» Articles » PMID: 17060524

Semimechanistic Pharmacokinetic/pharmacodynamic Model for Assessment of Activity of Antibacterial Agents from Time-kill Curve Experiments

Overview
Specialty Pharmacology
Date 2006 Oct 25
PMID 17060524
Citations 75
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Model-based translation of results from in vitro to in vivo experiments for afabicin activity against Staphylococcus aureus.

Saporta R, Nielsen E, Menetrey A, Cameron D, Nicolas-Metral V, Friberg L J Antimicrob Chemother. 2024; 79(12):3150-3159.

PMID: 39315768 PMC: 11638087. DOI: 10.1093/jac/dkae334.


Formulation and Validation of an Extended Sigmoid Emax Model in Pharmacodynamics.

Byun J Pharm Res. 2024; 41(9):1787-1795.

PMID: 39143408 DOI: 10.1007/s11095-024-03752-9.


A physiologically based pharmacokinetic/pharmacodynamic model to determine dosage regimens and withdrawal intervals of aditoprim against .

Mi K, Sun L, Zhang L, Tang A, Tian X, Hou Y Front Pharmacol. 2024; 15:1378034.

PMID: 38694922 PMC: 11061430. DOI: 10.3389/fphar.2024.1378034.


Time-Kill Analysis of Canine Skin Pathogens: A Comparison of Pradofloxacin and Marbofloxacin.

Azzariti S, Mead A, Toutain P, Bond R, Pelligand L Antibiotics (Basel). 2023; 12(10).

PMID: 37887249 PMC: 10603860. DOI: 10.3390/antibiotics12101548.


PK-PD integration of enrofloxacin and cefquinome alone and in combination against using an dynamic model.

Wei Y, Ji X, Zhang F, Zhang S, Deng Q, Ding H Front Pharmacol. 2023; 14:1226936.

PMID: 37869750 PMC: 10587432. DOI: 10.3389/fphar.2023.1226936.


References
1.
Gustafsson I, Lowdin E, Odenholt I, Cars O . Pharmacokinetic and pharmacodynamic parameters for antimicrobial effects of cefotaxime and amoxicillin in an in vitro kinetic model. Antimicrob Agents Chemother. 2001; 45(9):2436-40. PMC: 90674. DOI: 10.1128/AAC.45.9.2436-2440.2001. View

2.
Marshall S, Macintyre F, James I, Krams M, Jonsson N . Role of mechanistically-based pharmacokinetic/pharmacodynamic models in drug development : a case study of a therapeutic protein. Clin Pharmacokinet. 2006; 45(2):177-97. DOI: 10.2165/00003088-200645020-00004. View

3.
Critchley I, Sahm D, Thornsberry C, Blosser-Middleton R, Jones M, Karlowsky J . Antimicrobial susceptibilities of Streptococcus pyogenes isolated from respiratory and skin and soft tissue infections: United States LIBRA surveillance data from 1999. Diagn Microbiol Infect Dis. 2002; 42(2):129-35. DOI: 10.1016/s0732-8893(01)00327-3. View

4.
MacGowan A, Bowker K . Developments in PK/PD: optimising efficacy and prevention of resistance. A critical review of PK/PD in in vitro models. Int J Antimicrob Agents. 2002; 19(4):291-8. DOI: 10.1016/s0924-8579(02)00027-4. View

5.
Frimodt-Moller N . How predictive is PK/PD for antibacterial agents?. Int J Antimicrob Agents. 2002; 19(4):333-9. DOI: 10.1016/s0924-8579(02)00029-8. View