Pharmacokinetic-pharmacodynamic modeling and simulation for bactericidal effect in an in vitro dynamic model

Direct-from-specimen microbial growth inhibition spectrums under antibiotic exposure and comparison to conventional antimicrobial susceptibility testing

Increasing global travel and changes in the environment may escalate the frequency of contact with a natural host carrying an infection and, therefore, increase our chances of encountering microorganisms previously unknown to humans. During an emergency, the etiology of infection may be unknown at the time of patient treatment. The existing local or global Antimicrobial Stewardship Programs may not be fully prepared for emerging/re-emerging infectious disease outbreaks, especially if they are caused by an unknown organism, engineered bioterrorist attack, or rapidly evolving superbug. We demonstrate an antimicrobial efficacy profiling method that can be performed in hours directly from clinical urine specimens. The antimicrobial potency was determined by the level of microbial growth inhibition and compared to conventional antimicrobial susceptibility testing results. The oligonucleotide probe pairs on the sensors were designed to target Gram-negative bacteria, specifically Enterobacterales and Pseudomonas aeruginosa. A pilot study of 10 remnant clinical specimens from the Clinical Laboratory Improvement Amendments-certified labs of New York-Presbyterian Queens was conducted, and only one sample was not detected by the probes. The remaining nine samples agreed with reference AST methods (Vitek and broth microdilution), resulting in 100% categorical agreement. In a separate feasibility study, we evaluated a dual-kinetic response approach, in which we inoculated two antibiotic stripwells containing the same antimicrobial concentrations with clinical specimens at the original concentration (1x) and at a 10-fold dilution (0.1x) to cover a broader range of microbiological responses. The combined categorical susceptibility reporting of 12 contrived urine specimens was 100% for ciprofloxacin, gentamicin, and meropenem over a range of microbial loads from 10⁵ to 10⁸ CFU/mL.

Prolonged Versus Intermittent Infusion of β-Lactam Antibiotics: A Systematic Review and Meta-Regression of Bacterial Killing in Preclinical Infection Models

BACKGROUND

Administering β-lactam antibiotics via prolonged infusions for critically ill patients is mainly based on preclinical evidence. Preclinical data on this topic have not been systematically reviewed before.

OBJECTIVES

The aim of this study was to describe the pharmacokinetic/pharmacodynamic (PK/PD) indices and targets reported in preclinical models and to compare the bactericidal efficacy of intermittent and prolonged infusions of β-lactam antibiotics.

METHODS

The MEDLINE and EMBASE databases were searched. To compare the bactericidal action of β-lactam antibiotics across different modes of infusion, the reported PK/PD outcomes, expressed as the percentage of time (T) that free (f) β-lactam antibiotic concentrations remain above the minimal inhibitory concentration (MIC) (%fT_>MIC) or trough concentration (C_min)/MIC of individual studies, were recomputed relative to the area under the curve of free drug to MIC ratio (fAUC₂₄/MIC). A linear mixed-effects meta-regression was performed to evaluate the impact of the β-lactam class, initial inoculum, Gram stain, in vivo or in vitro experiment and mode of infusion on the reduction of bacterial cells (in colony-forming units/mL).

RESULTS

Overall, 33 articles were included for review, 11 of which were eligible for meta-regression. For maximal bactericidal activity, intermittent experiments reported a PK/PD target of 40-70% fT_>MIC, while continuous experiments reported a steady-state concentration to MIC ratio of 4-8. The adjusted effect of a prolonged as opposed to intermittent infusion on bacterial killing was small (coefficient 0.66, 95% confidence interval - 0.78 to 2.11).

CONCLUSIONS

Intermittent and prolonged infusions of β-lactam antibiotics require different PK/PD targets to obtain the same level of bacterial cell kill. The additional effect of a prolonged infusion for enhancing bacterial killing could not be demonstrated.

Therapeutic drug monitoring of β-lactam antibiotics in the ICU

INTRODUCTION

Individualizing antibiotic therapy is paramount to improve clinical outcomes while minimizing the risk of toxicity and antimicrobial therapy. β-lactam antibiotics are amongst the drugs most commonly prescribed in the Intensive Care Unit (ICU). The pharmacokinetics of β-lactam antibiotics are profoundly altered in critically ill patients, leading to the failure of standard drug dosing regimens to result in adequate drug concentrations. Therapeutic Drug Monitoring (TDM) of β-lactam antibiotics is a promising tool to help optimize β-lactam antibiotic therapy.

AREAS COVERED

The rationale behind TDM for β-lactam antibiotics is explained, as well as some more practical aspects such as when to sample, what concentrations to strive for and how to use it in clinical practice. We also discuss microbiological and analytical considerations, knowledge gaps, and future perspectives of β-lactam antibiotics TDM in ICU patients.

EXPERT OPINION

TDM of β-lactam antibiotics has been studied intensively in recent years. While TDM may not yet be widely available, and targets need to be further refined, TDM of β-lactam antibiotics will help to optimize antibiotic therapy in the critically ill patient, as an integrated part of an antimicrobial stewardship program.

Generating Robust and Informative Nonclinical In Vitro and In Vivo Bacterial Infection Model Efficacy Data To Support Translation to Humans

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration.

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration. This and the accompanying review on clinical PK/PD summarize the workshop discussions and recommendations. Nonclinical PK/PD models play a critical role in designing human dosage regimens and are essential tools for drug development. These include in vitro and in vivo efficacy models that provide valuable and complementary information for dose selection and translation from the laboratory to human. It is crucial that studies be designed, conducted, and interpreted appropriately. For antibacterial PK/PD, extensive published data and expertise are available. These have been leveraged to develop recommendations, identify common pitfalls, and describe the applications, strengths, and limitations of various nonclinical infection models and translational approaches. Despite these robust tools and published guidance, characterizing nonclinical PK/PD relationships may not be straightforward, especially for a new drug or new class. Antimicrobial PK/PD is an evolving discipline that needs to adapt to future research and development needs. Open communication between academia, pharmaceutical industry, government, and regulatory bodies is essential to share perspectives and collectively solve future challenges.

Distinguishing Antimicrobial Models with Different Resistance Mechanisms via Population Pharmacodynamic Modeling

Semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling is increasingly used for antimicrobial drug development and optimization of dosage regimens, but systematic simulation-estimation studies to distinguish between competing PD models are lacking. This study compared the ability of static and dynamic in vitro infection models to distinguish between models with different resistance mechanisms and support accurate and precise parameter estimation. Monte Carlo simulations (MCS) were performed for models with one susceptible bacterial population without (M1) or with a resting stage (M2), a one population model with adaptive resistance (M5), models with pre-existing susceptible and resistant populations without (M3) or with (M4) inter-conversion, and a model with two pre-existing populations with adaptive resistance (M6). For each model, 200 datasets of the total bacterial population were simulated over 24h using static antibiotic concentrations (256-fold concentration range) or over 48h under dynamic conditions (dosing every 12h; elimination half-life: 1h). Twelve-hundred random datasets (each containing 20 curves for static or four curves for dynamic conditions) were generated by bootstrapping. Each dataset was estimated by all six models via population PD modeling to compare bias and precision. For M1 and M3, most parameter estimates were unbiased (<10%) and had good imprecision (<30%). However, parameters for adaptive resistance and inter-conversion for M2, M4, M5 and M6 had poor bias and large imprecision under static and dynamic conditions. For datasets that only contained viable counts of the total population, common statistical criteria and diagnostic plots did not support sound identification of the true resistance mechanism. Therefore, it seems advisable to quantify resistant bacteria and characterize their MICs and resistance mechanisms to support extended simulations and translate from in vitro experiments to animal infection models and ultimately patients.

Mathematical models are increasingly used for analysis and interpretation of in vitro efficacy results of antimicrobial drugs. Various models are employed in the scientific literature and it seems that they are equally able to describe the observed data. The aim of the present study was to compare different models in various experimental designs and with different resistance mechanisms of bacteria. For that purpose we have generated experimental data through Monte-Carlo simulations and then used six different mathematical models to analyze these results. We showed that statistical comparison of models did not allow determining which was the true mechanism of resistance, i.e. the one used for the simulation step. Moreover mathematical parameters for bacterial resistance were estimated with bias and with a low precision except for the simpler cases. This suggests that the choice of the mathematical model for data analysis should be guided by experimental characterization of the bacterial mechanism of resistance.

A Diffusion-Based and Dynamic 3D-Printed Device That Enables Parallel in Vitro Pharmacokinetic Profiling of Molecules

The process of bringing a drug to market involves many steps, including the preclinical stage, where various properties of the drug candidate molecule are determined. These properties, which include drug absorption, distribution, metabolism, and excretion, are often displayed in a pharmacokinetic (PK) profile. While PK profiles are determined in animal models, in vitro systems that model in vivo processes are available, although each possesses shortcomings. Here, we present a 3D-printed, diffusion-based, and dynamic in vitro PK device. The device contains six flow channels, each with integrated porous membrane-based insert wells. The pores of these membranes enable drugs to freely diffuse back and forth between the flow channels and the inserts, thus enabling both loading and clearance portions of a standard PK curve to be generated. The device is designed to work with 96-well plate technology and consumes single-digit milliliter volumes to generate multiple PK profiles, simultaneously. Generation of PK profiles by use of the device was initially performed with fluorescein as a test molecule. Effects of such parameters as flow rate, loading time, volume in the insert well, and initial concentration of the test molecule were investigated. A prediction model was generated from this data, enabling the user to predict the concentration of the test molecule at any point along the PK profile within a coefficient of variation of ∼ 5%. Depletion of the analyte from the well was characterized and was determined to follow first-order rate kinetics, indicated by statistically equivalent (p > 0.05) depletion half-lives that were independent of the starting concentration. A PK curve for an approved antibiotic, levofloxacin, was generated to show utility beyond the fluorescein test molecule.

Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model

One critical approach to preclinical evaluation of anti-tuberculosis (anti-TB) drugs is the study of correlations between drug exposure and efficacy in animal TB infection models. While such pharmacokinetic/pharmacodynamic (PK/PD) studies are useful for the identification of optimal clinical dosing regimens, they are resource intensive and are not routinely performed. A mathematical model capable of simulating the PK/PD properties of drug therapy for experimental TB offers a way to mitigate some of the practical obstacles to determining the PK/PD index that best correlates with efficacy. Here, we present a preliminary physiologically based PK/PD model of rifampin therapy in a mouse TB infection model. The computational framework integrates whole-body rifampin PKs, cell population dynamics for the host immune response to Mycobacterium tuberculosis infection, drug-bacteria interactions, and a Bayesian method for parameter estimation. As an initial application, we calibrated the model to a set of available rifampin PK/PD data and simulated a separate dose fractionation experiment for bacterial killing kinetics in the lungs of TB-infected mice. The simulation results qualitatively agreed with the experimentally observed PK/PD correlations, including the identification of area under the concentration-time curve as best correlating with efficacy. This single-drug framework is aimed toward extension to multiple anti-TB drugs in order to facilitate development of optimal combination regimens.

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

Pharmacokinetic/pharmacodynamic (PK/PD) indices of antibiotics predicted by a semimechanistic PKPD model: a step toward model-based dose optimization

A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, a dose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fC(max)]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT(>MIC)]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices, with fT(>MIC) being the best predictor of the effect for β-lactams and fAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.

In vitro pharmacokinetic/pharmacodynamic models in anti-infective drug development: focus on TB

For rapid anti-tuberculosis (TB) drug development in vitro pharmacokinetic/pharmacodynamic (PK/PD) models are useful in evaluating the direct interaction between the drug and the bacteria, thereby guiding the selection of candidate compounds and the optimization of their dosing regimens. Utilizing in vivo drug-clearance profiles from animal and/or human studies and simulating them in an in vitro PK/PD model allows the in-depth characterization of antibiotic activity of new and existing antibacterials by generating time–kill data. These data capture the dynamic interplay between mycobacterial growth and changing drug concentration as encountered during prolonged drug therapy. This review focuses on important PK/PD parameters relevant to anti-TB drug development, provides an overview of in vitro PK/PD models used to evaluate the efficacy of agents against mycobacteria and discusses the related mathematical modeling approaches of time–kill data. Overall, it provides an introduction to in vitro PK/PD models and their application as critical tools in evaluating anti-TB drugs.

Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model

We have previously described a general semimechanistic pharmacokinetic-pharmacodynamic (PKPD) model that successfully characterized the time course of antibacterial effects seen in bacterial cultures when exposed to static concentrations of five antibacterial agents of different classes. In this PKPD model, the total bacterial population was divided into two subpopulations, one growing drug-susceptible population and one resting drug-insensitive population. The drug effect was included as an increase in the killing rate of the drug-susceptible bacteria with a maximum-effect (E(max)) model. The aim of the present study was to evaluate the ability of this PKPD model to describe and predict data from in vitro experiments with dynamic concentration-time profiles. Dynamic time-kill curve experiments were performed by using an in vitro kinetic system, where cultures of Streptococcus pyogenes were exposed to benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, or vancomycin using different starting concentrations (2 and 16 times the MIC) and elimination conditions (human half-life, reduced half-life, and constant concentrations). The PKPD model was applied, and the observations for the static as well as dynamic experiments were compared to model predictions based on parameter estimation using (i) static data, (ii) dynamic data, and (iii) combined static and dynamic data. Differences in experimental settings between static and dynamic experiments did not affect the growth kinetics of the bacteria significantly. With parameter reestimation, the structure of our previously proposed PKPD model could well characterize the bacterial growth and killing kinetics when exposed to dynamic concentrations with different elimination rates of all five investigated antibiotics. Furthermore, the model with parameter estimates based on data from only the static time-kill curve experiments could predict the majority of the time-kill curves from the dynamic experiments reasonably well. Adding data from dynamic experiments in the estimation improved the model fit for cefuroxime and vancomycin, indicating some differences in sensitivity to experimental conditions among the antibiotics studied.

Prediction of exposure-response relationships to support first-in-human study design

In drug development, phase 1 first-in-human studies represent a major milestone as the drug moves from preclinical discovery to clinical development activities. The safety of human subjects is paramount to the conduct of these studies and regulatory considerations guide activities. Forces of evolution on the pharmaceutical industry are re-shaping the first-in-human dose selection strategy. Namely, high attrition rates in part due to lack of efficacy have led to the re-organization of research and development organizations around the umbrella of translational research. Translational research strives to bring basic research advances into the clinic and support the reverse transfer of information to enhance compound selection strategies. Pharmacokinetic/pharmacodynamic (PK/PD) modeling holds a unique position in translational research by attempting to integrate diverse sets of information. PK/PD modeling has demonstrated utility in dose selection and trial design for later stages of drug development and is now being employed with greater prevalence in the translational research setting to manage risk (i.e., oncology and inflammation/immunology). Moving from empirical E (max) models to more mechanistic representations of the biological system, a higher fidelity of human predictions is expected. Strategies that have proven useful for PK predictions are being applied to PK/PD predictions. This review article examines examples of the application of PK/PD modeling in establishing target concentrations for supporting first-in-human study design.

Pharmacokinetic/pharmacodynamic modeling and simulation to determine effective dosage regimens for doripenem

The aim of this study was to obtain information on effective dosage regimens of doripenem by a modeling and simulation approach based on pharmacokinetic (PK)/pharmacodynamic (PD) theory. The PK/PD model we have already developed was modified to explain in vitro bactericidal kinetics of doripenem for several Pseudomonas aeruginosa strains. Time-course profiles of bacterial counts in patients infected with P. aeruginosa were simulated for typical clinical dosage regimens in Japan considering the variability of PK and the patients' backgrounds by a Monte Carlo simulation. Moreover, time-course profiles of probability achieving the criterion (log(CFU/mL) < 0) were predicted for the evaluation of antibacterial efficacy by renal function. The in vitro bacterial profiles at various dosage regimens could be well explained by the PK/PD model. The simulations suggested the dependence of antibacterial efficacy on the frequency of administration, indicating time-dependent antibacterial activity. It was also suggested that 500 mg t.i.d. showed significant bacterial reduction in patients for any degree of renal function and any severities in 2 weeks after the start of treatment. Our approach to simulate time-course profiles of bacterial counts should be useful for determining and examining effective dosage regimens, including the treatment period, in drug development.

Characteristics of doripenem: a new broad-spectrum antibiotic

Doripenem (S-4661) is a new parenteral antibiotic from the carbapenem class; similarly to imipenem and meropenem, it has a broad-spectrum activity against Gram-positive, Gram-negative, and anaerobic bacteria. It is active against multiresistant Gram-negative bacilli such as extended-spectrum beta-lactamase-producing (ESBL) Gram-negative Enterobacteriaceae and nonfermentative Gram-negative bacilli including some strains of Pseudomonas aeruginosa that are resistant to other carbapenems. Doripenem’s chemical structure is similar to that of meropenem (substitution of one sulfamoxil-aminomethyl chain for the dimethyl-carboxyl chain), and has one 1-beta-methyl chain which provides resistance to dehydropeptidase-I enzyme. The clinical trials conducted so far have focused on the treatment of severe infections such as complicated intra-abdominal infections, complicated urinary tract infections and pyelonephritis, nosocomial pneumonia, and ventilator-associated pneumonia. Given its activity profile and the results from the clinical trials, this antibiotic may be used for empirical treatment of multibacterial infections produced by potentially multiresistant Gram-negative bacilli. In 2007, the US Food and Drug Administration approved the use of doripenem for the treatment of complicated intra-abdominal infections and complicated urinary tract infections. The European Medicines Agency has approved the use of doripenem for the same indications in addition to nosocomial pneumonia regardless of whether it is ventilator-associated or not.

A simple in vitro PK/PD model system to determine time-kill curves of drugs against Mycobacteria

In vivo tuberculosis is exposed to continually changing drug concentrations for which static minimum inhibitory concentration (MIC) testing may be a poor surrogate. While in vitro approaches to determine time-kill curves for antibiotics have been widely applied in assessing antimicrobial activity against fast growing microorganisms, their availability and application for slow-growing microorganisms including Mycobacterium tuberculosis has so far been scarce. Thus, we developed a novel simple in vitro pharmacokinetic/pharmacodynamic (PK/PD) model for establishing time-kill curves and applied it for evaluating the antimicrobial activity of different dosing regimens of isoniazid (INH) against Mycobacterium bovis BCG as a surrogate for virulent M. tuberculosis. In the in vitro model M. bovis BCG was exposed to INH concentration-time profiles as usually encountered during multiple dose therapy with 25, 100 and 300mg/day in humans who are fast or slow INH metabolizers. Bacterial killing was followed over time by determining viable counts and the resulting time-kill data was analyzed using a semi-mechanistic PK/PD model with an adaptive IC(50) function to describe the emergence of insensitive populations of bacteria over the course of treatment. In agreement with previous studies, the time-kill data suggest that AUC(0-24)/MIC is the PK/PD index that is the most explanatory of the antimicrobial effect of INH. The presented in vitro PK/PD model and associated modeling approach were able to characterize the time-kill kinetics of INH in M. bovis BCG, and may in general serve as a potentially valuable, low cost tool for the assessment of antibacterial activity in slow-growing organisms in drug development and applied pharmacotherapy.

Doripenem in hospital infections: a focus on nosocomial pneumonia, complicated intra-abdominal infections, and complicated urinary tract infections

Doripenem is the latest carbapenem on the market to date. Although not an antibiotic in a new class, it offers a glimmer of hope in combating serious infections secondary to multidrug-resistant Gram-negative bacteria when we have not seen a new class of antibacterial, particularly for Gram-negative bacteria, for more than 10 years. In vitro, doripenem exhibits a broad spectrum of activity against Gram-positive and Gram-negative bacteria, including extended-spectrum β-lactamase (ESBL) and Amp-C β-lactamase producing Enterobacteriaceae and anaerobes. Doripenem also exhibits better in vitro activity against Pseudomonas aeruginosa compared to other anti-pseudomonal carbapenems. It combines the desirable activities of both imipenem and meropenem. It has similar activity to imipenem against Gram-positive pathogens and has the antimicrobial spectrum of meropenem against Gram-negative organisms. Several randomized clinical trials have demonstrated that doripenem is non-inferior to meropenem, imipenem, piperacillin/tazobactam, or levofloxacin in its efficacy and safety profile in treating a wide range of serious bacterial infections including intra-abdominal infection, complicated urinary tract infection, and nosocomial pneumonia. Due to its wide spectrum of activity and good safety profile it is susceptible to misuse leading to increasing rates of resistance. Judicious use should be considered when using doripenem as a first-line agent or drug of choice for serious infections. Doripenem is a well-tolerated drug with common adverse effects including headache, nausea and diarrhea. Caution should be used in patients with hypersensitivity to carbapenems and adverse reactions to β-lactam agents. Dosage adjustment is needed for patients with renal impairment. Doripenem has demonstrated economic and clinical benefits. It has been shown to reduce hospital length of stay and duration of mechanical ventilation for intensive care unit (ICU) patients. Therefore, doripenem is a welcome addition to our limited armamentarium of antibiotics available to treat serious bacterial infections in hospitalized patients.