Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach...

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing


Hydroxychloroquine (HCQ) is a promising candidate for Coronavirus disease of 2019 (COVID-19) treatment. The optimal dosing of HCQ is unknown. Our goal was to integrate historic and emerging pharmacological and toxicity data to understand safe and efficacious HCQ dosing strategies for COVID-19 treatment. The data sources included were (i) longitudinal clinical, pharmacokinetic (PK), and virologic data from patients with severe acute respiratory syndrome-2 (SARS-CoV-2) infection who received HCQ with or without azithromycin (n = 116), (ii) in vitro viral replication data and SARS-CoV-2 viral load inhibition by HCQ, (iii) a population PK model of HCQ, and (iv) a model relating chloroquine PKs to corrected QT (QTc) prolongation. A mechanistic PK/virologic/QTc model for HCQ was developed and externally validated to predict SARS-CoV-2 rate of viral decline and QTc prolongation. SARS-CoV-2 viral decline was associated with HCQ PKs (P < 0.001). The extrapolated patient half-maximal effective concentration (EC50) was 4.7 µM, comparable to the reported in vitro EC50s. HCQ doses > 400 mg b.i.d. for ≥5 days were predicted to rapidly decrease viral loads, reduce the proportion of patients with detectable SARS-CoV-2 infection, and shorten treatment courses, compared with lower dose (≤ 400 mg daily) regimens. However, HCQ doses > 600 mg b.i.d. were also predicted to prolong QTc intervals. This prolongation may have clinical implications warranting further safety assessment. Due to COVID-19's variable natural history, lower dose HCQ regimens may be indistinguishable from controls. Evaluation of higher HCQ doses is needed to ensure adequate safety and efficacy.



In vitro HCQ and CQ drug sensitivity data for SARS-CoV-2, reported as percent inhibition, were obtained from 24-hour and 48-hour experiments in Vero or VeroE6 cells derived from African green monkey kidney epithelium. The experiments are described in detail in the original publications. Estimated, apparent half-maximal effective concentration (EC50) values were reported, whereas the 90% effective concentration values were obtained by digitizing the graph of antiviral activity for HCQ using the software WebPlotDigitizer version 4.2.11

In vitro viral replication data were obtained from longitudinal data profiling the growth of SARS-CoV-1 in Vero cells over 11 days.12 RNA extraction and quantitative real-time polymerase chain reaction (PCR) were performed, and viral load was reported as cycle threshold (CT). Viral load was calculated from the CT reported in the original publication as 1/log2(CT).13

In vivo data was obtained from a published nonrandomized single arm open label study of HCQ 200 mg t.i.d., with or without azithromycin, for treatment of SARS-CoV-2 infection in France.6 Participants had PCR confirmed SARS-CoV-2 infection by nasopharyngeal swab and swab samples were obtained daily. Controls were selected by convenience at multiple hospitals in France. Viral load was calculated from CT.13 One sparse serum HCQ concentration was reported for each patient on days 2, 4, or 6 of treatment. Sixteen patients in the control arm (samples = 69), 14 patients in the HCQ arm (samples = 93), and 6 patients in the HCQ with azithromycin arm (samples = 40) contributed viral load samples over 6 days. Additional patient characteristics and results are reported in the original publication.6 An external cohort of 80 patients receiving 200 mg t.i.d. for 10 days was used for external model validation.14

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing

Pharmacokinetic/pharmacodynamic (PK/PD)-viral kinetics model diagram. A previously published two-compartment plasma PK model was used to simulate plasma concentration.15 PD compartments included a one-compartment model describing viral growth, death, and drug effect, and a model describing drug effect on QTc prolongation.19 represents the PD relationship for hydroxychloroquine (HCQ) plasma concentration (Cp). In the in vitro model, is characterized by a maximum effect (Emax) function , whereas for the clinical model it is described using a linear function (. CL, clearance; EC50, half-maximal effective concentration; QTc, corrected QT; V, volume of distribution.

Clinical PK/PD model

HCQ concentrations from the clinical cohort fell within the range expected from historical population profiles (Figure 3a left).6, 15 Figure 3b shows the reconstructed individual PK profiles in the treatment group. The patients' viral load is displayed in Figure 3a (right).

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing

Data and model for clinical data. (a) Raw pharmacokinetic (PK) and viral load data. In the PK graph, raw data is shown in red, whereas black and grey lines represent the typical and population plasma PK simulation (n = 200) using the PK model. In vitro half-maximal effective concentration (EC50s) indicated in the graph were calculated considering total drug using the values reported in Yao et al.3 In the viral load graph (left), thick lines represent the mean profiles of each group, whereas the thin ones represent the individual profiles. (b) Individual PK plasma profile predicted with the PK model for each patient treated with hydroxychloroquine (HCQ). (c) Visual predictive check of population PK/pharmacodynamic model. The solid continuous line represents the 50th percentile of the observations, dashed lines represent 2.5th and 97.5th percentiles of observations, and shaded areas represent the 95% prediction intervals for median, 2.5th, and 97.5th percentiles obtained from 1,000 simulated datasets. The lower panel shows the proportions of below the limit of quantification values observed (solid line), with 95% prediction variability shown by shaded area. CT, cycle threshold; LLOQ, lower limit of quantification.

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing

Comparison of EC50 values. (a) Comparison of percentage of viral inhibition for hydroxychloroquine (HCQ) by data source, including digitized 48-hour in vitro data (green), 24-hour in vitro data (orange) obtained from Yao et al.,3in vitro data from Liu et al.9 and Touret et al.,10 and longitudinal clinical data (blue; solid line = available data; dashed line = extrapolated data. Raw data and curves from Yao et al.3 were digitized and displayed directly in the plot. The model used for these data is shown in the original manuscript and used a sigmoidal concentration-response function . For the recently added references (Liu et al.9 and Touret et al.10), a Hill coefficient equal to 1 was assumed, and the different points for plotting purposes were calculated from the half-maximal effective concentration (EC50) values provided in the original manuscripts. (b) Table including the EC50 values and in vitro experimental conditions from Yao et al., Liu et al., and Touret et al.3, 9, 10 *Adjusted EC50 was calculated to obtain the total drug value as follows: , where fu = 0.5.

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing

Pharmacokinetic (PK) simulations for optimal dose. (a) Population PK plasma profiles following different twice daily regimens. (b) Population PK plasma profiles following different combinations of loading and maintenance dosing. (c) Proposed dosing schemes with interindividual variability. Hydroxychloroquine (HCQ) concentration is detectable in plasma for up to 21 days. Apparent in vitro half-maximal effective concentration (EC50s) were adjusted to account for plasma protein binding. QTc, corrected QT.

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing

Efficacy and safety simulations. (a) External validation of the model using original structure and extended structure with immune effect. (b) Predicted proportion of adults with detectable viral loads over time, stratified by regimen. (c) Median simulated proportion of adults with detectable viral loads at the end of treatment, stratified by regimen. (d) Predicted delta corrected QT (QTc; using a baseline QTc of 394 ± 30 SD) for each regimen of interest. PCR, polymerase chain reaction; PKPD, pharmacokinetic pharmacodynamic.


For HCQ to maximally suppress SARS-CoV-2 replication in vivo, the HCQ dose may need to be optimized. To best define the effective HCQ concentrations for treatment of COVID-19, all available data from in vitro and clinical studies using HCQ for SARS-CoV-2 were pooled to quantify the relationship between HCQ PK and SARS-CoV-2 viral decline in patients with COVID-19. We predicted that higher HCQ daily doses (e.g., as high as 800 mg b.i.d.), were associated with rapid rates of viral decline and increased the percentage of PCR-negative patients but could result in increased risk of QTc prolongation. Regimens that give ~ 800 mg/day either loaded upfront or as 400 mg b.i.d., could be safely tolerated and would reduce the time with a detectable SARS-CoV-2 viral load, and, thus, improve treatment outcomes. Higher HCQ doses of up to 800 mg b.i.d. could result in even faster rates of viral decline but there is limited safety information for these high doses.

HCQ pharmacology is complex; HCQ distributes extensively into erythrocytes (whole blood to plasma ratio ~ 3.8, exhibits a long half-life (123 hours) and a large volume of distribution, all attributed to extensive tissue uptake, clearly important for treatment of COVID-19 systemic illness.22, 23 HCQ and CQ are diprotic weak bases (with PKa of 9.67 and 8.27 vs. 10.18 and 8.38 for HCQ and CQ, respectively).24 Interestingly, both drugs experience ion-trapping in which the drug becomes ionized in acidic environments like the lysosome (pH ~ 5.0). This causes an irreversible accumulation, explains the large volume of distribution, and potentially impacts the amount of free drug available in tissues.25, 26 HCQ is converted into at least three metabolites (desethylhydroxychloroquine, desethylchloroquine, and bidesethylhdroxychloroquine). Desethylhydroxychloroquine HCQ, the primary metabolite, is pharmacologically active for some nonviral illnesses, and formed by various cytochrome P450 isozymes. For our analysis, we focused on the parent HCQ, as potent in vitro activity against SARS-CoV-2 has only been described for the parent compound.

For derivation of our dosing rationale, we have utilized HCQ levels in plasma, instead of the lungs. Lung accumulation has been observed for HCQ and CQ in animal PK studies and reported to be substantial (a partition coefficient of 281 (102.45)). The partition coefficient ratio enables quantification of the total drug concentration in the tissue, and by assuming the same fraction of unbound drug in plasma and tissue, one can further estimate unbound concentrations in the tissue. By using this approach, a wide range of doses, including doses as low as 10 mg, seem to be potentially therapeutic. The drug efficacy at the site of action is determined by the fraction of drug unbound in the tissue, which has not been studied for HCQ, and, thus, the amount of free drug in tissue remains unknown. Highly lipophilic drugs for other infectious diseases, like bedaquiline and clofazimine, accumulate in lungs as well, however, the accumulation correlates with binding to macromolecules in tissue, not necessarily to the free fraction.27, 28 Based on the physicochemical parameters of HCQ (log P of 3.85 and pKa of 9.67, 8.27), the fraction unbound in tissue is likely low.29 Therefore, in our study, we conservatively assume that the free fraction in plasma equilibrates between plasma and tissue and consider that to be the fraction of drug that can contribute to drug effect. Tissue binding studies using a rapid equilibrium dialysis assay with lung homogenate should be performed to define an accurate fraction unbound in the tissue.

Using a mechanistic PK/PD modeling approach, we were able to quantify a relationship between HCQ concentration and SARS-CoV-2 viral decline. However, we were not able to differentiate if azithromycin offered any additional benefit. The group receiving HCQ and azithromycin had the lowest baseline viral load and showed a similar rate of viral decline compared with the HCQ group.6 Therefore, it remains unclear if azithromycin offers any additional benefit.

Clinically significant QTc prolongation associated with HCQ have been reported.30-32 Only two small observational studies have reported associations between HCQ doses of 200–400 mg daily and QTc prolongation32, 33 and a concentration-dependent QTc relationship is not available. As a result, we used CQ as a model to predict QTc prolongation risk.19 HCQ and CQ have an identical structure with the substitution of a hydroxyl group for HCQ, and both have been found in vitro to inhibit the inward rectifier K+ channels.34, 35 This has been associated with QTc prolongation, and docking studies suggest nitrogen in the alkylamine and quinoline ring found in both compounds are responsible for binding with potassium channels.36 Although a dedicated study is needed, the hydroxyl group in HCQ is unlikely to affect rectifier K+ channels binding as the pKa for the alkylamine nitrogen is similar to that of chloroquine's.37 In vitro data from CQ identified an hERG IC50 of 2,500 nM.38 We leveraged a recent study of high-dose CQ for malaria treatment to predict potential risk of QTc prolongation with HCQ.19 In support of our findings, a maximum dose of 1,200 mg daily for 2–6 weeks has been well-tolerated without reported cardiac toxicity.39, 40 Based on this evidence, and the PK-QTc relationship for CQ presented here, we expect a HCQ course of 400–600 mg b.i.d. for 10 days or less is unlikely to be associated with clinically significant cardiac toxicity in patients without a known risk factor for QTc prolongation.41 As data for HCQ and QTc prolongation are limited, we recommend the highest doses of HCQ be reserved for study in dose escalation studies.

Additional toxicities associated with HCQ include retinopathy and gastrointestinal adverse events.39, 42 The mechanism of irreversible retinal damage associated with HCQ is unknown, but it has been associated with HCQ doses > 5 mg/kg and in patients who receive HCQ for > 5 years.42 Retinopathy associated with use < 1 month of HCQ has not been reported, and this side effect is less likely in the acute setting.30, 43 Gastrointestinal toxicity with HCQ is concentration-related and could be a limiting factor to dosage of HCQ but doses up to 1,200 mg have been reported to be well-tolerated without adverse events in patients with cancer and rheumatologic disease in other studies.39, 40

There were a few limitations to this study. First, clinical HCQ data are limited to nonrandomized studies, and a clear model for the natural rate of viral decline is not well defined. To explore this effect, we compared viral kinetic trends on treatment to the extracted baseline data from Cao et al. (n = 100 hospitalized patients who received supportive care).21 Second, the translational viral replication was obtained from SARS-CoV-1 data. SARS-CoV-1 and SARS-CoV-2 share an estimated 79.6% sequence homology.44 Third, we imputed the PK profiles for HCQ using population PK parameters derived from a pool of both healthy and malaria-infected patients. Fourth, we were not able to predict how concomitant HCQ and azithromycin may impact the risk of QTc prolongation or anticipate how underlying risk factors for QTc prolongation could impact the PK-QTc relationship due to the lack of available data. Closely monitored clinical trials will be needed to confirm that high-dose HCQ is safe with or without azithromycin. Finally, our model used plasma HCQ concentrations to predict nasopharyngeal viral loads, which may not fully correlate with clinical improvement or viral load measured at different sites, however, it has generally been accepted that viral decline is a desirable marker leading to clinical improvement.45-50 In addition, all relevant assumptions made during the analysis are summarized in Supplementary Table S4.

Treatment options for COVID-19 can most effectively be advanced by utilizing all available data and pharmacologically driven drug repurposing. Suboptimal dosing can result in wasted time and resources. Even more problematic is the potential to declare a drug ineffective because of misdosing. Using PK-exposure modeling, we predict that higher doses of HCQ will be needed to achieve cure within 7 days for all patients. Given the observed prolonged viral shedding in patients with COVID-19, these data support the possibility that early treatment with high-dose HCQ could reduce transmissibility and potentially reduce the risk of late clinical decompensation. However, given the possibility of QTc prolongation with high-dose regimens, rigorous trials must precede widespread clinical usage. We predict that higher HCQ doses, (> 400 mg b.i.d.) are most efficacious for viral suppression and should be further examined in clinical trials to evaluate safety and efficacy.


The investigators thank the Savic laboratory for all their help and support during this project. We would also like to thank Tia Tummino for her help addressing reviewer comments.


No funding was received for this work.

Conflict of Interest

The authors declared no conflict of interest.

Credited to ASCPT


6 views0 comments