Pharmacokinetics and Pharmacodynamics of Antimicrobials (original) (raw)
Related papers
2006
Antimicrobial efficacy in vivo is not exclusively defined by the activity of an antibiotic as determined in the in vitro susceptibility test. Knowledge of the pharmacokinetics and pharmacodynamics of antimicrobials and all phenomena occurring between antimicrobial agents and microorganisms is imperative. The pharmacodynamic (PD) parameters most often used in studies of antibiotic effect include the following relationships: the maximum free concentration (fCmax) to minimum inhibitory concentration (MIC) ratio, the free area under the curve (fAUC/MIC) ratio and the duration of time the free concentration exceeds the MIC (fT>MIC). Utilization of known pharmacokinetic / pharmacodynamic surrogate relationships should help to optimize treatment outcome, especially in the face of emerging resistance among Gram-positive and Gram-negative bacteria. Clinical studies in the field of antibacterial PD are still relatively scarce, and much information is needed to enable relevant dosing strate...
Pharmacodynamic Functions: a Multiparameter Approach to the Design of Antibiotic Treatment Regimens
Antimicrobial Agents and Chemotherapy, 2004
There is a complex quantitative relationship between the concentrations of antibiotics and the growth and death rates of bacteria. Despite this complexity, in most cases only a single pharmacodynamic parameter, the MIC of the drug, is employed for the rational development of antibiotic treatment regimens. In this report, we use a mathematical model based on a Hill function-which we call the pharmacodynamic function and which is related to previously published E max models-to describe the relationship between the bacterial net growth rates and the concentrations of antibiotics of five different classes: ampicillin, ciprofloxacin, tetracycline, streptomycin, and rifampin. Using Escherichia coli O18:K1:H7, we illustrate how precise estimates of the four parameters of the pharmacodynamic function can be obtained from in vitro time-kill data. We show that, in addition to their respective MICs, these antibiotics differ in the values of the other pharmacodynamic parameters. Using a computer simulation of antibiotic treatment in vivo, we demonstrate that, as a consequence of differences in pharmacodynamic parameters, such as the steepness of the Hill function and the minimum bacterial net growth rate attained at high antibiotic concentrations, there can be profound differences in the microbiological efficacy of antibiotics with identical MICs. We discuss the clinical implications and limitations of these results.
PLoS Computational Biology, 2011
Pharmacodynamic modeling has been increasingly used as a decision support tool to guide dosing regimen selection, both in the drug development and clinical settings. Killing by antimicrobial agents has been traditionally classified categorically as concentration-dependent (which would favor less fractionating regimens) or time-dependent (for which more frequent dosing is preferred). While intuitive and useful to explain empiric data, a more informative approach is necessary to provide a robust assessment of pharmacodynamic profiles in situations other than the extremes of the spectrum (e.g., agents which exhibit partial concentration-dependent killing). A quantitative approach to describe the interaction of an antimicrobial agent and a pathogen is proposed to fill this unmet need. A hypothetic antimicrobial agent with linear pharmacokinetics is used for illustrative purposes. A non-linear functional form (sigmoid Emax) of killing consisted of 3 parameters is used. Using different parameter values in conjunction with the relative growth rate of the pathogen and antimicrobial agent concentration ranges, various conventional pharmacodynamic surrogate indices (e.g., AUC/MIC, Cmax/MIC, %T.MIC) could be satisfactorily linked to outcomes. In addition, the dosing intensity represented by the average kill rate of a dosing regimen can be derived, which could be used for quantitative comparison. The relevance of our approach is further supported by experimental data from our previous investigations using a variety of gram-negative bacteria and antimicrobial agents (moxifloxacin, levofloxacin, gentamicin, amikacin and meropenem). The pharmacodynamic profiles of a wide range of antimicrobial agents can be assessed by a more flexible computational tool to support dosing selection. Citation: Tam VH, Nikolaou M (2011) A Novel Approach to Pharmacodynamic Assessment of Antimicrobial Agents: New Insights to Dosing Regimen Design. PLoS Comput Biol 7(1): e1001043.
The application of population pharmacokinetic modeling to individualized antibiotic therapy
International Journal of Antimicrobial Agents, 2002
This paper describes applications of population pharmacokinetic modeling to the optimization of antibiotic dosing. Parametric and nonparametric pharmacokinetic modeling approaches are discussed. Population models can be important extensions of therapeutic drug monitoring (TDM) in infectious disease. The concept of population model-based individualized antimicrobial therapy is described. With the availability of population modeling for obtaining PK parameter estimates, the focus has shifted to quantifying the antimicrobial effect and linking kinetics to drug effects. Examples of integrated pharmacokinetic Á/pharmacodynamic (PK Á/PD) models to describe bacterial killing as a function of drug concentration are discussed. Application of PK Á/PD mathematical models that correlate with microbiological and clinical outcomes will provide us with a better rationale for the proper dose selection of anti-infective therapy in different patient populations.
The pharmacokinetic–pharmacodynamic approach to a rational dosage regimen for antibiotics
Research in Veterinary Science, 2002
Pharmacokinetic-pharmacodynamic (PK/PD) surrogate indices (AUIC, AUC/MIC, C max /MIC, T > MIC) for measuring antibiotic efficacy are presented and reviewed. As clinical trials are not sufficiently sensitive to establish a dosage regimen which guarantees total bacteriological cure (Pollyanna phenomenon), PK/PD indexes have been proposed from in vitro, ex vivo, and in vivo infection models and subsequently validated in retrospective or prospective human clinical trials. The target value for time-dependent antibiotics (b-lactams, macrolides) is a time above the MIC (T > MIC) of 50-80% of the dosage interval, while for concentration-dependent antibiotics (quinolones and aminoglycosides), the area under the inhibitory curve (AUIC, or more simply AUC/MIC of about 125 h) is the best surrogate indicator of activity. Using the latter drugs, high concentrations achieved early during therapy are desirable to prevent the development of resistance. A C max /MIC ratio greater than 10-12 seems to be an appropriate target for aminoglycosides. Ó
Expert opinion on drug discovery, 2007
The selection of appropriate doses and dosing regimens is extremely important in antimicrobial therapy in order to increase the chances of clinical success and decrease the likelihood of toxic side effects and the development of resistance. Therefore, empirical treatment of bacterial infections is not reliable enough and more rational approaches are needed. Pharmacokinetic/pharmacodynamic (PK/PD) modeling provides a powerful tool to systematically link PK and PD properties in order to predict antimicrobial efficacy. However, commonly used minimum inhibitory concentration (MIC) based PK/PD indices, although easy to obtain, have a number of limitations. Thus, more reliable PK/PD indices need to be developed. The following article provides an overview of the present PK/PD approaches used in anti-infective therapy and discusses their role in improving drug therapy.
PLoS Pathogens, 2013
There are both pharmacodynamic and evolutionary reasons to use multiple rather than single antibiotics to treat bacterial infections; in combination antibiotics can be more effective in killing target bacteria as well as in preventing the emergence of resistance. Nevertheless, with few exceptions like tuberculosis, combination therapy is rarely used for bacterial infections. One reason for this is a relative dearth of the pharmaco-, population-and evolutionary dynamic information needed for the rational design of multi-drug treatment protocols. Here, we use in vitro pharmacodynamic experiments, mathematical models and computer simulations to explore the relative efficacies of different two-drug regimens in clearing bacterial infections and the conditions under which multi-drug therapy will prevent the ascent of resistance. We estimate the parameters and explore the fit of Hill functions to compare the pharmacodynamics of antibiotics of four different classes individually and in pairs during cidal experiments with pathogenic strains of Staphylococcus aureus and Escherichia coli. We also consider the relative efficacy of these antibiotics and antibiotic pairs in reducing the level of phenotypically resistant but genetically susceptible, persister, subpopulations. Our results provide compelling support for the proposition that the nature and form of the interactions between drugs of different classes, synergy, antagonism, suppression and additivity, has to be determined empirically and cannot be inferred from what is known about the pharmacodynamics or mode of action of these drugs individually. Monte Carlo simulations of within-host treatment incorporating these pharmacodynamic results and clinically relevant refuge subpopulations of bacteria indicate that: (i) the form of drug-drug interactions can profoundly affect the rate at which infections are cleared, (ii) two-drug therapy can prevent treatment failure even when bacteria resistant to single drugs are present at the onset of therapy, and (iii) this evolutionary virtue of two-drug therapy is manifest even when the antibiotics suppress each other's activity.