Semimechanistic Pharmacokinetic/Pharmacodynamic Model for Assessment of Activity of Antibacterial Agents from Time-Kill Curve Experiments

Abstract

Dosing of antibacterial agents is generally based on point estimates of the effect, even though bacteria exposed to antibiotics show complex kinetic behaviors. The use of the whole time course of the observed effects would be more advantageous. The aim of the present study was to develop a semimechanistic pharmacokinetic (PK)/pharmacodynamic (PD) model characterizing the events seen in a bacterial system when it is exposed to antibacterial agents with different mechanisms of action. Time-kill curve experiments were performed with a strain of Streptococcus pyogenes exposed to a wide range of concentrations of the following antibiotics: benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, and vancomycin. Bacterial counts were monitored with frequent sampling during the experiment. A simultaneous fit of all data was accomplished. The degradation of the drugs was monitored and corrected for in the model, and a link model was used to account for an effect delay. In the final PK/PD model, the total bacterial population was divided into two subpopulations: one growing drug-susceptible population and one resting insusceptible population. The drug effect was included as an increase of the killing rate of bacteria in the susceptible state, according to a maximum-effect (E max ) model. An internal model validation showed that the model was robust and had good predictability. In conclusion, for all drugs, the final PK/PD model successfully described bacterial growth and killing kinetics when the bacteria were exposed to different antibiotic concentrations. The semimechanistic model that was developed might, after further refinement, serve as a tool for the development of optimal dosing strategies for antibacterial agents.The MIC is the most commonly used parameter to describe the efficacy of an antibacterial agent against a bacterial strain. This is an in vitro measure reflecting the efficacy of a constant antibiotic exposure to a specified bacterial inoculum after an incubation period of 16 to 20 h (19). The MIC is an estimate of the susceptibility of a bacterial strain to an antibiotic that can guide the choice of appropriate antibiotic treatment in the clinical setting. However, it is not an optimal pharmacodynamic (PD) marker since it reflects only a point estimate of the effect and does not take the time course of the effect into account. Nevertheless, the pharmacokinetic (PK)/PD relationship for antibiotics has generally been characterized by using point estimates of the pharmacodynamics (e.g., the bacterial load after 24 h of exposure) and the pharmacokinetics. This approach has led to the classification of the antibacterial effect being dependent either on the antibiotic exposure (the maximum concentration in serum/MIC or the area under the concentration-time curve/MIC) or on the time that the antibiotic concentration is kept above the MIC (4, 18). The design of dosing schedules may, however, be further optimized if it is based on models that take the whole time course of the PK/PD relation, i.e., the time cour...

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

“…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 ). Bacteria being not susceptible to antibiotic exposure and not replicating (“persisters”) were assumed to be generated during replication and quantified in a compartment with nonreplicating persisting bacteria,12 (“PER”). The differential equations initialized for “GRO” with the bacterial concentration at t = 0 (CFU 0 ) as initial condition (IC) was as follows: $\frac{dGRO}{dt}=-k_{rep}\times GRO+k_{doub}\times REP\times 2\hspace{1em}\mathrm{IC}\hspace{0.17em}=\hspace{0.17em}{\text{CFU}}_0$ $\frac{dREP}{dt}=k_{rep}\times GRO-k_{doub}\times REP-k_{per}\times REP\hspace{1em}\text{IC}\hspace{0.17em}=\hspace{0.17em}0$ $\frac{dPER}{dt}=k_{per}\times REP-k_{death,per}\times PER\hspace{1em}\text{IC}\hspace{0.17em}=\hspace{0.17em}0$ …”

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

“…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.…”

Antifungal activity of alpha-mangostin against Candida albicans

“…Measuring the rate of fungicidal activity by time-kill assay can assess the speed with which killing may occur at a given drug concentration (19)(20). C. albicans in the log growth phase were used to prepare suspensions of 10 4 CFU/ml in Sabouraud dextrose broth.…”