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\begin{document}

\title{Bedaquiline exposure in pregnancy and breastfeeding in women with
rifampicin-resistant tuberculosis}



\author[1]{Richard Court}%
\author[1]{Kamunkhwala Gausi}%
\author[1]{Buyisile  Mkhize}%
\author[1]{Lubbe  Wiesner}%
\author[2]{Catriona Waitt}%
\author[1]{Helen McIlleron}%
\author[1]{Gary Maartens}%
\author[1]{Paolo Denti}%
\author[3]{Marian Loveday}%
\affil[1]{University of Cape Town}%
\affil[2]{University of Liverpool Institute of Translational Medicine}%
\affil[3]{Medical Research Council of South Africa}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Aim We aimed to explore the effect of pregnancy on bedaquiline
pharmacokinetics and describe bedaquiline exposure in the human milk of
mothers treated for rifampicin-resistant TB, where there is no human
data available. Methods We performed a longitudinal pharmacokinetic
study in pregnant women treated for rifampicin-resistant TB to explore
the effect of pregnancy on bedaquiline exposure. Pharmacokinetic
sampling was performed at four time-points over six hours in the third
trimester, and again at approximately six weeks postpartum. We obtained
serial human milk samples from breastfeeding mothers, and a single
plasma sample taken from breastfed and non-breastfed infants to assess
bedaquiline exposure. We used liquid chromatography-tandem mass
spectrometry to perform the human milk and plasma bedaquiline assays,
and population pharmacokinetic modelling to interpret the bedaquiline
concentrations. Results We recruited 13 women, six of whom completed the
ante- and post-partum PK sampling. All participants were HIV-positive on
antiretroviral therapy. We observed lower ante- and post-partum
bedaquiline exposures than reported in non-pregnant controls.
Bedaquiline concentrations in human milk were higher than maternal
plasma (milk to maternal plasma ratio: 24:1). A single random plasma
bedaquiline and M2 concentration was available in four infants (median
age: 6.5 weeks): concentrations in the one breastfed infant were similar
to maternal plasma concentrations; concentrations in the three
non-breastfed infants were detectable but lower than maternal plasma
concentrations. Conclusion We report low exposure of bedaquiline in
pregnant women treated for rifampicin-resistant TB. Bedaquiline
significantly accumulates in human milk; breastfed infants receive mg/kg
doses of bedaquiline equivalent to maternal doses.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Acquisition of data quantifying the exposure of second-line TB drugs in
pregnant woman treated for rifampicin-resistant TB (RR-TB) is a
priority. Until recently, pregnant and breastfeeding women have
typically been excluded from clinical trials of new drugs, including TB
treatment. (1) The World Health Organization (WHO) currently recommends
individualised treatment regimens with drugs with a preferred safety
profile for pregnant women with RR-TB, (2) although there are limited
human data guiding these recommendations.

Bedaquiline is a group A drug, recommended for inclusion in all RR-TB
treatment regimens, and can be used in pregnant women, although safety
data is lacking. The design of pharmacokinetic studies to explore the
effect of pregnancy on long half-life drugs like bedaquiline is
challenging, as cumulative drug concentrations could mask
pregnancy-related effects on drug exposure. Physiological changes in
pregnancy result in decreased concentrations of many drugs, particularly
in the third trimester. Pharmacokinetics in pregnancy may be complex:
one of the reasons for reduced drug concentrations in pregnancy is the
reduction in plasma concentrations of the two key drug-binding proteins:
albumin and \selectlanguage{greek}α\selectlanguage{english}1-acid glycoprotein.~(3) Reduction in these binding protein
concentrations reduces the total (bound and unbound) concentrations of
drugs, but the unbound fraction typically increases, resulting in
unbound drug concentrations that are similar to non-pregnant women. As
only the unbound drug is pharmacologically active, recommendations to
increase the dose of drugs in pregnancy to approximate total
concentrations in non-pregnant women could therefore increase the risk
of toxicity. However, pregnancy also increases several drug clearance
mechanisms, which could reduce unbound drug concentrations. Although
measurement of unbound bedaquiline concentrations is preferable to
rationally optimize dosing in pregnancy (bedaquiline is
\textgreater{}99\% protein-bound) (4), an understanding of the effect of
pregnancy on total bedaquiline concentrations would provide a
much-needed foundation to understand the effect of pregnancy on the
unbound fraction.

Data on the secretion of key drugs for RR-TB into human milk are scarce.
The studies describing RR-TB drug exposure in human milk are small with
little or no infant plasma pharmacokinetic data available - the study
designs are also unclear or unstated. Linezolid (5), levofloxacin (6)
and cycloserine (7) penetrate poorly into human milk and exposure to
breastfed infants is therefore likely to be low. Clofazimine, in
contrast, demonstrates effective human milk penetration with skin
discoloration observed in the infants of breastfeeding mothers treated
with clofazimine for leprosy;\textsuperscript{.}(8,9) clofazimine
exposure in human milk in the context of mothers treated for TB is
unfortunately lacking. Animal studies have shown that bedaquiline is
concentrated in rat milk with concentrations 6- to 12- fold higher than
the concentration in maternal plasma (10), but there are currently no
human data available. Information on clinically relevant infant exposure
to RR-TB drugs through breastfeeding with mother-infant pairs has not
been done, and is an important knowledge gap.

An international consensus panel on the inclusion of pregnant and
postpartum women in TB drug trials, convened by the NIH, identified the
safety, tolerability and pharmacokinetics of novel agents and regimens
for treatment of RR-TB as research priorities~(11). It is ethically
imperative to study drug dosing and safety in populations where drugs
are used - this has not been done satisfactorily for RR-TB. (12) We
therefore conducted an observational study of bedaquiline exposure in
pregnant and breastfeeding women with RR-TB.

\textbf{Method}

Study design

We performed a longitudinal pharmacokinetic study in pregnant women [?] 18
years of age treated for RR-TB, and their infants, at King Dinuzulu
Hospital in Durban, Kwazulu-Natal - nested within a cohort, which has
been previously described. (13) KDH is a specialist provincial RR-TB
hospital where, until recently, all pregnant women with RR-TB were
referred for care. With some individual regimen variability, all
participants were treated with a minimum of five drugs including
bedaquiline. Other drugs included: pyrazinamide, isoniazid, clofazimine,
linezolid, moxifloxacin, and less commonly: ethambutol, terizidone,
levofloxacin, ethionamide and para-aminosalycylic acid. We performed
pharmacokinetic sampling pre-dose, and two, four and six hours post-dose
in the third trimester of pregnancy ([?]28 weeks), and at the six week
postpartum visit. Dosing on both sampling days was observed after a
standard breakfast; the tablets/capsules were ingested with 250 mL of
water. Considering bedaquiline is dosed three times a week (after the
two week loading dose), it was not always logistically possible to
schedule pharmacokinetic sampling on a day when bedaquiline was
administered. We therefore recorded the last date and time when
bedaquiline was dosed to interpret the exposures with our modelling. All
concurrent medications, including antiretroviral therapy were recorded.
If available, human milk samples were taken from breastfeeding mothers
by manual expression at the same timepoints that blood was drawn at the
postpartum visit (i.e. pre-dose, and two, four and six hours post-dose);
samples were frozen within 30 minutes of sampling at minus 80\selectlanguage{ngerman}° C. To
evaluate infant drug exposure, a single random plasma sample was taken
from infants at the postpartum visit. If applicable, the time of the
most recent breastfeeding prior to the infant blood draw was recorded.

Pharmacokinetics

Plasma and human milk samples were stored at minus 80°C and transported
to the University of Cape Town, Division of Clinical Pharmacology
laboratory where total plasma~and human milk bedaquiline assays were
performed using liquid chromatography with tandem mass spectrometry. The
plasma assay for total bedaquiline has previously been described. (14)
Bedaquiline and its M2 metabolite in human milk were analysed with a
validated assay developed at the Division of Clinical Pharmacology
laboratory; the standards and quality checks were performed using blank
donated human milk. The extraction procedure consisted of protein
precipitation and solid phase extraction, followed by gradient liquid
chromatography on an Agilent Poroshell 120 SB-C18 (2.1 mm x 50 mm, 2.7
\selectlanguage{greek}μ\selectlanguage{english}m) analytical column with tandem mass spectrometry detection. An AB
Sciex API 3000 mass spectrometer at unit resolution in the multiple
reaction monitoring mode was used to monitor the transitions of the
protonated precursor ions m/z 555.1, m/z 561.1, m/z 541.1, and m/z 545.1
to the product ions m/z 58.2, m/z 64.1, m/z 480.3, and m/z 480.4 for
bedaquiline, TMC207-d6, M2, and M2-d3C13, respectively. Electro Spray
Ionisation was used for ion production. The calibration curves fitted
quadratic (weighted by 1/x) regressions based on peak area ratios over
the ranges 0.0780 -- 5.00 \selectlanguage{greek}µ\selectlanguage{english}g/mL for bedaquiline and 0.0312 -- 2.00 \selectlanguage{greek}µ\selectlanguage{english}g/mL
for M2. The combined accuracy (\%Nom) and precision (\%CV) statistics of
the lower limit of quantification, low, medium, and high quality
controls of bedaquiline and M2 during intra- and inter validations were
between 96.7\% and 106.5\%, and 3.4\% and 7.5\%, respectively.

Modelling

Bedaquiline concentrations were interpreted using population
pharmacokinetic modelling in NONMEM version 7.4.5 (15).
Perl-speaks-NONMEM version 5.2.6, Pirana version 3.0, and R with the
package xpose4 were used to facilitate the model development process,
data manipulation, and generation of model diagnostics (16). As a
starting point, we used a published population pharmacokinetic model of
bedaquiline in non-pregnant adults with HIV and drug-resistant
tuberculosis (14). Briefly, the published model consists of three
disposition compartments for bedaquiline and one disposition compartment
for M2. There was a correlation between bedaquiline and M2
between-subject-variability on clearance, and residual variabilities.
The effect of body weight on all disposition parameters was included
using allometric scaling, and albumin also affects the drug disposition
parameters. The co-administration of ritonavir-boosted lopinavir reduced
bedaquiline and M2 clearance by 65\% and 42\%, respectively. Molar
concentrations were used during model development to account for mass
balance between bedaquiline and its metabolite M2. Participant albumin
information were not captured in the current study, therefore we imputed
a reported albumin concentration from a previously study in South Africa
patients with RR-TB~(17).

When analysing the data, we first fit the original model as published,
without re-estimating any of the population parameters, but using the
study covariate, doses and dosing regimen information. This is similar
to using the current data as an ``external'' validation of the model,
i.e. assessing how the previous model predicts the current data based
solely on covariate information and assuming no effect of pregnancy
(which was not part of the origina model). Afterwards, we attempted to
use the data to re-estimate parameter values, using the general
principles of model development including drops in NONMEM objective
function value (OFV) for assessment of statistical significance and
inspection of diagnostic plots.

\subsection*{Calculation of infant bedaquiline intake with human
milk}

{\label{calculation-of-infant-bedaquiline-intake-with-human-milk}}

To estimate how much bedaquiline is ingested per day by a typical child
breastfed by a mother receiving bedaquiline, we assumed an average
infant milk ingestion of 0.15 L/kg/day~(18). The following equation was
used to calculate the infant dose (19):

\begin{equation}
D_{\text{infant}}=\ C_{m}\bullet\ V_{m}\nonumber \\
 \end{equation}

where \emph{Vm} is the volume of milk ingested by breastfeeding,
and\emph{C\textsubscript{m}} is the drug concentration in human milk.
This was calculated using the formula below:

\begin{equation}
C_{m}=M:P\ \bullet\ C_{\text{pss}}\nonumber \\
 \end{equation}

where \emph{C\textsubscript{pss}} is the average maternal plasma level
at a steady-state, and \emph{M:P} is the human milk-to-plasma ratio.

Ethics

Ethics approval for the study was granted by the South African Medical
Research Council Ethics Committee (EC017-6/2016), and the University of
Cape Town Human Research Ethics Committee (HREC: 666/2018). Informed
consent was taken from all participants in a language of their choice
(either English or isiXhosa).

\textbf{Results}

\subsection*{Study profile}

{\label{study-profile}}

Bedaquiline pharmacokinetic samples were available from 13 women in the
third trimester of pregnancy, at 30 (IQR: 25 - 37) weeks gestation, six
of whom returned for postpartum sampling at 7 (IQR: 6.5 -- 8) weeks.
Seventy-one plasma samples of bedaquiline parent and metabolite
concentration were available for analysis. Participant characteristics
are shown in Table 1. All participants were living with HIV were treated
with antiretroviral therapy~(ART), most commonly on nevirapine-based ART
(n=10, 83.3\%), but one woman received lopinavir/ritonavir. Serial human
milk samples at the same time-points that plasma was sampled, were
available in two breasfeeding participants. A single random plasma
bedaquiline concentration was available from four infants on the
postpartum PK sampling day, of whom one was breastfed. The serial
post-dose bedaquiline concentrations at each time point are shown in
Table 2.

\subsection*{Pharmacokinetic model}

{\label{pharmacokinetic-model}}

When we used the published model (14) to predict the expected exposures
in these patients (thus using the original population parameter
estimates and assuming no effect of pregnancy), the model overpredicted
both bedaquiline and M2 concentration on both antepartum and postpartum
visits, as presented in the visual predictive check (VPC)
in\textbf{Figure 1} . The VPC shows that the PK terminal elimination
phase of the participant not on lopinavir/ritonavir were approximately
50\% lower that the model prediction (for both the metabolite and
parent) as illustrated by the deviation of the 50\textsuperscript{th}
percentiles of the observations (red line) from the median of the model
predicted confidence interval (black line). If the pharmacokinetic
parameters in this study were in line with the previous report, we would
have expected to observe higher bedaquiline concentrations. Only the
data from the participant co-administered lopinavir/ritonavir, who had
higher bedaquiline concentrations due to a drug-interaction, was in line
with the model prediction.

When encountered several challenges when attempting to fit the original
model to the current data by re-estimating the parameter values. The
model structure is complex, with multiple disposition compartments, and
the current data did not reliably support the re-estimation of all
parameters - some of the parameter estimates obtained when attempting to
re-fit were unstable and/or implausible. In other words, while the model
could be adapted to fit the study data, this could be achieved in
multiple different ways, e.g. assuming a larger clearance or lower
bioavailability (both pre- and postpartum), but also a larger peripheral
volume of distribution. We experienced further complications when trying
to estimate a significant difference between the two pharmacokinetic
sampling visits, i.e. possibly due to pregnancy status. All the
scenarios were nearly equivalent in terms of goodness of fit, and there
was no meaningful difference in terms of statistical significance, thus
leaving the choice largely in the domain of speculation. Choosing a
different scenario (on which a difference is ascribed to) would imply a
different interpretation of the results, and if the different options
for the model were to be used to predict concentrations and suggest dose
adjustments they could come to very different conclusions. We also
attempted to use a frequentist prior approach (20) to try and stabilise
the parameter estimates, but the results became highly dependent on the
assumptions on the prior precision of each parameter, thus not solving
the problem. For this reason, we decided to simply use the model as
originally published and acknowledge that the concentrations we observed
are lower than expected, assuming that the pharmacokinetics are the same
as non-pregnant patients.

A graphical overview of the infant and human milk data is provided in
Figures 2 and 3, together with the plasma concentrations in the
respective mothers. The PK profiles for bedaquiline and M2 are shown:
maternal plasma concentrations ante- and postpartum, human milk and
infant concentrations. To calculate the human milk to maternal plasma
(M:P) ratio, we used matched milk and plasma samples captured more than
24 hours after the last bedaquiline dose. This was done because during
drug absorption the ratio was rapidly varying and the later samples
better reflect the steady-state equilibrium of bedaquiline. We observed
an average M:P ratio of 24:1 and 5.9:1 for bedaquiline and M2,
respectively. Therefore, assuming a maternal average steady-state
concentration of 0.3 mg/L, the infant bedaquiline dose would be 1.08
mg/kg/day. Similarly, assuming the M2 average steady-state concentration
is 0.1, the infant M2 dose would be 0.09 mg/kg/day. In comparison, a
70-kg individual administered the standard dose of 200 mg bedaquiline
three times a week would result in approximately 1.22 mg/kg/day dose of
bedaquiline.

Bedaquiline and M2 concentrations in the infant who was breastfed were
similar to maternal plasma concentrations, while for the three infants
who were not breastfed, bedaquiline and M2 concentrations were
detectable but lower than maternal plasma values~(see Figures 2 and 3).

\textbf{Discussion}

To our knowledge, this report is the first description of the exposure
of bedaquiline in pregnant women. We found bedaquiline and M2 exposure
in pregnant women to be approximately 50\% lower than expected in
non-pregnant patients (14). Although we were underpowered, we found no
significant difference between ante- and postpartum exposures.

There are several possible reasons for the low bedaquiline exposures we
observed in the third trimester. First, increased metabolism of
bedaquiline is a possible explanation - pregnancy is known to induce
CYP3A4, which is the major route of bedaquiline metabolism~(21). The
increase in CYP3A4 expression would lead to higher clearance and lower
bioavailability of bedaquiline, since it is present in both entero- and
hepatocytes. Second, pregnancy reduces plasma albumin concentrations, to
which bedaquiline is highly bound (22). The unbound fraction of
bedaquiline may therefore increase, subsequently increasing its
clearance and tissue distribution. In such a scenario, the total
(bound+unbound) concentrations of bedaquiline in plasma would decrease,
but this effect could be counter balanced by the large unbound fraction,
thus maintaining relatively unchanged unbound levels. However,
exploration of free bedaquiline exposure is required before a
recommendation for a dose adjustment can be made. Finally, changes in
body size (and possibly composition) may have affected bedaquiline
disposition, but it is unlikely that the increased weight in pregnancy
affected the exposure of bedaquiline as we used allometric scaling to
account for this in the model, and changes in body size alone are
therefore unlikely to explain the decreased bedaquiline concentrations
we observed.

Similarly, we observed lower-than-expected bedaquiline levels at the
postpartum visit. While it is generally accepted that pharmacokinetic
sampling approximately six weeks postpartum is a reasonable time-point
to allow the physiologic changes related to pregnancy to subside (23),
there are some limitations in using this timeline as a control when
exploring the effect of pregnancy on drugs with a long half-life such as
bedaquiline. Given that the terminal half-life of bedaquiline is more
than five months, (4) any change in pharmacokinetic parameters may only
become apparent on drug exposure after a considerable amount of time,
possibly months. Thus, even if most of the pregnancy effects~(if any)
had reversed in the first weeks after delivery, there may not have been
sufficient time for the exposure of bedaquiline to reach a new
equilibrium before the scheduled postpartum pharmacokinetic visit. An
alternative explanation is that adherence could have decreased in the
postpartum period; a systematic review reported poor postpartum
adherence in patients on ART (24). Sub-therapeutic bedaquiline exposures
could affect clinical outcomes and increase the risk of selecting for
drug resistance.

We observed concerningly high concentrations of bedaquiline in the human
milk samples we analysed, markedly higher than the maternal bedaquiline
plasma concentrations, in keeping with the findings of an animal study.
(10) The breastfeeding infant had a plasma bedaquiline concentration
similar to maternal plasma (Figure 2), which could have implications for
infant safety. In a previous animal study, rat pups who were breastfed
to mothers treated with bedaquiline were reported to have low body
weight. (10) In contrast, therapeutic concentrations of bedaquiline in
infants (possible with long half-life drugs, which accumulate slowly,
such as bedaquiline) could potentially be protective in infants exposed
to RR-TB, obviating the need for TB preventive therapy. The three
infants who were not breastfed had sub-therapeutic bedaquiline
concentrations, likely from transplacental exposure, which could select
for drug resistance should the infants develop RR-TB.

The main factors determining the transfer of a drug into human milk are
its physicochemical characteristics (such as lipid solubility and degree
of ionisation at different pH conditions) and its plasma
pharmacokinetics~(25). Fat-soluble drugs like bedaquiline cross
lipid-protein cell membranes easily, hence transferring readily into
human milk (25). The ease, with which drug molecules cross cellular
membranes, depends on the drug's degree of ionisation, which may vary in
different pH conditions. Weak bases like bedaquiline (pKa = 8.9) (26)
tend to be slightly less ionised in plasma compared to milk. This means
that unionised plasma bedaquiline will transfer into human milk, were it
is more likely to be ionised, favouring milk accumulation of the drug
(27). Transfer of drugs into human milk may also be greater in drugs
with a low affinity for maternal plasma proteins, but bedaquiline is
highly protein-bound (\textgreater{}99.9\%).~(4) An additional factor is
molecular weight, as drugs with low weight (\textless{}200 Da) reach
human milk more easily, but the molecular weight of bedaquiline is
555.504 Da~(28). Drugs, which have a long plasma half-life and therefore
accumulate, such as bedaquiline, are prone to transfer into human milk
compared with molecules which are cleared rapidly. The high
concentration of bedaquiline in human milk suggests that the mammary
glands could be a clearing site for bedaquiline. Excretion could be
significant, since, on average, a baby consumes about 0.15 L/kg/day of
human milk (18). Moreover, bedaquiline metabolism in breast tissue
cannot be excluded, as there are contradictory reports on the expression
of CYP3A4 in human breast tissue. (29--31)

Our study has several limitations. First, we did not measure unbound
bedaquiline concentrations or albumin levels, so we are unable to
conclusively determine if the reasons for the low concentrations
observed are related to protein binding. Second, there was a high rate
of participant loss to follow up, which limited our sample size, as many
participants were unable for logistical reasons, to complete the
postpartum PK sampling day. Third, PK sampling was not always performed
on a day when bedaquiline was scheduled to be administered (dosing is
three times a week). Although this was accounted for in our modelling,
considering we did not use an adherence measure, the date and time of
the last bedaquiline dose was obtained via participant self-report,
which could be unreliable.

We report low exposures of bedaquiline in this series of pregnant women
treated for RR-TB. Future studies should analyse bound and unbound
bedaquiline concentrations with adherence measures to better understand
the effect of pregnancy on bedaquiline exposure, ~and assess whether a
different dosing recommendation for bedaquiline in pregnancy is
warranted. Bedaquiline appeared to significantly accumulate into human
milk, which could be an exposure risk for breastfeeding babies and
should therefore be investigated.

Acknowledgments

The authors would like to acknowledge the participants who volunteered
for the study, and Sindisiwe Hlangu for her assistance with study
implementation.

Conflicts of interest

The authors declare no conflicts of interest

Funding

The University of Cape Town Clinical PK Laboratory is supported in part
via the Adult Clinical Trial Group (ACTG), by the National Institute of
Allergy and Infectious Diseases (NIAID) of the National Institutes of
Health under award numbers UM1 AI068634, UM1 AI068636, and UM1 AI106701;
as well as the Infant Maternal Pediatric Adolescent AIDS Clinical Trials
Group (IMPAACT), funding provided by National Institute of Allergy and
Infectious Diseases (U01 AI068632), The Eunice Kennedy Shriver National
Institute of Child Health and Human Development, and National Institute
of Mental Health grant AI068632. The content is solely the
responsibility of the authors and does not necessarily represent the
official views of the sponsors.~Catriona Waitt is supported by a
Wellcome Clinical Research Career Development Fellowship 222075/Z/20/Z.

Data availability

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

\textbf{References}

1. Gupta A, Hughes MD, Garcia-Prats AJ, McIntire K, Hesseling AC.
Inclusion of key populations in clinical trials of new antituberculosis
treatments: Current barriers and recommendations for pregnant and
lactating women, children, and HIV-infected persons. PLoS Med
{[}Internet{]}. 2019;16(8):1--26. Available from:
http://dx.doi.org/10.1371/journal.pmed.1002882

2. WHO. WHO Consolidated Guidelines on Tuberculosis, Module 4: Treatment
- Drug-Resistant Tuberculosis Treatment {[}Internet{]}. 2021 {[}cited
2021 Oct 12{]}. Available from:
https://www.who.int/publications/i/item/9789240007048

3. Schalkwijk S, Greupink R, Burger D. Free dug concentrations in
pregnancy: Bound to measure unbound? Br J Clin Pharmacol.
2017;83(12):2595--8.

4. van Heeswijk RPG, Dannemann B, Hoetelmans RMW. Bedaquiline: a review
of human pharmacokinetics and drug-drug interactions. J Antimicrob
Chemother. 2014 Sep;69(9):2310--8.

5. Lim FH, Lovering AM, Currie A, Jenkins DR. Linezolid and lactation:
measurement of drug levels in breast milk and the nursing infant. J
Antimicrob Chemother. 2017;72(9):2677--8.

6. Cahill JB, Bailey EM, Chien S, Johnson GM. Levofloxacin secretion in
breast milk: A case report. Pharmacotherapy. 2005;25(1):116--8.

7. Tran J, Montakantikul P. The safety of antituberculosis medications
during breastfeeding. J Hum Lact. 1998;14(4):337--40.

8. Loveday M, Hlangu S, Furin J. Breastfeeding in women living with
tuberculosis. Int J Tuberc Lung Dis. 2020;24(9):880--91.

9. Ozturk Z, Tatliparmak A. Leprosy treatment during pregnancy and
breastfeeding: A case report and brief review of literature. Dermatol
Ther. 2017;30(1):2016--7.

10. FDA. Full prescribing information: SIRTURO {[}Internet{]}. {[}cited
2021 Oct 12{]}. Available from:
https://www.accessdata.fda.gov/drugsatfda\_docs/label/2012/204384s000lbl.pdf

11. Gupta A, Mathad JS, Abdel-Rahman SM, Albano JD, Botgros R, Brown V,
et al. Toward Earlier Inclusion of Pregnant and Postpartum Women in
Tuberculosis Drug Trials: Consensus Statements from an International
Expert Panel. Clin Infect Dis. 2015;62(6):761--9.

12. Gupta A, Hughes MD, Garcia-Prats AJ, McIntire K, Hesseling AC.
Inclusion of key populations in clinical trials of new antituberculosis
treatments: Current barriers and recommendations for pregnant and
lactating women, children, and HIV-infected persons. PLoS Med.
2019;16(8):1--26.

13. Loveday M, Hughes J, Sunkari B, Master I, Hlangu S, Reddy T, et al.
Maternal and Infant Outcomes among Pregnant Women Treated for
Multidrug/Rifampicin-Resistant Tuberculosis in South Africa. Clin Infect
Dis. 2021;72(7):1158--68.

14. Brill MJE, Svensson EM, Pandie M, Maartens G, Karlsson MO.
Confirming model-predicted pharmacokinetic interactions between
bedaquiline and lopinavir/ritonavir or nevirapine in patients with HIV
and drug-resistant tuberculosis. Int J Antimicrob Agents.
2017;49(2):212--7.

15. Boeckmann AJ, Beal SL, Sheiner LB. NONMEM User's Guide, Part V.
Introductory Guide. NONMEM Proj Gr. 2011;(April):48.

16. Keizer RJ, Karlsson MO, Hooker A. Modeling and Simulation Workbench
for NONMEM: Tutorial on Pirana, PsN, and Xpose. CPT Pharmacometrics Syst
Pharmacol. 2013 Jun;2(6):e50.

17. Ngwalero P, Brust JCM, van Beek SW, Wasserman S, Maartens G,
Meintjes G, et al. Relationship between Plasma and Intracellular
Concentrations of Bedaquiline and Its M2 Metabolite in South African
Patients with Rifampin-Resistant Tuberculosis. Antimicrob Agents
Chemother {[}Internet{]}. 2021 Oct 18;65(11):e0239920. Available from:
https://journals.asm.org/doi/10.1128/AAC.02399-20

18. Wilson JT. Determinants and Consequences of Drug Excretion in Breast
Milk. Drug Metab Rev. 1983 Jan;14(4):619--52.

19. Koren G. Maternal-Fetal Toxicology: A Clinician's Guide. Third edit.
Vol. 149, Archives of Pediatrics \& Adolescent Medicine. New York:
American Medical Association; 1995. 177--221 p.

20. Chan Kwong AHXP, Calvier EAM, Fabre D, Gattacceca F, Khier S. Prior
information for population pharmacokinetic and
pharmacokinetic/pharmacodynamic analysis: overview and guidance with a
focus on the NONMEM PRIOR subroutine. Journal of Pharmacokinetics and
Pharmacodynamics. 2020. p. 1--16.

21. Svensson EM, Aweeka F, Park J-G, Marzan F, Dooley KE, Karlsson MO.
Model-Based Estimates of the Effects of Efavirenz on Bedaquiline
Pharmacokinetics and Suggested Dose Adjustments for Patients Coinfected
with HIV and Tuberculosis. Antimicrob Agents Chemother. 2013
Jun;57(6):2780--7.

22. Feghali MN, Mattison DR. Clinical Therapeutics in Pregnancy. J
Biomed Biotechnol. 2011 Jul;2011:1--13.

23. Eke AC, Olagunju A, Momper J, Penazzato M, Abrams EJ, Best BM, et
al. Optimizing Pharmacology Studies in Pregnant and Lactating Women
Using Lessons From HIV: A Consensus Statement. Clin Pharmacol Ther.
2021;110(1):36--48.

24. Nachega JB, Uthman OA, Anderson J, Peltzer K, Wampold S, Cotton MF,
et al. Adherence to antiretroviral therapy during and after pregnancy in
low-income, middle-income, and high-income countries: A systematic
review and meta-analysis. Aids. 2012;26(16):2039--52.

25. Chaves RG, Lamounier JA. Uso de medicamentos durante a lacta\selectlanguage{ngerman}ção. J
Pediatr (Rio J). 2004 Nov;80(5):s189--98.

26. Feng X, Zhu W, Schurig-Briccio LA, Lindert S, Shoen C, Hitchings R,
et al. Antiinfectives targeting enzymes and the proton motive force.
Proc Natl Acad Sci. 2015 Dec;112(51):E7073--82.

27. Howard CR, Lawrence RA. Drugs and Breastfeeding. Clin Perinatol.
1999 Jun;26(2):447--78.

28. Svensson EM, Murray S, Karlsson MO, Dooley KE. Rifampicin and
rifapentine significantly reduce concentrations of bedaquiline, a new
anti-TB drug. J Antimicrob Chemother. 2014;70(4):1106--14.

29. Iscan M, Klaavuniemi T, Çoban T, Kapucuo\selectlanguage{english}V Glu N, Pelkonen O, Raunio
H. The expression of cytochrome P450 enzymes in human breast tumours and
normal breast tissue. Breast Cancer Res Treat. 2001;70:47--54.

30. Hellmold H, Rylander T, Magnusson M, Reihne\selectlanguage{english}r E, Warner M, Gustafsson
J-A. Characterization of Cytochrome P450 Enzymes in Human Breast Tissue
from Reduction Mammaplasties. J Clin Endocrinol Metab. 1998
Mar;83(3):886--95.

31. Huang Z, Fasco MJ, Figge HL, Keyomarsi K, Kaminsky LS. Expression of
cytochromes P450 in human breast tissue and tumors. Drug Metab Dispos.
1996;24(8).

Table 1: Characteristics of pregnant women treated for
rifampicin-resistant tuberculosis\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Median (range) & Antepartum (n=13) & Postpartum (n=6) & Infants (n=4) --
PK visit\tabularnewline
\midrule
\endhead
\textbf{Baseline characteristics} & & &\tabularnewline
Age (years) & 30 (23 - 48) & 30 (23 - 48) & 6.5 (6 - 8)
weeks\tabularnewline
Height (cm) & 160 (140 - 176) & 162 (152 - 163) & 53 (50 -
55)\tabularnewline
HIV status (Pos/neg) & (13/0) & (6/0) &\tabularnewline
TB
type(RR\textsuperscript{a}/MDR\textsuperscript{b}/Pre-XDR\textsuperscript{c}/XDR/missing)
& (6/3/2/1/1) & (2/2/0/1/1) &\tabularnewline
Previous TB (yes/no/missing) & (7/5/1) & (2/3/1) &\tabularnewline
CD4 (cells/mm3) & 311 (44-1008) & 545 (253-1008) &\tabularnewline
ART\textsuperscript{d}(NVP\textsuperscript{e}/LPV\textsuperscript{f}/DTG\textsuperscript{g})
& 10/1/2 & \textbf{5}/0/1 &\tabularnewline
\textbf{Characteristics on the Pharmacokinetic day} &
\textbf{Characteristics on the Pharmacokinetic day} &
\textbf{Characteristics on the Pharmacokinetic day} &
\textbf{Characteristics on the Pharmacokinetic day}\tabularnewline
Weight (Kg) & 61 (55 - 104) & 67(52 - 84) & 4.1 (2.6 -
7.1)\tabularnewline
Time since EFV\textsuperscript{h} switch (Days) & 27 (13 - 96) & 154 (81
- 201) &\tabularnewline
Gestational age/time after delivery (weeks) & 30 (25 - 37) & 7 (6.5 - 8)
&\tabularnewline
Inpatients/outpatients & 12/1 & 0/6 &\tabularnewline
\bottomrule
\end{longtable}

RR\textsuperscript{a}: rifampicin-resistant; MDR\textsuperscript{b}:
multidrug-resistant; XDR\textsuperscript{c}: extremely drug-resistant;
ART\textsuperscript{d}: antiretroviral therapy; NVP\textsuperscript{e}:
nevirapine; LPV: lopinavir\textsuperscript{f}; DTG:
Dolutegravir\textsuperscript{g}; EFV\textsuperscript{h}: efavirenz

Table 2: Median (IQR) bedaquiline and metabolite (M2) concentrations per
time point\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Time point\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Bedaquiline (n=13)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Bedaquiline (n=13)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Bedaquiline (n=13)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Bedaquiline (n=13)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Metabolite, M2 (n=6)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Metabolite, M2 (n=6)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Metabolite, M2 (n=6)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Metabolite, M2 (n=6)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Metabolite, M2 (n=6)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Antepartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Antepartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Postpartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Postpartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Antepartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Antepartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Antepartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Postpartum\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Postpartum\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Time\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
No.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Concentration (mg/L)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
No.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Concentration (mg/L)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Concentration (mg/L)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
No.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Concentration (mg/L)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
No.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Concentration (mg/L)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Predose\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.419 (0.184 -- 0.711)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.186 (0.161 -- 0.212)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.186 (0.161 -- 0.212)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.183 (0.0922 -- 0.218\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0584 (0.0512- 0.0656)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.621 (0.288 -- 1.12)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.237 (0.221 -- 0.252)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.237 (0.221 -- 0.252)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.160 (0.115 -- 0.231)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0630 (0.0527 -- 0.0732)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.05 (0.863 -- 1.32)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.06\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.06\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.144 (0.124 -- 0.204)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0467\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.69 (1.59 -- 2.24)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.14\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.14\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.181 (0.123 -- 0.206)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0547\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
24 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.308 (0.287 -- 0.458)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.3085 (0.298 -- 0.312)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.3085 (0.298 -- 0.312)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.168 (0.148 -- 0.458)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.128 (0.117-0.142)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
26 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.250 (0.245 -- 0.443)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.281 (0.269-0.286)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.281 (0.269-0.286)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.142 (0.132 -- 0.241)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.107 (0.105-0.112)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.228 (0.225 -- 0.391)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.275 (0.269-0.283)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.275 (0.269-0.283)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.145 (0.138 - 0.226)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.112 (0.109-0.119)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
30 hrs\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.205 (0.182- 0.329)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.284 (0.259-0.292)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.284 (0.259-0.292)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.133 (0.118 -- 0.205)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.111 (0.104-0.116)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

No.: Number of participants at each timepoint

Figure 1

Visual predictive check (VPC) of the bedaquiline and M2, the top panels
represent the parent, and the bottom panels represent the metabolite
bedaquiline concentrations. The first column displays antepartum
concentrations, while the last and middle columns shows postpartum
concentration and antepartum concentration in participants
co-administered lopinavir/ritonavir, respectively. Due to the small
sample size in each panel, we plotted the
50\textsuperscript{th}percentiles of the observations~(red line) - the
shaded areas represent the 95\% model-predicted confidence intervals,
and the black line is the median of the model predicted confidence
interval.

Figure 2:

Pharmacokinetic profiles of bedaquiline concentrations stratified by
participant ID. The red dots and purple crosses represent maternal
plasma concentrations ante- and postpartum, respectively. The green
triangles represent human milk; the blue squares represent infant plasma
concentrations.

Figure 3:

Pharmacokinetic profiles of M2 concentrations stratified by participant
ID. The red dots and purple crosses represent maternal plasma
concentrations ante- and postpartum, respectively. The green triangles
represent human milk; the blue squares represent infant plasma
concentrations.

\textbf{Hosted file}

\verb`Figure1.docx`  available at \url{https://authorea.com/users/446884/articles/546012-bedaquiline-exposure-in-pregnancy-and-breastfeeding-in-women-with-rifampicin-resistant-tuberculosis}

\textbf{Hosted file}

\verb`Figure2.docx`  available at \url{https://authorea.com/users/446884/articles/546012-bedaquiline-exposure-in-pregnancy-and-breastfeeding-in-women-with-rifampicin-resistant-tuberculosis}

\textbf{Hosted file}

\verb`Figure3.docx`  available at \url{https://authorea.com/users/446884/articles/546012-bedaquiline-exposure-in-pregnancy-and-breastfeeding-in-women-with-rifampicin-resistant-tuberculosis}

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

