Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments.
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.
Nielsen
Elisabet I
EI
Division of Pharmacokinetics and Drug Therapy, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden. elisabet.nielsen@farmbio.uu.se
Viberg
Anders
A
Löwdin
Elisabeth
E
Cars
Otto
O
Karlsson
Mats O
MO
Sandström
Marie
M
D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics / Q000494:pharmacology*; D001372:Aza Compounds / Q000493:pharmacokinetics / Q000494:pharmacology; D002444:Cefuroxime / Q000493:pharmacokinetics / Q000494:pharmacology; D004305:Dose-Response Relationship, Drug; D004917:Erythromycin / Q000493:pharmacokinetics / Q000494:pharmacology; D024841:Fluoroquinolones; D008826:Microbial Sensitivity Tests / Q000379:methods*; D008954:Models, Biological; D000077266:Moxifloxacin; D010400:Penicillin G / Q000493:pharmacokinetics / Q000494:pharmacology; D011804:Quinolines / Q000493:pharmacokinetics / Q000494:pharmacology; D013297:Streptococcus pyogenes / Q000187:drug effects* / Q000254:growth & development / Q000378:metabolism; D014640:Vancomycin / Q000493:pharmacokinetics / Q000494:pharmacology
D000900:Anti-Bacterial Agents; D001372:Aza Compounds; D024841:Fluoroquinolones; D011804:Quinolines; D004917:Erythromycin; D014640:Vancomycin; D002444:Cefuroxime; D010400:Penicillin G; D000077266:Moxifloxacin