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Childhood vaccine hesitancy has been studied extensively before the COVID-19 pandemic. The pandemic presented new barriers to pediatric vaccinations. Furthermore, the development of COVID-19 vaccines has complicated factors underlying vaccine hesitancy. We performed a cross-sectional mobile phone-based survey at Children’s Hospital Los Angeles querying parents regarding perspectives on vaccines before and during the pandemic. Our primary aim was to understand the impact of the pandemic on routine childhood vaccine hesitancy. Secondarily, we examined intent to vaccinate, COVID-19 vaccine hesitancy, and key contributing demographic factors. Among 252 participants, we found overall increased childhood vaccine hesitancy (p = 0.006), increased risk perception (p = 0.006), and unchanged vaccine confidence during the COVID-19 pandemic. Increased hesitancy did not translate into decreased intent to vaccinate with routine childhood vaccines or influenza vaccines. During the pandemic, households with higher income (50–99 K, > 100 K) correlated with decreased routine childhood vaccine hesitancy, while Hispanic ethnicity and African American race had increased risk perception. For COVID-19 vaccine hesitancy, households with higher income (> 100 K) correlated with decreased hesitancy, while non-White ethnicity and race had increased risk perception. We found that routine childhood vaccine hesitancy increased during the COVID-19 pandemic, mainly due to increased risk perception. Key contributing demographic factors behind both childhood vaccine hesitancy and COVID-19 vaccine hesitancy included household income and race. Understanding factors behind routine childhood vaccine hesitancy is crucial to maintaining pediatric vaccination rates and promoting vaccine confidence during and after the COVID-19 pandemic.

Nearly all international regimens for pediatric acute lymphoblastic leukemia (ALL) incorporate intravenous "high dose" methotrexate (HDMTX, ≥1 gram/m2 ) to penetrate the central nervous system. Dosing is routinely adjusted for body surface area (BSA) but limited data describe the pharmacokinetics of HDMTX, particularly in obese and/or large patients. To understand the impact of body size (BSA) and body fat percentage (BFP) on HDMTX pharmacokinetics, we performed a secondary analysis of 36 children and adolescents 10-21 years old treated for newly diagnosed ALL and who were enrolled in a prospective study examining body composition. All patients received 5 grams/m2 of HDMTX infused over 24 hours. Plasma methotrexate concentrations were measured at +24, +42, and +48 hours. At 48 hours, ≥0.4 μmol/L was defined as "delayed elimination" necessitating prolonged supportive care. BFP was measured using dual-energy x-ray absorptiometry. A nonparametric population pharmacokinetic model was constructed with subsequent simulations to explore effects of BSA and BFP extremes. Despite standard BSA-adjusted dosing, we found significant intra-patient variability in mean MTX concentration (38%, range 1.2-86%). BSA and BFP were not linearly associated with increased area under the curve (AUC, p = 0.74 and p = 0.12), but both larger size (BSA) and greater obesity (BFP) were associated with an approximately twofold higher risk for delayed elimination at 48 hours. HDMTX AUC was not associated with toxicity. MTX pharmacokinetics vary among and even within patients despite BSA-adjusted dosing. Obesity and large size are identified as new risk factors for delayed elimination requiring further investigation. This article is protected by copyright. All rights reserved.

Understanding exposures of cefepime, a β-lactam antibiotic, is crucial for developing regimens to achieve optimal exposure and improved clinical outcomes. This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment. Multiple physiologically relevant models were fit to pediatric and adult subject data. To evaluate the final model performance, a withheld group of twelve pediatric and two separate adult populations were assessed. Seventy subjects with a total of 604 cefepime concentrations were included in this study. All adults (n=34) on average weighed 82.7 kg and displayed a mean creatinine clearance (CrCL) of 106.7 mL/min. All pediatric subjects (n=36) had mean weight and CrCL of 16.0 kg and 195.64 mL/min, respectively. A covariate-adjusted two compartment model described the observed concentrations well (population model R2, 87.0%; Bayesian model R2, 96.5%). In the evaluation subsets, the model performed similarly well (population R2, 84.0%; Bayesian R2, 90.2%). The identified model serves well for population dosing and as a Bayesian prior for precision dosing.Key pointsA unified cefepime population pharmacokinetic model has been developed from adult and pediatric patients and evaluates well in independent populations.When paired with real time beta-lactam assays, precision dosing approach will optimize drug exposure and improve clinical outcomes.

Linezolid is an antibiotic used to treat infections caused by drug-resistant gram-positive organisms, including vancomycin-resistant Enterococcus faecium, multi-drug resistant Streptococcus pneumoniae, and methicillin-resistant Staphylococcus aureus. The adverse effects of linezolid can include thrombocytopenia and neuropathy, which are more prevalent with higher exposures and longer treatment durations. Although linezolid is traditionally administered at a standard 600 mg dose every 12 hours, the resulting exposure can vary greatly between patients and can lead to treatment failure or toxicity. The efficacy and toxicity of linezolid are determined by the exposure achieved in the patient; numerous clinical and population pharmacokinetics (popPK) studies have identified threshold measurements for both parameters. Several special populations with an increased need for linezolid dose adjustments have also been identified. Therapeutic Drug Monitoring (TDM) is a clinical strategy that assesses the response of an individual patient and helps adjust the dosing regimen to maximize efficacy while minimizing toxicity. Adaptive feedback control and model-informed precision dosing are additional strategies that use Bayesian algorithms and PK models to predict patient-specific drug exposure. TDM is a very useful tool for patient populations with sparse clinical data or known alterations in pharmacokinetics, including children, patients with renal insufficiency or those receiving renal replacement therapy, and patients taking co-medications known to interact with linezolid. As part of the clinical workflow, clinicians can use TDM with the thresholds summarized from the current literature to improve linezolid dosing for patients and maximize the probability of treatment success.

Imagine it is 2030, and the drug label is in the cloud, is interactive, and can provide model-informed precision dosing support based on an individual's genomic and physiologic makeup that is uploaded via a user-friendly interface. Precision medicine has vastly improved our ability to provide tailored therapeutics, and groundbreaking advances in noninvasive systems have generated smart wearable devices that can follow our physiologic state and drug exposure in real time. One device consists of a microfluidic drug biosensor with a transmitter attached to the skin and a mobile app that displays concentration values and trends while issuing alerts and providing suggestions on dose changes when out of range. This sounds compelling, but why has model-informed precision dosing not yet become common clinical reality in 2020?

BACKGROUND: Up to 90% of children develop Pseudomonas aeruginosa (Pa)-positive respiratory cultures after tracheotomy. OBJECTIVE: To identify factors associated with chronic Pa-positive respiratory cultures in the first two years after tracheotomy. METHOD: We conducted a retrospective cohort study of 210 children ≤18 years old who underwent tracheotomy at a single freestanding children’s hospital who 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 two 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 two 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, 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.

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.

The inadequate adherence of patients whose hyperlipidemia is treated with atorvastatin (ATR) to medical instructions presents a serious health risk. Our aim was to develop a flexible approach based on therapeutic drug monitoring (TDM), nonparametric population pharmacokinetic modeling, and Monte Carlo simulation to differentiate adherent patients from partially and nonadherent individuals in a nonrandomized, unicentric, observational study. Sixty‐five subjects were enrolled. Nonparametric, mixed‐effect population pharmacokinetic models of the sums of atorvastatin and atorvastatin lactone concentrations (ATR+ATRL) and of the concentrations of the acid and lactone forms of ATR and its 2‐ and 4‐hydroxylated pharmacologically active metabolites (ATR+MET) were elaborated by including the TDM results obtained in 128 samples collected from thirty‐nine subjects. Monte Carlo simulation was performed based on the elaborated models to establish the probabilities of attaining a specific ATR+ATRL or ATR+MET concentration in the range of 0.002–10 nmol (mg dose)−1 L−1 at 1–24 h postdose by adherent, partially adherent, and nonadherent patients. The results of the simulations were processed to allow the estimation of the adherence of further 26 subjects who were phlebotomized at sampling times of 2–20 h postdose by calculating the probabilities of attaining the ATR+ATRL and ATR+MET concentrations measured in these subjects in adherent, partially adherent, and nonadherent individuals. The best predictive values of the estimates of adherence could be obtained with sampling at early sampling times. 61.54% and 38.46% of subjects in the adherence testing set were estimated to be fully and partially adherent, respectively, while in all cases the probability of nonadherence was extremely low. The evaluation of patient adherence to ATR therapy based on pharmacokinetic modeling and Monte Carlo simulation has important advantages over the collection of trough samples and the use of therapeutic ranges., We set forth a pharmacokinetics‐based clinical laboratory procedure and decision‐making algorithm for the therapeutic monitoring of atorvastatin concentrations at any time that has passed from drug intake to sample collection.

BackgroundVancomycin is a first line antibiotic for many common infectious diseases and is the most commonly prescribed antibiotic in the United States hospital setting. Vancomycin is also well known to cause kidney injury; two recent prospective studies have identified that increasing vancomycin area under the concentration curve predicts vancomycin induced kidney injury (VIKI). However, outside of clinical trials, it is unclear if pre-clinical data can quantitatively describe VIKI in patients.MethodsData were simultaneously analyzed from a pre-clinical rat model and two prospective clinical studies. Logged vancomycin area under the concentration curve (AUC) data for rats (n=48) and patients from PROVIDE (n=263) and CAMERA2 (n=291) were included. VIKI was defined as urinary KIM-1 concentrations ≥9.42 ng/mL in the rat and according to KDIGO stage 1 kidney injury for all human patients. Multiple generalized linear models were explored, and the order of magnitude was calculated between the probability of acute kidney injury (AKI) from the average obtained in the clinical studies (i.e. CAMERA2 and PROVIDE) and the rat for 0.1 increments in Log10AUC bounded common concentrations obtained in the therapeutic range (i.e. ~200 −800 mg*24h/L).ResultsA logit link model best fit the data. When calculating the multiplicative factors between the studies therapeutic range AUCs, the rat was an average 2.7 to 4.2 times more sensitive to AKI between AUCs of 199.5 (i.e. log 10 AUC=2.3) and 794.3 mg*h/L (i.e. log 10 AUC=2.9), respectively.ConclusionsA pre-clinical rat model was quantitatively linked to toxicity data from two large human studies. The rat is an attractive pre-clinical model to explore exposure toxicity relationships with vancomycin. External validation is required.

Isavuconazole, administered as the water-soluble prodrug isavuconazonium sulfate, is a new triazole agent used to treat invasive fungal infections. This phase 1 study evaluated the pharmacokinetics (PK), safety, and tolerability of isavuconazole in 46 immunocompromised pediatric patients, stratified by age (1 to 80% and >76% of simulated pediatric patients following i.v. or oral administration, respectively. Intravenous and oral administration of isavuconazonium sulfate at the studied dosage of 10 mg/kg was well tolerated and resulted in exposure in pediatric patients similar to that in adults. (This study has been registered at ClinicalTrials.gov under identifier NCT03241550).

Antimicrobial resistance (particularly through extended-spectrum β-lactamase and aminoglycoside-modifying enzyme production) in neonatal sepsis is a global problem, particularly in low- and middle-income countries, with significant mortality rates. High rates of resistance are reported for the current WHO-recommended first-line antibiotic regimen for neonatal sepsis, i.e., ampicillin and gentamicin. We assessed the utility of fosfomycin and amikacin as a potential alternative regimen to be used in settings of increasingly prevalent antimicrobial resistance. The combination was studied in a 16-arm dose-ranged hollow-fiber infection model (HFIM) experiment. The combination of amikacin and fosfomycin enhanced bactericidal activity and prevented the emergence of resistance, compared to monotherapy with either antibiotic. Modeling of the experimental quantitative outputs and data from checkerboard assays indicated synergy. We further assessed the combination regimen at clinically relevant doses in the HFIM with nine Enterobacterales strains with high fosfomycin and amikacin MICs and demonstrated successful kill to sterilization for 6/9 strains. From these data, we propose a novel combination breakpoint threshold for microbiological success for this antimicrobial combination against Enterobacterales strains, i.e., MIC(F) × MIC(A) < 256 (where MIC(F) and MIC(A) are the fosfomycin and amikacin MICs, respectively). Monte Carlo simulations predict that a standard fosfomycin-amikacin neonatal regimen would achieve >99% probability of pharmacodynamic success for strains with MICs below this threshold. We conclude that the combination of fosfomycin with amikacin is a viable regimen for the empirical treatment of neonatal sepsis and is suitable for further clinical assessment in a randomized controlled trial.

The high interindividual variability in the pharmacokinetics (PK) of linezolid has been described, which results in an unacceptably high proportion of patients with either suboptimal or potentially toxic concentrations following the administration of a fixed regimen. The aim of this study was to develop a population pharmacokinetic model of linezolid and use this to build and validate alogorithms for individualized dosing. A retrospective pharmacokinetic analysis was performed using data from 338 hospitalized patients (65.4% male, 65.5 [±14.6] years) who underwent routine therapeutic drug monitoring for linezolid. Linezolid concentrations were analyzed by using high-performance liquid chromatography. Population pharmacokinetic modeling was performed using a nonparametric methodology with Pmetrics, and Monte Carlo simulations were employed to calculate the 100% time >MIC after the administration of a fixed regimen of 600 mg administered every 12 h (q12h) intravenously (i.v.). The dose of linezolid needed to achieve a PTA ≥ 90% for all susceptible isolates classified according to EUCAST was estimated to be as high as 2,400 mg q12h, which is 4 times higher than the maximum licensed linezolid dose. The final PK model was then used to construct software for dosage individualization, and the performance of the software was assessed using 10 new patients not used to construct the original population PK model. A three-compartment model with an absorptive compartment with zero-order i.v. input and first-order clearance from the central compartment best described the data. The dose optimization software tracked patients with a high degree of accuracy. The software may be a clinically useful tool to adjust linezolid dosages in real time to achieve prespecified drug exposure targets. A further prospective study is needed to examine the potential clinical utility of individualized therapy.

The need for precision dosing has been challenged on the basis of insufficient evidence. Herein, we argue that adequate evidence exists to conduct therapeutic drug monitoring (TDM) and precisely target antibiotic exposures. While achievement of any antibiotic concentration does not guarantee efficacy sans toxicity for any single patient, stochastic control optimizes the probability of achieving favourable responses across patients. We argue that variability in targets (such as the organism’s MIC) can be considered with models. That is, complexity alone does not relegate the decision-making framework to ‘clinician intuition’. We acknowledge the exposure–response relationships are modified by patient-specific factors (other drugs, baseline organ functional status etc.) and describe how precision dosing can inform clinical decision making rather than protocolize it. Finally, we call for randomized, controlled trials; however, we suggest that these trials are not necessary to make TDM standard of care for multiple classes of antibiotics.

Gentamicin is known to have concentration-dependent bactericidal activity, and its nephrotoxic effect is well described. We developed a population pharmacokinetic/pharmacodynamic model to optimize gentamicin dosing in pediatrics. Data were retrospectively collected for pediatric patients 1 month to 12 years of age, admitted to general pediatric wards or intensive care units and received gentamicin for suspected or proven Gram-negative infections at King Saud University Medical City, Riyadh, Saudi Arabia. A total of 306 gentamicin peak and trough concentrations sets from 107 patients were analyzed with mean (±standard deviation) patient age and weight of 4.5 ± 3.5 years and 16.7 ± 10.8 kg, respectively. Gentamicin pharmacokinetics were adequately described with a one compartment system (R = 0.82, bias = 1.75% and precision = 88% for population predictions and R = 0.94, bias = 5% and precision = 29% for individual predictions). The gentamicin pharmacokinetic parameters were as follows: volume of distribution = 8.9 L, total body clearance = 2.8 L/h for a 20-kg patient. Monte Carlo simulations showed that doses of 5-6 mg/kg/dose once daily are adequate only to treat infections with Gram-negative organisms having minimal inhibitory concentration less than 1 µg/mL. While, at minimal inhibitory concentration of 1 µg/mL, higher doses (7-8 mg/kg/dose once daily) are needed to maximize the efficacy of gentamicin. However, at minimal inhibitory concentration of 2 µg/mL, even a 10 mg/kg dose showed poor target attainment (52%). The finding of this study highlights the need to reevaluate the current breakpoints of gentamicin and also to assess the safety of higher doses of gentamicin in pediatrics.

The repurposed agent moxifloxacin has become an important addition to the physician’s armamentarium for the therapy of Mycobacterium tuberculosis. When a drug is administered, we need to have metrics for success. As for most antimicrobial chemotherapy, we contend that for Mycobacterium tuberculosis therapy, these metrics should be a decline in the susceptible bacterial burden and the suppression of amplification of less-susceptible populations. To achieve optimal outcomes relative to these metrics, a dose and schedule of administration need to be chosen. For large populations of patients, there are true between-patient differences in important pharmacokinetic parameters. These distributions of parameter values may have an impact on these metrics, depending on what measure of drug exposure drives the metrics. To optimize dose and schedule choice of moxifloxacin, we performed a dose fractionation experiment in the hollow fiber infection model. We examined 12-, 24-, and 48-h dosing intervals with doses of 200, 400, and 800 mg for each interval, respectively. Within each interval, we had an arm where half-lives of 12, 8, and 4 h were simulated. We attempted to keep the average concentration (C(avg)) or area under the concentration-time curve (AUC) constant across arms. We found that susceptible bacterial load decline was linked to C(avg), as we had indicated previously. Resistance suppression, a nonmonotonic function, had minimum concentration (C(min)) as the linked index. The 48-h interval with the 4-h half-life had the largest less-susceptible population. Balancing bacterial kill, resistance suppression, toxicity (linked to peak concentration [C(peak)]), and adherence, we conclude that the dose of 400 mg daily is optimal for moxifloxacin.

Vancomycin induces exposure-related acute kidney injury. However, the pharmacokinetic-toxicodynamic (PK-TD) relationship remains unclear. Sprague-Dawley rats received intravenous (i.v.) vancomycin doses of 300 mg/kg/day and 400 mg/kg/day, divided into once-, twice-, three-times-, or four-times-daily doses (i.e., QD, BID, TID, or QID) over 24 h. Up to 8 samples plus a terminal sample were drawn during the 24-h dosing period. Twenty-four-hour urine was collected and assayed for kidney injury molecule-1 (KIM-1). Vancomycin was quantified via liquid chromatography-tandem mass spectrometry (LC-MS/MS). Following terminal sampling, nephrectomy and histopathologic analyses were conducted. PK analyses were conducted using Pmetrics. PK exposures (i.e., area under the concentration-time curve from 0 to 24 h [AUC0-24] and maximum concentration from 0 to 24 h [Cmax0-24]) were calculated for each rat, and PK-TD relationships were discerned. A total of 53-rats generated PK-TD data. A 2-compartment model fit the data well (Bayesian observed versus predicted concentrations; R2 = 0.96). KIM-1 values were greater in QD and BID groups (P for QD versus TID

The Mycobacterium tuberculosis drug discovery effort has generated a substantial number of new/repurposed drugs for therapy for this pathogen. The arrival of these drugs is welcome, but another layer of difficulty has emerged. Single agent therapy is insufficient for patients with late-stage tuberculosis because of resistance emergence. To achieve our therapeutic ends, it is requisite to identify optimal combination regimens. These regimens go through a lengthy and expensive evaluative process. If we have a modest group of 6 to 8 new or repurposed agents, this translates into 15 to 28 possible 2-drug combinations. There is neither time nor resources to give an extensive evaluation for all combinations. We sought a screening procedure that would identify combinations that had a high likelihood of achieving good bacterial burden decline. We examined pretomanid, moxifloxacin, linezolid, and bedaquiline in log-phase growth, acid-phase growth, and nonreplicative persister (NRP) phase in the Greco interaction model. We employed the interaction term α and the calculated bacterial burden decline as metrics to rank different regimens in different metabolic states. No relationship was found between α and bacterial kill. We chose bacterial kill as the prime metric. The combination of pretomanid plus moxifloxacin emerged as the clear frontrunner, as the largest bacterial declines were seen in log phase and acid phase with this regimen and it was second best in NRP phase. Bedaquiline also produced good kill. This screening process may identify optimal combinations that can be further evaluated in both the hollow-fiber infection model and in animal models of Mycobacterium tuberculosis infection.

Preclinical animal models of infection are employed to develop new agents but also to screen among molecules to rank them. There are often major differences between human pharmacokinetic (PK) profiles and those developed by animal models of infection, and these may lead to substantial differences in efficacy relative to that seen in humans. Linezolid is a repurposed agent employed to great effect for therapy of Mycobacterium tuberculosis. In this study, we used the hollow-fiber infection model (HFIM) to evaluate the impact of different pharmacokinetic profiles of mice and nonhuman primates (NHP) versus humans on bacterial cell kill as well as resistance suppression. We examined both plasma and epithelial lining fluid (ELF) profiles. We examined simulated exposures equivalent to 600 mg and 900 mg daily of linezolid in humans. For both plasma and ELF exposures, the murine PK profile provided estimates of effect that were biased low relative to human and NHP PK profiles. Mathematical modeling identified a linkage between minimum concentrations (C(min)) and bacterial kill and peak concentrations (C(peak)) and resistance suppression, with the latter being supported by a prospective validation study. Finding new agents with novel mechanisms of action against M. tuberculosis is difficult. It would be a tragedy to discard a new agent because of a biased estimate of effect in a preclinical animal system. The HFIM provides a system to benchmark evaluation of new compounds in preclinical animal model systems against human PK effects (species scale-up estimates of PK), to safeguard against unwarranted rejection of promising new agents.

The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter., The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter. Secondarily, the AUC/MIC targets of 450, 865, and 1,185 were used to calculate PTAs for Candida glabrata, C. albicans, and C. parapsilosis, respectively. The final 2-compartment model included weight as a covariate on volume of distribution (V). The mean V of the central compartment was 7.71 (standard deviation [SD], 2.70) liters/kg of body weight, the mean elimination constant (Ke) was 0.09 (SD, 0.04) h−1, the rate constant for the caspofungin distribution from the central to the peripheral compartment was 0.44 (SD, 0.39) h−1, and the rate constant for the caspofungin distribution from the peripheral to the central compartment was 0.46 (SD, 0.35) h−1. A loading dose of 2 mg/kg on the first day, followed by 1.25 mg/kg as a maintenance dose, was chosen. With this dose, 98% of the patients were expected to reach the AUC target on the first day and 100% of the patients on the third day. The registered caspofungin dose might not be suitable for critically ill patients who were all overweight (≥120 kg), over 80% of median weight (78 kg), and around 25% of lower weight (≤50 kg). A weight-based dose regimen might be appropriate for achieving adequate exposure of caspofungin in intensive care unit patients.

BackgroundVancomycin induces exposure-related acute kidney injury. However, the pharmacokinetic-toxicodynamic (PK-TD) relationship remains unclear.MethodsSprague-Dawley rats received IV vancomycin doses of 300mg/kg/day and 400mg/kg/day, divided once, twice, thrice or 4xdaily (i.e., QD, BID, TID or QID) over 24-hours. Up to 8-samples were drawn during the 24-hour dosing period. Twenty-four-hour urine was collected and assayed for kidney injury molecule-1 (KIM-1). Vancomycin was quantified via LC-MS/MS. Following terminal sampling, nephrectomy and histopathologic analyses were conducted. PK analyses were conducted using Pmetrics. PK exposures (i.e. AUC0-24h, CMAX0-24h,) were calculated for each rat, and PK-TD relationships were discerned.ResultsA total of 53-rats generated PK-TD data. A 2-compartment model fit the data well (Bayesian observed vs. predicted concentrations, R2=0.96). KIM-1 values were greater in QD and BID groups (P-values: QD vs TID:0–24h and AUC0-24h (R2□=□ 0.7 and 0.68). Corrected Akaike’s information criterion showed CMAX0-24h as most predictive PK-TD driver for vancomycin-induced kidney injury (VIKI) (−5.28 versus −1.95).ConclusionsWhile PK-TD indices are often inter-correlated, maximal concentrations and fewer doses (for the same total daily amount) resulted in increased VIKI in our rat model.

Given that aminoglycosides, such as amikacin, may be used for multi-drug resistant infections, optimization of therapy is paramount for improved treatment outcomes. This study aims to investigate the pharmacodynamics of different simulated intravenous amikacin doses on susceptible to inform ventilator-associated pneumonia and sepsis treatment choices.A hollow-fibre infection model with two isolates (MIC 2 and 8 mg/L) with an initial inoculum ∼10 colony-forming unit/mL was used to test different amikacin dosing regimens. Three regimens (15, 25 and 50 mg/kg) simulating a blood exposure and a 30 mg/kg regimen simulating the epithelial lining fluid (ELF) for potential respiratory tract infection were tested. Data were described using a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model. Whole genome sequencing was used to identify mutations associated with resistance emergence.While bacterial density was reduced by >6-logs within the first 12 h in simulated blood exposures, following this initial bacterial kill, there was amplification of a resistant sub-population with ribosomal mutations that were likely mediating amikacin resistance. No appreciable bacterial killing occurred with subsequent doses. There was less (

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 (k(el)), and CrCl and age group as covariates on k(el). 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.

Objectives Area under the time–concentration curve (AUC) -guided dosing provides better estimates of exposure than vancomycin trough concentrations. Though clinical benefits have been reported, the costs of AUC-guided dosing are uncertain. The objective of this study was to quantify the costs of single-sample Bayesian or two-sample AUC strategies versus trough-guided dosing. Methods A cost–benefit analysis from the institutional perspective was conducted using a decision tree to model the probabilities and costs of acute kidney injury (AKI) associated with vancomycin administered over 48 hours up to 21+ days. Costs included vancomycin concentrations, Bayesian software and AKI hospitalization costs, and probabilities were obtained from primary literature. Robustness was assessed via both one-way and probabilistic sensitivity analyses. Results In the base-case model, two-sample AUC versus trough dosing saved an average of US$ 846 per patient encounter, and single-sample Bayesian AUC versus trough dosing saved an average of US$ 2065 per patient encounter. This translates into annual cost-savings of US$ 846 810 and US$ 2 065 720 for two-sample and single-sample Bayesian methods versus trough dosing, respectively, assuming 1000 vancomycin-treated patients per year. Assuming a budget of US$ 100 000 per year for Bayesian software, an institution would need to treat ≥41 patients with vancomycin for at least 48 hours to break even. Conclusions There are significant institutional cost benefits using two-sample AUC or single-sample Bayesian methods over trough dosing, even after accounting for the annual costs of Bayesian programs. The potential to decrease rates of AKI, improve clinical outcomes and reduce costs to the institution strongly warrants consideration of improved dosing methods for vancomycin.

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 (α(s), α(r-p), and α(r-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.

Busulfan therapeutic drug monitoring (TDM) is necessary to better achieve the target exposure in children before hematopoietic stem cell transplantation (HSCT). However, TDM-based dosing may be challenging if intra-individual pharmacokinetic variability (also denoted inter-occasion variability [IOV]) occurs during therapy. The objectives of this study were to describe and quantify busulfan IOV in children, and to investigate its potential determinants. We performed a new analysis of published data from children who received intravenous busulfan over 4 days before HSCT. We calculated individual pharmacokinetic parameters on each day of therapy using a published population pharmacokinetic model of busulfan and analyzed their changes. Population estimation of IOV was also performed with non-linear mixed effects (NLME) modeling. Potential predictors of significant decrease in busulfan clearance (CL) were assessed by using machine learning approaches. IOV could be assessed in 136 children. Between day (D) 1 and D2, most patients (80%) experienced a decrease in busulfan CL, with a median change of − 7.9%. However, both large decreases (minimum, − 48.5%) and increases in CL (maximum, + 44%) were observed. Over D1–D3 of therapy, mean CL significantly decreased (− 15%), with a decrease of ≥ 20% in 22% of patients. Some patients also showed unstable CL from day to day. NLME modeling of IOV provided a coefficient of variation of 10.6% and 13.1% for volume of distribution (Vd) and CL, respectively. Some determinants of significant decreases in busulfan CL were identified, but predictive performance of the models was limited. Significant busulfan intra-individual variability may occur in children who receive a HSCT and is hardly predictable. The main risk is busulfan overexposure. Performing TDM repeatedly over therapy appears to be the best way to accurately estimate busulfan exposure and perform precision dosing.

Population pharmacokinetic (PK) modeling is a widely used approach to analyze PK data obtained from groups of individuals, in both industry and academic research. The approach can also be used to analyze pharmacodynamic (PD) data and pooled PK/PD data. There are 2 main families of population PK methods: parametric and nonparametric. The objectives of this article are to present an overview of nonparametric methods used in population pharmacokinetic modeling and to explain their specific characteristics to inform scientists and clinicians about their potential value for data analysis, simulation, dosage design, and therapeutic drug monitoring (TDM). Nonparametric methods have several interesting characteristics for population PK analysis, including computation of exact likelihoods, the ability to accommodate parameter probability distributions of any shape (eg, non-Gaussian), and to detect subpopulations and outliers. Nonparametric population methods are also highly relevant for model-based TDM and design of individualized drug dosage regimens. Several algorithms have been developed to estimate model parameter values within an individual and compute that individual's dosage to achieve target drug exposure with maximum precision and accuracy. Nonparametric modeling methods for both population and individual PK analysis are available under user-friendly packages.

When a drug has a narrow margin of safety and requires therapeutic drug monitoring or management, we must keep its dosage not too low, but not too high. We will see more clearly what this means as we go on. If it is too low, the result may be a poor effect. If too high, the patient may become toxic. We must carefully individualize the dosage for each patient, to achieve most precisely a specific target goal (not a range!) which we judge is clinically most appropriate for his or her unique need, such as a serum concentration or effect (a target prothrombin time or INR, for example, and not just one range for everyone, as is the case now), its profile over time, a desired area under the serum concentration curve, or the response of a biomarker.

Purpose This Position Paper aims to review and discuss the available data on therapeutic drug monitoring (TDM) of antibacterials, antifungals and antivirals in critically ill adult patients in the intensive care unit (ICU). This Position Paper also provides a practical guide on how TDM can be applied in routine clinical practice to improve therapeutic outcomes in critically ill adult patients. Methods Literature review and analysis were performed by Panel Members nominated by the endorsing organisations, European Society of Intensive Care Medicine (ESICM), Pharmacokinetic/Pharmacodynamic and Critically Ill Patient Study Groups of European Society of Clinical Microbiology and Infectious Diseases (ESCMID), International Association for Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) and International Society of Antimicrobial Chemotherapy (ISAC). Panel members made recommendations for whether TDM should be applied clinically for different antimicrobials/classes. Results TDM-guided dosing has been shown to be clinically beneficial for aminoglycosides, voriconazole and ribavirin. For most common antibiotics and antifungals in the ICU, a clear therapeutic range has been established, and for these agents, routine TDM in critically ill patients appears meritorious. For the antivirals, research is needed to identify therapeutic targets and determine whether antiviral TDM is indeed meritorious in this patient population. The Panel Members recommend routine TDM to be performed for aminoglycosides, beta-lactam antibiotics, linezolid, teicoplanin, vancomycin and voriconazole in critically ill patients. Conclusion Although TDM should be the standard of care for most antimicrobials in every ICU, important barriers need to be addressed before routine TDM can be widely employed worldwide. Electronic supplementary material The online version of this article (10.1007/s00134-020-06050-1) contains supplementary material, which is available to authorized users.

Background Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results. Methods Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m2 (interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software. Results For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (Ke). The parametric population parameter estimates were Ke 0.637 h−1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (Vc) 29.6 L (without BSV). The nonparametric values were Ke 0.681 h−1 (34.0% CV) and Vc 31.1 L (42.6% CV). Conclusions Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies. Electronic supplementary material The online version of this article (10.1007/s40262-020-00859-1) contains supplementary material, which is available to authorized users.

OBJECTIVES The objectives of this study were to 1) define extent and potential clinical impact of increased or decreased renal elimination of meropenem in children with sepsis, based on analysis of renal function during the first 2 days of PICU stay; and 2) estimate the risk of subtherapeutic meropenem exposure attributable to increased renal clearance. METHODS This retrospective study evaluated patients with a diagnosis of sepsis, receiving meropenem from the PICU at Rady Children's Hospital San Diego from 2015–2017. Meropenem exposure was estimated by using FDA-approved doses (20 and 40 mg/kg/dose) on day 1 and day 2 of PICU stay, based on a population pharmacokinetic (PK) model. For this population with sepsis, we assessed time-above-minimum inhibitory concentration (T>MIC) for pathogen MICs. RESULTS Meropenem treatment was documented in 105 episodes of sepsis with a 48% rate of pathogen detection. By day 2, increased eGFR (>120 mL/min/1.73 m2) was documented in 49% of patients, with 17% meeting criteria for augmented renal clearance ([ARC] >160 mL/min/1.73 m2) and 10%, for decreased function. Simulations documented that 80% of PICU patients with ARC did not achieve therapeutic meropenem exposure for Pseudomonas aeruginosa with a MIC of 2, using standard doses to achieve a pharmacodynamic goal of 80% T>MIC. CONCLUSIONS Approximately 3 of every 20 children with sepsis exhibited ARC during the first 48 hours of PICU stay. Simulations documented an increased risk for subtherapeutic meropenem exposure, suggesting that higher meropenem doses may be required to achieve adequate antibiotic exposure early in the PICU course.

Veno-occlusive disease (VOD) is a severe adverse reaction to busulfan-containing regimens used in the preparation of children for hematopoietic stem cell transplantation (HSCT). We conducted a retrospective analysis of data to examine determinants of VOD in children who received IV busulfan for HSCT conditioning. Busulfan PK parameters as well as various indices (maximal concentration—Cmax, area under the concentration-time curve—AUC) were estimated using a validated Bayesian approach. The influence of available PK, demographic, and clinical variables on the incidence of VOD was evaluated by using logistic regression and classification and regression tree (CART) analyses. Among the 293 patients included, the mean age was 6.5 years and the mean actual body weight was 26.3 kg. The incidence of VOD was 25.6%. Busulfan Cmax as well as weight

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.

OBJECTIVES:To identify factors associated with longer length of stay (LOS) and higher 30-day hospital revisit rates for children hospitalized with bacterial tracheostomy–associated respiratory tract infections (bTARTIs).METHODS:This was a multicenter, retrospective cohort study using administrative data from the Pediatric Health Information System database between 2007 and 2014 of patients 30 days to 17 years old with a principal discharge diagnosis of bTARTI or a principal discharge diagnosis of bTARTI symptoms with a secondary diagnosis of bTARTI. Primary outcomes of LOS (in days) and 30-day all-cause revisit rates (inpatient, observation, or emergency department visit) were analyzed by using a 3-level hierarchical regression model (discharges within patients within hospital).RESULTS:We included 3715 unique patients and 7355 discharges. The median LOS was 4 days (interquartile range: 3–8 days), and the 30-day revisit rate was 30.5%. Compared with children 1 to 4 years old, children aged 30 days to 12 months had both longer LOS (adjusted length of stay [aLOS] = +0.9 days; 95% confidence interval [CI]: 0.6 to 1.3) and increased hospital revisit risk (adjusted odds ratio [aOR] = 1.5; 95% CI: 1.3 to 1.7). Other factors associated with longer LOS included public insurance (aLOS = +0.5 days; 95% CI: 0.2 to 0.8), 3 or more complex chronic conditions (CCCs), mechanical ventilation (acute or chronic), and empirical anti-Pseudomonas aeruginosa antibiotics (aLOS = +0.6 days; 95% CI: 0.3 to 0.9). Other factors associated with 30-day revisit included 4 or more CCCs (aOR = 1.3; 95% CI: 1.1 to 1.6) and chronic ventilator dependency (aOR = 1.1; 95% CI: 1.0 to 1.3).CONCLUSIONS:Ventilator-dependent patients

OBJECTIVE: Identify characteristics associated with hospital readmission due to bacterial respiratory tract infections (bRTI) after tracheotomy. STUDY DESIGN: Retrospective study of 8009 children 0–17 years undergoing tracheotomy from 2007–2013 at 48 children’s hospitals in the Pediatric Health Information System database. The primary outcome was first hospital admission after tracheotomy for bRTI (i.e. primary diagnosis of bRTI or a primary diagnosis of bRTI symptom and secondary diagnosis of bRTI). We used Cox-proportional hazard modeling to assess associations between patient demographic and clinical characteristics and bRTI hospital readmission. RESULTS: Median age at tracheotomy admission was 5 months [interquartile range (IQR): 1–50 months]. Thirty-six percent (n=2,899) had at least one bRTI admission. Median time-to-readmission for bRTI was 275 days (IQR: 141–530). Factors independently associated with increased risk for bRTI readmission were younger age [e.g., age 2 complex chronic conditions (vs. zero; aHR 1.96; 95% CI: 1.34–2.86) and discharge to home (vs. post-acute care setting; aHR 1.19; 95% CI: 1.08–1.32). Trauma diagnosis at tracheotomy (aHR 0.83; 95% CI: 0.69–1) and ventilator dependency (aHR 0.88; 95% CI: 0.81–0.97) were associated with decreased risk. CONCLUSIONS: Young, Hispanic children with multiple complex chronic conditions who use Medicaid insurance and are not discharged to post-acute care are at the highest risk for hospital readmission for bRTI post-tracheotomy. Future research should investigate strategies to mitigate this risk for these children.

Jason Shafrin,1 Jeffrey Sullivan,1 Jacquelyn W Chou,1 Michael N Neely,2 Justin F Doan,3 J Ross Maclean1 1Precision Health Economics, LosAngeles, CA, USA; 2Department of Pediatrics, Keck School of Medicine, University of Southern California, LosAngeles, CA, USA; 3Worldwide Health Economics and Outcomes Research, Bristol-Myers Squibb, Princeton, NJ, USA Objective: To examine how observed medication nonadherence to 2 second-line, oral anticancer medications (axitinib and everolimus) affects progression-free survival (PFS) among patients with renal cell carcinoma. Methods: We used an adherence–exposure–outcome model to simulate the impact of adherence on PFS. Using a pharmacokinetic/pharmacodynamic (PK/PD) population model, we simulated drug exposure measured by area under the plasma concentration–time curve (AUC) and minimum blood or trough concentration (Cmin) under 2 scenarios: 1) optimal adherence and 2) real-world adherence. Real-world adherence was measured using the medication possession ratios as calculated from health insurance claims data. A population PK/PD model was simulated on individuals drawn from the Medical Expenditure Panel Survey (MEPS), a large survey broadly representative of the US population. Finally, we used previously published PK/PD models to estimate the effect of drug exposure (i.e., Cmin and AUC) on PFS outcomes under optimal and real-world adherence scenarios. Results: Average adherence measured using medication possession ratios was 76%. After applying our simulation model to 2164 individuals in MEPS, drug exposure was significantly higher among adherent patients compared with nonadherent patients for axitinib (AUC: 249.5 vs. 159.8 ng×h/mL, P

Objective Identify risk factors for readmission due to a bacterial tracheostomy-associated respiratory tract infection (bTARTI) within 12 months of discharge after tracheotomy. Design/Methods We performed a retrospective cohort study of 240 children who underwent tracheotomy and were discharged with tracheotsomy in place between January 1, 2005 and June 30, 2013. Children with prolonged total or post-tracheotomy length of stay (LOS), less than 12 months of follow-up, or who died during the index hospitalization were excluded. Readmission for a bTARTI (eg, pneumonia, tracheitis) treated with antibiotics, as ascertained by manual chart review, was the outcome variable. We used multivariate logistic regression to identify the independent association between risk factors and hospital readmission for bTARTI within 12 months. Results At index hospitalizations for tracheotomy, the median admission age was 5 months (interquartile range [IQR] 2-43 months) and median LOS was 73 days (IQR 43-121 days). Most patients were of Hispanic ethnicity (n = 162, 68%) and were publicly insured (n = 213, 89%). Nearly half (n = 112, 47%) were discharged on positive pressure mechanical ventilation. Many (n = 103, 43%) were admitted for bTARTI within 12 months of discharge. Only Hispanic ethnicity (adjusted odds ratio [AOR] 2.0; 95% confidence interval [CI]: 1.1-3.9; P = 0.03) and acquisition of Pseudomonas aeruginosa between tracheotomy and discharge from index hospitalization (AOR 3.2; 95%CI: 1.2-8.3; P = 0.02) were independently associated with increased odds of bTARTI readmission, while discharge on gastrointestinal pro-motility agents was associated with decreased risk (AOR = 0.4; 95%CI: 0.2-0.8; P = 0.01). Conclusions Hispanic ethnicity and post-tracheotomy acquisition of P. aeruginosa during initial hospitalization are associated with bTARTI readmission.

Monte Carlo simulations allow prediction and comparison of concentration-time profiles arising from different dosing regimens in a defined population, provided a population pharmacokinetic model has been established. The aims of this study were to evaluate the population pharmacokinetics of imipenem in critically ill patients with hospital-acquired pneumonia (HAP) and to assess the probability of target attainment (PTA) and cumulative fraction of response (CFR) using EUCAST data. A two-compartment model based on a data set of 19 subjects was employed. Various dosage regimens at 0.5-h and 3-h infusion rates and as continuous infusion were evaluated against the pharmacodynamic targets of 20%fT>MIC, 40%fT>MIC and 100%fT>MIC. For the target of 40%fT>MIC, all 0.5-h infusion regimens achieved optimal exposures (CFR ≥ 90%) against Escherichia coli and Staphylococcus aureus, with nearly optimal exposure against Klebsiella pneumoniae (CFR ≥ 89.4%). The 3-h infusions and continuous infusion exceeded 97% CFR against all pathogens with the exception of Pseudomonas aeruginosa and Acinetobacter spp., where the maximum CFRs were 85.5% and 88.4%, respectively. For the 100%fT>MIC target, only continuous infusion was associated with nearly optimal exposures. Higher PTAs for the targets of 40%fT>MIC and 100%fT>MIC were achieved with 3-h infusions and continuous infusion in comparison with 0.5-h infusions; however, continuous infusion carries a risk of not reaching the MIC of less susceptible pathogens in a higher proportion of patients. In critically ill patients with HAP with risk factors for Gram-negative non-fermenting bacteria, maximum doses administered as extended infusions may be necessary.

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 ( r s ) values for the area under the concentration-time curve during the first 24 h (AUC 0–24 ), the maximum concentration in plasma during the first 24 h (C max 0–24 ), and the lowest (minimum) concentration in plasma after the dose closest to 24 h (C min 0–24 ). A total of 52 rats received vancomycin ( n = 42) or NS ( n = 10). The strongest exposure-response correlations were observed between AUC 0–24 and C max 0–24 and urinary AKI biomarkers. Exposure-response correlations ( r s values) for AUC 0–24 , C max 0–24 , and C min 0–24 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 AUC 0–24 or C max 0–24 correlates with increases in urinary AKI biomarkers.

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.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% Tminimum inhibitory concentration (MIC), with a probability0.9) for doubling MICs was determined for different regimens of meropenem.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, TMIC of 100%, was higher for 3-hour infusion regimens compared with 30-minute infusion regimens for all ranges of CLcreat.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.

Background Cefepime (CEF) is commonly used for adult and pediatric infections. Several studies have examined CEF’s pharmacokinetics (PK) in various populations; however, a unifying PK model for adult and pediatric subjects does not yet exist. We developed a combined population model for adult and pediatric patients and validated the model. Methods The initial model includes adult and pediatric patients with a rich cefepime sampling design. All adults received 2 g CEF while pediatric subjects received a mean of 49 (SD 5) mg/kg. One- and two-compartment models were considered as base models and were fit using a non-parametric adaptive grid algorithm within the Pmetrics package 1.5.2 (Los Angeles, CA) for R 3.5.1. Compartmental model selection was based on Akaike information criteria (AIC). Covariate relationships with PK parameters were visually inspected and mathematically assessed. Predictive performance was evaluated using bias and imprecision of the population and individual prediction models. External validation was conducted using a separate adult cohort. Results A total of 45 subjects (n = 9 adults; n = 36 pediatrics) were included in the initial PK model build and 12 subjects in the external validation cohort. Overall, the data were best described using a two-compartment model with volume of distribution (V) normalized to total body weight (TBW/70 kg) and an allometric scaled elimination rate constant (Ke) for pediatric subjects (AIC = 4,138.36). Final model observed vs. predicted plots demonstrated good fit (population R2 = 0.87, individual R2 = 0.97, Figure 1a and b). For the final model, the population median parameter values (95% credibility interval) were V0 (total volume of distribution), 11.7 L (10.2–14.6); Ke for adult, 0.66 hour−1 (0.38–0.78), Ke for pediatrics, 0.82 hour−1 (0.64–0.85), KCP (rate constant from central to peripheral compartment), 1.4 hour−1 (1.3–1.8), KPC (rate constant from peripheral to central compartment), 1.6 hour−1 (1.2–1.8). The validation cohort has 12 subjects, and the final model fit the data well (individual R2 = 0.75). Conclusion In this diverse group of adult and pediatrics, a two-compartment model described CEF PK well and was externally validated with a unique cohort. This model can serve as a population prior for real-time PK software algorithms. Disclosures All authors: No reported disclosures.

Background Resistance in Neisseria gonorrhoeae to all therapeutic antimicrobials for gonorrhoea has emerged. Novel antimicrobials for treatment are imperative and the first-in-class spiropyrimidinetrione zoliflodacin appears promising. Zoliflodacin could be introduced in dual antimicrobial therapies to prevent the emergence and/or spread of resistance. We investigated the in vitro activity and induction/selection of resistance to zoliflodacin alone and in combination with six novel, currently or previously used therapeutic antimicrobials against N. gonorrhoeae. Methods The international gonococcal reference strains examined were WHO F (wild-type), and WHO O, WHO V, and WHO X (strains with different AMR profiles). Zoliflodacin was evaluated alone or in combination with ceftriaxone, spectinomycin, gentamicin, tetracycline, cethromycin, and sitafloxacin in checkerboard assays, time-kill curve analysis, and induction/selection of resistance studies. Results Zoliflodacin alone or in combination with all six antimicrobials showed rapid rates of in vitro bacterial killing against all examined strains in time-kill studies. Tetracycline or cethromycin combined with zoliflodacin decreased the rate of zoliflodacin growth inhibition, while ceftriaxone or gentamicin increased the rate of cell killing. The frequency of induced/selected zoliflodacin resistance mutations was low for zoliflodacin and further reduced for all antimicrobial combinations. All resistant mutants contained the GyrB mutations D429N, K450T or K450N, resulting in zoliflodacin MICs of 0.5–4 mg/L consistent with previous results. Conclusion Zoliflodacin, alone or in combination with STI therapeutic antimicrobials has a rapid and high in vitro efficacy against gonococci with low resistance emergence. Zoliflodacin remains a promising novel oral therapeutic for gonorrhoea monotherapy and as part of dual antimicrobial therapy with low resistance emergence potential. A phase III clinical trial evaluating efficacy and safety of zoliflodacin for uncomplicated gonorrhoea treatment is planned in 2019. Disclosure No significant relationships.

ObjectivesResistance in Neisseria gonorrhoeae to all gonorrhoea therapeutic antimicrobials has emerged. Novel therapeutic antimicrobials are imperative and the first-in-class spiropyrimidinetrione zoliflodacin appears promising. Zoliflodacin could be introduced in dual antimicrobial therapies to prevent the emergence and/or spread of resistance. We investigated the in vitro activity of and selection of resistance to zoliflodacin alone and in combination with six gonorrhoea therapeutic antimicrobials against N. gonorrhoeae.MethodsThe international gonococcal reference strains WHO F (WT) and WHO O, WHO V and WHO X (strains with different AMR profiles) were examined. Zoliflodacin was evaluated alone or combined with ceftriaxone, cefixime, spectinomycin, gentamicin, tetracycline, cethromycin or sitafloxacin in chequerboard assays, time–kill curve analysis and selection-of-resistance studies.ResultsZoliflodacin alone or in combination with all six antimicrobials showed rapid growth inhibition against all examined strains. The time–kill curve analysis indicated that tetracycline or cethromycin combined with zoliflodacin can significantly decrease the zoliflodacin kill rate in vitro. The frequency of selected zoliflodacin-resistance mutations was low when evaluated as a single agent and further reduced for all antimicrobial combinations. All resistant mutants contained the GyrB mutations D429N, K450T or K450N, resulting in zoliflodacin MICs of 0.5–4 mg/L.ConclusionsZoliflodacin, alone or in combination with sexually transmitted infection therapeutic antimicrobials, rapidly kills gonococci with infrequent resistance emergence. Zoliflodacin remains promising for gonorrhoea oral monotherapy and as part of dual antimicrobial therapy with low resistance emergence potential. A Phase III trial evaluating efficacy and safety of zoliflodacin for uncomplicated gonorrhoea treatment is planned in 2019.

Voriconazole is a first-line antifungal agent. Therapeutic drug monitoring is a standard of care., Voriconazole is a first-line antifungal agent. Therapeutic drug monitoring is a standard of care. The best way to adjust dosages to achieve desired drug exposure endpoints is unclear due to nonlinear and variable pharmacokinetics. Previously described software was used to prospectively adjust voriconazole dosages. The CYP2C19, CYP3A4, and CYP3A5 genotypes were determined. The primary endpoint was the proportion of patients with a Cmin at 120 h in the range 1 to 3 mg/liter using software to adjust voriconazole dosages. A total of 19 patients were enrolled, and 14 were evaluable. Of these, 12/14 (85.7%; 95% confidence interval = 57.2 to 98.2%) had a Cmin at 120 h posttreatment initiation of 1 to 3 mg/liter, which was higher than the a priori expected proportion of 33%. There was no association of CYP genotype-derived metabolizer phenotype with voriconazole AUC. Software can be used to adjust the dosages of voriconazole to achieve drug exposures that are safe and effective. (The clinical trial discussed in this paper has been registered in the European Clinical Trials Database under EudraCT no. 2013-0025878-34 and in the ISRCTN registry under no. ISRCTN83902726.)