Time-kill curve analysis and pharmacodynamic modelling for in vitro evaluation of antimicrobials against Neisseria gonorrhoeae
© The Author(s). 2016
Received: 19 March 2016
Accepted: 14 September 2016
Published: 17 September 2016
Gonorrhoea is a sexually transmitted infection caused by the Gram-negative bacterium Neisseria gonorrhoeae. Resistance to first-line empirical monotherapy has emerged, so robust methods are needed to evaluate the activity of existing and novel antimicrobials against the bacterium. Pharmacodynamic models describing the relationship between the concentration of antimicrobials and the minimum growth rate of the bacteria provide more detailed information than the MIC only.
In this study, a novel standardised in vitro time-kill curve assay was developed. The assay was validated using five World Health Organization N. gonorrhoeae reference strains and a range of ciprofloxacin concentrations below and above the MIC. Then the activity of nine antimicrobials with different target mechanisms was examined against a highly antimicrobial susceptible clinical strain isolated in 1964. The experimental time-kill curves were analysed and quantified with a previously established pharmacodynamic model. First, the bacterial growth rates at each antimicrobial concentration were estimated with linear regression. Second, we fitted the model to the growth rates, resulting in four parameters that describe the pharmacodynamic properties of each antimicrobial. A gradual decrease of bactericidal effects from ciprofloxacin to spectinomycin and gentamicin was found. The beta-lactams ceftriaxone, cefixime and benzylpenicillin showed bactericidal and time-dependent properties. Chloramphenicol and tetracycline were purely bacteriostatic as they fully inhibited the growth but did not kill the bacteria. We also tested ciprofloxacin resistant strains and found higher pharmacodynamic MICs (zMIC) in the resistant strains and attenuated bactericidal effects at concentrations above the zMIC.
N. gonorrhoeae time-kill curve experiments analysed with a pharmacodynamic model have potential for in vitro evaluation of new and existing antimicrobials. The pharmacodynamic parameters based on a wide range of concentrations below and above the MIC provide information that could support improving future dosing strategies to treat gonorrhoea.
KeywordsNeisseria gonorrhoeae Gonorrhoea Antimicrobial resistance Time-kill curves Pharmacodynamics
Antimicrobial resistance in Neisseria gonorrhoeae is a major public health problem. Strains that have developed resistance to all antimicrobials used for treatment have been classified as superbugs [1–3]. Clinical resistance to the last option for empirical antimicrobial monotherapy, ceftriaxone, was first described in 2009 . Currently, treatment recommendations for gonorrhoea and prediction of the efficacy of antimicrobials mainly rely on a single measurement: the MIC of the antimicrobial, sometimes supported by data from old clinical trials and pharmacokinetic/pharmacodynamic (PK/PD) simulations. However, antimicrobials that have different modes of action and lead to different treatment outcomes can have identical MICs . A better understanding of the in vitro pharmacodynamic properties of antimicrobials could be used to optimise dosing strategies and help prevent treatment failures .
Information about the effects of antimicrobials covering a wide range of antimicrobial concentrations below and above the MIC is particularly valuable for pathogens like N. gonorrhoeae, because data about PK/PD effects are limited. There is no standardised and quality assured time-kill curve analysis or animal model for the fastidious obligate human pathogen N. gonorrhoeae. Most published time-kill protocols for N. gonorrhoeae [10–12] are not generalizable, owing to the highly divergent growth requirements of different strains and interpretation of results generally relies on qualitative expert judgement. To study a wide range of N. gonorrhoeae strains, growth in absence of antimicrobials must be consistent and bacterial growth phases at the time of exposure to antimicrobial need to be synchronised in early to mid-log phase.
In this study, a standardised in vitro time-kill curve assay for N. gonorrhoeae was developed using Graver-Wade (GW) medium. GW medium is a chemically defined, nutritious, liquid medium that supports growth of a wide range of N. gonorrhoeae auxotypes and clinical isolates starting from very low inocula . The novel time-kill curve assay was validated on five World Health Organization N. gonorrhoeae reference strains with fluoroquinolone resistance determinants. A highly susceptible clinical N. gonorrhoeae isolate (DG666, isolated in 1964) was subsequently studied in detail and time-kill curve experiments performed for nine antimicrobials that have been, or currently are, used to treat gonorrhoea. In a second step we analysed the time-kill data using a pharmacodynamic model  for a comparative analysis of the pharmacodynamic properties of different antimicrobials.
Neisseria gonorrhoeae isolates and media
The five international N. gonorrhoeae reference strains WHO G, WHO K, WHO L, WHO M, and WHO N with different ciprofloxacin conferring mutations in gyrA, parC and parE [14, 15] and a clinical isolate susceptible to all antimicrobials that were examined (wild type) cultured in 1964 (DG666), were studied. Isolates were cultured, from frozen stocks (−70 °C), on GCAGP agar plates (3.6 % Difco GC Medium Base agar [BD, Diagnostics, Sparks, MD, USA] supplemented with 1 % haemoglobin [BD, Diagnostics], 1 % IsoVitalex [BD, Diagnostics] and 10 % horse serum) for 18–20 h at 37 °C in a humid 5 % CO2-enriched atmosphere. Gonococcal colonies were subcultured once more on GCAGP agar for 18–20 h at 37 °C in a humid 5 % CO2-enriched atmosphere, before being transferred to the liquid sterile GW medium, prepared as earlier described , for growth curve and time-kill experiments.
Viable cell counts
Bacterial viability was measured using a modified Miles and Misra method as previously described . Growing bacteria were removed from 96-well plates at specified time points using a multichannel pipette and diluted in sterile phosphate buffered saline (PBS) in six subsequent 1:10 dilutions (20 μl culture in 180 μl diluent). Ten μl droplets of each dilution were spotted on GCRAP (3.6 % Difco GC Medium Base agar [BD, Diagnostics] supplemented with 1 % haemoglobin [BD, Diagnostics] and 1 % IsoVitalex [BD, Diagnostics]). GCRAP plates were dried with the lid open in a sterile environment for 30–60 min before use. After drying the droplets (approximately 5–10 min), plates were incubated for 24 h at 37 °C in a humid 5 % CO2-enriched atmosphere. For every concentration and time point, colonies were counted for the first dilution that resulted in a countable range of 3–30 colonies and the CFU/ml calculated.
Prior to growth curve experiments, strains were subcultured once on chocolate agar PolyViteX (Biomerieux). A 0.5 McFarland inoculum was prepared and diluted to 100 CFU/ml (1:106) in GW Medium (35 °C). A volume of 100 μl diluted bacteria per well was transferred to Sarstedt round-bottom 96 well plates. The plates were tightly sealed with adhesive polyester foil (Sarstedt) and bacteria were grown shaking at 100 rpm at 35 °C in a humid 5 % CO2-enriched atmosphere. Bacterial growth was monitored over a time-course of 60 h (0, 2, 4, 6, 8, 10, 12, 20, 22, 24, 26, 28, 30, 32, 34, 40, 44, 48, 60 h). For every sampled time point, the content of one well was removed and viable counts determined . Growth curves were analysed by plotting the log CFU/ml against the time and fitting a Gompertz growth model  to the data as implemented in the package cellGrowth  for the R software environment for statistical computing . Only lag, log and stationary phases were included in the analysis and the decline phase excluded.
Time-kill curve analyses were performed by culturing N. gonorrhoeae in GW medium , in the presence of 11 antimicrobial concentrations in doubling dilutions ranging from 0.016 × MIC to 16 × MIC. For DG666, the MICs were determined before the experiment using Etest (bioMérieux, Marcy l’Etoile, France) according to the manufacturer’s instructions. For all other strains, previously published MIC values were used . The antimicrobials examined were ciprofloxacin (Sigma Aldrich, China), gentamicin (Sigma Aldrich, Israel), spectinomycin (Sigma Aldrich, Israel), azithromycin (Sigma Aldrich, USA), benzylpenicillin (Sigma Aldrich, USA), ceftriaxone (Sigma Aldrich, Israel), cefixime (European pharmacopeia reference standard, France), chloramphenicol (Sigma Aldrich, China) and tetracycline (Sigma Aldrich, China). Growth curves were initially performed to confirm that all strains would reach a stable early- to mid-log phase after 4 h of pre-incubation in antimicrobial-free GW medium. A 0.5 McFarland inoculum of N. gonorrhoeae was then prepared in sterile PBS from cultures grown on GCAGP agar plates for 18–20 h at 37 °C in a humid 5 % CO2-enriched atmosphere. For each strain, 30 μl of the inoculum was diluted in 15 ml pre-warmed (37 °C) antimicrobial-free GW medium and 90 μl per well was dispersed in round bottom 96-well Sarstedt microtiter plates. The plates were pre-incubated for 4 h shaking at 150 rpm, 35 °C in a humid 5 % CO2-enriched atmosphere. To each well containing 90 μl of pre-incubated bacteria, 10 μl of one of the antimicrobial concentrations (or PBS) was added, resulting in eight identical rows (one row for each time-point) containing bacteria exposed to 11 different antimicrobial concentrations and one untreated control.
Estimating bacterial growth rates
Growth of N. gonorrhoeae
Growth curves for the five different WHO N. gonorrhoeae reference strains (Additional file 1: Figure S1) confirmed that growth was well supported in GW medium. All strains could be grown from a starting inoculum of fewer than 103 CFU/ml and typically had a lag phase of under 4 h. The stationary phase lasted until 36 h for all strains, followed by a steep decline phase. Growth was similar for all strains, with WHO L the only strain that had a slightly longer lag phase (4 h).
Strains with resistance determinants to ciprofloxacin resulted in significantly changed pharmacodynamic parameters (Fig. 4c and Additional file 1: Table S2). The DG666 strain had a low pharmacodynamic MIC (zMIC) and a low minimal growth rate (ψ min), indicating the strong bactericidal effect of ciprofloxacin. The five WHO reference strains showed that the ciprofloxacin resistance determinants shifted the zMIC to higher values and resulted in an increase of the minimal growth rate (ψ min) compared to DG666 strain.
Parameter estimates of the pharmacodynamic function for nine antimicrobials in the antimicrobial susceptible Neisseria gonorrhoeae strain DG666
ψ min (h−1)a
ψ max (h−1)a
1.1 ± 0.1
−8.9 ± 2.2
0.7 ± 0.4
0.002 ± 0.0001
1.0 ± 0.2
−106.9 ± 140c
0.9 ± 0.07
0.2 ± 0.04
2.0 ± 0.6
−9.6 ± 1
0.7 ± 0.03
5 ± 0.7
2.5 ± 0.1
−2.2 ± 0.1
0.6 ± 0.05
0.03 ± 0.002
1.1 ± 0.1
−1.6 ± 0.6
0.9 ± 0.2
0.004 ± 0.002
1.6 ± 0.1
−0.6 ± 0.2
0.8 ± 0.07
0.0003 ± 0.0001
1.7 ± 0.5
−0.8 ± 0.2
0.8 ± 0.1
0.0002 ± 0.0002
1.8 ± 0.4
−0.1 ± 0.01
0.7 ± 0.2
0.5 ± 0.1
1.0 ± 0.2
−0.2 ± 0.08
0.8 ± 0.07
0.5 ± 0.3
A robust and reliable method to evaluate antimicrobial treatment options in vitro is urgently needed to help tackle the problem of antimicrobial resistant N. gonorrhoeae. In this study, a standardised in vitro time-kill curve assay was developed and the resulting data were analysed using a pharmacodynamic model that describes the relationship between the concentration of antimicrobials and the bacterial growth rate . We obtain and compare in vitro pharmacodynamic parameters of antimicrobials in susceptible and resistant strains of the same pathogenic species, opening up avenues into understanding the effects of different resistance determinants on strain phenotype.
The time-kill assay we developed worked well for different N. gonorrhoeae strains, including highly resistant isolates. Time-kill assays are usually very laborious but growing the bacteria in 96-microwell plates and using the modified Miles and Misra method  for plating made it possible to study 12 antimicrobial concentrations in the same experiment. The assay time was limited to 6 h and growth in the absence of antimicrobials was highly consistent and exponential for all strains during that time. The analysis of time-kill data for the susceptible strain DG666 showed strong bactericidal effects of ciprofloxacin, gentamicin and spectinomycin. Ciprofloxacin is a prime example of a bactericidal antimicrobial, representing the class of topoisomerase II inhibiting fluoroquinolones . The five WHO reference strains used in this study have different ciprofloxacin resistance-conferring mutations in gyrA, parC and parE. This was reflected in an increased pharmacodynamic MIC (zMIC) and a weaker bactericidal effect of ciprofloxacin, showing that even exposure to high concentrations (16 fold MIC) had a limited effect on the growth of these resistant strains.
Spectinomycin and gentamicin both inhibit protein translation [23–25]. Spectinomycin is a well-recognised treatment option for gonorrhoea and resistance is found rarely [26, 27]. Gentamicin is currently the recommended first-line treatment for gonorrhoea in Malawi, where it is used together with doxycycline in the syndromic management of urethritis . This aminoglycoside has been suggested for wider use in the treatment of gonorrhoea recently [29–31] and our time-kill data suggest that further exploration of this treatment option could be rewarding.
The cell wall inhibiting β-lactam antimicrobials are known to have a time-dependent mode of action [32, 33]. Therefore it was not surprising that benzylpenicillin, ceftriaxone and cefixime were characterised by time-dependent, bactericidal killing (−1.6 h−1 < ψ min < 0.6 h−1). Although currently not used for treatment of N. gonorrhoeae, chloramphenicol and tetracycline often act as model compounds for bacteriostatic effects [34, 35]. These effects were confirmed by growth rates close to zero at high antimicrobial concentrations (ψ min). Resistance to tetracycline is widespread  and chloramphenicol is relatively toxic and has undesirable side effects , so neither of these antimicrobials is currently routinely used for the treatment of gonorrhoea.
The Hill coefficient k describes the steepness of the pharmacodynamic curve around the zMIC. Higher values of k result in a steeper curve and a more dramatic increase in bacterial killing for increasing antimicrobial concentrations. For low values of k, increasing antimicrobial concentrations result in only marginal increases in bacterial killing, suggesting that the time above the zMIC may be a more important correlate for efficacy than a high ratio of maximum concentration to zMIC. Hence, Regoes et al.  hypothesised that high and low Hill coefficients are associated with concentration- and time-dependent antimicrobials such as ciprofloxacin and tetracycline, respectively. In our study, we did not find significantly different Hill coefficients for ciprofloxacin and the time-dependent beta-lactams. However time-dependent antimicrobials were clearly associated with higher minimal growth rates (ψ min) in our data. These results are in line with a review of pharmacodynamic parameters from different organisms and antibiotics. Czock and Keller  found a lower maximum kill rate for time-dependent compared to concentration-dependent antimicrobials. The association with time-dependency was less clear for the Hill coefficient and further studies were suggested to confirm a tendency towards higher values in some of the studies . The Hill coefficient (к) might also depend on the genetic background and metabolism of different strains therefore isogenic strains should be studied to systematically explore this parameter.
There are some limitations of the methods used in the present study. First, the rapid bactericidal effects of some antimicrobials occurred immediately after the compound was added resulting in bacterial counts below limit of detection at the first time point. These effects can make it challenging to estimate the minimal growth rate at high concentrations (ψ min) below values of −10 h−1, as observed for gentamicin for example. Second, the estimated bacterial growth rate at high antimicrobial concentrations did not always follow the sigmoidal four parameter model (Fig. 4b). This was the case for the beta-lactams and in one instance for chloramphenicol. Dose-response curves with multiphasic features and more than one inflection point have been observed previously and potentially indicate multiple targets . We therefore hypothesize that these high concentrations induce a biological effect distinct from the primary target and removed them for the scope of this study. Fitting a multiphasic model that could capture this effect would make it difficult to compare the parameters within this study and also to previous studies using the same model . Hence, the pharmacodynamic parameters are only valid within the studied range of antimicrobial concentrations, and benzylpenicillin, cefixime and ceftriaxone could well exhibit stronger bactericidal effects at higher concentrations. Third, the assay time was limited to 6 h to ensure synchronised growth for all strains. Hence, potential regrowth at later time points and post antibiotic effects could not be studied. Fourth, the time-kill curves appeared to level off over time for bactericidal compounds in susceptible strains. Interestingly, this phenomenon might represent a physiological adaptation to those antimicrobials, often described as persister cell formation [40–45]. This non-exponential decline makes it difficult to estimate the growth rate with linear regression. The clinical relevance of persister cells has been demonstrated for chronic infections such as tuberculosis  and infections caused by Staphylococcus aureus . Homologues to toxin-antitoxin modules involved in persister cell formation have been described also for N. gonorrhoeae  making it worthwhile considering this phenomenon in future studies. Furthermore, the proposed time-kill method allows the comparative evaluation of antimicrobials against N. gonorrhoeae in vitro only and pharmacokinetic effects were not studied.
The in vitro pharmacodynamic parameters can provide relative comparisons across different strains and antimicrobials which can be extremely valuable in preclinical studies. A novel compound can, for example, be categorised and compared to mechanistically well-understood antibiotics . As a next step, the pharmacodynamic properties that are obtained in vitro should be compared to data from clinical PK/PD studies that include additional parameters such as serum concentrations and half-life of the antimicrobial. This will be important to validate whether pharmacodynamic modeling based on in vitro data can be used to predict the outcome of different dosing strategies in vivo. For benzylpenicillin, ceftriaxone and cefixime the time of free antimicrobial above the MIC value should be maximised [50–52], suggesting that multiple dose treatment would be a rational strategy. Fluoroquinolones and aminoglycosides, which act in a concentration dependent and bactericidal manner, should be given as a single high dose . This is typically achieved by maximising the AUC/MIC and peak serum concentration/MIC ratio [54–56]. Our results suggest that this could be the case for ciprofloxacin, gentamicin and spectinomycin, which were found to be strongly bactericidal and concentration dependent. Azithromycin has been described to be bacteriostatic in Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae  but appears to act bactericidal on Pseudomonas aeruginosa . The in vitro pharmacodynamic parameters suggest that there is a continuous gradient from bacteriostatic to bactericidal effects and that azithromycin might fall in between these two categories.
The present study shows that evaluation of the parameters of a pharmacodynamic model based on in vitro time-kill data can add valuable information beyond that of MIC values for different antimicrobials. The quantitative assessment of pharmacodynamic parameters provides a more detailed picture of antimicrobial-induced effects on N. gonorrhoeae. The pharmacodynamic parameters can be applied for the evaluation of new antimicrobials and to study the effects of combining antimicrobials against N. gonorrhoeae.
We would like to thank Sandro Gsteiger and Valentino Desilvestro for statistical support.
This study was funded through an Interdisciplinary PhD (IPhD) project from SystemsX.ch (The Swiss Initiative for Systems Biology) evaluated by the Swiss National Science Foundation, the Rapid Diagnosis of Antibiotic Resistance in Gonorrhoea project (RaDAR-Go, funded by the Swiss Platforms for Translational Medicine initiative, SwissTransMed project number #25/2013), and the Örebro County Council Research Committee and the Foundation for Medical Research at Örebro University Hospital, Sweden. The funders had no role in study design, data collection, analysis and interpretation, writing the manuscript, or decision to publish.
Availability of data and materials
All data that were generated and analysed during this study are publicly available from the following GitHub repository: https://github.com/sunnivas/PDfunction.
All authors designed the study. SF performed all experiments and the statistical analysis. MU and LJH supervised the experiments, and CLA supervised the statistical analysis. NL and all other authors contributed to interpreting the data and writing the manuscript, and read and approved the final version of the manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Ethical committee approval was not required for this study.
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- Ohnishi M, Golparian D, Shimuta K, Saika T, Hoshina S, Iwasaku K, et al. Is Neisseria gonorrhoeae initiating a future era of untreatable gonorrhea? Detailed characterization of the first strain with high-level resistance to ceftriaxone. Antimicrob Agents Chemother. 2011;55:3538–45.View ArticlePubMedPubMed CentralGoogle Scholar
- Unemo M, Golparian D, Nicholas R, Ohnishi M, Gallay A, Sednaoui P. High-level cefixime- and ceftriaxone-resistant N. gonorrhoeae in France: novel penA mosaic allele in a successful international clone causes treatment failure. Antimicrob Agents Chemother. 2012;56:1273–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Cámara J, Serra J, Ayats J, Bastida T, Carnicer-Pont D, Andreu A, Ardanuy C. Molecular characterization of two high-level ceftriaxone-resistant Neisseria gonorrhoeae isolates detected in Catalonia, Spain. J Antimicrob Chemother. 2012;67:1858–60.View ArticlePubMedGoogle Scholar
- Unemo M, Shafer WM. Antimicrobial resistance in Neisseria gonorrhoeae in the 21st century: past, evolution, and future. Clin Microbiol Rev. 2014;27:587–613.View ArticlePubMedPubMed CentralGoogle Scholar
- Mueller M, de la Peña A, Derendorf H. Issues in pharmacokinetics and pharmacodynamics of anti-infective agents: kill curves versus MIC. Antimicrob Agents Chemother. 2004;48:369–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Li DRC, Zhu M, Schentag JJ. Achieving an optimal outcome in the treatment of infections. Clin Pharmacokinet. 2012;37:1–16.View ArticleGoogle Scholar
- Regoes RR, Wiuff C, Zappala RM, Garner KN, Baquero F, Levin BR. Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens. Antimicrob Agents Chemother. 2004;48:3670–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Johnson PJT, Levin BR. Pharmacodynamics, population dynamics, and the evolution of persistence in Staphylococcus aureus. PLoS Genet. 2013;9:e1003123. doi:10.1371/journal.pgen.1003123.View ArticlePubMedPubMed CentralGoogle Scholar
- Ankomah P, Levin BR. Two-drug antimicrobial chemotherapy: a mathematical model and experiments with Mycobacterium marinum. PLoS Pathog. 2012;8:e1002487. doi:10.1371/journal.ppat.1002487.View ArticlePubMedPubMed CentralGoogle Scholar
- Takei M, Yamaguchi Y, Fukuda H, Yasuda M, Deguchi T. Cultivation of Neisseria gonorrhoeae in liquid media and determination of its in vitro susceptibilities to quinolones. J Clin Microbiol. 2005;43:4321–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Jeverica S, Golparian D, Hanzelka B, Fowlie AJ, Matičič M, Unemo M. High in vitro activity of a novel dual bacterial topoisomerase inhibitor of the ATPase activities of GyrB and ParE (VT12-008911) against Neisseria gonorrhoeae isolates with various high-level antimicrobial resistance and multidrug resistance. J Antimicrob Chemother. 2014;69:1866–72.View ArticlePubMedGoogle Scholar
- Hamilton-Miller JM, Bruzzese T, Nonis A, Shah S. Comparative anti-gonococcal activity of S-565, a new rifamycin. Int J Antimicrob Agents. 1996;7:247–50.View ArticlePubMedGoogle Scholar
- Wade JJ, Graver MA. A fully defined, clear and protein-free liquid medium permitting dense growth of Neisseria gonorrhoeae from very low inocula. FEMS Microbiol Lett. 2007;273:35–7.View ArticlePubMedGoogle Scholar
- Unemo M, Fasth O, Fredlund H, Limnios A, Tapsall J. Phenotypic and genetic characterization of the 2008 WHO Neisseria gonorrhoeae reference strain panel intended for global quality assurance and quality control of gonococcal antimicrobial resistance surveillance for public health purposes. J Antimicrob Chemother. 2009;63:1142–51.View ArticlePubMedGoogle Scholar
- Unemo M, Golparian D, Sánchez-Busó L, Grad Y, Jacobsson S, Ohnishi M, et al. The novel 2016 WHO Neisseria gonorrhoeae reference strains for global quality assurance of laboratory investigations: phenotypic, genetic and reference genome characterization. J Antimicrob Chemother. 2016. [Epub ahead of print]Google Scholar
- Chen CY, Nace GW, Irwin PL. A 6 × 6 drop plate method for simultaneous colony counting and MPN enumeration of Campylobacter jejuni, Listeria monocytogenes, and Escherichia coli. J Microbiol Methods. 2003;55:475–9.View ArticlePubMedGoogle Scholar
- Zwietering MH, Jongenburger I, Rombouts FM, van’t Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990;56:1875–81.PubMedPubMed CentralGoogle Scholar
- Gagneur J, Neudecker A. cellGrowth: fitting cell population growth models. 2012. http://www.bioconductor.org/packages/release/bioc/manuals/cellGrowth/man/cellGrowth.pdf. Accessed 16 Aug 2016.
- R Core Team. R: a language and environment for statistical computing. https://www.r-project.org/. Accessed 16 Aug 2016.
- Ritz C, Streibig J. Bioassay analysis using R. J Stat Softw. 2005;12:1–22.View ArticleGoogle Scholar
- GitHub repository. https://github.com/sunnivas/PDfunction. Accessed 16 Aug 2016.
- LeBel M. Ciprofloxacin: chemistry, mechanism of action, resistance, antimicrobial spectrum, pharmacokinetics, clinical trials, and adverse reactions. Pharmacotherapy. 1988;8:3–33.View ArticlePubMedGoogle Scholar
- Borovinskaya MA, Pai RD, Zhang W, Schuwirth BS, Holton JM, Hirokawa G, Kaji H, Kaji A, Cate JHD. Structural basis for aminoglycoside inhibition of bacterial ribosome recycling. Nat Struct Mol Biol. 2007;14:727–32.View ArticlePubMedGoogle Scholar
- Borovinskaya MA, Shoji S, Holton JM, Fredrick K, Cate JHD. A steric block in translation caused by the antibiotic spectinomycin. ACS Chem Biol. 2007;2:545–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Wilson DN. The A–Z of bacterial translation inhibitors. Crit Rev Biochem Mol Biol. 2009;44:393–433.View ArticlePubMedGoogle Scholar
- Ward ME. The bactericidal action of spectinomycin on Neisseria gonorrhoeae. J Antimicrob Chemother. 1977;3:323–9.View ArticlePubMedGoogle Scholar
- Ilina EN, Malakhova MV, Bodoev IN, Oparina NY, Filimonova AV, Govorun VM. Mutation in ribosomal protein S5 leads to spectinomycin resistance in Neisseria gonorrhoeae. Front Microbiol. 2013;4:186.View ArticlePubMedPubMed CentralGoogle Scholar
- Brown LB, Krysiak R, Kamanga G, Mapanje C, Kanyamula H, Banda B, et al. Neisseria gonorrhoeae antimicrobial susceptibility in Lilongwe, Malawi. Sex Transm Dis. 2010;37:169–72.View ArticlePubMedGoogle Scholar
- Ross JDC, Lewis DA. Cephalosporin resistant Neisseria gonorrhoeae: time to consider gentamicin? Sex Transm Infect. 2012;88:6–8.View ArticlePubMedGoogle Scholar
- Dowell D, Kirkcaldy RD. Effectiveness of gentamicin for gonorrhoea treatment: systematic review and meta-analysis. Sex Transm Infect. 2012;88:589–94.View ArticlePubMedGoogle Scholar
- Hathorn E, Dhasmana D, Duley L, Ross JD. The effectiveness of gentamicin in the treatment of Neisseria gonorrhoeae: a systematic review. Syst Rev. 2014;3:104.View ArticlePubMedPubMed CentralGoogle Scholar
- Williamson R, Tomasz A. Inhibition of cell wall synthesis and acylation of the penicillin binding proteins during prolonged exposure of growing Streptococcus pneumoniae to benzylpenicillin. Eur J Biochem FEBS. 1985;151:475–83.View ArticleGoogle Scholar
- Drusano GL. Antimicrobial pharmacodynamics: critical interactions of “bug and drug”. Nat Rev Microbiol. 2004;2:289–300.View ArticlePubMedGoogle Scholar
- Comby S, Flandrois JP, Carret G, Pichat C. Mathematical modelling of growth of Escherichia coli at subinhibitory levels of chloramphenicol or tetracyclines. Res Microbiol. 1989;140:243–54.View ArticlePubMedGoogle Scholar
- Greulich P, Scott M, Evans MR, Allen RJ. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol Syst Biol. 2015;11:796.View ArticleGoogle Scholar
- Lewis DA. The Gonococcus fights back: is this time a knock out? Sex Transm Infect. 2010;86:415–21.View ArticlePubMedGoogle Scholar
- Duck PD, Dillon JR, Eidus L. Effects of thiamphenicol and chloramphenicol in inhibiting Neisseria gonorrhoeae isolates. Antimicrob Agents Chemother. 1978;14:788–90.View ArticlePubMedPubMed CentralGoogle Scholar
- Czock D, Keller F. Mechanism-based pharmacokinetic-pharmacodynamic modeling of antimicrobial drug effects. J Pharmacokinet Pharmacodyn. 2007;34:727–51.View ArticlePubMedGoogle Scholar
- Di Veroli GY, Fornari C, Goldlust I, Mills G, Koh SB, Bramhall JL, et al. An automated fitting procedure and software for dose-response curves with multiphasic features. Sci Rep. 2015;5:14701.View ArticlePubMedPubMed CentralGoogle Scholar
- Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. Bacterial persistence as a phenotypic switch. Science. 2004;305:1622–5.View ArticlePubMedGoogle Scholar
- Dörr T, Vulić M, Lewis K. Ciprofloxacin causes persister formation by inducing the TisB toxin in Escherichia coli. PLoS Biol. 2010; doi:10.1371/journal.pbio.1000317.
- Feng J, Kessler DA, Ben-Jacob E, Levine H. Growth feedback as a basis for persister bistability. Proc Natl Acad Sci U S A. 2014;111:544–9.View ArticlePubMedGoogle Scholar
- Maisonneuve E, Gerdes K. Molecular mechanisms underlying bacterial persisters. Cell. 2014;157:539–48.View ArticlePubMedGoogle Scholar
- Lewis K. Persister cells, dormancy and infectious disease. Nat Rev Microbiol. 2007;5:48–56.View ArticlePubMedGoogle Scholar
- Kint CI, Verstraeten N, Fauvart M, Michiels J. New-found fundamentals of bacterial persistence. Trends Microbiol. 2012;20:577–85.View ArticlePubMedGoogle Scholar
- Fauvart M, De Groote VN, Michiels J. Role of persister cells in chronic infections: clinical relevance and perspectives on anti-persister therapies. J Med Microbiol. 2011;60:699–709.View ArticlePubMedGoogle Scholar
- Conlon BP. Staphylococcus aureus chronic and relapsing infections: evidence of a role for persister cells: an investigation of persister cells, their formation and their role in S. aureus disease. BioEssays. 2014;36:991–6.View ArticlePubMedGoogle Scholar
- Hayes CS, Low DA. Signals of growth regulation in bacteria. Curr Opin Microbiol. 2009;12:667–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Foerster S, Golparian D, Jacobsson S, Hathaway LJ, Low N, Shafer WM, et al. Genetic resistance determinants, in vitro time-kill curve analysis and pharmacodynamic functions for the novel topoisomerase II inhibitor ETX0914 (AZD0914) in Neisseria gonorrhoeae. Front Microbiol. 2015;6:1377. http://dx.doi.org/10.3389/fmicb.2015.01377.View ArticlePubMedPubMed CentralGoogle Scholar
- Jaffe HW, Schroeter AL, Reynolds GH, Zaidi AA, Martin JE, Thayer JD. Pharmacokinetic determinants of penicillin cure of gonococcal urethritis. Antimicrob Agents Chemother. 1979;15:587–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Deguchi T, Yasuda M, Yokoi S, Ishida K-I, Ito M, Ishihara S, et al. Treatment of uncomplicated gonococcal urethritis by double-dosing of 200 mg cefixime at a 6-h interval. J Infect Chemother. 2003;9:35–9.View ArticlePubMedGoogle Scholar
- Chisholm SA, Mouton JW, Lewis DA, Nichols T, Ison CA, Livermore DM. Cephalosporin MIC creep among gonococci: time for a pharmacodynamic rethink? J Antimicrob Chemother. 2010;65:2141–8.View ArticlePubMedGoogle Scholar
- Drusano GL. Pharmacokinetics and pharmacodynamics of antimicrobials. Clin Infect Dis. 2007;45:S89–95.View ArticlePubMedGoogle Scholar
- Levison ME, Levison JH. Pharmacokinetics and pharmacodynamics of antibacterial agents. Infect Dis Clin North Am. 2009;23:791–815.View ArticlePubMedPubMed CentralGoogle Scholar
- Craig WA. Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin Infect Dis. 1998;26:1–10. quiz 11–12.View ArticlePubMedGoogle Scholar
- Frimodt-Møller N. How predictive is PK/PD for antibacterial agents? Int J Antimicrob Agents. 2002;19:333–9.View ArticlePubMedGoogle Scholar
- Dorfman MS, Wagner RS, Jamison T, Bell B, Stroman DW. The pharmacodynamic properties of azithromycin in a kinetics-of-kill model and implications for bacterial conjunctivitis treatment. Adv Ther. 2008;25:208–17.View ArticlePubMedGoogle Scholar
- Imamura Y, Higashiyama Y, Tomono K, Izumikawa K, Yanagihara K, Ohno H, et al. Azithromycin exhibits bactericidal effects on Pseudomonas aeruginosa through interaction with the outer membrane. Antimicrob Agents Chemother. 2005;49:1377–80.View ArticlePubMedPubMed CentralGoogle Scholar