Azithromycin may antagonize inhaled tobramycin when targeting P. aeruginosa in cystic fibrosis

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1 Data Supplement Azithromycin may antagonize inhaled tobramycin when targeting P. aeruginosa in cystic fibrosis Jerry A. Nick 1, Samuel M. Moskowitz 2, James F. Chmiel 3, Anna V. Forssén 4, Sun Ho Kim 2, Milene T. Saavedra 1, Lisa Saiman 5, Jennifer L. Taylor-Cousar 1,6, David P. Nichols 1,6 Supplemental Methods: Clinical Trial Dataset, Outcome Measures and Statistical Analyses: Additional demographic data, eligibility criteria, study design, and results from the original trial providing data for these analyses have been published(1). Azithromycin use was based on self-report and commonly used three times/week. Subjects reporting other dosing regimens (e.g. daily) were also categorized as being users of azithromycin. The CFQ- R is a tool to capture self-reported, disease related quality of life(2). The Respiratory Domain Symptom Score (RSS) of the CFQ-R was measured during this study. When normalized to a 100-point scale, the minimal clinically significant change is an increase (improvement) of 5.5 points(3). The need for IV or inhaled antibiotics for P. aeruginosa infection outside of the study protocol was measured and used as a surrogate for likely pulmonary exacerbations of CF lung disease. The need for oral antibiotics, which would also be outside of the study protocol, was not recorded. For crossover analyses, linear mixed models were used. Repeated measures across six visits were accounted for with a first-order autoregressive covariance matrix. Random intercepts were allowed for each subject. P-values comparing 3 pre-crossover and 3 post - crossover values were obtained by averaging model estimates across visit. E1

2 Flow-cell Biofilm Culture: Fifteen clinical strains of P. aeruginosa were isolated and cryopreserved during the clinical trial, each from a unique study subject randomized to inhaled tobramycin and reporting concomitant azithromycin use. Clinical strains were chosen such that some subjects had experienced an increase in FEV1 % predicted while receiving inhaled tobramycin, whereas others had experienced a decrease. Stock solutions of azithromycin (APP Pharmaceuticals, Schaumburg, IL), tobramycin (X-GEN Pharmaceuticals, Inc., Northport, NY), and aztreonam lysine (Gilead Sciences, Forest City, CA) were prepared in sterile water, aliquoted, and stored at -80 C. Biofilms of each clinical strain were grown in three-channel flow cells (Technical University of Denmark, Lyngby) using SCFM medium (4) with one-tenth the concentration of amino acids for 3 days at 37 C (5). Biofilms were then exposed to azithromycin (20 mcg/ml) and tobramycin (40 mcg/ml) or aztreonam lysine (900 mcg/ml) for 22 h at a constant flow rate of approximately 3.5 ml/h. Biofilms exposed to each antibiotic alone were included as controls. The tobramycin and aztreonam lysine concentrations used in combination experiments were those required to achieve ~50% biomass reduction in such controls. The azithromycin concentration approximated that found in the airways of healthy volunteers after standard oral azithromycin dosing in previous studies (6). Each drug combination or control was run in duplicate for each clinical strain. E2

3 Syto9 (5 µm) and propidium iodide (15 µm) were added to the medium to stain live and dead cells, respectively. Biofilms were imaged on an LSM 700 confocal scanning laser microscope (Carl Zeiss, Jena, Germany) for detection of Syto9 and propidium iodide, as described (5). Image stacks were acquired at three random positions in each flow cell lane and analyzed using COMSTAT software to quantify the volume of live and dead biomass (7). The mean % decrease in live biomass volume provided an estimate of the anti-biofilm effects of each drug combination or control. To analyze biofilm data, we developed an interpretative algorithm to classify the antibiofilm effects as antagonistic, additive / synergistic, or neutral. This algorithm uses the difference in live biomass volume of antibiotic-treated biofilms as the numerator, and live biomass volume of untreated biofilm as denominator. (TBRA = tobramycin, AZM = azithromycin, NON = no antibiotic) P-value for [Biomass(TBRA&AZM) Biomass(TBRA) 0] < Biomass (TBRA&AZM) Biomass(TBRA) Biomass (TBRA&AZM) Biomass(TBRA) Biomass(NON) Interpretation > Antagonistic 0.1 and < 0.2 Possibly antagonistic 0.2 Additive / Synergistic < and < 0.2 Possibly additive / synergistic Any value < 0.1 Indeterminate Any value 0.1 Indeterminate < 0.1 Neutral Based on this algorithm, azithromycin-tobramycin antagonism was observed for six of 15 P. aeruginosa clinical strains tested, and additive / synergistic effects were observed for three other strains. Neutral effects were observed for three of the strains, while the E3

4 remaining three strains gave indeterminate results. Representative images from biofilm analyses are available in the on-line supplement, Fig E2. Supplemental Data References E1. Assael BM, Pressler T, Bilton D, Fayon M, Fischer R, Chiron R, Larosa M, Knoop C, McElvaney N, Lewis SA, Bresnik M, Montgomery AB, Oermann CM. Inhaled aztreonam lysine vs. Inhaled tobramycin in cystic fibrosis: A comparative efficacy trial. J Cyst Fibros 2013;12: E2. Quittner AL, Buu A, Messer MA, Modi AC, Watrous M. Development and validation of the cystic fibrosis questionnaire in the united states: A health-related quality-of-life measure for cystic fibrosis. Chest 2005;128: E3. Quittner AL, Modi AC, Wainwright C, Otto K, Kirihara J, Montgomery AB. Determination of the minimal clinically important difference scores for the cystic fibrosis questionnaire-revised respiratory symptom scale in two populations of patients with cystic fibrosis and chronic pseudomonas aeruginosa airway infection. Chest 2009;135: E4. Palmer KL, Aye LM, Whiteley M. Nutritional cues control pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum. J Bacteriol 2007;189: E5. Nichols DP, Caceres S, Caverly L, Fratelli C, Kim SH, Malcolm K, Poch KR, Saavedra M, Solomon G, Taylor-Cousar J, Moskowitz S, Nick JA. Effects of azithromycin in pseudomonas aeruginosa burn wound infection. The Journal of surgical research 2013;183: E6. Olsen KM, San Pedro G, Gann LP, Gubbins PO, Halinski DM, Campbell GD, Jr. Intrapulmonary pharmacokinetics of azithromycin in healthy volunteers given five oral doses. Antimicrob Agents Chemother 1996;40: E7. Heydorn A, Nielsen AT, Hentzer M, Sternberg C, Givskov M, Ersboll BK, Molin S. Quantification of biofilm structures by the novel computer program comstat. Microbiology 2000;146: E4

5 FIGURE LEGENDS Figure E1. When considering only the 49 subjects who reported azithromycin use, were randomized to inhaled tobramycin, and chose to participate in the extension phase with inhaled aztreonam lysine, we observe improvement in the primary outcome measure with aztreonam. Figure E1a shows the relative change in FEV1%, averaged over 3 courses of each inhaled antibiotic. After adjusting for baseline FEV1, the estimated difference in FEV1% is 8.56% in favor of aztreonam lysine (P<0.001, std error 1.71). The difference in CFQ-R RSS (Fig E1b) and sputum bacterial density (Fig E1c) favored aztreonam lysine but did not reach statistical significance in this group. Fig E1b, P=0.48. Fig E1c, P-=0.10. TBRAtobramycin; AZTR- aztreonam lysine. Figure E2. Representative stained and imaged photos of biofilms grown with clinical P. aeruginosa isolates are shown. Live bacteria are stained green and dead bacteria are stained red. The growth patterns, as impacted by antibiotic exposure and compared with no antibiotic control, include: neutral, additive/synergistic ( 20% reduced live biofilm), and antagonistic ( 20% increased live biofilm). E5

6 Supplemental Figure E1. Crossover comparison in 49 subjects reporting azithromycin and randomized to inhaled tobramycin who enrolled in the extension study with aztreonam lysine Fig E1a. FEV1% (Mean +/- SEM)

7 Fig E1b. CFQ-R RSS (Mean +/- SEM)

8 Fig E1c. Sputum CFU (log10) (Mean +/- 95% CI)

9 Supplemental Figure E2. Representative images of Biofilm Analyses Scale bars represent 50 microns.