key: cord-0939988-dliml3ps
authors: Hughes, Jim H.; Sweeney, Kevin; Ahadieh, Sima; Ouellet, Daniele
title: Predictions of Systemic, Intracellular, and Lung Concentrations of Azithromycin with Different Dosing Regimens used in COVID‐19 Clinical Trials
date: 2020-06-08
journal: CPT Pharmacometrics Syst Pharmacol
DOI: 10.1002/psp4.12537
sha: ed34005e6e33e16740612d13ee9e26b20cb789b4
doc_id: 939988
cord_uid: dliml3ps

Azithromycin, a broad‐spectrum macrolide antibiotic, is being investigated in patients with COVID‐19. A population pharmacokinetic model was implemented to predict lung, intracellular poly/mononuclear cell (PBM/PML), and alveolar macrophage (AM) concentrations using published data and compared against preclinical EC90 for SARS‐CoV‐2. The final model described the data reported in 8 publications adequately. Consistent with its known properties, concentrations were higher in AM and PBM/PML, followed by lung tissue, and lowest systemically. Simulated PBM/PML concentrations exceeded EC90 following the first dose and for approximately 14 days following 500 mg QD for 3 days or 500 mg QD for 1 day/250 mg QD on days 2‐5, 10 days following a single 1000 mg dose, and for more than 20 days with 500 mg QD for 10 days. AM concentrations exceeded the IC90 for more than 20 days for all regimens. These data will better inform optimization of dosing regimens for azithromycin clinical trials.

Azithromycin (AZ), a broad-spectrum macrolide antibiotic with a long half-life and extensive tissue distribution, is being investigated in multiple clinical trials in patients with coronavirus 2019 disease (clinicaltrials.gov). An early uncontrolled clinical study in a small number of patients showed that azithromycin combined with hydroxychloroquine contributed to the reduction in viral load in patients with COVID-19. 1 The study was expanded to a pilot study in a total of 80 mildly infected subjects who showed clinical improvement with administration of the combined medications. 2 Controlled clinical studies in a larger number of patients are required to understand the clinical effect of azithromycin in this patient population.

To support the planning and safe administration of azithromycin alone and in combination with other agents, the clinical pharmacology, activity against different viral agents, and safety of azithromycin were recently reviewed by Damle et al. 3 AZ is not approved for antiviral therapy including infections with SARS-CoV-2, the virus causing COVID-19.

However, several mechanisms have been postulated to support its activity against viral pathogens. 3 Azithromycin is a weak base ionized at cellular pH and thus accumulates in intracellular compartments, more specifically in lysosomes, and remains in these cells for an extended period of time. The accumulation of ionized azithromycin in these cells would increase pH, altering the acidic environment required for uncoating of enveloped viruses and potentially impairing viral replication. In a recent preclinical study, the in vitro EC50 and EC90 (50% and 90% effective concentration, respectively) of azithromycin against SARS-CoV-2 was 2.12 µM (~1600 ng/mL) and 8.65 µM (~6500 ng/mL), respectively, as determined in a viral assay model using VeroE6 cells. 4 To better understand its efficacy against bacterial infections in the lung, investigators have measured azithromycin in different tissues including epithelial lining fluid (ELF), lung tissue homogenate, lung alveolar macrophages (AM) and intracellularly in peripheral blood mononuclear cells (monocyte or lymphocyte (PBM/MNL)) or polymorphonuclear leukocyte (PML) ( Table 1) . [5] [6] [7] [8] [9] [10] [11] [12] [13] A population pharmacokinetic (PopPK) model describing tissue distribution of unbound drug in muscle and subcutis and in PML separately for azithromycin un-ionized in cytosol and ionized in lysosome compartments has been developed by Zheng et al 5 , based on data originally published in Matzneller et al 6 . This paper summarizes the extension of the population pharmacokinetic model to predict tissue distribution in the lung and AMs, as intracellular concentrations are most relevant to antiviral activity. The model can be utilized to assess the effectiveness of

This article is protected by copyright. All rights reserved different dosing regimens based on comparison of the predictions in tissues to the in vitro antiviral EC90 value. Assessed regimens included those approved by the United States Food and Drug Administration (FDA) for different mild to moderate bacterial infections including 500 mg as a single dose on Day 1 followed by 250 mg once daily (QD) on days 2 through 5; 500 mg once daily for 3 days; and 1000 or 2000 mg as a single dose in adults.

For the current analysis, model development and simulations were undertaken using the following steps which are further described below. First, the azithromycin specific PopPK model developed by Zheng et al. 5 was utilized as the initial base model. This model consisted of a three-compartment model with first order absorption and elimination, with an absorption lag describing the disposition of unbound azithromycin in plasma, and both fast and slow equilibrating tissues (Figure 1 ). The model also included two tissue compartments one each for muscle and subcutis, and another compartment for PMLs, each with an associated deep-tissue compartment. Distribution to tissue compartments was driven by unbound drug from plasma, while distribution to PML cytosol was driven by unbound un-ionized drug from plasma. Tissue and cell concentrations were scaled according to estimated distribution factors representing the ratio between tissue/cell concentrations and plasma concentrations.

This article is protected by copyright. All rights reserved with fu equal to 0.4984 at very low azithromycin concentration and up to 0.8 at a concentration of 300 ng/mL. A full description of the model can be found in the original publication. 5 Second, an evaluation of the typical plasma predictions and parameter estimates of the initial base model was conducted against other azithromycin PopPK models in the literature (Supplemental Table S1 ) 9, 12, [14] [15] [16] [17] and published summary PBM and PML data 6, 7, 9, 13 as defined in the section below (Data for External Validation). Changes to the initial three-compartment structure (systemic disposition) parameters were made based on comparison to other published azithromycin popPK models, after adjusting for the difference in free and total concentrations. Azithromycin PopPK models fitted to total plasma concentrations required the f u to be incorporated when drug was distributed to the muscle, subcutis and PML compartments of the base model (Equation 2):

where Atis=amount in tissue, Aplas=amount in plasma, Atis,deep=amount in deep compartment connected to tissue, kin=rate constant for uptake in tissue/PML, kout=rate constant for distribution out of tissue/PML, kon/koff=on and off rate constants in tissue/PML compartments.

In addition, the model was validated using a VPC against external individual plasma, PML, and PBM data. PML and PBM concentrations were assumed to be similar based on data from Sampson et al, 2014. 7 Third, once the improvement of plasma and PML/PBM predictions was completed, development then focused on the extension of the model to predict concentrations in lung tissue and AMs ( Figure 1 ). The lung compartment was parameterized assuming unit density (1 g/cm 3 ) when comparing lung predictions with digitized lung concentrations. 8, 10, 11 Extension of the model to AMs assumed a similar description regarding intracellular concentrations as that described in PMLs after adjusting for distribution in lung tissue. These compartments shared the parameters used for distribution into tissue and cell compartments from the base

This article is protected by copyright. All rights reserved 

where the parameter f un-ionized is calculated based on azithromycin pK a (pK a1 : 8.1, pK a2 : 8.8) and physiologic pH as reported in Zheng et al 5 , using the same assumption that plasma and tissue compartment pH are similar, and kin, kout, koff, kon, Aplas have been defined above. The rate constants of distribution into and out of the lung and AMs (kin/kout ratios) were assumed to be the same as muscle/subcutis and PMLs, respectively. It should be noted that this assumption does not cover all distribution parameters, as these rate parameters were allowed to scale with the estimation of a distribution factor.

A local sensitivity analysis was performed to assess the sensitivity of AUC in AM and lung tissue to changes in model parameters. Parameters were perturbed by up to ± 20% with 2% intervals (-20%, -18%, -16% etc.) to simulate AUC and were compared against the reference AUC simulated from unperturbed parameters.

Model simulations were conducted using the R statistical and programming language (v3.6.1) with mrgsolve and tidyverse packages [18] [19] [20] 

This article is protected by copyright. All rights reserved consisted of mean and standard deviation values over time for 6 unique dosing regimens. Table 1 presents tissue/cell concentrations and a high-level summary of the study designs that were available from each literature source.

Individual clinical trial data were available from 2 published Phase I studies. 9, 22 The first study used an openlabel randomized single dose design (n = 40), to estimate the bioavailability of a fixed dose combination of azithromycin-chloroquine tablets relative to co-administration of separate azithromycin and chloroquine tablets. 22 These formulations were considered bioequivalent based on the conclusions of the study.

The second study, already mentioned above, was an open-label randomized parallel-group study (n = 24)

comparing the plasma, PML and MNL pharmacokinetics of azithromycin single-dose sustained release azithromycin (2 g) and a three-day regimen of immediate release azithromycin (3 x 500 mg). 9 Only the immediate release data (n = 12) were used in the present analysis.

The final model was used for simulations of drug concentrations based on planned azithromycin dosing regimens in COVID-19 clinical trials as reported on registry clinicaltrials.gov (accessed on April 08, 2020, Supplemental 

Implementation Table S1 ).

When implementing the parameters from the Zhao three-compartment model, the apparent volume of distribution in the fast distributing compartment was corrected (2890 L to 2490 L) to account for the added volume of 400 L attributed to the addition of both the muscle and subcutis compartments. The value was derived based on Kin and Kout values reported by Zheng et al. 5 The combination of parameters in the hybrid model (Table 1) confidence intervals of the distribution factor estimate for PML cytosol (95% CI 39-423) 5 and this adjustment visually improved model predictions.

As described in Methods, the hybrid model was extended to represent the lung tissue and AM cells (Figure 1 ).

Volume displacement caused by adding the lung compartment was accounted for by reducing the apparent

This article is protected by copyright. All rights reserved fast-distributing peripheral compartment, as described previously. The initial values for distribution factors (DF) to the new lung and AM compartments were determined by calculating the ratio between tissue and plasma from Lucchi et al. 8 and Danesi et al. 10 and further adjusted based on visual evaluation. The final model parameters are presented in Table 2 

Based on the most commonly reported dosing regimens for azithromycin investigated in clinical trials of COVID-19 (Supplemental Table S2 

This article is protected by copyright. All rights reserved 500 mg on Day 1, followed 250 mg QD Days 2 to 5), 10 days following a single 1000 mg dose, and more than 20 days with administration of 500 mg QD for 10 days. Trough concentrations in AM were approximately 4fold greater than those in WBC after first dose and exceeded the IC90 for more than 20 days based on the 4 regimens tested. Concentrations in total lung tissue were generally below IC90 except following administration of 500 mg QD for 10 days.

This article is protected by copyright. All rights reserved 

This article is protected by copyright. All rights reserved

One limitation of the approach used for implementation of the present model was the use of data collected in healthy volunteers, while lung data was obtained in patients without infection undergoing lung resection. In the presence of viral infection, distribution in lung tissue may be increased, and thus the model may underestimate lung concentrations in infected individuals. Despite this limitation, the present analysis and web application enables the evaluation of alternate scenarios, and will better inform optimization of dosing regimens for ongoing and future azithromycin clinical trials.

Azithromycin is currently being used in clinical trials of patients with COVID-19, although its optimal dose is unknown.

The study was able to predict azithromycin concentrations in relevant tissues for antiviral activity including lung, polymorphonuclear and mononuclear cells, and alveolar macrophages using different dosing regimens and compare against in vitro EC90 for SARS-CoV-2.

Azithromycin predicted exposure exceeded target EC90 in relevant tissues. The analysis provides a rationale to support dosing of azithromycin in clinical trials using azithromycin alone or in combination with other agents.

How might this change drug discovery, development, and/or therapeutics? This article is protected by copyright. All rights reserved 

This article is protected by copyright. All rights reserved Time after First Dose (hours) Concentration (ng/mL) B q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 

Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an openlabel non-randomized clinical trial

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 observation study

Clinical Pharmacology Perspectives on the Antiviral Activity of Azithromycin and Use in COVID-19

In vitro screening of a FDA approved chemical library reveals potential inhibitors of SARS-CoV-2 replication

Development of a population pharmacokinetic model characterizing the tissue distribution of azithromycin in healthy subjects

Blood, tissue, and intracellular concentrations of azithromycin during and after end of therapy

Population pharmacokinetics of azithromycin in whole blood, peripheral blood mononuclear cells, and polymorphonuclear cells in healthy adults

Pharmacokinetics of azithromycin in serum, bronchial washings, alveolar macrophages and lung tissue following a single oral dose of extended or immediate release formulations of azithromycin

Comparative pharmacokinetics of azithromycin in serum and white blood cells of healthy subjects receiving a single-dose extended-release regimen versus a 3-day immediate-release regimen

Comparative distribution of azithromycin in lung tissue of patients given oral daily doses of 500 and 1000 mg

Accepted Article This article is protected by copyright. All rights reserved

Pharmacokinetics of azithromycin in lung tissue, bronchia washing, and plasma in patients given multiple oral doses of 500 and 1000 mg daily

Pharmacokinetics of Oral Azithromycin in Serum, Urine, Polymorphonuclear Leucocytes and Inflammatory vs Non-Inflammatory Skin Blisters in Healthy Volunteers

Intrapulmonary pharmacokinetics of azithromycin in healthy volunteers given five oral doses

Population pharmacokinetics of azithromycin and chloroquine in healthy adults and paediatric malaria subjects following oral administration of fixed-dose azithromycin and chloroquine combination tablets

Development of a population pharmacokinetic model to describe azithromycin whole-blood and plasma concentrations over time in healthy subjects

Pharmacokinetic properties of azithromycin in pregnancy

Population pharmacokinetics study of azithromycin oral formulations using NONMEM

mrgsolve: Simulate from ODE-Based Models. R package version 0

Easily Install and Load the 'Tidyverse