Comparative Killing Rates of Fluoroquinolones and Cell Wall-Active Agents

Fung‐Tomc, Joan; Gradelski, Elizabeth; Valera, Lourdes; Kolek, B; Bonner, Daniel P.

doi:10.1128/aac.44.5.1377-1380.2000

Cited by 27 publications

(16 citation statements)

References 11 publications

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“…The optimal bactericidal concentrations of quinolones are close to 8 times their MIC (6). The time-kill study in the presence of 8 µg/ml ofloxacin for MFM is shown in Fig.…”

Section: Killing Rate By Ofloxacin and Ciprofloxacinmentioning

confidence: 88%

Functional Analysis of spoT, relA and dksA Genes on Quinolone Tolerance in Pseudomonas aeruginosa under Nongrowing Condition

Viducic

Ono

Murakami

et al. 2006

Microbiology and Immunology

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Abstract:To assess the contribution of ppGpp in antibiotic tolerance to quinolone in Pseudomonas aeruginosa, knockout mutants of the genes involved or linked with the stringent response, such as relA, spoT and dksA, were constructed and investigated for their antibiotic susceptibility to quinolones. The survival of the dksA and spoT mutants in the presence of 8 g/ml of ofloxacin and 1 g/ml of ciprofloxacin were shown to be approximately 20-180 and 10-40 times respectively, higher than the same for the wild type strain. The intracellular levels of ppGpp determined with high performance liquid chromatography (HPLC) demonstrated that spoT and dksA mutants possess higher basal levels of ppGpp. The data suggest that elevated basal levels of ppGpp may be responsible for rendering these mutants tolerant to quinolones and expand the importance of ppGpp as an antimicrobial target in P. aeruginosa.

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“…The optimal bactericidal concentrations of quinolones are close to 8 times their MIC (6). The time-kill study in the presence of 8 µg/ml ofloxacin for MFM is shown in Fig.…”

Section: Killing Rate By Ofloxacin and Ciprofloxacinmentioning

confidence: 88%

Functional Analysis of spoT, relA and dksA Genes on Quinolone Tolerance in Pseudomonas aeruginosa under Nongrowing Condition

Viducic

Ono

Murakami

et al. 2006

Microbiology and Immunology

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“…For all of the antibiotics that we studied, the onset of killing is dependent on their concentration. And, as is well known for some antibiotics and bacteria, the rate at which the bacteria are killed declines with time (12,14,16,19,22,23,26,38). This decline in the killing rate is likely to be due to adaptive resistance (2)-a mechanism by which the bacterial population becomes increasingly refractory to the antibiotic for physiological reasons, rather than due to inherited resistance.…”

Section: Discussionmentioning

confidence: 99%

Pharmacodynamic Functions: a Multiparameter Approach to the Design of Antibiotic Treatment Regimens

Regoes

Wiuff

Zappala

et al. 2004

Antimicrob Agents Chemother

302

432

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There is a complex quantitative relationship between the concentrations of antibiotics and the growth and death rates of bacteria. Despite this complexity, in most cases only a single pharmacodynamic parameter, the MIC of the drug, is employed for the rational development of antibiotic treatment regimens. In this report, we use a mathematical model based on a Hill function-which we call the pharmacodynamic function and which is related to previously published E max models-to describe the relationship between the bacterial net growth rates and the concentrations of antibiotics of five different classes: ampicillin, ciprofloxacin, tetracycline, streptomycin, and rifampin. Using Escherichia coli O18:K1:H7, we illustrate how precise estimates of the four parameters of the pharmacodynamic function can be obtained from in vitro time-kill data. We show that, in addition to their respective MICs, these antibiotics differ in the values of the other pharmacodynamic parameters. Using a computer simulation of antibiotic treatment in vivo, we demonstrate that, as a consequence of differences in pharmacodynamic parameters, such as the steepness of the Hill function and the minimum bacterial net growth rate attained at high antibiotic concentrations, there can be profound differences in the microbiological efficacy of antibiotics with identical MICs. We discuss the clinical implications and limitations of these results.Fundamental to the rational design (35, 55) of effective antibiotic treatment protocols are accurate measures of the absorption, distribution, and decay of the drug in treated patients (pharmacokinetics) and the functional relationship between the concentration of the antibiotic and the rate of growth or death of the target bacteria (pharmacodynamics). Typically the pharmacodynamics of antibiotics are studied in vitro by exposing exponentially growing bacteria to a range of drug concentrations and monitoring the changes in density of viable cells over time and thereby generating time-kill curves (7, 9, 16, 17, 23, 25, 31, 50-52, 54, 58, 61). From these data, the growth or death rates of the bacteria at different concentrations of antibiotics can be estimated and the functional relationship between bacterial growth (or death) and the concentration of the antibiotic can thereby be determined. We refer to this relationship as the pharmacodynamic function. The pharmacodynamic function can then be used in combination with pharmacokinetic data to investigate the efficacy of antibiotic treatment. Frequently the pharmacodynamic relationship is reduced to a single parameter, the MIC (3,14,15,20,21,27,37,[39][40][41]46,56), even though antibiotics with the same MIC can have very different pharmacodynamic functions (1,44).In this study, we examined the pharmacodynamic relationship between antibiotic concentration and bacterial growth and death rates. We generated time-kill curves for Escherichia coli exposed to antibiotics of five different classes: rifampin, ampicillin, ciprofloxacin, streptomycin, and tetracycline. These...

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“…1A and B, blue arrows). These cells displayed several characteristics in common with previously characterized drug-tolerant or persistent phenotypes (4,11,(23)(24)(25).…”

Section: Response Of P Aeruginosa To 48-h Ciprofloxacin Exposurementioning

confidence: 75%

The Development of Ciprofloxacin Resistance in Pseudomonas aeruginosa Involves Multiple Response Stages and Multiple Proteins

Su¹,

Ramkissoon²,

Doolittle³

et al. 2010

Antimicrob Agents Chemother

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Microbes have developed resistance to nearly every antibiotic, yet the steps leading to drug resistance remain unclear. Here we report a multistage process by which Pseudomonas aeruginosa acquires drug resistance following exposure to ciprofloxacin at levels ranging from 0.5؋ to 8؋ the initial MIC. In stage I, susceptible cells are killed en masse by the exposure. In stage II, a small, slow to nongrowing population survives antibiotic exposure that does not exhibit significantly increased resistance according to the MIC measure. In stage III, exhibited at 0.5؋ to 4؋ the MIC, a growing population emerges to reconstitute the population, and these cells display heritable increases in drug resistance of up to 50 times the original level. We studied the stage III cells by proteomic methods to uncover differences in the regulatory pathways that are involved in this phenotype, revealing upregulation of phosphorylation on two proteins, succinate-semialdehyde dehydrogenase (SSADH) and methylmalonate-semialdehyde dehydrogenase (MMSADH), and also revealing upregulation of a highly conserved protein of unknown function. Transposon disruption in the encoding genes for each of these targets substantially dampened the ability of cells to develop the stage III phenotype. Considering these results in combination with computational models of resistance and genomic sequencing results, we postulate that stage III heritable resistance develops from a combination of both genomic mutations and modulation of one or more preexisting cellular pathways.In the ongoing war between bug and drug, Pseudomonas aeruginosa is a frequent victor because it rapidly develops new defenses to any drug that is generated (31,41). This is of grave concern for prevention and spread of infectious disease and is a significant mystery to bacteriologists. While there are a number of known resistance mechanisms that develop in P. aeruginosa, the mystery stems from how these are rapidly generated and accumulate in a population to quickly form high-level resistance to an antimicrobial drug after exposure. Finding solutions to inhibit the rise of resistance in P. aeruginosa is important because the organism is responsible for chronic lung infection in individuals with cystic fibrosis (CF) (6) or chronic obstructive pulmonary disease (COPD) (13,15,28), and it also accounts for nearly 10% of hospital-acquired infections (47, 52).There is a small set of drugs commonly used to treat P. aeruginosa infection, including ciprofloxacin, tobramycin, gentamicin, ceftazidime, and imipenem. While P. aeruginosa has developed various levels of resistance to each of these, its response to ciprofloxacin is of particular interest because the drug is initially very effective, but P. aeruginosa rapidly acquires high-level resistance, rendering the drug impotent. In clinical isolates, approximately 30% of strains now present high-level ciprofloxacin resistance (31).While there have been many studies of factors involved in ciprofloxacin resistance in P. aeruginosa (e.g., references 17,...

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Comparative Killing Rates of Fluoroquinolones and Cell Wall-Active Agents

Cited by 27 publications

References 11 publications

Functional Analysis of spoT, relA and dksA Genes on Quinolone Tolerance in Pseudomonas aeruginosa under Nongrowing Condition

Functional Analysis of spoT, relA and dksA Genes on Quinolone Tolerance in Pseudomonas aeruginosa under Nongrowing Condition

Pharmacodynamic Functions: a Multiparameter Approach to the Design of Antibiotic Treatment Regimens

The Development of Ciprofloxacin Resistance in Pseudomonas aeruginosa Involves Multiple Response Stages and Multiple Proteins

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