\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Model informed dosing of Hydroxycholoroquine in COVID-19 patients:
Learnings from the recent experience, remaining uncertainties and Gaps}



\author[1]{Pauline Themans}%
\author[2]{Nicolas DAUBY}%
\author[3]{Loic Schrooyen}%
\author[2]{Faustine Lebout}%
\author[3]{Marc Delforge}%
\author[4]{Rakan Nasreddine}%
\author[5]{Agnes Libois}%
\author[5]{Marie-Christine  Payen}%
\author[3]{Deborah  Konopnicki}%
\author[6]{Francoise Wuillaume}%
\author[6]{Cécile Lescrainier}%
\author[6]{Veerle Verlinden}%
\author[1]{Jean-Michel Dogné}%
\author[6]{Jamila Hamdani}%
\author[6]{Flora Musuamba}%
\affil[1]{University of Namur}%
\affil[2]{Centre Hospitalier Saint-Pierre, Université Libre de Bruxelles (ULB)}%
\affil[3]{Centre Hospitalier Saint-Pierre, Université Libre de Bruxelles (ULB), Brussels}%
\affil[4]{Public Health School, Université Libre de Bruxelles (ULB)}%
\affil[5]{Centre Hospitalier Saint-Pierre, Université Libre de Bruxelles (ULB), }%
\affil[6]{Federaal Agentschap voor Geneesmiddelen en Gezondheidsproducten}%


\vspace{-1em}



  
  \date{May 20, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Aims In the absence of a commonly agreed dosing protocol based on
pharmacokinetic considerations, the dose and treatment duration for
hydroxychloroquine (HCQ) COVID-19 disease currently vary across national
guidelines and clinical study protocols. We have used a model-based
approach to explore the relative impact of alternative dosing regimens
proposed in different dosing protocols for hydroxychloroquine in
COVID-19. Methods We compared different PK exposures using Monte Carlo
simulations based on a previously published population pharmacokinetic
model in patients with rheumatoid arthritis, externally validated using
both independent data in lupus erythematous patients and recent data in
French COVID-19 patients. Clinical efficacy and safety information from
COVID-19 patients treated with HCQ were used to contextualize and assess
the actual clinical value of the model predictions. Results Literature
and observed clinical data confirm the variability in clinical responses
in COVID-19 when treated with the same fixed doses. Confounding factors
were identified that should be taken into account for dose
recommendation. For 80\% of patients, doses higher than 800mg day on D1
followed by 600mg daily on following days might not be needed for being
cured. Limited adverse drug reactions have been reported so far for this
dosing regimen, most often confounded by co-medications, comorbidities
or underlying COVID-19 disease effects. Conclusion Our results were
clear indicating the unmet need for characterization of target PK
exposures to inform HCQ dosing optimization in COVID-19. Dosing
optimization for HCQ in COVID-19 is still an unmet need. Efforts in this
sense are a prerequisite for best the benefit/risk balance.%
\end{abstract}%



\sloppy


\emph{Learnings from the recent experience, remaining uncertainties and
Gaps}

Pauline Th\selectlanguage{ngerman}émans\textsuperscript{1} , Nicolas Dauby\textsuperscript{2,3},
Loïc Schrooyen\textsuperscript{2}, Faustine Lebout\textsuperscript{2},
Marc Delforge\textsuperscript{2}, Rakan Nasreddinne\textsuperscript{2},
Agnès Libois\textsuperscript{2}, Marie-Christine
Payen\textsuperscript{2}, Déborah Konopnicki\textsuperscript{2},
Francoise Wuillaume\textsuperscript{4}, Cecile
Lescrainier\textsuperscript{4}, Veerle Verlinden\textsuperscript{4},
Jean-Michel Dogné\textsuperscript{4,5}, Jamila
Hamdani\textsuperscript{4}, and Flora T. Musuamba\textsuperscript{4,6}

1. Namur Institute for Complex System (naXys) and Department of
Mathematics, University of Namur, Namur, Belgium

2. Department of Infectious Diseases, Centre Hospitalier Saint-Pierre,
Université Libre de Bruxelles (ULB), Brussels

3. Environmental Health Research Centre, Public Health School,
Université Libre de Bruxelles (ULB), Brussels, Belgium

4. Belgian Federal Agency for Medicines and Health Products, Brussels,
Belgium

5 Namur Thrombosis and Hemostasis Center, Department of Pharmacy,
University of Namur, Namur, Belgium

6. Faculty of Pharmaceutical sciences, University of Lubumbashi,
Lubumbashi, DR Congo

\textbf{Corresponding author:}

Flora T Musuamba

Belgian Federal Agency for Medicines and Health Products, B-1060
Brussels, Belgium

flora.musuambashinanu@afmps.be

\textbf{Principal Investigator statement:} The authors confirm that
there is no principal investigator in this study because this study only
includes observational data from standard clinical practice data.

\textbf{Conflict of interest:} The authors declared no competing
interest for this work.

\textbf{Data availability statement} : The data that support the
findings of this study are available on request from the corresponding
author. The data are not publicly available due to privacy or ethical
restrictions.

\textbf{Word count:} Abstract: 250/ Text: 2988

\textbf{Table count} : 3

\textbf{Figure count} : 3

\textbf{Keywords:} Hydroxychloroquine, modelling and simulations, dosing
rationale, dosing optimization, pharmacokinetics

\textbf{Running title:} Hydroxychloroquine dosing in COVID-19 patients

1. What is already known about this subject:

- Inconsistent doses of Hydroxychloroquine are included in national
guidelines and clinical studies protocols for management of COVID-19
disease.

- Modelling and simulation approaches have recently been proposed for
dose selection but (external) clinical validation was either lacking or
carrying important limitations and unverified assumptions.

2. What this study adds:

- We propose a model-based approach for hydroxychloroquine dose
rationale with clinical validation using literature pharmacokinetic data
in autoimmune disease and COVID-19.

- Clinical efficacy and safety data in COVID-19 patients are used for
contextualization.

- Uncertainties and gaps are identified as well as data needed to
address them.



\section*{ABSTRACT}

{\label{abstract}}



\textbf{Aims}

In the absence of a commonly agreed dosing protocol based on
pharmacokinetic considerations, the dose and treatment duration for
hydroxychloroquine (HCQ) COVID-19 disease currently vary across national
guidelines and clinical study protocols. We have used a model-based
approach to explore the relative impact of alternative dosing regimens
proposed in different dosing protocols for hydroxychloroquine in
COVID-19.

\textbf{Methods}

We compared different PK exposures using Monte Carlo simulations based
on a previously published population pharmacokinetic model in patients
with rheumatoid arthritis, externally validated using both independent
data in lupus erythematous patients and recent data in French COVID-19
patients. Clinical efficacy and safety information from COVID-19
patients treated with HCQ were used to contextualize and assess the
actual clinical value of the model predictions.

\textbf{Results}

Literature and observed clinical data confirm the variability in
clinical responses in COVID-19 when treated with the same fixed doses.
Confounding factors were identified that should be taken into account
for dose recommendation. For 80\% of patients, doses higher than 800mg
day on D1 followed by 600mg daily on following days might not be needed
for being cured. Limited adverse drug reactions have been reported so
far for this dosing regimen, most often confounded by co-medications,
comorbidities or underlying COVID-19 disease effects.

\textbf{Conclusion}

Our results were clear indicating the unmet need for characterization of
target PK exposures to inform HCQ dosing optimization in COVID-19.
Dosing optimization for HCQ in COVID-19 is still an unmet need. Efforts
in this sense are a prerequisite for best the benefit/risk balance.

\section*{1 Background}

{\label{background}}

SARS Coronavirus disease 2019 is the most severe pandemic since almost a
century with more than 1,000,000 infections and 60000 deaths all over
the world within less than 6 months {[}1{]}, creating an unprecedented
urgent need for an effective and safe drug to stop its spread and
protect population less skilled to manage the crisis.

Standard drugs and vaccines development approaches are lengthy and
expensive: they require years/decades of research and development: they
are therefore not the optimal response for the current outbreak in view
of the rapid spread of the disease. It is therefore commonly agreed that
there is a more pressing refinement need for alternative solutions,
including drug repurposing and modelling and simulations.

Drug repurposing consists in this context on using of already marketed
drugs or therapeutics under development for other indications based on
their potential pharmacological interest or the available non-clinical
or clinical data with SARS coronavirus. It can be combined to
alternative evidence generation approaches including modelling and
simulation to address some important questions such as determining the
acceptable dose for the different drugs to be either tested in clinical
trials or implemented in compassionate/off-label use during the
outbreak.

Hydroxychloroquine (HCQ), a 4-aminoquinolein drug approved and used
since decades for treatment of malaria {[}2{]}, rheumatoid arthritis
{[}3{]} and cutaneous lupus erythematosus (CLE) {[}4{]} is being
considered as a potential therapeutic option in COVID-19. Recent\emph{in
vitro} studies demonstrated the antiviral activity of chloroquine (CQ)
and HCQ on SARS-CoV-2 (see e.g. {[}5{]}, {[}6{]} and references therein)
with results showing higher potency (lower \emph{in vitro} EC50) for HCQ
as compared to CQ, so that lower doses (than in approved indications)
could be used in COVID19.

HCQ has been used in clinical trials for COVID-19 treatment with
different outcomes/results {[}7-10{]}, it is currently investigated in a
series of additional ongoing/planned clinical trials {[}11{]} and is
included in some national guidelines for management of COVID-19.
However, in the absence of a clear dosing protocol based on drug
exposure in plasma/blood and at the site of infection, dosage and
duration of treatment currently vary across national and clinical study
protocols. Inappropriate dosing regimen can lead to an increase risk of
either therapeutic failure or adverse events such as cardiotoxicity (QT
prolongation) and retinopathies.

Modelling and simulation have the potential to optimize the dose based
on the pharmacokinetics (PK) behavior of the drug, provided that
exposure-response is understood and target concentrations are
characterized for both efficacy and safety. It is therefore considered
timely to explore how the evidence gathered with the clinical use of HCQ
could feed the understanding of its PK and PD and inform the dosing in
COVID-19 patients. In March 2020, a physiologically based
pharmacokinetic (PBPK) model was published by Yao et al to
simulate/predict the HCQ concentrations in blood, plasma and lung fluid
of Chinese patients {[}5{]}. Based on the PBPK model results, the
authors recommend an oral loading dose of 400 mg twice daily of
hydroxychloroquine sulfate, followed by a maintenance dose of 200 mg
given twice daily for 4 days However, no pharmacokinetic data in
COVID-19 patients were available to clinically validate the model. A
different and much higher dosing regimen (at least 800 mg daily over 10
days) has recently been recommended by Garcia-Cremades et al. based on
PK/PD simulation of HCQ effects on SARS COV-2 viral load the one hand,
and on QT prolonging effects of chloroquine (CQ) (a similar drug), on
the other hand. {[}12{]}

The first aim of this work is to assess and compare different dosing
regimens using Monte Carlo simulations based on a previously published
population pharmacokinetic (popPK) models in patients with rheumatoid
arthritis (RA) {[}3{]}, externally validated using both independent data
in patients with cutaneous lupus erythematosus (CLE) {[}ref : Morita{]}
and recent data in COVID-19 patients {[}4{]}. Moreover clinical efficacy
and safety information from COVID-19 patients treated with HCQ at
Saint-Pierre hospital (Brussels/Belgium) and as included in the recently
published studies are used to assess the clinical value of the model
predictions.

This work also aims to present and discuss the strength of evidence and
the uncertainties for a model informed approach based on the currently
available data as well as the current gaps in information for HCQ dose
optimization in COVID-19.

\section*{2 Methods}

{\label{methods}}

Population pharmacokinetic modelling and simulation

Two previous published population pharmacokinetic models have been
published for hydroxychloroquine (HCQ) in patients with RA {[}3{]} and
lupus erythematosus {[}4{]} using whole-blood HCQ concentrations. Four
additional models describe plasma concentrations or merged blood and
plasma concentrations {[}13-15{]}. Blood concentrations are known to be
more reproducible (because higher, with lesser analytical sensitivity
issues). We therefore used the model by Caramichael et al {[}3{]} for
subsequent simulation after independent validation. The authors
developed a one-compartment model with first-order elimination and
absorption and an absorption lag time. Nine of the patients received
oral dose and intravenous infusion for the bioavailability study.
Patients received Plaquenil® tablets, each tablet having 200 mg of
racemic HCQ sulfate equivalent to 155 mg of racemic HCQ base. The only
covariate retained in the model is the methotrexate, a drug most
commonly used in RA.

This model was externally validated using digitised blood concentrations
obtained from Morito et al. paper. In this study, HCQ was used to treat
patients with cutaneous lupus erythematosus {[}4{]}. The 90 patients
received one of these three dosing regimens of HCQ sulfate, depending on
their ideal body weight: 200 mg daily (n=20), 200 mg and 400 mg every
other day (n=55) or 400 mg daily (n=15). The steady-date blood
concentration data (3 samples/patient) were digitised from Figure 1 in
the paper by Morito et al {[}4{]}, using MATLAB R2016b software (The
MathWorks Inc, Natick City, MA, USA). Monte Carlo simulations were
performed using NONMEM software, version 7.3 (Icon Development
Solutions, Ellicott City, MD, USA).

This model was also used for simulations of the serum concentrations
serum PK data including data from 20 patients from Gautret et al. study
{[}6{]} with a serum/whole blood correction ratio of 0.53 {[}16{]}.
Gautret et al. study patients were confirmed COVID-19 and received 200
mg every 8 hours during 10 days. It is assumed that they were trough
concentrations, measured before the first dose of the indicated day.
NONMEM software was used for this purpose.

Subsequently, we used this model to perform simulations of blood
concentrations of HCQ base for different dosing protocols for treatment
of COVID-19. Table 1 includes the relevant information on the
simulations performed. The figures were generated using MATLAB software.

Additional clinical data

In addition to data used for model validation, additional data were used
to get some insight on dose-exposure-response as regards clinically
relevant beneficial and toxic effects of HCQ in COVID-19. Even though
these data could not permit a formal exposure-response analysis, they
were used to describe the doses and PK exposure distributions in
COVID-19 patients either with favourable responses (discharge from the
hospital based on criteria described below) or experiencing adverse drug
reactions after treatment with HCQ. Summary level and patient level data
were used for this purpose.

\emph{Summary level data}

These include data from 3 previously published clinical data in COVID-19
patients treated either with HCQ monotherapy {[}8-9{]} or with HCQ
combined with azithromycin (AZM) {[}7, 10{]}.

\emph{Patient level data}

Clinical data were obtained from 172 COVID-19 in-patients hospitalised
at Saint-Pierre Hospital in Brussels (Belgium) from
1\textsuperscript{st} of March to 6\textsuperscript{th} of April 2020
and treated with HCQ monotherapy. Summary of patient characteristics as
well as relevant information available on their disease stage and
response to HCQ monotherapy are included in Table 2. Statistical
analysis was performed using SAS JMP v.10 software. Criteria for
hospitalization included COVID-19 suspicion based on radiological
findings in patients with known severity factors (hypertension,
diabetes, lung disease, age \textgreater{}60) and/or with one the
following criteria oxygen desaturation \textless{}94\% while breathing
ambient air, respiratory rate \textgreater{} 22/min, heart rate
\textgreater{}125, decrease of oxygen saturation \textless{}94\% after
one minute walking test altered consciousness; The following criteria
were taken into account before discharge: no requirement of oxygen
supplementation, no evidence of desaturation while walking without
oxygen supplementation, ability of oral medication intake and
appropriate condition for isolation at home.

Moreover, EudraVigilance (EV), an European public vigilance database
including spontaneous reports of adverse events with medications, was
consulted and cases related to the use of HCQ in COVID-19 extracted.

\section*{3 Results}

{\label{results}}

The popPK model by Carmichael et al was successfully replicated as shown
in Figure 1. Results of external validation were satisfactory: the model
by Carmichael was able to acceptably predict previously published data
from external sources in CLE and in COVID-19 patients (see Figure 1).

This model was therefore used to simulated different dosing scenarios
including in national guidelines and ongoing/planned clinical study
protocols in Belgium. Results of the different simulations are shown in
Figure 2.

Selected clinical response markers including time to discharge from the
hospital, survival, C-reactive protein, blood oxygen levels and absolute
lymphocyte counts were collected from a cohort of 172 patients treated
with HCQ monotherapy at Saint Pierre Hospital in Brussels. Summary
descriptive statistics are included in Table2 together with patient age
and relevant comorbidities. Most of these patients received 5 day
treatment scheme with 400mg BID on day 1 followed by 200mg BID from days
2 to 5. Three patients received 6 or 7 days of treatment. As regards
impact of HCQ treatment on CRP, blood oxygen levels and absolute
lymphocyte counts, while all the patients received the same dosing
regimen for HCQ, variable responses were observed for each of the
biomarkers: a subset had their levels decreasing (i.e. negative slope)
after start of treatment with HCQ, while others had an increase of the
levels (positive slope). Moreover, time delay from apparition of
symptoms to implementation of treatment was a significant predictor of
admission to the intensive care unit (p\textless{}0.01, AUC ROC curve=
0.63) and death (p\textless{}0.01, AUC ROC curve= 0.61) based on
logistic regression analysis, while patient age, hypertension,
cardiomyopathies, cancer and obesity were significantly correlated with
patient death (p\textless{}0.05).

As regards clinical safety, case reports were found from literature data
and public vigilance database (EV). The dosing regimens information were
extracted when provided in the report and in most of the cases 800 mg
daily or lower doses were reported for day 1 and 400-600 mg daily were
reported for the following days. The time delay from start of treatment
to onset of ADR was very variable (from 1 to several days). Cardiac
disorders and especially QT prolongation were the most frequently
reported adverse drug reaction. At least one of the following additional
risk factors were reported in patients experiencing adverse drug
reactions: concomitant medication with at least one drug known to carry
QT prolonging drugs or cardiac toxicity, relevant comorbidities (e.g.
renal impairment), cardiovascular disease, cardiomyopathies and
hypokalaemia (see Table 2).

\section*{4 Discussion}

{\label{discussion}}

HCQ is approved and used worldwide for treatment of malaria, and RA (SLE
and CLE). The approved doses are higher in magnitude and of shorter
duration in malaria (1200mg on day 1 followed by 400mg daily over 10
days ) as compared to RA (loading dose of 400-800mg BID followed by
maintenance dose of 200mg BID chronically with sometimes therapeutic
drug monitoring targeting trough plasma concentrations of 0.6-1mg/L
{[}4{]}). Several PK models are available in the literature for HCQ in
these indications. {[}1, 2, 9{]} Our choice to use the PK model by
Carmichael et al was motivated by the fact that it was developed to
describe a relatively large cohort of blood concentrations (known to be
less variable). The estimated apparent clearance values were quite
consistent across previously published popPK models: 10-11L/h when whole
blood concentrations were analysed {[}3,4{]} vs 51-68L/h when plasma
concentrations were analysed {[}13-15{]}. The predictive performances of
Caramichael model were confirmed on external data including data in CLE
patients and in COVID-19 patients with overall satisfactory fitting of
digitized concentrations. It is therefore judged adequate be used to
explore the differential/relative impact of alternative dosing regimens
in COVID-19 patients in the absence of a refined popPK model developed
using COVID-19 patient data. It should however be noted that this model
still carries an high unexplained variability component on the volume of
distribution and clearance parameters: there is therefore a need for
refinement of this model and better characterisation of PK in COVID-19
patients, including by adequate description of covariate effects.
Potential covariates include bodyweight, CYP2D6 modulators and
underlying renal impairment.

In the absence of a high loading dose, the results of the dosing
simulations scenarios show that the drug progressively accumulates over
the dosing periods of 5 or 10 days: safety monitoring can therefore be
needed all the dosing time along and even after. This is confirmed by
case reports of patients experiencing adverse drug reactions such as QT
prolongations even after drug withdrawal. The appropriate
characterization of the loading and maintenance doses needed is
therefore important not only for drug efficacy but also for drug safety.
The use of high loading doses need to be justified in view of the hazard
for serious adverse events. There is still uncertainty on the
target/relevant systemic concentrations for drug efficacy and safety.
This is an important gap to be filled in the current situation because
systemic concentrations are more accessible for monitoring than could be
lung concentrations. There is an unmet need for adequately conducted
clinical PK and exposure-response studies.

Yao and al {[}5{]} have shown in their recent publication that in vitro
EC50 for prophylactic and treatment antiviral effects on SARS COV2 were
0.72 and 5.74 \selectlanguage{greek}µ\selectlanguage{english}M, respectively. Based on a PBPK modelling approach they
have proposed dosing regimens that allow reaching empirically determined
ratios between free lung concentrations and the \emph{in vitro} EC50.
However, in addition to the fact that this model was not validated using
clinical data in COVID-19 patients, the recommended doses should still
be cautiously considered because the relevant target ratios between lung
or systemic concentrations and in vitro active levels are still to be
established as well as the ranges for effective whole blood and plasma
total concentrations. A more recent paper {[}12{]} was published in this
sense using a model-based approach and PK/PD modelling of viral load and
QT prolongation. However, it should be noted that this was a
retrospective analysis of either aggregated or limited previously
published data generated in different settings and for different
purposes. Several unverified assumptions were therefore needed for the
PK/viral load and the PK/QT modelling. Of note, the assumed/modelled QT
prolonging effects were those of choloroquine and not
hydroxychloroquine. Moreover, The overall unexplained variability was
very high and covariate modelling was not implemented. Research is still
needed to determine target HCQ level for in vivo (human) antiviral
effect in COVID-19 and the link with clinically relevant outcome such as
patient cure and survival for the different disease stages. Given the
known multiphasic features of the COVID-19 disease and the importance of
the inflammatory component of the disease, it is still unclear how
relevant are viral load clearance by antiviral drugs for the patient
clinical outcome in early vs later stages of the disease.

While it is not possible to identify the optimal dose in the absence of
properly conducted dose-exposure-response analyses using relevant data
in the target indication, the currently available clinical efficacy and
safety data in different doses used in COVID-19 patients can already
provide some useful information on the dose requirement when interpreted
in link with the related PK information. High rates of positive clinical
outcomes have been reported with doses of 600mg to 800mg daily on day 1
followed by 400 to 600mg daily for a total treatment duration of 5 to 10
days {[}6-10{]}, also confirmed in the cohort of 172 patients treated at
Saint Pierre hospital (see Table 2 and Figure 3). While these studies
were all either single arm (no placebo arm), uncontrolled or of limited
size, and therefore precluding the robust identification of the actual
drug effect size, the important learning from these data is that higher
doses might not needed for an important proportion of the patients. The
determinants of positive patient outcomes are still to be identified,
and HCQ dose optimization can certainly be one of them. Additionally, as
extensively discussed in the recent literature, disease stage, patient
age, and comorbidities might also play key roles{[}17-19{]}

As regards safety, the overall safety profile seems quite good when the
drug is given at dose of 400mg - 800 mg on day 1 followed by 400 -- 600
mg daily during 5 to 10 days, under close clinical monitoring. Available
concerning cases reported in EV or in the literature are consistent with
the known safety concerns with HCQ which are potentiated by either PK
overexposure due to pharmacokinetic drug-drug interactions and/or renal
impairment or PD drug-drug interactions due to additive toxicities with
co-medications. Aggravation of the toxicity due to comorbidities or
underlying renal or liver diseases related to COVID-19 pathophysiology
cannot be excluded either {[}17-19{]} (also see Table 3). It is
therefore essential that patient treated with HCQ are closely monitored
for these risk factors, and that appropriate risk minimisation measures
are implemented as needed.

It should however be noted that the clinical safety data from EV should
be cautiously interpreted due to the potential bias related to
spontaneous underreporting.

\section*{5 Conclusion}

{\label{conclusion}}

We have successfully used a model based approach to explore the relative
impact of alternative dosing regimens proposed in different dosing
protocols for HCQ.

It was clear from our results that there is an unmet need for adequate
characterization of target PK exposures in COVID-19 patients to inform
the dosing optimization. Literature data and clinical data from a
Belgian hospital confirm the variability in clinical responses when same
fixed doses are given to all patients. Some confounding factors were
identified that should be taken into account for dose recommendation.
For 80\% of patients in Saint Pierre cohort, doses higher than 600-800mg
daily on day 1 followed by 400-600mg daily on following days might not
be needed for positive outcome. Very limited ADRs have been reported so
far for this dosing regimen, moreover they were most often confounded by
co-medications, comorbidities or underlying disease effects.

Acknowledgements

\textbf{Author contribution}

Pauline Th\selectlanguage{ngerman}émans: performed the analysis and wrote the manuscript.
Nicolas Dauby, Loïc Schrooyen, Faustine Lebout, Marc Delforge, Rakan
Nasreddinne, Agnès Libois, Marie-Christine Payen, Déborah Konopnicki:
treated the patients, collected the data and wrote the manuscript.
Francoise Wuillaume, Cecile Lescrainie, Veerle Verlinden, Jean-Michel
Dogné, Jamila Hamdani~: collected the data and wrote the mauscript.
Flora T. Musuamba designed the study, performed the analysis and wrote
the manuscript.

\section*{References}

{\label{references}}

\begin{enumerate}
\tightlist
\item
  https://coronavirus.jhu.edu/map.html
\item
  Lim HS, Im JS, Cho JY, et al. Pharmacokinetics of hydroxychloroquine
  and its clinical implications in chemoprophylaxis against malaria
  caused by Plasmodium vivax. Antimicrob Agents Chemother. 2009
  Apr;53(4):1468-75. doi: 10.1128/AAC.00339-08. Epub 2009 Feb 2. PubMed
  PMID: 19188392; PubMed Central PMCID: PMC2663072.
\item
  Carmichael SJ, Charles B, Tett SE. Population Pharmacokinetics of
  Hydroxychloroquine in Patients with Rheumatoid Arthritis. Ther Drug
  Monit. 2003 Dec;25(6):671-81. PubMed PMID: 14639053.
\item
  Morita S, Takahashi T, Yoshida Y, Yokota N. Population
  pharmacokinetics of hydroxychloroquine in Japanese patients with
  cutaneous or systemic lupus erythematosus. Ther Drug Monit.
  2016;38:259--67.
\item
  Yao X, Ye F, Zhang M, Cui C, Huang B, Niu P, et al. In Vitro Antiviral
  Activity and Projection of Optimized Dosing Design of
  Hydroxychloroquine for the Treatment of Severe Acute Respiratory
  Syndrome Main point\selectlanguage{english} : Hydroxychloroquine was found to be more potent
  than chloroquine at inhibiting SARS-CoV-2 in vit. Clin Infect Dis.
  2020;2:1--25.
\item
  Gautret al. Hydroxychloroquine and azithromycin as a treatment of
  COVID-19: results of an open-label non-randomized clinical trial.
  International Journal of Antimicrob Agents -- In Press 17 March 2020.
  -- DOI: 10.1016/j.ijantimicag.2020.105949
\item
  Gautret P, Lagier JP, Parola P et al. Clinical and microbiological
  effect of a combination of hydroxychloroquine and azithromycin in 80
  COVID-19 patients with at least a six-day follow up: an observational
  study. (2020)
\item
  Chen J, Liu D, Lui L, et al. A pilot study of hydroxychloroquine in
  treatment of patients with common coronavirus disease-19 COVID-19).
  Journal of Zhejiang University 2020; 03-03
\item
  Zhaowei Chen, Jijia Hu, Zongwei Zhang, et al. Efficacy of
  hydroxychloroquine in patients with COVID-19: results of a randomized
  clinical trial. medRxiv 2020.03.22.20040758; doi:
  https://doi.org/10.1101/2020.03.22.20040758)http://kns.cnki.net/kcms/detail/33.1248.R.20200309.1507.006.html
\item
  Molina JM, Delaugerre C, Goff JL, Mela-Lima B, Ponscarme D, Goldwirt
  L, de Castro N, No Evidence of Rapid Antiviral Clearance or Clinical
  Benefit with the Combination of Hydroxychloroquine and Azithromycin in
  Patients with Severe COVID-19 Infection, Medecine et Maladies
  Infectieuses (2020), doi: https://doi.org/10.1016/j.medmal.2020.03.006
\item
  https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov/solidarity-clinical-trial-for-covid-19-treatments
\item
  Garcia-Cremades M, Solans BP, Hughes E, et al. Optimizing
  hydroxychloroquine dosing for patients with COVID-19: An integrative.
  Clin Pharmacol Ther 2020 (in press)
\item
  Lim et al. Pharmacokinetics of Hydroxychloroquine and Its Clinical
  Implications in hemoprophylaxis against Malaria Caused by Plasmodium
  vivax. Antimicrob Agents Chemother. Apr 2009; 53 (4):
  1468-1475.Abdulaziz N, Shah AR, McCune WJ. Hydroxychloroquine:
  balancing the need to maintain therapeutic levels with ocular safety:
  an update. Curr Opin Rheumatol. 2018 May;30(3):249-255. doi:
  10.1097/BOR.0000000000000500. Review. PubMed PMID: 29517495.
\item
  Tett SE et al. Bioavailability of hydroxychloroquine tablets in
  healthy volunteers. Br J Clin Pharmacol. 1989 Jun;27(6):771-9.
\item
  Balevic SJ et al. Pharmacokinetics of Hydroxychloroquine in
  Pregnancies with Rheumatic Diseases. Clin Pharmacokinet. 2019
  Apr;58(4):525-533.
\item
  Blanchet B, Moez J, Allard M, le guern vet al. Hydroxychloroquine
  Levels in Patients with Systemic Lupus Erythematosus: Comparison of
  Whole Blood and Serum Levels {[}abstract{]}. Arthritis Rheumatol.
  2019; 71 (suppl 10).
  https://acrabstracts.org/abstract/hydroxychloroquine-levels-in-patients-with-systemic-lupus-erythematosus-comparison-of-whole-blood-and-serum-levels/.
  Accessed April 20, 2020.
\item
  Wang F-S, Zhang C. What to do next to control the 2019-nCoV
  epidemic;\emph{Lancet} 2020; \textbf{395:} 391--93
\item
  Zheng, Y., Ma, Y., Zhang, J. et al. COVID-19 and the cardiovascular
  system. Nat Rev Cardiol (2020).
  https://doi.org/10.1038/s41569-020-0360-5
\item
  Wu C-I, Postema PG, Arbelo E, Behr ER, Bezzina CR, Napolitano C,
  Robyns T, Probst V, Schulze-Bahr E, Remme CA, Wilde AAM, SARS-CoV-2,
  COVID-19 and inherited arrhythmia syndromes, Heart Rhythm (2020), doi:
  https://doi.org/10.1016/j.hrthm.2020.03.024.
\end{enumerate}

\section*{Tables:}

{\label{tables}}

\textbf{Table 1. Hydroxychloroquine sulfate dosing regimens assessed by
Monte Carlo simulations}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Dosing regimen & Loading dose (D1) & Total loading dose & Maintenance
dose & Total daily dose & Duration of treatment\tabularnewline
\midrule
\endhead
scenario 0(a) & 400 mg BID & 800 mg & 200 mg BID & 400 mg & 5
days\tabularnewline
& 400 mg BID & 800 mg & 200 mg BID & 400 mg & 10 days\tabularnewline
Alternative scenario 0 & / & / & 200 mg TID & 600 mg & 10
days\tabularnewline
Alternative scenario 1 (b) & 600 mg BID & 1200 mg & 200 mg BID & 400 mg
& 5 days\tabularnewline
& 600 mg BID & 1200 mg & 200 mg BID & 400 mg & 10 days\tabularnewline
Alternative scenario 2 (c) & 200 mg TID & 600 mg & 200 mg BID & 400 mg &
5 days\tabularnewline
& 200 mg TID & 600 mg & 200 mg BID & 400 mg & 10 days\tabularnewline
Alternative scenario 3 (d) & / & / & 200 mg BID & 400 mg & 5
days\tabularnewline
& / & / & 200 mg BID & 400 mg & 10 days\tabularnewline
Alternative scenario 4 (e) & 400 mg BID & 800 mg & 400 mg daily & 400 mg
& 5 days\tabularnewline
& 400 mg BID & 800 mg & 400 mg daily & 400 mg & 10 days\tabularnewline
Alternative scenario 5 (f) & 800 mg followed by 400 mg 6 hours later &
1200 mg & 200 mg BID & 400 mg & 5 days\tabularnewline
& 800 mg followed by 400 mg 6 hours later & 1200 mg & 200 mg BID & 400
mg & 10 days\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2 Summary characteristics of patients treated with HCQ
monotherapy at Saint Pierre hospital}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Parameter & Median{[}range {]}/Frequency & Total (N)\tabularnewline
\midrule
\endhead
Age (years) & 60 {[}26;96{]} & 172\tabularnewline
Gender (Males/females) & 101/71 & 172\tabularnewline
Time interval from start of symptoms to start of treatment (days) & 8
{[}1; 31{]} & 172\tabularnewline
Treatment duration (days) & 5 {[}3; 8{]} & 172\tabularnewline
Patients in intensive care unit (Yes/No) & 40 /131 & 172\tabularnewline
Time interval from start of treatment to discharge from the hospital
(days) & 6 {[}1;18{]} & 172\tabularnewline
Death (Yes/No) & 20/152 & 172\tabularnewline
Diabetes (Yes/No) & 55/117 & 172\tabularnewline
Hypertension (Yes/No) & 78/94 & 172\tabularnewline
Cardiomyopathies (Yes/No) & 34/138 & 172\tabularnewline
Obesity (Yes/No) & 49/122 & 172\tabularnewline
C-reactive protein (mg/L) & 146.1 {[}2.2; 547.6{]} & 317*\tabularnewline
Blood oxygen (\%) & 92 {[}23;100{]} & 152*\tabularnewline
Absolute lymphocytes (cells/nL) & 1.03 {[}0.16; 8.55{]} &
286*\tabularnewline
\bottomrule
\end{longtable}

*: some of the patients had more than one value measured

\textbf{Table 3 Summary of reported cases of adverse drug reactions
retrieved in EV, for HCQ indicated for COVID19, as of 06.04.2020}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
System Organ Classes \textbf{/Preferred term}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Number of patients\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Dose of HCQ sulfate\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Confounders\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Gastrointestinal disorders\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
400 mg/day (when stated)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Concomitant lopinavir-ritonavir (3 cases) or amoxicillin-clavulanate (1
case)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Skin disorders\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
200 mg/day\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Concomitant amoxicillin-clavulanate (in one case)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Investigations/ \textbf{ECG QT prolonged}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
28\textbf{/24}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
200 -- 800 mg/day One case reported the dose of 600 mg 2 times a day
(one day prior QT prolongation)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Concomitant QT prolonging drugs (azithromycin, levofloxacin,
escitalopram), hypokalaemia, renal failure, pre-existing drug-induced QT
prolongation\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Eye disorders\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
600 mg/day\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Cataract operation 3 weeks prior (1 case)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

*two cases with sudden death, one case of fatal cardiac arrest, one case
of fatal QT prolongation. Patient ages varied between 62 and 88 years
old. Three patients had serious co-morbidities. The fourth patient for
which no co-morbidity was reported had received co-suspected
azithromycin, levofloxacin and lopinavir-ritonavir.

\section*{Figure legends:}

{\label{figure-legends}}

\textbf{Figure 1.} Model validation. \textbf{(a)} Prediction of
Carmichael et al. PK data (200 mg daily). Blue line: median. Black
lines: 2.5 and 97.5 percentiles (95 \% prediction interval). Red starts:
observed (blood) concentrations in RA patients digitised from Carmichael
et al. publication {[}3{]}. \textbf{(b-e)} Prediction of Morita et al.
PK data for different dosing regimens: (b) 200 mg daily (c) 200 or 400
mg every other day, when the last dosing just before blood sampling is
200 mg (d) 200 or 400 mg every other day, when the last dosing just
before blood sampling is 400 mg (e) 400 mg daily. Blue line: medians.
Black lines: 2.5 and 97.5 percentiles (95 \% prediction intervals). Red
starts: observed (blood) concentrations in CLE patients digitised from
Morita et al. publication {[}4{]} \textbf{(f)} Prediction of Gautret et
al. PK data (200 mg TID for 10 days). Blue line: median. Black lines:
2.5 and 97.5 percentiles (95 \% prediction interval). Stars: observed
corrected serum concentrations in COVID-19 patients from Gautret et al.
publication {[}6{]} (blue/black refers to PCR-negative patients on day
6, red refers to PCR-positive patients on day 6, black refers to
patients with azithromycin added to HCQ treatment).

\textbf{Figure 2.} Simulated blood PK profiles for different dosing
regimens: \textbf{(a)} 400 mg BID on day 1 followed by 200 mg BID
(\textbf{Belgian protocol} ), \textbf{(b)} 600 mg BID on day 1 followed
by 200 mg BID, \textbf{(c)} 200 mg TID on day 1 followed by 200 mg
BID,\textbf{(d)} no loading dose, 200 mg BID, \textbf{(e)} 400 mg BID on
day 1 followed by 400 mg daily, \textbf{(f)} 800 mg and 400 mg 6 hours
later on day 1, followed by 200 mg BID. Blue lines: medians. Black
lines: 5\textsuperscript{th} and 95\textsuperscript{th} percentiles (90
\% prediction intervals). Solid lines: treatment for 10 days. Dotted
lines: treatment for 5 days.

\textbf{Figure 3.} Histogram of times to discharge for patients of Saint
Pierre hospital.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figures-1-2-BJCP/Figures-1-2-BJCP}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figue-3/Figue-3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

