504.
17.
Dahl EL, Rosenthal PJ. 2007. Multiple antibiotics exert delayed effects against the Plasmodium falciparum apicoplast. Antimicrob Agents Chemother 51:3485–3490.
18.
Stanway RR, Witt T, Zobiak B, Aepfelbacher M, Heussler VT. 2009. GFP-targeting allows visualization of the apicoplast throughout the life cycle of live malaria parasites. Biol Cell 101:415–430.
19.
Friesen J, Silvie O, Putrianti ED, Hafalla JCR, Matuschewski K, Borrmann S. 2010. Natural immunization against malaria: causal prophylaxis with antibiotics. Sci Transl Med 2:404a49.
20.
Shimizu S, Osada Y, Kanazawa T, Tanaka Y, Arai M. 2010. Suppressive effect of azithromycin on Plasmodium berghei mosquito stage development and apicoplast replication. Malar J 9:73.
21.
Kennedy K, Cobbold SA, Hanssen E, Birnbaum J, Spillman NJ, McHugh E, Brown H, Tilley L, Spielmann T, McConville MJ, Ralph SA. 2019. Delayed death in the malaria parasite Plasmodium falciparum is caused by disruption of prenylation-dependent intracellular trafficking. PLoS Biol 17:e3000376.
22.
van Eijk AM, Terlouw DJ. 2011. Azithromycin for treating uncomplicated malaria. Cochrane Database Syst Rev 16:CD006688.
23.
Pereira MR, Henrich PP, Sidhu AB, Johnson D, Hardink J, Van Deusen J, Lin J, Gore K, O’Brien C, Wele M, Djimde A, Chandra R, Fidock DA. 2011. In vivo and in vitro antimalarial properties of azithromycin-chloroquine combinations that include the resistance reversal agent amlodipine. Antimicrob Agents Chemother 55:3115–3124.
24.
Phong NC, Quang HH, Thanh NX, Trung TN, Dai B, Shanks GD, Chavchich M, Edstein MD. 2016. In vivo efficacy and tolerability of artesunate-azithromycin for the treatment of falciparum malaria in Vietnam. Am J Trop Med Hyg 95:164–167.
25.
Moore BR, Benjamin JM, Auyeung SO, Salman S, Yadi G, Griffin S, Page-Sharp M, Batty KT, Siba PM, Mueller I, Rogerson SJ, Davis TM. 2016. Safety, tolerability, and pharmacokinetic properties of coadministered azithromycin and piperaquine in pregnant Papua New Guinean women. Br J Clin Pharmacol 82:199–212.
26.
Phiri K, Kimani J, Mtove GA, Zhao Q, Rojo R, Robbins J, Duparc S, Ayoub A, Vandenbroucke P. 2016. Parasitological clearance rates and drug concentrations of a fixed dose combination of azithromycin-chloroquine in asymptomatic pregnant women with Plasmodium falciparum parasitemia: an open-label, non-comparative study in Sub-Saharan Africa. PLoS One 11:e0165692.
27.
Chandra R, Ansah P, Sagara I, Sie A, Tiono AB, Djimde AA, Zhao Q, Robbins J, Penali LK, Ogutu B. 2015. Comparison of azithromycin plus chloroquine versus artemether-lumefantrine for the treatment of uncomplicated Plasmodium falciparum malaria in children in Africa: a randomized, open-label study. Malar J 14:108.
28.
Moore BR, Benjamin JM, Tobe R, Ome-Kaius M, Yadi G, Kasian B, Kong C, Robinson LJ, Laman M, Mueller I, Rogerson S, Davis TME. 2019. A randomized open-label evaluation of the antimalarial prophylactic efficacy of azithromycin-piperaquine versus sulfadoxine-pyrimethamine in pregnant Papua New Guinean women. Antimicrob Agents Chemother 63:e00302-19.
29.
Kimani J, Phiri K, Kamiza S, Duparc S, Ayoub A, Rojo R, Robbins J, Orrico R, Vandenbroucke P. 2016. Efficay and safety of azithromycin-chloroquine versus sulfadoxine-pyrimethamine for intermittent preventive treatment of Plasmodium falciparum malaria infection in pregnant women in Africa: an open-label, randomized trial. PLoS One 11:e0157045.
30.
Chandramohan D, Dicko A, Zongo I, Sagara I, Cairns M, Kuepfer I, Diarra M, Barry A, Tapily A, Nikiema F, Yerbanga S, Coumare S, Thera I, Traore A, Milligan P, Tinto H, Doumbo O, Ouedraogo JB, Greenwood B. 2019. Effect of adding azithromycin to seasonal malaria chemoprevention. N Engl J Med 380:2197–2206.
31.
Li FL, Wang LH, Ding DB, Yang JD, Gao XS. 1982. Studies on antimalarials synthesis of 4-arylamino-tert-butylaminomethyl phenols. Yao Xue Xue Bao 17:77–79. (In Chinese.)
32.
Wang JY, Cao WC, Shan CQ, Zhang M, Li GF, Ding DB, Shi YL, Wu BA. 2004. Naphthoquine phosphate and its combination with artemisinin. Acta Trop 89:375–381.
33.
Moore BR, Laman M, Salman S, Batty KT, Page-Sharp M, Hombhanje F, Manning L, Davis TM. 2016. Naphthoquine: an emerging candidate for artemisinin combination therapy. Drugs 76:789–804.
34.
Batty KT, Salman S, Moore BR, Benjamin J, Lee ST, Page-Sharp M, Pitus N, Ilett KF, Mueller I, Hombhanje FW, Siba P, Davis TM. 2012. Artemisinin-naphthoquine combination therapy for uncomplicated pediatric malaria: a pharmacokinetic study. Antimicrob Agents Chemother 56:2472–2484.
35.
Chen J, Luo P, Shi K, Lin Y, Wang J. 1998. Observation on malaria prophylaxis using naphthoquine phosphate. Zhongguo Ji Sheng Chong Xue Yu Ji Sheng Chong Bing Za Zhi 16:236. (In Chinese.)
36.
Yang H, Li X, Yang P, Li C, Wu C, Zhang Z, Gao B. 2003. Preventive effect on naphthoquine against vivax malaria and drug-resistant falciparum malaria in Yunnan, China. Zhongguo Ji Sheng Chong Bing Fang Zhi Za Zhi 16:137–139. (In Chinese.)
37.
Laman M, Benjamin JM, Moore BR, Salib M, Tawat S, Davis WA, Siba PM, Robinson LJ, Davis TM. 2015. Artemether-lumefantrine versus artemisinin-naphthoquine in Papua New Guinean children with uncomplicated malaria: a six-months posttreatment follow-up study. Malar J 14:121.
38.
Yang H, Wang J, Liu H, Li X, Nie R, Li C, Wang H, Wang Q, Cao Y, Cui Y. 2018. Randomized, double-blind, placebo-controlled studies to assess safety and prophylactic efficacy of naphthoquine-azithromycin combination for malaria prophylaxis in Southeast Asia. Antimicrob Agents Chemother 62:e00793-18.
39.
Peters W, Portus JH, Robinson BL. 1975. The chemotherapy of rodent malaria. XXII. The value of drug-resistant strains of Plasmodium berghei in screening for blood schizonticidal activity. Ann Trop Med Parasitol 69:155–171.
40.
Chou TC. 2006. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev 58:621–681.
41.
Zhao L, Au JL, Wientjes MG. 2010. Comparison of methods for evaluating drug-drug interaction. Front Biosci 2:241–249.
42.
Roell KR, Reif DM, Motsinger-Reif AA. 2017. An introduction to terminology and methodology of chemical synergy: perspectives from across disciplines. Front Pharmacol 8:158.
43.
Chou TC, Talalay P. 1984. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul 22:27–55.
44.
Chou TC. 2010. Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Res 70:440–446.
45.
Li Y, Chen H, Yang H. 2011. In vitro response of Plasmodium falciparum to naphthoquine combined with azithromycin. Zhongguo Bing Yuan Sheng Wu Xue Za Zhi 6:505–506. (In Chinese.)
46.
Krogstad DJ, Schlesinger PH. 1987. The basis of antimalarial action: non-weak base effects of chloroquine on acid vesicle pH. Am J Trop Med Hyg 36:213–220.
47.
Sullivan DJ, Jr, Gluzman IY, Russell DG, Goldberg DE. 1996. On the molecular mechanism of chloroquine’s antimalarial action. Proc Natl Acad Sci U S A 93:11865–11870.
48.
Togami K, Chono S, Morimoto K. 2013. Subcellular distribution of azithromycin and clarithromycin in rat alveolar macrophages (NR8383) in vitro. Biol Pharm Bull 36:1494–1499.
49.
Girard AE, Girard D, English AR, Gootz TD, Cimochowski CR, Faiella JA, Haskell SL, Retsema JA. 1987. Pharmacokinetic and in vivo studies with azithromycin (CP-62,993), a new macrolide with an extended half-life and excellent tissue distribution. Antimicrob Agents Chemother 31:1948–1954.
50.
Tian JG. 1996. Pharmacokinetics study of naphthoquine phosphate in normal and infected mice. MS thesis. Academy of Military Medical Sciences, Beijing, China.
51.
Wang H, Bei ZC, Wang JY, Cao WC. 2011. Plasmodium berghei K173: selection of resistance to naphthoquine in a mouse model. Exp Parasitol 127:436–439.
52.
R Core Team. 2019. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
53.
Friendly M, Meyer D. 2016. Discrete data analysis with r: visualization and modeling techniques for categorical and count data. Chapman & Hall/CRC, Boca Raton, FL.
54.
Meyer D, Zeileis A, Hornik K. 2017. vcd: visualizing categorical data. R package version 1.4–4. https://cran.r-project.org/web/packages/vcd/index.html.
55.
Zeileis A, Meyer D, Hornik K. 2007. Residual-based shadings for visualizing (conditional) independence. J Comput Graph Stat 16:507–525.

Information & Contributors

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Published In

cover image Antimicrobial Agents and Chemotherapy
Antimicrobial Agents and Chemotherapy
Volume 64Number 1120 October 2020
eLocator: 10.1128/aac.02307-19
PubMed: 32839220

History

Received: 19 November 2019
Returned for modification: 17 February 2020
Accepted: 18 August 2020
Published online: 20 October 2020

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Keywords

  1. naphthoquine
  2. azithromycin
  3. combination therapy
  4. malaria
  5. chemotherapy
  6. prophylaxis

Contributors

Authors

State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
Guo-Fu Li
State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
Jing-Hua Zhao
State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
Min Zhang
State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
Xiao-Guang Ji
State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
Jing-Yan Wang
State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China

Notes

Address correspondence to Zhu-Chun Bei, [email protected], or Jing-Yan Wang, [email protected].

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