The selection of bacterial resistance was examined in relationship to antibiotic pharmacokinetics (PK) and organism MICs in the patients from four nosocomial lower respiratory tract infection clinical trials. The evaluable database included 107 acutely ill patients, 128 pathogens, and five antimicrobial regimens. Antimicrobial pharmacokinetics were characterized by using serum concentrations, and culture and sensitivity tests were performed daily on tracheal aspirates to examine resistance. Pharmacodynamic (PD) models were developed to identify factors associated with the probability of developing bacterial resistance. Overall, in 32 of 128 (25%) initially susceptible cases resistance developed during therapy. An initial univariate screen and a classification and regression tree analysis identified the ratio of the area under the concentration-time curve from 0 to 24 h to the MIC (AUC0–24/MIC) as a significant predictor of the development of resistance (P < 0.001). The final PK/PD model, a variant of the Hill equation, demonstrated that the probability of developing resistance during therapy increased significantly when antimicrobial exposure was at an AUC0–24/MIC ratio of less than 100. This relationship was observed across all treatments and within all organism groupings, with the exception of β-lactamase-producing gram-negative organisms (consistent with type I β-lactamase producers) treated with β-lactam monotherapy. Combination therapy resulted in much lower rates of resistance than monotherapy, probably because all of the combination regimens examined had an AUC0–24/MIC ratio in excess of 100. In summary, the selection of antimicrobial resistance appears to be strongly associated with suboptimal antimicrobial exposure, defined as an AUC0–24/MIC ratio of less than 100.

Pharmacokinetic/pharmacodynamic surrogate relationships have been used to describe the antibacterial activity of various classes of antimicrobial agents. Studies that have evaluated these relationships were reviewed to determine which of these surrogate markers were further dependent on antimicrobial class. The fluoroquinolone and aminoglycoside agents exhibit concentration-dependent killing. Studies have demonstrated that peak serum concentration: minimum inhibitory concentration (MIC) and area under the serum concentration-time curve (AUC): MIC ratios are important predictors of outcome for these antimicrobial agents. Area under the inhibitory concentration-time curve (AUIC24) [i.e. AUC24/MIC] is a useful parameter for describing efficacy for these agents, while an adequate peak concentration: MIC ratio seems necessary to prevent selection of resistant organisms. For beta-lactam antibiotics, the duration of time that the serum concentration exceeds the MIC (T > MIC) was the significant pharmacokinetic/pharmacodynamic surrogate in cases where the bacterial inoculum was low, or where very sensitive organisms were tested. However, in studies using more resistant organisms or larger inoculum sizes there is some concentration-dependence to the observed effect. Studies using reasonable dosage intervals have demonstrated covariance between T > MIC and AUC/MIC ratio for beta-lactam antibiotics. Since glycopeptide antibiotics display relatively slow but concentration-independent killing, and are cell wall active agents similar to beta-lactams, it has been presumed that T > MIC is the important pharmacokinetic surrogate related to efficacy for these agents. Some studies have shown that a concentration multiple of the MIC may be necessary for successful outcome with vancomycin. AUIC24 may prove to be an important pharmacokinetic surrogate if both time and concentration are indeed important parameters. To select an appropriate antimicrobial agent, the clinician must consider many patient-specific as well as organism-specific factors. Utilisation of known pharmacokinetic/pharmacodynamic surrogate relationships should help to optimise treatment outcome.

We extensively studied the epidemiology and time course of endemic methicillin-resistant Staphylococcus aureus (MRSA) in the Millard Fillmore Hospital, a 600-bed teaching hospital in Buffalo. The changeover from methicillin-susceptible S. aureus to MRSA begins on the first hospital day, when patients are given cefazolin as presurgical prophylaxis. Under selective antibiotic pressure, colonizing flora change within 24 to 48 hours. For patients remaining hospitalized, subsequent courses of third-generation cephalosporins further select and amplify the colonizing MRSA population. Therefore, managing antibiotic selective pressure might be essential. Other strategies include attention to dosing, so that serum concentrations of drug exceed the minimum inhibitory concentration, and antibiotic cycling. Although there are some promising new antibiotics on the horizon, it is necessary to deal with many resistance patterns by using the combined strategies of infection control and antibiotic management.

The serum bactericidal activity of ciprofloxacin against strains of Streptococcus pneumoniae, Staphylococcus aureus, and Pseudomonas aeruginosa for which MICs are similar (0.4 ,g/ml) was assessed with serum ultrafiltrates from five healthy volunteers receiving ciprofloxacin at 400 mg intravenously every 8 h. In addition, human serum was supplemented with ciprofloxacin to achieve a mean steady-state concentration (Cs,) (716) 887-4566. (AUIC) and the area under the bactericidal titer curve (AUBC) have been described as measures of patient-specific antibiotic pharmacokinetics integrated with bacteria-and antimicrobial agent-specific pharmacodynamics (1). In order to measure the AUBC, SBT tests are performed with patient serum at several time points following antibiotic dosing. The measured AUBC is calculated as the area under the reciprocal SBT versus time curve (1). The AUIC is a measure of inhibitory activity and is calculated as the AUC of the reciprocal SITs versus time. Ellner and Neu (5)

Application of pharmacokinetic and pharmacodynamic principles to dosing of ciprofloxacin may reduce the risk of ciprofloxacin resistance to the level seen in isolates exposed to other agents.

The time-kill curve methodology was used to determine the pharmacodynamics of piperacillin, ciprofloxacin, piperacillin-tazobactam and the combinations piperacillin-ciprofloxacin and ciprofloxacin-piperacillin-tazobactam. Kill curve studies were performed for piperacillin, ciprofloxacin, and piperacillin-tazobactam at concentrations of 0.25 to 50 times the MICs for 13 strains of bacteria: four Pseudomonas aeruginosa, three Enterobacter cloacae, three Klebsiella pneumoniae, and three Staphylococcus aureus isolates (tazobactam concentrations of 0.5, 4, and 12 g/ml). By using a sigmoid E max model and nonlinear least squares regression, the 50% lethal concentrations and the maximum lethal rates of each agent were determined for each bacterial strain. For piperacillin-ciprofloxacin and ciprofloxacin-piperacillin-tazobactam, kill curve studies were performed with concentrations obtained by the fractional maximal effect method (R. C. Li, J. J. Schentag, and D. E. Nix, Antimicrob. Agents Chemother. 37:523-531, 1993) and from individual 50% lethal concentrations and maximum lethal rates. Ciprofloxacin-piperacillin-tazobactam was evaluated only against the four P. aeruginosa strains. Interactions between piperacillin and ciprofloxacin were generally additive. At physiologically relevant concentrations of piperacillin and ciprofloxacin, ciprofloxacin had the highest rates of killing against K. pneumoniae. Piperacillin-tazobactam (12 g/ml) had the highest rate of killing against E. cloacae. Piperacillin-ciprofloxacin with relatively higher ciprofloxacin concentrations had the greatest killing rates against S. aureus. This combination had significantly higher killing rates than piperacillin (P < 0.002). For all the bacterial strains tested, killing rates by ciprofloxacin were significantly higher than those by piperacillin (P < 0.001). Piperacillin-tazobactam (4 and 12 g/ml) had significantly higher killing rates than piperacillin alone (P < 0.02 and P < 0.004, respectively). The effect of the combination of piperacillin-ciprofloxacin, in which piperacillin concentrations were relatively higher, was not statistically different from that of piperacillin alone (P > 0.71). The combination of ciprofloxacin-piperacillin-tazobactam achieved greater killing than other combinations or monotherapies against P. aeruginosa. The reduction in the initial inoculum was 1 to 4 logs greater with ciprofloxacin-piperacillin-tazobactam at 4 and 12 g/ml than with any other agent or combination of agents. On the basis of the additive effects prevalently demonstrated in the in vitro study, the combinations piperacillin-ciprofloxacin and piperacillin-tazobactam are rational therapeutic options. Greater killing of P. aeruginosa was demonstrated with ciprofloxacin-piperacillin-tazobactam. Since treatment failure of P. aeruginosa pneumonia is a significant problem, clinical studies are warranted.Many studies have attempted to examine the efficacies of various combinations of antimicrobial agents by a wide range of susceptibility test methods includin...

The pharmacokinetics (PK) and pharmacodynamics (PD) of cefotaxime and ofloxacin and of their combination were examined in a three-period randomized crossover study involving 12 healthy adults. The PK of cefotaxime and ofloxacin were modeled. PD was assessed from the predicted concentrations in serum and serum untrafiltrate inhibitory titers for 10 test organisms. An inhibitory sigmoid Emax model based on the probability of bacterial growth was used, where Emax = 1 and EC50 is the concentration resulting in a 50% probability of growth. The total body clearance (CL(T)) and volume of distribution at steady state (V(SS)) for cefotaxime were 0.236 liters/kg/h and 0.207 liters/kg, respectively, for the monotherapy and 0.231 liters/kg/h and 0.208 liters/kg for the combination therapy. Ofloxacin exhibited PK parameters of 0.143 liters/kg/h for CL(T) and 1.20 liters/kg for V(SS) following the monotherapy and of 0.141 liters/kg/h for CL(T) and 1.16 liters/kg for V(SS) following combination therapy. For the combination therapy, an interaction term, theta, defined the type and relative extent of interaction. The range of observed theta values (-0.033 to 0.067) is consistent with an additive PD interaction according to standards similar to those used for the in vitro fractional inhibitory concentration index.

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