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What Do We Really Know About Antibiotic Pharmacodynamics?


Brent W. Gunderson, Pharm.D., Gigi H. Ross, Pharm.D., Khalid H. Ibrahim, Pharm.D., and John C. Rotschafer, Pharm.D., FCCP

[Pharmacotherapy 21(11s):302s-318s, 2001. © 2001 Pharmacotherapy Publications, Inc.]

Abstract and Introduction

Abstract

Antibiotic pharmacodynamics is an evolving science that focuses on the relationship between drug concentration and pharmacologic effect, which is an antibiotic-induced bacterial death that also can manifest as an adverse drug reaction. The pharmacologic action of antibiotics usually can be described as concentration dependent or independent, although such classifications are highly reliant on the specific antibiotic and bacterial pathogen being studied. Quantitative pharmacodynamic parameters, such as ratio of the area under the concentration-time curve during a 24-hour dosing period to minimum inhibitory concentration (AUC0-24:MIC), ratio of maximum serum antibiotic concentration to MIC (Cmax:MIC), and duration of time that antibiotic concentrations exceed MIC (T>MIC), have been proposed as likely predictors of clinical and microbiologic success or failure for different pairings of antibiotic and bacteria. Thus far, most pharmacodynamic data reported have focused on fluoroquinolones, but work has been conducted on vancomycin, ß-lactams, macrolides, aminoglycosides, and other antibiotics. Despite the development of a number of different pharmacodynamic modeling systems, remarkable agreement exists between in vitro, animal, and limited human data. Although still somewhat premature and requiring additional clinical validation, antibiotic pharmacodynamics will likely advance on four fronts: the science should prove to be extremely useful and represent a cost-effective and efficient method to help develop new antibiotics; formulary committees will likely use pharmacodynamic parameters to assist in differentiating antibiotics of the same chemical class in making antibiotic formulary selections; pharmacodynamic principles will likely be used to design optimal antibiotic strategies for patients with severe infections; and limited data to date suggest that the application of pharmacodynamic concepts may limit or prevent the development of antibiotic resistance. The study of antibiotic pharmacodynamics appears to hold great promise and will likely become a routine part of our daily clinical practices.

Introduction

Overall, the antibiotic prescribing process has been extremely subjective and has promoted a naive understanding of how to optimize antibiotic performance. During the past 30 years, clinicians have become very familiar with the science of pharmacokinetics, which is a very useful tool for describing how drugs behave in the human host, but it does not promote an understanding of a drug's desired or undesired pharmacologic effects. Pharmacodynamics has the potential to provide clinicians with the missing tools required to make antibiotic prescribing more objective and to expand our understanding of the interaction between bacteria and antibiotics.

During the past 10 years, investigators have been able to identify possible pharmacodynamic outcome predictors and assign quantitative values that predict the success or failure of antibiotic regimens. In addition, pharmaco-dynamics offers a way to expedite the antibiotic development process and provide an additional dimension for distinguishing various members of a particular antibiotic class.

Background of Pharmacodynamics and Pharmacodynamic Outcome Parameters

Antibiotics can be classified as either concentration- or time-dependent bactericidal agents. Concentration-dependent (or time-independent) antibiotics kill at a greater rate and to a greater extent with increasing antibiotic concentrations, whereas time-dependent (or concentration-independent) antibiotics kill bacteria at the same rate and to the same extent once an appropriate antibiotic threshold concentration has been achieved. Increasing the antibiotic concentration beyond this point typically does not augment the antibacterial activity.

In general, aminoglycosides, fluoroquinolones, and metronidazole (against anaerobic bacteria) are considered concentration-dependent killers, whereas vancomycin, clindamycin, macrolides (with the possible exception of azithromycin), and ß-lactams are considered concentration-independent killers. Our understanding is compromised, however, due to limited data involving a relatively small number of bacterial isolates over a confined range of antibiotic concentrations.

Presently, only three quantifiable pharmaco-dynamic parameters have been thoroughly investigated: the duration of time in which antibiotic concentrations exceed the minimum inhibitory concentration (T>MIC), the ratio of maximum serum antibiotic concentration (Cmax) to MIC (Cmax:MIC), and the ratio of the area under the concentration-time curve during a 24-hour dosing period (AUC0-24) to MIC (AUC0-24:MIC). As only free drug is therapeutically active, these parameters probably should be adjusted to reflect the degree of protein binding for highly bound agents. In general, concentration-independent killers are represented best by T>MIC, whereas concentration-dependent killers appear best represented by Cmax:MIC or AUC0-24:MIC. As there is a direct relationship among antibiotic dose, Cmax, AUC0-24, and T>MIC, the covariance among these parameters complicates the investigator's ability to identify the optimal predictor of antibiotic performance. Conflicting data promoting different pharmacodynamic parameters are easily found in the literature because of this covariance.

Thus far, pharmacodynamics has used either serum antibiotic concentrations or the derived pharmacokinetic parameters as surrogate markers of efficacy at the actual infection site to predict outcome. This may be a misrepresentation because the antibacterial concentration at an infection site is highly dependent on antibiotic distribution properties. Undoubtedly, as the science progresses and the level of sophistication increases, new mathematical relationships that better predict activity will be discovered and validated. This is essential, as current models do not adequately predict activity for select types of infections or specific antibiotics. An example of the former is intracellular infection such as with species of Mycoplasma, Legionella, and Chlamydia, and an example of the latter is macrolides, where intracellular and tissue concentrations are high but concentration of antibiotic remaining in the blood or serum is modest.

The optimal value of a pharmacodynamic parameter is a function of the physiochemical proprieties of the antibiotic and the biologic properties of the target bacteria (see the list of valuable pharmacodynamic data provided by in vitro testing in the In Vitro Pharmacodynamic Models section). The activity of an antibiotic is likely to be influenced by factors at the site of infection, such as pH, bacterial load, phase of bacterial growth, or the presence or absence of oxygen.

ß-lactams

ß-lactams primarily exhibit concentration-independent activity, and the focus has been on T>MIC as a predictive parameter. Results of an experiment published in 1950 found that the major determinant of penicillin activity against pneumococci in an animal model was the amount of time in which drug concentration remained above minimally effective levels.[1] Increased efficacy with larger doses was attributed to an extension in the time the drug remained above the effective concentration, rather than to an increase in absolute concentrations. The results of further studies in both in vitro models and in vivo confirm the importance of T>MIC in optimizing ß-lactam activity (Table 1).[1-12]

In vivo data from a murine model of Klebsiella pneumoniae pneumonia in a neutropenic host suggest that continuous infusion of ceftazidime may be especially useful when host defenses are impaired.[5] Implicit in this finding is the importance of T>MIC in predicting efficacy. Further studies in a neutropenic murine thigh infection model have shown T>MIC to be the best predictor of efficacy of ticarcillin against Pseudomonas aeruginosa, ceftazidime against Escherichia coli, cefazolin against E. coli and Staphylococcus aureus, and both penicillin and erythromycin against Streptococcus pneumoniae.[12,13] For maximum efficacy, ticarcillin levels needed to remain above the MIC for P. aeruginosa for 100% of the dosage interval, a finding that also applied to cefazolin against E. coli and penicillin against S. pneumoniae. However, cefazolin activity against S. aureus was maximized when antibiotic concentration remained above the MIC for 55% of the dosing interval; a similar percentage was observed to maximize erythromycin activity against S. pneumoniae.

The parameters AUC0-24 and Cmax may be at least as important as T>MIC in predicting optimal ß-lactam activity in certain types of infection. A model of ampicillin activity against Haemophilus influenzae in an in vivo fibrin clot model demonstrated that higher doses produced stronger bactericidal activity compared with continuous infusion.[14] Apparently, higher doses are needed in this setting to drive drug concentration levels into the dense bacterial growth, further demonstrating the complex relationship between optimal pharmacodynamic parameters and site of infection or infecting microorganism.

As with other antibiotic classes, most of the pharmacodynamic data generated for ß-lactams are derived from in vitro or animal models, though available clinical data generally agree with in vitro findings.[15] Clinical data relevant to ß-lactam pharmacodynamics are generally limited to studies testing the efficacy of very high doses, continuous infusion, or multiple-dose administration, although a lone study of cefmenoxime in the setting of gram-negative pneumonia found relationships between AUC0-24:DRC (the ratio of AUC0-24 to dynamic response concentration, an alternative to MIC), as well as T>DRC and time to eradication of bacteria from tracheal secretions.[11]

Most of these studies have found that continuous infusion is at least as effective as more conventional intermittent dosing, and that very high doses do not increase efficacy. However, no well-designed, randomized, placebo-controlled studies have been performed to substantiate the wide-spread use of the former strategy.[16-18] Furthermore, to our knowledge no trials have tested the effect of varying the interval where concentration is kept above MIC while simultaneously evaluating the effect of AUC0-24:MIC and Cmax:MIC. As such, there are no clinical data available that complement contemporary pharmacodynamics. This lack of validation has led to considerable variation in approaches to optimize ß-lactam activity. Recommendations to exceed the MIC by 1-5 multiples for between 40% and 100% of the dosage interval have been advanced.[7, 9, 15, 19-24] A reasonable conclusion holds that, for ß-lactams that are highly protein bound (> 90%), the time free drug is kept above the MIC should be maximized.

If T>MIC can be validated as an outcome parameter, several strategies designed to keep serum drug concentrations above the MIC can be employed: using agents with long serum half-lives; giving doses more frequently; administering drug as a continuous infusion; using repository dosage forms (procaine and benzathine penicillin G) if an appropriate match between antibiotic and bacterial pathogen is present; prescribing agents with active metabolites; concomitantly administering inhibitors of antibiotic elimination, such as probenecid; and selecting agents with the lowest MIC toward the infecting microorganism.

In conclusion, ß-lactams are generally considered concentration-independent killers of bacteria. Both in vitro and animal data support T>MIC as a pharmacodynamic parameter that predicts activity, though other parameters take on increased importance in select settings. A relatively large body of data from human studies supports administering these agents as a continuous infusion or in shorter dosage intervals, especially in neutropenic patients, to maximize the interval during which drug concentration is kept above the MIC. However, no convincing data are available to support choosing continuous infusions over more conventional dosing schemes in the majority of clinical situations. In addition, whereas limited clinical data have speculated on the optimal duration of T>MIC, definitive values have yet to be established. Clearly, more in vitro and much more human data are needed to validate the importance of T>MIC across the spectrum of infectious diseases.

Aminoglycosides

Applying pharmacodynamics to aminoglycoside dosing has proved controversial. Aminoglycosides have clearly been shown to have concentration-dependent activity against gram-negative bacteria; however, their activity when used as adjunctive therapy for S. aureus or enterococci may be concentration independent.[25-33] In addition, animal models have demonstrated that the parameter best able to predict efficacy against gram-negative pathogens such as P. aeruginosa and E. coli may be the dosing frequency.[12, 25]

Early in vitro studies illustrated the relation-ship between the Cmax:MIC and the emergence of resistance. In an in vitro pharmacodynamic model, netilmicin was able to prevent regrowth of P. aeruginosa, K. pneumoniae, E. coli, and S. aureus only if Cmax:MIC exceeded eight.[31] The MICs were 4- to 8-fold higher for regrowing bacteria versus the original isolates.

In one study, the author evaluated the correlation of pharmacodynamic parameters and therapeutic efficacy in a neutropenic murine thigh infection model.[12] Log10 AUC0-24 was found to best correlate with efficacy against P. aeruginosa (decrease in bacterial load) when given every 1-8 hours, whereas T>MIC was the best correlate when tobramycin was administered every 12-24 hours. Similar results were found for gentamicin; as dosing interval lengthened, T>MIC correlated more strongly with activity than did AUC0-24. In a similar study, this same group evaluated the effects of impaired renal function on the correlation between pharmaco-dynamic parameters and efficacy.[25] Here, T>MIC correlated best with efficacy for amikacin against K. pneumoniae, E. coli, and Serratia marcescens, whereas AUC0-24 correlated best against P. aeruginosa in mice with normal renal function. However, in mice with impaired renal function, which have aminoglycoside half-lives more similar to those seen in humans, AUC0-24 correlated best with efficacy against all four pathogens. Yet another study in the murine model examined the comparative importance of T>MIC and AUC over MIC (the area under the entire concentration-time curve minus the area where concentration was less than the MIC, AUC>MIC) in neutropenic mice infected with K. pneumoniae in either the lung or thigh.[3] Here, T>MIC once again correlated more strongly with efficacy in the thigh models when doses were given every 1-6 hours, and AUC0-24:MIC emerged as more predictive if the interval was extended. However, in the lung model, AUC0-24:MIC was a better predictor of activity, regardless of dosing interval.

Early clinical studies demonstrated that higher peak aminoglycoside levels correlated with improved clinical outcome.[34-36] Further, mostly retrospective clinical studies have attempted to validate the utility of Cmax:MIC by analyzing pharmacokinetic and microbiologic data. An examination of clinical response to aminoglycoside therapy in 188 patients with documented gram-negative infections found increasing Cmax:MIC ratios were strongly correlated with clinical response.[26] A relatively small retrospective study found that several pharmacodynamic indices correlated with therapeutic response to aminoglycosides.[37] In this study of infections attributed mostly to gram-negative pathogens, Cmax:MIC, T>MIC, T>4xMIC, and two other related parameters all correlated with therapeutic response. The authors concluded that Cmax:MIC is the parameter that is most easily monitored and interpreted.

Prospective studies have attempted to test the effects of dosing regimens designed to theoretically achieve a Cmax:MIC of 10. The authors of one study reported success from both an efficacy and a toxicity standpoint by using this protocol.[38] However, wide interpatient variability in aminoglycoside pharmacokinetics, along with toxicity concerns, has made the application of this technique controversial.[39, 40] Patients in whom the aminoglycoside serum half-life is 4 hours or more will accumulate drug despite a 24-hour dosage interval, whereas patients in whom the aminoglycoside serum half-life is short will have trough concentrations approaching zero for a disproportionate duration of the 24-hour dosage interval.[40] Although this antibiotic-free interval may help overcome adaptive resistance and limit tissue accumulation, the optimal duration for this interval has not been defined.[29, 32, 41-44] To our knowledge, no clinical studies in which aminoglycosides have been dosed to achieve a Cmax:MIC of 10 based on individual patient pharmacokinetics and antibacterial susceptibility data have been performed.

In addition, no head-to-head studies have been conducted to compare individualized regimens with once-daily regimens for efficacy and toxicity. Although single daily dosing of aminoglycosides theoretically takes advantage of the agents' concentration-dependent activity, the effect of Cmax:MIC values greater than 10, the utility and optimal duration of the antibiotic-free period, and the relative efficacy of single daily dosing compared with individualized dosing need to be investigated to determine the rightful place of single daily dosing of aminoglycosides.

In summary, in vitro data demonstrate that aminoglycosides have concentration-dependent activity against gram-negative pathogens. Against gram-positive pathogens, aminoglycosides may show concentration-independent killing. In addition, findings from in vitro work seem to indicate Cmax:MIC ratios in excess of 8 are capable of preventing regrowth of resistant isolates (Table 2). Data from in vivo animal studies are somewhat conflicting, but in models that presumably more closely resemble infections in the human host, AUC0-24 and Cmax are important predictors of efficacy relative to T>MIC (Table 2). The results of human studies seem to indicate that increasing aminoglycoside concentrations kills bacteria more rapidly and that the concentration-dependent activity of aminoglycosides can be optimized through the use of once-daily dosing. However, this approach has not been proven more safe or efficacious than individualized dosing in well-designed studies. Clearly, more in vitro, animal, and human data testing the activity of aminoglycosides against gram-negative and gram-positive pathogens are needed to identify and validate the pharmaco-dynamic parameters that are the best predictors of microbiologic and clinical success.

Fluoroquinolones

The fluoroquinolones represent the first new antibiotic class to have extensive pharmacodynamic studies incorporated into the antibiotic development process. However, the majority of fluoroquinolone pharmacodynamic data have been generated with in vitro pharmacodynamic or animal models (Table 3). Most studies in humans have been retrospective and have predominantly investigated fluoroquinolone activity against gram-negative infections.

Early in vitro pharmacodynamic work identified the Cmax:MIC ratio as an important parameter in the clinical use of the fluoro-quinolone, enoxacin.[31] Regrowth of isolates of P. aeruginosa, K. pneumoniae, E. coli, and S. aureus, which had MICs 4- to 8-fold higher than before exposure, could not be prevented if Cmax:MIC ratios were less than 8. Further in vitro work with ciprofloxacin against P. aeruginosa also found that a Cmax:MIC of greater than 8 was required to prevent the emergence of resistant isolates.[45] More recently, investigators have described a relationship between AUC0-24:MIC and antimicrobial activity of fluoroquinolones.[46] In an in vitro model of infection with P. aeruginosa, ciprofloxacin and ofloxacin demonstrated equal activity when AUC0-24:MIC approached 100, suggesting this level might describe generic fluoroquinolone activity against P. aeruginosa.

Considerable effort has been put into the study of the activity of fluoroquinolones against gram-positive bacteria as well as anaerobes. Data from the many in vitro studies seem to indicate the newer fluoroquinolones possess concentration-independent activity against selected gram-positive pathogens and anaerobes. The authors of one study tested the activity of levofloxacin against S. pneumoniae by infusing drug at a constant rate of 1, 2, and 10 times the MIC for varying percentages of the dosing interval.[48] As the killing rate remained constant despite the varying concentrations, the authors concluded that levofloxacin did in fact exhibit concen-tration-independent activity. Furthermore, contrary to original hypotheses, these agents may retain activity against gram-positive microorganisms at lower AUC0-24:MIC ratios than required for gram-negative microorganisms. Indeed, AUC0-24:MIC ratios as low as 30-50 may maximize fluoroquinolone activity against S. pneumoniae and Bacteroides sp.[48-51,55,56,67]

The finding that different types of bacteria require different AUC0-24:MIC values for efficacy illustrates that pharmacodynamic activity is species dependent. This is not unexpected, as biologic differences between gram-positive and gram-negative bacteria would likely lead to differences in antibiotic uptake and the efficiency with which fluoroquinolones bind to gram-positive and gram-negative topoisomerase II and IV.

Fluoroquinolone pharmacodynamics has also been thoroughly studied in animal models. Pharmacodynamic studies in animal models can be misinterpreted because of confounding variables, including variable protein binding and host immune system activity. Nevertheless, results from animal studies are generally in agreement with those from in vitro studies. A study of lomefloxacin in a neutropenic rat model of pseudomonal sepsis revealed that the probability of a successful outcome was greatest when Cmax:MIC exceeded 10.[57] Several studies of the newer fluoroquinolones against S. pneumoniae and S. aureus in immunocompetent and neutropenic mice have found AUC0-24:MIC ratios from 30-50 produce a net bacteriostatic effect.[58-60]

An early human study relevant to fluoroquinolone pharmacodynamics was conducted with cipro-floxacin in patients with nosocomial pneumonia.[66] In this study of 50 patients, bacterial eradication from the lower respiratory tract was correlated with high Cmax:MIC ratios, high AUC0-24:MIC ratios, and T>MIC. A retrospective analysis of the activity of intravenous ciprofloxacin against moderate-to-severe infections found AUC0-24:MIC to be the most important predictor of clinical and microbiologic success, as well as of bacterial eradication.[62] A ratio of 125 was found to be the minimally effective value in the 74 patients in this study. Of note, most infections involved the lower respiratory tract, and 82% of the infecting microorganisms were gram-negative.

The first clinical study to examine differences in fluoroquinolone activity against gram-positive and gram-negative bacteria was actually carried out in the serum ultrafiltrates from five healthy volunteers against S. pneumoniae, S. aureus, and P. aeruginosa.[61] Maximal effects were seen at 15-40 times MIC for gram-positive organisms and 20-50 times MIC for P. aeruginosa.

A study of grepafloxacin in the treatment of acute bacterial exacerbations of chronic bronchitis in 73 patients aged 23-81 years found AUC0-24:MIC ratios of 175 or greater were needed for optimal antibacterial response.[65] Most isolates in this study were gram-negative, but of special note, although the subgroup size was very small, patients with S. pneumoniae had a bacteriologic cure rate of 87.5% when AUC0-24:MIC was 0-92, marking clinical evidence that lower AUC0-24:MIC ratios may be sufficient in the treatment of gram-positive infection.

The activity of levofloxacin against infections of the respiratory tract, skin, and urinary tract was evaluated in what was, to our knowledge, the only prospective study relevant to pharmaco-dynamics.[64] In this study, a Cmax:MIC of at least 10 was identified as correlating with successful clinical outcome and microbiologic cure. This study did not alter levofloxacin doses or obviously attempt to change levofloxacin clearance, and, as such, the study cannot discriminate among Cmax:MIC, AUC0-24:MIC, or T>MIC as the most important outcome parameter.

A recent retrospective review attempted to relate pharmacodynamics and the emergence of resistance.[63] Here, the records of 107 patients acutely ill with nosocomial lower respiratory tract infections who were treated with cefmenoxime, imipenem, ceftazidime, or ciprofloxacin in clinical trials were evaluated for the probability of developing bacterial resistance. Gram-negative organisms accounted for 86% of recovered isolates. The probability of developing resistance increased when AUC0-24:MIC was less than 100. This relationship was observed to be strongest when P. aeruginosa was treated with ciprofloxacin.

In summary, the wealth of fluoroquinolone in vitro pharmacodynamic data indicate these agents have concentration-dependent activity against gram-negative bacteria, especially P. aeruginosa. Conversely, in vitro data seem to show concentration-independent activity against S. pneumoniae and Bacteroides sp. As there has generally been good correlation between data gleaned from in vitro and animal models and data from the few human studies, a reasonable working hypothesis is that AUC0-24:MIC ratios of approximately 125 predict optimal activity of fluoroquinolones against gram-negative pathogens, whereas ratios as low as 30-50 may be sufficient for S. pneumoniae and anaerobes such as Bacteroides fragilis.[3,46,49-51,55,62,68] However, supporting human data confirming these observations are extremely limited.

Because of the concordance of in vitro and in vivo data, fluoroquinolones represent the first new class of antibiotics for which pharmaco-dynamics could be integrated into clinical decision making by determining AUC0-24:MIC ratios given antibiotic susceptibility data (Table 4). However, because of the limited number of clinical trials validating these data, the routine application of these concepts to patient care may be premature at this time.

Macrolides

Owing to the individual pharmacokinetic profiles of macrolides, their pharmacodynamic outcome parameters cannot be definitively characterized as they can for other antimicrobial agents. The difficulty encountered in developing adequate models representing in vivo macrolide activity is manifested in the relative lack of available pharmacodynamic data (Table 5). Macrolides concentrate considerably in both the extracellular space and in phagocytic cells. Therefore, their activity relies not only on the measurable serum concentrations, but also on the intracellular drug levels.[75, 76]

A vivid example of the importance of this phenomenon was described by comparing the activities of azithromycin and clarithromycin against S. pneumoniae and H. influenzae.[77] Against S. pneumoniae, both macrolides exceeded the MIC over the recommended dosage intervals, 12 hours for clarithromycin and 24 hours for azithromycin (MIC < 0.25 µg/ml for both agents). However, against H. influenzae, azithromycin, clarithromycin, and the 14-hydroxyclarithromycin metabolite failed to reach the MIC levels (0.5-2 µg/ml, 4-8 µg/ml , and 2-4 µg/ml, respectively). Nonetheless, this in vitro trial and clinical experience document the activity of both azithromycin and clarithromycin against H. influenzae. Thus, the reported MICs do not account for the intracellular and extracellular macrolide concentrations. In addition, the pharmacokinetic profiles of macrolides, specifically azithromycin, afford them well-documented postantibiotic effects (PAEs).[70, 78] Azithromycin is slowly released from within cells, subjecting bacteria to prolonged exposures.

Generally, macrolides are believed to perform as concentration-independent drugs.[76, 77] In a study conducted in a mouse thigh model, the authors evaluated erythromycin against S. pneumoniae and found T>MIC to be the most significant parameter determining efficacy.[12] The study administered erythromycin injections of 0.5-96 mg/kg given at intervals of 1, 2, 3, 4, 6, 8, 12, and 24 hours, allowing for the minimization of pharmacokinetic parameter interdependence. The log10 AUC0-24 (R2 = 0.22) showed large deviation from the regression line, whereas the log10 Cmax data varied inversely with efficacy. The T>MIC (R2 = 0.73), however, was selected by stepwise regression analysis and by the ability for additional kill as the most significant predictor of efficacy.

Another study conducted with clarithromycin against S. pneumoniae compared the number of bacteria remaining in the thigh after 24 hours of therapy with each of three pharmacokinetic-pharmacodynamic parameters (AUC0-24:MIC, Cmax:MIC, and T>MIC).[69, 78] The correlation between bacterial counts and outcome parameter was poor for AUC0-24:MIC and Cmax:MIC, but the T>MIC showed a highly significant correlation.

Surprisingly, a study conducted with azithromycin against the same strain of S. pneumoniae found different relationships between the pharmacokinetic-pharmacodynamic outcome parameters and bacterial counts.[70, 78] The best correlation was observed with the AUC0-24:MIC ratio, followed by the Cmax:MIC ratio. The discrepancy once again was attributed to the prolonged PAE that resulted from the pharmacokinetic profile of azithromycin.

In summary, the pharmacodynamics of macrolides are not clear-cut. Owing to the pharmacokinetic profiles of macrolides, their activity relies not only on the measurable serum concentrations, but also on the intracellular drug levels. The available data seem to identify T>MIC as the optimal pharmacodynamic parameter for clarithromycin and erythromycin, but not for azithromycin.

Other Agents

Pharmacodynamic data for other classes of antibiotics are extremely limited. Limitations to the pharmacodynamic data set of ß-lactams, aminoglycosides, and fluoroquinolones, including the limited range of drug concentrations and bacterial strains studied and lack of clinical validation, are even more apparent with clindamycin, metronidazole, and quinupristin-dalfopristin (Table 5). As such, extreme caution is warranted when extrapolating results from in vitro work beyond the studied antibiotic-pathogen combination, and, especially, into the human host.

A single in vitro study was performed to characterize the activity of metronidazole against Trichomonas vaginalis under anaerobic conditions.[73] Metronidazole demonstrated concentration-independent killing at concen-trations of 0.1-10 times the minimum lethal concentration; maximal kill rates were at concentrations of 10-25 times the minimum lethal concentration, although the study did not test higher concentrations.

Clindamycin activity against S. aureus and S. pneumoniae was evaluated with an in vitro pharmacodynamic model.[74] In this study, lower Cmax:MIC and AUC0-24:MIC ratios did not correlate with a loss of antibacterial efficacy. All regimens tested produced clindamycin concentrations in excess of the MIC for the entire dosage interval.

Although the literature for vancomycin is not extensive, studies appear to support the belief that vancomycin is a concentration-independent killer of gram-positive pathogens, despite the proposals by some authors of its need to achieve certain Cmax and Cmin levels for optimal activity.[71, 79-81] These identified levels simply allow vancomycin to remain above the MIC for adequate periods of time. Animal work evaluating vancomycin 30 mg/kg every 12 hours versus 30 mg/kg every 6 hours in rat endocarditis models infected with S. aureus found that increased administration frequency resulted in a decreased number of bacteria recovered from aortic valve vegetations.[82] However, not all trials supported the concentration-independent hypotheses. A mouse peritonitis model subjecting S. pneumoniae and S. aureus to varying vancomycin treatment regimens also tested for the optimal pharmacodynamic parameter.[72] The investigation spanned four trials: single escalating doses to determine the dose that protected 50% of lethally infected mice (ED50); doses administered as one or two doses; multidosing regimens given over 48 hours, ranging from 2-24 doses; and intraperitoneal multidose treatments over 48 hours ranging from 1-20 mg/kg. This experiment reported that the doses required to achieve ED50 were higher in a statistically significant fashion when administered as a single dose. In addition, the pharmacodynamic parameter selected by Spearman rank correlation (r) determination in the multidose trials found T>MIC, Cmax:MIC, and AUC0-24:MIC to correlate with effect.

In vitro work also confirmed that vancomycin activity is concentration independent. An experiment conducted with vancomycin against S. aureus demonstrated that bolus doses achieving Cmax values of 5, 10, 20, and 40 mg/L yielded time-kill curves that were not significantly different from one another (p=0.20).[71] Of note, the time to achieve a 3-log kill of bacteria was approximately twice as long in an anaerobic environment as in an aerobic one.

An in vitro evaluation was conducted with quinupristin-dalfopristin (formerly RP 59500) against fibrin-platelet clots infected with two S. aureus isolates, one resistant and one sensitive to erythromycin and methicillin.[83] Quinupristin-dalfopristin was administered by continuous and intermittent infusion, 7.5 mg/kg given as a regimen every 6, 8, and 12 hours. Although the interval over which the antibiotic was administered did not seem to change the mean bacterial recovery from the clots for the methicillin-erythromycin-sensitive isolate, dosing frequency trended toward a correlation in the methicillin-erythromycin-resistant isolate. In addition, AUC was significantly correlated to a reduction in bacterial density over 72 hours, but only for the methicillin-erythromycin-resistant isolate. Such differences can be attributed to the intrinsic activity of quinupristin-dalfopristin against resistant versus susceptible strains. Other studies conducted with methicillin-erythromycin-resistant S. aureus also identified differences in quinupristin-dalfopristin activities, depending on the methicillin-erythromycin susceptibility.[84]

A second evaluation of quinupristin-dalfopristin against vancomycin-resistant Enterococcus faecium was conducted under static conditions.[85] The bacteria were exposed to concentration multiples ranging from 0.5 to 48 times the MIC. A strong correlation was found between the quinupristin-dalfopristin concen-tration:minimum bactericidal concentration (MBC) ratio and bacterial kill rate. The authors interpreted the results to indicate concentration-dependent killing. The quinupristin-dalfopristin MBC may prove to be an important indicator of pharmacodynamic activity, as correlation was linked to activity in S. aureus as well.[84,86] The data set for quinupristin-dalfopristin is limited to only a few strains of bacteria, and many of the experiments have been conducted in special environments. In addition, other studies have found quinupristin-dalfopristin to be a time-dependent killer.[87]

Postantibiotic Effect

Another important consideration in pharmaco-dynamics is the presence or absence of a PAE, which refers to the ability to suppress bacterial growth after a scripted exposure to an antibiotic.[88] In general, ß-lactams produce a PAE only against gram-positive organisms, although some authors suggest the class, especially carbapenems, have a sustained PAE (as well as concentration-dependent activity) against gram-negative organisms.[9,19,88-92] Only agents that interrupt protein or nucleic acid synthesis, such as the macrolides, fluoroquinolones, and aminoglycosides, have been consistently shown to produce a sustained PAE against gram-negative bacteria.[88,93,94]

Translating the PAE found with in vitro experiments into a treatment for human infections is potentially problematic.[88] Frequently, the PAE is longer in vivo than in vitro, but in some cases it may be nonexistent in vivo, as is the case with streptococci exposed to penicillins and cephalosporins.[88] Although in vitro PAE data are often generated after single antibiotic exposures and therefore do not simulate a clinical course of multiple antibiotic treatments, clinicians are dependent on literature values because this type of laboratory testing is not routinely done. In summary, the PAE is unique to the pathogen and is generally longer in vivo than in vitro. The PAE may wane with multiple dosing and is not a clinically reported value. Therefore, justifying extended dosage intervals based on PAE is difficult.

In Vitro Pharmacodynamic Models

In vitro pharmacodynamic systems are usually designed to emulate the serum concentration-time curve of a human antibiotic exposure and to study the effect of changing antibiotic concen-trations on bacteria over time. Experiments can be run in a controlled, reproducible environment where antibiotic exposures can be repeated to simulate conditions not possible in a human host. These experiments can be done relatively quickly and at a fraction of the cost of a clinical trial. Efficacy and toxicity in these systems are not a concern as patient outcome is not an issue.

Data gleaned from appropriately designed in vitro experiments can quickly depict an antibiotic as a time- or concentration-dependent killer of a specific microorganism. Phenomena such as the PAE, the effect of protein binding on drug activity, and relative activity in aerobic and anaerobic environments are easily studied. However, in vitro pharmacodynamic models have limitations. They do not account for the effects of the immune system and cannot assess toxicity. They are also limited in the duration of experiments and hampered by technical challenges, such as antibiotic carryover between the actual model and agar plates used to quantify bacteria. Data from in vitro pharmacodynamic models are often not validated, and peer-reviewed journal standards for publishing such work are not presently available. Because of these limitations, work done with in vitro models is intended to and should be complementary to work done with in vivo systems.

Nonetheless, in vitro models do play a valuable role in the antibiotic development process. In vitro pharmacodynamic testing represents a complementary step in the development process from benchtop to animal models and eventually to human trials. Demonstrating that pharmaco-dynamic data are reproducible in vitro, in animals, and through clinical trials should expedite the optimization of antibiotic dosing and should result in the ongoing validation of pharmacodynamic parameters. In summary, the in vitro pharmacodynamic model is a cost-effective and rapid means for gathering preliminary information on antibiotic activity and establishing pharmacodynamic parameters under the influence of a variety of clinical variables.

Identifying pharmacodynamic parameters is becoming an important part of the new drug development process. Characterizing an antibiotic as a concentration- or time-dependent killer of the bacteria in question and under-standing the pharmacodynamic parameters that best predict activity allows for the design of rational dosage regimens in humans, thereby reducing the costs and time needed for the antibiotic development process. In addition, identifying optimal pharmacodynamic parameters along with other variables influencing activity and designing theoretically effective regimens in preclinical testing will give a new antibiotic the best possible chance of establishing clinical efficacy in phase III and IV trials and beyond. The following is a list of valuable pharmaco-dynamic data that can be provided with in vitro testing:
In vitro models may be particularly useful in sorting through covariance among outcome parameters. The ability of in vitro pharmaco-dynamic models to explore the predictive value of a variety of pharmacodynamic parameters rapidly and relatively inexpensively will play an increasingly important role in the early stages of a drug's development.

Pharmacodynamics and Managed Care

As the pharmaceutical industry continues to develop and market more antibiotics within a given chemical class, institutions and practitioners are left with the dilemma of deciding which agent to place on the formulary. Within a managed care organization or a hospital, the formulary committee is charged with identifying which products are thera-peutically equivalent and which are superior as formulary choices. This decision is made based on the results of clinical trials, contracting issues, and professional judgement. Antibiotic pharmaco-kinetic properties, antimicrobial spectrum of activity, and antibiotic-related toxicity, among others, are important considerations in selecting products. Pharmacodynamics offers more data and an additional approach for comparing antibiotic agents.

If T>MIC, Cmax:MIC, and AUC0-24:MIC truly represent parameters capable of predicting clinical and microbiologic success, pharmaco-dynamics will become an important tool in the antibiotic formulary selection process. The pharmaceutical industry is already using pharmacodynamic data to market antibiotics. Although the use of such data would seem to be a logical extension of the science, there must be sequential validation of these data in vitro, in animal models, and most important, in the human host.

At the present time, choosing the appropriate parameter for predicting clinical and micro-biologic success and determining the appropriate value for this parameter remain controversial issues. Undoubtedly, over time these questions will be adequately addressed, and the resultant data will be clinically validated. Until that time, however, use of pharmacodynamic data to delineate the potential merits of one antibiotic versus another must be approached with caution.

Pharmacodynamics and Resistance

Fluoroquinolone dosing schedules that do not produce AUC0-24:MIC ratios greater than 100 (or Cmax:MIC ratios > 8) have been reported to be an independent risk factor for the development of drug resistance in gram-negative pathogens.[31,62,95,96] Data from in vitro models indicate fluoroquinolone AUC0-24:MIC less than 50 fosters resistance in B. fragilis, and that ratios less than 30 are unable to prevent regrowth of S. pneumoniae.[48,55] The results of one study demonstrated in B. fragilis that underexposure to one fluoroquinolone can confer resistance to the entire class.[67] A recent retrospective analysis of five different dosing regimens applied to nosocomial lower respiratory tract infections within the confines of a clinical trial demonstrated that an AUC0-24:MIC of 100 or greater, with any antibiotic, was necessary to prevent selective pressures that could lead to resistance.[63] Collectively, these data would seem to verify that antibiotic underdosing fosters bacterial antibiotic resistance.

Use of pharmacodynamics to take advantage of the activity of an antibiotic likely plays a role in minimizing the risk of resistance. A retrospective review of patients acutely ill with lower respiratory tract infections found that the probability of developing bacterial resistance increased when the AUC0-24:MIC ratio achieved was less than 100.[63] This study would seem to indicate that dosing antibiotics to a target level defined by pharmacokinetics and bacterial susceptibility decreases the risk of underexposing pathogens to antibacterial agents, a phenomenon speculated to foster resistance.

Theoretically, the most potent agent should be used to treat an infection, but there is a clinical paradox in that other bacterial reservoirs (intestinal, vaginal, respiratory) within the host are also challenged with the same antimicrobial chemotherapy and that the best agent or dosage strategy for the infecting pathogen may not be the best for the other bacterial flora. As a result, suboptimal antibiotic exposure of the host's bacterial flora could result in a collateral damage scenario generating resistant flora. For example, using the most potent fluoroquinolone to treat a urinary tract infection due to E. coli could theoretically result in the development of a B. fragilis class resistant to all other fluoro-quinolones in the intestine.[97] This collateral damage scenario is even more problematic in that the clinician is almost certainly not monitoring for this potential.

Patient-Specific Pharmacodynamics

Making patient-specific treatment decisions based on individual or population pharmaco-kinetics in conjunction with bacterial susceptibility data would mark the ultimate step in the development of the field of pharmaco-dynamics. Unfortunately, there are a paucity of pharmacodynamic data from human studies to support such a practice. The authors of one study have shown that ß-lactams given as a continuous infusion are effective against susceptible gram-negative bacteria in patients with neutropenia.[17] Some researchers have demonstrated the ability to predict success when treating select infections with ß-lactams given to achieve a specified time or percentage of the dosage interval above the MIC.[98] Ciprofloxacin has been shown to be effective against gram-negative infections of the lower respiratory tract when dosed to achieve AUC0-24:MIC ratios in excess of 125.[62] Although not validated within the study, aminoglycoside regimens designed to produce Cmax:MIC ratios of 10 were shown to be effective.[38]

A major deficiency in available pharmaco-dynamic data relates to the use of multidrug regimens. Combinations of antibiotics can result in an additive effect, synergy, indifference, or an antagonistic effect, and few in vitro and practically no in vivo studies are available that address these concepts. Most of the available in vitro data involve antibiotic combinations that include an aminoglycoside.[99, 100] Questions about dose amounts arise when antibiotics are given in combination. For example, an AUC0-24:MIC ratio of greater than 125 predicts optimal activity of fluoroquinolones against P. aeruginosa. Is a lower ratio adequate if aminoglycosides are given concomitantly, or are pharmacodynamic outcome parameters generic and additive, as proposed by one author?[95] There are few answers to such questions, but pharmacodynamics holds the potential to help resolve these issues in the future.

Pharmacodynamics: Present and Future

The field of pharmacodynamics is evolving rapidly. To date, most of the pharmacodynamic data have been generated in in vitro experiments, and more patient experience is needed to validate the clinical application of the science. Pharmacodynamic outcome predictors would seem to hold great promise as objective measures of clinical outcome and toward prevention of bacterial resistance. So far, pharmacodynamic data generated in vitro, in animals, and in humans seem to be in general agreement with one another. With intracellular infections, or with antibiotics that concentrate extensively in cells or tissue but maintain modest serum concentrations, pharmacodynamics will need to evolve to effectively model these situations and develop meaningful outcome parameters. Over time, however, pharmacodynamics should advance on four fronts: the science should be extremely useful and represent a cost-effective and efficient method for developing new antibiotics; formulary committees will likely use pharmacodynamic parameters to differentiate antibiotics of the same chemical class when selecting antibiotics for the formulary; pharmacodynamics will likely assist prescribers in designing optimal antibiotic strategies for patients with severe infections; and pharmaco-dynamic concepts may curtail or prevent the development of antibiotic resistance.

Table 1. ß-lactam Pharmacodynamic Data from Select In Vitro, Animal, and Human Studies

Antibiotic Organism Class
or Organism		 Outcome Parameter
and Value		 Source
Penicillin G[1] Diplococcus pneumoniae T>MIC Mouse and rabbit
Cefazolin[2] E. coli, Klebsiella sp T>MIC IVPDM
Cefazolin, imipenem, ceftazidime[3] K. pneumoniae T>MIC Neutropenic murine model, lung and thigh infections
Ampicillin[4] E. coli AUC0-24, to prevent regrowth of resistant isolates IVPDM
Ceftazidime[5] K. pneumoniae T>MIC Neutropenic mouse, pneumonia
Cephalosporins[6] S. pneumoniae T>MIC In vitro, murine
Ceftriaxone[7] S. pneumoniae T>MBC 95-100% Rabbit model of meningitis
Cefazolin, cefaloridine, cefalothin[8] E. coli T>MIC, maximal effect at 1-4 x MIC IVPDM
Broad-spectrum cephalosporins[9] Enterobacteriaceae
Streptococci
S. aureus		 T>MIC 60-70%
T>MIC 60-70%
T>MIC 40-50%		 Review of animal data
Ceftibuten, cefaclor[10] K. pneumoniae, E. coli, S. aureus, S. pneumoniae T>MIC Nonneutropenic murine model of intraabdominal infections
Cefmenoxime[11] Gram-negative T>DRC, AUC0-24:DRC Human, nosocomial pneumonia
Cefazolin, ticarcillin, penicillin[12] E. coli
S. aureus
P. aeruginosa
S. pneumoniae		 T>MIC 100%
T>MIC 55%
T>MIC 100%
T>MIC 100%		 Neutropenic murine thigh infection models
T>MIC = duration of time that antibiotic concentration exceeds microbial minimum inhibitory concentration (fraction of interval represented by percentage); IVPDM = in vitro pharmacodynamic model; AUC0-24 = area under the concentration-time curve during a 24-hour dosing period; T>MBC = duration of time drug concentration exceeds the microbial minimum bactericidal concentration; T>DRC = duration of time that antibiotic concentration exceeds dynamic response concentration; AUC0-24:DRC = ratio of AUC0-24 to the DRC.

Table 2. Aminoglycoside Pharmacodynamic Data from Select Animal and Human Studies

Antibiotic Organism Class or Organism Outcome Parameter
and Value		 Source
Tobramycin, gentamicin[12] P. aeruginosa, E. coli AUC0-24 and T>MIC Neutropenic murine thigh infection model
Gentamicin, netilmicin[3] K. pneumoniae AUC0-24:MIC in thigh model if short dosing interval, T>MIC if long dosing interval, AUC0-24:MIC regardless of interval duration in lung model Neutropenic murine thigh and lung infection model
Amikacin[25] K. pneumoniae, E. coli, P. aeruginosa, S. marcescens T>MIC if normal renal function (except P. aeruginosa), AUC0-24 if impaired renal function Neutropenic murine thigh infection model
Gentamicin, tobramycin, amikacin[26] Gram-negative (63% E. coli, 27% Klebsiella sp); UTI, LRTI, intraabdominal or cutaneous infection, bacteremia Cmax:MIC > 10 Human, retrospective
Aminoglycosides[37] Mostly gram-negative, respiratory infection, soft tissue, sepsis, and UTI Cmax:MIC > 8, also T>MIC and T > 4 x MIC among others Human, retrospective
Gentamicin, tobramycin[38] Gram-negative Dosage designed to produce Cmax:MIC > 10 Human
AUC0-24 = area under the concentration-time curve during a 24-hour dosing period; T>MIC = duration of time that antibiotic concentration exceeds microbial minimum inhibitory concentration (fraction of interval represented by percentage); AUC0-24:MIC = ratio of AUC0-24 to MIC; UTI = urinary tract infection LRTI = lower respiratory tract infection; Cmax:MIC = ratio of maximum serum antibiotic concentration to MIC.

Table 3: Fluoroquinolone Pharmacodynamic Data from Select In Vitro, Animal, and Human Studies

Antibiotic Organism Class or
Organism		 Outcome Parameter
and Value		 Source
Enoxacin, netilmicin[31] P. aeruginosa
K. pneumoniae
E. coli
S. aureus		 Cmax:MIC > 8 IVPDM
Ciprofloxacin[45] P. aeruginosa Cmax:MIC > 8 IVPDM
Ciprofloxacin, ofloxacin[46] P. aeruginosa AUC0-24:MIC > 100 IVPDM
Levofloxacin, ciprofloxacin, trovafloxacin[47] S. pneumoniae AUC0-24:MIC > 35 IVPDM
Levofloxacin[48] Fluoroquinolone-resistant S. pneumoniae AUC0-24:MIC 26-36 IVPDM
Ciprofloxacin, levofloxacin[49] S. pneumoniae AUC0-24:MIC 30-55 IVPDM
Ciprofloxacin, ofloxacin, trovafloxacin[50] S. pneumoniae AUC0-24:MIC 44-49 IVPDM
Ciprofloxacin, levofloxacin[51] S. pneumoniae AUC0-24:MIC 32-64 IVPDM
Trovafloxacin, ciprofloxacin, clinafloxacin, sparfloxacin, levofloxacin[52] S. aureus AUC0-24:MIC > 57 IVPDM
Levofloxacin, trovafloxacin, gatifloxacin, ciprofloxacin[53] Fluoroquinolone-resistant S. pneumoniae AUC0-24:MIC > 20 or Cmax:MIC > 2.2 IVPDM
Levofloxacin, trovafloxacin, gatifloxacin, clinafloxacin, sparfloxacin, ciprofloxacin[54] S. pneumoniae AUC0-24:MIC > 40 or T>MIC 55% IVPDM, infected fibrin clot
Levofloxacin, trovafloxacin[55] B. fragilis AUC0-24:MIC > 50 IVPDM
Levofloxacin, sparfloxacin, trovafloxacin[56] B. thetaiotamicron AUC0-24:MIC 10-50 IVPDM
Lomefloxacin[57] P. aeruginosa Cmax:MIC > 10 Neutropenic rat sepsis
Sitafloxacin[58] S. pneumoniae
S. aureus
Enterobacteriaceae		 AUC0-24:MIC = 37
AUC0-24:MIC = 71
AUC0-24:MIC = 43		 Murine thigh and lung infection models
Gatifloxacin[59] S. pneumoniae
S. aureus
Enterobacteriaceae		 AUC0-24:MIC = 52
AUC0-24:MIC = 39
AUC0-24:MIC = 48		 Murine thigh and lung infection models
Gemifloxacin[60] S. pneumoniae
S. aureus
Enterobacteriaceae		 AUC0-24:MIC = 35 Murine thigh and lung infection models
Ciprofloxacin[61] S. pneumoniae
S. aureus
P. aeruginosa		 Maximal effect at 15-40 x MIC for gram-positive, 20-50 x MIC for PA Serum ultrafiltrates from healthy volunteers
Ciprofloxacin[62] 82% gram-negative AUC0-24:MIC > 125 Human, retrospective, mostly infections of the LRT
Cefmenoxime, imipenem, ceftazidime,
ciprofloxacin[63]		 86% gram-negative AUC0-24:MIC > 100 to prevent emergence of resistance Human, retrospective, nosocomial LRT infections
Levofloxacin[64] 16% S. pneumoniae
11% S. aureus		 Cmax:MIC > 10 Human trial with urinary tract, pulmonary, and skin or skin structure infections
Grepafloxacin[65] Acute bacterial exacerbations of chronic bronchitis, mostly gram-negative, few S. pneumoniae AUC0-24:MIC > 175, AUC0-24:MIC > 0-92 vs S. pneumoniae Human, acute bacterial exacerbations of chronic bronchitis
Ciprofloxacin[66] Gram-negative Cmax:MIC, AUC0-24:MIC, T>MIC Ventilator-dependent patients with nosocomial pneumonia
Cmax:MIC = ratio of maximum serum antibiotic concentration to microbial minimum inhibitory concentration; IVPDM = in vitro pharmacodynamic model; AUC0-24:MIC = ratio of area under the concentration-time curve during a 24-hour dosing period to MIC; PA = Pseudomonas aeruginosa; LRT = lower respiratory tract; T>MIC = duration of time that antibiotic concentration exceeds MIC.

Table 4. Typical AUC0-24:MIC Ratios Produced by Standard Doses of Selected Fluoroquinolones in Humans

Fluoroquinolone AUC0-24
(mgbullethr/L)		 Minimum Inhibitory Concentration (mg/L)
2.0 1.0 0.5 0.25 0.125 0.06
Ciprofloxacin
20
10
20
40
80
160
330
Sparfloxacin
19
9.5
19
38
76
152
320
Levofloxacin
48
24
48
96
192
384
400
Trovafloxacin
34
17
34
68
136
272
570
Moxifloxacin
48
24
48
96
192
384
800
Gatifloxacin
51
26
51
102
204
408
850
Gemifloxacin
9
4.5
9
18
36
72
150

Table 5. Pharmacodynamic Data for Other Antibiotics from Select In Vitro and Animal Studies

Antibiotic Organism Outcome Parameter
and Value		 Source
Erythromycin[12] S. pneumoniae T>MIC 60% Neutropenic murine thigh infection model
Clarithromycin[69] S. pneumoniae T>MIC Neutropenic murine thigh infection model
Azithromycin[70] S. pneumoniae AUC0-24:MIC Neutropenic murine thigh infection model
Vancomycin[71] S. aureus T>MIC ~ 100% IVPDM
Vancomycin[72] S. pneumoniae, S. aureus T>MIC, Cmax:MIC, AUC0-24:MIC Murine peritonitis
Metronidazole[73] T. vaginalis C:MLC > 10-25 IVPDM, anaerobic
Clindamycin[74] S. pneumoniae, S. aureus T>MIC IVPDM
T>MIC = duration of time that antibiotic concentration exceeds microbial minimum inhibitory concentration (fraction of interval represented by percentage); AUC0-24:MIC = ratio of area under the concentration-time curve during a 24-hour dosing period to MIC; IVPDM = in vitro pharmacodynamic model; Cmax:MIC = ratio of maximum serum antibiotic concentration to MIC; C:MLC = ratio of concentration to minimum lethal concentration.

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John C. Rotschafer, Pharm.D., Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, 308 Harvard Street, Weaver-Densford Hall 9-151, Minneapolis, MN 55455.

Authors from: the Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, Minnesota, and the Department of Clinical Pharmacy, Regions Hospital, St. Paul, Minnesota.


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