Individual meropenem epithelial lining fluid and plasma PK/PD target attainment.

It is unclear whether plasma is a reliable surrogate for target attainment in the epithelial lining fluid (ELF). The objective of this study was to characterize meropenem target attainment in plasma and ELF using prospective samples. The first 24-hour T>MIC was evaluated vs 1xMIC and 4xMIC targets at the patient (i.e., fixed MIC of 2 mg/L) and population [i.e., cumulative fraction of response (CFR) according to EUCAST MIC distributions] levels for both plasma and ELF. Among 65 patients receiving ≥24 hours of treatment, 40% of patients failed to achieve >50% T>4xMIC in plasma and ELF, and 30% of patients who achieved >50% T>4xMIC in plasma had <50% T>4xMIC in ELF. At 1xMIC and 4xMIC targets, 3% and 25% of patients with >95% T>MIC in plasma had <50% T>MIC in ELF, respectively. Those with a CRCL >115 mL/min were less likely to achieve >50%T>4xMIC in ELF (P < 0.025). In the population, CFR for Escherichia coli at 1xMIC and 4xMIC was >97%. For Pseudomonas aeruginosa, CFR was ≥90% in plasma and ranged 80%-85% in ELF at 1xMIC when a loading dose was applied. CFR was reduced in plasma (range: 75%-81%) and ELF (range: 44%-60%) in the absence of a loading dose at 1xMIC. At 4xMIC, CFR for P. aeruginosa was 60%-86% with a loading dose and 18%-62% without a loading dose. We found that plasma overestimated ELF target attainment inup to 30% of meropenem-treated patients, CRCL >115 mL/min decreased target attainment in ELF, and loading doses increased CFR in the population.

Cefepime Precision Dosing Tool: from Standard to Precise Dose Using Nonparametric Population Pharmacokinetics.

Cefepime is the second most common cephalosporin used in U.S. hospitals. We aim to develop and validate a cefepime population pharmacokinetic (PK) model and integrate it into a precision dosing tool for implementation. Two data sets (680 patients) were used to build the cefepime PK model in Pmetrics, and three data sets (34 patients) were used for the validation. A separate application data set (115 patients) was used for the implementation and validation of a precision dosing tool. The model support points and covariates were used to generate the optimal initial dose (OID). Cefepime PK was described by a two-compartment model including weight and creatinine clearance (CrCl) as covariates. The median rate of elimination was 0.30 h-1 (adults) and 0.96 h-1 (children), the central volume of distribution was 13.85 L, and the rate of transfer from the central to the peripheral compartments was 1.22 h-1 and from the peripheral to the central compartments was 1.38 h-1. After integration in BestDose, the observed versus predicted cefepime concentration fit using the application data set was excellent (R2 > 0.98), and the median difference between what was observed and what BestDose predicted on a second occasion was 4%. For the OID, cefepime at a 0.5- to 1-g 4-h infusion every 8 to 24 h (q8 to 24 h) with a CrCl of <70 mL/min was needed to achieve a target range of free trough:MIC 1 to 4 at a MIC of 8 mg/L, while continuous infusion was needed for higher CrCl and weight values. In conclusion, we developed and validated a cefepime model for clinical application. The model was integrated in a precision dosing tool for implementation, and the median concentration prediction bias was 4%. The OID algorithm was provided.

Pharmacokinetic modelling of caspofungin to develop an extended dosing regimen in paediatric patients.

BACKGROUND: Echinocandins are commonly used in treatment and prophylaxis of invasive fungal diseases. Intravenous daily dosing for prophylaxis in the outpatient setting can however become a hurdle for adequate compliance in the paediatric population. OBJECTIVES: Simulations were performed to assess extended twice-weekly dosing for antifungal prophylaxis using caspofungin. METHODS: A population pharmacokinetic model was developed based on previously published data from children aged 3 months to 17 years. Using the final model, Monte Carlo simulations were performed to assess the dose needed for adequate exposure in a twice-weekly setting. Mean weekly AUC0-24 h/MIC together with reported AUC0-24 h from previously reported paediatric trials were used to guide adequate exposure. RESULTS AND CONCLUSIONS: A two-compartment model with linear elimination and allometric scaling using fixed exponents was found most adequate to describe the given paediatric populations. Simulations showed that a 200 mg/m2 twice-weekly regimen with maximal 200 mg total dose should result in exposures matching registered daily dosing as well as commonly used pharmacokinetic/pharmacodynamic targets.

Individual target pharmacokinetic/pharmacodynamic attainment rates among meropenem-treated patients admitted to the ICU with hospital-acquired pneumonia.

OBJECTIVES: Critical illness reduces ß-lactam pharmacokinetic/pharmacodynamic (PK/PD) attainment. We sought to quantify PK/PD attainment in patients with hospital-acquired pneumonia. METHODS: Meropenem plasma PK data (n = 70 patients) were modelled, PK/PD attainment rates were calculated for empirical and definitive targets, and between-patient variability was quantified [as a coefficient of variation (CV%)]. RESULTS: Attainment of 100% T>4×MIC was variable for both empirical (CV% = 92) and directed (CV% = 33%) treatment. CONCLUSIONS: Individualization is required to achieve suggested PK/PD targets in critically ill patients.

Interchangeability of generic drugs for subpopulations: Bioequivalence simulation from a nonparametric PK model of gabapentin generic drugs.

Patients are often switched between generic formulations of the same drug, but in some cases generic interchangeability is questioned. For generic drugs to be approved, bioequivalence with the innovator drug should be demonstrated, but evidence of bioequivalence is not required in the intended patient population or relative to other approved generics. AIM: We aim to identify pathophysiological pharmacokinetic subpopulations for whom there is a difference in comparative bioavailability compared to a healthy population. METHODS: We used simulated exposures from a nonparametric model of multiple generics and the originator gabapentin. Exposure was simulated for virtual populations with pharmacokinetic characteristics beyond those of healthy subjects with regard to rate of absorption, volume of distribution and reduced renal function. Virtual parallel design bioequivalence studies were performed using a random sample of 24 simulated subjects, with standard acceptance criteria. RESULTS: Results indicated increased pharmacokinetic variability for patient populations with a lower rate of absorption or a reduced renal function, but no change in the average comparable bioavailability ratio. This increased variability results in a reduced likelihood of demonstrating bioequivalence. Observations were similar for comparisons between all different formulations, as well as between subjects who received the identical formulation in a repeated fashion. No relevant effect was observed for simulations with increased volume of distribution. CONCLUSION: Our simulations indicate that the reduced likelihood of demonstrating bioequivalence for subjects with altered pharmacokinetics is not influenced by a formulation switch, nor does the average comparable bioavailability ratio change, therefore these results support generic interchangeability and current approval requirements for generics.

Building Optimal Three-Drug Combination Chemotherapy Regimens To Eradicate Mycobacterium tuberculosis in Its Slow-Growth Acid Phase.

Mycobacterium tuberculosis metabolic state affects the response to therapy. Quantifying the effect of antimicrobials in the acid and nonreplicating metabolic phases of M. tuberculosis growth will help to optimize therapy for tuberculosis. As a brute-force approach to all possible drug combinations against M. tuberculosis in all different metabolic states is impossible, we have adopted a model-informed strategy to accelerate the discovery. Using multiple concentrations of each drug in time-kill studies, we examined single drugs and two- and three-drug combinations of pretomanid, moxifloxacin, and bedaquiline plus its active metabolite against M. tuberculosis in its acid-phase metabolic state. We used a nonparametric modeling approach to generate full distributions of interaction terms between pretomanid and moxifloxacin for susceptible and less susceptible populations. From the model, we could predict the 95% confidence interval of the simulated total bacterial population decline due to the 2-drug combination regimen of pretomanid and moxifloxacin and compare this to observed declines with 3-drug regimens. We found that the combination of pretomanid and moxifloxacin at concentrations equivalent to average or peak human concentrations effectively eradicated M. tuberculosis in its acid growth phase and prevented emergence of less susceptible isolates. The addition of bedaquiline as a third drug shortened time to total and less susceptible bacterial suppression by 8 days compared to the 2-drug regimen, which was significantly faster than the 2-drug kill.

Building Optimal Three-Drug Combination Chemotherapy Regimens.

Multidrug therapy is often required. Examples include antiviral therapy, nosocomial infections, and, most commonly, anti-Mycobacterium tuberculosis therapy. Our laboratory previously identified a mathematical approach to identify 2-drug regimens with a synergistic or additive interaction using a full factorial study design. Our objective here was to generate a method to identify an optimal 3-drug therapy. We studied M. tuberculosis isolate H37Rv in log-phase growth in flasks. Pretomanid and moxifloxacin were chosen as the base 2-drug regimen. Bedaquiline (plus M2 metabolite) was chosen as the third drug for evaluation. Total bacterial burden and bacterial burden less-susceptible to study drugs were enumerated. A large mathematical model was fit to all the data. This allowed extension to evaluation of the 3-drug regimen by employing a Monte Carlo simulation. Pretomanid plus moxifloxacin demonstrated excellent bacterial kill and suppressed amplification of less-susceptible pathogens. Total bacterial burden was driven to extinction in 3 weeks in 6 of 9 combination therapy evaluations. Only the lowest pretomanid/moxifloxacin exposures in combination did not extinguish the bacterial burden. No combination regimen allowed resistance amplification. Generation of 95% credible intervals about estimates of the interaction parameters α (αs, αr-p, and αr-m) by bootstrapping showed the interaction was near synergistic. The addition of bedaquiline/M2 metabolite was evaluated by forming a 95% confidence interval regarding the decline in bacterial burden. The addition of bedaquiline/M2 metabolite shortened the time to eradication by 1 week and was significantly different. A model-based system approach to evaluating combinations of 3 agents shows promise to rapidly identify the most promising combinations that can then be trialed.

Population Pharmacokinetics and Target Attainment of Cefepime in Critically Ill Patients and Guidance for Initial Dosing.

Cefepime is commonly used in the intensive care unit (ICU) to treat bacterial infections. The time during which the free cefepime concentration is above the MIC (fT>MIC) should be optimized to increase the efficacy of the regimen. We aim to optimize the exposure of cefepime in ICU patients by using population pharmacokinetic (PK) modeling and simulations. Two data sets were included in this study. The first was a prospective study of pediatric patients who received cefepime at 50 mg/kg of body weight and had extensive PK sampling. The second study comprised retrospective data for adult ICU patients admitted to UF Health Shands Hospital who received cefepime and had their cefepime concentrations measured. The population PK model was developed, and simulations were performed, using Pmetrics. The target exposures were 100% fT>MIC and 100% fT>4×MIC The studies included a total of 266 patients, and the mean ages were 3.9 years in the pediatric group and 55 years in adult group. More than half of the patients were males. The mean (standard deviation [SD]) creatinine clearance (CrCl) was 125 (93) ml/min. The mean (SD) daily dose for adults was 4.9 (1.6) g. Cefepime was well described by a two-compartment model with weight as a covariate on the volume of distribution and elimination rate constant (kel), and CrCl and age group as covariates on kel At a MIC of 8 mg/liter, a cefepime loading dose of 4 g as an extended infusion followed by a 6-g continuous infusion was needed for good target attainment. In conclusion, prolonged or continuous infusions will be needed to achieve optimal cefepime exposure for ICU patients. Given the observed variability, therapeutic drug monitoring can help individualize therapy.

Nonparametric Population Pharmacokinetic Modeling of Isoniazid in Colombian Patients With Tuberculosis.

BACKGROUND: Isoniazid (INH) is a first-line antituberculosis (TB) agent with a pharmacokinetic profile characterized by high interindividual variation; however, population pharmacokinetic studies in patients with TB are scarce. The aim was to develop a population model for INH in Colombian patients with TB suitable for predicting drug exposure and assessing the probability of target attainment of pharmacodynamic goals. METHODS: Ten hospitalized adult patients with TB undergoing INH treatment were recruited. After an 8-hour fasting, subjects took 300 mg of INH, and 10 samples were taken from 0 to 12 hours. INH was quantified by high-performance liquid chromatography-UV, and data were analyzed with the Pmetrics R package software. A Monte Carlo simulation with the model parameters was run to determine the probability of target attainment for optimal efficacy. RESULTS: The best model included 2 compartments, first-order absorption (Ka), delayed absorption (Tlag), and linear clearance (CL). Median Tlag was 0.25 hours, 5.54 hour for Ka, (Equation is included in full-text article.)for CL, (Equation is included in full-text article.)for the volume of the central compartment (Vc), 1.04 L/h for intercompartmental clearance (Q), and 788 L for the volume of the peripheral compartment (Vp). CL and Vc were allometrically scaled on basis of the normalized body weight. CONCLUSIONS: The Monte Carlo simulation indicated that 300 mg of INH per day is appropriate for Mycobacterium tuberculosis strains with minimal inhibitory concentration (MIC) up to 0.03 mg/L (target: area under the concentration-time curve/MIC >597); however, to cover strains with MIC up to 0.125 mg/L (80% of clinical isolates), a dose of 900 mg per day would be required.

Development of Chronic Pseudomonas aeruginosa-Positive Respiratory Cultures in Children with Tracheostomy.

BACKGROUND: Up to 90% of children develop Pseudomonas aeruginosa (Pa)-positive respiratory cultures after tracheotomy. OBJECTIVE: To identify the factors associated with chronic Pa-positive respiratory cultures in the first 2 years after tracheotomy. METHODS: We conducted a retrospective cohort study of 210 children ≤ 18 years old who underwent tracheotomy at a single freestanding children's hospital that had two or more years of respiratory cultures post-tracheotomy available for analysis. We conducted multivariable logistic regression to test the association between demographic and clinical factors to our primary outcome of chronic Pa infection, defined as > 75% of respiratory cultures positive for Pa in the first 2 years after tracheotomy. RESULTS: Of the primarily male (61%), Hispanic (68%), and publicly insured (88%) cohort, 18% (n = 37) developed chronic Pa-positive respiratory cultures in the first 2 years. On multivariable logistic regression, pre-tracheotomy Pa-positive respiratory culture (aOR 11.3; 95% CI 4-1.5) and discharge on beta agonist (aOR 6.3; 95% CI 1.1-36.8) were independently associated with chronic Pa-positive respiratory cultures, while discharge on chronic mechanical ventilation was associated with decreased odds (aOR 0.3; 95% CI 0.1-0.7). On sensitivity analysis examining those without a pre-tracheotomy Pa-positive respiratory culture, discharge on MV continued to be associated with decreased odds of chronic Pa (aOR 0.1; 95% CI 0.02-0.4) and three other variables (male gender, chronic lung disease, and discharge on inhaled corticosteroids) were associated with increased odds of chronic Pa. CONCLUSION: Because pre-tracheotomy Pa growth on respiratory culture is associated with post-tracheotomy chronic Pa-positive respiratory cultures, future research should examine pre-tracheotomy Pa eradication or suppression protocols.

Prospective Trial on the Use of Trough Concentration versus Area under the Curve To Determine Therapeutic Vancomycin Dosing.

We hypothesized that dosing vancomycin to achieve trough concentrations of >15 mg/liter overdoses many adults compared to area under the concentration-time curve (AUC)-guided dosing. We conducted a 3-year, prospective study of vancomycin dosing, plasma concentrations, and outcomes. In year 1, nonstudy clinicians targeted trough concentrations of 10 to 20 mg/liter (infection dependent) and controlled dosing. In years 2 and 3, the study team controlled vancomycin dosing with BestDose Bayesian software to achieve a daily, steady-state AUC/MIC ratio of ≥400, with a maximum AUC value of 800 mg · h/liter, regardless of trough concentration. For Bayesian estimation of AUCs, we used trough samples in years 1 and 2 and optimally timed samples in year 3. We enrolled 252 adults who were ≥18 years old with ≥1 available vancomycin concentration. Only 19% of all trough concentrations were therapeutic versus 70% of AUCs (P < 0.0001). After enrollment, median trough concentrations by year were 14.4, 9.7, and 10.9 mg/liter (P = 0.005), with 36%, 7%, and 6% over 15 mg/liter (P < 0.0001). Bayesian AUC-guided dosing in years 2 and 3 was associated with fewer additional blood samples per subject (3.6, 2.0, and 2.4; P = 0.003), shorter therapy durations (8.2, 5.4, and 4.7 days; P = 0.03), and reduced nephrotoxicity (8%, 0%, and 2%; P = 0.01). The median inpatient stay was 20 days among nephrotoxic patients versus 6 days (P = 0.002). There was no difference in efficacy by year, with 42% of patients having microbiologically proven infections. Compared to trough concentration targets, AUC-guided, Bayesian estimation-assisted vancomycin dosing was associated with decreased nephrotoxicity, reduced per-patient blood sampling, and shorter length of therapy, without compromising efficacy. These benefits have the potential for substantial cost savings. (This study has been registered at ClinicalTrials.gov under registration no. NCT01932034.).

Evaluation of Vancomycin Exposures Associated with Elevations in Novel Urinary Biomarkers of Acute Kidney Injury in Vancomycin-Treated Rats.

Vancomycin has been associated with acute kidney injury (AKI). However, the pharmacokinetic/toxicodynamic relationship for AKI is not well defined. Allometrically scaled vancomycin exposures were used to assess the relationship between vancomycin exposure and AKI. Male Sprague-Dawley rats received clinical-grade vancomycin in normal saline (NS) as intraperitoneal (i.p.) injections for 24- to 72-h durations with doses ranging 0 to 200 mg/kg of body weight divided once or twice daily. Urine was collected over the protocol's final 24 h. Renal histopathology was qualitatively scored. Urinary biomarkers (e.g., cystatin C, clusterin, kidney injury molecule 1 [KIM-1], osteopontin, lipocalin 2/neutrophil gelatinase-associated lipocalin 2) were assayed using a Luminex xMAP system. Plasma vancomycin concentrations were assayed by high-performance liquid chromatography with UV detection. A three-compartment vancomycin pharmacokinetic model was fit to the data with the Pmetrics package for R. The exposure-response in the first 24 h was evaluated using Spearman's nonparametric correlation coefficient (rs) values for the area under the concentration-time curve during the first 24 h (AUC0-24), the maximum concentration in plasma during the first 24 h (Cmax0-24 ), and the lowest (minimum) concentration in plasma after the dose closest to 24 h (Cmin0-24 ). A total of 52 rats received vancomycin (n = 42) or NS (n = 10). The strongest exposure-response correlations were observed between AUC0-24 and Cmax0-24 and urinary AKI biomarkers. Exposure-response correlations (rs values) for AUC0-24, Cmax0-24 , and Cmin0-24 were 0.37, 0.39, and 0.22, respectively, for clusterin; 0.42, 0.45, and 0.26, respectively, for KIM-1; and 0.52, 0.55, and 0.42, respectively, for osteopontin. However, no differences in histopathological scores were observed. Optimal sampling times after administration of the i.p. dose were 0.25, 0.75, 2.75, and 8 h for the once-daily dosing schemes and 0.25, 1.25, 14.5, and 17.25 h for the twice-daily dosing schemes. Our observations suggest that AUC0-24 or Cmax0-24 correlates with increases in urinary AKI biomarkers.

Voriconazole pharmacokinetics following HSCT: results from the BMT CTN 0101 trial.

BACKGROUND: Voriconazole is a first-line agent for the prevention and treatment of a number of invasive fungal diseases. Relatively little is known about the relationship between drug exposure and the prevention of invasive fungal infections. PATIENTS AND METHODS: A pharmacokinetic-pharmacodynamic substudy was performed as part of the BMT CTN 0101 trial, which was a randomized clinical trial comparing voriconazole with fluconazole for the prevention of invasive fungal infections in HSCT recipients. A previously described population pharmacokinetic model was used to calculate the maximum a posteriori Bayesian estimates for 187 patients. Drug exposure in each patient was quantified in terms of the average AUC and average trough concentrations. The relationship between drug exposure and the probability of breakthrough infection was investigated using logistic regression. AUC and trough concentrations in patients with and without breakthrough infection were compared. RESULTS: Pharmacokinetic data from each patient were readily described using the maximum a posteriori Bayesian estimates. There were only five patients that had a breakthrough infection while receiving voriconazole in the first 100 days post-HSCT. For these patients, there was no statistically significant relationship between the average AUC or average trough concentration and the probability of breakthrough infection [OR (95% CI) 1.026 (0.956-1.102) and 1.108 (0.475-2.581), respectively]. P value for these estimates was 0.474 and 0.813, respectively. CONCLUSIONS: Given the very small number of proven/probable infections, it was difficult to identify any differences in drug exposure in HSCT recipients with and without breakthrough fungal infections.

A Nonparametric Pharmacokinetic Approach to Determine the Optimal Dosing Regimen for 30-Minute and 3-Hour Meropenem Infusions in Critically Ill Patients.

BACKGROUND: Pharmacokinetics of meropenem differ widely in the critically ill population. It is imperative to maintain meropenem concentrations above the inhibitory concentrations for most of the interdose interval. A population pharmacokinetic/pharmacodynamic model was developed to determine the probability of target attainment for 3-hour and 30-minute infusion regimens in this population. METHODS: This study was performed in an intensive care setting among adult patients who were initiated on meropenem at a dose of 1000 mg. Multiple blood specimens were collected at predetermined time points during the interdose period, and meropenem concentrations were measured using high performance liquid chromatography. Using Pmetrics, a pharmacokinetic/pharmacodynamic model was developed and validated. Monte Carlo simulation was performed, and probability of target attainment (100% T > minimum inhibitory concentration (MIC), with a probability >0.9) for doubling MICs was determined for different regimens of meropenem. RESULTS: A 2-compartment multiplicative gamma error model best described the population parameters from 34 patients. The pharmacokinetic parameters used in the final model were Ke (elimination rate constant from the central compartment), Vc (volume of distribution of central compartment), KCP and KPC (intercompartmental rate constants), and IC2 (the fitted amount of meropenem in the peripheral compartment). Inclusion of creatinine clearance (CLcreat) and body weight as covariates improved the model prediction (Ke = Ke0 × (Equation is included in full-text article.), Vc = Vc0 × Weight). The Ke and Vc [geometric mean (range)] of the individuals were 0.54 (0.01-2.61)/h and 9.36 (4.35-21.62) L, respectively. The probability of attaining the target, T > MIC of 100%, was higher for 3-hour infusion regimens compared with 30-minute infusion regimens for all ranges of CLcreat. CONCLUSIONS: This study emphasizes that extended regimens of meropenem are preferable for treating infections caused by bacteria with higher MICs. The nonparametric analysis using body weight and CLcreat as covariate adequately predicted the pharmacokinetics of meropenem in critically ill patients with a wide range of renal function.

An exploratory analysis of the ability of a cefepime trough concentration greater than 22 mg/L to predict neurotoxicity.

INTRODUCTION: Cefepime trough concentrations >22 mg/L (T(>22)) have been associated with neurotoxicity in a single study. PATIENTS AND METHODS: Neurotoxicity outcomes for 28 cefepime-treated adult patients with febrile neutropenia were abstracted from the literature. The precision of T(>22) to predict neurotoxicity was quantified using 95% confidence intervals. Thirty-two cefepime-treated patients contributed serum concentrations for a pharmacokinetic model, fit using the Nonparametric Adaptive Grid algorithm within the Pmetrics package for R. Concentration-time curves were simulated for common dosing schemes and 3 renal dispositions. Probabilities of neurotoxicity and numbers needed to harm were calculated from simulations according to the proposed pharmacokinetic/toxicodynamic threshold of T(>22). Bayesian modeling was utilized to explore other pharmacokinetic parameters relationships with neurotoxicity. RESULTS: The mean probability of neurotoxicity at T(>22) was 51.4% (95% CI: 16.4-85.0%). Among the schemes and renal dispositions simulated, the combination of cefepime 2 g every 8 h and a creatinine clearance of 60 mL/min produced the greatest probability of neurotoxicity (48.3%). Estimated numbers needed to harm according to T(>22) ranged from 2.1 to 18.5 persons. Explorations of maximal serum concentration and area under the curve demonstrated high levels of collinearity, making it impossible to identify trough concentrations as the driver of neurotoxicity. DISCUSSION: T(>22) had low precision as a predictive neurotoxic threshold. When a neurotoxic threshold of T(>22) was assumed, projected neurotoxicity rates and numbers needed to harm greatly exceeded observed neurotoxicity rates in the general population and in high risk subpopulations. Other drug exposure metrics should be explored.

Combination treatment with meropenem plus levofloxacin is synergistic against Pseudomonas aeruginosa infection in a murine model of pneumonia.

BACKGROUND: Meropenem plus levofloxacin treatment was shown to be a promising combination in our in vitro hollow fiber infection model. We strove to validate this finding in a murine Pseudomonas pneumonia model. METHODS: A dose-ranging study with meropenem and levofloxacin alone and in combination against Pseudomonas aeruginosa was performed in a granulocytopenic murine pneumonia model. Meropenem and levofloxacin were administered to partially humanize their pharmacokinetic profiles in mouse serum. Total and resistant bacterial populations were estimated after 24 hours of therapy. Pharmacokinetic profiling of both drugs was performed in plasma and epithelial lining fluid, using a population model. RESULTS: Meropenem and levofloxacin penetrations into epithelial lining fluid were 39.3% and 64.3%, respectively. Both monotherapies demonstrated good exposure responses. An innovative combination-therapy analytic approach demonstrated that the combination was statistically significantly synergistic (α = 2.475), as was shown in the hollow fiber infection model. Bacterial resistant to levofloxacin and meropenem was seen in the control arm. Levofloxacin monotherapy selected for resistance to itself. No resistant subpopulations were observed in any combination therapy arm. CONCLUSIONS: The combination of meropenem plus levofloxacin was synergistic, producing good bacterial kill and resistance suppression. Given the track record of safety of each agent, this combination may be worthy of clinical trial.

Influence of renal function estimation on pharmacokinetic modeling of vancomycin in elderly patients.

Vancomycin is a renally excreted drug, and its body clearance correlates with creatinine clearance. However, the renal function estimation equation that best predicts vancomycin clearance has not been established yet. The objective of this study was to compare the abilities of different renal function estimation equations to describe vancomycin pharmacokinetics in elderly patients. The NPAG algorithm was used to perform population pharmacokinetic analysis of vancomycin concentrations in 78 elderly patients. Six pharmacokinetic models of vancomycin clearance were built, based on the following equations: Cockcroft-Gault (CG), Jelliffe (JEL), Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) (both in milliliters per minute per 1.73 m(2)), and modified MDRD and CKD-EPI equations (both in milliliters per minute). Goodness-of-fit and predictive performances of the six PK models were compared in a learning set (58 subjects) and a validation set (20 patients). Final analysis was performed to estimate population parameters in the entire population. In the learning step, the MDRD-based model best described the data, but the CG- and JEL-based models were the least biased. The mean weighted errors of prediction were significantly different between the six models (P = 0.0071). In the validation group, predictive performances were not significantly different. However, the use of a renal function estimation equation different from that used in the model building could significantly alter predictive performance. The final analysis showed important differences in parameter distributions and AUC estimation across the six models. This study shows that methods used to estimate renal function should not be considered interchangeable for pharmacokinetic modeling and model-based estimation of vancomycin concentrations in elderly patients.

Defining Clinical Exposures of Cefepime for Gram-Negative Bloodstream Infections That Are Associated with Improved Survival.

The percentage of time that free drug concentrations remain above the MIC (fT>MIC) that is necessary to prevent mortality among cefepime-treated patients with Gram-negative bloodstream infections (GNBSI) is poorly defined. We conducted a retrospective study of adult patients with GNBSI. Eligible cases were frequency matched to ensure categorical representation from all MICs. Organism, MIC, infection source, gender, age, serum creatinine, weight, antibiotic history, and modified APACHE II score were collected from hospital records. Two population pharmacokinetic models (models 1 and 2) were used to impute exposures over the first 24 h in each patient from mean model parameters, covariates, and dosing history. From the imputed exposures, survival thresholds for fT>MIC were identified using classification and regression tree (CART) analysis and analyzed as nominal variables for univariate and multivariate regressions. A total of 180 patients were included in the analysis, of whom 13.9% died and 86.1% survived. Many patients (46.7% [n = 84/180]) received combination therapy with cefepime. Survivors had higher mean (standard deviation [SD]) fT>MIC than those who died (model 1, 74.2% [29.6%] versus 52.1% [33.8%], P < 0.001; model 2, 85.9% [24.0%] versus 64.4% [31.4%], P < 0.001). CART identified fT>MIC threshold values for greater survival according to models 1 and 2 at >68% and >74%, respectively. Survival was improved for those with fT>MIC of >68% (model 1 adjusted odds ratio [aOR], 7.12; 95% confidence interval [CI], 1.90 to 26.7; P = 0.004) and >74% (model 2 aOR, 6.48; 95% CI, 1.90 to 22.1) after controlling for clinical covariates. Similarly, each 1% increase in cefepime fT>MIC resulted in a 2% improvement in multivariate survival probability (P = 0.015). Achieving a cefepime fT>MIC of 68 to 74% was associated with a higher odds of survival for patients with GNBSI. Regimens targeting this exposure should be aggressively pursued.

Optimal timing of oral fosfomycin administration for pre-prostate biopsy prophylaxis.

OBJECTIVES: As the optimal administration time for fosfomycin peri-procedural prophylaxis is unclear, we sought to determine optimal administration times for fosfomycin peri-procedural prophylaxis. METHODS: Plasma, peripheral zone and transition zone fosfomycin concentrations were obtained from 26 subjects undergoing transurethral resection of the prostate (TURP), following a single oral dose of 3 g of fosfomycin. Population pharmacokinetic modelling was completed with the Nonparametric Adaptive Grid (NPAG) algorithm (Pmetrics package for R), with a four-compartment model. Plasma and tissue concentrations were simulated during the first 24 h post-dose, comparing these with EUCAST susceptibility breakpoints for Escherichia coli, a common uropathogen. RESULTS: Non-compartmental-determined pharmacokinetic values in our population were similar to those reported in the package insert. Predicted plasma concentrations rapidly increased after the first hour, giving more than 90% population coverage for organisms with an MIC ≤4 mg/L over the first 12 h post-dose. Organisms with higher MICs fared much worse, with organisms at the EUCAST breakpoint being covered for <10% of the population at any time. Transitional zone prostate concentrations exceeded 4 mg/L for 90% of the population between hours 1 and 9. Peripheral zone prostate concentrations were much lower and only exceeded 4 mg/L for 70% of the population between hours 1 and 4. CONCLUSIONS: Until more precise plasma and tissue data are available, we recommend that fosfomycin prophylaxis be given 1-4 h prior to prostate biopsy. We do not recommend fosfomycin prophylaxis for subjects with known organisms with MICs >4 mg/L.