Development and Qualification of a Pharmacodynamic Model for the Pronounced Inoculum Effect of Ceftazidime against Pseudomonas aeruginosa

Abstract

Evidence is mounting in support of the inoculum effect (i.e., slow killing at large initial inocula [CFUo]) for numerous antimicrobials against a variety of pathogens. Our objectives were to (i) determine the impact of the CFUo of Pseudomonas aeruginosa on ceftazidime activity and (ii) to develop and validate a pharmacokinetic/ pharmacodynamic (PKPD) mathematical model accommodating a range of CFUo. Time-kill experiments using ceftazidime at seven concentrations up to 128 mg/liter (MIC, 2 mg/liter) were performed in duplicate against P. aeruginosa PAO1 at five CFUo from 10 5 to 10 9 CFU/ml. Samples were collected over 24 h and fit by candidate models in NONMEM VI and S-ADAPT 1.55 (all data were comodeled). External model qualification integrated data from eight previously published studies. Ceftazidime displayed approximately 3 to 4 log 10 CFU/ml net killing at 10 6.2 CFUo and concentrations of 4 mg/liter (or higher), less than 1.6 log 10 CFU/ml killing at 10 7.3CFUo, and no killing at 10 8.0 CFUo for concentrations up to 128 mg/liter. The proposed mechanism-based model successfully described the inoculum effect and the concentration-independent lag time of killing. The mean generation time was 28.3 min. The effect of an autolysin was assumed to inhibit successful replication. Ceftazidime concentrations of 0.294 mg/liter stimulated the autolysin effect by 50%. The model was predictive in the internal cross-validation and had excellent in silico predictive performance for published studies of P. aeruginosa ATCC 27853 for various CFUo. The proposed PKPD model successfully described and predicted the pronounced inoculum effect of ceftazidime in vitro and integrated data from eight literature studies to support translation from time-kill experiments to in vitro infection models.Pseudomonas aeruginosa is an opportunistic, gram-negative pathogen responsible for high morbidity and mortality (19). P. aeruginosa has multiple mechanisms of resistance to antibiotics, including efflux pumps, the enzymatic degradation of antibiotics by, e.g., beta-lactamases, and target structure alteration (19,25,45). Due to its remarkable ability to resist killing by antibiotics (45), many P. aeruginosa isolates from nosocomial infections are multidrug resistant. The proportion of ceftazidime-resistant P. aeruginosa isolates from intensive care units increased from approximately 14% in 1997 to 24% in 2003 in the United States (23).Infections with a high bacterial density at the initiation of antibiotic therapy may present a therapeutic problem, including a higher risk for the emergence of resistance due to the larger number of bacteria present and the higher probability of having at least one resistant bacterial cell within a large initial inoculum (CFUo) (32). The probability of the emergence of resistance may be substantially increased at high CFUo, as the amplification of resistant subpopulations has been demonstrated to occur secondarily to low-intensity antimicrobial exposure (60). The inoculum effect was first described by Kirby (34)...

Translational Pharmacometric Evaluation of Typical Antibiotic Broad-Spectrum Combination Therapies AgainstStaphylococcus AureusExploitingIn VitroInformation

“…To be able to capture replication dependent and independent drug effects, we used a simplified blueprint of the bacterial life cycle11 for simultaneously modeling of the single and combined effects. The life‐cycle model enabled to include the drug effects motivated by their respective mode of action.…”

“…A simplified life‐cycle model11 was utilized as the core of the PD model, which consisted of two bacterial growth states: bacteria in the growing state (“GRO”) transferred into the replicating state (“REP”). In “REP,” bacteria replicated (“doubling”) and transferred back to “GRO.” The first‐order rate‐constant k rep was assumed to be rate‐limiting and actual replication was assumed to be very fast (k doub fixed to 100 h −1 ).…”

Quantifying Subpopulation Synergy for Antibiotic Combinations via Mechanism-Based Modeling and a Sequential Dosing Design

“…For each population, a life cycle growth model (22,23) was applied that accounts for the underlying biology of bacterial growth (24) and contains bacteria that are preparing for replication (state 1) and bacteria immediately before the replication step (state 2). The transition from state 1 to state 2 occurred via a first-order growth The combination of drug A and drug B will eradiate these bacterial populations due to the lack of a population resistant to both drugs.…”

“…As CFU ALL approaches CFU max , REP approaches 1, representing a 50% probability of successful replication where bacteria continue to transition between states 1 and 2 but the total viable count is constant (22). The differential equation for CFU SS,2 also included killing by nisin and amikacin:…”

“…Models with one, two, or three preexisting populations with different susceptibilities to the respective antibiotic were considered similarly to previously described models (8,14,19,22,(25)(26)(27)(28)(29)(30). The susceptibility of each population was estimated via a specific rate of bacterial killing by the respective antibiotic (Fig.…”