Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first broke out in 2019 and subsequently spread worldwide. Chloroquine has been sporadically used in treating SARS-CoV-2 infection. Hydroxychloroquine shares the same mechanism of action as chloroquine, but its more tolerable safety profile makes it the preferred drug to treat malaria and autoimmune conditions. We propose that the immunomodulatory effect of hydroxychloroquine also may be useful in controlling the cytokine storm that occurs late phase in critically ill patients with SARS-CoV-2. Currently, there is no evidence to support the use of hydroxychloroquine in SARS-CoV-2 infection.
The pharmacological activity of chloroquine and hydroxychloroquine was tested using SARS-CoV-2–infected Vero cells. Physiologically based pharmacokinetic (PBPK) models were implemented for both drugs separately by integrating their in vitro data. Using the PBPK models, hydroxychloroquine concentrations in lung fluid were simulated under 5 different dosing regimens to explore the most effective regimen while considering the drug’s safety profile.
Hydroxychloroquine (EC50 = 0.72 μM) was found to be more potent than chloroquine (EC50 = 5.47 μM) in vitro. Based on PBPK models results, a loading dose of 400 mg twice daily of hydroxychloroquine sulfate given orally, followed by a maintenance dose of 200 mg given twice daily for 4 days is recommended for SARS-CoV-2 infection, as it reached 3 times the potency of chloroquine phosphate when given 500 mg twice daily 5 days in advance.
Hydroxychloroquine was found to be more potent than chloroquine to inhibit SARS-CoV-2 in vitro.
In December 2019 the outbreak of a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-2019), was first reported in Wuhan, China. The outbreak has since rapidly spread to other provinces in mainland China, as well as other countries around the world. Currently, the number of people diagnosed with SARS-CoV-2 infection is increasing by approximately 1000 cases per day. Unfortunately, to date, no drugs have been approved by regulatory agencies for the treatment of SARS-CoV-2 infection.
Chloroquine is a widely used antimalarial with immunomodulatory effects [1–5]. In a recent in vitro study chloroquine was found to inhibit the growth of SARS-CoV-2 in vitro [6]. This finding has been supported by clinical studies conducted in approximately 100 patients with SARS-CoV-2 [7, 8].
Hydroxychloroquine is an analog of chloroquine that has fewer concerns about drug–drug interactions. In the previous SARS outbreak, hydroxychloroquine was reported to have anti–SARS-CoV activity in vitro [9]. This suggests that hydroxychloroquine may be a potential pharmacological agent for the treatment of COVID-19 infection. However, to date, there is no clinical evidence to support the use of hydroxychloroquine as a treatment for SARS-CoV-2 infection.
The molecular mechanism of action of chloroquine and hydroxychloroquine has not been fully elucidated. Findings from previous studies have suggested that chloroquine and hydroxychloroquine may inhibit the coronavirus through a series of steps. First, the drugs can change the pH at the surface of the cell membrane and, thus, inhibit the fusion of the virus to the cell membrane. It can also inhibit nucleic acid replication, glycosylation of viral proteins, virus assembly, new virus particle transport, virus release, and other processes to achieve its antiviral effects [10].
A reliable estimation of hydroxychloroquine and chloroquine concentrations in the lung, the target tissue, may be used for guiding dose recommendations. Physiologically based pharmacokinetic (PBPK) models are a mathematical modeling technique that can predict drug concentrations in human tissues in silico by integrating physiological and drug disposition parameters. PBPK models are widely used in drug development to help identify whether a clinical trial is warranted as well as help guide the use of drugs based on predictions from well-validated models [11, 12].
In this study we aimed to (1) investigate the antiviral and prophylactic activity of hydroxychloroquine and chloroquine in vitro, (2) build a PBPK model for hydroxychloroquine and chloroquine using data from the literature, and (3) predict drug concentrations under different dosing regimens using the developed PBPK models.
METHODS
In Vitro Antiviral Activity Experiment
Experiment Materials
Chloroquine phosphate and hydroxychloroquine sulfate were purchased from Beijing Innochem Science & Technology Co, Ltd. The lyophilized powder was diluted in double-distilled water to 10 mM. Hydroxychloroquine sulfate was readily soluble in water. Chloroquine phosphate was dissolved by shaking the solution at room temperature for 2 hours. The chloroquine and hydroxychloroquine solutions were filtered through a 0.22-μm membrane and were then stored at − 80°C. The clinically isolated SARS-CoV-2 virus strain, C-Tan-nCoV Wuhan strain 01, was propagated in Vero cells.
Cell Culture
The Vero cells were derived from the African green monkey kidney and were grown in Dulbecco’s Modified Eagle Medium (DMEM; Sigma Aldrich, Boston, MA) supplemented with 5% fetal bovine serum (HyClone, Logan, UT). The cells were maintained in a humidified atmosphere with 5% CO2 at 37°C. The culture medium was replaced each day.
Antiviral Activity Assay
The anti–SARS-CoV-2 activity of chloroquine and hydroxychloroquine was investigated in vitro. Cells were seeded into 96-well plates at a density of 1 × 104 cells/well and were grown for 24 hours. The in vitro experiment was divided into 2 sections, as follows: (1) the treatment study and (2) the prophylactic study.
Treatment Study
In the treatment study Vero cells were infected at a multiplicity of infection (MOI) of 0.01 (100 plaque-forming units/well) for 2 hours at a temperature of 37°C.Virus input was washed with DMEM and the cells were then treated with medium containing either chloroquine or hydroxychloroquine at 0.032, 0.16, 0.80, 4, 20, or 100 μM for 24 or 48 hours.
Prophylactic Study
Vero cells were pretreated with chloroquine or hydroxychloroquine for 2 hours and then were removed from the drug-containing medium and washed by DMEM. The virus was then added to the infected Vero cells (as described for the treatment study) for 2 hours. Following this, the fresh drug-free medium was added and the cells were incubated for 24 or 48 hours.
The supernatant was collected, and, the RNA was extracted and analyzed by relative quantification using real-time reverse transcriptase–polymerase chain reaction (RT-PCR) (methods described in a previously published study) [13, 14].
Viral RNA Extraction and Reverse Transcriptase–Polymerase Chain Reaction
Viral RNA was extracted from 100 μL of supernatant of infected cells using the automated nucleic acid extraction system (Tianlong, China) and the manufacturer’s instructions. Detection of the SARS-CoV-2 virus was performed using the One Step Prime Script RT-PCR kit (TaKaRa, Japan) on the Light Cycler 480 Real-Time PCR system (Roche, Rotkreuz, Switzerland) with primers. The following sequences were used:
forward primer: 5′-AGAAGATTGGTTAGATGATGATAGT-3′;
reverse primer:5′-TTCCATCTCTAATTGAGGTTGAACC-3′;
and probe:5′-FAM-TCCTCACTGCCGTCTTGTTG ACCA- BHQ1–3′.
All experiments were conducted in triplicates. The relative expression was estimated using the 2-△△Ct method.
Statistical Analysis
A sigmoidal concentration-response function, Y = Bottom + (Top-Bottom)/ [1 + 10^([LogEC50 − X] × HillSlope)], was fit to the data using nonlinear regression. The EC50 values were calculated using PRISM (GraphPad Software, San Diego, CA).
PBPK Model Development, Validation, and Simulation
The PBPK models for chloroquine and hydroxychloroquine were developed using the Simcyp simulator (version 18). The chloroquine compound file was provided by Simcyp Limited (a Certara company; Blades Enterprise Centre, Sheffield, UK), and the hydroxychloroquine compound file was self-built. Physical and chemical parameters were obtained from the literature [15]. Pharmacokinetic parameters, such as liver intrinsic clearance, the fraction of dose entering the enterocytes (fa) and first-order absorption rate constant (ka), were determined from clinical data [16]. These data are summarized in Tables 1 and 2 in Supplement File 1. To further predict drug concentrations in lung fluid, we assumed perfusion limited tissue distribution and included an additional tissue compartment (other than default lung organ) in the model to represent lung tissue. This set up allowed us to use the lung to blood concentration ratio for chloroquine and hydroxychloroquine obtained from animal studies [17, 18].
Validation Data
Published chloroquine and hydroxychloroquine clinical trial data were used to validate the developed PBPK models (details summarized in Table 3 in Supplement File 2) [16, 19–23]. Data obtained from the literature in graphical form were extracted using Plot Digitizer (version 2.26, GetData). Pharmacokinetic parameters that could not be sourced from the literature were estimated using extracted data in Phoenix (version 8.6; Certara Company).
Validation Method
Concentration-time profiles were simulated under different published clinical trial protocols using the developed PBPK models for hydroxychloroquine and chloroquine [16, 19–23]. The Simcyp “Healthy volunteer,” “Chinese healthy volunteer,” and “Pediatric” virtual populations were used in the validation simulations as the reported clinical trials were conducted in Caucasian, Chinese, and children populations, respectively.
Simulated exposure data were compared with observed data. The criterion to determine model accuracy was based on whether the observed data fell within the 90% confidence interval of the predicted values. The ratio of predicted pharmacokinetic (PK) parameters (eg, the maximum concentration [Cmax] and area under the curve [AUC]) to observed values was used to evaluate model performance. The predicted values were considered reasonable if the ratio of predicted to observed data was within a predefined 2-fold range (0.5 ≤ ratio ≤ 2.0).
Simulation Method
The exposure of chloroquine and hydroxychloroquine in the lung fluid (lung), plasma, and blood was simulated under different dosing regimens (shown in Table 1) using the validated PBPK models. A correction factor for chloroquine base and hydroxychloroquine base was input into the model simulations. Chloroquine phosphate 500 mg is equivalent to 300 mg of chloroquine base, and 200 mg of hydroxychloroquine sulfate is equivalent to 155 mg of hydroxychloroquine base. The “Chinese healthy volunteers” virtual population provided in Simcyp was used for the simulations. All simulations were performed with 10 trials and 10 subjects per trial. Virtual subjects were aged between 20 to 50 years of age, and 50% of the subjects were male and 50% female.
Ratios of Free Lung Tissue Trough Concentration/EC50 (RLTEC) Under Different Dosage Regimens
| RLTEC | |||||
|---|---|---|---|---|---|
| Drug and ID | Dosing Regimen | Day 1 | Day 3 | Day 5 | Day 10 |
| Chloroquine phosphate | |||||
| A. | D1–D10 500 mg BID | 2.38 | 5.92 | 18.9 | 40.7 |
| Hydroxychloroquine sulfate | |||||
| B. | D1 800 mg + 400 mg; D2–D10 400 mg QD | 33.3 | 55.1 | 103 | 168 |
| C. | D1 600 mg BID; D2–D10 400 mg QD | 31.7 | 54.7 | 103 | 169 |
| D. | D1 600 mg BID; D2–D10 200 mg BID | 31.7 | 53.1 | 101 | 167 |
| E. | D1 400 mg BID; D2–D10 200 mg BID | 21.0 | 38.9 | 85.4 | 154 |
| F. | D1 400 mg BID; D2–D5 200 mg BID | 21.0 | 38.9 | 85.4 | 83.3 |
| RLTEC | |||||
|---|---|---|---|---|---|
| Drug and ID | Dosing Regimen | Day 1 | Day 3 | Day 5 | Day 10 |
| Chloroquine phosphate | |||||
| A. | D1–D10 500 mg BID | 2.38 | 5.92 | 18.9 | 40.7 |
| Hydroxychloroquine sulfate | |||||
| B. | D1 800 mg + 400 mg; D2–D10 400 mg QD | 33.3 | 55.1 | 103 | 168 |
| C. | D1 600 mg BID; D2–D10 400 mg QD | 31.7 | 54.7 | 103 | 169 |
| D. | D1 600 mg BID; D2–D10 200 mg BID | 31.7 | 53.1 | 101 | 167 |
| E. | D1 400 mg BID; D2–D10 200 mg BID | 21.0 | 38.9 | 85.4 | 154 |
| F. | D1 400 mg BID; D2–D5 200 mg BID | 21.0 | 38.9 | 85.4 | 83.3 |
Abbreviations: BID, twice daily; QD, once daily; RLTEC, ratio of free lung tissue trough concentration/EC50.
Ratios of Free Lung Tissue Trough Concentration/EC50 (RLTEC) Under Different Dosage Regimens
| RLTEC | |||||
|---|---|---|---|---|---|
| Drug and ID | Dosing Regimen | Day 1 | Day 3 | Day 5 | Day 10 |
| Chloroquine phosphate | |||||
| A. | D1–D10 500 mg BID | 2.38 | 5.92 | 18.9 | 40.7 |
| Hydroxychloroquine sulfate | |||||
| B. | D1 800 mg + 400 mg; D2–D10 400 mg QD | 33.3 | 55.1 | 103 | 168 |
| C. | D1 600 mg BID; D2–D10 400 mg QD | 31.7 | 54.7 | 103 | 169 |
| D. | D1 600 mg BID; D2–D10 200 mg BID | 31.7 | 53.1 | 101 | 167 |
| E. | D1 400 mg BID; D2–D10 200 mg BID | 21.0 | 38.9 | 85.4 | 154 |
| F. | D1 400 mg BID; D2–D5 200 mg BID | 21.0 | 38.9 | 85.4 | 83.3 |
| RLTEC | |||||
|---|---|---|---|---|---|
| Drug and ID | Dosing Regimen | Day 1 | Day 3 | Day 5 | Day 10 |
| Chloroquine phosphate | |||||
| A. | D1–D10 500 mg BID | 2.38 | 5.92 | 18.9 | 40.7 |
| Hydroxychloroquine sulfate | |||||
| B. | D1 800 mg + 400 mg; D2–D10 400 mg QD | 33.3 | 55.1 | 103 | 168 |
| C. | D1 600 mg BID; D2–D10 400 mg QD | 31.7 | 54.7 | 103 | 169 |
| D. | D1 600 mg BID; D2–D10 200 mg BID | 31.7 | 53.1 | 101 | 167 |
| E. | D1 400 mg BID; D2–D10 200 mg BID | 21.0 | 38.9 | 85.4 | 154 |
| F. | D1 400 mg BID; D2–D5 200 mg BID | 21.0 | 38.9 | 85.4 | 83.3 |
Abbreviations: BID, twice daily; QD, once daily; RLTEC, ratio of free lung tissue trough concentration/EC50.
Dose Regimen Optimization
The PBPK models were used to predict the lung tissue concentrations of chloroquine and hydroxychloroquine under different dosing regimens (Table 1). The lung trough concentrations on days 1, 3, 5, and 10 were adjusted by the plasma unbound fraction (fu,plasma) to obtain the free lung trough concentration. The ratio of the free lung trough concentration to the in vitro EC50 values (RLTEC) was calculated and the results tabulated. In a recent clinical trial, 500 mg of chloroquine phosphate given twice daily was shown to be effective on study day 5 (RLTEC, day5). This dosing regimen for chloroquine was used as the target for dose optimization for hydroxychloroquine (ie, the RLTEC of hydroxychloroquine should not be lower than the RLTEC, day5 of chloroquine at any time).
RESULTS
Antiviral Activity In Vitro
Results from the in vitro study showed that both chloroquine and hydroxychloroquine have good antiviral activity. Chloroquine and hydroxychloroquine were found to decrease the viral replication in a concentration-dependent manner. The EC50 values for chloroquine were 23.90 and 5.47 μM at 24 and 48 hours, respectively (Figure 1A). EC50 values for hydroxychloroquine were 6.14 and 0.72 μM at 24 and 48 hours, respectively (Figure 1B).
The antiviral activities of chloroquine and hydroxychloroquine for treatment or prophylactic treatment against SARS-CoV-2 in vitro. The antiviral activities of chloroquine and hydroxychloroquine for therapeutic and prophylactic use were tested on the Vero cells infected with a SARS-CoV-2 clinically isolated strain. A and B, For the treatment group, chloroquine and hydroxychloroquine were added to culture medium after the infection of Vero cells and cells were incubated for 24 or 48 hours. C and D, For the prophylactic treatment group, the Vero cells were pretreated with chloroquine and hydroxychloroquine for 2 hours, and then washed by medium. Virus was added to infect cells. After that, the fresh drug-free medium was added and cells were incubated for 24 or 48 hours. The viral yield in the cell supernatant was quantified by RT-PCR. Abbreviations: RT-PCR, reverse transcriptase–polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
Antiviral Pretreatment Activity In Vitro
Hydroxychloroquine exhibited a superior in vitro antiviral effect in comparison to chloroquine when the drug was added prior to the viral challenge. The EC50 values for chloroquine were greater than 100 and 18.01 μM at 24 and 48 hours, respectively. EC50 values for hydroxychloroquine were 6.25 and 5.85 μM at 24 and 48 hours, respectively. It was noted that, with longer incubation times, the EC50 values for chloroquine and hydroxychloroquine tended to decrease. The inhibitory effect of chloroquine was poor. This was particularly evident at 24 hours whereby, even at the highest concentration of chloroquine used in the study, the inhibition rate did not exceed 50% (Figure 1C, D).
PBPK Model Development, Validation, and Simulation Validation Results
The predicted and observed plasma/blood concentration time profiles for chloroquine and hydroxychloroquine are shown in Figure 2. Intravenous data were used to understand the distribution and elimination phase of the 2 drugs, and oral administration data were used to understand the intracorporal absorption process. Most of the observed data fell within the 90% prediction interval. The ratio of predicted to observed PK parameters (Cmax and AUC) were within the range of 0.5 to 2.0 (details summarized in Table 4 in Supplement File 2), indicating that the prediction accuracy of the developed PBPK models was acceptable and could be used to simulate the different dosing scenarios.
Predicted and observed mean arithmetic concentration profiles. A and B, Validation for HCQ PBPK model by blood data after intravenous administration, C, Validation for HCQ PBPK model by blood data after oral administration. D, Validation for HCQ PBPK model by plasma data after oral administration. E, Validation for CQ PBPK model by blood data after oral administration. F, Validation for CQ PBPK model by blood data after intravenous administration. G, Validation for CQ PBPK model by plasma data after oral administration. H, Validation for CQ PBPK model by plasma data after oral administration. Details are summarized in Tables 3 and 4 in Supplement File 2. Abbreviations: conc, concentration; CQ, chloroquine; HCQ, hydroxychloroquine; Obs, observed; PBPK, physiologically based pharmacokinetic; Pred, predicted.
Simulation Results
The simulated lung, blood, and plasma concentration time profiles for chloroquine and hydroxychloroquine under the different dosing regimens are shown in Figure 3. It can be seen that the lung, blood, and plasma concentrations of chloroquine increased slowly after the first dose was given and were yet to reach steady state on day 10. The simulated chloroquine concentration in lung tissue was much higher than in plasma, where the lung to plasma ratio increased with time and reached a ratio of approximately 400. The projected lung, blood, and plasma concentrations of hydroxychloroquine rapidly increased and reached steady state following the initial loading dose and subsequent maintenance doses (Figure 3B, C).
Predicted plasma (A), blood (B), and lung (C) concentration–time profiles of CQ under the dose regimen A, and HCQ under dose regimen B, regimen C, regimen D, regimen E, and regimen F. Lung tissue concentrations were predicted based on animal data (see Methods). Abbreviations: conc, concentration; CQ, chloroquine; HCQ, hydroxychloroquine.
Suggested Dosing Regimens for Hydroxychloroquine to Treat SARS-CoV-2 Infection
The free lung trough concentrations were also projected from the simulations. The RLTEC under the different dosing regimens is shown in Table 1. The RLTEC values of hydroxychloroquine were found to be higher than the RLTEC values of chloroquine on days 1, 3, 5, and 10. This suggests that hydroxychloroquine may achieve ideal clinical efficacy under the simulated dosing regimens.
The RLTEC on day 1 was notably higher for hydroxychloroquine than for chloroquine. This is likely due to the loading dose of hydroxychloroquine given, thus enabling a faster clinical effect. There was no significant difference between the once- and twice-daily maintenance dosing regimens (regimen C and D, respectively) when used from day 2 to day 10; hence, the once-daily dosing regimen may be preferred to improve patient compliance. Despite regimen F being a 5-day treatment regimen, the lung trough concentrations were still above the target concentration on day 10. However, if the treatment duration of regimen F was extended to 10 days (ie, regimen E), it resulted in a higher drug concentration on day 10. Overall, regimen F may be the best regimen while considering both efficacy, safety, and patient compliance. These simulations were used to provide timely support on proper dose selection of CQ and HCQ in a clinical study commenced in February, 2020 in Wuhan City (http://www.chictr.org.cn/showproj.aspx?proj=49482).
DISCUSSION
In this study, hydroxychloroquine exhibited better in vitro anti–SARS-CoV-2 activity than chloroquine. This was demonstrated by the EC50 values for hydroxychloroquine always being smaller than the EC50 values for chloroquine, indicating that hydroxychloroquine has a more potent antiviral activity (shown in Figure 1). In the study by Wang et al [6], chloroquine was shown to have an inhibitory effect on SARS-CoV-2 with an EC50 value of 1.13 μM after a 48-hour incubation time. These findings are comparable to our in vitro chloroquine results of an EC50 value of 5.47 μM. In addition, an unpublished clinical trial has demonstrated the therapeutic effect of chloroquine in patients with SARS-CoV-2. On the basis of hydroxychloroquine’s superior antiviral and prophylactic activity, as well as its more tolerable safety profile in comparison to chloroquine, we believe that hydroxychloroquine may be a promising drug for the treatment of SARS-CoV-2 infection [24].
In our study we noted that the EC50 values for hydroxychloroquine and chloroquine decreased with longer incubation times. This suggests that incubation time may influence the drug’s antiviral activity. Both hydroxychloroquine and chloroquine have been reported to accumulate in cells [25]. It is possible that a longer incubation time may provide more time for the drug to accumulate to higher intracellular concentrations and ultimately exhibit a better antiviral effect [26]. Another possible explanation is that the drug-induced cytotoxicity may take time to develop, and hence, the drug effect may increase with time [27].
The PBPK model for hydroxychloroquine and chloroquine was validated with in vivo PK data from humans, with predictions of lung tissue drug concentrations supported by findings in rats and mice. A high lung to plasma partition coefficient ratio (Kp ratio) reported in animal studies was used to imitate the drugs’ high accumulation in lung tissue. The Kp ratio of both drugs for humans was assumed to be same as the ratio for chloroquine in rats because there were no human data available. This assumption may be reasonable as the transportation of both drugs is completely via passive diffusion (ie, no transporters are involved). Further cell and animal researches are underway to update these PBPK models (manuscripts in preparation).
In some patients it has been reported that their immune response to the SARS-CoV-2 virus results in an increase of cytokines interleukin (IL)-6 and IL-10 [13, 28]. This may progress to a cytokine storm, followed by multiorgan failure and potentially death. Both hydroxychloroquine and chloroquine have immunomodulatory effects and can suppress the increase of immune factors [29, 30]. Bearing this in mind, it is possible that early treatment with either of the drugs may help prevent the progression of the disease to a critical, life-threatening state. In critically ill patients with SARS-CoV-2 the use of corticosteroids may be harmful [31]; however, the use of immunosuppressants (eg, tocilizumab) is not ideal either as they can suppress the immune system and lead to an increased risk of infection [32]. In this setting, hydroxychloroquine may be an ideal drug to treat SARS-CoV-2 infection as it can inhibit the virus via its antiviral effects and help mediate the cytokine storm via its immunomodulatory effects. Based on work conducted in our laboratory, we recommend the concomitant use of low-dose hydroxychloroquine with an anti-inflammatory drug to help mitigate the cytokine storm in critically ill patients with SARS-CoV-2.
Several clinical trials are currently investigating the use of hydroxychloroquine to treat SARS-CoV-2 infection. However, it is worth noting that the dosing regimens used in these trials are mainly based on previous clinical experience, raising the concern that adverse effects may occur in study participants (The dose regimen of chloroquine phosphate and hydroxychloroquine sulfate from registered clinical trials were summarized in Supplement File 3). In this study, an optimized dosing regimen was designed for hydroxychloroquine to have a high loading dose and low maintenance dose based on its unique pharmacokinetics (ie, high accumulation in cells and long elimination half-life). Using PBPK modeling and simulation techniques the optimal dosing regimen for hydroxychloroquine was evaluated in silico. The simulation results demonstrated that regimen F was able to achieve treatment efficacy as well as have a good safety profile, even considering possible underestimation of drug efficacy to some extent. However, future clinical trials evaluating this regimen are required before it can be widely used to treat COVID-19. The combination of the in vitro antiviral activity data and predicted drug concentrations in this study is being used to support the design of dosing regimens used in a clinical study in COVID-19 patients (http://www.chictr.org.cn/showproj.aspx?proj=49482).
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
Notes
Acknowledgments. The authors thank Dr Lisa Almond, Alice Ke, Mian Zhang and Luke Guilliatt from Simcyp Limited for providing the chloroquine compound file. The authors would also like to thank Dr. Isabelle Hui-San Kuan and Dr. Xiao Zhu of New Zealand and Dr. Ping Zhao of the Bill & Melinda Gates Foundation for their proofreading work. At the same time, authors would also like to thank Dr. Gaohua Lu of GlaxoSmithKline UK, who guided the optimization of the PBPK model.
Author contributions. D. L. and H. L. conceived and designed the study. X. Y. designed and W. T., F. Y., B. H., P. N., L. Z., and R. L. conducted the experiments of in vitro antiviral activity. X. L. analyzed the experiment data. X. Y. and M. Z. developed and optimized the chloroquine and hydroxychloroquine PBPK model. C. C. designed the dose regiments of hydroxychloroquine. X. Y., M. Z., C. C., X. L., and D. L. prepared this manuscript. H. L., E. D., C. S., S. Z., and W. T. reviewed this manuscript.
Financial support. This work was supported by the “13th Five-Year” National Major New Drug Projects of China, Ministry of Science and Technology of the People’s Republic of China (grant number 2017ZX09101001–002–001) and the Bill & Melinda Gates Foundation (grant number OPP1204780).
Potential conflicts of interest. C. S., H. L., and D. L. have patents pending for antimicrobial infection pharmoceutical composition and its application. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
References
Author notes
X. Y., F. Y., M. Z., C. C., and B. H. contributed equally to this work.
D. L., W. T., and H. L. contributed equally to this work.
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