Microbial kinetics and dependencies of individual and combined antibiotic inhibitors of protein biosynthesis

Achieving an optimal outcome in the treatment of infections. The role of clinical pharmacokinetics and pharmacodynamics of antimicrobials

Over the past few decades, the importance of applying pharmacokinetic principles to the design of drug regimens has been increasingly recognised by clinicians. From the perspective of antimicrobial chemotherapy, an improvement in clinical outcome and/or a reduction in toxicity are of primary interest. Before application of these pharmacokinetic theories can be effective, the interrelationships between antimicrobial, pathogen and host factors must be clearly defined. Information regarding the pharmacokinetics of the antimicrobial and the quantification of pathogen susceptibility is required. Even though susceptibility end-points such as minimum inhibitory concentration (MIC) and minimum bactericidal concentration are widely employed, they do not provide any information on dynamic changes of bacterial densities. In this regard, time-kill studies can provide more basic knowledge of the complex bacterial responses to the antimicrobial. Better prediction of these responses can be afforded by the use of mathematical models. More recently, various surrogate end-points employing a combination of suitable pharmacokinetic parameters and susceptibility data, for example the ratio of peak concentration to MIC, the area under the concentration-time curve above the MIC (AUC > MIC), the time above the MIC, or the area under the inhibitory curve (AUIC), have been suggested for better prediction of the activity of different classes of antimicrobials. To allow more extensive investigations of the contribution of pharmacokinetics to the pharmacodynamics of antimicrobials, various in vitro kinetic models have been developed. However, certain limitations exist, and it is necessary to avoid over-interpretation of the data generated by these models. Two important microbial dynamic responses, postantibiotic effect and resistance selection, must be further explored before the full impact of pharmacokinetics on antimicrobial chemotherapy can be depicted. The present paper aims at discussing all the relevant factors and provides some pertinent information on the use of pharmacokinetic-pharmacodynamic principles in antimicrobial therapy.

Performance of the fractional maximal effect method: comparative interaction studies of ciprofloxacin and protein synthesis inhibitors

The performance of the recently developed Fractional Maximal Effect (FME) method was evaluated along with the conventional checkerboard technique and time-kill method. Ciprofloxacin in combination with tobramycin was tested against Escherichia coli and Pseudomonas aeruginosa and in combination with tetracycline, chloramphenicol, erythromycin against Escherichia coli. Two combinations, amoxicillin-tetracycline (antagonistic) and tobramycin-ticarcillin (synergistic), were included as reference interactions. The FME method unequivocally showed an antagonistic interaction between ciprofloxacin and all the protein synthesis inhibitors (PSIs) tested, while the other two methods yielded variable results. At a total FME (TFME) level of 1, the FME method demonstrated a similar degree of antagonism against ciprofloxacin by tetracycline, chloramphenicol, and erythromycin, and much lower by tobramycin. Internal consistency of the FME operation was demonstrated by the identical conclusions obtained at both TFME levels of 0.5 and 1. The FME method appears to be a practical alternative for resolving the inconsistencies observed in conventional methods of antibiotic combination testing.

Pharmacodynamic modeling of bacterial kinetics: beta-lactam antibiotics against Escherichia coli

A simple pharmacodynamic model has been developed to describe the bacterial kinetics exhibited by beta-lactam antibiotics. In contrast with previous models that only characterized the early killing phase of a time-kill curve, the present model is capable of simultaneously describing both the killing and regrowth phases. The model relied on the use of both first-order bactericidal and resistance formation rate constants to accurately define the time-dependent changes in the bacterial populations of an antibiotic-treated culture. The concentration dependency of the bactericidal rate constant was further delineated using a saturable-receptor model. Furthermore, an exponential decrease in the resistance formation rate with increasing antibiotic concentrations was demonstrated. The evolving pharmacodynamic model was also explored via computer simulations by perturbing the two governing rate constants. The model was subsequently applied to the description of time-kill data for amoxicillin, penicillin G, and cephalexin against Escherichia coli. The description of amdinocillin's action against E. coli was not as comprehensive because of the existence of a second killing phase. However, this model can be applicable to many classes of antibiotics that display the usual killing and regrowth phases in time-kill studies. The pharmacodynamic model can potentially improve the prediction of bacterial killing and regrowth and foster an improved understanding of complex antimicrobial pharmacodynamics.

The fractional maximal effect method: a new way to characterize the effect of antibiotic combinations and other nonlinear pharmacodynamic interactions

The checkerboard technique leading to the fractional inhibitory concentration indexes and the killing curve method are currently the most widely used methods to study antibiotic combinations. For both methods, experimental conditions and interpretation criteria are somewhat arbitrary. The relevance of the fractional inhibitory concentration index computation, in the classic case of additivity [P = d1/(D1)p + d2/(D2)p, where d1 and d2 are the doses of drugs 1 and 2 in combination to produce an effect at a percent level (P) and (D1)p and (D2)p are the doses required for the two respective drugs alone to produce the same effect] relies on the assumption of a linear relationship between the MIC and the concentration of the test antibiotics. In addition, there is no consensus as to the definition of synergy in killing curve interpretation. The fractional maximal effect (FME) method is a new approach which was developed to handle the nonlinear pharmacodynamics exhibited by antibiotics and other drugs. This method relies on the mathematical linearization of the nonlinear concentration-effect scales and eventual construction of an isobologram-type data plot. The FME method was applied to study interactions between several antibiotic combinations: amoxicillin and tetracycline, ciprofloxacin and erythromycin, and ticarcillin and tobramycin. These combinations were selected because the pharmacologic basis for their interactions has been previously described. The FME method correctly identified antagonism for the first two combinations and synergism for the last combination. Conclusions were reproducible across the range of concentrations studied. Besides providing information on the nature of the interaction, the method can rapidly explore the effect of changing concentration ratios of two antimicrobial agents on the degrees of interaction. The FME method may be applied to interactions between drugs or agents with either a linear or nonlinear endpoint measurement. Methods frequently used for drug combination testing are also discussed in the paper.

Evaluation of an indanyl ester of carbenicillin

Cultures of tetracycline-treated Staphylococcus aureus exhibited monophasic steady-state growth curves similar to that observed for tetracycline-treated Escherichia coli. Apparent growth rate constants of the respective drug-treated cultures showed the same formal dependence on drug concentration, which was linear at a low concentration but asymptotically approached zero at higher concentration levels and implied the saturation of a limited number of receptor sites engaged in microbial protein synthesis. The relative potency of tetracycline action of S. aureus/E. coli was 6.50:1 at 37.5°C and pH 7.05. This is attributed to relative differences in drug permeation and/or binding affinity for biophase receptors in the respective organisms. It is concluded from kinetic dependencies of growth inhibition of the cultures that tetracycline has the same mode of action on S. aureus and E. coli. It is bacteriostatic at concentrations below the minimal inhibitory concentration level but bactericidal at the higher concentration levels.

Kinetics and mechanisms of drug action on microorganisms XXII: Effects of aminosidine with and without organism pretreatment with bacteriostatic agents

Escherichia coli generation in the logarithmic growth phase was inhibited in peptone broth USP at pH 7.0 without kill below 3.0 mug/ml of aminosidine. Above this value, the logarithms of the number of viables of the drug-treated culture ultimately decreased linearly with time and the slopes of these plots were independent of concentration. A concentration-dependent lag in the time of attainment of the cidal action was observed, and the extent of this lag was related to the ease of emergence of resistant organisms. The minimal concentration for cidal action increased with increasing concentrations of nutrients and with decreasing pH. Pretreatment of the cultures with novobiocin and tetracycline lessened the minimum bactericidal concentration of aminosidine whereas chloramphenicol pretreatment increased it. Tetracycline pretreatment inhibited the emergence of aminosidine-resistant organisms.

Effect of novobiocin and its combination with tetracycline, chloramphenicol, erythromycin, and lincomycin on the microbial generation of Escherichia coli

Inhibition of the steady-state generation of Escherichia coli by the bacteriostatic antibiotic novobiocin is linearly related to drug concentration in the range of 0 to 30 mug/ml. Increased cell sizes result because the drug inhibits cell division. The generation rate dependence on drug concentration depends on the nonionized fraction of novobiocin and is invariant with inoculum size or medium composition. However, the antibacterial activity of novobiocin decreases as the concentration of nutrients and Mg(2+) increases, although the inhibitory action of novobiocin on generation rate remains unchanged for concentrations of Mg(2+) above 8.1 x 10(-4) M. Novobiocin is synergistic in combinations with tetracycline in broth, but not when the Mg(2+) was maintained at 4.05 x 10(-3) M. Combinations of novobiocin with the 50S ribosomal subunit inhibitors chloramphenicol, erythromycin, or lincomycin are antagonistic, and the degree of growth inhibition is determined only by that component of the binary combination that would have the greater potency if it were acting alone.