Pharmacokinetic/Pharmacodynamic (PK/PD) Evaluation of a Once-Daily Treatment Using Ciprofloxacin in an Extended-Release Dosage Form*

Schuck, Edgar; Dalhoff, A.; Staß, Heino; Derendorf, Hartmut

doi:10.1007/s15010-005-8204-0

Cited by 34 publications

(32 citation statements)

References 35 publications

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“…The dynamics between constitutively polymyxin-resistant (mutants) and adaptively polymyxin-resistant bacterial populations during polymyxin therapy have not been previously investigated. In line with recently published recommendations (51), the new mechanism-based model described in this report built upon prior work (35,36,52) on modeling antimicrobial activity and incorporated both of these resistance mechanisms, in addition to reversible dormancy. The model was able to describe well the observed viable counts on both antibioticfree and antibiotic-containing agar plates (Fig.…”

Section: Discussionmentioning

confidence: 84%

“…The effective polymyxin concentration (C polymyxin,eff ) was then calculated, accounting for the occupancy of the lipid A binding site (F polymyxin,eff ) and the concentration of divalent cations in cation-adjusted Mueller-Hinton broth (equation 6). Bacterial killing by polymyxin (Kill polymyxin,eff ) was described by a Hill equation (equation 7) that is commonly used to describe antimicrobial activity (35,36). Polymyxin resistance.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Colistin and Polymyxin B Dosage Regimens against Acinetobacter baumannii: Differences in Activity and the Emergence of Resistance

Cheah

Tsuji

et al. 2016

Antimicrob Agents Chemother

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dInfections caused by multidrug-resistant Acinetobacter baumannii are a major public health problem, and polymyxins are often the last line of therapy for recalcitrant infections by such isolates. The pharmacokinetics of the two clinically used polymyxins, polymyxin B and colistin, differ considerably, since colistin is administered as an inactive prodrug that undergoes slow conversion to colistin. However, the impact of these substantial pharmacokinetic differences on bacterial killing and resistance emergence is poorly understood. We assessed clinically relevant polymyxin B and colistin dosage regimens against one reference and three clinical A. baumannii strains in a dynamic one-compartment in vitro model. A new mechanism-based pharmacodynamic model was developed to describe and predict the drug concentrations and viable counts of the total and resistant populations. Rapid attainment of target concentrations was shown to be critical for polymyxin-induced bacterial killing. All polymyxin B regimens achieved peak concentrations of at least 1 mg/liter within 1 h and caused >4 log 10 killing at 1 h. In contrast, the slow rise of colistin concentrations to 3 mg/liter over 48 h resulted in markedly reduced bacterial killing. A significant (4 to 6 log 10 CFU/ ml) amplification of resistant bacterial populations was common to all dosage regimens. The developed mechanism-based model explained the observed bacterial killing, regrowth, and resistance. The model also implicated adaptive polymyxin resistance as a key driver of bacterial regrowth and predicted the amplification of preexisting, highly polymyxin-resistant bacterial populations following polymyxin treatment. Antibiotic combination therapies seem the most promising option for minimizing the emergence of polymyxin resistance.

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Section: Discussionmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Colistin and Polymyxin B Dosage Regimens against Acinetobacter baumannii: Differences in Activity and the Emergence of Resistance

Cheah

Tsuji

et al. 2016

Antimicrob Agents Chemother

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“…An alternative to the assumption that the total bacterial population consists of a few distinct and discrete bacterial populations is to model the emergence of resistance as a gradual process evolving over time. A time-dependent adaptation factor has been included to describe a time-dependent decrease in the maximum kill rate, E max (Schuck et al, 2005), or in the bacterial growth-rate constant (Mouton et al, 1997). This modeling strategy has been further developed by Tam et al (2005b) by introducing an adaptation factor (a) that is dependent on time as well as the drug concentration according to a saturable adaptation function (eq.…”

Section: Pharmacokinetic-pharmacodynamic Models Describing Antibioticmentioning

confidence: 99%

Pharmacokinetic-Pharmacodynamic Modeling of Antibacterial Drugs

Nielsen

Friberg

2013

Pharmacological Reviews

265

326

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“…We and others have previously developed mathematical models to capture the dynamic relationship between a heterogeneous microbial population and constant drug concentrations (10,13,15,16,21,24). Our models were further refined to predict the microbial response to multiple antimicrobial agent dosing regimens (fluctuating drug concentration over time) efficiently (14,22).…”

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confidence: 99%

Mathematical Modeling To Characterize the Inoculum Effect

Bhagunde¹,

Chang

Singh

et al. 2010

Antimicrob Agents Chemother

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Killing by beta-lactams is well known to be reduced against a dense bacterial population, commonly known as the inoculum effect. However, the underlying mechanism of this phenomenon is not well understood. We proposed a semimechanistic mathematical model to account for the reduced in vitro killing observed. Time-kill studies were performed with 4 baseline inocula (ranging from approximately 1 ؋ 10 5 to 1 ؋ 10 8 CFU/ml) of Escherichia coli ATCC 25922 (MIC, 2 mg/liter). Constant but escalating piperacillin concentrations used ranged from 0.25؋ to 256؋ MIC. Serial samples were taken over 24 h to quantify viable bacterial burden, and all the killing profiles were mathematically modeled. The inoculum effect was attributed to a reduction of effective drug concentration available for bacterial killing, which was expressed as a function of the baseline inoculum. Biomasses associated with different inocula were examined using a colorimetric method. Despite identical drug-pathogen combinations, the baseline inoculum had a significant impact on bacterial killing. Our proposed mathematical model was unbiased and reasonable in capturing all 28 killing profiles collectively (r 2 ‫؍‬ 0.88). Biomass was found to be significantly more after 24 h with a baseline inoculum of 1 ؋ 10 8 CFU/ml, compared to one where the initial inoculum was 1 ؋ 10 5 CFU/ml (P ‫؍‬ 0.002). Our results corroborated previous observations that in vitro killing by piperacillin was significantly reduced against a dense bacterial inoculum. This phenomenon can be reasonably captured by our proposed mathematical model, and it may improve prediction of bacterial response to various drug exposures in future investigations.

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Pharmacokinetic/Pharmacodynamic (PK/PD) Evaluation of a Once-Daily Treatment Using Ciprofloxacin in an Extended-Release Dosage Form*

Cited by 34 publications

References 35 publications

Colistin and Polymyxin B Dosage Regimens against Acinetobacter baumannii: Differences in Activity and the Emergence of Resistance

Colistin and Polymyxin B Dosage Regimens against Acinetobacter baumannii: Differences in Activity and the Emergence of Resistance

Pharmacokinetic-Pharmacodynamic Modeling of Antibacterial Drugs

Mathematical Modeling To Characterize the Inoculum Effect

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