Pharmacokinetic-pharmacodynamic modeling of activity of ceftazidime during continuous and intermittent infusion

Translational PK/PD for the Development of Novel Antibiotics-A Drug Developer's Perspective

Antibiotic development traditionally involved large Phase 3 programs, preceded by Phase 2 studies. Recognizing the high unmet medical need for new antibiotics and, in some cases, challenges to conducting large clinical trials, regulators created a streamlined clinical development pathway in which a lean clinical efficacy dataset is complemented by nonclinical data as supportive evidence of efficacy. In this context, translational Pharmacokinetic/Pharmacodynamic (PK/PD) plays a key role and is a major contributor to a "robust" nonclinical package. The classical PK/PD index approach, proven successful for established classes of antibiotics, is at the core of recent antibiotic approvals and the current antibacterial PK/PD guidelines by regulators. Nevertheless, in the case of novel antibiotics with a novel Mechanism of Action (MoA), there is no prior experience with the PK/PD index approach as the basis for translating nonclinical efficacy to clinical outcome, and additional nonclinical studies and PK/PD analyses might be considered to increase confidence. In this review, we discuss the value and limitations of the classical PK/PD approach and present potential risk mitigation activities, including the introduction of a semi-mechanism-based PK/PD modeling approach. We propose a general nonclinical PK/PD package from which drug developers might choose the studies most relevant for each individual candidate in order to build up a "robust" nonclinical PK/PD understanding.

Distinguishing Inducible and Non-Inducible Resistance to Colistin in Pseudomonas aeruginosa by Quantitative and Systems Pharmacology Modeling at Low and Standard Inocula

Colistin is a polymyxin and peptide antibiotic that can yield rapid bacterial killing, but also leads to resistance emergence. We aimed to develop a novel experimental and Quantitative and Systems Pharmacology approach to distinguish between inducible and non-inducible resistance. Viable count profiles for the total and less susceptible populations of Pseudomonas aeruginosa ATCC 27853 from static and dynamic in vitro infection models were simultaneously modeled. We studied low and normal initial inocula to distinguish between inducible and non-inducible resistance. A novel cutoff filter approach allowed us to describe the eradication and inter-conversion of bacterial populations. At all inocula, 4.84 mg/L of colistin (sulfate) yielded ≥4 log10 killing, followed by >4 log10 regrowth. A pre-existing, less susceptible population was present at standard but not at low inocula. Formation of a non-pre-existing, less susceptible population was most pronounced at intermediate colistin (sulfate) concentrations (0.9 to 5 mg/L). Both less susceptible populations inter-converted with the susceptible population. Simultaneously modeling of the total and less susceptible populations at low and standard inocula enabled us to identify the de novo formation of an inducible, less susceptible population. Inducible resistance at intermediate colistin concentrations highlights the importance of rapidly achieving efficacious polymyxin concentrations by front-loaded dosage regimens.

Characterization of Pseudomonas aeruginosa resistance to ceftolozane-tazobactam due to ampC and/or ampD mutations observed during treatment using semi-mechanistic PKPD modeling

A double ampC (AmpCG183D) and ampD (AmpDH157Y) genes mutations have been identified by whole genome sequencing in a Pseudomonas aeruginosa (PaS) that became resistant (PaR) in a patient treated by ceftolozane/tazobactam (C/T). To precisely characterize the respective contributions of these mutations on the decreased susceptibility to C/T and on the parallel increased susceptibility to imipenem (IMI), mutants were generated by homologous recombination in PAO1 reference strain (PAO1- AmpCG183D, PAO1-AmpDH157Y, PAO1-AmpCG183D/AmpDH157Y) and in PaR (PaR-AmpCPaS/AmpDPaS). Sequential time-kill curve experiments were conducted on all strains and analyzed by semi-mechanistic PKPD modeling. A PKPD model with adaptation successfully described the data, allowing discrimination between initial and time-related (adaptive resistance) effects of mutations. With PAO1 and mutant-derived strains, initial EC50 values increased by 1.4, 4.1, and 29-fold after AmpCG183D , AmpDH157Y and AmpCG183D/AmpDH157Y mutations, respectively. EC50 values were increased by 320, 12.4, and 55-fold at the end of the 2 nd experiment. EC50 of PAO1-AmpCG183D/AmpDH157Y was higher than that of single mutants at any time of the experiments. Within the PaR clinical background, reversal of AmpCG183D, and AmpDH157Y mutations led to an important decrease of EC50 value, from 80.5 mg/L to 6.77 mg/L for PaR and PaR-AmpCPaS/AmpDPaS, respectively. The effect of mutations on IMI susceptibility mainly showed that the AmpCG183D mutation prevented the emergence of adaptive resistance. The model successfully described the separate and combined effect of AmpCG183D and AmpDH157Y mutations against C/T and IMI, allowing discrimination and quantification of the initial and time-related effects of mutations. This method could be reproduced in clinical strains to decipher complex resistance mechanisms.

Comparison of the pharmacokinetics of continuous and intermittent infusions of ampicillin-sulbactam in dogs with septic peritonitis

OBJECTIVE

To evaluate the time-course of ampicillin-sulbactam and percentage of time that its concentration is above a given MIC (T% > MIC) in dogs with septic peritonitis when delivered as either a continuous infusion (CI) or intermittent infusion (II).

ANIMALS

11 dogs with septic peritonitis.

PROCEDURES

Dogs were randomized to receive ampicillin-sulbactam as either CI or II. Continuous infusions were delivered as a 50 mg/kg bolus IV followed by a rate of 0.1 mg/kg/min. Intermittent infusions were administered as 50 mg/kg IV q8h. Serum ampicillin-sulbactam concentrations were measured at hours 0, 1, 6, and every 12 hours after until patients were transitioned to an oral antimicrobial equivalent. All other care was at the discretion of the attending clinician. Statistical analysis was used to determine each patient's percentage of time T% > MIC for 4 MIC breakpoints (0.25, 1.25, 8, and 16 µg/mL).

RESULTS

No dogs experienced adverse events related to ampicillin-sulbactam administration. Both CI and II maintained a T% > MIC of 100% of MIC 0.25 µg/mL and MIC 1.25 µg/mL. The CI group maintained a higher T% > MIC for MIC 8 µg/mL and MIC 16 µg/mL; however, these differences did not reach statistical significance (P = .15 and P = .12, respectively).

CLINICAL RELEVANCE

This study could not demonstrate that ampicillin-sulbactam CI maintains a greater T% > MIC in dogs with septic peritonitis than II; however, marginal differences were noted at higher antimicrobial breakpoints. While these data support the use of antimicrobial CI in septic and critically ill patients, additional prospective trials are needed to fully define the optimal doses and the associated clinical responses.

Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review

Pharmacokinetics and pharmacodynamics are areas in pharmacology related to different themes in the pharmaceutical sciences, including therapeutic drug monitoring and different stages of drug development. Although the knowledge of these disciplines is essential, they have historically been treated separately. While pharmacokinetics was limited to describing the time course of plasma concentrations after administering a drug-dose, pharmacodynamics describes the intensity of the response to these concentrations. In the last decades, the concept of pharmacokinetic/pharmacodynamic modeling (PK/PD) emerged, which seeks to establish mathematical models to describe the complete time course of the dose-response relationship. The integration of these two fields has had applications in optimizing dose regimens in treating antibacterial and antifungals. The anti-infective PK/PD models predict the relationship between different dosing regimens and their pharmacological activity. The reviewed studies show that PK/PD modeling is an essential and efficient tool for a better understanding of the pharmacological activity of antibacterial and antifungal agents.

Pharmacokinetic Modeling of Hydrocortisone by Including Protein Binding to Corticosteroid-Binding Globulin

Background: Patients with adrenal insufficiency are treated with oral hydrocortisone (HC) to compensate for the loss of endogenous cortisol production. Intrinsic imperfections of cortisol replacement strategies in mimicking normal cortisol secretion are the underlying cause of the increased morbidity and mortality of patients suffering from secondary adrenal insufficiency (SAI). To improve oral hydrocortisone substitution therapy, a better understanding of its pharmacokinetics (PK) is necessary. The previous PK model did not include protein binding. It is known that protein binding can impact hydrocortisone pharmacokinetics. The aim of this study is to describe HC pharmacokinetics including the protein-binding state using Edsim++ (Mediware, Prague) pharmacokinetic modeling software, paving the way for an in-silico tool suitable for drug delivery design. Methods: A total of 46 patients with SAI participated in a randomized double-blind crossover study Patients randomly received a low dose of HC (0.2–0.3 mg/kg body weight/day) for 10 weeks, followed by a high dose (0.4–0.6 mg/kg body weight/day) for another 10 weeks, or vice versa. Plasma samples were obtained and analyzed for free and total hydrocortisone. Single compartment population pharmacokinetic analysis was performed using an extended Werumeus-Buning model built in Edsim++. This model includes a mathematical approach for estimating free cortisol by Nguyen et al., taking the protein binding of HC to albumin and hydrocortisone-binding globulin (CBG, transcortin) into consideration, as well as different states of CBG which affect binding kinetics to HC. The goodness of fit for observed versus predicted values was calculated. Results and conclusions: Nguyen’s formula for free cortisol estimation was successfully implemented in a pharmacokinetic model. The model shows high Spearman’s correlation for observed versus predicted hydrocortisone concentrations. Significantly higher correlations (Spearman’s r, 0.901 vs. 0.836) between total and free hydrocortisone AUC₂₄ (area-under the curve over 24 h) are found when comparing new and old models. This new model was used to simulate the plasma concentration–time behavior of a more suitable hydrocortisone formulation.

Combating Multidrug-Resistant Bacteria by Integrating a Novel Target Site Penetration and Receptor Binding Assay Platform Into Translational Modeling

Multidrug-resistant bacteria are causing a serious global health crisis. A dramatic decline in antibiotic discovery and development investment by pharmaceutical industry over the last decades has slowed the adoption of new technologies. It is imperative that we create new mechanistic insights based on latest technologies, and use translational strategies to optimize patient therapy. Although drug development has relied on minimal inhibitory concentration testing and established in vitro and mouse infection models, the limited understanding of outer membrane permeability in Gram-negative bacteria presents major challenges. Our team has developed a platform using the latest technologies to characterize target site penetration and receptor binding in intact bacteria that inform translational modeling and guide new discovery. Enhanced assays can quantify the outer membrane permeability of β-lactam antibiotics and β-lactamase inhibitors using multiplex liquid chromatography tandem mass spectrometry. While β-lactam antibiotics are known to bind to multiple different penicillin-binding proteins (PBPs), their binding profiles are almost always studied in lysed bacteria. Novel assays for PBP binding in the periplasm of intact bacteria were developed and proteins identified via proteomics. To characterize bacterial morphology changes in response to PBP binding, high-throughput flow cytometry and time-lapse confocal microscopy with fluorescent probes provide unprecedented mechanistic insights. Moreover, novel assays to quantify cytosolic receptor binding and intracellular drug concentrations inform target site occupancy. These mechanistic data are integrated by quantitative and systems pharmacology modeling to maximize bacterial killing and minimize resistance in in vitro and mouse infection models. This translational approach holds promise to identify antibiotic combination dosing strategies for patients with serious infections.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Calculated initial parenteral treatment of bacterial infections: Pharmacokinetics and pharmacodynamics

This is the third chapter of the guideline "Calculated initial parenteral treatment of bacterial infections in adults - update 2018" in the 2nd updated version. The German guideline by the Paul-Ehrlich-Gesellschaft für Chemotherapie e.V. (PEG) has been translated to address an international audience. The chapter features the pharmacokinetic and pharmacodynamics properties of the most frequently used antiinfective agents.

Meropenem-Tobramycin Combination Regimens Combat Carbapenem-Resistant Pseudomonas aeruginosa in the Hollow-Fiber Infection Model Simulating Augmented Renal Clearance in Critically Ill Patients

Augmented renal clearance (ARC) is common in critically ill patients and is associated with subtherapeutic concentrations of renally eliminated antibiotics. We investigated the impact of ARC on bacterial killing and resistance amplification for meropenem and tobramycin regimens in monotherapy and combination. Two carbapenem-resistant Pseudomonas aeruginosa isolates were studied in static-concentration time-kill studies. One isolate was examined comprehensively in a 7-day hollow-fiber infection model (HFIM). Pharmacokinetic profiles representing substantial ARC (creatinine clearance of 250 ml/min) were generated in the HFIM for meropenem (1 g or 2 g administered every 8 h as 30-min infusion and 3 g/day or 6 g/day as continuous infusion [CI]) and tobramycin (7 mg/kg of body weight every 24 h as 30-min infusion) regimens. The time courses of total and less-susceptible bacterial populations and MICs were determined for the monotherapies and all four combination regimens. Mechanism-based mathematical modeling (MBM) was performed. In the HFIM, maximum bacterial killing with any meropenem monotherapy was ∼3 log₁₀ CFU/ml at 7 h, followed by rapid regrowth with increases in resistant populations by 24 h (meropenem MIC of up to 128 mg/liter). Tobramycin monotherapy produced extensive initial killing (∼7 log₁₀ at 4 h) with rapid regrowth by 24 h, including substantial increases in resistant populations (tobramycin MIC of 32 mg/liter). Combination regimens containing meropenem administered intermittently or as a 3-g/day CI suppressed regrowth for ∼1 to 3 days, with rapid regrowth of resistant bacteria. Only a 6-g/day CI of meropenem combined with tobramycin suppressed regrowth and resistance over 7 days. MBM described bacterial killing and regrowth for all regimens well. The mode of meropenem administration was critical for the combination to be maximally effective against carbapenem-resistant P. aeruginosa.

Modeling and Simulation of Pretomanid Pharmacodynamics in Pulmonary Tuberculosis Patients

Pretomanid (PA-824) is a nitroimidazole in clinical testing for the treatment of tuberculosis. A population pharmacodynamic model for pretomanid was developed using a Bayesian analysis of efficacy data from two early bactericidal activity (EBA) studies, PA-824-CL-007 and PA-824-CL-010, conducted in Cape Town, South Africa. The two studies included 122 adult male and female participants with newly diagnosed pulmonary tuberculosis who received once daily oral pretomanid doses of either 50, 100, 150, 200, 600, 1,000, or 1,200 mg for 14 days. The structural model described capacity-limited growth and saturable drug-induced bacterial killing with separate rate equations for sputum solid culture colony forming unit (CFU) counts and liquid culture time to positivity (TTP) that were linked through a time constant. The posterior population geometric means and interindividual variability percent coefficients of variation were, respectively; 0.152±0.013 log₁₀ CFU/mL sputum/day and 54%±6% for the maximum kill rate constant, 20.4±1.0 h and 20.8%±0.1% for the time constant of proportionality between the CFU and TTP rate equations, and 770±140 ng/mL and 48%±17% for the pretomanid half-maximum effect plasma concentration. Model simulations showed once daily pretomanid at 100 mg, 200 mg, and 300 mg, attained 58%, 73%, and 80%, respectively, of an expected maximum 14-day EBA of 0.136 log₁₀CFU/mL sputum/day. These results establish a pretomanid exposure-efficacy relationship with dual outcomes for CFU counts and TTP, and with potential applications to dose optimization of pretomanid-containing regimens.

Impact of Antibiotic Gut Exposure on the Temporal Changes in Microbiome Diversity

Although the global deleterious impact of antibiotics on the intestinal microbiota is well known, temporal changes in microbial diversity during and after an antibiotic treatment are still poorly characterized. We used plasma and fecal samples collected frequently during treatment and up to one month after from 22 healthy volunteers assigned to a 5-day treatment by moxifloxacin (n = 14) or no intervention (n = 8). Moxifloxacin concentrations were measured in both plasma and feces, and bacterial diversity was determined in feces by 16S rRNA gene profiling and quantified using the Shannon index and number of operational taxonomic units (OTUs). Nonlinear mixed effect models were used to relate drug pharmacokinetics and bacterial diversity over time. Moxifloxacin reduced bacterial diversity in a concentration-dependent manner, with a median maximal loss of 27.5% of the Shannon index (minimum [min], 17.5; maximum [max], 27.7) and 47.4% of the number of OTUs (min, 30.4; max, 48.3). As a consequence of both the long fecal half-life of moxifloxacin and the susceptibility of the gut microbiota to moxifloxacin, bacterial diversity indices did not return to their pretreatment levels until days 16 and 21, respectively. Finally, the model characterized the effect of moxifloxacin on bacterial diversity biomarkers and provides a novel framework for analyzing antibiotic effects on the intestinal microbiome.

Antibiotic use in critical illness

OBJECTIVE

To provide a review on the current use of antimicrobials with a discussion on the pharmacokinetic and pharmacodynamic profiles of antimicrobials in critically ill patients, the challenges of drug resistance, the use of diagnostic testing to direct therapy, and the selection of the most likely efficacious antimicrobial protocol.

ETIOLOGY

Patients in the intensive care unit often possess profound pathophysiologic changes that can complicate antimicrobial therapy. Although many antimicrobials have known pharmacodynamic profiles, critical illness can cause wide variations in their pharmacokinetics. The two principal factors affecting pharmacokinetics are volume of distribution and drug clearance. Understanding the interplay between critical illness, drug pharmacokinetics, and antimicrobial characteristics (ie, time-dependent vs concentration-dependent) may improve antimicrobial efficacy and patient outcome.

DIAGNOSIS

Utilizing bacterial culture and susceptibility can aid in identifying drug resistant infections, selecting the most appropriate antimicrobials, and hindering the future development of drug resistance.

THERAPY

Having a basic knowledge of antimicrobial function and how to use diagnostics to direct therapeutic treatment is paramount in managing this patient population. Diagnostic testing is not always available at the time of initiation of antimicrobial therapy, so empiric selections are often necessary. These empiric choices should be made based on the location of the infection and the most likely infecting bacteria.

PROGNOSIS

Studies have demonstrated the importance of moving away from a "one dose fits all" approach to antimicrobial therapy. Instead there has been a move toward an individualized approach that takes into consideration the pharmacokinetic and pharmacodynamic variabilities that can occur in critically ill patients.

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.

Soup with or without meatballs: Impact of nutritional factors on the MIC, kill-rates and growth-rates

BACKGROUND

The Minimum Inhibitory Concentration (MIC) is a reference value for susceptibility testing of bacteria. However, the MIC is a net result of growth and killing after a certain duration of exposure under standardized favourable in vitro conditions. Killing and growth characteristics of a drug may yield more information on its activity and help to explain discrepancies between efficacy observed in vitro and in vivo.

METHODS

The MIC of meropenem was determined for P. aeruginosa ATCC 27853 both by microdilution and the E-test in dilutions of Mueller Hinton (MH) broth from 100% to 1%. Time-kill curves were obtained for twofold dilutions of meropenem. Growth rates and kill rates at each concentration and dilution were obtained by linear regression. The Hill equation was fit to the kill rates vs concentrations.

RESULTS

Growths rates decreased log linearly from 0.63/h at 100% to 0.29/h at 6% MH. Over the 100-6% MH dilution range, there was a log-linear decrease of the MIC of meropenem of both the E-test and microdilution. The EC50s decreased from 0.29 mg/L to 0.07 mg/L, which is in agreement with the MIC results. There was a log-linear relationship between MIC and EC50 for the various dilutions MH.

CONCLUSIONS

The availability of nutritional factors is related to the MIC, and a lower availability is related to both a lower growth rate and higher kill rate. Since nutritional factors are less abundantly available in vivo as compared to in vitro, this should be taken into account when translating in vitro to in vivo pharmacodynamics.

Optimization of a Meropenem-Tobramycin Combination Dosage Regimen against Hypermutable and Nonhypermutable Pseudomonas aeruginosa via Mechanism-Based Modeling and the Hollow-Fiber Infection Model

Hypermutable Pseudomonas aeruginosa strains are prevalent in patients with cystic fibrosis and rapidly become resistant to antibiotic monotherapies. Combination dosage regimens have not been optimized against such strains using mechanism-based modeling (MBM) and the hollow-fiber infection model (HFIM). The PAO1 wild-type strain and its isogenic hypermutable PAOΔmutS strain (MIC_meropenem of 1.0 mg/liter and MIC_tobramycin of 0.5 mg/liter for both) were assessed using 96-h static-concentration time-kill studies (SCTK) and 10-day HFIM studies (inoculum, ∼10^8.4 CFU/ml). MBM of SCTK data were performed to predict expected HFIM outcomes. Regimens studied in the HFIM were meropenem at 1 g every 8 h (0.5-h infusion), meropenem at 3 g/day with continuous infusion, tobramycin at 10 mg/kg of body weight every 24 h (1-h infusion), and both combinations. Meropenem regimens delivered the same total daily dose. Time courses of total and less susceptible populations and MICs were determined. For the PAOΔmutS strain in the HFIM, all monotherapies resulted in rapid regrowth to >10^8.7 CFU/ml with near-complete replacement by less susceptible bacteria by day 3. Meropenem every 8 h with tobramycin caused >7-log₁₀ bacterial killing followed by regrowth to >6 log₁₀ CFU/ml by day 5 and high-level resistance (MIC_meropenem, 32 mg/liter; MIC_tobramycin, 8 mg/liter). Continuous infusion of meropenem with tobramycin achieved >8-log₁₀ bacterial killing without regrowth. For PAO1, meropenem monotherapies suppressed bacterial growth to <4 log₁₀ over 7 to 9 days, with both combination regimens achieving near eradication. An MBM-optimized meropenem plus tobramycin regimen achieved synergistic killing and resistance suppression against a difficult-to-treat hypermutable P. aeruginosa strain. For the combination to be maximally effective, it was critical to achieve the optimal shape of the concentration-time profile for meropenem.

Substantial Impact of Altered Pharmacokinetics in Critically Ill Patients on the Antibacterial Effects of Meropenem Evaluated via the Dynamic Hollow-Fiber Infection Model

Critically ill patients frequently have substantially altered pharmacokinetics compared to non-critically ill patients. We investigated the impact of pharmacokinetic alterations on bacterial killing and resistance for commonly used meropenem dosing regimens. A Pseudomonas aeruginosa isolate (MIC_meropenem 0.25 mg/liter) was studied in the hollow-fiber infection model (inoculum ∼10^7.5 CFU/ml; 10 days). Pharmacokinetic profiles representing critically ill patients with augmented renal clearance (ARC), normal, or impaired renal function (creatinine clearances of 285, 120, or ∼10 ml/min, respectively) were generated for three meropenem regimens (2, 1, and 0.5 g administered as 8-hourly 30-min infusions), plus 1 g given 12 hourly with impaired renal function. The time course of total and less-susceptible populations and MICs were determined. Mechanism-based modeling (MBM) was performed using S-ADAPT. All dosing regimens across all renal functions produced similar initial bacterial killing (≤∼2.5 log₁₀). For all regimens subjected to ARC, regrowth occurred after 7 h. For normal and impaired renal function, bacterial killing continued until 23 to 47 h; regrowth then occurred with 0.5- and 1-g regimens with normal renal function (fT_>5×MIC = 56 and 69%, fC_min/MIC < 2); the emergence of less-susceptible populations (≥32-fold increases in MIC) accompanied all regrowth. Bacterial counts remained suppressed across 10 days with normal (2-g 8-hourly regimen) and impaired (all regimens) renal function (fT_>5×MIC ≥ 82%, fC_min/MIC ≥ 2). The MBM successfully described bacterial killing and regrowth for all renal functions and regimens simultaneously. Optimized dosing regimens, including extended infusions and/or combinations, supported by MBM and Monte Carlo simulations, should be evaluated in the context of ARC to maximize bacterial killing and suppress resistance emergence.

Daptomycin Pharmacokinetics and Pharmacodynamics in Septic and Critically Ill Patients

Infections, including sepsis, are associated with high mortality rates in critically ill patients in the intensive care unit (ICU). Appropriate antibiotic selection and adequate dosing are important for improving patient outcomes. Daptomycin is bactericidal in bloodstream infections caused by Staphylococcus aureus and other Gram-positive pathogens cultured in ICU patients. The drug has concentration-dependent activity, and the area under the curve/minimum inhibitory concentration ratio is the pharmacokinetic/pharmacodynamic (PK/PD) index that best correlates with daptomycin activity, whereas toxicity correlates well with daptomycin plasma trough concentrations (or minimum concentration [C min]). Adequate daptomycin exposure can be difficult to achieve in ICU patients; multiple PK alterations can result in highly variable plasma concentrations, which are difficult to predict. For this reason, therapeutic drug monitoring could help clinicians optimize daptomycin dosing, thus improving efficacy while decreasing the likelihood of serious adverse events. This paper reviews the literature on daptomycin in ICU patients with sepsis, focusing on dosing and PK and PD parameters.

Defining the Active Fraction of Daptomycin against Methicillin-Resistant Staphylococcus aureus (MRSA) Using a Pharmacokinetic and Pharmacodynamic Approach

Our objective was to study the pharmacodynamics of daptomycin in the presence of varying concentrations of human serum (HS) in vitro to quantify the fraction of daptomycin that is ‘active’. Time kill experiments were performed with daptomycin (0 to 256 mg/L) against two MRSA strains at log-phase growth, in the presence of HS (0%, 10%, 30%, 50%, 70%) combined with Mueller-Hinton broth. Daptomycin ≥ 2 mg/L achieved 99.9% kill within 8 h at all HS concentrations; early killing activity was slightly attenuated at higher HS concentrations. After 1 h, bacterial reduction of USA300 upon exposure to daptomycin 4 mg/L ranged from -3.1 to -0.5 log₁₀CFU/mL in the presence of 0% to 70% HS, respectively. Bactericidal activity was achieved against both strains at daptomycin ≥ 4 mg/L for all fractions of HS exposure. A mechanism-based mathematical model (MBM) was developed to estimate the active daptomycin fraction at each %HS, comprising 3 bacterial subpopulations differing in daptomycin susceptibility. Time-kill data were fit with this MBM with excellent precision (r² >0.95). The active fraction of daptomycin was estimated to range from 34.6% to 25.2% at HS fractions of 10% to 70%, respectively. Despite the reported low unbound fraction of daptomycin, the impact of protein binding on the activity of daptomycin was modest. The active fraction approach can be utilized to design in vitro experiments and to optimize therapeutic regimens of daptomycin in humans.

A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro

OBJECTIVES

Mycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states.

METHODS

The natural growth data of M. tuberculosis H37Rv used in this study consisted of viability defined as cfu versus time based on data from an in vitro hypoxia system. External validation of the natural growth model was conducted using data representing the rate of incorporation of radiolabelled methionine into proteins by the bacteria. Rifampicin time-kill curves from log-phase (0.25-16 mg/L) and stationary-phase (0.5-64 mg/L) cultures were used to assess the model's ability to describe drug effects by evaluating different linear and non-linear exposure-response relationships.

RESULTS

The final pharmacometric model consisted of a three-compartment differential equation system representing fast-, slow- and non-multiplying bacteria. Model predictions correlated well with the external data (R(2) = 0.98). The rifampicin effects on log-phase and stationary-phase cultures were separately and simultaneously described by including the drug effect on the different bacterial states. The predicted reduction in log10 cfu after 14 days and at 0.5 mg/L was 2.2 and 0.8 in the log-phase and stationary-phase systems, respectively.

CONCLUSIONS

The model provides predictions of the change in bacterial numbers for the different bacterial states with and without drug effect and could thus be used as a framework for studying anti-tubercular drug effects in vitro.

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.

Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonas aeruginosa as quantified in a PK/PD model

OBJECTIVES

Combination therapy can be a strategy to ensure effective bacterial killing when treating Pseudomonas aeruginosa, a Gram-negative bacterium with high potential for developing resistance. The aim of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and meropenem alone and in combination for one WT and one meropenem-resistant strain of P. aeruginosa.

METHODS

In vitro time-kill curve experiments were conducted with a P. aeruginosa WT (ATCC 27853) (MICs: meropenem 1 mg/L; colistin 1 mg/L) and a meropenem-resistant type (ARU552) (MICs: meropenem 16 mg/L; colistin 1.5 mg/L). PK/PD models characterizing resistance were fitted to the observed bacterial counts in NONMEM. The final model was applied to predict the bacterial killing of ARU552 for different combination dosages of colistin and meropenem.

RESULTS

A model with compartments for growing and resting bacteria, where the bacterial killing by colistin reduced with continued exposure and a small fraction (0.15%) of the start inoculum was resistant to meropenem, characterized the bactericidal effect and resistance development of the two antibiotics. For a typical patient, a loading dose of colistin combined with a high dose of meropenem (2000 mg q8h) was predicted to result in a pronounced kill of the meropenem-resistant strain over 24 h.

CONCLUSIONS

The developed PK/PD model successfully described the time course of bacterial counts following exposures to colistin and meropenem, alone and in combination, for both strains, and identified a dynamic drug interaction. The study illustrates the application of a PK/PD model and supports high-dose combination therapy of colistin and meropenem to overcome meropenem resistance.

Pharmacodynamic modelling of in vitro activity of tetracycline against a representative, naturally occurring population of porcine Escherichia coli

Background

The complex relationship between drug concentrations and bacterial growth rates require not only the minimum inhibitory concentration but also other parameters to capture the dynamic nature of the relationship. To analyse this relationship between tetracycline concentration and growth of Escherichia coli representative of those found in the Danish pig population, we compared the growth of 50 randomly selected strains. The observed net growth rates were used to describe the in vitro pharmacodynamic relationship between drug concentration and net growth rate based on E_max model with three parameters: maximum net growth rate (α_max); concentration for a half-maximal response (E_max); and the Hill coefficient (γ).

Results

The net growth rate in the absence of antibiotic did not differ between susceptible and resistant isolates (P = 0.97). The net growth rate decreased with increasing tetracycline concentrations, and this decline was greater in susceptible strains than resistant strains. The lag phase, defined as the time needed for the strain to reach an OD₆₀₀ value of 0.01, increased exponentially with increasing tetracycline concentration. The pharmacodynamic parameters confirmed that the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \alpha_{max} $$\end{document}αmax between susceptible and resistant strains in the absence of a drug was not different. EC₅₀ increased linearly with MIC on a log–log scale, and γ was different between susceptible and resistant strains.

Conclusions

The in vitro model parameters described the inhibition effect of tetracycline on E. coli when strains were exposed to a wide range of tetracycline concentrations. These parameters, along with in vivo pharmacokinetic data, may be useful in mathematical models to predict in vivo competitive growth of many different strains and for development of optimal dosing regimens for preventing selection of resistance.

Measurement of ceftazidime concentration in human plasma by ultra-performance liquid chromatography-tandem mass spectrometry. Application to critically ill patients and patients with osteoarticular infections

Ceftazidime is an antibiotic belonging to the third generation of the cephalosporin family. It is indicated in the treatment of serious, simple or mixed bacterial infections, and its administration in continuous or intermittent infusion allows optimization of the concentration of antibiotic to keep it above the minimum inhibitory concentration. We developed and validated a chromatographic method by ultra-performance liquid chromatography-tandem mass spectrometry to measure ceftazidime concentration in human plasma. Following extraction with acetonitrile and 1,2-dichloroethane, the chromatographic separation was achieved using an Acquity ® UPLC ® BEH(TM) (2.1 × 100 mm i.d., 1.7 µm) reverse-phase C18 column, with a water-acetonitrile linear gradient containing 0.1% formic acid at a 0.4 mL/min flow rate. Ceftazidime and its internal standard (cefotaxime) were detected by electrospray ionization mass spectrometry in positive ion multiple reaction monitoring mode using mass-to-charge transitions of 547.0 → 467.9/396.1 and 456.0 → 395.8/324.1, respectively. The limit of quantification was 0.58 mg/L and linearity was observed in the range 0.58-160 mg/L. Coefficients of variation and absolute relative biases were <9.8 and 8.4%. The mean recovery for ceftazidime was 74.4 ± 8.1%. Evaluation of the matrix effect showed ion enhancement, and no carry-over was observed. The validated method could be applied to daily clinical laboratory practice to measure the concentration of ceftazidime in plasma.

SYTO-13, a Viability Marker as a New Tool to Monitor In Vitro Pharmacodynamic Parameters of Anti-Pneumocystis Drugs

While Pneumocystis pneumonia (PcP) still impacts the AIDS patients, it has a growing importance in immunosuppressed HIV-negative patients. To determine the anti-Pneumocystis therapeutic efficacy of new compounds, animal and in vitro models have been developed. Indeed, well-designed mouse or rat experimental models of pneumocystosis can be used to describe the in vivo anti-Pneumocystis activity of new drugs. In vitro models, which enable the screening of a large panel of new molecules, have been developed using axenic cultures or co-culture with feeder cells; but no universally accepted standard method is currently available to evaluate anti-Pneumocystis molecules in vitro. Thus, we chose to explore the use of the SYTO-13 dye, as a new indicator of Pneumocystis viability. In the present work, we established the experimental conditions to define the in vitro pharmacodynamic parameters (EC50, Emax) of marketed compounds (trimethoprim/sulfamethoxazole, pentamidine, atovaquone) in order to specifically measure the intrinsic activity of these anti-P. carinii molecules using the SYTO-13 dye for the first time. Co-labelling the fungal organisms with anti-P. carinii specific antibodies enabled the measurement of viability of Pneumocystis organisms while excluding host debris from the analysis. Moreover, contrary to microscopic observation, large numbers of fungal cells can be analyzed by flow cytometry, thus increasing statistical significance and avoiding misreading during fastidious quantitation of stained organisms. In conclusion, the SYTO-13 dye allowed us to show a reproducible dose/effect relationship for the tested anti-Pneumocystis drugs.

Lung concentrations of ceftazidime administered by continuous versus intermittent infusion in patients with ventilator-associated pneumonia

Ceftazidime is a beta-lactam compound that exerts a time-dependent bactericidal effect. Numerous arguments are in favor of continuous administration of ceftazidime, both for reasons of clinical efficacy and to preserve bacteriological mutation. We report a prospective, single-center, parallel-group, randomized, controlled trial comparing two modes of administration of ceftazidime, namely, continuous administration (loading dose of 20 mg/kg of body weight followed by 60 mg/kg/day) versus intermittent administration (20 mg/kg over 30 min every 8 h) in 34 patients with ventilator-associated pneumonia due to Gram-negative bacilli. The study was performed over 48 h with 13 and 18 assessments of serum ceftazidime in the continuous-infusion group (group A) and the intermittent-fusion group (group B), respectively. Bronchoalveolar lavage (BAL) was performed at steady state in both groups at 44 h to determine ceftazidime levels in the epithelial lining fluid. We chose a predefined threshold of 20 mg/liter for serum concentrations of ceftazidime because of ecological conditions in our center. The median time above 20 mg/liter (T>20 mg) was 100% in group A versus 46% in group B. In group A, 14/17 patients had 100% T>20 mg, versus only 1/17 patients in group B. In the epithelial lining fluid, the median concentration of ceftazidime was 12 mg/liter in group A versus 6 mg/liter in group B. A threshold of 8 mg/liter in the epithelial lining fluid was achieved twice as often in group A as in group B. This study of ceftazidime concentrations in the epithelial lining fluid indicates that continuous infusion presents advantages in terms of pharmacodynamics and predictable efficacy in patients presenting ventilator-associated pneumonia.

Optimization of continuous infusion of piperacillin-tazobactam in children with fever and neutropenia

The study through Monte Carlo simulation of β-lactam pharmacokinetic/pharmacodynamic target attainment and determination of subsequent serum concentrations of piperacillin-tazobactam administered through continuous infusion to children treated for fever and neutropenia shows that 400 mg/kg/day has the highest probability of target attainment against Pseudomonas aeurginosa in our oncology ward compared with the standard regimen of 300 mg/kg/day.

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.

A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models

The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK-PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK-PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.

Quantifying subpopulation synergy for antibiotic combinations via mechanism-based modeling and a sequential dosing design

Quantitative modeling of combination therapy can describe the effects of each antibiotic against multiple bacterial populations. Our aim was to develop an efficient experimental and modeling strategy that evaluates different synergy mechanisms using a rapidly killing peptide antibiotic (nisin) combined with amikacin or linezolid as probe drugs. Serial viable counts over 48 h were obtained in time-kill experiments with all three antibiotics in monotherapy against a methicillin-resistant Staphylococcus aureus USA300 strain (inoculum, 10(8) CFU/ml). A sequential design (initial dosing of 8 or 32 mg/liter nisin, switched to amikacin or linezolid at 1.5 h) assessed the rate of killing by amikacin and linezolid against nisin-intermediate and nisin-resistant populations. Simultaneous combinations were additionally studied and all viable count profiles comodeled in S-ADAPT and NONMEM. A mechanism-based model with six populations (three for nisin times two for amikacin) yielded unbiased and precise (r = 0.99, slope = 1.00; S-ADAPT) individual fits. The second-order killing rate constants for nisin against the three populations were 5.67, 0.0664, and 0.00691 liter/(mg · h). For amikacin, the maximum killing rate constants were 10.1 h(-1) against its susceptible and 0.771 h(-1) against its less-susceptible populations, with 14.7 mg/liter amikacin causing half-maximal killing. After incorporating the effects of nisin and amikacin against each population, no additional synergy function was needed. Linezolid inhibited successful bacterial replication but did not efficiently kill populations less susceptible to nisin. Nisin plus amikacin achieved subpopulation synergy. The proposed sequential and simultaneous dosing design offers an efficient approach to quantitatively characterize antibiotic synergy over time and prospectively evaluate antibiotic combination dosing strategies.

High β-lactamase levels change the pharmacodynamics of β-lactam antibiotics in Pseudomonas aeruginosa biofilms

Resistance to β-lactam antibiotics is a frequent problem in Pseudomonas aeruginosa lung infection of cystic fibrosis (CF) patients. This resistance is mainly due to the hyperproduction of chromosomally encoded β-lactamase and biofilm formation. The purpose of this study was to investigate the role of β-lactamase in the pharmacokinetics (PK) and pharmacodynamics (PD) of ceftazidime and imipenem on P. aeruginosa biofilms. P. aeruginosa PAO1 and its corresponding β-lactamase-overproducing mutant, PAΔDDh2Dh3, were used in this study. Biofilms of these two strains in flow chambers, microtiter plates, and on alginate beads were treated with different concentrations of ceftazidime and imipenem. The kinetics of antibiotics on the biofilms was investigated in vitro by time-kill methods. Time-dependent killing of ceftazidime was observed in PAO1 biofilms, but concentration-dependent killing activity of ceftazidime was observed for β-lactamase-overproducing biofilms of P. aeruginosa in all three models. Ceftazidime showed time-dependent killing on planktonic PAO1 and PAΔDDh2Dh3. This difference is probably due to the special distribution and accumulation in the biofilm matrix of β-lactamase, which can hydrolyze the β-lactam antibiotics. The PK/PD indices of the AUC/MBIC and C(max)/MBIC (AUC is the area under concentration-time curve, MBIC is the minimal biofilm-inhibitory concentration, and C(max) is the maximum concentration of drug in serum) are probably the best parameters to describe the effect of ceftazidime in β-lactamase-overproducing P. aeruginosa biofilms. Meanwhile, imipenem showed time-dependent killing on both PAO1 and PAΔDDh2Dh3 biofilms. An inoculum effect of β-lactams was found for both planktonic and biofilm P. aeruginosa cells. The inoculum effect of ceftazidime for the β-lactamase-overproducing mutant PAΔDDh2Dh3 biofilms was more obvious than for PAO1 biofilms, with a requirement of higher antibiotic concentration and a longer period of treatment.

β-Lactam therapeutic drug monitoring in the critically ill: optimising drug exposure in patients with fluctuating renal function and hypoalbuminaemia

β-Lactams are routinely prescribed in the treatment of serious infections. Empirical dosing schedules are typically derived from studies in healthy volunteers and largely fail to consider the significant changes in antibacterial pharmacokinetics often encountered in the critically ill. These changes are primarily driven by the underlying pathophysiology and the interventions provided, leading to altered protein binding, poor tissue penetration, and fluctuations in the volume of distribution and drug clearance. Each separately, and in combination, is likely to complicate successful β-lactam administration in this setting. Although antibacterial therapeutic drug monitoring (TDM) has traditionally been employed to minimise drug toxicity, the challenges to achieving 'optimal' drug concentrations in the critically ill suggest β-lactam TDM as an attractive means to optimise drug exposure. Whilst there is currently little evidence to support routine widespread application of such a service, β-lactam TDM may still have a role in select patients where difficulty in establishing therapeutic concentrations can be illustrated. This series utilises three representative cases from a β-lactam TDM service that highlight the utility of this intervention in optimising antibacterial dosing. These preliminary data support an expanding role for β-lactam TDM in select critically ill patients and provide insight into the subpopulations most at risk of suboptimal drug exposure. Future studies investigating the clinical outcome benefits of β-lactam TDM in these patient groups are now warranted.

Continuous beta-lactam infusion in critically ill patients: the clinical evidence

There is controversy over whether traditional intermittent bolus dosing or continuous infusion of beta-lactam antibiotics is preferable in critically ill patients. No significant difference between these two dosing strategies in terms of patient outcomes has been shown yet. This is despite compelling in vitro and in vivo pharmacokinetic/pharmacodynamic (PK/PD) data. A lack of significance in clinical outcome studies may be due to several methodological flaws potentially masking the benefits of continuous infusion observed in preclinical studies. In this review, we explore the methodological shortcomings of the published clinical studies and describe the criteria that should be considered for performing a definitive clinical trial. We found that most trials utilized inconsistent antibiotic doses and recruited only small numbers of heterogeneous patient groups. The results of these trials suggest that continuous infusion of beta-lactam antibiotics may have variable efficacy in different patient groups. Patients who may benefit from continuous infusion are critically ill patients with a high level of illness severity. Thus, future trials should test the potential clinical advantages of continuous infusion in this patient population. To further ascertain whether benefits of continuous infusion in critically ill patients do exist, a large-scale, prospective, multinational trial with a robust design is required.

Therapeutic drug monitoring of antimicrobials

Optimizing the prescription of antimicrobials is required to improve clinical outcome from infections and to reduce the development of antimicrobial resistance. One such method to improve antimicrobial dosing in individual patients is through application of therapeutic drug monitoring (TDM). The aim of this manuscript is to review the place of TDM in the dosing of antimicrobial agents, specifically the importance of pharmacokinetics (PK) and pharmacodynamics (PD) to define the antimicrobial exposures necessary for maximizing killing or inhibition of bacterial growth. In this context, there are robust data for some antimicrobials, including the ratio of a PK parameter (e.g. peak concentration) to the minimal inhibitory concentration of the bacteria associated with maximal antimicrobial effect. Blood sampling of an individual patient can then further define the relevant PK parameter value in that patient and, if necessary, antimicrobial dosing can be adjusted to enable achievement of the target PK/PD ratio. To date, the clinical outcome benefits of a systematic TDM programme for antimicrobials have only been demonstrated for aminoglycosides, although the decreasing susceptibility of bacteria to available antimicrobials and the increasing costs of pharmaceuticals, as well as emerging data on pharmacokinetic variability, suggest that benefits are likely.

In vivo pharmacokinetics/pharmacodynamics of colistin and imipenem in Pseudomonas aeruginosa biofilm infection

Many Pseudomonas aeruginosa isolates from the airways of patients with cystic fibrosis (CF) are sensitive to antibiotics in susceptibility testing, but eradication of the infection is difficult. The main reason is the biofilm formation in the airways of patients with CF. The pharmacokinetics (PKs) and pharmacodynamics (PDs) of antimicrobials can reliably be used to predict whether antimicrobial regimens will achieve the maximum bactericidal effect against infections. Unfortunately, however, most PK/PD studies of antimicrobials have been done on planktonic cells and very few PK/PD studies have been done on biofilms, partly due to the lack of suitable models in vivo. In the present study, a biofilm lung infection model was developed to provide an objective and quantitative evaluation of the PK/PD profile of antimicrobials. Killing curves were set up to detect the antimicrobial kinetics on planktonic and biofilm P. aeruginosa cells in vivo. Colistin showed concentration-dependent killing, while imipenem showed time-dependent killing on both planktonic and biofilm P. aeruginosa cells in vivo. The parameter best correlated to the elimination of bacteria in lung by colistin was the area under the curve (AUC) versus MIC (AUC/MIC) for planktonic cells or the AUC versus minimal biofilm inhibitory concentration (MBIC; AUC/MBIC) for biofilm cells. The best-correlated parameter for imipenem was the time that the drug concentration was above the MIC for planktonic cells (T(MIC)) or time that the drug concentration was above the MBIC (T(MBIC)) for biofilm cells. However, the AUC/MIC of imipenem showed a better correlation with the efficacy of imipenem for biofilm infections (R(2) = 0.89) than planktonic cell infections (R(2) = 0.38). The postantibiotic effect (PAE) of colistin and imipenem was shorter in biofilm infections than planktonic cell infections in this model.

"One-size-fits-all"? Optimizing treatment duration for bacterial infections

Historically, antibiotic treatment guidelines have aimed to maximize treatment efficacy and minimize toxicity, but have not considered the evolution of antibiotic resistance. Optimizing the duration and dosing of treatment to minimize the duration of symptomatic infection and selection pressure for resistance simultaneously has the potential to extend the useful therapeutic life of these valuable life-saving drugs without compromising the interests of individual patients.Here, using mathematical models, we explore the theoretical basis for shorter durations of treatment courses, including a range of ecological dynamics of bacteria that cause infections or colonize hosts as commensals. We find that immunity is an important mediating factor in determining the need for long duration of treatment. When immunity to infection is expected, shorter durations that reduce the selection for resistance without interfering with successful clinical outcome are likely to be supported. Adjusting drug treatment strategies to account for the impact of the differences in the ecological niche occupied by commensal flora relative to invasive bacteria could be effective in delaying the spread of bacterial resistance.

Antibiotics in critically ill patients: a systematic review of the pharmacokinetics of β-lactams

INTRODUCTION

Several reports have shown marked heterogeneity of antibiotic pharmacokinetics (PK) in patients admitted to ICUs, which might potentially affect outcomes. Therefore, the pharmacodynamic (PD) parameter of the efficacy of β-lactam antibiotics, that is, the time that its concentration is above the bacteria minimal inhibitory concentration (T > MIC), cannot be safely extrapolated from data derived from the PK of healthy volunteers.

METHODS

We performed a full review of published studies addressing the PK of intravenous β-lactam antibiotics given to infected ICU patients. Study selection comprised a comprehensive bibliographic search of the PubMed database and bibliographic references in relevant reviews from January 1966 to December 2010. We selected only English-language articles reporting studies addressing β-lactam antibiotics that had been described in at least five previously published studies. Studies of the PK of patients undergoing renal replacement therapy were excluded.

RESULTS

A total of 57 studies addressing six different β-lactam antibiotics (meropenem, imipenem, piperacillin, cefpirome, cefepime and ceftazidime) were selected. Significant PK heterogeneity was noted, with a broad, more than twofold variation both of volume of distribution and of drug clearance (Cl). The correlation of antibiotic Cl with creatinine clearance was usually reported. Consequently, in ICU patients, β-lactam antibiotic half-life and T > MIC were virtually unpredictable, especially in those patients with normal renal function. A better PD profile was usually obtained by prolonged or even continuous infusion. Tissue penetration was also found to be compromised in critically ill patients with septic shock.

CONCLUSIONS

The PK of β-lactam antibiotics are heterogeneous and largely unpredictable in ICU patients. Consequently, the dosing of antibiotics should be supported by PK concepts, including data derived from studies of the PK of ICU patients and therapeutic drug monitoring.

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.

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.

A novel approach to pharmacodynamic assessment of antimicrobial agents: new insights to dosing regimen design

Pharmacodynamic modeling has been increasingly used as a decision support tool to guide dosing regimen selection, both in the drug development and clinical settings. Killing by antimicrobial agents has been traditionally classified categorically as concentration-dependent (which would favor less fractionating regimens) or time-dependent (for which more frequent dosing is preferred). While intuitive and useful to explain empiric data, a more informative approach is necessary to provide a robust assessment of pharmacodynamic profiles in situations other than the extremes of the spectrum (e.g., agents which exhibit partial concentration-dependent killing). A quantitative approach to describe the interaction of an antimicrobial agent and a pathogen is proposed to fill this unmet need. A hypothetic antimicrobial agent with linear pharmacokinetics is used for illustrative purposes. A non-linear functional form (sigmoid Emax) of killing consisted of 3 parameters is used. Using different parameter values in conjunction with the relative growth rate of the pathogen and antimicrobial agent concentration ranges, various conventional pharmacodynamic surrogate indices (e.g., AUC/MIC, Cmax/MIC, %T>MIC) could be satisfactorily linked to outcomes. In addition, the dosing intensity represented by the average kill rate of a dosing regimen can be derived, which could be used for quantitative comparison. The relevance of our approach is further supported by experimental data from our previous investigations using a variety of gram-negative bacteria and antimicrobial agents (moxifloxacin, levofloxacin, gentamicin, amikacin and meropenem). The pharmacodynamic profiles of a wide range of antimicrobial agents can be assessed by a more flexible computational tool to support dosing selection.

Pharmacodynamic modeling of in vitro activity of marbofloxacin against Escherichia coli strains

A mathematical pharmacodynamic model was developed to describe the bactericidal activity of marbofloxacin against Escherichia coli strains with reduced susceptibility levels (determined using MICs) under optimal and intestinal growth conditions. Model parameters were estimated using nonlinear least-square curve-fitting procedures for each E. coli strain. Parameters related to bactericidal activity were subsequently analyzed using a maximum-effect (E(max)) model adapted to account for a direct and a delayed effect. While net growth rates did not vary significantly with strain susceptibility, culture medium had a major effect. The bactericidal activity of marbofloxacin was closely associated with the concentration and the duration of exposure of the bacteria to the antimicrobial agent. The value of the concentration inducing a half-maximum effect (C(50)) was highly correlated with MIC values (R(2) = 0.87 and R(2) = 0.94 under intestinal and optimal conditions, respectively). Our model reproduced the time-kill kinetics with good accuracy (R(2) of >0.90) and helped explain observed regrowth.

Dose-response relationships of three amphotericin B formulations in a non-neutropenic murine model of invasive aspergillosis

New lipid-associated formulations of amphotericin B (AmB) have been developed in order to reduce toxicity and enhance the efficacy of AmB by allowing administration of higher doses of the drug. We determined the in vivo dose-response relationships of 1 day and 7 day treatment of AmB, Ambisome (AmBi) and Abelcet (ABLC) in a non-neutropenic murine model of invasive aspergillosis by using survival as an endpoint. Female CD-1 mice were infected intravenously 48 h prior to start therapy with Aspergillus fumigatus (1 x 10(7) conidia/mouse). Groups of 10 mice were treated iv for 1 day or 7 days with increasing 2-fold doses of AmB, ABLC and AmBi up to a maximum of 20 mg/kg/day. Mortality was determined twice daily until day 15. Results were analyzed using product-moment survival analysis and by determining the dose response relationships on day 15. Survival at day 15 of mice with 7 day AmBi or ABLC treatment was significantly better than that of controls or AmB. The ED50s of AmBi and ABLC were 0.06 (95% CI: 0.03-0.127) mg/kg and 0.21 (0.06-0.66) mg/kg respectively. In addition, the maximum effect was higher for AmBi than ABLC, 90% survival versus 68%, respectively. Most of the effects of treatment with AmBi were reached after 1 day of treatment, indicating that the first dose given is most important in predicting survival. This study shows that AmBi and ABLC were significantly more efficacious than AmB in a non-neutropenic murine model of invasive aspergillosis, and that the effect observed was primarily dependent on the first dose administered.

Therapeutic drug monitoring of beta-lactams in critically ill patients: proof of concept

The extreme pharmacokinetic behaviour of drugs sometimes observed in critically ill patients poses a significant threat to the achievement of optimal antibiotic treatment outcomes. Scant information on beta-lactam antibiotic therapeutic drug monitoring (TDM) is available. The objective of this prospective study was to evaluate the practicality and utility of a beta-lactam TDM programme in critically ill patients. TDM was performed twice weekly on all eligible patients at a 30-bed tertiary referral critical care unit. Blood concentrations were determined by fast-throughput high-performance liquid chromatography (HPLC) assays and were available within 12h of sampling. Dose adjustment was instituted if the trough or steady-state blood concentration was below 4-5x the minimum inhibitory concentration (MIC) or above 10x MIC. A total of 236 patients were subject to TDM over an 11-month period. The mean+/-standard deviation age was 53.5+/-18.3 years. Dose adjustment was required in 175 (74.2%) of the patients, with 119 of these patients (50.4%) requiring dose increases after the first TDM. For outcome of therapy, 206 (87.3%) courses resulted in a positive treatment outcome and there were 30 (12.7%) treatment failures observed including 14 deaths and 15 courses requiring escalation to broader-spectrum agents; 1 course was ceased due to an adverse drug reaction. Using binomial logistic regression, only an elevated Acute Physiology and Chronic Health Evaluation (APACHE) II score (P<0.01) and elevated plasma creatinine concentration (P=0.05) were found to be predictive of mortality. In conclusion, further research is required to determine definitively whether achievement of optimal beta-lactam pharmacodynamic targets improves clinical outcomes.